Author response:

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

Response to Reviewer #1:

Comment 1:

Summary:

The authors sought to investigate the associations of age at breast cancer onset with the incidence of myocardial infarction (MI) and heart failure (HF). They employed a secondary data analysis of the UK Biobank. They used descriptive and inferential analysis including Cox proportional hazards models to investigate the associations. Propensity score matching was also used. They found that Among participants with breast cancer, younger onset age was significantly associated with elevated risks of MI (HR=1.36, 95%CI: 1.19 to 1.56, P<0.001) and HF (HR=1.31, 95% CI: 1.18 to 1.46, P<0.001). the reported similar findings after propensity matching.

Strengths:

The use of a large dataset is a strength of the study as the study is well-powered to detect differences. Reporting both the unmatched and the propensity-matched estimates was also important for statistical inference.

Weaknesses:

Despite the merits of the paper, readers may get confused as to whether authors are referring to “age at breast cancer onset” or “age at breast cancer diagnosis”. I suppose the title refers to the latter, in which case it will be best to be consistent in using “age at breast cancer diagnosis” throughout the manuscripts. I would recommend a revision to the title to make it explicit that the authors are referring to “age at breast cancer diagnosis”.

Thank you for your nice comments and suggestions. Yes, as you mentioned, in this study, we focused on age at breast cancer diagnosis, which was obtained from the cancer registry data in the UK Biobank and was used in all the analyses. We agree with you that it would be better to consistently use “age at diagnosis of breast cancer” throughout the manuscripts for a better understanding; therefore, we have replaced “age at breast cancer onset” with “age at diagnosis of breast cancer”.

Change in the manuscript:

“Age at breast cancer onset” was replaced with “age at diagnosis of breast cancer” in the title and throughout the manuscripts.

Recommendations For The Authors:

Kindly review the references for the location of the full stop. Putting the full stop at the end of the parenthesis makes reading smother than its current form as it is difficult to know when the new sentence begins.

Thank you for your suggestion. We have made revisions to the location of the full stop next to a reference.

Change in the manuscript:

The full stop was put at the end of the parenthesis of a reference throughout the manuscripts.

Response to Reviewer #2:

Comment 1:

This is a well-presented large analysis from the UK Biobank of nearly 250,000 female adults. The authors examined the associations of breast cancer diagnosis with incident myocardial infarction and heart failure by different onset age groups. Based on results from a series of statistical analyses, the authors concluded that younger onset age of breast cancer was associated with myocardial infarction and heart failure, highlighting the necessity of careful monitoring of cardiovascular status in women diagnosed with breast cancer, especially those younger ones.

Comments to consider:

It’s thoughtful for the authors to have included and adjusted for menopausal status, breast cancer surgery, and hormone replacement therapy in their sensitivity analysis. It would be informative if the authors presented the number and percentages of menopause and cancer treatments.

Thank you for your comments. As suggested, we have provided more detailed information on the number and percentage of menopausal status and breast cancer treatments.

Change in the manuscript:

Page 11, Lines 208 to 211: added “Among participants with breast cancer, 11 460 (70.6%) participants were postmenopausal, 14 255 (87.6%) participants had undergone breast cancer surgery, and 6 784 (41.8%) participants had received hormone replacement therapy.”

Change in the supplementary material:

The number and percentage of menopausal status, breast cancer surgery, and hormone replacement therapy were added to Table S13.

aAdjusted for age, ethnicity, education, current smoking, current drinking, obesity, exercise, low-density lipoprotein cholesterol, depressed mood, hypertension, diabetes, antihypertensive drug use, antidiabetic drug use, statin use, menopausal status, breast cancer surgery, and hormone replacement therapy.

HR, hazard ratio; CI, confidence interval.

Comment 2:

The analytical baseline used for follow-up should be pointed out in the methods section. It’s confusing whether the analytic baseline was defined as the study baseline or the time at breast cancer diagnosis.

We apologize for the confusion. In this study, the analytical baseline used for follow-up was defined as the baseline of UK Biobank (2006-2010) and we have pointed it out in the methods section as suggested.

Change in the manuscript:

Page 9, Lines 165 to 166: added: “The analytical baseline used for follow-up was defined as the baseline of UK Biobank (2006-2010).”

Comment 3:

Did the older onset age group have a longer follow-up duration? Could the authors provide information on the length of follow-up by age of onset in Supplementary Table S4? It would give the readers more information regarding different age groups.

Thank you for your question. We compared the time of follow-up among the three diagnosis age groups and found that although the durations of follow-up among the three groups were quite similar (as shown in Table S4), statistical analysis revealed a significant difference with the older diagnosis age group demonstrating a longer follow-up duration (P for Kruskal-Wallis test <0.001). This is understandable as with large sample sizes, even a slight difference could lead to statistical significance. According to your suggestion, we have added information on the length of follow-up by age of diagnosis in Supplementary Table S4.

Change in the supplementary material:

Added the median and interquartile range of follow-up in Supplementary Table S4.

The results are presented as the mean ± standard deviation, or No. (%).

aThe effect sizes are standardized mean differences for continuous outcomes and the Phi coefficient for dichotomous outcomes.

LDL-C, low-density lipoprotein cholesterol.

Author response:

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

Reviewer #1 (Public Review):

This study addresses the temporal patterning of a specific Drosophila CNS neuroblast lineage, focusing on its larval development. They find that a temporal cascade, involving the Imp and Syb genes changes the fate of one daughter cell/branch, from glioblast (GB) to programmed cell death (PCD), as well as gates the decommissioning of the NB at the end of neurogenesis.

I believe there are some inaccuracies in this summary. We address temporal patterning during larval and pupal stages until the adult stage. The Imp and Syp genes change the fate of one daughter cell/branch from survival to programmed cell death (PCD). The change from glioblast (GB) to PCD, which occurs at an early time point, is not addressed here. The main point of the paper is missing:

• Last-born MNs undergo apoptosis due to their failure to express a functional TF code, and this code is post-transcriptionally regulated by the opposite expression of Imp and Syp in immature MNs.

Reviewer #2 (Public Review):

Summary:

Guan and colleagues address the question of how a single neuroblast produces a defined number of progeny, and what influences its decommissioning. The focus of the experiments are two well-studied RNA-binding proteins: Imp and Syp. The Authors find that these factors play an important role in determining the number of neurons in their preferred model system of VNC motor neurons coming from a single lineage (LinA/15) by separate functions taking place at specific stages of development of this lineage: influencing the life-span of the LinA neuroblast to control its timely decommissioning and functioning in the Late-born post-mitotic neurons to influence cell death after the appropriate number of progeny is generated. The post-mitotic role of Imp/Syp in regulating programmed-cell death (PCD) is also correlated with a specific code of key transcription factors that are suspected to influence neuronal identity, linking the fate of neuronal survival with its specification. This paper addresses a wide scope of phenotypes related to the same factors, thus providing an intriguing demonstration of how the nervous system is constructed by context-specific changes in key developmental regulators.

The bulk of conclusions drawn by the authors are supported by careful experimental evidence, and the findings are a useful addition to an important topic in developmental neuroscience.

I cannot summarize better the paper.

Strengths:

A major strength is the use of a genetic labeling tool that allows the authors to specifically analyze and manipulate one neuronal lineage. This allows for simultaneous study of both the progenitors and post-mitotic progeny. As a result the paper conveys a lot of useful information for this particular neuronal lineage. Furthermore addressing the association of cell fate specification, taking advantage of this lab's extensive prior work in the system, with developmentally-regulated programmed celldeath is an important contribution to the field.

Beyond Imp/Syp, additional characterization of this model system is provided in characterizing a previously unrecognized death of a hemilineage in early-born neurons.

Thanks!

Weaknesses:

The main observations that distinguish this study from others that have investigated Imp/Syp in the fly nervous system is the role played in late-born post-mitotic neurons to regulate programmed cell death. This is an important and plausible (based on the presented findings) newly discovered role for these proteins. However the precision of experiments is not particularly strong, which limits the authors claims. The genetic strategy used to manipulate Imp/Syp or the TF code appears to be done throughout the entire lineage, or all neuronal progeny, and not restricted to only the late born cells. Can the authors rule out survival of the early born hemi-lineage normally fated to die? Therefore statements such as this: 

To further investigate this possibility, we used the MARCM technique to change the TF code of lastborn MNs without affecting the expression of Imp and Syp should be qualified to specify that the result is obtained by misexpressing these factors throughout the entire lineage.

We agree that our genetic manipulations affect the entire lineage or all neuronal progeny. We do not have genetic tools to gain such precision. We have changed our descriptions to specify the entire lineage or all neuronal progeny. As the reviewer raised, we were also concerned about the possibility that the overexpression of Imp or knockdown of Syp could induce the survival of the early-born hemilineage. We have two experiments that rule out this possibility:

(1) In late LL3 larvae, Imp OE or syp MARCM clones do not change the number of cells in LL3 larvae (see Guan et al., 2022), indicating that the hemilineage that died by PCD is not affected. If Imp or Syp played a role in the survival of the hemilineage, we would see at least a 50% increase in the number of MNs at this stage.

(2) The MARCM experiment using the VGlut driver to overexpress P35 or Imp allows us to manipulate only elav+ VGlut+ neurons. The hemilineage removed by PCD is elav- VGlut- and is not affected by this experiment. Consequently, the increase in MNs in adults with genetic manipulation can only be the result of the survival of the other hemilineage (elav+, VGlut+). Moreover, this experiment shows an increase in the number of neurons in the adult but not in LL3, demonstrating that the hemilineage (elav- VGlut-) is still removed by PCD with this genetic manipulation.

The authors make an observation that differs from other systems in which Imp/Syp have been studied: that the expression of the two proteins appears to be independent and not influenced by cross-regulation. However there is a lack of investigation as to what effect this may have on how Imp/Syp regulate temporal identity. A key implication of the previously observed cross-regulation in the fly mushroom body is that the ratio of Imp/Syp could change over the life of the NB which would permit different neuronal identities. Without cross-regulation, do the authors still observe a gradient in the expression pattern of time? Because the data is presented with Imp and Syp stained in different brain samples, and without quantification across different stages, this is unclear. The authors use the term 'gradient' but changes in levels of these factors are not evident from the presented data.

We have now quantified the transcriptional activity of Imp and Syp in the NB over time using smFISH. We have also quantified the relative expression of Imp and Syp protein in the NB over time by co-immunostaining. Additionally, we quantified the relative expression of Imp and Syp protein in postmitotic neurons as a function of their birth order in late LL3 larvae. All these data show an opposite temporal gradient of Imp and Syp in the NB and an opposite spatial gradient in immature neurons according to their birth order (Figure. 4). How these gradients are established in our system remains to be elucidated. 

Reviewer #3 (Public Review):

This study by Guan and co-workers focuses on a model neuronal lineage in the developing Drosophila nervous system, revealing interesting aspects about: a) the generation of supernumerary cells, later destined for apoptosis; and, b) new insights into the mechanisms that regulate this process. The two RNA-binding proteins, Imp and Syp, are shown to be expressed in temporally largely complementary patterns, their expression defining early vs later born neurons in this lineage, and thus also regulating the apoptotic elimination. Moreover, neuronal 'fate' transcription factors that are downstream of Imp and signatures of early-born neurons, can also be sufficient to convert later born cells to an earlier 'fate', including survival.

The authors provide solid evidence for most of their statements, including the temporal windows during which the early and the later-born motoneurons are generated by this model lineage, how this relates to patterns of cell death by apoptosis and that mis-expression of early-born transcription factors in later-born cells can be sufficient to block apoptosis (part of, and perhaps indicative of the late-born identity).

Other studies have previously outlined analogous, mutually antagonistic roles for Imp and Syp during nervous system development in Drosophila, in different parts and at different stages, with which the working model of this study aligns.

Overall, this study adds to and extends current working models and evidence on the developmental mechanisms that underlie temporal cell fate decisions.

I cannot summarize better the paper.

Reviewer #1 (Recommendations For The Authors):

While this is an interesting topic, I raised two issues in my original review.

(1) Against the backdrop of numerous previous studies linking many developmental regulators, including tTFs, to programmed cell death in the developing CNS, which in several cases have involved identifying key PCD genes and decoding the molecular regulatory interplay between regulators and PCD genes, this study does not provide any new insight into the regulation of developmental PCD in the CNS.

The authors have not added any new data to address this shortcoming.

I agree with the reviewer that we did not attempt to link Imp/Syp with the temporal transcription factor (tTF) cascade or spatial selectors such as Hox genes. However, this decision was intentional as our primary focus was on studying immature MNs. It is worth noting that the decommissioning of NBs by autophagic cell death or terminal differentiation, which is mediated by Imp/Syp in other lineages, has not been correlated with tTFs or spatial selectors. Although we have not directly examined the involvement of the hb + sv > kr > pdm > cas > cas-svp > Grh cascade in the decommissioning of the Lin A neuroblast, our preliminary data indicate that Hb, Sv, Pdm, and Cas are not expressed in the Lin A NB, while Grh is consistently expressed in the NB (Wenyue et al., 2022). Thus, it is less likely that this particular tTF cascade is not implicated in Lin A neuroblast decommissioning. In contrast, spatial selectors, such as the Hox gene Antp, play an opposing role compared to HOX transcription factors in abdominal NBs. In the Lin A lineage, Antp promotes survival (Baek, Enriquez, & Mann, 2013). Here, to avoid repeating what has already been described in the literature, we focused on the role of Imp/Syp in postmitotic neurons and revealed that the precise elimination of MNs is linked to the control of TFs expressed in the MNs.

(2) I raised the issue that it is unclear if Imp/Syp acts in the NB, and/or in IMC/GMC, and/or in the daughter cells generated from these.

I agree with the reviewer's concern regarding the unclear function of Imp/Syp, i.e., whether it acts in the NB, IMC/GMC, or daughter cells. To address this, one possible approach would be to attempt rescuing Imp and Syp mutants by transgenic expression in specific cell types, such as NBs, IMC/GMC, or GB/daughter cells. However, we have not conducted such experiments as we were skeptical about the outcome. Previous published work has used drivers expressed in NBs, IMC/GMC, or postmitotic neurons to decipher the function of a gene in a specific cell type. But the results of these experiments must be taken with caution. Using NB/GMC drivers to study gene function can lead to effects not only in the NB but also in its progeny, including GMC or postmitotic neurons, due to the perdurance and stability of the Gal4 and UAS-gene expression system. For instance, dpn-Gal4 UASGFP not only labels the NB but also many of its progeny, even if Dpn is only expressed in NBs. And elav-Gal4 is expressed in the NB and GMCs.

However, our overexpression of Imp in immature neurons using Vglut demonstrates that Imp promotes cell survival through an autonomous function in these neurons. This driver is only expressed in postmitotic neurons (elav+) and not in the NB, IMC/GMC, or in the hemilineage eliminated by cell death (elav-vglut-).

Reviewer #2 (Recommendations For The Authors):

Oddly knockdown of Imp in the neuroblast (Fig. 5D) only led to death at 8h APF, when Imp is no longer expressed. Do the authors have an explanation as to how the stem cell can survive until this point? A discussion would be helpful.

The simple explanation is the efficiency of RNAi. The imp-/- MARCM clones (Guan et al., 2022) lead to a stronger reduction of MNs in LL3.

A simple experiment I would recommend is to repeat the antibody stainings of staged larvae/pupae (Fig. 4) having the anti-Imp/Syp antibodies in the same brain sample, and perhaps a quantification of the ratio in the NB. Given the species in which the ABs were raised seem compatible, this should be feasible. As it stands now, there is no indication of whether the ratio of Imp vs Syp change over time.

We have now quantified the transcriptional activity of Imp and Syp in the NB over time. We have also quantified the relative expression of Imp and Syp proteins in the NB over time and quantified the relative expression of Imp and Syp proteins in postmitotic neurons as a function of their birth in late LL3 larvae. How these gradients are established in our system still remains to be 

Minor errors/suggestions:

Fig 4. Time legend at the top goes A, B, C, E, F (no D). So it doesn't match the panels below

Yes, we have made the corrections.

Sentence repeated in Intro:

The process of terminating NB neurogenesis through autophagic cell death or terminal differentiation is commonly referred to as decommissioning.

Yes, corrections have been made.

IN FIGURE 1 THEY SAY 'TYPE IB' AND IN FIGURE 2 THEY SAY 'TYPE 1B'

We have changed it to type 1b.

In Fig2A-It's hard to see lack of Elav and Fig2G-It's hard to see presence of Dcp1. Panels could be adjusted to emphasize these results

We have increased the size of the panels and made two separate panels where only the elav and Dcp1 signals are present.

Observations that the result is equivalent in all thoracic segments is expected, since all legs need the same number of neurons. This is nice to have but can be in the supplement.

Overall the figure number seems excessive, especially considering much of the results included(particularly the NB results) are findings consistent with previous papers and some is characterization of the system that does not fit well with the main focus regarding Imp/Syp (i.e death of one hemi-lineage:

Figure 5 and 6 can be joined as one.

We have combined Figures 5 and 6, showing only the T1 segments.

There is some discrepancy between graphs Fig7F and K: At LL3 the number of neurons is different for the control in 7F and the count in K

Yes, because the genetic backgrounds are not the same and we are not counting the same type of cells. In 7F, we are counting the elav+ and VGlut+ cells, whereas in Figure 7K, we are counting all the elav+ in Lin A, including those elav+ VGlut-. VGlut expression arrives a bit later after elav+, which is why we have fewer elav+ cells in 7F. In other words, VGlut MARCM clones do not label all Lin A elav+ cells. I have clarified this in the figure.

Reviewer #3 (Recommendations For The Authors):

Main comment: on the notion of Imp and Syp gradients:

p. 5, related to figure 4 - there are clearly distinct windows for predominantly (if not exclusively) Imp, and later, Syp expression in lineage 15, with a phase of co-expression.

However, based on the data shown, it is unclear whether these windows represent gradients, as repeatedly stated. If the notion of gradients is derived from other studies, on other lineages, then this would be good to clarify. Alternatively, the idea of temporally opposing gradients of Imp and Syp would need to be demonstrated for this lineage.

For example, a more accurate way to describe this study's data is given on p.7 "In conclusion, our findings demonstrate that the opposite expression pattern of Imp and Syp in postmitotic neurons precisely shapes the size of Lin A/15 lineage by controlling the pattern of PCD in immature MNs (Fig. 8)."

We have now quantified the transcriptional activity of Imp and Syp in the NB over time. We have also quantified the relative expression of Imp and Syp proteins in the NB over time. We have also quantified the relative expression of Imp and Syp proteins in postmitotic neurons as a function of their birth in late LL3 larvae. How these gradients are established in our system still remains to be identified.

Minor points:

p.6, related to figure 7: Are numbers of EDU- early born and EDU+, late born, MNs expressed as means in the main text? As written, it suggests absence of any variability, which one would expect and which is shown in Fig.7 data.

Yes, we have added averages in the text.

Methods: the author name 'Lacin' has been mis-spelled

Sorry about that, it's been corrected.

Author response:

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

eLife assessment

This paper proposes a valuable new method for the assessment of the mean kurtosis for diffusional kurtosis imaging by utilizing a recently introduced sub-diffusion model. The evidence supporting the claims that this technique is robust and accurate in brain imaging is incomplete. The work could be of interest in the research and clinical arena.

We thank the editors for their assessment and the reviewers for their careful reading and feedback that helped to improve the manuscript. We have addressed all the reviewers’ concerns and would like to request an update of the assessment to reflect the revisions we have made.

Below, we address the reviewers’ comments.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This study introduces an innovative method for assessing the mean kurtosis, utilizing the mathematical foundation of the sub-diffusion framework. In particular, a new fitting technique that incorporates two different diffusion times is proposed to estimate the parameters of the sub-diffusion model. The evaluation of this technique, which generates kurtosis maps based on the sub-diffusion framework, is conducted through simulations and the examination of data obtained from human subjects.

We thank Reviewer #1 for pointing out the novelty and innovation of our work.

Strengths:

The utilization of the sub-diffusion model for tissue characterization is a significant conceptual advancement for the field of diffusion MRI. This study adeptly harnesses this approach for an accurate estimation of the parameters of the widely employed diffusion model, DKI, leveraging their established analytical interconnection as evidenced in prior research. Notably, this approach not only proposes a robust, fast, and accurate technique for DKI parameter estimation but also underscores the viability of deploying the sub-diffusion model for tissue characterization, substantiated by both simulated and human subject analyses. The paper is very-well written; well-organized; and coherent. The simulation study included different aspects of water diffusion as captured by diffusion-weighted MRI such as varying diffusion times and different b-value subpopulations, resulting in a comprehensive and thorough discussion.

We thank Reviewer #1 for highlighting the the strengths of our work.

Weaknesses:

The primary objective of this study is to demonstrate a robust approach for estimating DKI parameters by directly calculating them using the parameters of the sub-diffusion model. This premise, however, relies on the assumption that the sub-diffusion model effectively characterizes the diffusion MRI signal and that its parameters are both robust and accurate. Throughout the manuscript, the term "ground truth kurtosis K" is frequently used to denote the "true K" value in the context of the simulation study. Nonetheless, given that the data is simulated using the new sub-diffusion model - an approximation of the DKI-based signal expression- this value cannot truly be considered the "ground truth K". The simulation study highlights the robustness and accuracy of D* and K*, but it inherently operates under the assumption that the observed data is in the form of the sub-diffusion model.

It is correct that our study operates under the assumption that the observed data is in the form of the sub-diffusion model, and indeed one of the key outcomes of this work is to demonstrate the effectiveness of that assumption and the new possibilities it brings. Naturally, using any mathematical model at all carries assumptions. Over the past two decades, many mathematical and biophysical models have been proposed to characterise diffusion MRI signals. However, model validation remains an open challenge in the field. In this, as well as in our previous work (Yang et al, NeuroImage, 2022), we have shown that our proposed sub-diffusion model not only provides a much better fitting compared to the traditional DKI method, overcoming the major limitation of the traditional DKI method on the maximum b-value, but also generates brain maps with superior tissue contrast and elucidates previously unseen structure.

We have replaced the term “ground truth kurtosis K” with “true kurtosis K”.

The comment “… using the new sub-diffusion model – an approximation of the DKI-based signal expression…” is a bit misleading. In fact we propose that the reverse interpretation is the more suitable way to view the relationship: the DKI model is a degree-2 approximation of the sub-diffusion model, as in eq. (7).

Reviewer #2 (Public Review):

Summary: The authors present a technique for fitting diffusion magnetic resonance images (dMRI) to a sub-diffusion model of the diffusion process within brain imaging. The authors suggest that their technique provides robust and accurate calculation of diffusional kurtosis imaging parameters from which high quality images can be calculated from short dMRI data acquisitions at two diffusion times.

Strengths: If the authors can show that the dMRI signal in brain tissue follows a sub-diffusion model decay curve then their technique for accurately and robustly calculating diffusional kurtosis parameters from multiple diffusion times would be of benefit for tissue microstructural imaging in research and clinical arenas.

In Figure 7, we showed that the diffusion MRI signals follow the sub-diffusion model decay curves.

Weaknesses: The applied sub-diffusion model has two parameters that are invariant to diffusion time, D_β and β which are used to calculate the diffusional kurtosis measures of a diffusion time dependent D* and a diffusion time invariant K*. However, the authors do not demonstrate that the D_β, β and K* parameters are invariant to diffusion time in brain tissue.

In our proposed sub-diffusion model, D_β and β are assumed to be time-independent parameters, which is a key strength of the approach. The goal is to characterise tissue-specific properties (D_β for diffusivity and β for the extent of tissue complexity) that do not rely on the diffusion time setting in diffusion MRI experiments. To extract such time-independent properties, we proposed a new sampling and fitting strategy – fitting at least two diffusion time data together.

The authors' results visually show that there is time dependence of the K* measure (in Figure 6) that is more apparent in white matter with K* values being higher for diffusion times of ∆=49 ms than ∆ = 19 ms. The diffusion time dependence of K* indicates there is also diffusion time dependence of β.

The discrepancies in the fitted K* for ∆ = 19 ms and ∆ = 49 ms separately do not necessarily imply that there is a true time dependence in these parameters. Rather, this can be explained by a deficiency of data when fitting a two-dimensional surface (S is a function of q and ∆) based on data along a single curve for a fixed value of ∆.  Without properly sampling the surface across two independent coordinates, one cannot expect a fully reliable fit.  Indeed, a great advantage of our proposed method is to allow fitting data with multiple values of ∆, and thereby getting a richer data set with which to fit the full signal surface S(q, ∆).  The results for fitting ∆ = 19 ms and ∆= 49 ms data together clearly show the benefits of this approach, with superior contrast achieved.

Furthermore, Figure 7 shows that there is a tissue specific root mean squared error in model fitting over the two diffusion times which indicates greater deviation from the model fit in white matter than grey matter.

Although the errors are not completely tissue-independent, please note the magnitude of the RMSE is very small. The quality of the fitting in both white and grey matter is shown in sub-figures (A)-(H) for several representative voxels.

To show that the sub-diffusion model is robust and accurate (and consequently that K* is robust and accurate) the authors would have to demonstrate that there is no diffusion time-dependence in both D_β and β in application to brain imaging data for each diffusion time separately. Simulated data should not be used to demonstrate the robustness and accuracy of the sub-diffusion model or to determine optimization of dMRI acquisition parameters without first demonstrating that D_β and β are invariant to diffusion time. This is because simulated signals calculated by using the sub-diffusion characteristic equation of dMRI signal decay will necessarily have diffusion time invariant D_β and β parameters. Without further information demonstrating diffusion time invariance of D_β, β and K* it is not possible to determine whether the authors have achieved their aims or that their results support their conclusions.

First, as explained above, the dMRI signal S is a function of q and ∆, i.e., a two-dimensional surface S(q, ∆), and hence fitting data sampled from single diffusion time (i.e., one curve on the surface) cannot provide reliable parameters, as seen in the discrepancies in K* in Figure 6 (bottom two rows). Our proposed new sampling and fitting strategy overcomes this issue. That is, to obtain a reliable fitting, one should fit data from at least two diffusion times together (i.e., sampling data from at least two curves on the signal surface).

Second, to demonstrate that D_β and β are time invariant, one would require data at several diffusion times with high b values. Such data cannot be easily obtained. The data used in this current study is the MGH Connectome 1.0 human brain data, which only contains two diffusion times, ∆ = 19 ms and ∆ = 49 ms.

Hence, we conducted numerical experiments to demonstrate our idea. In Figure 3, we showed that (i) the variability of the fitted parameters is significantly reduced when moving from fitting single diffusion time data to two diffusion time data, and (ii) the difference in fitting three diffusion times compared to two is very minor, indicating convergence towards the correct time-independent parameter values. The results from fitting human brain data (Figure 6 and Tables 2-4) agree with the expectations from our numerical experiments. Hence, we believe that we have provided sufficient evidence to support our proposed sub-diffusion model and its optimal fitting strategy.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

It is clear that the authors preferred generating the data by using sub-diffusion model's signal expression as it has many benefits, such as allowing different diffusion times to be incorporated, and hence investigation of the effect of the number of diffusion times on the accuracy of the parameter fitting. I recommend adding another simulation study by generating the data with the DKI model expression (as the goal of the study is to provide an accurate mapping of diffusional mean kurtosis), fitting the data to the sub-diffusion model's expression in Eq. (10), and then calculating K* and D* by Eqs. (8) and (9) only for a fixed diffusion time and one b-value subset.

We appreciate the suggestion. However, unfortunately it is not appropriate to generate data with the DKI model, as the maximum b-value is limited to 2000~3000s/mm^2 and hence the DKI model cannot represent diffusion MRI signals from a full spectrum of b-values. A key strength of our proposed model is that it removes this limitation.

There is a typo on Page 24, Line 581; "b<=2400" should be b>=2400.

We have fixed this typo.

Reviewer #2 (Recommendations For The Authors):

As the authors state the sub-diffusion model has two parameters, D_β and β that are invariant to diffusion time, and give rise to a time-varying diffusion coefficient in mm^2s^-1 and a time invariant kurtosis. However, there is a need to be clearer and more specific about the implications of the sub-diffusion model. The manuscript would be improved by the authors:

(a) Defining the time-varying diffusion coefficient that arises from the model, its functional form and properties.

We refer Reviewer#2 to eq.(5) and eq.(8) for the definition of time-varying diffusion coefficients D* and D_SUB and their relationship.

(b) Clearly discuss the implications of this with respect to other time-varying diffusion coefficient methods in the current literature.

We refer Reviewer#2 to the section “Time-dependence of diffusivity and kurtosis” under “Discussions”.

(c) Demonstrating that D_β and β do not vary with diffusion time when estimated from dMRI acquired on human participants.

We have addressed this comment in the public review.

The manuscript would benefit from increases in clarity in all sections and the authors identifying typographical errors.

We have updated the relevant text in the revised manuscript to make it clearer, including fixing typos.

Specific improvements to clarity in the methods and results section would include:

Line 620: Why were parameter approximations for model fitting to simulated data restricted to the ranges D_β∈[10^(-4),10^(-3) ] and β∈[0.5,1] but in fitting to brain imaging data the ranges were D_β>0 and 0<β<=1.

The parameter ranges for model fitting to both the simulated and human data were set to the same: D_β>0 and 0<β<=1. To generate simulated data, D_β and β ranges were restricted to reflect observations in human brain data. We have updated the text to make this clearer.

Lines 622, 628 & 629: Which goodness of fit measure was used?

The goodness of fit measure for all simulated results is the coefficient of determination, or R^2 value, as noted in the “Goodness-of-fit and region-based statistical analysis” section under Methods. We have updated the text to make this clearer.

Line 666: The method for computation of R^2 within the coefficient of determination should be stated as there are several ways of calculating an R^2 value.

The formula for computing R^2 has been added to the text.

Line 685: A t-test is mentioned but it is not clear as to the inputs to this test, or where the results of this analysis are presented.

We have updated the text to make this clearer. The results of this analysis are presented in Table 5. The entries identified in italic under the optimal b-value heading were found to be significantly different from the benchmark mean K* reported in Table 2.

Line 696: It is not clear how the intra-class correlation coefficient histograms are computed from six subjects. This applies to results in Figure 10 that require greater clarity in the description.

The formula for computing the intra-class correlation coefficient has been added to the sub-section “Scan-rescan analysis using intraclass correlation coefficient (ICC)” under “Methods”.

It would be helpful if the authors primarily report results pertaining to the model parameters D_β and β. This is because D* and K* are calculated from D_β and β. Conditions for robust and accurate estimation of D_β and β will provide robust and accurate measures for D* and K*.

Two new tables for the model parameters D_β and β have been added. Please see Tables 3 and 4 in the revised manuscript.

The authors state that fitted model parameters are not affected by maximum b-value (paragraph beginning line 366). This statement is based on their model simulation results. Could the authors provide data to support this based on the application of their model to the human brain imaging data?

We would like to clarify that our statement is indeed based on human brain imaging. As stated in the paragraph beginning line 366, both results in Table 2 (using full dataset) and Table 5 (using dataset with optimal b-value sampling) are generated from the Connectome human brain data. If maximum b-value dependence is present, benchmark (Table 2) versus optimal region-specific results (Table 5, or previously Table 3) should show some systematic difference.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

The authors investigate the role of chirping in a species of weakly electric fish. They subject the fish to various scenarios and correlate the production of chirps with many different factors. They find major correlations between the background beat signals (continuously present during any social interactions) or some aspects of social and environmental conditions with the propensity to produce different types of chirps. By analyzing more specifically different aspects of these correlations they conclude that chirping patterns are related to navigation purposes and the need to localize the source of the beat signal (i.e. the location of the conspecific).

The study provides a wealth of interesting observations of behavior and much of this data constitutes a useful dataset to document the patterns of social interactions in these fish. Some data, in particular the high propensity to chirp in cluttered environments, raises interesting questions. Their main hypothesis is a useful addition to the debate on the function of these chirps and is worth considering and exploring further.

After the initial reviewers' comments, the authors performed a welcome revision of the way the results are presented. Overall the study has been improved by the revision. However, one piece of new data is perplexing to me. The new Figure 7 presents the results of a model analysis of the strength of the EI caused by a second fish to localize when the focal fish is chirping. From my understanding of this type of model, EOD frequency is not a parameter in the model since it evaluates the strength of the field at a given point in time. Therefore the only thing that matters is the phase relationship and strength of the EOD. Assuming that the second fish's EOD is kept constant and the phases relationship is also the same, the only difference during a chirp that could affect the result of the calculation is the potential decrease in EOD amplitude during the chirp. It is indeed logical that if the focal fish decreased its EOD amplitude the target fish's EOD becomes relatively stronger. Where things are harder to understand is why the different types of chirps (e.g. type 1 vs type 2) lead to the same increase in signal even though they are typically associated with different levels of amplitude modulations. Also, it is hard to imagine that a type 2 chirps that is barely associated with any decrease in EOD amplitude (0-10% maybe), would cause doubling of the EI strength. There might be something I don't understand but the authors should provide a lot more details on how this result is obtained and convince us that it makes sense.

We thank the author for the comments and we agree that the approach could have been better detailed. As anticipated by the Reviewer, the Boundary Element Method (BEM) model can be used simply to calculate the electric field and electric image at a specific point in time (instantaneously), regardless of EOD frequency. However, our model allows for the concatenation of consecutive instants and thus is able to render an entire sequence of electric fields - and resulting electric images - incorporating realistic EOD characteristics such as shape, duration, and frequencies (see Pedraja et al., 2014).

Chirp-triggered EIs were modeled using real chirps produced by interacting fish. Each chirp was thus associated to its duration and peak parameters, as well as the fish positional information (distance and angle). 

However, since we did not know the beat phase at which chirps were produced, we computed electric images for each fish position and chirp scenario by simulating various phases (here referred to the initial offset of the two EODs, set at 4 phases, equally spaced). These are intended as phases of the sender EOD and simply refer to the initial OFFSET between the two interacting EODs. However, since our simulations were run over a time window of 500 msec, all phases are likely to be covered, with a different temporal order relative to the chirp (always centered within the 500 msec).

The simulation was run maintaining consistent timing for both chirp and non-chirp conditions, across approximately 800 body nodes. At each node, the current flow was calculated from the peak-to-peak of the EOD sum (i.e. the point-to-point of the difference between the beat positive and negative envelopes). Analyzing the EIs over this fixed time window enables us to assess the unitary changes of current flow induced by chirps over units of time (ΔI/Δt). From this, we can calculate a cumulative sum of current flow changes - expressed as delta(EI) and use it to show the effect of the chirps on the spatiotemporal EI (Figure 7C).

One can express this cumulative change mapped onto the fish body (keeping the 800 points separated, as in Figure 7C) or further sum the current changes to obtain a single total (as shown in Figure 7D).

One can check this by considering that a sum for example of a set of 500/800 points - judging from the size of the blue areas in C not all 800 points have a detectable change - each valued 0.1-to-0.3 mA/s, one could get circa 100 mA/s, which is what is shown in D. (is this what is happening ?)

We do not know why chirps of different types triggered similar effects. It is possible that, since EI measurements are pooled over several chirps produced at different angles and distances, in case of a lower amount of chirps considered for a given type (as in the case of rises, very low) these measurements may not highlight more marked differences among types. In a publication we are currently working on, we are considering a larger dataset to better assess these results.

The methods section has been edited to clarify the approach (not yet).

Reviewer #2 (Public Review):

Studying Apteronotus leptorhynchus (the weakly electric brown ghost knifefish), the authors provide evidence that 'chirps' (brief modulations in the frequency and amplitude of the ongoing electric signal) function in active sensing (specifically homeoactive sensing) rather than communication. Chirping is a behavior that has been well studied, including numerous studies on the sensory coding of chirps and the neural mechanisms for chirp generation.

Chirps are largely thought to function in communication behavior, so this alternative function is a very exciting possibility that could have a great impact on the field.

We thank the Reviewer for the extensive and constructive comments. We would like to add that, while it is true that many detailed studies have been published on the anatomy and physiology of the circuits implicated in the production and modulation of “electric chirps”, most of this  research assumed, and focused exclusively on, their possible role in communication.  In addition, most behavioral studies did the same and a meta-analysis of the existing literature on chirping allows to trace back the communication idea mainly to two studies: Hagedorn and Heiligenberg, 1985 (“Court and spark: electric signals in the courtship and mating of gymnotoid fish”) and Hopkins, 1974 (“Electric Communication: Functions in the Social Behavior of Eigenmannia Virescens”), among the main sources. Importantly, in these studies only contextual observations have been made (no playback experiment or other attempts to analyze more quantitatively the correlation of chirping with other behaviors).

The authors do provide convincing evidence that chirps may function in homeoactive sensing. However, their evidence arguing against a role for chirps in communication is not as strong, and fails to sufficiently consider the evidence from a large body of existing research. Ultimately, the manuscript presents very interesting data that is sure to stimulate discussion and follow-up studies, but it suffers from dismissing evidence in support of, or consistent with, a communicative function for chirps.

Although the tone of some statements present in our earlier draft may suggest otherwise, through our revisions, we have made an effort to clarify that we do not intend to dismiss a function of chirps in communication, we only intend to debate and discuss valid alternative hypothesis, advanced from reasonable considerations.

Before writing this manuscript, we have attempted to survey  literally all the existing literature on chirps (including studies focused on behavior, peripheral sensory physiology as well as brain physiology). Although it is not unlikely that some studies have eluded our attention, an effort for a comprehensive review was made. Based on this survey we realized that none of the studies provided a clear  and  unambiguous piece of evidence to support the communication hypothesis (we refer here to the weak points highlighted in the discussion and mentioned in the previous comment). Which in fact does not come without its weak points and contradictions (see later comments).

It follows a summary of the mentions made to the communication theory in the different section of the manuscript including several edits we have applied in response to the Reviewer’s concern:

In the abstract we clearly state that we are considering an alternative that is only hypothetically complementary, not for sure.  Nonetheless, we have identified a couple of instances that could sound dismissive of the “communication hypothesis” in the following section.

In the introduction we write in fact about the possibility of interference between communication signals and conspecific electrolocation cues, as they are both detected as beat perturbations. We did not mean to use “Interference” here as “reciprocal canceling”, rather we intended it as “partial or more or less conspicuous overlap” in the responses triggered in electroreceptors.

Hoping to convey a clearer message, we have edited the related statement and changed it to “both types of information are likely to overlap and interact in highly variable ways”.

We have also removed the statement: “According to this idea, beats and chirps are not only detected through the same input channel, but also used for the same purpose.” as at this point in the manuscript it may be too strong.

In the results section we do not include statements that might be seen as dismissive of the communication hypothesis but only statements in support of the “probing with chirps” idea (which is the central hypothesis of the study).

In the discussion paragraphs we elaborate on why the current functional view is either flawed or incomplete (first paragraph “existing functional hypotheses''). Namely: 1)  multiple triggering factors implied in chirp responses covary and need to be disentangled (example DF/ sex), 2) findings on brown ghosts and a few other gymnotiforms have been used to advance the hypothesis of “communication through chirps'' in all weakly electric fish (including pulse species). 3) social encounters - in which chirps are recorded - imply also other behaviors (such as probing) which have not been considered so far. This point is related to the first one on covariates. 4) most studies referring to big chirps as courtship chirps were not done in reproductive animals (added now)  and 5) no causal evidence has been provided so far to justify a role of chirps in social communication.

We are discussing these points as challenges to the communication hypothesis, not to dismiss the hypothesis, but rather to motivate future studies addressing these challenges.

We do not want to appear dismissive of the communication hypothesis and had therefore previously edited the manuscript to avoid the impression of exclusivity of the probing hypothesis. We have now gone over the manuscript once more and edited several sentences. Nevertheless, we want to point out again that - despite the large consensus - the communication hypothesis has, until now, never been investigated with the kind of rigor applied here.

The authors do acknowledge that chirps could function as both a communication and homeactive sensing signal, but it seems clear they wish to argue against the former and for the latter, and the evidence is not yet there to support this.

In both rounds of revision we have made an effort to convey a more inclusive interpretation of our findings. We tried our best to express our ideas as hypothetical, not as proof that communication through chirps does not exist. The aim of this study is to propose an alternative view, and this cannot be done without underlining the weak points of an existing hypothesis while providing and supporting reasonable arguments in favor of the alternative we advance. The actual evidence for a role of chirping in communication is much less strong than appears from the pure number of articles that have discussed chirps in this context.

Regarding the weak evidence against communication, here we can list a few additional important points related to the proposed interpretations of chirp function (more specific than those made earlier):

(1) A formally sound assessment of signal value/meaning - as typically done in animal communication studies should involve: 

a) the isolation of a naturally occurring signal and determination of the context in which it is produced 

b) the artificial replication of the signal

c) the observation that such mimic is capable of triggering reliable and stereotyped responses in a group of individuals (identified by sex and/or species) under the same conditions (conditioned, unconditioned, state-dependent, etc.). As discussed for instance in Bradbury and Vehrencamp, 2011; Laidre and Johnstone, 2013; Wyatt, 2015; Rutz et al., 2023.

This approach has so far not been applied to weakly electric fish. The initial purpose of the present study was in fact to conduct this type of validation.

(2) The hypothesis of chirps used for DF-sign discrimination - for “social purposes” - although plausible in the face of theoretical considerations,  does not seem to be reasonable in practice, when one considers emission rates of 150 chirps per minute. We do find a strong correlation of chirp type with DF, which is often very abrupt and sudden (as if the fish were tracking beat frequency to guess its value) but the consideration made above on chirp rates seems to discourage this interpretation.

(3) The hypothesis of chirp-patterning (i.e. chirping may have meaning based on the sequence of chirps of different types, a bit like syllables in birdsongs) - assessed by only one study conducted in our group - has not been enough substantiated by replication. We have surveyed all possible combinations of chirps produced by interacting pairs in different behavioral conditions using different value for chirp sequence size: 2, 3,... ,8 chirps (both considering the sender alone as well as sender+receiver together). In all cases we found no evidence for  a context dependent “modulation” of chirp types (i.e. no specific chirp type sequence in specific contexts).

(4) The hypothesized role of “large chirps” as courtship signals could be easily criticized by noting the symmetrical distribution of these events around  a DF of 0 Hz . Although one could argue about a failure to discriminate DF-sign, to explain this well known pattern. However, we know from Walter Heiligenberg’s work and physiological considerations that such task can be solved easily through t-units and … in principle even just by motion (which would change the EOD phase in frequency dependent ways, thus potentially revealing the DF sign).

Overall, these considerations made us think that certainly chirping occurs in a social context, but it is the meaning of this behavior that remains elusive.  We noticed that environmental factors are also strongly implied … we then formulate an alternative hypothesis to explain chirping but we do so  without dismissing the communication idea.

All this seems to us just a careful way to critically discuss our results and those of other studies, without considering the issue resolved.

In the introduction, the authors state, "Since both chirps and positional parameters (such as size, orientation or motion) can only be detected as perturbations of the beat, and via the same electroreceptors, the inputs relaying both types of information are inevitably interfering." I disagree with this statement, which seems to be a key assumption. Both of these features certainly modulate the activity of electroreceptors, but that does not mean those modulations are ambiguous as to their source. You do not know whether the two types of modulations can be unambiguously decoded from electroreceptor afferent population activity.

We thank the Reviewer for noting this imprecision. We have addressed the Reviewer’s concern in another reply (see above).

My biggest issue with this manuscript is that it is much too strong in dismissing evidence that chirping correlates with context. In your behavioral observations, you found sex differences in chirping as well as differences between freely interacting and physically separated fish. Chirps tended to occur in close proximity to another fish. Your model of chirp variability found that environmental experience, social experience, and beat frequency (DF) are the most important factors explaining chirp variability. Are these not all considered behavioral or social context? Beat frequency (DF) in particular is heavily downplayed as being a part of "context" but it is a crucial part of the context, as it provides information about the identity of the fish you're interacting with. The authors show quite convincingly that the types of chirps produced do not vary with these contexts, but chirp rates do.

We believe the “perceived claim” may be an issue of unclear writing. We have now tried to better clarify that “context” affects chirp rates, but it does not affect chirp types as much (except when beat frequency is high).  

We have edited two statements possibly susceptible to misinterpretation: 

(1) In the results: “It also indicates that chirp parameters such as duration and FM do not seem to be associated with any particular context in a meaningful way, other than being affected by beat frequency.”

(2) In the discussion: the statement

“Recordings from interacting fish pairs confirmed the absence of any significant correlation between chirp type choice and behavioral context (Figure S2) although the variance of chirp parameters appears to be significantly affected by this factor (Figure 2). This may suggest that the effect of behavioral context is mainly detectable in the number of chirps produced (Figure S1), rather than the type (Figure S2).”

has been changed to:

“Recordings from interacting fish pairs confirmed the absence of any significant correlation between chirp type choice and behavioral context, except for those cases characterized by higher beat frequencies  (Figure S2). This suggests that the effect of behavioral context highlighted in our factor analysis (Figure 2) is mainly due to the number of chirps produced (Figure S1), rather than their type (Figure S2).”

Eventually, in the results we emphasize the relatively higher impact of previously unexplored factors on chirp variance: “The plot of individual chirps (Figure 2C) shows the presence of clustering around different categorical variables and it reveals that experience levels or swimming conditions are important factors affecting chirp distribution (note for instance the large central “breeding” cluster in which fish are divided and the smaller ones in which fish are free). Sender or receiver identity does not individuate any clear clustering relative to either sex (see the overlap of male_s/male_r and female_s/female_r) or social status (dominant/subordinate). Chirps labeled based on tank experience (i.e. resident vs intruder) are instead clearly separated.”.

Further, in your playback experiments, fish responded differently to small vs. large DFs, males chirped more than females, type 2 chirps became more frequent throughout a playback, and rises tended to occur at the end of a playback. These are all examples of context-dependent behavior.

We do note that male brown ghosts chirp more than females. But we do also say - and show in figure 8 - that males move more in proximity to and around conspecifics. We do acknowledge that chirp time-course may be different during playbacks in a type-dependent manner. But how this can support the communication hypothesis - or other alternatives - is unclear. This result could equally imply the use of different chirp types for different probing needs. Since we cannot be sure about either, we do not want to put too much emphasis to it. Eventually, the fact that “context” (here meant broadly to define different experimental situations in which social but also physical and environmental parameters are altered) affects chirping is undeniable: cluttered and non-cluttered environments do represent different contexts which differently affect chirping in conspicuous ways.

In the results, the authors state, "Overall, the majority of chirps were produced by male subjects, in comparable amounts regardless of environmental experience (resident, intruder or equal; Figure S1A,C), social status (dominant or subordinate; Figure S1B) or social experience (novel or experienced; Figure S1D)." This is not what is shown in Figure S1. S1A shows clear differences between resident vs. intruder males, S1B shows clear differences between dominant vs. subordinate males, and S1D shows clear differences between naïve and experienced males. The analysis shown in Figure 2 would seem to support this. Indeed, the authors state, "Overall, this analysis indicated that environmental and social experience, together with beat frequency (DF) are the most important factors explaining chirp variability."

The Reviewer is right in pointing at this imprecise reference and we are grateful for spotting this incongruence. The writing refers probably to an earlier version of the figure in which data were grouped and analyzed differently. We now edited the text and changed it to: “Overall, the majority of chirps were produced by male subjects, at rates that seemed  affected by environmental experience (resident, intruder or equal; Figure S1A,C), social status (dominant or subordinate; Figure S1B) and social experience (novel or experienced; Figure S1D).”

The choice of chirp type varied widely between individuals but was relatively consistent within individuals across trials of the same experiment. The authors interpret this to mean that chirping does not vary with internal state, but is it not likely that the internal states of individuals are stable under stable conditions, and that individuals may differ in these internal states across the same conditions? Stable differences in communication signals between individuals are frequently interpreted as reflecting differences between those individuals in certain characteristics, which are being communicated by these signals.

It seems here we have been unclear in the writing: while it is true that behavioral states are stable and can imply stable chirp patterning (if the two are related), since chirp types vary abruptly and in a reliable DF-dependent manner, different types of chirps are unlikely to be matched to different internal states following the same temporal order in such a reliable way (similarly repeated through consecutive trials).

This would imply the occurrence of different internal states in rapid sequence, reliably triggered by repeated EOD ramps, regardless of whether the playback is 20 sec long or 180 sec long.

We have edited this paragraph to better explain this: “The reliability by which the chirping response adapts to both the rate and direction of beat frequency is variable across individuals but rather stable across trials (relative to a given subject), further suggesting that chirp type variations may not reflect changes in internal states or in the animal motivation to specific behavioral displays (which are presumably subject to less abrupt variations and stereotypical patterning based on DF).”

I am not convinced of the conclusion drawn by the analysis of chirp transitions. The transition matrices show plenty of 1-2 and 2-1 transitions occurring.

The only groups in which 1-2 and 2-1 transitions are as frequent as 1-1 and 2-2 (being 1 and 2 the numerical IDs of the two interacting fish) are F-F pairs. This is a result of the fact that in females chirp rates are so low that within-fish-correlations end up being as low as between-fish-correlations. We believe the impression of the Reviewer could be due to the fact that these are normalized maps (see legend of Figure 5A-B).

Further, the cross-correlation analysis only shows that chirp timing between individuals is not phase-locked at these small timescales. It is entirely possible that chirp rates are correlated between interacting individuals, even if their precise timing is not.

We agree with the Reviewer, this is a possibility. To address this point, we did edit the results section to acknowledge that what we see may be related to the time window chosen (i.e. 4 sec):

“More importantly, they show that - at least in the social conditions analyzed here and within small-sized time windows - chirp time series produced by different fish during paired interactions are consistently independent of each other.”

Further, it is not clear to me how "transitions" were defined. The methods do not make this clear, and it is not clear to me how you can have zero chirp transitions between two individuals when those two individuals are both generating chirps throughout an interaction.

We thank the Reviewer for bringing up this unclear point. We have now clarified how transitions were calculated in the method section: “The number of chirp transitions present in each recording (dataset used for Figures 1, 2, 5) was measured by searching in a string array containing the 4 chirp types per fish pair, all their possible pairwise permutations (i.e. all possible permutations of 4+4=8 elements are: 1-1, 1-2, 1-3 … 7-6, 7-7, 7-8; considering the following legend 1 = fish1 type 1, 2 = fish 1 type 2, 3 = fish1 type 3 … 6 = fish2 type 2, 7 = fish2 type 3 and 8 = fish2 rise).”.

Zero transitions are possible if two fish (or groups of fish) do not produce chirps of all types. Only transitions of produced types can be counted.

In the results, "Although all chirp types were used during aggressive interactions, these seemed to be rather less frequent in the immediate surround of the chirps (Figure 6A)." A lack of precise temporal correlation on short timescales does not mean there is no association between the two behaviors. An increased rate of chirping during aggression is still a correlation between the two behaviors, even if chirps and specific aggressive behaviors are not tightly time-locked.

The Reviewer is right in pointing out the limited temporal scaling of our observations/analysis. We have now edited the last paragraph of the results related to figure 6 to include the possibility mentioned by the Reviewer: “The significantly higher extent of chirping during swimming and locomotion, consistently confirmed by 4 different approaches (PSTH, TM, CN, MDS), suggests that - although chirp-behavior correlations may exist at time-scales larger than those here considered - chirping may be linked more strongly with scanning and environmental exploration than with a particular motivational state, thus confirming findings from our playback experiments.”

The Reviewer here remarks an important point, yet, due to space limitations, we have considered only a sub-second scale. Most playback experiments in weakly electric fish implied the use of EOD mimics for a few tens of seconds - to avoid habituation in the fish behavioral responses -  while inter-chirp intervals usually range between a few hundreds of milliseconds to seconds (depending on how often a fish would chirp). This suggested to us that a 4 second time window may not be a bad choice to start with.

In summary, it is simply too strong to say that chirping does not correlate with context, or to claim that there is convincing evidence arguing against a communication function of chirps. Importantly, however, this does not detract from your exciting and well-supported hypothesis that chirping functions in homeoactive sensing. A given EOD behavior could serve both communication and homeoactive sensing. I actually suspect this is quite common in electric fish (both gymnotiforms and mormyrids), and perhaps in other actively sensing species such as echolocating animals. The two are not mutually exclusive.

We agree with the Reviewer that context - broadly speaking - does affect chirping (as we mentioned above). We hope we have improved the writing and clarified that we do not dismiss communication functions of chirping, but we do lean towards electrolocation based on the considerations above made and our results.

We do conclude the manuscript remarking that communication and electrolocation are not mutually exclusive: ”probing cues could function simultaneously as proximity signals to signal presence, deter approaches, or coordinate behaviors like spawning, if properly timed (Henninger et al., 2018).” (see the conclusion paragraph of the discussion) .

Therein, we further add “These findings aim to stir the pot and initiate a discussion on possible alternative functions of chirps beyond their presumed communication role.”.

With this, we hope we’ve made it clear how we intend our manuscript to be read.

Reviewer #3 (Public Review):

Summary:

This important paper provides the best-to-date characterization of chirping in weakly electric fish using a large number of variables. These include environment (free vs divided fish, with or without clutter), breeding state, gender, intruder vs resident, social status, locomotion state and social and environmental experience, without and with playback experiments. It applies state-of-the-art methods for reducing the dimensionality of the data and finding patterns of correlation between different kinds of variables (factor analysis, K-means). The strength of the evidence, collated from a large number of trials with many controls, leads to the conclusion that the traditionally assumed communication function of chirps may be secondary to its role in environmental assessment and exploration that takes social context into account. Based on their extensive analyses, the authors suggest that chirps are mainly used as probes that help detect beats caused by other fish and as well as objects.

Strengths:

The work is based on completely novel recordings using interaction chambers. The amount of new data and associated analyses is simply staggering, and yet, well organized in presentation. The study further evaluates the electric field strength around a fish (via modelling with the boundary element method) and how its decay parallels the chirp rate, thereby relating the above variables to electric field geometry.

The main conclusions are that the lack of any significant behavioural correlates for chirping, and the lack of temporal patterning in chirp time series, cast doubt on a primary communication goal for most chirps. Rather, the key determinants of chirping are the difference frequency between two interacting conspecifics as well as individual subjects' environmental and social experience. The paper concludes that there is a lack of evidence for stereotyped temporal patterning of chirp time series, as well as of sender-receiver chirp transitions beyond the known increase in chirp frequency during an interaction.

These conclusions by themselves will be very useful to the field. They will also allow scientists working on other "communication" systems to perhaps reconsider and expand the goals of the probes used in those senses. A lot of data are summarized in this paper, with thorough referencing to past work.

The alternative hypotheses that arise from the work are that chirps are mainly used as environmental probes for better beat detection and processing and object localization, and in this sense are self-directed signals. This led to their prediction that environmental complexity ("clutter") should increase chirp rate, which is fact was revealed by their new experiments. The authors also argue that waveform EODs have less power across high spatial frequencies compared to pulse-type fish, with a resulting relatively impoverished power of resolution. Chirping in wave-type fish could temporarily compensate for the lower frequency resolution while still being able to resolve EOD perturbations with a good temporal definition (which pulse-type fish lack due to low pulse rates).

The authors also advance the interesting idea that the sinusoidal frequency modulations caused by chirps are the electric fish's solution to the minute (and undetectable by neural wetware) echo-delays available to it, due to the propagation of electric fields at the speed of light in water. The paper provides a number of experimental avenues to pursue in order to validate the non-communication role of chirps.

We thank the reviewer for the kind assessment.

Weaknesses:

My main criticism is that the alternative putative role for chirps as probe signals that optimize beat detection could be better developed. The paper could be clearer as to what that means precisely, especially since beating - and therefore detection of some aspects of beating due to the proximity of a conspecific - most often precedes chirping. One meaning the authors suggest, tentatively, is that the chirps could enhance electrosensory responses to the beat, for example by causing beat phase shifts that remediate blind spots in the electric field of view.

We agree with the Reviewer that a better and more detailed explanation of how beat processing for conspecific electrolocation may be positively affected by chirps would be important to provide. We are currently working on a follow-up manuscript in which we intend to include these aspects. For space limitations and readability we had to discard from the current manuscript a lot of results that could further clarify these issues.

A second criticism is that the study links the beat detection to underwater object localization. The paper does not significantly develop that line of thought given their data - the authors tread carefully here given the speculative aspect of this link. It is certainly possible that the image on the fish's body of an object in the environment will be slightly modified by introducing a chirp on the waveform, as this may enhance certain heterogeneities of the object in relation to its environment. The thrust of this argument derives mainly from the notion of Fourier analysis with pulse type fish EOD waveforms (see above, and radar theory more generally), where higher temporal frequencies in the beat waveform induced by the chirp will enable a better spatial resolution of objects. It remains to be seen whether experiments can show this to be significant.

Perhaps the Reviewer refers to the last discussion paragraph before the conclusions in which we mention the performance of pulse or wave-type EODs in electrolocation (referring here to ideas illustrated in a recent review by Crampton, 2019). We added to this paragraph a statement which could better clarify that we do not propose that chirping could enhance object electrolocation. What we mean is that, in a context in which object electrolocation occurs through wave-type EODs - given the generally lower performance of such narrow-band signals in resolving the spatial features of any object, even a 3D electric field  - chirping could improve beat detection during social encounters by increasing the amount of information obtained by the fish.

The edited paragraph now reads: “While broadband pulse signals may be useful to capture highly complex environments rich in foliage, roots and other structures common in vegetation featuring the more superficial habitats in which pulse-type fish live, wave-type EODs may be a better choice in the relatively simpler river-bed environments in which many wave-type fish live (e.g., the benthic zone of deep river channels; Crampton, 2019). In this case, achieving a good spatial resolution is critical during social encounters, especially considering the limited utility of visual cues in these low-light conditions. In such habitats, social encounters may “electrically” be less “abrupt”, but spatially less “conspicuous” or blurred (as a 3D electric field may be). In such a scenario, chirps could serve as a means to supplement the spatial information acquired via the beat, accentuating these cues during periods of reduced resolution.”

Recommendations for the authors:

Reviewer #3 (Recommendations For The Authors):

None, my points in the original review have been properly addressed in this resubmission.

Author response:

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

Public Reviews: 

Reviewer #1 (Public Review): 

Summary: 

This manuscript presented a useful toolkit designed for CyTOF data analysis, which integrates 5 key steps as an analytical framework. A semi-supervised clustering tool was developed, and its performance was tested in multiple independent datasets. The tool was compared to human experts as well as supervised and unsupervised methods. 

Strengths: 

The study employed multiple independent datasets to test the pipeline. A new semi-supervised clustering method was developed. 

Weaknesses: 

The examination of the whole pipeline is incomplete. Lack of descriptions or justifications for some analyses. 

We thank the reviewer’s overall summary and comments of this manuscript. In the last part of the results, we showcased the functionalities of ImmCellTyper in covid dataset, including quality check, BinaryClust clustering, cell abundance quantification, state marker expression comparison within each identified cell types, cell population extraction, subpopulation discovery using unsupervised methods, and data visualization etc. We added more descriptions in the text based on the reviewer’s suggestions. 

Reviewer #2 (Public Review): 

Summary: 

The authors have developed marker selection and k-means (k=2) based binary clustering algorithm for the first-level supervised clustering of the CyTOF dataset. They built a seamless pipeline that offers the multiple functionalities required for CyTOF data analysis. 

Strengths: 

The strength of the study is the potential use of the pipeline for the CyTOF community as a wrapper for multiple functions required for the analysis. The concept of the first line of binary clustering with known markers can be practically powerful. 

Weaknesses: 

The weakness of the study is that there's little conceptual novelty in the algorithms suggested from the study and the benchmarking is done in limited conditions. 

We thank the reviewer’s overall summary and comments of this manuscript. While the concept of binary clustering by k-means is not novel, BinaryClust only uses it for individual markers to identify positive and negative cells, then combine it with the pre-defined matrix for cell type identification. This has not been introduced elsewhere. Furthermore, ImmCellTyper streamlines the entire analysis process and enhances data exploration on multiple levels. For instance, users can evaluate functional marker expression level/cellular abundance across both main cell types and subpopulations; Also, this computational framework leverages the advantages of both semi-supervised and unsupervised clustering methods to facilitate subpopulation discovery. We believe these contributions warrant consideration as advancements in the field.  

As for the benchmarking, we limited the depth only to main cell types rather than subpopulations. The reason is because we only apply BinaryClust to identify main cell types; For the cell subsets discovery, unsupervised methods integrated in this pipeline has already been published and widely used by the research community. Therefore, it does not seem to be necessary for additional benchmarking.

Reviewer #3 (Public Review): 

Summary: 

ImmCellTyper is a new toolkit for Cytometry by time-of-flight data analysis. It includes BinaryClust, a semi-supervised clustering tool (which takes into account prior biological knowledge), designed for automated classification and annotation of specific cell types and subpopulations. ImmCellTyper also integrates a variety of tools to perform data quality analysis, batch effect correction, dimension reduction, unsupervised clustering, and differential analysis. 

Strengths: 

The proposed algorithm takes into account the prior knowledge. 

The results on different benchmarks indicate competitive or better performance (in terms of accuracy and speed) depending on the method. 

Weaknesses: 

The proposed algorithm considers only CyTOF markers with binary distribution. 

We thank the reviewer’s overall summary and comments of this manuscript. Binary classification can be considered as an imitation of human gating strategy, as it is applied to each marker. For example, when characterizing the CD8 T cells, we aim for CD19-CD14-CD3+CD4- population, which is binary in nature (either positive and negative) and follows the same logic as the method (BinaryClust) we developed. Results indicated that it works very well for well-defined main cell lineages, particularly when the expression of the defining marker is not continuous. However, the limitation is for subpopulation identification, because a handful makers behave in a continuum manner, so we suggest unsupervised method after BinaryClust, which also brings another advantage of identifying unknown subsets beyond our current knowledge, and none of the semi-supervised tools can achieve that. To address the reviewer’s concern, we considered the limitation of binary distribution, but it does not profoundly affect the application of the pipeline.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors): 

Many thanks for the reviewers’ comments and suggestions, please see below the point-to-point response:

(1) The style of in-text reference citation is not consistent. Many do not have published years.

The style of the reference citation has been revised and improved.  

(2) The font size in the table of Figure 1 is too small, so is Figure 2. 

The font size has been increased.

(3) Is flowSOM used as part of BinaryClust? How should the variable running speed of BinaryClust be interpreted, given that it is occasionally slower and sometimes faster than flowSOM in the datasets?

To answer reviewer’s question, flowSOM is not a part of BinaryClust. They are separate clustering methods that have been incorporated into the ImmCellTyper pipeline. As described in Figure 1, BinaryClust, a semi-supervised method, is used to classify the main cell lineages; while flowSOM, an unsupervised method, is recommended here for further subpopulation discovery. So, they operate independently of each other. To avoid confusions, we slightly modified Figure 1 for clarification.

Regarding the variability in running speed in Figure 4. The performance of algorithms can indeed be influenced by the characteristics of the datasets, such as size and complexity. The differences observed between the covid dataset and the MPN dataset, such as marker panel, experimental protocol, and data acquisition process etc., could account for this variation. Our explanation is that flowSOM suits better the data structure of covid dataset, which might be the reason why it is slightly faster to analyse compared to the MPN dataset. Moreover, for the covid dataset, the runtime for both BinaryClust and flowSOM is less than 100s, and the difference is not notable. 

(4) In the Method section ImmCellTyper workflow overview, it is difficult to link the description of the pipeline to Figure 8. There are two sub-pipelines in the text and seven steps in the figure. What are their relations? Some steps are not introduced in the text, such as Data transformation and SCE object construction. What is co-factor 5?

Figure 8 provides an overview of the entire workflow for CyTOF data analysis, starting from the raw fcs file data and proceeding until downstream analysis (seven steps). But the actual implementation of the pipeline was divided into two separate sections, as outlined in the vignettes of the ImmCellTyper GitHub page (https://github.com/JingAnyaSun/ImmCellTyper/tree/main/vignettes).

Users will initially run ‘Intro_to_batch_exam_correct’ to perform data quality check and identify potential batch effects, followed by ‘Intro_to_data_analysis’ for data exploration. We agree with the reviewer that the method for this section is a bit confusing, so we’ve added more description for clarification.

In processing mass cytometry data, arcsine transformation is commonly applied to handle zero values, skewed distributions, and to improve visualization as well as clustering performance. The co-factor here is used as a parameter to scale down the data to control the width of the linear region before arcsine transformation. We usually get the best results by using co-factor 5 for CyTOF data.   

(5) For differential analysis, could the pipeline analyze paired/repeated samples?

For the statistical step, ImmCellTyper supports both two-study group comparison using Mann-Whitney Wilcoxon test, and multiple study group comparison (n>2) using Kruskal Wallis test followed by post hoc analysis (pairwise Wilcoxon test or Dunn’s test) with multiple testing correction using Benjamini-Hochberg Procedure.

Certainly, this pipeline allows flexibilities, users can also extract the raw data of cell frequencies and apply suitable statistical methods for testing.

(6) In Figure 2A, the range of the two axes is different for Dendritic cells, which could be misleading. Why the agreement is bad for dendritic cells?

The range for the axes is automatically adapted to the data structure, which explains why they may not necessarily be equal. The co-efficient factor for the correlation of DCs is 0.958, compared to other cell types (> 0.99), it is relatively worse but does not indicate poor agreement.

Moreover, the abundance of DCs is much less than other cell types, comprising approximately 2-5% of whole cells. As a result, even small differences in abundance may appear to as significant variations. For example, a difference of 1% in DC abundance represents a 2-fold change, which can be perceived as substantial.

Overall, while the agreement for DCs may appear comparatively lower, it is not necessarily indicative of poor performance, considering both the coefficient factor and the relative abundance of DCs compared to other cell types.

(7) In the Results section BinaryClust achieves high accuracy, what method was used to get the p-value, such as lines 212, 213, etc.?

The accuracy of BinaryClust was tested using F-measure and ARI against ground truth (manual gating), the detailed description/calculation can be found in methods. For line 212 and 213, the p-value was calculated using ANOVA for the interaction plot shown in Figure 3. We’ve now added the statistical information into the figure legend.   

(8) The performance comparison between BinaryClust and LDA is close. The current comparison design looks unfair. Given LDA only trained using half data, LDA may outperform BinaryClust.

It is true that LDA was trained using half data, which is because this method requires manual gating results as training dataset to build a model, then apply the model to the rest of the files to label cell types. Here we used 50% of the whole dataset as training set. We are of course very happy to implement any additional suggestions for a better partition ratio.

(9) There are 5 key steps in the proposed workflow. However, not every step was presented in the Results.

Thanks for the comments. The results primarily focused on demonstrating the precision and performance of BinaryClust in comparison with ground truth and existing tools. Additionally, a case study showcasing the application/functions of the entire pipeline in a dataset was also presented. Due to limitation in space, the implementation details of the pipeline were described in the method section and github documentations, which users/readers can easily access.

Reviewer #2 (Recommendations For The Authors): 

The tools suggested by the authors could be potentially useful to the community. However, it's difficult to understand the conceptual novelty of the algorithms suggested here. The concept of binary clustering has been described before (https://doi.org/10.1186/s12859-022-05085-z, https://doi.org/10.1152/ajplung.00104.2022), and it mainly utilizes k-means clustering set to generate binary clusters based on selected markers. Other algorithms associated with the package are taken from other studies. 

We acknowledge the reviewer’s comment regarding the novelty of our method. While the concept of binary clustering by k-means has been previously described to transcriptome data, our approach applies it to CyTOF data analysis, which has not been introduced elsewhere. Furthermore, ImmCellTyper streamlines the entire analysis process and enhances data exploration on multiple levels. For instance, users can evaluate functional marker expression level/cellular abundance across both main cell types and subpopulations; Also, as stated in the manuscript, this computational framework leverages the advantages of both semi-supervised and unsupervised clustering methods to facilitate subpopulation discovery. We believe these contributions warrant consideration as advancements in the field.  

In addition, the benchmarking of clustering performance, especially to reproduce manual gating and comparison to tools such as flowSOM is not comprehensive enough. The result for the benchmarking test could significantly vary depending on how the authors set the ground truth (resolution of cell type annotations). The authors should compare the tool's performance by changing the depth of cell type annotations. Especially, the low abundance cell types such as gdT cells or DCs were not effectively captured by the suggested methods. 

Thanks for the comment. We appreciate the reviewer’s concern. However, as illustrated in figure 1, our approach uses BinaryClust, a semi-supervised method, to identify main cell types rather than directly targeting subpopulations. The reason is because semi-supervised method relies on users’ prior definition thus is limited to discover novel subsets. In the ImmCellTyper framework, unsupervised method was subsequently applied for subset exploration following the BinaryClust step.

Regarding benchmarking, we focused on testing the precision of BinaryClust for main cell type characterization, because it is what the method is used for in the pipeline, and we believe this is sufficient. As for the cell subsets discovery, the unsupervised methods we integrated has already been published and widely used by the research community. Therefore, it does not seem to be necessary for additional benchmarking.

Moreover, as shown in Figure 3 and Table 1, our results indicated that the F-measure for DCs and gdT cells in BinaryClust is 0.80 and 0.92 respectively, which were very close to ground truth and outperformed flowSOM, demonstrating its effectiveness. 

We hope these clarifications address the reviewer’s concern.

Minor comments: 

(1) In Figure 4, it's perplexing to note that BinaryClust shows the slowest runtime for the COVID dataset, compared to the MPN dataset, which features a similar number of cells. What causes this variation? Is it dependent on the number of markers utilized for the clustering? This should be clarified/tested. 

Thanks for the comment, but we are not sure that we fully understand the question. As shown in figure 4 that BinaryClust has slightly higher runtime in MPN dataset than covid dataset, which is reasonable because and the cell number in MPN dataset is around 1.6 million more than covid dataset.

(2) Some typos are noted: 

- DeepCyTOF and LDA use a maker expression matrix extracted → "marker"?* 

Corrected.

- Datasets(Chevrier et al.)which → spacing* 

Corrected.

- This is due to the method's reliance → spacing*

Corrected.

Reviewer #3 (Recommendations For The Authors): 

Is it possible to accommodate more than two levels within the clustering process, i.e., can the proposed semi-supervised clustering tool be extended to multi-levels instead of binary?

Thanks for the comments. Binary classification can be considered as an imitation of human gating strategy, as it is applied to each marker. For example, when characterizing the CD8 T cells, we aim for CD19-CD14-CD3+CD4- population, which is binary in nature (either positive and negative) and follows the same logic as the method (BinaryClust) we developed. Results indicated that it works very well for well-defined main cell lineages. However, the limitation is for subpopulation identification, because a handful of makers behave in a continuum manner, so we would suggest unsupervised method after BinaryClust, which also brings another advantage of identifying unknown subsets beyond our current knowledge, and none of the semi-supervised tools can achieve that. To answer the reviewer’s question, it is possible to set the number to 3,4,5 rather than just 2, but considering the design and rationale of the entire framework (as describe in the manuscript and above), it doesn’t seem to be necessary.

Could you please comment on why on the COVID dataset, BinaryClust was slower as compared to flowSOM?

Thanks for the question. The performance of algorithms can indeed be affected by the characteristics of the datasets, such as their size and complexity. The covid and MPN datasets differ in various aspects including marker panel, experimental protocol, and data acquisition process, among others, which wound account for the observed variation in speed. So, our explanation is flowSOM suits better for the structure of covid dataset than MPN dataset.  Additionally, for covid dataset, both BinaryClust and flowSOM have runtimes of less than 100s, and the difference between the two isn’t particularly dramatic.

Minor errors: 

Line#215 "(ref) " reference is missing

Added.

Figure 3, increase the font of the text in order to improve readability. 

Increased.

Line#229 didn't --> did not. 

Corrected

Line#293 repetition of the reference. 

The repetition is due to the format of the citation, which has been revised.

Author response:

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

Reviewer #1 (Public Review):  

Summary: 

In this study, Nandy and colleagues examine neural and behavioral correlates of perceptual variability in monkeys performing a visual change detection task. They used a laminar probe to record from area V4 while two macaque monkeys detected a small change in stimulus orientation that occurred at a random time in one of two locations, focusing their analysis on stimulus conditions where the animal was equally likely to detect (hit) or not-detect (miss) a briefly presented orientation change (target). They discovered two behavioral measures that are significantly different between hit and miss trials - pupil size tends to be slightly larger on hits vs. misses, and monkeys are more likely to miss the target on trials in which they made a microsaccade shortly before target onset. They also examined multiple measures of neural activity across the cortical layers and found some measures that are significantly different between hits and misses. 

Strengths: 

Overall the study is well executed and the analyses are appropriate (though multiple issues do need to be addressed). 

We thank the reviewer for their enthusiasm and their constructive comments which we address below.

Weaknesses: 

My main concern with this study is that with the exception of the pre-target microsaccades, the physiological and behavioral correlates of perceptual variability (differences between hits and misses) appear to be very weak and disconnected. Some of these measures rely on complex analyses that are not hypothesis-driven and where statistical significance is difficult to assess. The more intuitive analysis of the predictive power of trial outcomes based on the behavioral and neural measures is only discussed at the end of the paper. This analysis shows that some of the significant measures have no predictive power, while others cannot be examined using the predictive power analysis because these measures cannot be estimated in single trials. Given these weak and disconnected effects, my overall sense is that the current results do not significantly advance our understanding of the neural basis of perceptual variability. 

Reviewer #1 (Recommendations For The Authors): 

(1) Most of the effects are very small. For example, the difference in pupil size between hits and misses is ~0.08 z-score units. The differences in firing rates between hits and misses are in the order of 1-2% of normalized firing rates. While these effects may be significant, their contribution to perceptual variability could be negligible, as suggested by the analysis of predictive power at the end of the result section. On a related note, it would be useful to mention the analysis of predictive power earlier in the paper. The finding that some of the measures do not have significant predictive power w/r to behavioral outcome raises questions regarding their importance. Finally, it would strengthen the paper if the authors could come up with methods to assess the predictive power of the PPC and interlaminar SSC. Without such analyses, it is difficult to assess the importance of these measures. 

We expect that relatively small differences in early to intermediate sensory areas could cumulatively result in large differences in higher areas and contribute to the binary distinction between hits and misses. We certainly do not claim that these results completely explain state-dependent differences that determine the outcome of these trials. Instead, we have focused on neural signatures at the level of the V4 columnar microcircuit that might ultimately contribute to the variability in perception.

We would like to emphasize that, based on the reviewer’s recommendation, we have now analyzed our results separately for each animal (see below). The consistency and significance of our findings across both animals give us confidence that what we have reported here are important neural signatures underlying perceptual variability at threshold.

We would also like to note that SSC and PPC are now part of the standard toolkit of systems neuroscience and have been employed in numerous studies to our knowledge. While all measures come with their set of caveats and limitations, these two measures provide a frequency-resolved metric of the relationship between two temporal processes (point or continuous), which we believe provide insights into the interlaminar flow of information that we report here.

Unfortunately, limitations in the GLM method and the reliability of these analyses with limited data make it impossible for these two measures to be included. The GLM requires all variables to be defined for each trial in the input. SSC and PPC can be undefined at low firing rates and require a substantial amount of data to be reliably calculated. While we did consider imputing data or estimating SSC and PPC using multiple trials, we ultimately did not pursue this idea as the purpose of the GLM was to use simultaneous measurements from single trials. 

(2) What is the actual predictive power of the GLM model (i.e., what is the accuracy of predicting whether a given held-out trial will lead to a hit or a miss)? How much of this predictive power is accounted for by the effect of microsaccades? 

As the GLM is not a decoder, it does not classify whether a given left out trial will be a hit or a miss. However, the GLM was highly predictive compared to a constant model. This information has been added to Table 3. The deviance of the GLM with and without microsaccades as a variable was not significantly different (p >0.9).  

(3) The role of stimulus contrast is not explained clearly. Are all the analyses and figures restricted to a single contrast level? Was the contrast the same on both sides? If multiple contrasts are used, could contrast account for some of the observed neural-behavioral covariations? 

All of the analyses include stimuli of all tested contrast levels. Stimulus contrasts were the same at both locations (attended and unattended). We have added a more detailed description of the contrast in hit and miss trials (Lines 289-296 and reproduced that here: 

“Non-target stimulus contrasts were slightly different between hits and misses (mean:

33.1% in hits, 34.0% in misses, permutation test, 𝑝 = 0.02), but the contrast of the target was higher in hits compared to misses (mean: 38.7% in hits, 27.7% in misses, permutation test, 𝑝 = 1.6 𝑒 − 31). Firing rates were normalized by contrast in Figure 3. In all other figures, we considered only non-target stimuli, which had very minor differences in contrast (<1%) across hits and misses. While we cannot completely rule out any other effects of stimulus contrast, the normalization in Figure 3 and minor differences for non-target stimuli should minimize them.”

(4) Do the animals make false alarms (i.e., report seeing a target in non-target epochs)?

If not, then it is not clear that the animals are performing near their perceptual threshold. If the false-alarm rate is non-zero, it should be reported and analyzed for neural/behavioral correlates. Does the logistic regression fit allow for a false alarm rate? More generally, it would be useful to see a summary of behavioral performance, such as distribution of thresholds, lower and upper asymptotes, and detection rates on foil trials vs. matched target trials. 

The logistic regression does allow for a false alarm rate. We have reported additional behavioral parameters in Figure 1-figure supplement 3A-G.  

(5) As far as I can tell, all the analyses in the paper are done on data combined across the two animals. Given that these effects are weak and that the analyses are complex, it is important to demonstrate for each analysis/figure that the results hold for each animal separately before combining the data across animals. This can be done in supplementary figures. 

We have updated the paper to include all main results plotted separately for each animal as supplementary figures. 

- Figure 2-figure supplement 2

- Figure 3-figure supplement 1

- Figure 3-figure supplement 2

- Figure 4-figure supplement 1

- Figure 5-figure supplement 2

- Figure 7-figure supplement 1

All the results except for the canonical correlation analysis were present, consistent, and significant when we analyzed them in each monkey independently.

(6) The selection of the temporal interval used for the various analyses appears somewhat post hoc and is not explained clearly. Some analyses are restricted to the period immediately before or during target onset (e.g., 400 ms before target onset for analysis of the effect of microsaccade, 60 ms before stimulus onset for the analysis of the effect of neural variability). Other analyses are done on non-target rather than target stimuli. What is the justification for selecting these particular periods for these analyses? The differences in firing rates between hits and misses are restricted to the target epoch and are not present in the non-target epochs. Given these results, it seems important to compare the effects in target and non-target epochs in other analyses as well.

Restricting the analysis of the Fano Factor to 60 ms before non-target onset seems odd. Given that the duration of the interval between stimulus presentations is random, how could this pre-stimulus effect be time-locked to target onset? 

We selected a 200ms time window during the pre-stimulus or stimulus-evoked period for almost all our analyses. The results relating to microsaccade occurrence were robust to narrower time windows more consistent with the other pre-stimulus windows we used, but we chose to use the 400ms window to capture a larger fraction of trials with microsaccades. 

Only the Fano factor time window was selected post-hoc based on the traces in Figure 4A, and the result is robust across animals (new Figure 4-figure supplement 1). The inter-stimulus intervals are random, and we do not believe the neural variability is timelocked to upcoming stimuli, but that lower variability in this pre-stimulus window is characteristic of hits. 

We believe that the consistency of our results across both animals provides further evidence that our time window selection was appropriate. 

We are interested in the extent to which these effects would remain consistent when applied only to target stimuli. However, restricting our analyses to only target stimuli substantially reduces the amount of neural data available for analysis. We plan to explore target stimulus representation more thoroughly in future studies.   

(7) Can the measured neural response be used to discriminate between target and nontarget stimuli? If so, is the discriminability between target and non-target higher in hits vs. misses? 

Thank you for raising this interesting point. We performed this analysis and find that target stimuli are more discriminable from non-targets in hits compared to misses. This has been added as a new Figure 3A.  

(8) How many trials were performed per session? Did miss probability tend to increase over time over the session? If so, could this slow change in hit probability account for some of the observed neural and behavioral correlations with perceptual decisions? 

Monkeys initiated a median of 905 trials (range of 651 to 1086). This has been added to the manuscript (Line 106). Approximately 1/8 of those trials were at perceptual threshold. Hit probability at threshold does not change substantially over the course of the session. We now report this in new Figure 1- figure Supplement 3I (error bars show standard deviation). 

(9) Did miss probability depend on the time of the change within the trial? If so, do any of the behavioral/neural metrics share a similar within-trial time course? 

Change times were not significantly different across hit and miss trials (p=0.15, Wilcoxon rank sum test). We now report this in new Figure 1-figure supplement 3H.

(10) "Deep layer neurons exhibit reduced low-frequency phase-locking in hit trials than in misses (Figure 5B), suggesting an improvement in pooled signal-to-noise among this neural population." - why does this metric suggest improved SNR? Is there any evidence for improved SNR in the data? Why just in deep layers? 

Thank you for raising this question. We agree this statement is not fully supported by the data and have removed it.  

(11) I may have missed this but what were the sizes of the Gabor stimuli? 

This has been added to the methods section (Line 454). The Gaussian halfwidth was 2 degrees.  

Reviewer #2 (Public Review):  

In this manuscript, the authors conducted a study in which they measured eye movements, pupil diameter, and neural activity in V4 in monkeys engaged in a visual attention task. The task required the monkeys to report changes in the orientation of Gabors' visual stimuli. The authors manipulated the difficulty of the trials by varying the degree of orientation change and focused their analysis on trials of intermediate difficulty where the monkeys' hit rate was approximately 50%. Their key findings include the following: 1) Hit trials were preceded by larger pupil diameter, reflecting higher arousal, and by more stable eye positions; 2) V4 neurons exhibit larger visual responses in hit trials; 3) Superficial and deep layers exhibited greater coherence in hit trials during both the pre-target stimulus period and the non-target stimulus presentation period. These findings have useful implications for the field, and the experiments and analyses presented in this manuscript validly support the authors' claims. 

Strengths: 

The experiments were well-designed and executed with meticulous control. The analyses of both behavioural and electrophysiological data align with the standards in the field. 

We thank the reviewer for their enthusiasm about our study and their constructive comments which we address below.

Weaknesses: 

Many of the findings appear to be incremental compared to previous literature, including the authors' own work. While incremental findings are not necessarily a problem, the manuscript lacks clear statements about the extent to which the dataset, analysis, and findings overlap with the authors' prior research. For example, one of the main findings, which suggests that V4 neurons exhibit larger visual responses in hit trials (as shown in Fig. 3), appears to have been previously reported in their 2017 paper. Additionally, it seems that the entire Fig1-S1 may have been reused from the 2017 paper. These overlaps should have been explicitly acknowledged and correctly referenced. 

While the raw data used in this paper overlaps entirely with Nandy et al. (2017), all the analyses and findings in this manuscript are new and have not been previously reported. Figure 1-figure supplement 1 is modified and reproduced from that paper only to allow readers to understand the recording methods used to collect the data without needing to go back to the previous paper. We have added an explicit acknowledgment of this to the figure caption.

Previous studies have demonstrated that attention leads to decorrelation in V4 population activity. The authors should have discussed how and why the high coherence across layers observed in the current study can coexist with this decorrelation. 

We have updated the discussion section (Lines 347-351) to further elaborate on this interpretation. 

Furthermore, the manuscript does not explore potentially interesting aspects of the dataset. For instance, the authors could have investigated instances where monkeys made 'false' reports, such as executing saccades towards visual stimuli when no orientation change occurred. It would be valuable to provide the fraction of the monkeys' responses in a session, including false reports and correct rejections in catch trials, to allow for a broader analysis that considers the perceptual component of neural activity over pure sensory responses. 

We appreciate this feedback. While we agree these are interesting directions, we decided to limit the scope of this study to only focus on trials at threshold with an orientation change, and are considering these directions for future studies. 

Reviewer #2 (Recommendations For The Authors): 

• Figure Design: Since eLife does not impose space limitations, it is advisable for the authors to avoid using very small font sizes. Consistency in font size throughout the figures is recommended. Some figures are challenging to discern, for example, the mean+-sem in Fig. 2B, and the alpha values of green and purple colours for superficial/deep layers are too high, making them too transparent or pale. 

We have increased the size of some small fonts and improved font size consistency throughout the figures. We have changed the layer colors to improve legibility. 

• Line 119: trail, 

This has been fixed.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

The present study provides a phylogenetic analysis of the size prefrontal areas in primates, aiming to investigate whether relative size of the rostral prefrontal cortex (frontal pole) and dorsolateral prefrontal cortex volume vary according to known ecological or social variables.

I am very much in favor of the general approach taken in this study. Neuroimaging now allows us to obtain more detailed anatomical data in a much larger range of species than ever before and this study shows the questions that can be asked using these types of data. In general, the study is conducted with care, focusing on anatomical precision in definition of the cortical areas and using appropriate statistical techniques, such as PGLS.

I have read the revised version of the manuscript with interest. I agree with the authors that a focus on ecological vs laboratory variables is a good one, although it might have been useful to reflect that in the title.

I am happy to see that the authors included additional analyses using different definitions of FP and DLPFC in the supplementary material. As I said in my earlier review, the precise delineation of the areas will always be an issue of debate in studies like this, so showing the effects of different decisions in vital.

We thank the reviewer for these positive remarks and for these very useful suggestions on the previous version of this article.

I am sorry the authors are so dismissive of the idea of looking the models where brain size and area size are directly compared in the model, rather preferring to run separate models on brain size and area size. This seems to me a sensible suggestion.

We agree with the reviewer 1 and the response of reviewer 3 also made it clear to us of why it was an important issue. We have therefore addressed it more thoroughly this time.

First, we have added a new analysis, with whole brain volume included as covariate in the model accounting for regional volumes, together with the socio-ecological variables of interest. As expected given the very strong correlation across all brain measures (>90%), the effects of all socio-ecological factors disappear for both FP and DLPFC volumes when ‘whole brain’ is included as covariate. This is coherent with our previous analysis showing that the same combination of socio-ecological variables could account for the volume of FP, DLPFC and the whole brain. Nevertheless, the interpretation of these results remains difficult, because of the hidden assumptions underlying the analysis (see below).

Second, we have clarified the theoretical reasons that made us choose absolute vs relative measures of brain volumes. In short, we understand the notion of specificity associated with relative measures, but 1) the interpretation of relative measures is confusing and 2) we have alternative ways to evaluate the specificity of the effects (which are complementary to the idea of adding whole brain volume as covariate). 

Our goal here was to evaluate the influence of socio-ecological factors on specific brain regions, based on their known cognitive functions in laboratory conditions (working memory for the DLPFC and metacognition for the frontal pole). Thus, the null hypothesis is that socio-ecological challenges supposed to mobilize working memory and metacognition do not affect the size of the brain regions associated with these functions (respectively DLPFC and FP). This is what our analysis is testing, and from that perspective, it seems to us that direct measures are better, because within regions (across species), volumes provide a good index of neural counts (since densities are conserved), which are indicative fo the amount of computational resources available for the region. It is not the case when using relative measures, or when using the whole brain as covariate, since densities are heterogenous across brain regions (e.g. Herculano-Houzel, 2011; 2017, but see below for further details on this).

Quantitatively, the theoretical level of specificity of the relation between brain regions and socio-ecological factors is difficult to evaluate, given that our predictions are based on the cognitive functions associated with DLPFC and FP, namely working memory and metacognition, and that each of these cognitive functions also involved other brain regions. We would actually predict that other brain regions associated with the same cognitive functions as DLPFC or FP also show a positive influence of the same socioecological variables. Given that the functional mapping of cognitive functions in the brain remains debated, it is extremely difficult to evaluate quantitatively how specific the influence of the socio-ecological factors should be on DLPFC and FP compared to the rest of the brain, in the frame of our hypothesis.

Critically, given that FP and DLPFC show a differential sensitivity to population density, a proxy for social complexity, and that this difference is in line with laboratory studies showing a stronger implication of the FP in social cognition, we believe that there is indeed some specificity in the relation between specific regions of the PFC and socioecological variables. Thus, our results as a whole seem to indicate that the relation between prefrontal cortex regions and socio-ecological variables shows a small but significant level of specificity. We hope that the addition of the new analysis and the corresponding modifications of the introduction and discussion section will clarify this point.

Similarly, the debate about whether area volume and number of neurons can be equated across the regions is an important one, of which they are a bit dismissive.

We are sorry that the reviewer found us a bit dismissive on this issue, and there may have been a misunderstanding.

Based on the literature, it is clearly established that for a given brain region, area volume provides a good proxy for the number of neurons, and it is legitimate to generalize this relation across species if neuronal densities are conserved for the region of interest (see for example Herculano-Houzel 2011, 2017 for review). It seems to be the case across primates because cytoarchitectonic maps are conserved for FP and DLPFC, at least in humans and laboratory primates (Petrides et al, 2012; Sallet et al, 2013; Gabi et al, 2016; Amiez et al, 2019). But we make no claim about the difference in number of neurons between FP and DLPFC, and we never compared regional volumes across regions (we only compared the influence of socio-ecological factors on each regional volume), so their difference in cellular density is not relevant here. As long as the neuronal density is conserved across species but within a region (DLPFC or FP), the difference in volume for that region, across species, does provide a reliable proxy for the influence of the socioecological regressor of interest (across species) on the number of neurons in that region.

Our claims are based on the strength of the relation between 1) cross-species variability in a set of socio-ecological variables and 2) cross-species variability in neural counts in each region of interest (FP or DLPFC). Since the effects of interest relate to inter-specific differences, within a region, our only assumption is that the neural densities are conserved across distinct species for a given brain region. Again (see previous paragraph), there is reasonable evidence for that in the literature. Given that assumption, regional volumes (across species, for a given brain region) provide a good proxy for the number of neurons. Thus, the influence of a given socio-ecological variable on the interspecific differences in the volume of a single brain region provides a reliable estimate of the influence of that socio-ecological variable on the number of neurons in that region (across species), and potentially of the importance of the cognitive function associated with that region in laboratory conditions. None of our conclusions are based on direct comparison of volumes across regions, and we only compared the influence of socioecological factors (beta weights, after normalization of the variables).

Note that this is yet another reason for not using relative measures and not including whole brain as covariate in the regression model: Given that whole brain and any specific region have a clear difference in density, and that this difference is probably not conserved across species, relative measures (or covariate analysis) cannot be used as proxies for neuronal counts (e.g. Herculano-Houzel, 2011). In other words, using the whole brain to rescale individual brain regions relies upon the assumption that the ratios of volumes (specific region/whole brain) are equivalent to the ratios of neural counts, which is not valid given the differences in densities.

Nevertheless, I think this is an important study. I am happy that we are using imaging data to answer more wider phylogenetic questions. Combining detailed anatomy, big data, and phylogenetic statistical frameworks is a important approach.

We really thank the reviewer for these positive remarks, and we hope that this study will indeed stimulate others using a similar approach.

Reviewer #2 (Public Review):

In the manuscript entitled "Linking the evolution of two prefrontal brain regions to social and foraging challenges in primates" the authors measure the volume of the frontal pole (FP, related to metacognition) and the dorsolateral prefrontal cortex (DLPFC, related to working memory) in 16 primate species to evaluate the influence of socio-ecological factors on the size of these cortical regions. The authors select 11 socio-ecological variables and use a phylogenetic generalized least squares (PGLS) approach to evaluate the joint influence of these socio-ecological variables on the neuro-anatomical variability of FP and DLPFC across the 16 selected primate species; in this way, the authors take into account the phylogenetic relations across primate species in their attempt to discover the the influence of socio-ecological variables on FP and DLPF evolution.

The authors run their studies on brains collected from 1920 to 1970 and preserved in formalin solution. Also, they obtained data from the Mussée National d´Histoire Naturelle in Paris and from the Allen Brain Institute in California. The main findings consist in showing that the volume of the FP, the DLPFC, and the Rest of the Brain (ROB) across the 16 selected primate species is related to three socio-ecological variables: body mass, daily traveled distance, and population density. The authors conclude that metacognition and working memory are critical for foraging in primates and that FP volume is more sensitive to social constraints than DLPFC volume.

The topic addressed in the present manuscript is relevant for understanding human brain evolution from the point of view of primate research, which, unfortunately, is a shrinking field in neuroscience. But the experimental design has two major weak points: the absence of lissencephalic primates among the selected species and the delimitation of FP and DLPFC. Also, a general theoretical and experimental frame linking evolution (phylogeny) and development (ontogeny) is lacking.

We are sorry that the reviewer still believes that these two points are major weaknesses.

- We have added a point on lissencephalic species in the discussion. In short, we acknowledge that our work may not be applied to lissencephalic species because they cannot be studied with our method, but on the other hand, based on laboratory data there is no evidence showing that the functional organization of the DLPFC and FP in lissencephalic primates is radically different from that of other primates (Dias et al, 1996; Roberts et al, 2007; Dureux et al, 2023; Wong et al, 2023). Therefore, there is no a priori reason to believe that not including lissencephalic primates prevents us from drawing conclusions that are valid for primates in general. Moreover, as explained in the discussion, including lissencephalic primates would require using invasive functional studies, only possible in laboratory conditions, which would not be compatible with the number of species (>15) necessary for phylogenetic studies (in particular PGLS approaches). Finally, as pointed out by the reviewer, our study is also relevant for understanding human brain evolution, and as such, including lissencephalic species should not be critical to this understanding.

- In response to the remarks of reviewer 1 on the first version of the manuscript, we had included a new analysis in the previous version of the manuscript, to evaluate the validity of our functional maps given another set of boundaries between FP and DLPFC. But one should keep in mind that our objective here is not to provide a definitive definition of what the regions usually referred to as DLPFC and FP should be from an anatomical point of view. Rather, as our study aims at taking into account the phylogenetic relations across primate species, we chose landmarks that enable a comparison of the volume of cortex involved in metacognition (FP) and working memory (DLPFC) across species. We have also updated the discussion accordingly.

We agree that this is a difficult point and we have always acknowledged that this was a clear limitation in our study. In the light of the functional imaging literature in humans and non-human primates, as well as the neurophysiological data in macaques, defining the functional boundary between FP and DLPFC remains a challenging issue even in very well controlled laboratory conditions. As mentioned by reviewer 1, “the precise delineation of the areas will always be an issue of debate in studies like this, so showing the effects of different decisions in vital”. Again, an additional analyses using different boundaries for FP and DLPFC was included in the supplementary material to address that issue. Now, we are not aware of solid evidence showing that the boundaries that we chose for DLPFC vs FP were wrong, and we believe that the comparison between 2 sets of measures as well as the discussion on this topic should be sufficient for the reader to assess both the strength and the limits of our conclusion. That being said, if the reviewer has any reference in mind showing better ways to delineate the functional boundary between FP and DLPFC in primates, we would be happy to include it in our manuscript.

- The question of development, which is an important question per se,  is neither part of the hypothesis nor central for the field of comparative cognition in primates. Indeed, major studies in the field do not mention development (e.g. Byrne, 2000; Kaas, 2012; Barton, 2012). De Casien et al (2022) even showed that developmental constraints are largely irrelevant (see Claim 4 of their article): [« The functional constraints hypothesis […] predicts more complex, ‘mosaic’ patterns of change at the network level, since brain structure should evolve adaptively and in response to changing environments. It also suggests that ‘concerted’ patterns of brain evolution do not represent conclusive evidence for developmental constraints, since allometric relationships between developmentally linked or unlinked brain areas may result from selection to maintain functional connectivity. This is supported by recent computational modeling work [81], which also suggests that the value of mosaic or concerted patterns may fluctuate through time in a variable environment and that developmental coupling may not be a strong evolutionary constraint. Hence, the concept of concerted evolution can be decoupled from that of developmental constraints »].

Finally, when studies on brain evolution and cognition mention development, it is generally to discuss energetic constraints rather than developmental mechanisms per se (Heldstab et al 2022 ; Smaers et al, 2021;  Preuss & Wise, 2021; Dunbar & Schutz, 2017; MacLean et al, 2012. Mars et al, 2018; 2021). Therefore, development does not seem to be a critical issue, neither for our article nor for the field.

Reviewer #3 (Public Review):

This is an interesting manuscript that addresses a longstanding debate in evolutionary biology - whether social or ecological factors are primarily responsible for the evolution of the large human brain. To address this, the authors examine the relationship between the size of two prefrontal regions involved in metacognition and working memory (DLPFC and FP) and socioecological variables across 16 primate species. I recommend major revisions to this manuscript due to: 1) a lack of clarity surrounding model construction; and 2) an inappropriate treatment of the relative importance of different predictors (due to a lack of scaling/normalization of predictor variables prior to analysis).

We thank the reviewer for his/her remarks, and for the clarification of his /her criticism regarding the use of relative measures. We are sorry to have missed the importance of this point in the first place. We also thank the reviewer for the cited references, which were very interesting and which we have included in the discussion. As the reviewer 1 also shared these concerns, we wrote a detailed response to explain how we addressed the issue above.

First, we did run a supplementary analysis where whole brain volume was added as covariate, together with socio-ecological variables, to account for the volume of FP or DLPFC. As expected given the very high correlation across all 3 brain measures, none of the socio-ecological variables remained significant. We have added a long paragraph in the discussion to tackle that issue. In short, we agree with the reviewer that the specificity of the effects (on a given brain region vs the rest of the brain) is a critical issue, and we acknowledge that since this is a standard in the field, it was necessary to address the issue and run this extra-analysis. But we also believe that specificity could be assessed by other means: given the differential influence of ‘population density’ on FP and DLPFC, in line with laboratory data, we believe that some of the effects that we describe do show specificity. Also, we prefer absolute measures to relative measures because they provide a better estimate of the corresponding cognitive operation, because standard allometric rules (i.e., body size or whole brain scaling) may not apply to the scaling and evolution of FP and DLPFC in primates.. Indeed, given that we use these measures as proxies of functions (metacognition for FP and working memory for DLPFC), it is clear that other parts of the brain should show the same effect since these functions are supported by entire networks that include not only our regions of interest but also other cortical areas in the parietal lobe. Thus, the extent to which the relation with socio-ecological variables should be stronger in regions of interest vs the whole brain depends upon the extent to which other regions are involved in the same cognitive function as our regions of interest, and this is clearly beyond the scope of this study. More importantly, volumetric measures are taken as proxies for the number of neurons, but this is only valid when comparing data from the same brain region (across species), but not across brain regions, since neural densities are not conserved. Thus, using relative measures (scaling with the whole brain volume) would only work if densities were conserved across brain regions, but it is not the case. From that perspective, the interpretation of absolute measures seems more straightforward, and we hope that the specificity of the effects could be evaluated using the comparison between the 3 measures (FP, DLPFC and whole brain) as well as the analysis suggested by the reviewer. We hope that the additional analysis and the updated discussion will be sufficient to cover that question, and that the reader will have all the information necessary to evaluate the level of specificity and the extent to which our findings can be interpreted.

Recommendations for the authors:

Reviewer #2 (Recommendations For The Authors):

In my previous review of the present manuscript, I pointed out the fact that defining parts, modules, or regions of the primate cerebral cortex based on macroscopic landmarks across primate species is problematic because it prevents comparisons between gyrencephalic and lissencephalic primate species. The authors have rephrased several paragraphs in their manuscript to acknowledge that their findings do apply to gyrencephalic primates.

I also said that "Contemporary developmental biology has showed that the selection of morphological brain features happens within severe developmental constrains. Thus, the authors need a hypothesis linking the evolutionary expansion of FP and DLPFC during development. Otherwise, the claims form the mosaic brain and modularity lack fundamental support". I insisted that the author should clarify their concept of homology of cerebral cortex parts, modules, or regions cross species (in the present manuscript, the frontal pole and the dorsolateral prefrontal cortex). Those are not trivial questions because any phylogenetic explanation of brain region expansion in contemporary phylogenetic and evolutionary biology must be rooted in evolutionary developmental biology. In this regard, the authors could have discussed their findings in the frame of contemporary studies of cerebral cortex evolution and development, but, instead, they have rejected my criticism just saying that they are "not relevant here" or "clearly beyond the scope of this paper".

The question of development, which is an important question per se, is neither part of the hypothesis nor central for the field of comparative cognition in primates. Indeed, the major studies in the field do not mention development and some even showed that developmental constraints were not relevant (see De Casien et al., 2022 and details in our response to the public review). When studies on brain evolution and cognition mention development, it is generally to discuss energetic constraints rather than developmental mechanisms per se (Heldstab et al 2022 ; Smaers et al, 2021;  Preuss & Wise, 2021; Dunbar & Schutz, 2017;  MacLean et al, 2012. Mars et al, 2018; 2021).

If the other reviewers agree, the authors are free to publish in eLife their correlations in a vacuum of evolutionary developmental biology interpretation. I just disagree. Explanations of neural circuit evolution in primates and other mammalian species should tend to standards like the review in this link: https://royalsocietypublishing.org/doi/full/10.1098/ rstb.2020.0522

In this article, Paul Cizek (a brilliant neurophysiologist) speculates on potential evolutionary mechanisms for some primate brain functions, but there is surprisingly very little reference to the existing literature on primate evolution and cognition. There is virtually no mention of studies that involve a large enough number of species to address evolutionary processes and/or a comparison with fossils and/or an evaluation of specific socio-ecological evolutionary constraints. Most of the cited literature refers to laboratory studies on brain anatomy of a handful of species, and their relevance for evolution remains to be evaluated. These ideas are very interesting and they could definitely provide an original perspective on evolution, but they are mostly based on speculations from laboratory studies, rather than from extensive comparative studies. This paper is interesting for understanding developmental mechanisms and their constraints on neurophysiological processes in laboratory conditions, but we do not think that it would fit it in the framework of our paper as it goes far beyond our main topic.

Reviewer #3 (Recommendations For The Authors):

Yes, I am suggesting that the authors also include analyses with brain size (rather than body size) as a covariate to evaluate the effects of other variables in the model over and above the effect on brain size. In a very simplified theoretical scenario: two species have the same body sizes, but species A has a larger brain and therefore a larger FP. In this case, species A has a larger FP because of brain allometric patterns, and models including body size as a covariate would link FP size and socioecological variables characteristic of species A (and others like it). However, perhaps the FP of species A is actually smaller than expected for its brain size, while the FP of species B is larger than expected for its brain size.

As explained in our response to the public review, we did run this analysis and we agree with the reviewer’s point from a practical point of view: it is important to know the extent to which the relation with a set of socio-ecological variables is specific of the region of interest, vs less specific and present for other brain regions. Again, we are sorry to not have understood that earlier, and we acknowledge that since it is a standard in the field, it needs to be addressed thoroughly.

We understand that the scaling intuition, and the need to get a reference point for volumetric measures, but here the volume of each brain region is taken as a proxy for the number of neurons and therefore for the region’s computational capacities. Since, for a given brain region (FP or DLPFC) the neural densities seem to be well conserved across species, comparing regional volumes across species provides a good proxy for the contrast (across species) in neural counts for that region. All we predicted was that for a given brain region, associated with a given cognitive operation, the volume (number of neurons) would be greater in species for which socio-ecological constraints potentially involving that specific cognitive operation were greater. We do not understand how or why the rest of the brain would change this interpretation (of course, as discussed just above, beyond the question of specificity). And using whole brain volume as a scaling measure is problematic because the whole brain density is very different from the density of these regions of the prefrontal cortex (see above for further details). Again, we acknowledge that allometric patterns exist, and we understand how they can be interpreted, but we do not understand how it could prove or disprove our hypothesis (brain regions involved in specific cognitive operations are influenced by a specific set of socio-ecological variables). When using volumes as a proxy for computational capacities, the theoretical implications of scaling  procedures might be problematic. For example, it implies that the computational capacities of a given brain region are scaled by the rest of the brain. All other things being equal, the computational capacities of a given brain region, taken as the number of neurons, should decrease when the size of the rest of the brain increases. But to our knowledge there is no evidence for that in the literature. Clearly these are very challenging issues, and our position was to take absolute measures because they do not rely upon hidden assumptions regarding allometric relations and their consequence on cognition.

But since we definitely understand that scaling is a reference in the field, we have not only completed the corresponding analysis (including the whole brain as a covariate, together with socio-ecological variables) but also expended the discussion to address this issue in detail. We hope that between this new analysis and the comparison of effects between non-scaled measures of FP, DLPFC and the whole brain, the reader will be able to judge the specificity of the effect.

Models including brain (instead of body) size would instead link FP size and socioecological variables characteristic of species B (and others like it). This approach is supported by a large body of literature linking comparative variation in the relative size of specific brain regions (i.e., relative to brain size) to behavioral variation across species - e.g., relative size of visual/olfactory brain areas and diurnality/nocturnality in primates (Barton et al. 1995), relative size of the hippocampus and food caching in birds (Krebs et al. 1989).

Barton, R., Purvis, A., & Harvey, P. H. (1995). Evolutionary radiation of visual and olfactory brain systems in primates, bats and insectivores. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 348(1326), 381-392.

Krebs, J. R., Sherry, D. F., Healy, S. D., Perry, V. H., & Vaccarino, A. L. (1989). Hippocampal specialization of food-storing birds. Proceedings of the National Academy of Sciences, 86(4), 1388-1392. 

We are grateful to the reviewer for mentioning these very interesting articles, and more generally for helping us to understand this issue and clarify the related discussion. Again, we understand the scaling principle but the fact that these methods provide interesting results does not make other approaches (such as ours) wrong or irrelevant. Since we have used both our original approach and the standard version as requested by the reviewer, the reader should be able to get a clear picture of the measures and of their theoretical implications. We sincerely hope that the present version of the paper will be satisfactory, not only because it is clearer, but also because it might stimulate further discussion on this complex question.

Author response:

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

eLife assessment

This manuscript presents useful findings on several phage from deep sea isolates of Lentisphaerae strains WC36 and zth2 that further our understanding of deep sea microbial life. The manuscript's primary claim is that phage isolates augment polysaccharide use in Pseudomonas bacteria via auxiliary metabolic genes (AMGs). However, the strength of the evidence is incomplete and does not support the primary claims. Namely, there are not data presented to rule out phage contamination in the polysaccharide stock solution, AMGs are potentially misidentified, and there is missing evidence of successful infection.

Thanks for the Editor’s and Reviewers’ positive and constructive comments, which help us improve the quality of our manuscript entitled “Deep-sea bacteriophages facilitate host utilization of polysaccharides” (paper#eLife-RP-RA-2023-92345). The comments are valuable, and we have studied the comments carefully and have made corresponding revisions according to the suggestions. We removed some uncertain results and strengthened other parts of the manuscript, which evidently improved the accuracy and impact of the revised version. Revised portions are marked in blue in the modified manuscript. Please find the detailed responses as following.

Public Reviews:

Reviewer #1 (Public Review):

Summary: This manuscript describes the identification and isolation of several phage from deep sea isolates of Lentisphaerae strains WC36 and zth2. The authors observe induction of several putative chronic phages with the introduction of additional polysaccharides to the media. The authors suggest that two of the recovered phage genomes encode AMGs associated with polysaccharide use. The authors also suggest that adding the purified phage to cultures of Pseudomonas stutzeri 273 increased the growth of this bacterium due to augmented polysaccharide use genes from the phage. While the findings were of interest and relevance to the field, it is my opinion that several of the analysis fall short of supporting the key assertions presented.

Thanks for your comments. We removed some uncertain results and strengthened other parts of the manuscript, which evidently improved the accuracy and impact of the revised version. Please find the detailed responses as following.

Strengths: Interesting isolate of deep sea Lentisphaerae strains which will undoubtedly further our understanding of deep sea microbial life.

Thanks for your positive comments.  

Weaknesses:

(1) Many of the findings are consistent with a phage contamination in the polysaccharide stock solution. 

Thanks for your comments. We are very sure that the phages are specifically derived from the Lentisphaerae strain WC36 but not the polysaccharide stock solution. The reasons are as following: (1) the polysaccharide stock solution was strictly sterilized to remove any phage contamination; (2) we have performed multiple TEM checks of the rich medium supplemented with 10 g/L laminarin alone (Supplementary Fig. 1A) or in 10 g/L starch alone (Supplementary Fig. 1B), and there were not any phage-like structures, which confirmed that the polysaccharides (laminarin/starch) we used were not contaminated with any phage-like structures; in addition, we also observed the polysaccharides (laminarin/starch) directly by TEM and did not find any phage-like structures (Supplementary Fig. 2); (3) the polysaccharide (starch) alone could not promote the growth of Pseudomonas stutzeri 273, however, the supplement of starch together with the extracted Phages-WC36 could effectively facilitate the growth of Pseudomonas stutzeri 273 (Author response image 1). The above results clearly indicated the phages were derived from the Lentisphaerae strain WC36 but not the polysaccharide stock solution. 

Author response image 1.

Growth curve and status of Pseudomonas stutzeri 273 cultivated in basal medium, basal medium supplemented with 20 μl/mL Phages-WC36, basal medium supplemented with 5 g/L starch, basal medium supplemented with 5 g/L starch and 20 μl/mL Phages-WC36. 

 

(2) The genes presented as AMGs are largely well known and studied phage genes which play a role in infection cycles.

Thanks for your comments. Indeed, these AMGs may be only common in virulent phages, while have never been reported in chronic phages. In virulent phages, these genes typically act as lysozymes, facilitating the release of virions from the host cell upon lysis, or injection of viral DNA upon infection. However, the chronic phages do not lyse the host. Therefore, the persistence of these genes in chronic phages may be due to their ability to assist the host in metabolizing polysaccharides. Finally, according to your suggestions, we have weakened the role of AMGs and added “potential” in front of it. The detailed information is shown below.

(3) The evidence that the isolated phage can infect Pseudomonas stutzeri 273 is lacking, putting into question the dependent results.

Thanks for your comments. Actually, we selected many marine strains (Pseudomonadota, Planctomycetes, Verrucomicrobia, Fusobacteria, and Tenericutes isolates) to investigate whether Phages-WC36 could assist them in degradation and utilization of polysaccharides, and found that Phages-WC36 could only promote the growth of strain 273. It is reported that filamentous phages could recognize and bind to the host pili, which causes the pili to shrink and brings the filamentous phages closer to and possibly through the outer membrane of host cells. The possible mechanism of other chronic phages release without breaking the host might be that it was enclosed in lipid membrane and released from the host cells by a nonlytic manner. Thus, these chronic phages may have a wider host range. However, we were unable to further reveal the infection mechanism due to some techniques absence. Therefore, according to your suggestions, we have deleted this section in the revised manuscript.

Reviewer #1 (Recommendations For The Authors):

I have previously reviewed this manuscript as a submission to another journal in 2022. My recommendations here mirror those of my prior suggestions, now with further added details.

Thanks for your great efforts for reviewing our manuscript and valuable suggestions for last and this versions.

Specific comments:

Comment 1: Line 32. Rephrase to "polysaccharides cause the induction of multiple temperate phages infecting two strains of Lentisphaerae (WC36 and zth2) from the deep sea."

Thanks for your positive suggestion. We have modified this description as “Here, we found for the first time that polysaccharides induced the production of multiple temperate phages infecting two deep-sea Lentisphaerae strains (WC36 and zth2).” in the revised manuscript (Lines 31-33). 

Comment 2: Line 66. "Chronic" infections are not "lysogenic" as described here, suggesting the former is a subcategory of the latter. If you are going to introduce lifecycles you need a brief sentence distinguishing "chronic" from "lysogenic"

Thanks for your positive suggestion. We added this sentence as “Currently, more and more attention has been paid to chronic life cycles where bacterial growth continues despite phage reproduction (Hoffmann Berling and Maze, 1964), which was different from the lysogenic life cycle that could possibly lyse the host under some specific conditions.” in the revised manuscript (Lines 66-69).

Comment 3: Line 72. Please avoid generalized statements like "a hand-full" (or "plenty" line 85). Try to be at least somewhat quantitative regarding how many chronic phages are known. This is a fairly common strategy among archaeal viruses. 

Thanks for your suggestion. Given that some filamentous phages also have a chronic life cycle that is not explicitly reported, we cannot accurately estimate their numbers. According to your suggestions, we have modified these descriptions as “however, to our best knowledge, only few phages have been described for prokaryotes in the pure isolates up to date (Roux et al., 2019; Alarcón-Schumacher et al., 2022; Liu et al., 2022).” in the revised manuscript (Lines 73-75). In addition, the number of chronic phages in the biosphere cannot be accurately estimated, according to the latest report (Chevallereau et al., 2022), which showed that “a large fraction of phages in the biosphere are produced through chronic life cycles”. Therefore, we have modified this description as “Therefore, a large percentage of phages in nature are proposed to replicate through chronic life cycles” in the revised manuscript (Lines 87-88). 

Comment 4: Line 93. While Breitbart 2012 is a good paper to cite here, there have been several, much more advanced analysis of the oceans virome. https://doi.org/10.1016/j.cell.2019.03.040 is one example, but there are several others. A deeper literature review is required in this section.  

Thanks for your valuable suggestions. We have added some literatures and modified this description as “A majority of these viruses are bacteriophages, which exist widely in oceans and affect the life activities of microbes (Breitbart, 2012; Roux et al., 2016; Gregory et al., 2019; Dominguez-Huerta et al., 2022).” in the revised manuscript (Lines 94-97). 

References related to this response:

Roux, S., Brum, J.R., Dutilh, B.E., Sunagawa, S., Duhaime, M.B., Loy, A., Poulos, B.T., Solonenko, N., Lara, E., Poulain, J., et al. (2016) Ecogenomics and potential biogeochemical impacts of globally abundant ocean viruses. Nature 537:689-693. 

Gregory, A.C., Zayed, A.A., Conceição-Neto, N., Temperton, B., Bolduc, B., Alberti, A., Ardyna, M., Arkhipova, K., Carmichael, M., Cruaud, C., et al. (2019) Marine DNA Viral Macro- and Microdiversity from Pole to Pole. Cell 177:1109-1123.e1114. 

Dominguez-Huerta, G., Zayed, A.A., Wainaina, J.M., Guo, J., Tian, F., Pratama, A.A., Bolduc, B., Mohssen, M., Zablocki, O., Pelletier, E., et al. (2022) Diversity and ecological footprint of Global Ocean RNA viruses. Science 376:1202-1208.

Comment 5: Line 137. I see the phage upregulation in Figure 1, however in the text and figure it would be good to also elaborate on what the background expression generally looks like. Perhaps a transcriptomic read normalization and recruitment to the genome with a display of the coverage map, highlighting the prophage would be helpful. Are the polysacharides directly influencing phage induction or is there some potential for another cascading effect?  

Thanks for your comments. We have elaborated all expressions of phage-associated genes under different conditions in the Supplementary Table 1, which showed that the background expressions were very low. The numbers in Fig. 1C were the gene expressions (by taking log2 values) of strain WC36 cultured in rich medium supplemented with 10 g/L laminarin compared with the rich medium alone.

In addition, our RT-qPCR results (Fig. 1D) also confirmed that these genes encoding phage-associated proteins were significantly upregulated when 10 g/L laminarin was added in the rich medium. According to your suggestions, we have modified this description as “In addition to the up-regulation of genes related to glycan transport and degradation, when 10 g/L laminarin was added in the rich medium, the most upregulated genes were phage-associated (e. g. phage integrase, phage portal protein) (Fig. 1C and Supplementary Table 1), which were expressed at the background level in the rich medium alone.” in the revised manuscript (Lines 136-140). Based on the present results, we speculate that polysaccharides might directly induce phage production, which needs to be verified by a large number of experiments in the future.

Comment 6: Line 179. We need some assurance that phage was not introduced by your laminarin or starch supplement. Perhaps a check on the TEM/sequencing check of supplement itself would be helpful? This may be what is meant on Line 188 "without culturing bacterial cells" however this is not clearly worded if that is the case. Additional note, further reading reinforces this as a key concern. Many of the subsequent results are consistent with a contaminated starch stock. 

Thanks for your comments. We are very sure that the phages are specifically derived from the Lentisphaerae strain WC36 but not the polysaccharide stock solution. The reasons are as following: (1) we have performed multiple TEM checks of the rich medium supplemented with 10 g/L laminarin alone (Supplementary Fig. 1A) or in 10 g/L starch alone (Supplementary Fig. 1B), and there were not any phage-like structures, which confirmed that the polysaccharides (laminarin/starch) we used are not contaminated with any phage-like structures. In addition, we also observed the polysaccharides (laminarin/starch) directly by TEM and did not find any phage-like structures (Supplementary Fig. 2). According to your suggestions, we have modified this description as “We also tested and confirmed that there were not any phage-like structures in rich medium supplemented with 10 g/L laminarin alone (Supplementary Fig. 1A) or in 10 g/L starch alone (Supplementary Fig. 1B), ruling out the possibility of phage contamination from the polysaccharides (laminarin/ starch).” in the revised manuscript (Lines 158-162) and “Meanwhile, we also checked the polysaccharides (laminarin/ starch) in rich medium directly by TEM and did not find any phage-like structures (Supplementary Fig. 2).” in the revised manuscript (Lines 178-180). (2) the polysaccharide stock solution was strictly sterilized to remove any phage contamination. (3) the polysaccharide (starch) alone could not promote the growth of Pseudomonas stutzeri 273, however, the supplement of starch together with the extracted Phages-WC36 could effectively facilitate the growth of Pseudomonas stutzeri 273 (Response Figure 1). The above results clearly indicated the phage was derived from the Lentisphaerae strain WC36 but not the polysaccharide stock solution. 

In addition, given that polysaccharide was a kind of critical energy source for most microorganisms, we sought to ask whether polysaccharide also induces the production of bacteriophages in other deep-sea bacteria. To this end, we cultured deep-sea representatives from other four other phyla (including Chloroflexi, Tenericutes, Proteobacteria, and Actinobacteria) in the medium supplemented with laminarin/starch, and checked the supernatant of cells suspension through TEM as described above. We could not find any phage-like structures in these cells suspension (Author reaponse image 2), which also confirmed that there was no phage contamination in the polysaccharides.

Author response image 2.

Growth curve and status of Pseudomonas stutzeri 273 cultivated in basal medium, basal medium supplemented with 20 μl/mL Phages-WC36, basal medium supplemented with 5 g/L starch, basal medium supplemented with 5 g/L starch and 20 μl/mL Phages-WC36.   

Author response image 3.

TEM observation of the supernatant of cells suspension of a Chloroflexi strain, a Tenericutes strain, a Proteobacteria strain and an Actinobacteria strain that cultivated in the rich medium supplemented with 10 g/L laminarin and 10 g/L starch. No phage-like particles could be observed.  

Comment 7: Line 223. Correct generalized wording "long time". 

Thanks for your comments. We have changed “after for a long time” to “after 30 days” in the revised manuscript (Line 197).

Comment 8: Line 229. Please more explicitly describe what these numbers are (counts of virion like structures - filamentous and hexagonal respectively?), the units (per µL?), and how these were derived. The word "around" should be replaced with mean and standard deviation values for each count from replicates, without which these are not meaningful.

Thanks for your comments. The average numbers per microliter (µL) of filamentous and hexagonal phages in each condition were respectively calculated by randomly choosing ten TEM images. According to your suggestions, we have modified this description as “Specifically, the average number per microliter of filamentous phages (9.7, 29 or 65.3) extracted from the supernatant of strain WC36 cultured in rich medium supplemented with 10 g/L laminarin for 5, 10 or 30 days was higher than that cultured in rich medium supplemented with 5 g/L laminarin (4.3, 13.7 or 35.3) (Fig. 3B). The average number per microliter of hexagonal phages (9, 30, 46.7) extracted from the supernatant of strain WC36 cultured in rich medium supplemented with 10 g/L laminarin for 5, 10 or 30 days was higher than that cultured in rich medium supplemented with 5 g/L laminarin (4, 11.3 or 17.7) (Fig. 3C).” in the revised manuscript (Lines 203-210).

Comment 9: Line 242. This section should be included in the discussion of Figure 2 - around line 194.

Thanks. According to your suggestion, we have moved this section to the discussion corresponding to Figure 2 (Lines 183-191).

Comment 10: Figure 3. Stay consistent in the types of figures generated per strain. Figure 3A should be a growth curve.

Thanks for your comments. Actually, figure 3A was a growth curve, the corresponding description “(A) Growth curve of strain WC36 cultivated in either rich medium alone or rich medium supplemented with 5 g/L or 10 g/L laminarin for 30 days.” was shown in the Figure 3A legend in this manuscript.

Comment 11: Line 312. Move the discussion of AMGs to after the discussion of the phage genome identification.

Thanks for your valuable comments. According to your suggestions, we have moved the discussion of AMGs to after the discussion of the phage genome identification.

Comment 12: Line 312. It would be informative to sequence in-bulk each of your treatments as opposed to just sequencing the viral isolates (starch and no host included) to see what viruses can be identified in each. ABySS is also not a common assembler for viral analysis. Is there literature to support it as a sufficient tool in assembling viral genomes? What sequencing depths were obtained in your samples?

Thanks for your comments. In previous studies, we did sequence the starch or laminarin alone (no host included) and did not detect any phage-related sequences. The introduction of ABySS software was shown in these literatures (Jackman SD, Vandervalk BP, Mohamadi H, Chu J, Yeo S, Hammond SA, Jahesh G, Khan H, Coombe L, Warren RL, Birol I. ABySS 2.0: resource-efficient assembly of large genomes using a Bloom filter. Genome Res. 2017 May;27(5):768-777; Simpson JT, Wong K, Jackman SD, Schein JE, Jones SJ, Birol I. ABySS: a parallel assembler for short read sequence data. Genome Res. 2009 Jun;19(6):1117-23.), which were also used to assemble viral genomes in these literatures (Guo Y, Jiang T. First Report of Sugarcane Mosaic Virus Infecting Goose Grass in Shandong Province, China. Plant Dis. 2024 Mar 21. doi: 10.1094/PDIS-11-23-2514-PDN; Tang M, Chen Z, Grover CE, Wang Y, Li S, Liu G, Ma Z, Wendel JF, Hua J. Rapid evolutionary divergence of Gossypium barbadense and G. hirsutum mitochondrial genomes. BMC Genomics. 2015 Oct 12;16:770.). The sequencing depth of the phages of strain WC36 and zth2 were 350x and 365x, respectively.

Comment 13: Line 323. Replace "eventually" with more detail about what was done to derive the genomes. Were these the only four sequences identified as viral?

Thanks for your comments. We have used the ABySS software (http://www.bcgsc.ca/platform/bioinfo/software/abyss) to perform genome assembly with multiple-Kmer parameters. VIBRANT v1.2.1 (Kieft et al., 2020), DRAM-v (Shaffer et al., 2020), VirSorter v1.0.5 (with categories 1 (“pretty sure”) and 2 (“quite sure”)) (Roux et al., 2015) and VirFinder v1.1 (with statistically significant viral prediction: score > 0.9 and P-value < 0.05) (Ren et al., 2017) with default parameters were used to identify viral genomes from these assembly sequences by searching against the both cultured and non-cultured viral NCBI-RefSeq database (http://blast.ncbi.nlm.nih.gov/) and IMG/VR database (Camargo et al., 2023). The GapCloser software (https://sourceforge.net/projects/soapdenovo2/files/GapCloser/) was subsequently applied to fill up the remaining local inner gaps and correct the single base polymorphism for the final assembly results. All the detailed processes were described in the supplementary information. The virus sequences with higher scores are only these four, but they are not complete genomes. Some virus sequences with shorter sequences and lower scores were excluded.

Comment 14: Line 328. We need some details about the host genomes here. How were these derived? What is their completeness/contamination? What is their size? If the bins are poor, these would not serve as a reliable comparison to identify integrated phage.

Thanks for your comments. For genomic sequencing, strains WC36 and zth2 were grown in the liquid rich medium supplemented with 5 g/L laminarin and starch and harvested after one week of incubation at 28 °C. Genomic DNA was isolated by using the PowerSoil DNA isolation kit (Mo Bio Laboratories Inc., Carlsbad, CA). Thereafter, the genome sequencing was carried out with both the Illumina NovaSeq PE150 (San Diego, USA) and Nanopore PromethION platform (Oxford, UK) at the Beijing Novogene Bioinformatics Technology Co., Ltd. A complete description of the library construction, sequencing, and assembly was performed as previously described (Zheng et al., 2021). We used seven databases to predict gene functions, including Pfam (Protein Families Database, http://pfam.xfam.org/), GO (Gene Ontology, http://geneontology.org/) (Ashburner et al., 2000), KEGG (Kyoto Encyclopedia of Genes and Genomes, http://www.genome.jp/kegg/) (Kanehisa et al., 2004), COG (Clusters of Orthologous Groups, http://www.ncbi.nlm.nih.gov/COG/) (Galperin et al., 2015), NR (Non-Redundant Protein Database databases), TCDB (Transporter Classification Database), and Swiss-Prot (http://www.ebi.ac.uk/uniprot/) (Bairoch and Apweiler, 2000). A whole genome Blast search (E-value less than 1e-5, minimal alignment length percentage larger than 40%) was performed against above seven databases.

The completeness of the genomes of strains WC36 and zth2 were 100%, which were checked by the CheckM v1.2.2. The size of the genome of strains WC36 and zth2 were 3,660,783 bp and 3,198,720bp, respectively. The complete genome sequences of strains WC36 and zth2 presented in this study have been deposited in the GenBank database with accession numbers CP085689 and CP071032, respectively. 

Moreover, to verify whether the absence of microbial contamination in phage sequencing results, we used the new alignment algorithm BWA-MEM (version 0.7.15) to perform reads mapping of host WGS to these phages. We found that all the raw reads of host strains (WC36 and zth2) were not mapping to these phages sequences (Author response image 3, shown as below). In addition, we also performed the evaluation of the assembly graph underlying the host consensus assemblies. Clean reads were mapped to the bacterial complete genome sequences by the Bowtie 2 (version 2.5.0), BWA (version 0.7.8) and SAMTOOLS (version 0.1.18). The results showed that the total mismatch rate of strains WC36 and zth2 were almost 0% and 0.03%, respectively (Author response table 1, shown as below). In addition, we also collected the cells of strains WC36 and zth2, and then sent them to another company for whole genome sequencing (named WC36G and ZTH, GenBank accession numbers CP151801 and CP119760, respectively). The completeness of the genomes of strains WC36G and ZTH were also 100%. The size of the genome of strains WC36G and ZTH were 3,660,783bp and 3,198,714bp, respectively. The raw reads of strains WC36G and zth2 were also not mapping to the phages sequences. Therefore, we can confirm that these bacteriophage genomes were completely outside of the host chromosomes. 

Author response image 4.

The read mapping from WGS to phage sequences.

Author response table 1.

Sequencing depth and coverage statistics.

References related to this response:

Zheng, R., Liu, R., Shan, Y., Cai, R., Liu, G., and Sun, C. (2021b) Characterization of the first cultured free-living representative of Candidatus Izemoplasma uncovers its unique biology ISME J 15:2676-2691. 

Ashburner, M., Ball, C.A., Blake, J.A., Botstein, D., Butler, H., Cherry, J.M., Davis, A.P., Dolinski, K., Dwight, S.S., Eppig, J.T., et al. (2000) Gene ontology: tool for the unification of biology. The Gene Ontology Consortium Nat Genet 25:25-29. 

Kanehisa, M., Goto, S., Kawashima, S., Okuno, Y., and Hattori, M. (2004) The KEGG resource for deciphering the genome Nucleic Acids Res 32:D277-280. 

Galperin, M.Y., Makarova, K.S., Wolf, Y.I., and Koonin, E.V. (2015) Expanded microbial genome coverage and improved protein family annotation in the COG database Nucleic Acids Res 43:D261-269. 

Bairoch, A., and Apweiler, R. (2000) The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000 Nucleic Acids Res 28:45-48.

Comment 15: Line 333. This also needs some details. What evidence do you have that these are not chromosomal? If not chromosomal where can they be found? Sequencing efforts should also be able to yield extrachromosomal elements such as plasmids etc... If you were to sequence your purified isolate cultures from the rich media alone and include all assemblies (not just those binned for example) as a reference, would you be able to recruit viral reads? The way this reads suggests that Chevallereau et al., worked specifically with these phage, which is not the case - please rephrase.

Thanks for your comments. We carefully compared the bacteriophage genomes with those of the corresponding hosts (strains WC36 and zth2) using Galaxy Version 2.6.0 (https://galaxy.pasteur.fr/) (Afgan et al., 2018) with the NCBI BLASTN method and used BWA-mem software for read mapping from host whole genome sequencing (WGS) to these bacteriophages. These analyses both showed that the bacteriophage genomes are completely outside of the host chromosomes. Therefore, we hypothesized that the phage genomes might exist in the host in the form similar to that of plasmid.

Comment 16: Line 335. More to the point here that we need confirmation that these phages were not introduced in the polysaccharide treatment

Thanks for your comments. Please find our answers for this concern in the responses for comment 1 of “weakness” part and comment 6 of “Recommendations For The Authors” part.

Comment 17: Line 342. Lacking significant detail here. Phylogeny based on what gene(s), how were the alignments computed/refined, what model used etc..?

Thanks for your comments. According to your suggestions, all the related information was shown in this section “Materials and methods” of this manuscript. The maximum likelihood phylogenetic tree of Phage-WC36-2 and Phage-zth2-2 was constructed based on the terminase large subunit protein (terL). These proteins used to construct the phylogenetic trees were all obtained from the NCBI databases. All the sequences were aligned by MAFFT version 7 (Katoh et al., 2019) and manually corrected. The phylogenetic trees were constructed using the W-IQ-TREE web server (http://iqtree.cibiv.univie.ac.at) with the “GTR+F+I+G4” model (Trifinopoulos et al., 2016). Finally, we used the online tool Interactive Tree of Life (iTOL v5) (Letunic and Bork, 2021) to edit the tree. 

Comment 18: Line 346. How are you specifically defining AMGs in this study? Most of these are well-known and studied phage genes with specific life cycle functions and could not be considered as polysaccharide processing AMGs even though in host cells many do play a role in polysaccharide processing systems. A substantially deeper literature review is needed in this section, which would ultimately eliminate most of these from the potential AMG pools. Further, the simple HMM/BLASTp evalues are not sufficient to support the functional annotation of these genes. At a minimum, catalytic/conserved regions should be identified, secondary structures compared, and phylogenetic analysis (where possible) developed etc... My recommendation is to eliminate this section entirely from the manuscript. 

Categorically:

- Glycoside hydrolase (various families), glucosaminidases, and transglycosylase are all very common to phage and operate generally as a lysins, facilitating the release of virions from the host cell upon lysis, or injection of viral DNA upon infection https://doi.org/10.3389/fmicb.2016.00745 (and citations therein) https://doi.org/10.1016/j.cmi.2023.10.018 etc... In order to confirm these as distinct AMGs we would need a very detailed analysis indicating that these are not phage infection cycle/host recognition related, however I strongly suspect that under such interrogation, these would prove to be as such.

-TonB related systems including ExbB are well studied among phages as part of the trans-location step in infection. These could not be considered as AMGs. https://doi.org/10.1128/JB.00428-19. Other TonB dependent receptors play a role in host recognition.

-Several phage acetyltransferases play a role in suppressing host RNA polymerase in order to reserve host cell resources for virion production, including polysaccharide production. https://doi.org/10.3390/v12090976. Further it has been shown that the E. coli gene neuO (O-acetyltransferase) is a homologue of lambdoid phage tail fiber genes https://doi.org/10.1073/pnas.0407428102. I suspect the latter is also the case here and this is a tail fiber gene.

Thanks for your valuable comments. According to your suggestions, we have reanalyzed these AMGs and made some modifications (the new version Fig. 5A, shown as below). These genes encoding proteins associated with polysaccharide transport and degradation may be only common in virulent phages, and have never been reported in chronic phages. Unlike virulent phages, these genes typically act as lysozymes, facilitating the release of virions from the host cell upon lysis, or injection of viral DNA upon infection, chronic phages do not lyse the host. It is reported that, filamentous phages could recognize and bind to the host pili, which causes the pili to shrink and brings the filamentous phages closer to and possibly through the outer membrane of host cells (Riechmann et al., 1997; Sun et al., 1987). The possible mechanism of other chronic phage release without breaking the host might be that it was enclosed in lipid membrane and released from the host cells by a nonlytic manner. It has recently been reported that the tailless Caudoviricetes phage particles are enclosed in lipid membrane and are released from the host cells by a nonlytic manner (Liu et al., 2022), and the prophage induction contributes to the production of membrane vesicles by Lacticaseibacillus casei BL23 during cell growth (da Silva Barreira et al., 2022). Therefore, the persistence of these genes in chronic phages may be due to their ability to assist the host in metabolizing polysaccharides. 

Finally, according to your suggestions, we have weakened the role of AMGs and added “potential” in front of it.

References related to this response:

Riechmann L, Holliger P. (1997) The C-terminal domain of TolA is the coreceptor for filamentous phage infection of E. coli Cell 90:351-60.

Sun TP, Webster RE. (1987) Nucleotide sequence of a gene cluster involved in entry of E colicins and single-stranded DNA of infecting filamentous bacteriophages into Escherichia coli J Bacteriol 169:2667-74. 

Liu Y, Alexeeva S, Bachmann H, Guerra Martníez J.A, Yeremenko N, Abee T et al. (2022) Chronic release of tailless phage particles from Lactococcus lactis Appl Environ Microbiol 88: e0148321. da Silva Barreira, D., Lapaquette, P., Novion Ducassou, J., Couté, Y., Guzzo, J., and Rieu, A. Spontaneous prophage induction contributes to the production of membrane vesicles by the gram-positive bacterium Lacticaseibacillus casei BL23. mBio_._ 2022;13:e0237522.

Comment 19: Line 354. To make this statement that these genes are missing from the host, we would need to know that these genomes are complete.

Thanks for your comments. The completeness of the genomes of strains WC36 and zth2 were 100%, which were checked by the CheckM v1.2.2. The size of the genome of strains WC36 and zth2 were 3,660,783 bp and 3,198,720bp, respectively. The complete genome sequences of strains WC36 and zth2 presented in this study have been deposited in the GenBank database with accession numbers CP085689 and CP071032, respectively. In addition, we also collected the cells of strains WC36 and zth2, and then sent it to another company for whole genome sequencing (named WC36G and ZTH, GenBank accession numbers CP151801 and CP119760, respectively). The completeness of the genomes of strains WC36G and ZTH were also 100%. The size of the genome of strains WC36G and ZTH were 3,660,783bp and 3,198,714bp, respectively. Therefore, these genomes of strains WC36 and zth2 were complete and circular.    

Comment 20: Figure 5. Please see https://peerj.com/articles/11447/ and https://doi.org/10.1093/nar/gkaa621 for a detailed discussion on vetting AMGs. Several of these should be eliminated according to the standards set in the field. More specifically, and by anecdotal comparison with other inoviridae genomes, for Phage-WC36-1 and Phage-zth2-1, I am not convinced that the transactional regulator and glycoside hydrolase are a part of the phage genome. The phage genome probably ends at the strand switch.

Thanks for your comments. According to your suggestions, we have analyzed these two articles carefully and modified the genome of Phage-WC36-1 and Phage-zth2-1 by anecdotal comparison with other inoviridae genomes. As you said, the transactional regulator and glycoside hydrolase are not a part of the phage genome.

The new version Fig. 5A was shown.

References related to this response:

Shaffer, M., Borton, M.A., McGivern, B.B., Zayed, A.A., La Rosa, S.L., Solden, L.M., Liu, P., Narrowe, A.B., Rodrgíuez-Ramos, J., Bolduc, B., et al. (2020) DRAM for distilling microbial metabolism to automate the curation of microbiome function Nucleic Acids Res 48:8883-8900 

Pratama, A.A., Bolduc, B., Zayed, A.A., Zhong, Z.P., Guo, J., Vik, D.R., Gazitúa, M.C., Wainaina, J.M., Roux, S., and Sullivan, M.B. (2021) Expanding standards in viromics: in silico evaluation of dsDNA viral genome identification, classification, and auxiliary metabolic gene curation PeerJ 9:e11447

Comment 21: Line 380. This section needs to start with detailed evidence that this phage can even infect this particular strain. Added note, upon further reading the serial dilution cultures are not sufficient to prove these phage infect this Pseudomonas. We need at a minimum a one-step growth curve and wet mount microscopy. It is much more likely that some carry over contaminant is invading the culture and influencing OD600. With the given evidence, I am not at all convinced that these phages have anything to do with Pseudomonas polysaccharide use and I recommend either drastically revising this section or eliminating it entirely.

Line 386-389. Could this be because you are observing your added phage in the starch enriched media while no phage were introduced with the "other types of media" so none would be observed? This could have nothing to do with infection dynamics. Further, this would also be consistent with your starch solution being contaminated by phage.

Line 399. Again consistent with the starch media being contaminated.

Line 401-408. This is more likely to do with the augmentation of the media with an additional carbon source and not involving the phage. 

Line 410. I am not convinced that these viruses infect the Pseudomonas strain. Extensive further evidence of infection is needed to make these assertions.  Figure 6A. We need confirmation that the isolate culture remains pure and there are no other contaminants introduced with the phage.

Thanks for your comments. We have proved that the polysaccharides (laminarin/ starch) didn't contaminate any phages above. Actually, we selected many marine strains (Pseudomonadota, Planctomycetes, Verrucomicrobia, Fusobacteria, and Tenericutes isolates) to investigate whether Phages-WC36 could assist them in degradation and utilization of polysaccharides, and found that Phages-WC36 could only promote the growth of strain 273. The presence of filamentous phages and hexagonal phages was detected in the supernatant of strain 273 cultured in basal medium supplemented with 5 g/L starch and 20 μl/mL Phages-WC36. After 3 passages of serial cultivation in basal medium supplemented with 5 g/L starch, we found that filamentous phages and hexagonal phages were also present in basal medium supplemented with starch, but not in the basal medium, which may mean that Phages-WC36 could infect strain 273 and starch is an important inducer. In addition, the Phages-WC36 used in the growth assay of strain 273 were multiple purified and eventually suspended in SM buffer (0.01% gelatin, 50 mM Tris-HCl, 100 mM NaCl and 10 mM MgSO4). Thus, these phages are provided do not contain some extracellular enzymes and/or nutrients. In addition, we set up three control groups in the growth assay of strain 273: basal medium, basal medium supplemented with Phages-WC36 and basal medium supplemented with starch. If the Phages-WC36 contains some extracellular enzymes and/or nutrients, strain 273 could also grow well in the basal medium supplemented only with Phages-WC36. However, the poor growth results of strain 273 cultivated in the basal medium supplemented with Phages-WC36 further confirmed that there were not some extracellular enzymes and/or nutrients in these phages.

Finally, the possible mechanism of the chronic phage release without breaking the host might be that it was enclosed in lipid membrane and released from the host cells by a nonlytic manner. Thus, these chronic phages may have a wider host range. However, we were unable to further disclose the infection mechanism in this paper. Therefore, according to your suggestions, we have deleted this section entirely.

Comment 27: Line 460. Details about how these genomes were reconstructed is needed here.  

Thanks for your comments. According to your suggestions, we have added the detailed information about the genome sequencing, annotation, and analysis as “Genome sequencing, annotation, and analysis of strains WC36 and zth2 For genomic sequencing, strains WC36 and zth2 were grown in the liquid rich medium supplemented with 5 g/L laminarin and starch and harvested after one week of incubation at 28 °C. Genomic DNA was isolated by using the PowerSoil DNA isolation kit (Mo Bio Laboratories Inc., Carlsbad, CA). Thereafter, the genome sequencing was carried out with both the Illumina NovaSeq PE150 (San Diego, USA) and Nanopore PromethION platform (Oxford, UK) at the Beijing Novogene Bioinformatics Technology Co., Ltd. A complete description of the library construction, sequencing, and assembly was performed as previously described (Zheng et al., 2021b). We used seven databases to predict gene functions, including Pfam (Protein Families Database, http://pfam.xfam.org/), GO (Gene Ontology, http://geneontology.org/) (Ashburner et al., 2000), KEGG (Kyoto Encyclopedia of Genes and Genomes, http://www.genome.jp/kegg/) (Kanehisa et al., 2004), COG (Clusters of Orthologous Groups, http://www.ncbi.nlm.nih.gov/COG/) (Galperin et al., 2015), NR (Non-Redundant Protein Database databases), TCDB (Transporter Classification Database), and Swiss-Prot (http://www.ebi.ac.uk/uniprot/) (Bairoch and Apweiler, 2000). A whole genome Blast search (E-value less than 1e-5, minimal alignment length percentage larger than 40%) was performed against above seven databases.” in the revised manuscript (Lines 333-351).

Comment 28: Line 462. Accession list of other taxa in the supplement would help here.  

Thanks for your comments. The accession numbers of these strains were displayed behind these strains in Figure 1A. According to your suggestions, we have added an accession list of these taxa (Supplementary Table 6) in the revised manuscript.

Comment 29: Line 463. Is there any literature to support that these are phylogenetically informative genes for Inoviridae?  

Thanks for your comments. There are some literatures (Zeng et al, 2021; Evseev et al, 2023) to support that these are phylogenetically informative genes for Inoviridae. We have added these literatures in the revised manuscript. 

References related to this response:

Zeng, J., Wang, Y., Zhang, J., Yang, S., and Zhang, W. (2021) Multiple novel filamentous phages detected in the cloacal swab samples of birds using viral metagenomics approach Virol J 18:240

Evseev, P., Bocharova, J., Shagin, D., and Chebotar, I. (2023) Analysis of Pseudomonas aeruginosa isolates from patients with cystic fibrosis revealed novel groups of filamentous bacteriophages. Viruses 15: 2215

Reviewer #2 (Public Review):

Summary: This paper investigates virus-host interactions in deep-sea bacteriophage systems which employ a seemingly mutualistic approach to viral replication in which the virus aids host cell polysaccharide import and utilization via metabolic reprogramming. The hypothesis being tested is supported with solid and convincing evidence and the findings are potentially generalizable with implications for our understanding of polysaccharide-mediated virus-host interactions and carbon cycles in marine ecosystems more broadly.

Thanks for your positive comments.

Strengths: This paper synthesizes sequencing and phylogenic analyses of two Lentisphaerae bacteria and three phage genomes; electron microscopy imaging of bacterial/phage particles; differential gene expression analyses; differential growth curve analyses, and differential phage proliferation assays to extract insights into whether laminarin and starch can induce both host growth and phage proliferation. The data presented convincingly demonstrate that both host culture density and phage proliferation increase as a result having host, phage, and polysaccharide carbon source together in culture.

Thanks for your positive comments.  

Weaknesses (suggestions for improvement): 

(1) The article would be strengthened by the following additional experiment: providing the phage proteins hypothesized to be aiding host cell growth (red genes from Figure 5...TonB system energizer ExbB, glycosidases, etc) individually or in combination on plasmids rather than within the context of the actual phage itself to see if such additional genes are necessary and sufficient to realize the boosts in host cell growth/saturation levels observed in the presence of the phages tested.

Thanks for your valuable comments. It is a really good idea to express individually or in combination on plasmids to see the effects of those polysaccharide-degradation proteins in the host cell. However, at present, we failed to construct the genetic and expression system for the strictly anaerobic strain WC36, which hindering our further detailed investigation of the functions of those polysaccharide-degradation proteins. In our lab, we are trying our best to build the genetic and expression system for strain WC36. We will definitely test your idea in the future. 

(2) The paper would also benefit from additional experiments focused on determining how the polysaccharide processing, transport, and metabolism genes are being used by the phages to either directly increase viral infection/replication or else to indirectly do so by supporting the growth of the host in a more mutualistic manner (i.e. by improving their ability to import, degrade, and metabolize polysaccharides).  

Thanks for your valuable comments. Indeed, due to the chronic phage genome is not within the chromosome of the host, it is very hard to disclose the exact auxiliary process and mechanism of chronic phages. At present, we are trying to construct a genetic manipulation system for the strictly anaerobic host WC36, and we will gradually reveal this auxiliary mechanism in the future. In addition, combined with the reviewer 1’s suggestions, the focus of revised manuscript is to emphasize that polysaccharides induce deep-sea bacteria to release chronic phages, and most of the content of phage assisting host metabolism of polysaccharides has been deleted.

(3) The introduction would benefit from a discussion of what is known regarding phage and/or viral entry pathways that utilize carbohydrate anchors during host entry. The discussion could also be improved by linking the work presented to the concept of "selfishness" in bacterial systems (see for instance Giljan, G., Brown, S., Lloyd, C.C. et al. Selfish bacteria are active throughout the water column of the ocean. ISME COMMUN. 3, 11 (2023) https://doi.org/10.1038/s43705-023-00219-7). The bacteria under study are gram negative and it was recently demonstrated (https://www.nature.com/articles/ismej201726) that "selfish" bacteria sequester metabolizable polysaccharides in their periplasm to advantage. It is plausible that the phages may be hijacking this "selfishness" mechanism to improve infectivity and ENTRY rather than helping their hosts to grow and profilerate so they can reap the benefits of simply having more hosts to infect. The current work does not clearly distinguish between these two distinct mechanistic possibilities. The paper would be strengthened by at least a more detailed discussion of this possibility as well as the author's rationale for interpreting their data as they do to favor the "mutualistic" interpretation. In the same light, the paper would benefit from a more careful choice of words which can also help to make such a distinction more clear/evident/intentional. As currently written the authors seem to be actively avoiding giving insights wrt this question.  

Thanks for your valuable comments. According to your suggestions, we have added the related discussion as “Moreover, it was recently demonstrated that selfish bacteria, which were common throughout the water column of the ocean, could bind, partially hydrolyze, and transport polysaccharides into the periplasmic space without loss of hydrolysis products (Reintjes et al., 2017; Giljan et al., 2023). Based on our results, we hypothesized that these chronic phages might also enter the host through this “selfishness” mechanism while assisting the host in metabolizing polysaccharides, thus not lysing the host. On the other hand, these chronic phages might hijack this “selfishness” mechanism to improve their infectivity and entry, rather than helping their hosts to grow and proliferate, so they could reap the benefits of simply having more hosts to infect. In the future, we need to construct a genetic operating system of the strictly anaerobic host strain WC36 to detailedly reveal the relationship between chronic phage and host.” in the revised manuscript (Lines 305-316). 

References related to this response:

Reintjes, G., Arnosti, C., Fuchs, B.M., and Amann, R. (2017) An alternative polysaccharide uptake mechanism of marine bacteria ISME J 11:1640-1650

Giljan, G., Brown, S., Lloyd, C.C., Ghobrial, S., Amann, R., and Arnosti, C. (2023) Selfish bacteria are active throughout the water column of the ocean ISME Commun 3:11

(4) Finally, I would be interested to know if the author’s sequencing datasets might be used to inform the question raised above by using bacterial immunity systems such as CRISPR/Cas9. For example, if the phage systems studied are truly beneficial/mutualistic for the bacteria then it’s less likely that there would be evidence of targeted immunity against that particular phage that has the beneficial genes that support polysaccharide metabolism.

Thanks for your comments. According to your suggestions, we have carefully analyzed the genome of strain WC36, and found that there were no CRISPR/Cas9-related genes. Considering our results that the number of chronic phages was increased with the prolongation of culture time, we speculated that host might have no targeted immunity against these chronic phages.

Reviewer #2 (Recommendations For The Authors):

There are some minor grammatical errors and unclear statements (lines 99-100, 107-109, 163, 222, 223, 249-250, 254) which should also be fixed before final publication. 

Thanks for your valuable comments. We have fixed these minor grammatical errors and unclear statements in the revised manuscript.

Lines 99-100: we have modified this description as “For instance, AMGs of marine bacteriophages have been predicted to be involved in photosynthesis (Mann et al., 2003), nitrogen cycling (Ahlgren et al., 2019; Gazitúa et al., 2021), sulfur cycling (Anantharaman et al., 2014; Roux et al., 2016), phosphorus cycling (Zeng and Chisholm, 2012), nucleotide metabolism (Sullivan et al., 2005; Dwivedi et al., 2013; Enav et al., 2014), and almost all central carbon metabolisms in host cells (Hurwitz et al., 2013).” in the revised manuscript (Lines 100-105).

Lines 107-109: we have modified this description as “However, due to the vast majority of deep-sea microbes cannot be cultivated in the laboratory, most bacteriophages could not be isolated.” in the revised manuscript (Lines 110-111).

Line 163: we have modified this description as “Based on the growth curve of strain WC36, we found that the growth rate of strictly anaerobic strain WC36 was relatively slow.” in the revised manuscript (Lines 149-151).

Lines 222-223: we have modified this description as “Regardless of whether the laminarin was present, the bacterial cells kept their cell shape intact, indicating they were still healthy after 30 days” in the revised manuscript (Lines 195-197).

Lines 249-250: we have modified this description as “However, the entry and exit of the hexagonal phages into the WC36 cells were not observed.” in the revised manuscript (Lines 190-191).

Line 254: we have modified this description as “To explore whether the production of bacteriophages induced by polysaccharide is an individual case, we further checked the effect of polysaccharides on another cultured deep-sea Lentisphaerae strain zth2.” in the revised manuscript (Lines 213-215).

Author response:

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

Reviewer 1:

Thank you for your review and pointing out multiple things to be discussed and clarified! Below, we go through the various limitations you pointed out and refer to the places where we have tried to address them.

(1) It's important to keep in mind that this work involves simplified models of the motor system, and often the terminology for 'motor cortex' and 'models of motor cortex' are used interchangeably, which may mislead some readers. Similarly, the introduction fails in many cases to state what model system is being discussed (e.g. line 14, line 29, line 31), even though these span humans, monkeys, mice, and simulations, which all differ in crucial ways that cannot always be lumped together.

That is a good point. We have clarified this in the text (Introduction and Discussion), to highlight the fact that our model isn’t necessarily meant to just capture M1. We have also updated the introduction to make it more clear which species the experiments which motivate our investigation were performed in.

(2) At multiple points in the manuscript thalamic inputs during movement (in mice) is used as a motivation for examining the role of preparation. However, there are other more salient motivations, such as delayed sensory feedback from the limb and vision arriving in the motor cortex, as well as ongoing control signals from other areas such as the premotor cortex.

Yes – the motivation for thalamic inputs came from the fact that those have specifically been shown to be necessary for accurate movement generation in mice. However, it is true that the inputs in our model are meant to capture any signals external to the dynamical system modeled, and as such are likely to represent a mixture of sensory signals, and feedback from other areas. We have clarified this in the Discussion, and have added this additional motivation in the Introduction.

(3) Describing the main task in this work as a delayed reaching task is not justified without caveats (by the authors' own admission: line 687), since each network is optimized with a fixed delay period length. Although this is mentioned to the reader, it's not clear enough that the dynamics observed during the delay period will not resemble those in the motor cortex for typical delayed reaching tasks.

Yes, we completely agree that the terminology might be confusing. While the task we are modeling is a delayed reaching task, it does differ from the usual setting since the network has knowledge of the delay period, and that is indeed a caveat of the model. We have added a brief paragraph just after the description of the optimal control objective to highlight this limitation.

We have also performed additional simulations using two different variants of a model-predictive control approach that allow us to relax the assumption that the go-cue time is known in advance. We show that these modifications of the optimal controller yield results that remain consistent with our main conclusions, and can in fact in some settings lead to preparatory activity plateaus during the preparation epoch as often found in monkey M1 (e.g in Elsayed et al. 2016). We have modified the Discussion to explain these results and their limitations, which are summarized in a new Supplementary Figure (S9).

(4) A number of simplifications in the model may have crucial consequences for interpretation.

a) Even following the toy examples in Figure 4, all the models in Figure 5 are linear, which may limit the generalisability of the findings.

While we agree that linear models may be too simplistic, much prior analyses of M1 data suggest that it is often good enough to capture key aspects of M1 dynamics; for example, the generative model underlying jPCA is linear, and Sussillo et al. (2015) showed that the internal activity of nonlinear RNN models trained to reproduce EMG data aligned best with M1 activity when heavily regularized; in this regime, the RNN dynamics were close to linear. Nevertheless, this linearity assumption is indeed convenient from a modeling viewpoint: the optimal control problem is more easily solved for linear network dynamics and the optimal trajectories are more consistent across networks. Indeed, we had originally attempted to perform the analyses of Figure 5 in the nonlinear setting, but found that while the results were overall similar to what we report in the linear regime, iLQR was occasionally trapped into local minimal, resulting in more variable results especially for inhibition-stabilized network in the strongly connected end of the spectrum. Finally, Figure 5 is primarily meant to explore to what extent motor preparation can be predicted from basic linear control-theoretic properties of the Jacobian of the dynamics; in this regard, it made sense to work with linear RNNs (for which the Jacobian is constant).

b) Crucially, there is no delayed sensory feedback in the model from the plant. Although this simplification is in some ways a strength, this decision allows networks to avoid having to deal with delayed feedback, which is a known component of closed-loop motor control and of motor cortex inputs and will have a large impact on the control policy.

This comment resonates well with Reviewer 3's remark regarding the autonomous nature (or not) of M1 during movement. Rather than thinking of our RNN models as anatomically confined models of M1 alone, we think of them as models of the dynamics which M1 implements possibly as part of a broader network involving “inter-area loops and (at some latency) sensory feedback”, and whose state appears to be near-fully decodable from M1 activity alone. We have added a paragraph of Discussion on this important point.

(5) A key feature determining the usefulness of preparation is the direction of the readout dimension. However, all readouts had a similar structure (random Gaussian initialization). Therefore, it would be useful to have more discussion regarding how the structure of the output connectivity would affect preparation, since the motor cortex certainly does not follow this output scheme.

We agree with this limitation of our model — indeed one key message of Figure 4 is that the degree of reliance on preparatory inputs depends strongly on how the dynamics align with the readout. However, this strong dependence is somewhat specific to low-dimensional models; in higher-dimensional models (most of our paper), one expects that any random readout matrix C will pick out activity dimensions in the RNN that are sufficiently aligned with the most controllable directions of the dynamics to encourage preparation.

We did consider optimizing C away (which required differentiating through the iLQR optimizer, which is possible but very costly), but the question inevitably arises what exactly should C be optimized for, and under what constraints (e.g fixed norm or not). One possibility is to optimize C with respect to the same control objective that the control inputs are optimized for, and constrain its norm (otherwise, inputs to the M1 model, and its internal activity, could become arbitrarily small as C can grow to compensate). We performed this experiment (new Supplementary Figure S7) and obtained a similar preparation index; there was one notable difference, namely that the optimized readout modes led to greater observability compared to a random readout; thus, the same amount of “muscle energy” required for a given movement could now be produced by a smaller initial condition. In turn, this led to smaller control inputs, consistent with a lower control cost overall.

Whilst we could have systematically optimized C away, we reasoned that (i) it is computationally expensive, and (ii) the way M1 affects downstream effectors is presumably “optimized” for much richer motor tasks than simple 2D reaching, such that optimizing C for a fixed set of simple reaches could lead to misleading conclusions. We therefore decided to stick with random readouts.

Additional comments:

(1) The choice of cost function seems very important. Is it? For example, penalising the square of u(t) may produce very different results than penalising the absolute value.

Yes, the choice of cost function does affect the results, at least qualitatively. The absolute value of the inputs is a challenging cost to use, as iLQR relies on a local quadratic approximation of the cost function. However, we have included additional experiments in which we penalized the squared derivative of the inputs (Supplementary Figure S8; see also our response to Reviewer 3's suggestion on this topic), and we do see differences in the qualitative behavior of the model (though the main takeaway, i.e. the reliance on preparation, continues to hold). This is now referred to and discussed in the Discussion section.

(2) In future work it would be useful to consider the role of spinal networks, which are known to contribute to preparation in some cases (e.g. Prut and Fetz, 1999).

(3) The control signal magnitude is penalised, but not the output torque magnitude, which highlights the fact that control in the model is quite different from muscle control, where co-contraction would be a possibility and therefore a penalty of muscle activation would be necessary. Future work should consider the role of these differences in control policy.

Thank you for pointing us to this reference! Regarding both of these concerns, we agree that the model could be greatly improved and made more realistic in future work (another avenue for this would be to consider a more realistic biophysical model, e.g. using the MotorNet library). We hope that the current Discussion, which highlights the various limitations of our modeling choices, makes it clear that a lot of these choices could easily be modified depending on the specific assumptions/investigation being performed.

Reviewer 2:

Thank you for your positive review! We very much agree with the limitations you pointed out, some of which overlapped with the comments of the other reviewers. We have done our best to address them through additional discussion and new supplementary figures. We briefly highlight below where those changes can be found.

(1) Though the optimal control theory framework is ideal to determine inputs that minimize output error while regularizing the input norm, it however cannot easily account for some other varied types of objectives especially those that may lead to a complex optimization landscape. For instance, the reusability of parts of the circuit, sparse use of additional neurons when learning many movements, and ease of planning (especially under uncertainty about when to start the movement), may be alternative or additional reasons that could help explain the preparatory activity observed in the brain. It is interesting to note that inputs that optimize the objective chosen by the authors arguably lead to a trade-off in terms of other desirable objectives. Specifically, the inputs the authors derive are time-dependent, so a recurrent network would be needed to produce them and it may not be easy to interpolate between them to drive new movement variants. In addition, these inputs depend on the desired time of output and therefore make it difficult to plan, e.g. in circumstances when timing should be decided depending on sensory signals. Finally, these inputs are specific to the full movement chain that will unfold, so they do not permit reuse of the inputs e.g. in movement sequences of different orders.

Yes, that is a good point! We have incorporated further Discussion related to this point. We have additionally included a new example in which we regularize the temporal complexity of the inputs (see also our response to Reviewer 3's suggestion on this topic), which leads to more slowly varying inputs, and may indeed represent a more realistic constraint and lead to simpler inputs that can more easily be interpolated between. We also agree that uncertainty about the upcoming go cue may play an important role in the strategy adopted by the animals. While we have not performed an extensive investigation of the topic, we have included a Supplementary Figure (S9) in which we used Model Predictive Control to investigate the effect of planning under uncertainty about the go cue arrival time. We hope that this will give the reader a better sense of what sort of model extensions are possible within our framework.

(2) Relatedly, if the motor circuits were to balance different types of objectives, the activity and inputs occurring before each movement may be broken down into different categories that may each specialize into one objective. For instance, previous work (Kaufman et al. eNeuron 2016, Iganaki et al., Cell 2022, Zimnik and Churchland, Nature Neuroscience 2021) has suggested that inputs occurring before the movement could be broken down into preparatory inputs 'stricto sensu' - relating to the planned characteristics of the movement - and a trigger signal, relating to the transition from planning to execution - irrespective of whether the movement is internally timed or triggered by an external event. The current work does not address which type(s) of early input may be labeled as 'preparatory' or may be thought of as a part of 'planning' computations.

Yes, our model does indeed treat inputs in a very general way, and does not distinguish between the different types of processes they may be composed of. This is partly because we do not explicitly model where the inputs come from, such that our inputs likely englobe multiple processes. We have added discussion related to this point.

(3) While the authors rightly point out some similarities between the inputs that they derive and observed preparatory activity in the brain, notably during motor sequences, there are also some differences. For instance, while both the derived inputs and the data show two peaks during sequences, the data reproduced from Zimnik and Churchland show preparatory inputs that have a very asymmetric shape that really plummets before the start of the next movement, whereas the derived inputs have larger amplitude during the movement period - especially for the second movement of the sequence. In addition, the data show trigger-like signals before each of the two reaches. Finally, while the data show a very high correlation between the pattern of preparatory activity of the second reach in the double reach and compound reach conditions, the derived inputs appear to be more different between the two conditions. Note that the data would be consistent with separate planning of the two reaches even in the compound reach condition, as well as the re-use of the preparatory input between the compound and double reach conditions. Therefore, different motor sequence datasets - notably, those that would show even more coarticulation between submovements - may be more promising to find a tight match between the data and the author's inputs. Further analyses in these datasets could help determine whether the coarticulation could be due to simple filtering by the circuits and muscles downstream of M1, planning of movements with adjusted curvature to mitigate the work performed by the muscles while permitting some amount of re-use across different sequences, or - as suggested by the authors - inputs fully tailored to one specific movement sequence that maximize accuracy and minimize the M1 input magnitude.

Regarding the exact shape of the occupancy plots, it is important to note that some of the more qualitative aspects (e.g the relative height of the two peaks) will change if we change the parameters of the cost function. Right now, we have chosen the parameters to ensure that both reaches would be performed at roughly the same speed (as a way to very loosely constrain the parameters based on the observed behavior). However, small changes to the hyperparameters can lead to changes in the model output (e.g one of the two consecutive reaches being performed using greater acceleration than the other), and since our biophysical model is fairly simple, changes in the behavior are directly reflected in the network activity. Essentially, what this means is that while the double occupancy is a consistent feature of the model, the exact shape of the peaks is more sensitive to hyperparameters, and we do not wish to draw any strong conclusions from them, given the simplicity of the biophysical model. However, we do agree that our model exhibits some differences with the data. As discussed above, we have included additional discussion regarding the potential existence of separate inputs for planning vs triggering the movement in the context of single reaches.

Overall, we are excited about the suggestions made by the Reviewer here about using our approach to analyze other motor sequence datasets, but we think that in order to do this properly, one would need to adopt a more realistic musculo-skeletal model (such as one provided by MotorNet).

(4) Though iLQR is a powerful optimization method to find inputs optimizing the author's cost function, it also has some limitations. First, given that it relies on a linearization of the dynamics at each timestep, it has a limited ability to leverage potential advantages of nonlinearities in the dynamics. Second, the iLQR algorithm is not a biologically plausible learning rule and therefore it might be difficult for the brain to learn to produce the inputs that it finds. It remains unclear whether using alternative algorithms with different limitations - for instance, using variants of BPTT to train a separate RNN to produce the inputs in question - could impact some of the results.

We agree that our choice of iLQR has limitations: while it offers the advantage of convergence guarantees, it does indeed restrict the choice of cost function and dynamics that we can use. We have now included extensive discussion of how the modeling choices affect our results.

We do not view the lack of biological plausibility of iLQR as an issue, as the results are agnostic to the algorithm used for optimization. However, we agree that any structure imposed on the inputs (e.g by enforcing them to be the output of a self-contained dynamical system) would likely alter the results. A potentially interesting extension of our model would be to do just what the reviewer suggested, and try to learn a network that can generate the optimal inputs. However, this is outside the scope of our investigation, as it would then lead to new questions (e.g what brain region would that other RNN represent?).

(5)  Under the objective considered by the authors, the amount of input occurring before the movement might be impacted by the presence of online sensory signals for closed-loop control. It is therefore an open question whether the objective and network characteristics suggested by the authors could also explain the presence of preparatory activity before e.g. grasping movements that are thought to be more sensory-driven (Meirhaeghe et al., Cell Reports 2023).

It is true that we aren’t currently modeling sensory signals explicitly. However, some of the optimal inputs we infer may be capturing upstream information which could englobe some sensory information. This is currently unclear, and would likely depend on how exactly the model is specified. We have added new discussion to emphasize that our dynamics should not be understood as just representing M1, but more general circuits whose state can be decoded from M1.

Reviewer #2 (Recommendations For The Authors):

Additionally, thank you for pointing out various typos in the manuscript, we have fixed those!

Reviewer 3:

Thank you very much for your review, which makes a lot of very insightful points, and raises several interesting questions. In summary, we very much agree with the limitations you pointed out. In particular, the choice of input cost is something we had previously discussed, but we had found it challenging to decide on what a reasonable cost for “complexity” could be. Following your comment, we have however added a first attempt at penalizing “temporal complexity”, which shows promising behavior. We have only included those additional analyses as supplementary figures, and we have included new discussion, which hopefully highlights what we meant by the different model components, and how the model behavior may change as we vary some of our choices. We hope this can be informative for future models that may use a similar approach. Below, we highlight the changes that we have made to address your comments.

The main limitation of the study is that it focuses exclusively on one specific constraint - magnitude - that could limit motor-cortex inputs. This isn't unreasonable, but other constraints are at least as likely, if less mathematically tractable. The basic results of this study will probably be robust with regard such issues - generally speaking, any constraint on what can be delivered during execution will favor the strategy of preparing - but this robustness cuts both ways. It isn't clear that the constraint used in the present study - minimizing upstream energy costs - is the one that really matters. Upstream areas are likely to be limited in a variety of ways, including the complexity of inputs they can deliver. Indeed, one generally assumes that there are things that motor cortex can do that upstream areas can't do, which is where the real limitations should come from. Yet in the interest of a tractable cost function, the authors have built a system where motor cortex actually doesn't do anything that couldn't be done equally well by its inputs. The system might actually be better off if motor cortex were removed. About the only thing that motor cortex appears to contribute is some amplification, which is 'good' from the standpoint of the cost function (inputs can be smaller) but hardly satisfying from a scientific standpoint.

The use of a term that punishes the squared magnitude of control signals has a long history, both because it creates mathematical tractability and because it (somewhat) maps onto the idea that one should minimize the energy expended by muscles and the possibility of damaging them with large inputs. One could make a case that those things apply to neural activity as well, and while that isn't unreasonable, it is far from clear whether this is actually true (and if it were, why punish the square if you are concerned about ATP expenditure?). Even if neural activity magnitude an important cost, any costs should pertain not just to inputs but to motor cortex activity itself. I don't think the authors really wish to propose that squared input magnitude is the key thing to be regularized. Instead, this is simply an easily imposed constraint that is tractable and acts as a stand-in for other forms of regularization / other types of constraints. Put differently, if one could write down the 'true' cost function, it might contain a term related to squared magnitude, but other regularizing terms would by very likely to dominate. Using only squared magnitude is a reasonable way to get started, but there are also ways in which it appears to be limiting the results (see below).

I would suggest that the study explore this topic a bit. Is it possible to use other forms of regularization? One appealing option is to constrain the complexity of inputs; a long-standing idea is that the role of motor cortex is to take relatively simple inputs and convert them to complex time-evolving inputs suitable for driving outputs. I realize that exploring this idea is not necessarily trivial. The right cost-function term is not clear (should it relate to low-dimensionality across conditions, or to smoothness across time?) and even if it were, it might not produce a convex cost function. Yet while exploring this possibility might be difficult, I think it is important for two reasons.

First, this study is an elegant exploration of how preparation emerges due to constraints on inputs, but at present that exploration focuses exclusively on one constraint. Second, at present there are a variety of aspects of the model responses that appear somewhat unrealistic. I suspect most of these flow from the fact that while the magnitude of inputs is constrained, their complexity is not (they can control every motor cortex neuron at both low and high frequencies). Because inputs are not complexity-constrained, preparatory activity appears overly complex and never 'settles' into the plateaus that one often sees in data. To be fair, even in data these plateaus are often imperfect, but they are still a very noticeable feature in the response of many neurons. Furthermore, the top PCs usually contain a nice plateau. Yet we never get to see this in the present study. In part this is because the authors never simulate the situation of an unpredictable delay (more on this below) but it also seems to be because preparatory inputs are themselves strongly time-varying. More realistic forms of regularization would likely remedy this.

That is a very good point, and it mirrors several concerns that we had in the past. While we did focus on the input norm for the sake of simplicity, and because it represents a very natural way to regularize our control solutions, we agree that a “complexity cost” may be better suited to models of brain circuits. We have addressed this in a supplementary investigation. We chose to focus on a cost that penalizes the temporal complexity of the inputs, as ||u(t+1) - u(t)||^2. Note that this required augmenting the state of the model, making the computations quite a bit slower; while it is doable if we only penalize the first temporal derivative, it would not scale well to higher orders.

Interestingly, we did find that the activity in that setting was somewhat more realistic (see new Supplementary Figure S8), with more sustained inputs and plateauing activity. While we have kept the original model for most of the investigations, the somewhat more realistic nature of the results under that setting suggests that further exploration of penalties of that sort could represent a promising avenue to improve the model.

We also found the idea of a cost that would ensure low-dimensionality of the inputs across conditions very interesting. However, it is challenging to investigate with iLQR as we perform the optimization separately for each condition; nevertheless, it could be investigated using a different optimizer.

At present, it is also not clear whether preparation always occurs even with no delay. Given only magnitude-based regularization, it wouldn't necessarily have to be. The authors should perform a subspace-based analysis like that in Figure 6, but for different delay durations. I think it is critical to explore whether the model, like monkeys, uses preparation even for zero-delay trials. At present it might or might not. If not, it may be because of the lack of more realistic constraints on inputs. One might then either need to include more realistic constraints to induce zero-delay preparation, or propose that the brain basically never uses a zero delay (it always delays the internal go cue after the preparatory inputs) and that this is a mechanism separate from that being modeled.

I agree with the authors that the present version of the model, where optimization knows the exact time of movement onset, produces a reasonably realistic timecourse of preparation when compared to data from self-paced movements. At the same time, most readers will want to see that the model can produce realistic looking preparatory activity when presented with an unpredictable delay. I realize this may be an optimization nightmare, but there are probably ways to trick the model into optimizing to move soon, but then forcing it to wait (which is actually what monkeys are probably doing). Doing so would allow the model to produce preparation under the circumstances where most studies have examined it. In some ways this is just window-dressing (showing people something in a format they are used to and can digest) but it is actually more than that, because it would show that the model can produce a reasonable plateau of sustained preparation. At present it isn't clear it can do this, for the reasons noted above. If it can't, regularizing complexity might help (and even if this can't be shown, it could be discussed).

In summary, I found this to be a very strong study overall, with a conceptually timely message that was well-explained and nicely documented by thorough simulations. I think it is critical to perform the test, noted above, of examining preparatory subspace activity across a range of delay durations (including zero) to see whether preparation endures as it does empirically. I think the issue of a more realistic cost function is also important, both in terms of the conceptual message and in terms of inducing the model to produce more realistic activity. Conceptually it matters because I don't think the central message should be 'preparation reduces upstream ATP usage by allowing motor cortex to be an amplifier'. I think the central message the authors wish to convey is that constraints on inputs make preparation a good strategy. Many of those constraints likely relate to the fact that upstream areas can't do things that motor cortex can do (else you wouldn't need a motor cortex) and it would be good if regularization reflected that assumption. Furthermore, additional forms of regularization would likely improve the realism of model responses, in ways that matter both aesthetically and conceptually. Yet while I think this is an important issue, it is also a deep and tricky one, and I think the authors need considerable leeway in how they address it. Many of the cost-function terms one might want to use may be intractable. The authors may have to do what makes sense given technical limitations. If some things can't be done technically, they may need to be addressed in words or via some other sort of non-optimization-based simulation.

Specific comments

As noted above, it would be good to show that preparatory subspace activity occurs similarly across delay durations. It actually might not, at present. For a zero ms delay, the simple magnitude-based regularization may be insufficient to induce preparation. If so, then the authors would either have to argue that a zero delay is actually never used internally (which is a reasonable argument) or show that other forms of regularization can induce zero-delay preparation.

Yes, that is a very interesting analysis to perform, which we had not considered before! When investigating this, we found that the zero-delay strategy does not rely on preparation in the same way as is seen in the monkeys. This seems to be a reflection of the fact that our “Go cue” corresponds to an “internal” go cue which would likely come after the true, “external go cue” – such that we would indeed never actually be in the zero delay setting. This is not something we had addressed (or really considered) before, although we had tried to ensure we referred to “delta prep” as the duration of the preparatory period but not necessarily the delay period. We have now included more discussion on this topic, as well as a new Supplementary Figure S10.

I agree with the authors that prior modeling work was limited by assuming the inputs to M1, which meant that prior work couldn't address the deep issue (tackled here) of why there should be any preparatory inputs at all. At the same time, the ability to hand-select inputs did provide some advantages. A strong assumption of prior work is that the inputs are 'simple', such that motor cortex must perform meaningful computations to convert them to outputs. This matters because if inputs can be anything, then they can just be the final outputs themselves, and motor cortex would have no job to do. Thus, prior work tried to assume the simplest inputs possible to motor cortex that could still explain the data. Most likely this went too far in the 'simple' direction, yet aspects of the simplicity were important for endowing responses with realistic properties. One such property is a large condition-invariant response just before movement onset. This is a very robust aspect of the data, and is explained by the assumption of a simple trigger signal that conveys information about when to move but is otherwise invariant to condition. Note that this is an implicit form of regularization, and one very different from that used in the present study: the input is allowed to be large, but constrained to be simple. Preparatory inputs are similarly constrained to be simple in the sense that they carry only information about which condition should be executed, but otherwise have little temporal structure. Arguably this produces slightly too simple preparatory-period responses, but the present study appears to go too far in the opposite direction. I would suggest that the authors do what they can to address these issue via simulations and/or discussion. I think it is fine if the conclusion is that there exist many constraints that tend to favor preparation, and that regularizing magnitude is just one easy way of demonstrating that. Ideally, other constraints would be explored. But even if they can't be, there should be some discussion of what is missing - preparatory plateaus, a realistic condition-invariant signal tied to movement onset - under the present modeling assumptions.

As described above, we have now included two additional figures. In the first one (S8, already discussed above), we used a temporal smoothness prior, and we indeed get slightly more realistic activity plateaus. In a second supplementary figure (S9), we have also considered using model predictive control (MPC) to optimize the inputs under an uncertain go cue arrival time. There, we found that removing the assumption that the delay period is known came with new challenges: in particular, it requires the specification of a “mental model” of when the Go cue will arrive. While it is reasonable to expect that monkeys will have a prior over the go time arrival cue that will be shaped by the design of the experiment, some assumptions must be made about the utility functions that should be used to weigh this prior. For instance, if we imagine that monkeys carry a model of the possible arrival time of the go cue that is updated online, they could nonetheless act differently based on this information, for instance by either preparing so as to be ready for the earliest go cue possible or alternatively to be ready for the average go cue. This will likely depend on the exact task design and reward/penalty structure. Here, we added simulations with those two cases (making simplifying assumptions to make the problem tractable/solvable using model predictive control), and found that the “earliest preparation” strategy gives rise to more realistic plateauing activity, while the model where planning is done for the “most likely go time” does not. We suspect that more realistic activity patterns could be obtained by e.g combining this framework with the temporal smoothness cost. However, the main point we wished to make with this new supplementary figure is that it is possible to model the task in a slightly more realistic way (although here it comes at the cost of additional model assumptions). We have now added more discussion related to those points. Note that we have kept our analyses on these new models to a minimum, as the main takeaway we wish to convey from them is that most components of the model could be modified/made more realistic. This would impact the qualitative behavior of the system and match to data but – in the examples we have so far considered – does not appear to modify the general strategy of networks relying on preparation.

On line 161, and in a few other places, the authors cite prior work as arguing for "autonomous internal dynamics in M1". I think it is worth being careful here because most of that work specifically stated that the dynamics are likely not internal to M1, and presumably involve inter-area loops and (at some latency) sensory feedback. The real claim of such work is that one can observe most of the key state variables in M1, such that there are periods of time where the dynamics are reasonably approximated as autonomous from a mathematical standpoint. This means that you can estimate the state from M1, and then there is some function that predicts the future state. This formal definition of autonomous shouldn't be conflated with an anatomical definition.

Yes, that is a good point, thank you for making it so clearly! Indeed, as previous work, we do not think of our “M1 dynamics” as being internal to M1, but they may instead include sensory feedback / inter-area loops, which we summarize into the connectivity, that we chose to have dynamics that qualitatively resemble data. We have now incorporated more discussion regarding what exactly the dynamics in our model represent.

Round 2 of reviews

Reviewer 3:

My remaining comments largely pertain to some subtle (but to me important) nuances at a few locations in the text. These should be easy for the authors to address, in whatever way they see fit.

Specific comments:

(1) The authors state the following on line 56: "For preparatory processes to avoid triggering premature movement, any pre-movement activity in the motor and dorsal pre-motor (PMd) cortices must carefully exclude those pyramidal tract neurons."

This constraint is overly restrictive. PT neurons absolutely can change their activity during preparation in principle (and appear to do so in practice). The key constraint is looser: those changes should have no net effect on the muscles. E.g., if d is the vector of changes in PT neuron firing rates, and b is the vector of weights, then the constraint is that b'd = 0. d = 0 is one good way of doing this, but only one. Half the d's could go up and half could go down. Or they all go up, but half the b's are negative. Put differently, there is no reason the null space has to be upstream of the PT neurons. It could be partly, or entirely, downstream. In the end, this doesn't change the point the authors are making. It is still the case that d has to be structured to avoid causing muscle activity, which raises exactly the point the authors care about: why risk this unless preparation brings benefits? However, this point can be made with a more accurate motivation. This matters, because people often think that a null-space is a tricky thing to engineer, when really it is quite natural. With enough neurons, preparing in the null space is quite simple.

That is a good point – we have now reformulated this sentence to instead say “to avoid triggering premature movement, any pre-movement activity in the motor and dorsal premotor (PMd) cortices must engage the pyramidal tract neurons in a way that ensures their activity patterns will not lead to any movement”.

(2) Line 167: 'near-autonomous internal dynamics in M1'.

It would be good if such statements, early in the paper, could be modified to reflect the fact that the dynamics observed in M1 may depend on recurrence that is NOT purely internal to M1. A better phrase might be 'near-autonomous dynamics that can be observed in M1'. A similar point applies on line 13. This issue is handled very thoughtfully in the Discussion, starting on line 713. Obviously it is not sensible to also add multiple sentences making the same point early on. However, it is still worth phrasing things carefully, otherwise the reader may have the wrong impression up until the Discussion (i.e. they may think that both the authors, and prior studies, believe that all the relevant dynamics are internal to M1). If possible, it might also be worth adding one sentence, somewhere early, to keep readers from falling into this hole (and then being stuck there till the Discussion digs them out).

That is a good point: we have now edited the text after line 170 to make it clear that the underlying dynamics may not be confined to M1, and have referenced the later discussion there.

(3) The authors make the point, starting on line 815, that transient (but strong) preparatory activity empirically occurs without a delay. They note that their model will do this but only if 'no delay' means 'no external delay'. For their model to prepare, there still needs to be an internal delay between when the first inputs arrive and when movement generating inputs arrive.

This is not only a reasonable assumption, but is something that does indeed occur empirically. This can be seen in Figure 8c of Lara et al. Similarly, Kaufman et al. 2016 noted that "the sudden change in the CIS [the movement triggering event] occurred well after (~150 ms) the visual go cue... (~60 ms latency)" Behavioral experiments have also argued that internal movement-triggering events tend to be quite sluggish relative to the earliest they could be, causing RTs to be longer than they should be (Haith et al. Independence of Movement Preparation and Movement Initiation). Given this empirical support, the authors might wish to add a sentence indicating that the data tend to justify their assumption that the internal delay (separating the earliest response to sensory events from the events that actually cause movement to begin) never shrinks to zero.

While on this topic, the Haith and Krakauer paper mentioned above good to cite because it does ponder the question of whether preparation is really necessary. By showing that they could get RTs to shrink considerably before behavior became inaccurate, they showed that people normally (when not pressured) use more preparation time than they really need. Given Lara et al, we know that preparation does always occur, but Haith and Krakauer were quite right that it can be very brief. This helped -- along with neural results -- change our view of preparation from something more cognitive that had to occur, so something more mechanical that was simply a good network strategy, which is indeed the authors current point. Working a discussion of this into the current paper may or may not make sense, but if there is a place where it is easy to cite, it would be appropriate.

This is a nice suggestion, and we thank the reviewer for pointing us to the Haith and Krakauer paper. We have now added this reference and extended the paragraph following line 815 to briefly discuss the possible decoupling between preparation and movement initiation that is shown in the Haith paper, emphasizing how this may affect the interpretation of the internal delay and comparisons with behavioral experiments.

Author response:

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

Reviewer #1:

(1) Peptides were synthesized with fluorescein isothiocyanate (FITC) and Tat tag, and then PEGylated with methoxy PEG Succinimidyl Succinate.

I have two concerns about the peptide design. First, FTIC was intended "for monitoring" (line 129), but was never used in the manuscript. Second, PEGylation targets the two lysine sidechains on the Tat, which would alter its penetration property.

(1) We conducted an analysis of the cellular trafficking of FITC-tagged peptides following their permeabilization into cells.

Author response image 1.

However, we did not include it in the main text because it is a basic result.

(2) As can be seen in the figure above, after pegylation and permeabilization, the cells were stained with FITC. It appears that this does not affect the ability to penetrate into the cells.

(2) "Superdex 200 increase 10/300 GL column" (line 437) was used to isolate mono/di PEGylated PDZ and separate them from the residual PEG and PDZ peptide. "m-PEG-succinimidyl succinate with an average molecular weight of 5000 Da" (lines 133 and 134).

To my knowledge, the Superdex 200 increase 10/300 GL column is not suitable and is unlikely to produce traces shown in Figure 1B.

As Superdex 200 increase 10/300 GL featrues a fractionation range of 10,000 to 600,000 Da, we used it to fractionate PEGylated products including DiPEGylated PDZ (approx. 15 kDa) and MonoPEGylated PDZ (approx. 10 kDa) from residuals (PDZ and PEG), demonstrating successful isolation of PEGylated products (Figure 1C). Considering the molecular weights of PDZ and PEG are approximately 4.1 kDa and and 5.0 kDa, respectively, the late eluting peaks from SEC were likely to represent a mixed absorbance of PDZ and PEG at 215 nm.

However, as the reviewer pointed out, it could be unreasonable to annotate peaks representing PDZ and PEG, respectively, from mixed absorbance detected in a region (11-12 min) beyond the fractionation range.

In our revised manuscript, therefore, multiple peaks in the late eluting volume (11-12 min) were labeled as 'Residuals' all together. As a reference, the revised figure 1B includes a chromatogram of pure PDZ-WT under the same analytic condition.

Therefore, we changed Fig.1B to new results.

(3) "the in vivo survival effect of LPS and PDZ co-administration was examined in mice. The pretreatment with WT PDZ peptide significantly increased survival and rescued compared to LPS only; these effects were not observed with the mut PDZ peptide (Figure 2a)." (lines 159-160).

Fig 2a is the weight curve only. The data is missing in the manuscript.

We added the survived curve into Fig. 2A.

(4) Table 1, peptide treatment on ALT and AST appears minor.

In mice treated with LPS, levels of ALT and AGT in the blood are elevated, but these levels decrease upon treatment with WT PDZ. However, the use of mut PDZ does not result in significant changes. Figure 3A shows inflammatory cells within the central vein, yet no substantial hepatotoxicity is observed during the 5-day treatment with LPS. Normally, the ranges of ALT and AGT in C57BL6 mice are 16 ~ 200 U/L and 46 ~ 221 U/L, respectively, according to UCLA Diagnostic Labs. Therefore, the values in all experiments fall within these normal ranges. In summary, a 5-day treatment with LPS induces inflammation in the liver but is too short a duration to induce hepatotoxicity, resulting in lower values.

(5) MitoTraker Green FM shouldn't produce red images in Figure 6.

We changed new results (GREEN one) into Figs 6A and B.

(6) Figure 5. Comparison of mRNA expression in PDZ-treated BEAS-2B cells. Needs a clearer and more detailed description both in the main text and figure legend. The current version is very hard to read.

We changed Fig. 5A to new one to understand much easier and added more detailed results and figure legend.

Results Section in Figure 5:

we performed RNA sequencing analysis. The results of RNA-seq analysis showed the expression pattern of 24,424 genes according to each comparison combination, of which the results showed the similarity of 51 genes overlapping in 4 gene categories and the similarity between each comparison combination (Figure 5a). As a result, compared to the control group, it was confirmed that LPS alone, WT PDZ+LPS, and mut PDZ+LPS were all upregulated above the average value in each gene, and when LPS treatment alone was compared with WT PDZ+LPS, it was confirmed that they were averaged or downregulated. When comparing LPS treatment alone and mut PDZ+LPS, it was confirmed that about half of the genes were upregulated. Regarding the similarity between comparison combinations, the comparison combination with LPS…

Figure 5 Legend Section:

Figure 5. Comparison of mRNA expression in PDZ-treated BEAS-2B cells.

BEAS-2B cells were treated with wild-type PDZ or mutant PDZ peptide for 24 h and then incubated with LPS for 2 h, after which RNA sequencing analysis was performed. (a) The heat map shows the general regulation pattern of about 51 inflammation-related genes that are differentially expressed when WT PDZ and mut PDZ are treated with LPS, an inflammatory substance. All samples are RED = upregulated and BLUE = downregulated relative to the gene average. Each row represents a gene, and the columns represent the values of the control group treated only with LPS and the WT PDZ and mut PDZ groups with LPS. This was used by converting each log value into a fold change value. All genes were adjusted to have the same mean and standard deviation, the unit of change is the standard deviation from the mean, and the color value range of each row is the same. (b) Significant genes were selected using Gene category chat (Fold change value of 2.00 and normalized data (log2) value of 4.00). The above pie chart shows the distribution of four gene categories when comparing LPS versus control, WT PDZ+LPS/LPS, and mut PDZ+LPS/LPS. The bar graph below shows RED=upregulated, GREEN=downregulated for each gene category, and shows the number of upregulated and downregulated genes in each gene category. (c) The protein-protein interaction network constructed by the STRING database differentially displays commonly occurring genes by comparing WT PDZ+LPS/LPS, mut PDZ+LPS/LPS, and LPS. These nodes represent proteins associated with inflammation, and these connecting lines denote interactions between two proteins. Different line thicknesses indicate types of evidence used in predicting the associations.

Reviewer #2:

(1) In this paper, the authors demonstrated the anti-inflammatory effect of PDZ peptide by inhibition of NF-kB signaling. Are there any results on the PDZ peptide-binding proteins (directly or indirectly) that can regulate LPS-induced inflammatory signaling pathway? Elucidation of the PDZ peptide-its binding partner protein and regulatory mechanisms will strengthen the author's hypothesis about the anti-inflammatory effects of PDZ peptide.

As mentioned in the Discussion section, we believe it is crucial to identify proteins that directly interact with PDZ and regulate it. This direct interaction can modulate intracellular signaling pathways, so we plan to express GST-PDZ and induce binding with cellular lysates, then characterize it using the LC-Mass/Mass method. We intend to further research these findings and submit them for publication.

(2) The authors presented interesting insights into the therapeutic role of the PDZ motif peptide of ZO-1. PDZ domains are protein-protein interaction modules found in a variety of species. It has been thought that many cellular and biological functions, especially those involving signal transduction complexes, are affected by PDZ-mediated interactions. What is the rationale for selecting the core sequence that regulates inflammation among the PDZ motifs of ZO-1 shown in Figure 1A?

The rationale for selecting the core sequence that regulates inflammation among the PDZ motifs of ZO-1, as shown in Figure 1A, is grounded in the specific roles these motifs play in signal transduction pathways that are crucial for inflammatory processes. PDZ domains are recognized for their ability to function as scaffolding proteins that organize signal transduction complexes, crucial for modulating cellular and biological functions. The chosen core sequence is particularly important because it is conserved across ZO-1, ZO-2, and ZO-3, indicating a fundamental role in maintaining cellular integrity and signaling pathways. This conservation suggests that the sequence’s involvement in inflammatory regulation is not only significant in ZO-1 but also reflects a broader biological function across the ZO family.

(3) In Figure 3, the authors showed the representative images of IHC, please add the quantification analysis of Iba1 expression and PAS-positive cells using Image J or other software. To help understand the figure, an indication is needed to distinguish specifically stained cells (for example, a dotted line or an arrow).

We added the semi-quantitative results into Figs. 3d,e,f.

Result section: The specific physiological mechanism by which WT PDZ peptide decreases LPS-induced systemic inflammation in mice and the signal molecules involved remain unclear. These were confirmed by a semi-quantitative analysis of Iba-1 immunoreactivity and PAS staining in liver, kidney, and lung,respectively (Figures 4d, e, and f). To examine whether WT PDZ peptide can alter LPS-induced tissue damage in the kidney, cell toxicity assay was performed (Figure 3g). LPS induced cell damage in the kidney, however, WT PDZ peptide could significantly alleviate the toxicity, but mut PDZ peptide could not. Because cytotoxicity caused by LPS is frequently due to ROS production in the kidney (Su et al., 2023; Qiongyue et al., 2022), ROS production in the mitochondria was investigated in renal mitochondria cells harvested from kidney tissue (Figure 3h)......

Figure legend section: Indicated scale bars were 20 μm. (d,e,f) Semi-quantitative analysis of each are positive for Iba-1 in liver and kidney, and positive cells of PAS in lung, respectively. (g) After the kidneys were harvested, tissue lysates were used for MTT assay. (h) After.....

(4) In Figure 6G, H, the authors confirmed the change in expression of the M2 markers by PDZ peptide using the mouse monocyte cell line Raw264.7. It would be good to add an experiment on changes in M1 and M2 markers caused by PDZ peptides in human monocyte cells (for example, THP-1).

We thank you for your comments. To determine whether PDZ peptide regulates M1/M2 polarization in human monocytes, we examined changes in M1 and M2 gene expression in THP-1 cells. As a result, wild-type PDZ significantly suppressed the expression of M1 marker genes (hlL-1β, hIL-6, hIL-8, hTNF-ɑ), while increasing the expression of M2 marker genes (hlL-4, hIL-10, hMRC-1). However, mutant PDZ did not affect M1/M2 polarization. These results suggest that PDZ peptide can suppress inflammation by regulating M1/M2 polarization of human monocyte cells. These results are for the reviewer's reference only and will not be included in the main content.

Author response image 2.

Minor point:

The use of language is appropriate, with good writing skills. Nevertheless, a thorough proofread would eliminate small mistakes such as:

• line 254, " mut PDZ+LPS/LPS (45.75%) " → " mut PDZ+LPS/LPS (47.75%) "

• line 296, " Figure 6f " → " Figure 6h "

We changed these points into the manuscript.

Author response:

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

eLife assessment

This important study presents a novel pipeline for the large-scale genomic prediction of members of the non-ribosomal peptide group of pyoverdines based on a dataset from nearly 2000 Pseudomonas genomes. The advance presented in this study is largely based on solid evidence, although some main claims are only incompletely supported. This study on bacterial siderophores has broad theoretical and practical implications beyond a singular subfield.

Thank you for the supportive and encouraging words. We appreciate the editor’s and reviewers’ careful and professional assessment of this manuscript. The reviewers’ scrutiny has helped us to improve the presentation and discussion of our work. We have now carefully revised the manuscript following their instructive suggestions and comments. Please find below our detailed responses (marked in blue) to each of the comments.

Public Reviews:

Reviewer #1 (Public Review):

The manuscript introduces a bioinformatic pipeline designed to enhance the structure prediction of pyoverdines, revealing an extensive and previously overlooked diversity in siderophores and receptors. Utilizing a combination of feature sequence and phylogenetic approaches, the method aims to address the challenging task of predicting structures based on dispersed gene clusters, particularly relevant for pyoverdines.

Predicting structures based on gene clusters is still challenging, especially pyoverdines as the gene clusters are often spread to different locations in the genome. An improved method would indeed be highly useful, and the diversity of pyoverdine gene clusters and receptors identified is impressive.

However, so far the method basically aligns the structural genes and domains involved in pyoverdine biosynthesis and then predicts A domain specificity to predict the encoded compounds. Both methods are not particularly new as they are included in other tools such as PRISM (10.1093/nar/gkx320) or Sandpuma (https://doi.org/10.1093/bioinformatics/btx400) among others. The study claims superiority in A domain prediction compared to existing tools, yet the support is currently limited, relying on a comparison solely with AntiSMASH. A more extensive and systematic comparison with other tools is needed.  

Thanks for pointing this out. In the revised manuscript, we have included a comprehensive comparative analysis, in which we compared our pipeline to six different commonly used methods, including NP.searcher, PRISM4, AdenPredictor, SeMPI2, SANDPUMA, antiSMASH5 (see Supplementary_table 6 for details, and lines 281-286). These approaches either consist of a single specific algorithm or integrate several methods. Our approach performs best (see table below), demonstrating a clear improvement over previous tool. The improvements are due to several methodological differences inherent to our approach. Additionally, while exploring existing prediction tools, we found that some had not been maintained for years. For instance, we were unable to access NRPSsp (www.nrpssp.com) and NRPSpredictor2 (http://nrps.informatik.uni-tuebingen.de/). Below, we briefly explain these differences, particularly in relation to PRISM and SANDPUMA, as highlighted by the reviewer. 

Author response table 1.

PRISM annotates biosynthetic gene clusters (BGC) and reconstructs the linear structures of NRPS synthetases, with this function depending on proper annotations of open reading frames. This pipeline can have difficulties in assembling the linear structure into a final product. In our approach, we found that the annotations of NRPS gene are frequently truncated because of sequencing errors and annotation issues. Our method fixes this problem through rescanning all possible reading frames of the BGC to rebuild complete pyoverdine synthetase genes. 

Sandpum and our approach are based on similar ideas (using the prediCAT algorithm) to predict A domain substrates, namely by using the closest reference A domain annotated. However, our method uses a self-adaptive feature extraction step to reduce the co-founding influence of phylogeny. This small adjustment significantly improves the performance of our approach and even works well for small training sets (101 experimentally validated A domains with our approach as opposed to 494 A domains used by Sandpuma from MIBiG).

Additionally, in contradiction to the authors' claims, the method's applicability seems constrained to well-known and widely distributed gene clusters. The absence of predictions for new amino acids raises concerns about its generalizability to NRPS beyond the studied cases.

We thank the reviewers for this comment. We acknowledge that our method cannot directly predict new amino acids. Nevertheless, for several reasons we believe that our approach is not constrained and can be widely applied in the future.

First, our method can identify A domains that select new unknown amino acid substrates. In fact, three of the four unresolved cases in our experimental verification analysis (Fig. 3d) represent new amino acids. Obviously, experimental verification is required to characterize the unknown substrate. Once verified, the new A domains and their substrates can expand the reference dataset, allowing targeted improvement of our phylogeny-focused prediction technique. We now discuss this aspect in lines 634-645.

Second, despite that the overall substrate diversity in NRPS is high across the microbial kingdom, our analysis suggests that the number of amino acids used for a specific group of secondary metabolites quickly reaches a saturation point. The discovery rate of new amino acids was 1.7% for our experimental Pseudomonas data set (Fig. 3d). The discovery rate of new amino acids was even 0.0 % for the Burkholderiales data set. This suggests that as the database expands, the discovery rate of novel amino acid substrates is expected to drop rapidly.

Third, we acknowledge that the inability to predict the substrates of unknown domains is a common limitation among all knowledge-guided learning algorithms, including ours. However, we have made significant improvements in prediction accuracy. As the database grows, we expect the rate of unknown substrates to decrease, and the prediction accuracy to increase.

The manuscript lacks clarity on how the alignment of structural genes operates when dealing with multiple NRPS gene clusters on different genome contigs. How would the alignment of each BGC work?

We thank the reviewers for this comment. The pyoverdine molecules consist of a conserved fluorescent chromophore (Flu) and a peptide chain (Pep), both synthesized by NRPS enzymes. In most instances (over 90%), Flu and Pep are produced by two separate biosynthetic gene clusters (BGCs). In these cases, we merge the two BGCs by positioning Flu at the head and Pep at the tail. For the remaining less than 10%, there are two scenarios: 1. Flu and Pep are located on the same BGC, which eliminates any issues with BGC alignment. 2. In very rare cases, Flu and Pep are synthesized by three BGCs. Here, Flu is still synthesized by one BGC at the head, while Pep is produced by two BGCs. We put the BGC containing the Thioesterase (TE) domain as the tail and the BGC not containing the TE domain in the middle.

(see lines 165-169).

Another critical concern is that a main challenge in NRPS structure prediction is not the backbone prediction but rather the prediction of tailoring reactions, which is not addressed in the manuscript at all, and this limitation extensively restricts the applicability of the method.

While we thank the reviewer for this comment, we only partly agree with it. Peptide backbone predictions are still a significant challenge. This challenge is clearly visible in our new analysis comparing prediction accuracies of different pipelines, such as antiSMASH5, PRISM4, AdenPredictor, SeMPI2, NP.searcher, Sandpuma. Unresolved and wrong substrate predictions are still common, highlighting the importance of our contribution in developing a new approach with improved high accuracy. 

However, we agree with the reviewer that our current algorithm does not predict tailoring reactions (now discussed on lines 680-685). Although tailoring reactions are important for predicting the final NRPS product structure, none of the other existing pipelines address this issue either, and it remains a challenge for future work. For our study, it is important to note that the specificity of pyoverdines is primarily determined by the backbone composition, whereas tailoring reactions seem to play a minor role.

The manuscript presents a potentially highly useful bioinformatic pipeline for pyoverdine structure prediction, showcasing a commendable exploration of siderophore diversity. However, some of the claims made remain unsubstantiated. Overall, while the study holds promise, further validation and refinement are required to fulfill its potential impact on the field of bioinformatic structure prediction.

Thank you for the supportive and encouraging words. We deeply appreciate your constructive comments and suggestions. 

Reviewer #2 (Public Review):

Pyoverdines, siderophores produced by many Pseudomonads, are one of the most diverse groups of specialized metabolites and are frequently used as model systems. Thousands of Pseudomonas genomes are available, but large-scale analyses of pyoverdines are hampered by the biosynthetic gene clusters (BGCs) being spread across multiple genomic loci and existing tools' inability to accurately predict amino acid substrates of the biosynthetic adenylation (A) domains. The authors present a bioinformatics pipeline that identifies pyoverdine BGCs and predicts the A domain substrates with high accuracy. They tackled a second challenging problem by developing an algorithm to differentiate between outer membrane receptor selectivity for pyoverdines versus other siderophores and substrates. The authors applied their dataset to thousands of Pseudomonas strains, producing the first comprehensive overview of pyoverdines and their receptors and predicting many new structural variants.

The A domain substrate prediction is impressive, including the correction of entries in the MIBiG database. Their high accuracy came from a relatively small training dataset of A domains from 13 pyoverdine BGCs. The authors acknowledge that this small dataset does not include all substrates, and correctly point out that new sequence/structure pairs can be added to the training set to refine the prediction algorithm. 

The authors could have been more comprehensive in finding their training set data. For instance, the authors claim that histidine "had not been previously documented in pyoverdines", but the sequenced strain P. entomophila L48, incorporates His (10.1007/s10534-009-9247-y). 

Thank you for highlighting this issue. We agree that stating histidine has not been reported before in pyoverdine was incorrect. We have reviewed the full text and made the necessary corrections.

The primary reason for excluding the sequenced strains P. syringae 1448a (10.1186/14712180-11-218) and P. entomophila L48 (10.1007/s10534-009-9247-y) from the training set is that the pyoverdine structures of these strains were not determined solely through experimental methods. In these works, the pyoverdine structures were predicted based on the synthetic gene sequence using bioinformatical analysis, followed by structural analysis experiments based on this predicted structure. We found that pre-prediction probably has introduced biases into downstream analyses. Specifically, in the case of Pseudomonas entomophila L48, we discovered inaccuracies in the annotation of certain domains (see figures below). For example, the third A domain of the peptide chain in P. entomophila L48 pyoverdine was initially annotated with Dab specificity. However, upon closer examination, it appears to differ significantly from other Dab references (top) or Dab from our experimentally validated (right) domains (left panel in the figure below). By analyzing the interface (I) domain (10.1073/pnas.1903161116) in its predicted site, we suggested that it should actually recognize OHHis. The OHAsp domain of P. entomophila L48 reported in the paper is actually close in sequence similarity to the OHAsp domain (left panel in the figure below), while the Ala domain reported is more similar to the Ser domain (right panel in the figure below). For these reasons, we did not include this supervised pyoverdine structure analysis strain in the training set data.

Author response image 1.

The workflow cannot differentiate between different variants of Asp and OHOrn, and it's not clear if this is a limitation of the workflow, the training data, or both. 

Thanks for pointing this out. It is generally challenging to differentiate between variants of the same amino acid (for all the algorithms existing to date). In this sense, it is a limitation of our but also of all other workflows. Nonetheless, we wish to stress that we observed feature sequence divergence (using the A motif4-5 region), which helped us to separate some (but not all) of the Asp and Orn variants. For example, separations between Asp-variants are distinct (left panel in the figure below). To be on the conservative side, we only differentiated between OHAsp and Asp for our predictions, but also differentiation between DOHAsp and OHAsp would be possible. In the case of Orn-variants, there was a clear separation between Orn and the OHOrn variants (right panel). In contrast, it was difficult to differentiate between the subgroups of OHOrn variants. We believe that no A domain prediction tool will be able to solve this issue. Instead, it would be important to include information on substrate-modifying enzymes in future approaches.

Author response image 2.

The prediction workflow holds up well in Burkholderiales A domains, however, they fail to mention in the main text that they achieved these numbers by adding more A domains to their training set.

We thank the reviewers for this comment. We apologize for not having mentioned the training data set in the main text, while we described it in detail in the methods section (lines 714-732). We now provided more details on the analysis procedure in the main text (lines 307313). Important to note is that we did not add more A domains to the training data set but built up a new independent data set for Burkholderiales. The aim was to mirror the analysis we performed for pyoverdines with a completely new data set, featuring 124 A domains for training and 178 A domains as test set.

To validate their predictions, they elucidated structures of several new pyoverdines, and their predictions performed well. However, the authors did not include their MS/MS data, making it impossible to validate their structures. In general, the biggest limitation of the submitted manuscript is the near-empty methods section, which does not include any experimental details for the 20 strains or details of the annotation pipeline (such as "Phydist" and "Syndist"). The source code also does not contain the requisite information to replicate the results or re-use the pipeline, such as the antiSMASH version and required flags. That said, skimming through the source code and data (kindly provided upon request) suggests that the workflow itself is sound and a clear improvement over existing tools for pyoverdine BGC annotation.

Thank you for highlighting these issues. We agree that the methods section is short. This is because the entire paper is a step-by-step methodological introduction to our pipeline. We have now carefully revised the main text to add the information requested by the reviewer. Moreover, we have included a supplementary file with the MS/MS data of the experimentally analyzed pyoverdine structures. Finally, we further include a link to a one-click online notebook that can be used to replicate the annotation and substrate prediction results See: https://drive.google.com/drive/folders/1JsfyPUGDTFo8BDDZk8JLSvKry8emzMhr?usp=drive_ link , following a more detail explanation on code.

Predicting outer membrane receptor specificity is likewise a challenging problem and the authors have made a promising achievement by finding specific gene regions that differentiate the pyoverdine receptor FpvA from FpvB and other receptor families. Their predictions were not tested experimentally, but the finding that only predicted FpvA receptors were proximate to the biosynthesis genes lends credence to the predictive power of the workflow. The authors find predicted pyoverdine receptors across an impressive 468 genera, an exciting finding for expanding the role of pyoverdines as public goods beyond Pseudomonas. However, whether or not these receptors can recognize pyoverdines (and if so, which structures!) remains to be investigated.

Thank you for the supportive and encouraging words. The bioinformatic analysis and experimental testing of pyoverdine-receptor matching is complicated and it is not part of this paper. We treated it in a separate manuscript in which we developed an experimentally verified co-evolution algorithm that matches pyoverdines to receptors. With this algorithm, we can identify self-receptors (i.e. receptors used to take up the self-produced pyoverdine), and therefore establish pyoverdine sharing and interaction networks across strains in communities.

Please see DOI:10.1101/2023.11.05.565711 for details.

In all, the authors have assembled a rich dataset that will enable large-scale comparative genomic analyses. This dataset could be used by a variety of researchers, including those studying natural product evolution, public good eco/evo dynamics, and NRPS engineering.

Thank you for the supportive and encouraging words. We are grateful for the reviewers’ instructive suggestions and comments.

Reviewer #3 (Public Review):

Summary:

Secondary metabolites are produced by numerous microorganisms and have important ecological functions. A major problem is that neither the function of a secondary metabolite enzyme nor the resulting metabolite can be precisely predicted from gene sequence data.

In the current paper, the authors addressed this highly relevant question.

The authors developed a bioinformatic pipeline to reconstruct the complete secondary metabolism pathway of pyoverdines, a class of iron-scavenging siderophores produced by Pseudomonas spp. These secondary metabolites are biosynthesized by a series of nonribosomal peptide synthetases and require a specific receptor (FpvA) for uptake. The authors combined knowledge-guided learning with phylogeny-based methods to predict with high accuracy encoding NRPSs, substrate specificity of A domains, pyoverdine derivatives, and receptors. After validation, the authors tested their pipeline with sequence data from 1664 phylogenetically distinct Pseudomonas strains and were able to determine 18,292 enzymatic A domains involved in pyoverdine synthesis, reliably predicted 97.8% of their substrates, identified 188 different pyoverdine molecule structures and 4547 FpvA receptor variants belonging to 94 distinct groups. All the results and predictions were clearly superior to predictions that are based on antiSMASH. Novel pyoverdine structures were elucidated experimentally by UHPLC-HR-MS/MS.

To assess the extendibility of the pipeline, the authors chose Burkholderiales as a test case which led to the results that the pipeline consistently maintains high prediction accuracy within Burkholderiales of 83% which was higher than for antiSMASH (67%).

Together, the authors concluded that supervised learning based on a few known compounds produced by species from the same genus probably outperforms generalized prediction algorithms trained on many products from a diverse set of microbes for NRPS substrate predictions. As a result, they also show that both pyoverdine and receptor diversity have been vastly underestimated.

Strengths:

The authors developed a very useful bioinformatic pipeline with high accuracy for secondary metabolites, at least for pyoverdines. The pipelines have several advantages compared to existing pipelines like the extensively used antiSMASH program, e.g. it can be applied to draft genomes, shows reduced erroneous gene predictions, etc. The accuracy was impressively demonstrated by the discovery of novel pyoverdines whose structures were experimentally substantiated by UHPLC-HR-MS/MS.

The manuscript is very well written, and the data and the description of the generation of pipelines are easy to follow.

Weaknesses:

The only major comment I have is the uncertainty of whether the pipeline can be applied to more complex non-ribosomal peptides. In the current study, the authors only applied their pipeline to a very narrow field, i.e., pyoverdines of Pseudomonas and Burkholderia strains.

Thanks for your positive and encouraging comment. Regarding your only major comment, we think that the design concept of our pipeline has the potential to be applied to more complex non-ribosomal peptides. Currently, our method is tailored to accurately predict the structural composition of the Pseudomonas siderophore pyoverdine (see also response 3). A key point emphasized in our article is the importance of considering phylogeny in developing substrate prediction algorithms for A domains. Currently, the main challenge in advancing these algorithms is the limited availability of data on A domains and their corresponding substrates. However, with the future accumulation of more reference data, we are confident that the design principles of our method will enable precise predictions of the structural compositions of all products synthesized by non-ribosomal peptide synthetases (see our discussions in lines 634-

645). 

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

I believe that the manuscript would benefit from focusing solely on the task of improving pyoverdine predictions. This aspect alone is significant, and robustly supporting this claim would strengthen the manuscript. The diversity analysis provided is valuable and would undoubtedly benefit the scientific community. However, additional systematic comparisons with other methods are necessary. Furthermore, clarification of certain terms, such as 'featurebased' (e.g., whether it refers to NRPS domains or CDS), would enhance clarity.

Thank you for the supportive and encouraging words. We followed the reviewer’s suggestion and now provide the requested method comparison, see also response 2 for details. Furthermore, we have carefully checked the main text to clarify terms whenever needed. Specifically, we now define the terms “feature sequence” and “feature sequence distance” in lines 227-229.  

Additionally, several minor points could be improved upon:

In line 85, clarification is needed on how pyoverdine genes were identified.

Thank you for your thorough review. In the introduction section, we provided a brief overview of our work, while the detailed methodology is outlined in the results section on lines 160-174.

In line 382, it would be helpful to know the source of the sequences.

We agree and have now carefully revised the manuscript following your suggestions (lines 403-405).

Line 392 could be explained more clearly. Does it mean that the authors used an hmm search to search pHMMs against each reference sequence?

Thanks for your comment. Yes, we used an hmm search to search pHMMs against each reference sequence. We have now revised the manuscript to improve explanations (lines 413-418).

Reviewer #2 (Recommendations For The Authors):

The authors state they "elucidated the chemical structure of the 20 pyoverdines using culturebased methods combined with UHPLC-HR-MS/MS", so I was alarmed to see that KR and LB already published several of those structures in the cited paper. I hope that this "double dipping" will be fixed in a revision process.

Thank you for pointing this out. We agree that we have not explained clearly enough what steps were conducted in this study and which data were used from a previous paper (https://doi.org/10.1007/s00216-022-03907-w). The genomes of the 20 strains used for the verification analysis (Fig. 3d) were sequenced as part of this study (access code now provided). 14 out of the 20 pyoverdine structures were elucidated with UHPLC-HR-MS/MS in this study. For 6 out of the 20 pyoverdines, we had structural information already at hand from the previous paper. We have now clarified these details in our manuscript (lines 276-280). 

Thank you for providing the source code and data, and I hope that the final non-redundant dataset will be uploaded to Zenodo or another repository. Please deposit the 20 newlysequenced genomes to GenBank or another public repository. Please also show the UHPLC-

HR-MS/MS data, preferably in the form of raw data uploaded to GNPS.

We have followed the reviewer’s advice and deposited our data:

- The sequences of the 20 newly sequenced strains are available on ENA accession PRJEB76792.

- The MS/MS plots of the 14 newly analyzed pyoverdines are shown in the Supplementary Materials.

- We provide a one-click online notebook to allow readers to replicate the pyoverdine cluster annotation and substrate prediction of the 20 experimentally analyzed strains.

I suggest adding "at least" or a similar qualifier when the 73 variants are mentioned unless the literature search was truly exhaustive. What were the criteria for inclusion of the 13 strains in Table S2? For instance, sequenced strains P. syringae 1448a (10.1186/1471-2180-11-218) and P. entomophila L48 (10.1007/s10534-009-9247-y) were not included.

Thank you for your comment. We have now carefully revised the manuscript following your suggestions (lines 291-295). Regarding the criteria for including the 13 strains in Table S2, we aimed to select strains with the high credibility for inclusion in the training set data. The primary reason for excluding the two strains from the training set is that their siderophore structures were analyzed through supervised experiments. We wanted to avoid any form of biases that bioinformatic pre-predictions could introduce to downstream analyses (see Response 13 for details).

OHAsp in pyoverdines has been reported to arise from hydroxylation of Asp after it's already been activated by the A domain (10.1073/pnas.1903161116). Was there a clear difference between A domains that lead to Asp and OHAsp? Conversely, acetylation and formylation of OHOrn occur before adenylation. Can your workflow be used to differentiate cOHOrn, fOHOrn, and AcOHOrn, which are currently difficult to predict through genome mining?

Thank you for these considerations. We treated these aspects in our response 8.  

Throughout, define non-proteinogenic AA substrate abbreviations (ex: Rsc, Dab).

Revised as per suggestion (lines 329-333).

Additional line comments:

189: Mention PhyloPhlAn in the main text.

Revised as per suggestion (lines 189).

191: Define these filtering/selection criteria.

Thanks for your comment, we have added the criteria in the main text (line 196 and line 198). 

309, 620: An A domain presumably loading histidine is present in sequenced strain P. entomophila L48 (10.1007/s10534-009-9247-y). Please also clarify that Val has previously been seen in a pyoverdine (it is in Table S1) albeit not sequenced.

We have clarified these aspects as per suggestion (lines 314-315 and line 630).

310: The pipeline can "highlight" new substrates, but not identify them.

Revised as per suggestion (line 295).

354: Please clarify "13 amino acid substrates form the core of all the 188 pyoverdine structures", considering that 279 A domain substrates couldn't be predicted.

Thanks for your comments. We have now clarified “our analysis found that 13 amino acids form the main structural substrates of all the 188 pyoverdine structures.” (lines

360-363)

630: "discovered" implies that there is experimental evidence. I suggest something like "here we predicted 151 putatively new variants".

Revised as per suggestion (line 648).

Reviewer #3 (Recommendations For The Authors):

Weakness:

The only major comment I have is the uncertainty of whether the pipeline can be applied to more complex non-ribosomal peptides. In the current study, the authors only applied their pipeline to a very narrow field, i.e., pyoverdines of Pseudomonas and Burkholderia strains

Thanks for your comment. Please see our Responses 3+13 above, where we treat this concern in detail. Moreover, we discussed the possibility of extension to other groups of secondary metabolites in our discussion. We believe that we deliver a balanced view on the applicability of our approach and the next steps to be taken.  

Please comment on this aspect.

Minor:

(1)  When you speak about "synthesis" it is rather biosynthesis. Synthesis is chemical synthesis.

Please replace all instances of the word synthesis with biosynthesis.

Revised as per suggestion.

(2)  Line 188: synthetase is rather synthetases

Revised as per suggestion (line 191).

Author response:

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

Point-by-point reply in response to the Reviewer’s comments

Reviewer #1

Public review:

[1] (a) Given that only a fraction of the FAPs express BDNF after injury, the authors need to demonstrate the specificity of the Prrx1-Cre for FAPs. This is particularly important because muscle stem cell also express GDNF receptors (Fig. 3C & D) and myogenic progenitors/satellite cells produce BDNF after nerve injury (Griesbeck et al., 1995 (PMID 8531223); Omura et al., 2005 (PMID 16221288)). (b) Moreover, as the authors point out, there are multipotent mesenchymal precursor cells in the nerve that migrate into the surrounding tissue following nerve injury and contribute to regeneration (Carr et al, PMID 30503141). Therefore, there are multiple possible sources of BDNF, highlighting the need to clearly demonstrate that FAP-derived BDNF is essential.

- (a) As the Reviewer noted, both GDNF receptor expression and increased BDNF expression in response to nerve injury are detectable in both FAPs and muscle stem cells (MuSCs). Therefore, we agree with the Reviewer that demonstrating the specificity of Prrx1-Cre in FAPs is crucial to support our claim. In our previous publication (Kim et al., 2022), using Prrx1-Cre; Rosa-eYFP mice, we showed that while most of the CD31-CD45-Vcam1-Sca1+ FAPs are eYFP+, CD31-CD45-Vcam1+Sca1- MuSCs do not express eYFP (Liu et al., 2015; Kim et al., 2022) (Attached Figure 1). Additionally, genomic DNA PCR using mononuclear cells sorted from our Prrx1Cre; Bdnffl/fl mice showed that DNA recombination in the floxed Bdnf gene could only be detected in FAPs and CD31-CD45-Vcam1-Sca1- cells, but not in MuSCs (Author response image 2). This is consistent with a previous report that showed Prrx1-Cre activity in FAPs, pericytes, vascular smooth muscle cells (vSMCs) and tenocytes (Leinroth et al.,

2022), where pericytes, vSMCs and tenocytes are included the CD31-CD45-Vcam1Sca1- population (Giordani et al., 2019). Together, these results demonstrate that while Prrx1-Cre is active in FAPs, it is absent in MuSCs.

Author response image 1.

Expression of eYFP in muscle-resident, lineage-negative, live mononuclear cells isolated from Prrx1Cre;RosaeYFP mice. Supplemental Figure 3A from Kim et al., 2022. Lin-: lineage-negative (CD31-CD45-); Neg.: Vcam1-Sca1-.

Author response image 2.

Recombination of the floxed Bdnf gene in the mononuclear cells sorted from muscles of Prrx1Cre; Bdnffl/fl or Bdnffl/fl mice. Genotypes and cell types sampled for each lane is specified. P4, P5, and P6 indicate primers used for each PCR. Lin+: lineage(CD31/CD45)-positive; DN: CD31-CD45-Vcam1-Sca1-.

- (b) We appreciate and agree with the Reviewer’s comment that additional experiments are needed to confirm that FAP-derived BDNF is indeed essential for nerve regeneration, considering other potential cellular sources of BDNF, such as nerve-resident mesenchymal precursor cells. One possible experiment that could demonstrate the requirement of FAP-derived BDNF in nerve regeneration would be the transplantation of wild-type FAPs into our Prrx1Cre; Bdnf fl/fl mice and to see if the delay in nerve regeneration and remyelination is recovered, making the process similar to that in control mice. Unfortunately, since the genetic background of our Prrx1Cre; Bdnffl/fl mice is a mixture of B6, 129S4, and BALB/c, immune rejection of the transplanted cells may occur, which makes the experiment technically difficult. Another experimental approach could involve the use of FAP-specific Cre mouse line, as we have mentioned in the Discussion of our original manuscript. However, such a line does not yet exist due to the lack of a marker gene that is expressed specifically in FAPs, but not in nerve-resident mesenchymal precursor cells. Overcoming such technical challenges and demonstrating the requirement of FAP-derived BDNF in nerve regeneration would significantly strengthen our report, though we regret that these methods are currently unavailable.

[2] Similarly, the authors should provide some evidence that BDNF protein is produced by FAPs. All of their data for BDNF expression is based on mRNA expression and that appears to only be increased in a small subset of FAPs. Perhaps an immunostaining could be done to demonstrate up-regulation of BDNF in FAPs after injury.

- We appreciate the Reviewer’s constructive comment. To demonstrate that BDNF protein is produced by FAPs upon nerve injury, we performed western blot analysis. FAPs were isolated from either sciatic nerve crush injury-affected muscles at 7 days post injury (dpi) or from the contralateral, uninjured muscles, and protein samples were prepared for SDS-PAGE and western blot using anti-BDNF, anti-PDGFRα and antiGAPDH antibodies. As a result, while both nerve injury-affected and uninjured musclederived FAPs expressed PDGFRα, the mature from of BDNF protein was only detected in nerve injury-affected FAPs, showing that BDNF is indeed expressed in FAPs at the protein level after injury. We have added this new result as Figure 4F in the New Figure 4 with the experimental scheme as New Figure 4—figure supplement 1, and revised the Results section (lines 364-374) and the Materials and Methods section (lines 687-705) in our manuscript to include the new results in detail.

[3] The suggestion that Schwann cell-derived GDNF is responsible for upregulation of BDNF in the FAPs is indirect, based largely on the data showing that injection of GDNF into the muscle is sufficient to up-regulate BDNF (Fig. 4F & G). However, to more directly connect the 2 observations in a causal way, the authors should inject a Ret/GDNF antagonist, such as a Ret-Fc construct, then measure the BDNF levels.

- We appreciate the Reviewer’s constructive comment, and we agree that testing the necessity of GDNF/RET signaling in BDNF upregulation is crucial to link the expression of the two neurotrophic factors in a causal way. As a means to antagonize GDNF/RET signaling, we injected anti-GDNF antibodies into the tibialis anterior and gastrocnemius muscles following sciatic nerve crush injury to block the activity of intramuscular GDNF protein. As a result, although the differences were not statistically significant, we observed a tendancy towards decreased Bdnf mRNA expression upon anti-GDNF injection compared to IgG controls. We have added this new result as New Figure 4—figure supplement 2, and revised our manuscript to include the details in both the Results section (lines 381-390) and the Materials and Methods section (lines 611-616). We have also changed the title of New Figure 4 (line 332) to encompass the new results. We are aware that further experiments that may involve increasing the number of animals tested, increasing the antibody injection dosage or frequency, or implementation of genetic models such as Plp1CreER; Gdnffl/fl should be carried out to validate our hypothesis with statistical significance. Unfortunately, due to limited time, resources, and research funds, we were unable to perform such additional experiments. We hope that the Reviewer understands these limitations.

[4] (a) In assessing the regeneration after nerve crush, the authors focus on remyelination, for example, assessing CMAP and g-ratios. However, they should also quantify axon regeneration, which can be done distal to the crush injury at earlier time points, before the 6 weeks scored in their study. Evaluating axon regeneration, which occurs prior to remyelination, would be especially useful because BDNF can act on both Schwann cells, to promote myelination, and axons, enhancing survival and growth. (b) They could also evaluate the stability of the neuromuscular junctions, particularly if a denervation was done with the conditional knock outs, although that may be a bit beyond the scope of this study.

- (a) As the Reviewer mentioned, BDNF is known to act on both Schwann cells and axons, where it promotes myelination and axonal growth, respectively (Oudega and

Hagg, 1998; Zhang et al., 2000; Chan et al., 2001; Xiao et al., 2009; English et al.,

2013). We fully agree with the Reviewer’s comment that quantification of axon regeneration, which could be achieved through immunostaining of the distal part of the sciatic nerve at earlier time points after injury, would shed light on whether FAPderived BDNF can also contribute to axon regeneration in addition to remyelination. Unfortunately, we could not perform such additional experiments within the limited time frame, since preparing enough numbers of control and conditional knockout mice that match the age groups used in this study (3-4 months old), followed by waiting for additional 2-4 weeks after nerve crush injury for sample collection, and subsequent immunostaining for quantification could take almost 6 months in total. We hope that the Reviewer understands this limitation.

- (b) We appreciate the Reviewer’s constructive comment. Although the number of animals used for neuromuscular junction (NMJ) analyses was not sufficient, we had briefly examined the structure of NMJs at 4 weeks post nerve crush injury in control (Ctrl) and conditional knockout (cKO) mice as a preliminary experiment. As a result, no significant differences were observed between Ctrl and cKO mice in terms of NMJ morphology and innervation (Author response image 3). 

Author response image 3.

Structures of neuromuscular junctions from Ctrl vs cKO mice at 4 weeks post nerve crush injury. Whole-mount immunostaining was done using the exterior digitorum longus muscles that were affected by sciatic nerve crush injury. Samples were stained with α-bungarotoxin (green), neurofilament (red), and synaptophysin (blue). Scale bar: 50 μm. 

Going back to part (a) of this Reviewer’s comment, considering the data presented in Author response image 3, where innervation of axons into acetylcholine receptor clusters was not significantly different between Ctrl versus cKO mice, FAP-derived BDNF may not be critical for the axonal growth upon nerve injury. Although we acknowledge that additional experiments are required to draw a meaningful conclusion on this point, we could not perform such additional experiments due to insufficient time and resources.

We hope that the Reviewer understands our limitation.

Recommendations for the authors:

[1] In citing the ability of BDNF to promote Schwann cell myelination the authors should include Chan et al., 2001 (PMID 11717413) in addition to the Zhang et al, 2000 and Xiao et al, 2009 references.

- We apologize for missing out the reference mentioned by the Reviewer. We have added the suggested reference in our revised manuscript (lines 395, 425, and 517).

Reviewer #2

Public review:

[1] Although, I find the data the authors generated enough for their claims. I do see them as relatively poor, and (a) a complementary analysis of protein expression would strengthen the paper through immunostaining of the different genes mentioned for FAPs and Schwann cells. The model is entirely supported by measuring mRNA levels and negative regulation of gene expression in specific cells. Additionally, (b) what happens to the structure of the neuromuscular junction after regeneration when GDNF or BDNF expression is reduced? (c) The determination of decreasing levels of FAPs BDNF mRNA during aging is interesting; is the gain of BDNF expression in FAPs reverting the phenotype?

- (a) We appreciate and agree with the Reviewer’s comment that validation of BDNF protein expression in FAPs and GDNF protein expression in Schwann cells upon nerve injury would strengthen this paper. Regarding GDNF protein expression in Schwann cells upon nerve injury, it has already been demonstrated by previous studies (Höke et al., 2002; Xu et al., 2013). For BDNF protein expression in FAPs upon nerve injury, we performed western blot analysis for validation, as mentioned in the response to Reviewer #1 Public review [2]. The results showed that while the mature form of BDNF protein could not be readily detected in FAPs isolated from uninjured muscles, it could be detected in FAPs isolated from sciatic nerve crush injury-affected muscles at 7 days post injury. We have added the new result as Figure 4F in the New Figure 4 with the experimental scheme as New Figure 4—figure supplement 1, and revised the Results section (lines 364-374) and the Materials and Methods section (lines 687-705) in our manuscript to include the new results in detail.

- (b) Though the data is preliminary, we examined the structures of neuromuscular junctions (NMJs) from control and Prrx1Cre; Bdnf fl/fl mice at 4 weeks post injury in the exterior digitorum longus muscles, as mentioned in the response to Reviewer #1 Publilc review [4](b). As a result, we could not identify significant differences between control versus Prrx1Cre; Bdnf fl/fl mice, where BDNF expression is reduced specifically in Prrx1-expressing cells, including FAPs (Attached Figure 3). Since other cellular sources of BDNF, such as Schwann cells, exist, regeneration of the NMJs may not have been as significantly affected as remyelination in our Prrx1Cre; Bdnf fl/fl mice. However, further experiments with a sufficient number of mice and more observation time points are required to statistically validate this hypothesis in detail. Unfortunately, preparing samples for such additional analyses would take more than four months, as we need to produce sufficient numbers of control and Prrx1Cre; Bdnf fl/fl mice that match the age groups used in this study. We hope that the Reviewer understands our limitation.

Regarding analyzing NMJ structures after regeneration affected by reduced GDNF levels, using genetic models such as Plp1CreER; Gdnffl/fl mice would be appropriate, as we have used the Prrx1Cre; Bdnffl/fl mice in this study to reduce BDNF levels produced by FAPs. Unfortunately, we do not have the Gdnffl mice, and obtaining these mice to produce Plp1CreER; Gdnffl/fl mice and performing the additional experiment would take too much time for this current revision. In a further study, we will try to perform the additional experiment by obtaining the required mouse line. We hope that the Reviewer understands our limitation.

- (c) We appreciate the Reviewer for highlighting this point. In this paper, we have shown that BDNF expression upon nerve injury is decreased in aged FAPs compared to young adult FAPs, and suggested that this may be one of the causes of the delayed nerve regeneration phenotype in aged mice. Previously, it has been reported that while intramuscular injection of BDNF accelerates nerve regeneration, intramuscular injection of anti-BDNF antibodies delays the regeneration process (Zheng et al., 2016). This implies that intramuscular levels of active BDNF can significantly influence the speed of nerve regeneration. Therefore, the gain of BDNF expression in aged FAPs may contribute to reversing the delayed nerve regeneration phenotype in aged mice, since it would result in additional supply of active, intramuscular BDNF, which has previously been shown to accelerate nerve regeneration. Though experimental validation is required to support such claim, we could not obtain sufficient numbers of aged mice within the limited time frame. We hope that the Reviewer understands our limitation.

Recommendations for the authors:

[1] The authors should include the experimental design and several drawings in the leading figures indicating, for example, how remyelination after injury was quantified and how the response of regenerated sciatic nerve to a depolarizing stimulus was studied.

- We apologize for any confusion caused by insufficient information provided in the leading figures. Unfortunately, due to limited space, we could not add experimental designs or drawings in the leading figures. Instead, to do our best to comply with the

Reviewer’s comment, we have revised the figure legends in the leading figures so that the experimental designs or diagrams can be referred to in the figure supplements.

We hope that the Reviewer understands this limitation.

Reviewer #3

Public review:

[1] In Fig. 1 and 2 authors provide data on scRNA seq and this is important information reporting the finding of RET and GFRa1 transcripts in the subpopulation of FAP cells. However, authors provide no data on the expression of RET and GFRa1 proteins in FAP cells.

- Reply for this comment by the Reviewer is in the Recommendations for the authors section below ([2]), as the same comment is repeated.

[2] Another problem is the lack of information showing that GDNF secreted by Schwann cells can activate RET and its down-stream signaling in FAP cells. There is no direct experimental proof that GDNF activating GFRa1-RET signaling triggers BDNF upregulation In FAP cells. The data that GDNF signaling is inducing the synthesis and secretion of BDNF is also not conclusive.

- Reply for this comment by the Reviewer is in the Recommendations for the authors section below ([3]), as the same comment is repeated.

Recommendations for the authors:

[1] Although this is a novel study and contains very well-performed parts, the GDNF section is preliminary and requires additional experimentation. In the introduction authors describe well FAPs but even do not mention how GDNF is signaling. Moreover, the reader may get an impression that Ras-MAPK pathway is the only or at least the main GDNF signaling pathway. In fact, for neurons Akt and Src signaling pathways play also crucial role.

- We apologize for the missing content in the Introduction section of our manuscript and for any confusion caused by our misleading description of the GDNF signaling pathway. We have revised our manuscript to include the GDNF signaling pathway in the Introduction section, along with a description of other downstream signaling pathways of GDNF that are known to play crucial roles, as mentioned by the Reviewer (lines 115-130). Additionally, we changed the expression in the Results section to avoid making any misleading impressions (lines 318-319).

[2] In Fig. 1 and 2 authors provide data on scRNA seq and this is important information reporting the finding of RET and GFRa1 transcripts in the subpopulation of FAP cells. However, authors provide no data on the expression of RET and GFRa1 proteins in FAP cells.

- We appreciate the Reviewer for the constructive comment. Though we fully agree with the Reviewer that validating the expression of RET and GFRα1 proteins in FAPs is needed, we were unable to obtain the antibodies required for such experiments within the limited time frame for this revision. We hope that the Reviewer understands our limitation. Although we could not directly show the expression of those GDNF receptor genes at the protein level in FAPs, based on the result where intramuscular GDNF injection could sufficiently induce Bdnf expression in FAPs compared to PBS control in the absence of nerve damage, it is likely that GDNF receptors are indeed expressed at the protein level in FAPs, since if otherwise, FAPs would not have been able to respond to the injected GDNF protein. Nevertheless, in a future study, we will try to validate the protein-level expression of GDNF receptors in FAPs to comply with the Reviewer’s suggestion and to further support this study.

[3] Another problem is the lack of information showing that GDNF secreted by Schwann cells can activate RET and its down-stream signaling in FAP cells. Authors can monitor activation of MAPK pathway by detecting phospho-Erk and PI3 kinase-Akt pathway measuring phospho-S6 using immunohistochemistry. We can recommend to use the following antibodies: pErk1/2 (1:300, Cell Signaling, Cat# 4370L RRID:AB_2297462), pS6 (1:300, Cell Signaling, Cat# 4858L RRID:AB_1031194). These experiments are crucial because RET and GFRa1 proteins maybe not expressed at the sufficient level on the cell surface.

- We sincerely appreciate the Reviewer’s constructive comment. In this study, we suggested that the GDNF-BDNF axis within FAPs would signal through the MAPK pathway based on the bioinformatic analysis of our single cell RNA-seq data and matching the results with the previously known pathways. We fully agree that monitoring the activation of the MAPK pathway and the PI3K-Akt pathway by immunohistochemistry would experimentally demostrate whether GDNF can activate those pathways within FAPs through GFRα1/RET activation. Unfortunately, we could not obtain the antibodies suggested by the Reviewer for this revision due to insufficient research funds and limited time frame. We hope that the Reviewer understands our limitation. In future studies, we will try to validate the detailed molecular pathway that mediates the GDNF-BDNF axis in FAPs by incorporating the methodology suggested by the Reviewer, along with implementation of genetic models such as Plp1CreER; Gdnffl/fl, Prrx1Cre; Retfl/fl or Prrx1Cre; Gfra1fl/fl to validate whether Schwann cell-derived

GDNF can actually signal through its canonical receptor RET/GFRα1 expressed in FAPs to induce expression of BDNF upon nerve injury.

[4] (a) There is no direct experimental proof that GDNF activating GFRa1-RET signaling triggers BDNF upregulation in FAP cells. Authors can use GDNF blocking antibodies, siRNA or use RET or GFRa1 cKO mice to delete them from FAP cells. (b) The data that GDNF signaling is inducing the synthesis and secretion of BDNF is also not conclusive. Authors should show that GDNF injection is increasing BDNF protein levels in FAPs. To get sufficient material for ELISA detection of BDNF is perhaps problematic. However, authors can use BDNF antibodies from Icosagen company and use IHC.

- (a) We appreciate the Reviewer for the critical comment. As mentioned in the reply for Reviewer #1 Public review [3], we used GDNF blocking antibodies to reduce GDNF signaling within the tibialis anterior and gastrocnemius muscles by intramuscular injection after sciatic nerve crush injury, and included the result as a new figure supplement in our revised manuscript (New Figure 4—figure supplement 2) with its details in both the Results section (lines 381-390) and the Materials and Methods section (lines 611-616). Though the results were not statistically significant, intramuscular injection of anti-GDNF antibodies showed a tendency toward reduced Bdnf expression in FAPs, compared to IgG controls. As mentioned in the reply for Reviewer #1 Public review [3], and as suggested by the Reviewer, using cKO mice such as Plp1CreER; Gdnffl/fl, Prrx1Cre; Retfl/fl, or Prrx1Cre; Gfra1fl/fl mice would further validate the GDNF-BDNF axis suggested in this study, likely with statistical significance. Unfortunately, obtaining these genetic models within the limited time frame of this current revision is not feasible. We will try to adopt such models in our future study to validate the role of Schwann cell-derived GDNF in inducing BDNF expression in FAPs via activation of RET/GFRα1.  

- (b) We appreciate the Reviewer for the constructive comment. Though we fully agree that the experiment suggested by the Reviewer would validate the synthesis and secretion of BDNF protein by GDNF signaling in FAPs, we were not able to perform it due to lack of research funds to obtain enough amount of the GDNF protein. We hope that the Reviewer understands our limitation. Still, combining the results from New Figure 4H in this study with the New Figure 4F, where GDNF injection induced Bdnf mRNA expression in FAPs, and BDNF protein expression in FAPs in response to nerve injury was demonstrated via western blot, we anticipate that GDNF injection would increase BDNF protein levels in FAPs, though direct validation of this statement would require conducting the additional experiments mentioned by the Reviewer.

References

Chan JR, Cosgaya JM, Wu YJ, and Shooter EM (2001). Neurotrophins are key mediators of the myelination program in the peripheral nervous system. Proceedings of the National Academy of Sciences 98:14661-14668.

English AW, Liu K, Nicolini JM, Mulligan AM, and Ye K (2013). Small-molecule trkB agonists promote axon regeneration in cut peripheral nerves. Proc Natl Acad Sci U S A 110:16217-22.10.1073/pnas.1303646110

Giordani L, He GJ, Negroni E, Sakai H, Law JY, Siu MM, Wan R, Corneau A, Tajbakhsh S, and Cheung TH (2019). High-dimensional single-cell cartography reveals novel skeletal muscle-resident cell populations. Molecular Cell 74:609-621. e6.

Höke A, Gordon T, Zochodne D, and Sulaiman O (2002). A decline in glial cell-linederived neurotrophic factor expression is associated with impaired regeneration after long-term Schwann cell denervation. Experimental neurology 173:77-85.

Kim J-H, Kang J-S, Yoo K, Jeong J, Park I, Park JH, Rhee J, Jeon S, Jo Y-W, and Hann S-H (2022). Bap1/SMN axis in Dpp4+ skeletal muscle mesenchymal cells regulates the neuromuscular system. JCI Insight 7:

Leinroth AP, Mirando AJ, Rouse D, Kobayahsi Y, Tata PR, Rueckert HE, Liao Y, Long JT, Chakkalakal JV, and Hilton MJ (2022). Identification of distinct non-myogenic skeletal-muscle-resident mesenchymal cell populations. Cell Reports 39:

Liu L, Cheung TH, Charville GW, and Rando TA (2015). Isolation of skeletal muscle stem cells by fluorescence-activated cell sorting. Nature protocols 10:1612-1624.

Oudega M, and Hagg T (1998). Neurotrophins promote regeneration of sensory axons in the adult rat spinal cord. Brain Research 818:431-438.10.1016/S0006-8993(98)01314-6

Xiao J, Wong AW, Willingham MM, Kaasinen SK, Hendry IA, Howitt J, Putz U, Barrett GL, Kilpatrick TJ, and Murray SS (2009). BDNF exerts contrasting effects on peripheral myelination of NGF-dependent and BDNF-dependent DRG neurons. J Neurosci 29:4016-22.10.1523/JNEUROSCI.3811-08.2009

Xu P, Rosen KM, Hedstrom K, Rey O, Guha S, Hart C, and Corfas G (2013). Nerve injury induces glial cell linederived neurotrophic factor (gdnf) expression in schwann cells through purinergic signaling and the pkcpkd pathway. Glia 61:1029-1040.

Zhang JY, Luo XG, Xian CJ, Liu ZH, and Zhou XF (2000). Endogenous BDNF is required for myelination and regeneration of injured sciatic nerve in rodents. European Journal of Neuroscience 12:4171-4180.10.1111/j.1460-9568.2000.01312.x

Zheng J, Sun J, Lu X, Zhao P, Li K, and Li L (2016). BDNF promotes the axonal regrowth after sciatic nerve crush through intrinsic neuronal capability upregulation and distal portion protection. Neuroscience letters 621:1-8.

Author Response:

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

We carefully read through the second-round reviews and the additional reviews. To us, the review process is somewhat unusual and very much dominated by referee 2, who aggressively insists that we mixed up the trigeminal nucleus and inferior olive and that as a consequence our results are meaningless. We think the stance of referee 2 and the focus on one single issue (the alleged mix-up of trigeminal nucleus and inferior olive) is somewhat unfortunate, leaves out much of our findings and we debated at length on how to deal with further revisions. In the end, we decided to again give priority to addressing the criticism of referees 2, because it is hard to go on with a heavily attacked paper without resolving the matter at stake. The following is a summary of, what we did:

Additional experimental work:

(1) We checked if the peripherin-antibody indeed reliably identifies climbing fibers.

To this end, we sectioned the elephant cerebellum and stained sections with the peripherin-antibody. We find: (i) the cerebellar white matter is strongly reactive for peripherin-antibodies, (ii) cerebellar peripherin-antibody staining of has an axonal appearance. (iii) Cerebellar Purkinje cell somata appear to be ensheated by peripherin-antibody staining. (iv) We observed that the peripherin-antibody reactivity gradually decreases from Purkinje cell somata to the pia in the cerebellar molecular layer. This work is shown in our revised Figure 2. All these four features align with the distribution of climbing fibers (which arrive through the white matter, are axons, ensheat Purkinje cell somata, and innervate Purkinje cell proximally not reaching the pia). In line with previous work, which showed similar cerebellar staining patterns in several species (Errante et al. 1998), we conclude that elephant climbing fibers are strongly reactive for peripherin-antibodies.

(2) We delineated the elephant olivo-cerebellar tract.

The strong peripherin-antibody reactivity of elephant climbing fibers enabled us to delineate the elephant olivo-cerebellar tract. We find the elephant olivo-cerebellar tract is a strongly peripherin-antibody reactive, well-delineated fiber tract several millimeters wide and about a centimeter in height. The unstained olivo-cerebellar tract has a greyish appearance. In the anterior regions of the olivo-cerebellar tract, we find that peripherin-antibody reactive fibers run in the dorsolateral brainstem and approach the cerebellar peduncle, where the tract gradually diminishes in size, presumably because climbing fibers discharge into the peduncle. Indeed, peripherin-antibody reactive fibers can be seen entering the cerebellar peduncle. Towards the posterior end of the peduncle, the olivo-cerebellar disappears (in the dorsal brainstem directly below the peduncle. We note that the olivo-cerebellar tract was referred to as the spinal trigeminal tract by Maseko et al. 2013. We think the tract in question cannot be the spinal trigeminal tract for two reasons: (i) This tract is the sole brainstem source of peripherin-positive climbing fibers entering the peduncle/ the cerebellum; this is the defining characteristic of the olivo-cerebellar tract. (ii) The tract in question is much smaller than the trigeminal nerve, disappears posterior to where the trigeminal nerve enters the brainstem (see below), and has no continuity with the trigeminal nerve; the continuity with the trigeminal nerve is the defining characteristic of the spinal trigeminal tract, however.

The anterior regions of the elephant olivo-cerebellar tract are similar to the anterior regions of olivo-cerebellar tract of other mammals in its dorsolateral position and the relation to the cerebellar peduncle. In its more posterior parts, the elephant olivo-cerebellar tract continues for a long distance (~1.5 cm) in roughly the same dorsolateral position and enters the serrated nucleus that we previously identified as the elephant inferior olive. The more posterior parts of the elephant olivo-cerebellar tract therefore differ from the more posterior parts of the olivo-cerebellar tract of other mammals, which follows a ventromedial trajectory towards a ventromedially situated inferior olive. The implication of our delineation of the elephant olivo-cerebellar tract is that we correctly identified the elephant inferior olive.

(3) An in-depth analysis of peripherin-antibody reactivity also indicates that the trigeminal nucleus receives no climbing fiber input.

We also studied the peripherin-antibody reactivity in and around the trigeminal nucleus. We had also noted in the previous submission that the trigeminal nucleus is weakly positive for peripherin, but that the staining pattern is uniform and not the type of axon bundle pattern that is seen in the inferior olive of other mammals. To us, this observation already argued against the presence of climbing fibers in the trigeminal nucleus. We also noted that the myelin stripes of the trigeminal nucleus were peripherin-antibody-negative. In the context of our olivo-cerebellar tract tracing we now also scrutinized the surroundings of the trigeminal nucleus for peripherin-antibody reactivity. We find that the ventral brainstem surrounding the trigeminal nucleus is devoid of peripherin-antibody reactivity. Accordingly, no climbing fibers, (which we have shown to be strongly peripherin-antibody-positive, see our point 1) arrive at the trigeminal nucleus. The absence of climbing fiber input indicates that previous work that identified the (trigeminal) nucleus as the inferior olive (Maseko et al 2013) is unlikely to be correct.

(4) We characterized the entry of the trigeminal nerve into the elephant brain.

To better understand how trigeminal information enters the elephant’s brain, we characterized the entry of the trigeminal nerve. This analysis indicated to us that the trigeminal nerve is not continuous with the olivo-cerebellar tract (the spinal trigeminal tract of Maseko et al. 2013) as previously claimed by Maseko et al. 2013. We show some of this evidence in Referee-Figure 1 below. The reason we think the trigeminal nerve is discontinuous with the olivo-cerebellar tract is the size discrepancy between the two structures. We first show this for the tracing data of Maseko et al. 2013. In the Maseko et al. 2013 data the trigeminal nerve (Referee-Figure 1A, their plate Y) has 3-4 times the diameter of the olivocerebellar tract (the alleged spinal trigeminal tract, Referee-Figure 1B, their plate Z). Note that most if not all trigeminal fibers are thought to continue from the nerve into the trigeminal tract (see our rat data below). We plotted the diameter of the trigeminal nerve and diameter of the olivo-cerebellar (the spinal trigeminal tract according to Maseko et al. 2013) from the Maseko et al. 2013 data (Referee-Figure 1C) and we found that the olivocerebellar tract has a fairly consistent diameter (46 ± 9 mm2, mean ± SD). Statistical considerations and anatomical evidence suggest that the tracing of the trigeminal nerve into the olivo-cerebellar (the spinal trigeminal tract according to Maseko et al. 2013) is almost certainly wrong. The most anterior point of the alleged spinal trigeminal tract has a diameter of 51 mm2 which is more than 15 standard deviations different from the most posterior diameter (194 mm2) of the trigeminal tract. For this assignment to be correct three-quarters of trigeminal nerve fibers would have to spontaneously disappear, something that does not happen in the brain. We also made similar observations in the African elephant Bibi, where the trigeminal nerve (Referee-Figure 1D) is much larger in diameter than the olivocerebellar tract (Referee-Figure 1E). We could also show that the olivocerebellar tract disappears into the peduncle posterior to where the trigeminal nerve enters (Referee-Figure 1F). Our data are very similar to Maseko et al. indicating that their outlining of structures was done correctly. What appears to have been oversimplified, is the assignment of structures as continuous. We also quantified the diameter of the trigeminal nerve and the spinal trigeminal tract in rats (from the Paxinos & Watson atlas; Referee-Figure 1D); as expected we found the trigeminal nerve and spinal trigeminal tract diameters are essentially continuous.

In our hands, the trigeminal nerve does not continue into a well-defined tract that could be traced after its entry. In this regard, it differs both from the olivo-cerebellar tract of the elephant or the spinal trigeminal tract of the rodent, both of which are well delineated. We think the absence of a well-delineated spinal trigeminal tract in elephants might have contributed to the putative tracing error highlighted in our Referee-Figure 1A-C.

We conclude that a size mismatch indicates trigeminal fibers do not run in the olivo-cerebellar tract (the spinal trigeminal tract according to Maseko et al. 2013).

Author response image 1.

The trigeminal nerve is discontinuous with the olivo-cerebellar tract (the spinal trigeminal tract according to Maseko et al. 2013)

A, Trigeminal nerve (orange) in the brain of African elephant LAX as delineated by Maseko et al. 2013 (coronal section; their plate Y).

B, Most anterior appearance of the spinal trigeminal tract of Maseko et al. 2013 (blue; coronal section; their plate Z). Note the much smaller diameter of the spinal trigeminal tract compared to the trigeminal nerve shown in C, which argues against the continuity of the two structures. Indeed, our peripherin-antibody staining showed that the spinal trigeminal tract of Maseko corresponds to the olivo-cerebellar tract and is discontinuous with the trigeminal nerve.

C, Plot of the trigeminal nerve and olivo-cerebellar tracts (the spinal trigeminal tract according to Maseko et al. 2013) diameter along the anterior-posterior axis. The trigeminal nerve is much larger in diameter than the olivocerebellar tract (the spinal trigeminal tract according to Maseko et al. 2013). C, D measurements, for which sections are shown in panels C and D respectively. The olivocerebellar tract (the spinal trigeminal tract according to Maseko et al. 2013) has a consistent diameter; data replotted from Maseko et al. 2013. At mm 25 the inferior olive appears.

D, Trigeminal nerve entry in the brain of African elephant Bibi; our data, coronal section, the trigeminal nerve is outlined in orange, note the large diameter.

E, Most anterior appearance of the olivo-cerebellar tract in the brain of African elephant Bibi; our data, coronal section, approximately 3 mm posterior to the section shown in A, the olivocerebellar tract is outlined in blue. Note the smaller diameter of the olivo-cerebellar tract compared to the trigeminal nerve, which argues against the continuity of the two structures.

F, Plot of the trigeminal nerve and olivo-cerebellar tract diameter along the anterior-posterior axis. The nerve and olivo-cerebellar tract are discontinuous and the trigeminal nerve is much larger in diameter than the olivocerebellar tract (the spinal trigeminal tract according to Maseko et al. 2013); our data. D, E measurements, for which sections are shown in panels D and E respectively. At mm 27 the inferior olive appears.

G, In the rat the trigeminal nerve is continuous in size with the spinal trigeminal tract. Data replotted from Paxinos and Watson.

Reviewer 2 (Public Review):

As indicated in my previous review of this manuscript (see above), it is my opinion that the authors have misidentified, and indeed switched, the inferior olivary nuclear complex (IO) and the trigeminal nuclear complex (Vsens). It is this specific point only that I will address in this second review, as this is the crucial aspect of this paper - if the identification of these nuclear complexes in the elephant brainstem by the authors is incorrect, the remainder of the paper does not have any scientific validity.

Comment: We agree with the referee that it is most important to sort out, the inferior olivary nuclear complex (IO) and the trigeminal nuclear complex, respectively.Change: We did additional experimental work to resolve this matter as detailed at the beginning of our response. Specifically, we ascertained that elephant climbing fibers are strongly peripherin-positive. Based on elephant climbing fiber peripherin-reactivity we delineated the elephant olivo-cerebellar tract. We find that the olivo-cerebellar connects to the structure we refer to as inferior olive to the cerebellum (the referee refers to this structure as the trigeminal nuclear complex). We also found that the trigeminal nucleus (the structure the referee refers to as inferior olive) appears to receive no climbing fibers. We provide indications that the tracing of the trigeminal nerve into the olivo-cerebellar tract by Maseko et al. 2023 was erroneous (Author response image 1). These novel findings support our ideas but are very difficult to reconcile with the referee’s partitioning scheme.

The authors, in their response to my initial review, claim that I "bend" the comparative evidence against them. They further claim that as all other mammalian species exhibit a "serrated" appearance of the inferior olive, and as the elephant does not exhibit this appearance, that what was previously identified as the inferior olive is actually the trigeminal nucleus and vice versa. 

For convenience, I will refer to IOM and VsensM as the identification of these structures according to Maseko et al (2013) and other authors and will use IOR and VsensR to refer to the identification forwarded in the study under review. <br /> The IOM/VsensR certainly does not have a serrated appearance in elephants. Indeed, from the plates supplied by the authors in response (Referee Fig. 2), the cytochrome oxidase image supplied and the image from Maseko et al (2013) shows a very similar appearance. There is no doubt that the authors are identifying structures that closely correspond to those provided by Maseko et al (2013). It is solely a contrast in what these nuclear complexes are called and the functional sequelae of the identification of these complexes (are they related to the trunk sensation or movement controlled by the cerebellum?) that is under debate.

Elephants are part of the Afrotheria, thus the most relevant comparative data to resolve this issue will be the identification of these nuclei in other Afrotherian species. Below I provide images of these nuclear complexes, labelled in the standard nomenclature, across several Afrotherian species. 

(A) Lesser hedgehog tenrec (Echinops telfairi) 

Tenrecs brains are the most intensively studied of the Afrotherian brains, these extensive neuroanatomical studies undertaken primarily by Heinz Künzle. Below I append images (coronal sections stained with cresol violet) of the IO and Vsens (labelled in the standard mammalian manner) in the lesser hedgehog tenrec. It should be clear that the inferior olive is located in the ventral midline of the rostral medulla oblongata (just like the rat) and that this nucleus is not distinctly serrated. The Vsens is located in the lateral aspect of the medulla skirted laterally by the spinal trigeminal tract (Sp5). These images and the labels indicating structures correlate precisely with that provide by Künzle (1997, 10.1016, see his Figure 1K,L. Thus, in the first case of a related species, there is no serrated appearance of the inferior olive, the location of the inferior olive is confirmed through connectivity with the superior colliculus (a standard connection in mammals) by Künzle (1997), and the location of Vsens is what is considered to be typical for mammals. This is in agreement with the authors, as they propose that ONLY the elephants show the variations they report. 

Peer Review Image 1.

(B) Giant otter shrew (Potomogale velox) 

The otter shrews are close relatives of the Tenrecs. Below I append images of cresyl violet (left column) and myelin (right column) stained coronal sections through the brainstem with the IO, Vsens and Sp5 labelled as per standard mammalian anatomy. Here we see hints of the serration of the IO as defined by the authors, but we also see many myelin stripes across the IO. Vsens is located laterally and skirted by the Sp5. This is in agreement with the authors, as they propose that ONLY the elephants show the variations they report.

Peer Response Image 2.

(C) Four-toed sengi (Petrodromus tetradactylus) 

The sengis are close relatives of the Tenrecs and otter shrews, these three groups being part of the Afroinsectiphilia, a distinct branch of the Afrotheria. Below I append images of cresyl violet (left column) and myelin (right column) stained coronal sections through the brainstem with the IO, Vsens and Sp5 labelled as per standard mammalian anatomy. Here we see vague hints of the serration of the IO (as defined by the authors), and we also see many myelin stripes across the IO. Vsens is located laterally and skirted by the Sp5. This is in agreement with the authors, as they propose that ONLY the elephants show the variations they report. 

Peer Response Image 3.

(D) Rock hyrax (Procavia capensis) 

The hyraxes, along with the sirens and elephants form the Paenungulata branch of the Afrotheria. Below I append images of cresyl violet (left column) and myelin (right column) stained coronal sections through the brainstem with the IO, Vsens and Sp5 labelled as per the standard mammalian anatomy. Here we see hints of the serration of the IO (as defined by the authors), but we also see evidence of a more "bulbous" appearance of subnuclei of the IO (particularly the principal nucleus), and we also see many myelin stripes across the IO. Vsens is located laterally and skirted by the Sp5. This is in agreement with the authors, as they propose that ONLY the elephants show the variations they report. 

Peer Review Image 4.

(E) West Indian manatee (Trichechus manatus) 

The sirens are the closest extant relatives of the elephants in the Afrotheria. Below I append images of cresyl violet (top) and myelin (bottom) stained coronal sections (taken from the University of Wisconsin-Madison Brain Collection, https://brainmuseum.org, and while quite low in magnification they do reveal the structures under debate) through the brainstem with the IO, Vsens and Sp5 labelled as per standard mammalian anatomy. Here we see the serration of the IO (as defined by the authors). Vsens is located laterally and skirted by the Sp5. This is in agreement with the authors, as they propose that ONLY the elephants show the variations they report.

Peer Review Image 5.

These comparisons and the structural identification, with which the authors agree as they only distinguish the elephants from the other Afrotheria, demonstrate that the appearance of the IO can be quite variable across mammalian species, including those with a close phylogenetic affinity to the elephants. Not all mammal species possess a "serrated" appearance of the IO. Thus, it is more than just theoretically possible that the IO of the elephant appears as described prior to this study. 

So what about elephants? Below I append a series of images from coronal sections through the African elephant brainstem stained for Nissl, myelin, and immunostained for calretinin. These sections are labelled according to standard mammalian nomenclature. In these complete sections of the elephant brainstem, we do not see a serrated appearance of the IOM (as described previously and in the current study by the authors). Rather the principal nucleus of the IOM appears to be bulbous in nature. In the current study, no image of myelin staining in the IOM/VsensR is provided by the authors. However, in the images I provide, we do see the reported myelin stripes in all stains - agreement between the authors and reviewer on this point. The higher magnification image to the bottom left of the plate shows one of the IOM/VsensR myelin stripes immunostained for calretinin, and within the myelin stripes axons immunopositive for calretinin are seen (labelled with an arrow). The climbing fibres of the elephant cerebellar cortex are similarly calretinin immunopositive (10.1159/000345565). In contrast, although not shown at high magnification, the fibres forming the Sp5 in the elephant (in the Maseko description, unnamed in the description of the authors) show no immunoreactivity to calretinin. 

Peer Review Image 6.

Comment: We appreciate the referee’s additional comments. We concede the possibility that some relatives of elephants have a less serrated inferior olive than most other mammals. We maintain, however, that the elephant inferior olive (our Figure 1J) has the serrated appearance seen in the vast majority of mammals.

Change: None.

Peripherin Immunostaining 

In their revised manuscript the authors present immunostaining of peripherin in the elephant brainstem. This is an important addition (although it does replace the only staining of myelin provided by the authors which is unusual as the word myelin is in the title of the paper) as peripherin is known to specifically label peripheral nerves. In addition, as pointed out by the authors, peripherin also immunostains climbing fibres (Errante et al., 1998). The understanding of this staining is important in determining the identification of the IO and Vsens in the elephant, although it is not ideal for this task as there is some ambiguity. Errante and colleagues (1998; Fig. 1) show that climbing fibres are peripherin-immunopositive in the rat. But what the authors do not evaluate is the extensive peripherin staining in the rat Sp5 in the same paper (Errante et al, 1998, Fig. 2). The image provided by the authors of their peripherin immunostaining (their new Figure 2) shows what I would call the Sp5 of the elephant to be strongly peripherin immunoreactive, just like the rat shown in Errant et al (1998), and more over in the precise position of the rat Sp5! This makes sense as this is where the axons subserving the "extraordinary" tactile sensitivity of the elephant trunk would be found (in the standard model of mammalian brainstem anatomy). Interestingly, the peripherin immunostaining in the elephant is clearly lamellated...this coincides precisely with the description of the trigeminal sensory nuclei in the elephant by Maskeo et al (2013) as pointed out by the authors in their rebuttal. Errante et al (1998) also point out peripherin immunostaining in the inferior olive, but according to the authors this is only "weakly present" in the elephant IOM/VsensR. This latter point is crucial. Surely if the elephant has an extraordinary sensory innervation from the trunk, with 400 000 axons entering the brain, the VsensR/IOM should be highly peripherin-immunopositive, including the myelinated axon bundles?! In this sense, the authors argue against their own interpretation - either the elephant trunk is not a highly sensitive tactile organ, or the VsensR is not the trigeminal nuclei it is supposed to be. 

Comment: We made sure that elephant climbing fibers are strongly peripherin-positive (our revised Figure 2). As we noted in already our previous ms, we see weak diffuse peripherin-reactivity in the trigeminal nucleus (the inferior olive according to the referee), but no peripherin-reactive axon bundles (i.e. climbing fibers) that are seen in the inferior olive of other species. We also see no peripherin-reactive axon bundles (i.e. the olivo-cerebellar tract) arriving in the trigeminal nucleus as the tissue surrounding the trigeminal nucleus is devoid of peripherin-reactivity. Again, this finding is incompatible with the referee’s ideas. As far as we can tell, the trigeminal fibers are not reactive for peripherin in the elephant, i.e. we did not observe peripherin-reactivity very close to the nerve entry, but unfortunately, we did not stain for peripherin-reactivity into the nerve. As the referee alludes to the absence of peripherin-reactivity in the trigeminal tract is a difference between rodents and elephants.

Change: Our novel Figure 2.

Summary: 

(1) Comparative data of species closely related to elephants (Afrotherians) demonstrates that not all mammals exhibit the "serrated" appearance of the principal nucleus of the inferior olive. 

(2) The location of the IO and Vsens as reported in the current study (IOR and VsensR) would require a significant, and unprecedented, rearrangement of the brainstem in the elephants independently. I argue that the underlying molecular and genetic changes required to achieve this would be so extreme that it would lead to lethal phenotypes. Arguing that the "switcheroo" of the IO and Vsens does occur in the elephant (and no other mammals) and thus doesn't lead to lethal phenotypes is a circular argument that cannot be substantiated. 

(3) Myelin stripes in the subnuclei of the inferior olivary nuclear complex are seen across all related mammals as shown above. Thus, the observation made in the elephant by the authors in what they call the VsensR, is similar to that seen in the IO of related mammals, especially when the IO takes on a more bulbous appearance. These myelin stripes are the origin of the olivocerebellar pathway, and are indeed calretinin immunopositive in the elephant as I show. 

(4) What the authors see aligns perfectly with what has been described previously, the only difference being the names that nuclear complexes are being called. But identifying these nuclei is important, as any functional sequelae, as extensively discussed by the authors, is entirely dependent upon accurately identifying these nuclei. 

(4) The peripherin immunostaining scores an own goal - if peripherin is marking peripheral nerves (as the authors and I believe it is), then why is the VsensR/IOM only "weakly positive" for this stain? This either means that the "extraordinary" tactile sensitivity of the elephant trunk is non-existent, or that the authors have misinterpreted this staining. That there is extensive staining in the fibre pathway dorsal and lateral to the IOR (which I call the spinal trigeminal tract), supports the idea that the authors have misinterpreted their peripherin immunostaining.

(5) Evolutionary expediency. The authors argue that what they report is an expedient way in which to modify the organisation of the brainstem in the elephant to accommodate the "extraordinary" tactile sensitivity. I disagree. As pointed out in my first review, the elephant cerebellum is very large and comprised of huge numbers of morphologically complex neurons. The inferior olivary nuclei in all mammals studied in detail to date, give rise to the climbing fibres that terminate on the Purkinje cells of the cerebellar cortex. It is more parsimonious to argue that, in alignment with the expansion of the elephant cerebellum (for motor control of the trunk), the inferior olivary nuclei (specifically the principal nucleus) have had additional neurons added to accommodate this cerebellar expansion. Such an addition of neurons to the principal nucleus of the inferior olive could readily lead to the loss of the serrated appearance of the principal nucleus of the inferior olive, and would require far less modifications in the developmental genetic program that forms these nuclei. This type of quantitative change appears to be the primary way in which structures are altered in the mammalian brainstem. 

Comment: We still disagree with the referee. We note that our conclusions rest on the analysis of 8 elephant brainstems, which we sectioned in three planes and stained with a variety of metabolic and antibody stains and in which assigned two structures (the inferior olive and the trigeminal nucleus). Most of the evidence cited by the referee stems from a single paper, in which 147 structures were identified based on the analysis of a single brainstem sectioned in one plane and stained with a limited set of antibodies. Our synopsis of the evidence is the following.

(1) We agree with the referee that concerning brainstem position our scheme of a ventromedial trigeminal nucleus and a dorsolateral inferior olive deviates from the usual mammalian position of these nuclei (i.e. a dorsolateral trigeminal nucleus and a ventromedial inferior olive).

(2) Cytoarchitectonics support our partitioning scheme. The compact cellular appearance of our ventromedial trigeminal nucleus is characteristic of trigeminal nuclei. The serrated appearance of our dorsolateral inferior olive is characteristic of the mammalian inferior olive; we acknowledge that the referee claims exceptions here. To our knowledge, nobody has described a mammalian trigeminal nucleus with a serrated appearance (which would apply to the elephant in case the trigeminal nucleus is situated dorsolaterally).

(3) Metabolic staining (Cyto-chrome-oxidase reactivity) supports our partitioning scheme. Specifically, our ventromedial trigeminal nucleus shows intense Cyto-chrome-oxidase reactivity as it is seen in the trigeminal nuclei of trigeminal tactile experts.

(4) Isomorphism. The myelin stripes on our ventromedial trigeminal nucleus are isomorphic to trunk wrinkles. Isomorphism is a characteristic of somatosensory brain structures (barrel, barrelettes, nose-stripes, etc) and we know of no case, where such isomorphism was misleading.

(5) The large-scale organization of our ventromedial trigeminal nuclei in anterior-posterior repeats is characteristic of the mammalian trigeminal nuclei. To our knowledge, no such organization has ever been reported for the inferior olive.

(6) Connectivity analysis supports our partitioning scheme. According to our delineation of the elephant olivo-cerebellar tract, our dorsolateral inferior olive is connected via peripherin-positive climbing fibers to the cerebellum. In contrast, our ventromedial trigeminal nucleus (the referee’s inferior olive) is not connected via climbing fibers to the cerebellum.

Change: As discussed, we advanced further evidence in this revision. Our partitioning scheme (a ventromedial trigeminal nucleus and a dorsolateral inferior olive) is better supported by data and makes more sense than the referee’s suggestion (a dorsolateral trigeminal nucleus and a ventromedial inferior olive). It should be published.

Reviewer #3 (Public Review):

Summary: 

The study claims to investigate trunk representations in elephant trigeminal nuclei located in the brainstem. The researchers identify large protrusions visible from the ventral surface of the brainstem, which they examined using a range of histological methods. However, this ventral location is usually where the inferior olivary complex is found, which challenges the author's assertions about the nucleus under analysis. They find that this brainstem nucleus of elephants contains repeating modules, with a focus on the anterior and largest unit which they define as the putative nucleus principalis trunk module of the trigeminal. The nucleus exhibits low neuron density, with glia outnumbering neurons significantly. The study also utilizes synchrotron X-ray phase contrast tomography to suggest that myelin-stripe-axons traverse this module. The analysis maps myelin-rich stripes in several specimens and concludes that based on their number and patterning that they likely correspond with trunk folds; however this conclusion is not well supported if the nucleus has been misidentified. 

Comment: The referee provides a summary of our work. The referee also notes that the correct identification of the trigeminal nucleus is critical to the message of our paper.

Change: In line with these assessments we focused our revision efforts on the issue of trigeminal nucleus identification, please see our introductory comments and our response to Referee 2.

Strengths: 

The strength of this research lies in its comprehensive use of various anatomical methods, including Nissl staining, myelin staining, Golgi staining, cytochrome oxidase labeling, and synchrotron X-ray phase contrast tomography. The inclusion of quantitative data on cell numbers and sizes, dendritic orientation and morphology, and blood vessel density across the nucleus adds a quantitative dimension. Furthermore, the research is commendable for its high-quality and abundant images and figures, effectively illustrating the anatomy under investigation.

Comment: We appreciate this positive assessment.

Change: None

Weaknesses: 

While the research provides potentially valuable insights if revised to focus on the structure that appears to be inferior olivary nucleus, there are certain additional weaknesses that warrant further consideration. First, the suggestion that myelin stripes solely serve to separate sensory or motor modules rather than functioning as an "axonal supply system" lacks substantial support due to the absence of information about the neuronal origins and the termination targets of the axons. Postmortem fixed brain tissue limits the ability to trace full axon projections. While the study acknowledges these limitations, it is important to exercise caution in drawing conclusions about the precise role of myelin stripes without a more comprehensive understanding of their neural connections. 

Comment: We understand these criticisms and the need for cautious interpretation. As we noted previously, we think that the Elife-publishing scheme, where critical referee commentary is published along with our ms, will make this contribution particularly valuable.

Change: Our additional efforts to secure the correct identification of the trigeminal nucleus.

Second, the quantification presented in the study lacks comparison to other species or other relevant variables within the elephant specimens (i.e., whole brain or brainstem volume). The absence of comparative data to different species limits the ability to fully evaluate the significance of the findings. Comparative analyses could provide a broader context for understanding whether the observed features are unique to elephants or more common across species. This limitation in comparative data hinders a more comprehensive assessment of the implications of the research within the broader field of neuroanatomy. Furthermore, the quantitative comparisons between African and Asian elephant specimens should include some measure of overall brain size as a covariate in the analyses. Addressing these weaknesses would enable a richer interpretation of the study's findings. 

Comment: We understand, why the referee asks for additional comparative data, which would make our study more meaningful. We note that we already published a quantitative comparison of African and Asian elephant facial nuclei (Kaufmann et al. 2022). The quantitative differences between African and Asian elephant facial nuclei are similar in magnitude to what we observed here for the trigeminal nucleus, i.e. African elephants have about 10-15% more facial nucleus neurons than Asian elephants. The referee also notes that data on overall elephant brain size might be important for interpreting our data. We agree with this sentiment and we are preparing a ms on African and Asian elephant brain size. We find – unexpectedly given the larger body size of African elephants – that African elephants have smaller brains than Asian elephants. The finding might imply that African elephants, which have more facial nucleus neurons and more trigeminal nucleus trunk module neurons, are neurally more specialized in trunk control than Asian elephants.

Change: We are preparing a further ms on African and Asian elephant brain size, a first version of this work has been submitted.

Reviewer #4 (Public Review): 

Summary: 

The authors report a novel isomorphism in which the folds of the elephant trunk are recognizably mapped onto the principal sensory trigeminal nucleus in the brainstem. Further, they identifiy the enlarged nucleus as being situated in this species in an unusual ventral midline position. 

Comment: The referee summarizes our work.

Change: None.

Strengths: 

The identity of the purported trigeminal nucleus and the isomorphic mapping with the trunk folds is supported by multiple lines of evidence: enhanced staining for cytochrome oxidase, an enzyme associated with high metabolic activity; dense vascularization, consistent with high metabolic activity; prominent myelinated bundles that partition the nucleus in a 1:1 mapping of the cutaneous folds in the trunk periphery; near absence of labeling for the anti-peripherin antibody, specific for climbing fibers, which can be seen as expected in the inferior olive; and a high density of glia.

Comment: The referee again reviews some of our key findings.

Change: None. 

Weaknesses: 

Despite the supporting evidence listed above, the identification of the gross anatomical bumps, conspicuous in the ventral midline, is problematic. This would be the standard location of the inferior olive, with the principal trigeminal nucleus occupying a more dorsal position. This presents an apparent contradiction which at a minimum needs further discussion. Major species-specific specializations and positional shifts are well-documented for cortical areas, but nuclear layouts in the brainstem have been considered as less malleable. 

Comment: The referee notes that our discrepancy with referee 2, needs to be addressed with further evidence and discussion, given the unusual position of both inferior olive and trigeminal nucleus in the partitioning scheme and that the mammalian brainstem tends to be positionally conservative. We agree with the referee. We note that – based on the immense size of the elephant trigeminal ganglion (50 g), half the size of a monkey brain – it was expected that the elephant trigeminal nucleus ought to be exceptionally large.

Change: We did additional experimental work to resolve this matter: (i) We ascertained that elephant climbing fibers are strongly peripherin-positive. (ii) Based on elephant climbing fiber peripherin-reactivity we delineated the elephant olivo-cerebellar tract. We find that the olivo-cerebellar connects to the structure we refer to as inferior olive to the cerebellum. (iii) We also found that the trigeminal nucleus (the structure the referee refers to as inferior olive) appears to receive no climbing fibers. (iv) We provide indications that the tracing of the trigeminal nerve into the olivo-cerebellar tract by Maseko et al. 2023 was erroneous (Referee-Figure 1). These novel findings support our ideas.

Reviewer #5 (Public Review): 

After reading the manuscript and the concerns raised by reviewer 2 I see both sides of the argument - the relative location of trigeminal nucleus versus the inferior olive is quite different in elephants (and different from previous studies in elephants), but when there is a large disproportionate magnification of a behaviorally relevant body part at most levels of the nervous system (certainly in the cortex and thalamus), you can get major shifting in location of different structures. In the case of the elephant, it looks like there may be a lot of shifting. Something that is compelling is that the number of modules separated but the myelin bands correspond to the number of trunk folds which is different in the different elephants. This sort of modular division based on body parts is a general principle of mammalian brain organization (demonstrated beautifully for the cuneate and gracile nucleus in primates, VP in most of species, S1 in a variety of mammals such as the star nosed mole and duck-billed platypus). I don't think these relative changes in the brainstem would require major genetic programming - although some surely exists. Rodents and elephants have been independently evolving for over 60 million years so there is a substantial amount of time for changes in each l lineage to occur.

I agree that the authors have identified the trigeminal nucleus correctly, although comparisons with more out groups would be needed to confirm this (although I'm not suggesting that the authors do this). I also think the new figure (which shows previous divisions of the brainstem versus their own) allows the reader to consider these issues for themselves. When reviewing this paper, I actually took the time to go through atlases of other species and even look at some of my own data from highly derived species. Establishing homology across groups based only on relative location is tough especially when there appears to be large shifts in relative location of structures. My thoughts are that the authors did an extraordinary amount of work on obtaining, processing and analyzing this extremely valuable tissue. They document their work with images of the tissue and their arguments for their divisions are solid. I feel that they have earned the right to speculate - with qualifications - which they provide. 

Comment: The referee summarizes our work and appears to be convinced by the line of our arguments. We are most grateful for this assessment. We add, again, that the skeptical assessment of referee 2 will be published as well and will give the interested reader the possibility to view another perspective on our work.

Change: None. 

Recommendations for the authors: 

Reviewer #1 (Recommendations For The Authors):

With this manuscript being virtually identical to the previous version, it is possible that some of the definitive conclusions about having identified the elephant trigeminal nucleus and trunk representation should be moderated in a more nuanced manner, especially given the careful and experienced perspective from reviewers with first hand knowledge elephant neuroanatomy.

Comment: We agree that both our first and second revisions were very much centered on the debate of the correct identification of the trigeminal nucleus and that our ms did not evolve as much in other regards. This being said we agree with Referee 2 that we needed to have this debate. We also think we advanced important novel data in this context (the delineation of elephant olivo-cerebellar tract through the peripherin-antibody).

Changes: Our revised Figure 2. 

The peripherin staining adds another level of argument to the authors having identified the trigeminal brainstem instead of the inferior olive, if differential expression of peripherin is strong enough to distinguish one structure from the other.

Comment: We think we showed too little peripherin-antibody staining in our previous revision. We have now addressed this problem.

Changes: Our revised Figure 2, i.e. the delineation of elephant olivo-cerebellar tract through the peripherin-antibody).

There are some minor corrections to be made with the addition of Fig. 2., including renumbering the figures in the manuscript (e.g., 406, 521). 

I continue to appreciate this novel investigation of the elephant brainstem and find it an interesting and thorough study, with the use of classical and modern neuroanatomical methods.

Comment: We are thankful for this positive assessment.

Reviewer #2 (Recommendations For The Authors):

I do realise the authors are very unhappy with me and the reviews I have submitted. I do apologise if feelings have been hurt, and I do understand the authors put in a lot of hard work and thought to develop what they have; however, it is unfortunate that the work and thoughts are not correct. Science is about the search for the truth and sometimes we get it wrong. This is part of the scientific process and why most journals adhere to strict review processes of scientific manuscripts. As I said previously, the authors can use their data to write a paper describing and quantifying Golgi staining of neurons in the principal olivary nucleus of the elephant that should be published in a specialised journal and contextualised in terms of the motor control of the trunk and the large cerebellum of the elephant. 

Comment: We appreciate the referee’s kind words. Also, no hard feelings from our side, this is just a scientific debate. In our experience, neuroanatomical debates are resolved by evidence and we note that we provide evidence strengthening our identification of the trigeminal nucleus and inferior olive. As far as we can tell from this effort and the substantial evidence accumulated, the referee is wrong.

Reviewer #4 (Recommendations For The Authors):

As a new reviewer, I have benefited from reading the previous reviews and Author response, even while having several new comments to add. 

(1) The identification of the inferior olive and trigeminal nuclei is obviously center stage. An enlargement of the trigeminal nuclei is not necessarily problematic, given the published reports on the dramatic enlargement of the trigeminal nerve (Purkart et al., 2022). At issue is the conspicuous relocation of the trigeminal nuclei that is being promoted by Reveyaz et al. Conspicuous rearrangements are not uncommon; for example, primary sensory cortical fields in different species (fig. 1 in H.H.A. Oelschlager for dolphins; S. De Vreese et al. (2023) for cetaceans, L. Krubitzer on various species, in the context of evolution). The difficult point here concerns what looks like a rather conspicuous gross anatomical rearrangement, in BRAINSTEM - the assumption being that the brainstem bauplan is going to be specifically conservative and refractory to gross anatomical rearrangement. 

Comment: We agree with the referee that the brainstem rearrangements are unexpected. We also think that the correct identification of nuclei needs to be at the center of our revision efforts.

Change: Our revision provided further evidence (delineation of the olivo-cerebellar tract, characterization of the trigeminal nerve entry) about the identity of the nuclei we studied.

Why would a major nucleus shift to such a different location? and how? Can ex vivo DTI provide further support of the correct identification? Is there other "disruption" in the brainstem? What occupies the traditional position of the trigeminal nuclei? An atlas-equivalent coronal view of the entire brainstem would be informative. The Authors have assembled multiple criteria to support their argument that the ventral "bumps" are in fact a translocated trigeminal principal nucleus: enhanced CO staining, enhanced vascularization, enhanced myelination (via Golgi stains and tomography), very scant labeling for a climbing fiber specific antibody ( anti-peripherin), vs. dense staining of this in the alternative structure that they identify as IO; and a high density of glia. Admittedly, this should be sufficient, but the proposed translocation (in the BRAINSTEM) is sufficiently startling that this is arguably NOT sufficient. <br /> The terminology of "putative" is helpful, but a more cogent presentation of the results and more careful discussion might succeed in winning over at least some of a skeptical readership. 

Comment: We do not know, what led to the elephant brainstem rearrangements we propose. If the trigeminal nuclei had expanded isometrically in elephants from the ancestral pattern, one would have expected a brain with big lateral bumps, not the elephant brain with its big ventromedial bumps. We note, however, that very likely the expansion of the elephant trigeminal nuclei did not occur isometrically. Instead, the neural representation of the elephant nose expanded dramatically and in rodents the nose is represented ventromedially in the brainstem face representation. Thus, we propose a ‘ventromedial outgrowth model’ according to which the elephant ventromedial trigeminal bumps result from a ventromedially direct outgrowth of the ancestral ventromedial nose representation.

We advanced substantially more evidence to support our partitioning scheme, including the delineation of the olivo-cerebellar tract based on peripherin-reactivity. We also identified problems in previous partitioning schemes, such as the claim that the trigeminal nerve continues into the ~4x smaller olivocerebellar tract (Referee-Figure 1C, D); we think such a flow of fibers, (which is also at odds with peripherin-antibody-reactivity and the appearance of nerve and olivocerebellar tract), is highly unlikely if not physically impossible. With all that we do not think that we overstate our case in our cautiously presented ms.

Change: We added evidence on the identification of elephant trigeminal nuclei and inferior olive.

(2) Role of myelin. While the photos of myelin are convincing, it would be nice to have further documentation. Gallyas? Would antibodies to MBP work? What is the myelin distribution in the "standard" trigeminal nuclei (human? macaque or chimpanzee?). What are alternative sources of the bundles? Regardless, I think it would be beneficial to de-emphasize this point about the role of myelin in demarcating compartments. <br /> I would in fact suggest an alternative (more neutral) title that might highlight instead the isomorphic feature; for example, "An isomorphic representation of Trunk folds in the Elephant Trigeminal Nucleus." The present title stresses myelin, but figure 1 already focuses on CO. Additionally, the folds are actually mentioned almost in passing until later in the manuscript. I recommend a short section on these at the beginning of the Results to serve as a useful framework.

Here I'm inclined to agree with the Reviewer, that the Authors' contention that the myelin stipes serve PRIMARILY to separate trunk-fold domains is not particularly compelling and arguably a distraction. The point can be made, but perhaps with less emphasis. After all, the fact that myelin has multiple roles is well-established, even if frequently overlooked. In addition, the Authors might make better use of an extensive relevant literature related to myelin as a compartmental marker; for example, results and discussion in D. Haenelt....N. Weiskopf (eLife, 2023), among others. Another example is the heavily myelinated stria of Gennari in primate visual cortex, consisting of intrinsic pyramidal cell axons, but where the role of the myelination has still not been elucidated. 

Comment: (1) Documentation of myelin. We note that we show further identification of myelinated fibers by the fluorescent dye fluomyelin in Figure 4B. We also performed additional myelin stains as the gold-myelin stain after the protocol of Schmued (Referee-Figure 2). In the end, nothing worked quite as well to visualize myelin-stripes as the bright-field images shown in Figure 4A and it is only the images that allowed us to match myelin-stripes to trunk folds. Hence, we focus our presentation on these images.

(2) Title: We get why the referee envisions an alternative title. This being said, we would like to stick with our current title, because we feel it highlights the major novelty we discovered.

(3) We agree with many of the other comments of the referee on myelin phenomenology. We missed the Haenelt reference pointed out by the referee and think it is highly relevant to our paper

Change: 1. Referee Figure. 2. Inclusion of the Haenelt-reference.

Author response image 2.

Myelin stripes of the elephant trunk module visualized by Gold-chloride staining according to Schmued

A, Low magnification micrograph of the trunk module of African elephant Indra stained with AuCl according to Schmued. The putative finger is to the left, proximal is to the right. Myelin stripes can easily be recognized. The white box indicates the area shown in B.

B, high magnification micrograph of two myelin stripes. Individual gold-stained (black) axons organized in myelin stripes can be recognized.

Schmued, L. C. (1990). A rapid, sensitive histochemical stain for myelin in frozen brain sections. Journal of Histochemistry & Cytochemistry, 38(5), 717-720.

Are the "bumps" in any way "analogous" to the "brain warts" seen in entorhinal areas of some human brains (G. W. van Hoesen and A. Solodkin (1993)? 

Comment: We think this is a similar phenomenon.

Change: We included the Hoesen and A. Solodkin (1993) reference in our discussion.

At least slightly more background (ie, a separate section or, if necessary, supplement) would be helpful, going into more detail on the several subdivisions of the ION and if these undergo major alterations in the elephant.

Comment: The strength of the paper is the detailed delineation of the trunk module, based on myelin stripes and isomorphism. We don’t think we have strong evidence on ION subdivisions, because it appears the trigeminal tract cannot be easily traced in elephants. Accordingly, we find it difficult to add information here.

Change: None.

Is there evidence from the literature of other conspicuous gross anatomical translocations, in any species, especially in subcortical regions? 

Comment: The best example that comes to mind is the star-nosed mole brainstem. There is a beautiful paper comparing the star-nosed mole brainstem to the normal mole brainstem (Catania et al 2011). The principal trigeminal nucleus in the star-nosed mole is far more rostral and also more medial than in the mole; still, such rearrangements are minor compared to what we propose in elephants.

Catania, Kenneth C., Duncan B. Leitch, and Danielle Gauthier. "A star in the brainstem reveals the first step of cortical magnification." PloS one 6.7 (2011): e22406.

Change: None.

(3) A major point concerns the isomorphism between the putative trigeminal nuclei and the trunk specialization. I think this can be much better presented, at least with more discussion and other examples. The Authors mention about the rodent "barrels," but it seemed strange to me that they do not refer to their own results in pig (C. Ritter et al., 2023) nor the work from Ken Catania, 2002 (star-nosed mole; "fingerprints in the brain") or other that might be appropriate. I concur with the Reviewer that there should be more comparative data. 

Comment: We agree.

Change: We added a discussion of other isomorphisms including the the star-nosed mole to our paper.

(4) Textual organization could be improved. 

The Abstract all-important Introduction is a longish, semi "run-on" paragraph. At a minimum this should be broken up. The last paragraph of the Introduction puts forth five issues, but these are only loosely followed in the Results section. I think clarity and good organization is of the upmost importance in this manuscript. I recommend that the Authors begin the Results with a section on the trunk folds (currently figure 5, and discussion), continue with the several points related to the identification of the trigeminal nuclei, and continue with a parallel description of ION with more parallel data on the putative trigeminal and IO structures (currently referee Table 1, but incorporate into the text and add higher magnification of nucleus-specific cell types in the IO and trigeminal nuclei). Relevant comparative data should be included in the Discussion.

Comment: 1. We agree with the referee that our abstract needed to be revised. 2. We also think that our ms was heavily altered by the insertion of the new Figure 2, which complemented Figure 1 from our first submission and is concerned with the identification of the inferior olive. From a standpoint of textual flow such changes were not ideal, but the revisions massively added to the certainty with which we identify the trigeminal nuclei. Thus, although we are not as content as we were with the flow, we think the ms advanced in the revision process and we would like to keep the Figure sequence as is. 3. We already noted above that we included additional comparative evidence.

Change: 1. We revised our abstract. 2. We added comparative evidence.

Reviewer #5 (Recommendations For The Authors): 

The data is invaluable and provides insights into some of the largest mammals on the planet. 

Comment: We are incredibly thankful for this positive assessment.

Author response:

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

Public Review: 

This study used ATAC-Seq to characterize chromatin accessibility during stages of GABAergic neuron development in induced pluripotent stem cells (iPSCs) derived from both Dravet Syndrome (DS) patients and healthy donors. The authors report accelerated GABAergic maturation to a point, followed by further differentiation into a perturbed chromatin profile, in the cells from patients. In a preliminary analysis, valproic acid, an anti-seizure medication commonly used in patients with DS, increased open chromatin in both patient and control iPSCs in a nonspecific manner, and to different degrees in cultures derived from different patients. These findings provide new information about DS-associated changes in chromatin, and provide further evidence for developmental abnormalities in interneurons with DS. 

Strengths:

This is a novel study that aims to investigate the epigenetic changes that occur in a sodium channel model of epilepsy; these changes are often ignored but may be an interesting area for future therapeutics. In general, the flow of the paper is good, and the figures are well-designed.  Reply: Thank you for your positive feedback about our work. 

Weaknesses:

The most substantial weakness relates to the observation that DS is often viewed as a monogenic form of epilepsy. It is directly linked to SCN1A gene haploinsufficiency (Yu et al, 2006; Ogiwara et al, 2007). The gene product is Nav1.1, the alpha subunit of voltage-gated sodium channel type I that regulates neuronal excitability. Yet, analysis was conducted at time points of GABAergic interneuron differentiation in which SCN1A is likely not expressed. The paper would be strengthened if SCN1A expression and Nav1.1 protein were examined across the experimental time course. If SCN1A is not yet expressed, this would complicate any explanation of how the observed epigenetic changes might arise. It also seems counterintuitive that the absence of a sodium channel can accelerate differentiation, when, a priori, one might expect the opposite (a 'less neuronal' signal). 

Thanks, this is an important point!  In our revised manuscript, we have incorporated data on the expression of SCN1A at d19 and d65 of GABAergic development in both the control and patient groups. We first retrieved data from our previous RNA-Seq analysis, showing SCN1A gene expression in our cells at both d19 and d65. We have now updated our text on the SCN1A gene expression in the revised manuscript (Revised Supplementary Figure 1A, revised text Line 108-109). Second, we confirmed the dynamics of SCN1A expression by real-time quantitative RT/PCR analysis at four time-pionts of GABAergic development (d0, d19, d35 and d65). Notably, expression of SCN1A was detected by qRT-PCR from d19 and the expression increased with differentiation. We have now included this information in the revised manuscript (Revised Supplementary Figure 1B, revised text Line 112). 

Related to this, another important limitation of the study is that the controls are cells derived from healthy individuals and not from isogenic lines. The usage of isogenic lines is extremely relevant for every study in which iPSC-derived somatic cells are used to model a disease, but specifically in diseases like DS, in which the genetic background has an ascertained impact on disease phenotype (Cetica et al, 2017 and others). This serious limitation should be considered.

Yes, we fully agree that isogenic and edited patient-derived iPSC would have been the ideal controls. At an early stage we therefore invested considerable time and efforts in order to generate isogenic lines from patientderived iPSC. However, editing of the SCN1A variants in patient-derived iPSC turned out unsuccessful after several trials and modifications so we finally turned to iPSC from healthy donors. This is now discussed together with other limitations of our study in the revised manuscript (end of discussion section, lines 499-506).

In addition, the authors should provide data on variability across cell lines and differentiations to help convince the reader that the results can be attributed to genetic defects, rather than variability across individuals. 

This is a valuable point. In the revised manuscript, we have now added plots and IF staining from individual samples to give the readers a complete picture on how they are distributed (Revised Supplementary Figure 1C, Revised Supplementary Figure 2, and Revised Supplementary Figure 4).

In the revised manuscript, we incorporated an explanation on the strategy used to compare the two groups (cases vs. controls) in more detail. In our analysis, we first compared the dynamic changes of chromatin accessibility cell line by cell line across differentiation. We then extracted the common changes from different cell lines at each time point (Revised text line 152-155, line 226-228). Using this strategy, we extracted the common changes confined to the control and patient groups, respectively. With this approach we avoid to capture the variability across individuals.

Additionally, the authors acknowledge the variability of the differentiations and cell lines, which is commendable, and they attribute this to "possibly reflecting cell line specific and endogenous differences reported previously", but could also have to do with cell death. This is a large confounding factor for ATAC-seq. Certainly, Sup Fig 1C shows lower FrIP scores, consistent with cell death, and there seems to be a lot of death in the representative images. Moreover, the iGABA neurons are very difficult to keep alive, especially to 65 days, without co-culturing with glia and/or glutamatergic neurons. The authors should comment on how much these factors may have influenced their results. 

With this point in mind, we re-examined QC of our ATAC-Seq across all samples: As shown in revised

Supplementary Figure 2C and Supplementary Figure 4C, our cutoff for FRiP is 15%, and all of samples have an FrIP of more than 15%. At the later time points (d35 or d65), we did not observe a FRiP <15%. We therefore feel confident that the quality of ATAC-Seq is good enough for downstream analysis and data interpretation.  

Regarding the differentiation protocol, we are following a directed protocol of iPSC towards interneurons. The protocol is described in detail by Maroof et al (reference 34) and slightly modified in our lab (described in reference 13). With our modified protocol, GABAergic cells are viable beyond day 65 without the need of co-cultures with astrocyte or microglia. This is also reflected by the electrophysiological activity of interneurons at d65 and at later time points (reference 13). Additionally, our ambition was to obtain a homogeneous cell population for further analysis. Adding other cell types to the cultures would have interfered with downstream processes and a need for cell sorting. Using our protocol, we obtain viable GABA interneurons after up to 100 days in culture. To assess the viability of our cells at the point of sampling (other than by morphological assessment), we used Trypan blue staining and an automated cell counter. Only samples with a viability >90% were processed for ATAC seq. which is a commonly used cut-off for cell viability. We have now modified the method section in the revised version to describe the GABAergic differentiation and sampling (line 519-529).

Finally, changes in gene expression are only inferred, as no RNA levels were measured. If RNA-seq was not possible it would have been good to see at least some of the key genes/findings corroborated with RNA/protein levels vs chromatin accessibility alone, particularly given that these molecular readouts do not always correlate. 

In our revised manuscript, we include our recently published RNA-seq performed at d19 and d65. We also correlated the RNAseq and ATACseq data obtained from the same samples.  The Pearson correlations between gene expression and chromatin accessibility were within the range 0.49-0.57 (Revised Supplementary Figure 2G, Revised supplementary Figure 4G), which is acceptable according to standard criteria. The results confirmed that the quality of ATAC-Seq is good enough for analysis of expression levels and chromatin openness in key genes. We also added gene expression levels from RNA-seq (d19 and d65) in our revised manuscript (Revised Figure 1G, Revised Figure 2G). Finally, we performed qRT-PCR analysis of key genes in each cluster and the results are now included in the revised version (Revised Supplementary Figure 3E, Revised Supplementary Figure 5E)

Additional Points:

(1) Representative images for cell-identity markers for only D65 are shown, and not D0, D19, and D35 though it is stated in the text that this was performed. At a minimum, these representative images should be shown for all lines. 

As suggested, we have now added images for cell identity markers of all iPSC lines in the revised version (Revised Supplementary Figure 1C).

(2) What QC was performed on iPSC lines, i.e. karyotype/CNV analysis and confirmation of genotypes?

All iPSC lines used in this study have been fully characterized according to standard and state-of-the art procedures: Expression of pluripotency and stemness genes has been shown by immunostaining, flow cytometry and scorecard analysis; integrity of the genome has been assessed by karyotyping using g-banding; differentiation capacity was characterized using an embryoid body assay in combination with scorecard analysis; and genotypes were verified by Sanger sequencing. Please, see the following publications for full datasets: Schuster et all, Neurobiol Dis 2019, Schuster et al Stem Cell Res 2019, Sobol et al Stem Cells and Development 2015. In our lab, the integrity of iPSC lines are routinely verified using flow cytometry (expression for TRA-1-60 and SSEA4), immunostaining (expression of NANOG, SOX2 and OCT4), Sanger sequencing (targeting variants in SCN1A gene), cell morphology analysis and analysis of mycoplasma by MycoAlert® (Lonza).

(3) Were all experiments performed on a single differentiation? Or multiples? Were the differentiations performed with the same type? If not, was batch considered in the analysis? 

Thank you for raising this question. The text Material and Methods has been modified as follows, to better describe the differentiation and sampling procedure:

“GABAergic interneuron differentiation from iPSCs was performed as previously described (reference 13). The protocol utilizes DUAL SMAD inhibition to induce neurogenesis towards neural stem cells for 10 days, followed by patterning with high levels of sonic hedgehog for nine days towards cortically fated neuronal progenitor cells (NPC) and subsequent maturation for 46 days, i.e. a total of 65 days (Figure 1A). Neuronal cells at day 65 and onwards are healthy and viable as judged by morphological assessment by light microscopy. Differentiation was performed at least 3 times per cell line.  

Cell cultures were sampled at days 0 (D0), D19, D35 and D65, respectively, by harvesting cells with TryplE and centrifugation (300 x g, 3 min). Harvested cells were counted and assessed for viability using trypan blue staining and an automated EVE cell counter (Nano Entek). Samples with a viability of >90% were chosen for ATAC-Seq library preparation (see below).”.  

I also assume that technical replicates were merged, and then all three biological replicates were kept for each analysis and outliers were not removed, e.g. Control_D19_8F seems like an example of an outlier. 

This is a valuable point. We agree on that there is variability across three health donors and patients, respevtively, but the quality of ATAC-Seq is good after multiple assessment of QC (Revised Supplementary Figure 2B-D). The color code in Supplementary Figure 1C may be mis-leading as the Pearsson correlation of all samples was displayed. Overall, the correlation from all ATAC-seq among replicates are over 0.8. At the same time, we observed that samples at d0 are clustered together, but not at the later time points. We interpret this as related to the cell-line specific plasticity of chromatin dynamic during differentiation. The observation agrees with our results from PCA (Revised Supplementary Figure 2F).  

(4) In Figure 1C, it is intriguing that the ATACseq signal gets stronger in imN. One might expect it to be strongest in the iPSCs which are undifferentiated and have the highest levels of open chromatin. Is this a function of sequencing depth, or are all the Y-axes normalized across all time points? 

This is another valuable point. Figure 1C present the average chromatin openness for clusters specific regions- not of chromatin openness from the entire genome, which is a reason for why the chromatin openness at

D35 is higher than at other time-points. The genome-wide chromatin openness is presented in revised

Supplementary Figure 2D and we have now updated the figure legend to avoid any potential misunderstanding. 

The sequencing depth for each sample is extracted in a similar range. To give the readers a complete picture, we also present the depth of sequencing reads for each sample (Revised Supplementary Figure 2A and Revised Supplementary Figure 4A). The Y-axes of genome browser tracks were normalized, and we added the normalized value in the figures. 

(5) In Figure 1F, are these all enriched terms, or were they prioritized somehow? 

Yes, the enriched terms are prioritized based on biological meanings, and we have now clarified this in the updated legend of the manuscript. In addition, all enriched terms are now included in revised Supplementary Table 2 and Supplementary Table 4. 

(6) In Figure 1G (also the same plots in Fig 2/3), are all these images normalized i.e. there is no scale bar for each track, and do they represent and aggregate BAM/bigwig?

Yes, the genome browser tracks were normalized and we have now revised the figures by adding scale bars.

It would be good to show in supplement the variability across cell lines/diffs - particularly given the variability in the heatmap/PCA - and demonstrate the rigor/reproducibility of these results. This comment applies to all these plots across the 3 figures, particularly as in some instances the samples appear to cluster by individual first and then time point (Sup Fig 3B). 

Thanks. We have now revised the figure with plots showing individual samples. 

How confident are the authors that these effects are driven by genotype and not a single cell line? In the Fig 3D representation of NANOG, it is very difficult to see any difference between patient and control. 

In Figure 3D, we showed common chromatin dynamics in the control and patient groups. To avoid any misunderstanding, we have now updated our legend in the revised manuscript. 

(7) For the changes in occupancy annotation (UTR/exon/intron etc), are these differences still significant after correcting for variability from cell line to cell line at each time point? I.e. rather than average across all three samples, what is the range?  Reply: Revised accordingly. 

(8) The VPA timepoint is not well-justified. Given that VPA would be administered in patients with fully mature inhibitory neurons, it is difficult to determine the biological relevance. I appreciate that this is a limitation of the model, but this should at least be addressed in the manuscript. 

We agree on that our model system of GABAergic interneuron development has limitations and that cells may not fully recapitulate the development and physiology in vivo. Obvious factors to consider in our system are the directed protocol to enrich for GABAergic interneurons and the differentiation time-line restricted to 65d. This is now discussed (lines 499-506).

Recommendations for the authors:

(1) The term 'mutation' has been replaced with the term ' pathogenic variant' or likely pathogenic variant depending on the context, please see PMID: 25741868 

Thank you for pointing this out. We have replaced all instances of “mutation” with “pathogenic variant” throughout the manuscript.

(2) It is unclear what the nomenclature for sample labelling is in Supplementary Figure 1, e.g. 7C, 8F, 1B.  

We apologize for this confusion. There are cell lines names. We labeled all data and images according to cell line name, i.e. control lines: Ctl1B, Ctl7C and Ctl8F; patient lines: DD1C, DD4A, DD5A. To avoid any potential confusion, we have added a note in the revised legend of Supplementary Figure 1B.

(3) Can the authors confirm that the Deseq2 FDR values are Benjamini-Hochberg procedure corrected per default settings? If so, this should ideally be added to methods or legend for clarity 

Yes, default settings were used in Deseq2 FDR values, which is added in the method part of revised manuscript. 

(4) While it makes sense that the authors present the data in the order of Figure 1, and Figure 2, this actually makes it quite difficult to compare the two datasets, especially for the functional enrichment in the "F" figures. It may be helpful to consider re-organizing the figure order. For instance, for the long-term potentiation signal in the DS-iPSCs, what does this mean in terms of biological relevance? Or maybe Figure 2 needs to be supplementary given that Figure 3 is a more direct comparison.  

Thank you for the suggestions. We attempted to reorganize during our revision. We still believe it is easier for the audience to grasp the main message if we organize it according to our current workflow—first presenting an individual differential landscape for controls and patients, and then comparing the common and unique aspects among them.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

In this manuscript, entitled " Merging Mul-OMICs with Proteome Integral Solubility Alteration Unveils Antibiotic Mode of Acon", Dr. Maity and colleagues aim to elucidate the mechanisms of action of antibiotics through combined approaches of omics and the PISA tool to discover new targets of five drugs developed against Helicobacter pylori.

Strengths:

Using transcriptomics, proteomic analysis, protein stability (PISA), and integrative analysis, Dr. Maity and colleagues have identified pathways targeted by five compounds initially discovered as inhibitors against H. pylori flavodoxin. This study underscores the necessity of a global approach to comprehensively understanding the mechanisms of drug action. The experiments conducted in this paper are well-designed and the obtained results support the authors' conclusions.

Weaknesses:

This manuscript describes several interesting findings. A few points listed below require further clarification:

(1) Compounds IVk exhibits markedly different behavior compared to the other compounds. The authors are encouraged to discuss these findings in the context of existing literature or chemical principles.

This is a good point. We have added the following paragraph (Page No-13).

“In several of our studies, compound IVk, which has a higher MIC, exhibits markedly different behavior. This difference in behavior may stem from different sources, including intercellular availability, inactivation inside the cell, or loss of target specificity. Multiple studies have previously demonstrated that there is only a 30% chance for a structurally similar compound to have similar biological activity32.”

(2) The incubation me for treating H. pylori with the drugs was set at 4 hours for transcriptomic and proteomic analyses, compared to 20 min for PISA analysis. The authors need to explain the reason for these differences in treatment duration.

This is now explained in Pages 17 and 19, where the following paragraphs have been included

“The incubation time for transcriptomics and proteomics assays was determined based on the Time-Kill Curves assay (Fig. 6(A)). The 4-hour time point shows a significant amount of cell death compared to the control population.”

“The target deconvolution method aims to evaluate the initial interaction with intracellular proteins. We selected a 20-minute time point based on intracellular ROS generation (not shown). It is a well-reported phenomenon that bactericidal drugs induce early production of ROS.”

(3) The PISA method facilitates the identification of proteins stabilized by drug treatment. DnaJ and Trigger factor (g), well-known molecular chaperones, prevent protein aggregation under stress. Their enrichment in the soluble fraction is expected and does not necessarily indicate direct stabilization by the drugs. The possibility that their stabilization results from binding to other proteins destabilized by the drugs should be considered. To prevent any misunderstanding, the authors should clarify that their methodology does not solely identify direct targets. Instead, the combination of their findings sheds light on various pathways affected by the treatment.

This is also a very valuable observation. We now clearly state that in new paragraphs at Pages 8 and 13

Another target shared among several compounds is the chaperone protein trigger factor (Tig), which plays a crucial role in facilitating proper protein folding and is indispensable for the survival of bacterial cells. The solubility of this protein has been altered by all the compounds except IVk (Fig. 2(I-J)) in a concentration-dependent manner (Fig. S4(B, D, and E)). The possibility of Tig interacting with other proteins destabilized by the drug, along with the influence of the heat gradient during the PISA assay, may introduce potential noise in the data. Further investigation is required to confirm the interaction of the drug with Tig.

“The module “black” associated with this compound contains Tig, which is involved in facilitating proper protein folding, as a target, and it down-regulates multiple proteins associated closely with S12 ribosomal protein of the 30S subunit (Fig. S9(D)) indicating its involvement in stabilization of ribosomal protein.”

(4) At the end of the manuscript, the authors conclude that four compounds "strongly interact with CagA". However, detailed molecule/protein interaction studies are necessary to definitively support this claim. The authors should exercise caution in their statement. As the authors mentioned, additional research (not mandated in the scope of this current paper) is necessary to determine the drug's binding affinity to the proposed targets.

We have modified the sentence (Page -15) to say:

“This study identifies four out of our five compounds that induce significant change in the solubility of CagA, the major virulence factor of H. pylori.”

(5) The authors should clarify the PISA-Express approach over standard PISA. A detailed explanation of the differences between both methods in the main text is important.

This was already explained in Page 5 (no changes have been made)

Reviewer #2 (Public Review):

Summary:

This work has an important and ambitious goal: understanding the effects of drugs, in this case antimicrobial molecules, from a holistic perspective. This means that the effect of drugs on a group of genes and whole metabolic pathways is unveiled, rather than its immediate effect on a protein target only. To achieve this goal the authors successfully implement the PISA-Express method (Protein Integral Solubility Alteration), using combined transcriptomics, proteomics, and drug-induced changes in protein stability to retrieve a large number of genes and proteins affected by the used compounds. The compounds used in the study (compound IVa, IVb, IVj, and IVk) were all derived from the precursors compound IV, they are effective against Helicobacter pylori, and their mode of action on clusters of genes and proteins has been compared to the one of the known pylori drug metronidazole (MNZ). Due to this comparison, and confirmed by the diversity of responses induced by these very similar compounds, it can be understood that the approach used is reliable and very informative. Notably, although all compound IV derivatives were designed to target pylori Flavodoxin (Fld), only one showed a statically significant shift of Fld solubility (compound IVj, FIG S11). For most other compounds, instead, the involvement of other possible targets affecting diverse metabolic pathways was also observed, notably concerning a series of genes with other important functions: CagA (virulence factor), FtsY/FtsA (cell division), AtpD (ATP-synthase complex), the essential GTPase ObgE, Tig (protein export), as well as other proteins involved in ribosomal synthesis, chemotaxis/motility and DNA replication/repairs. Finally, for all tested molecules, in vivo functional data have been collected that parallel the omics predictions, comforting them and showing that compound IV derivatives differently affect cellular generation of reactive oxygen species (ROS), oxygen consumption rates (OCR), DNA damage, and ATP synthesis.

Strengths:

The approach used is very potent in retrieving the effects of chemically active molecules (in this case antimicrobial ones) on whole cells, evidencing protein and gene networks that are involved in cell sensitivity to the studied molecules. The choice of these compounds against H. pylori is perfect, showcasing how different the real biological response is, compared to the hypothetical one. In fact, although all molecules were retrieved based on their activity on Fld, the authors unambiguously show that large unexpected gene clusters may, and in fact are, affected by these compounds, and each of them in different manners.

Impact:

The present work is the first report relying on PISA-Express performed on living bacterial cells. Because of its findings, this work will certainly have a high impact on the way we design research to develop effective drugs, allowing us to understand the fine effects of a drug on gene clusters, drive molecule design towards specific metabolic pathways, and eventually better plan the combination of multiple active molecules for drug formulation. Beyond this, however, we expect this article to impact other related and unrelated fields of research as well. The same holistic approaches might also allow gaining deep, and sometimes unexpected, insight into the cellular targets involved in drug side effects, drug resistance, toxicity, and cellular adaptation, in fields beyond the medicinal one, such as cellular biology and environmental studies on pollutants.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Please modify these few concerns:

-  It is unclear from the introduction and discussion whether conventional transcriptomic and proteomic analyses have previously been conducted on the compounds examined in this study. If only targeted studies have been performed please clarify this further.

To make it more clear, we have added the following paragraph in Page 5:

“Our investigation into understanding the mode of action of nitro-benzoxadiazole compounds commenced with a comparison of the conventional transcriptional and translational changes induced by these compounds, the vehicle control (DMSO), and the commercially used drug MNZ. RNA sequencing (RNA-seq) and expressional proteomics were employed to identify transcriptional and translational changes, respectively.”

-  The decision to monitor the oxygen consumption rate (OCR) is based on the hypothesis that the drugs would impact flavodoxins function. Could the authors cite specific studies that suggest a reduction in flavodoxin leads to decreased OCR that can be measured?

The reviewer is correct to say that we have done this study based on our hypothesis that a reduction in flavodoxin may lead to decreased OCR.  To our knowledge, there is no previous studies indicating that so we now clearly state (Page 14) that it is our hypothesis.

“On the other hand, given that these drugs indicated involvement of multiple factors from the electron transport chain including flavodoxin and we observed significant drop in the ATP production rate (Fig. 6(D)) associated to compounds IV and IVj, we have investigated the changes in oxygen consumption rate (OCR) as we hypothesize that a reduction in soluble flavodoxin could lead to decreased OCR”

-  Increase font size in some figures and supplemental materials for clarity.

We acknowledge the reviewer's comment and have addressed it to the best possible extent in the figures.

-  Correct figure references throughout the text (example of mistake p4, Fig S1D, p6 S1C).

We have corrected the figure references.

-  Check spelling errors, for example, Figure S1B: "library preparation".

We have revised the figures and corrected spelling errors.

-  Ensure H. pylori is in italics.

Done!

-  Figure S4: Replace (D) by (E).

Done! Thank you.

-  Page 7: Check the sentence: "...RpleE, InfC) and F Furthermore, we..." .

Corrected!  

“The 20 common essential targets are mostly associated with cell division (for example, FtsZ), small subunit ribosomal proteins (RspC, RspE, RspL, RplE, InfC). Furthermore, we identified a few unique changes for compound IV (DnaN, involved in DNA tethering and processivity of DNA polymerases, and C694_06445, which could be a functional equivalent of delta subunit of DNA polymerase III).”

-  Page 9: Please modify the name of one compound "Compounds IV, IVj (and not IVk) and MnZ downregulate...".

We have observed that both reviewers mentioned this point and we revisited the data, as suggested by Fig S8(B), that compounds IV, IVk, and MNZ cluster together and downregulate the genes associated with this pathway. Based on this, we have not changed anything in the text.  

-  Figure S9: please clarify symbols (triangles and others) in the Figure legend.

Done!

-  Page 9: Is it the Figure S9B you are referring to? Talking about proteomics?

Sorry, we have not understood the above comment.

Reviewer #2 (Recommendations For The Authors):

All figures are printed as one per page. In this format, almost all pictures suffer a severe problem with dimensions. Notably graph axes and axis values, subtitles, and legends within the pictures are too small, although the graphical part is almost always appropriate. Negative example (higher fonts are needed): Figure 1. Positive example (font ok): Figure 2A or Figure 3 right panels.

We have carefully revised our figures to address the issues you mentioned, ensuring that elements are visible when printed one per page. In Fig 1: We have increased the font sizes of the graph axes, axis values, subtitles, and legends to improve readability. Additionally, we have color-matched different Gene Ontology (GO) terms for better rideability. In Fig 2: To enhance clarity, we have resized the figure by removing the top 10 protein list, now presented in a separate table. This ensures that the figure's main content remains prominent.  These modifications have been made across figures to maintain consistency and readability.

For all figures, particularly for non-experts, not only a list of what is found in the picture should be provided, but also a minimal, simplified key of interpretation (of what is to be noticed). Particularly relevant for scatter plots.

We have modified the legends to provide simplified key interpretation for the scatter plots. 

In general for most analyses I see the involvement of FtsA, whereas most discussions concern FtsY and FtsZ. Maybe this point should be clarified. For example: i) FtsZ is quoted in the Second "Results" paragraph (page 6), but we can't find this gene in Figure 2, nor in the corresponding table (Figure 2A); ii) FtsY downregulation is quoted in the Fifth "Results" paragraph (page 9), but we can't find this gene in Figure 5, 9S or 10S.

We are not entirely sure if we have understood the reviewer's comment correctly, as we did not mention FtsY in our discussion section. In the discussion section, we have focused on the involvement of FtsZ and FtsA with some of our compounds. We decided to discuss them together because FtsZ is the primary component that is recruited to the membrane by the actin-related protein FtsA, while the role of FtsY remains highly debated.

Figure 1: same colour for the same GO: term in different panels should be used.

Done!

Figure 4: please specify (being it essential throughout the whole paper) that the group colouring only refers to Figure 4A, lower bar.

Done!

Figure 5, S9, and S10: having the combination of analysed sets (brown / IV , magenta / IVb, etc....) as a panel subtle is almost a necessity, to avoid constant page turning. I did rewrite all of them by hand to be able to follow the main text story.

Done!

What are the triangles? (this is not written anywhere).

We have now explained this in the legends of Fig5.

Figures S9 and S10 are too crowded (please refer to Figure 5 for a good format/size).

For supplementary figures S9 and S10 we prefer to keep the gene names, but in order to make them more legible we have now added subtitles to each panel.

Second and third "Results" paragraph. Explicitly saying that the Second is only focused on TOP 10 hits, at the beginning of the paragraph (while the third on essential genes) would help enormously the non-specialist in orienting among the different sections.

On page 7, we have revised the text to indicate that the paragraph is only focused on the top 10 hits. Additionally, we have included a table of top 10 hits for better clarity and accessibility. 

Page 6: the following sentence should be in the introduction, to stress the novelty of the work: "This is the first me PISA assay, in the form of PISA-Express, has been successfully performed in living bacterial cells, with protocols adapted and modified from previous PISA studies in mammalian cells".

Page- 2 

We agree this is an important point. However, having we stated it in both the abstract and in the PISA section in the results we prefer not to state it once more in the Introduction.

(no changes made)

I couldn't find any reference to Figure S3 in the text.

Included! (P 9)

"Compounds IV, IVk, and MNZ downregulate the genes associated with this pathway (Fig. 4(B) & S8(B))": it seems to me that it is IVj rather than IVk to downregulate. Please check carefully.

We have observed that both reviewers mentioned this point and we revisited the data, as suggested by Fig S8(B), that compounds IV, IVk, and MNZ cluster together and downregulate the genes associated with this pathway. Based on this, we have not changed anything in the text.  

Page 12: of the pre-defined target like flavodoxin => of the pre-defined target flavodoxin.

Thanks! We have removed “like” from the sentence.

Metronidazol (=MNZ) only appears on page 13 (MNZ already on page 8).

Corrected!  The correspondence is now first indicated in P. 3.

Please resolve the ambiguity metronidazol/metronidazole (main text and figures).

We now always say “metronidazole”

The Sixth "Results" paragraph (pages 10-11) should be developed a bit more. All Figure 6 results are summarized in 8 lines at the end of the paragraph. This doesn't bring much, particularly to a non-specialist reader. Please, for each panel, clearly explain what is to be noticed and what main conclusion(s) can be extracted.

We have improved the description of the section. The modified part now reads:

…This indicates that the nitro-bearing groups have a higher propensity to generate ROS. We have also observed that the genes associated with the generation of ROS are significantly overexpressed for compounds IV, IVb, IVj, and MNZ (Fig. S12(A)). As described above and depicted in Fig. S12(B), multiple DNA damage repair proteins and genes are down-regulated in the presence of compounds IV, IVb, IVj, and MNZ. Additionally, DNA PolA was found to be a major target for compound IVj. Following these results, we investigated compound-induced DNA damage using the APO BrdU TUNEL assay. All the compounds, particularly IV and IVj, caused significant DNA damage (Fig. 6(C)).

On the other hand, given that these drugs indicated involvement of multiple factors from the electron transport chain including flavodoxin and we observed significant drop in the ATP production rate (Fig. 6(D)) associated to compounds IV and IVj, we have investigated the changes in oxygen consumption rate (OCR) as we hypothesize that a reduction in soluble flavodoxin could lead to decreased OCR.  Though the signal-to-noise ratio of these data is poor…

and we added figure S12 for clarity.   

In the same section I found: "Compound IV and its derivatives cause a marked increase in ROS generation when compared to the control (DMSO)" => refers to THIS work or previous work? (in the later case, please quote it).

This data is from our current paper, as shown in Fig 6(B).

In the same paragraph, "the signal-to-noise ratio of these data is considerable" => does it mean that you have good (high signal-to-noise) data, or that you have too high noise for precise quantification? I rather understood the later, but this sentence definitely needs to be rewritten.

Thank you for pointing out the mistake. Your interpretation is correct. We have corrected the sentence.

Author response:

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

(1) The conclusions in the text are very broad and general but often based on a limited number of examples. It would be important that the authors hit the appropriate tone when most of the analysis (in Figure 5) is derived from n=3 events.

We have tried to hit the correct tone here by modifying our manuscript text. In particular we have we have added a pie chart to Figure 4 (Figure 4C, that summarises data from all RBMX targets, not just the original n=3, and shows that most RBMX targets are rescued by RBMXL2).

(2) The fractions of long/ultra-long exons actually bound by/regulated by RBMX are not clearly stated - which is in contrast to the general statement of the title (implying a global role for RBMX in proper splicing of ultra-long exons).

(i) We have changed our title (now “An anciently diverged family of RNA binding proteins maintain correct splicing of a class of ultra-long exons through cryptic splice site repression”).

(ii) We also include much more clear text about the fractions of long/ultralong exons bound by RBMX with the following text: 

“…..This led us to test whether RBMX protein is preferentially associated with long exons. For this we plotted the distribution of internal exons bound and regulated by RBMX together with all internal exons expressed from HEK293 mRNA genes (Liu et al., 2017) (Figure 2 – Source Data 1). We found that RBMX controls and binds two different classes of exons: the first have comparable length to the average HEK293 exon, while the second were extremely long, exceeding 1000 bp in length (Figure 2F). We defined this second class as ‘ultra-long exons’, which represented the 18.9% of internal exons regulated by RBMX and 17.6% of the ones that contained RBMX iCLIP tags. These proportions were significantly enriched compared to the general abundance of internal ultra-long exons expressed from HEK293 cells, which was only 0.4% (Figure 2G)……”

“…….We next wondered whether ultra-long exons regulated by RBMX (which represented 11.6% of all ultra-long internal exons from genes expressed in HEK293) had any particular feature compared to ultra-long exons that were RBMX-independent……..”

(3) The authors should state what fraction of ultra-long exons show cryptic splicing in the RBMX siRNA that are corrected by RBMXL2 overexpression (rather than just showing the 3 events). There's some confusion about the global nature of the conclusions relative to the data displayed.

This is a good point. We have used the RNAseq information as suggested, and included a pie chart (Figure 4C) that includes this information.

(4) It would be helpful if the authors could identify if there are some motifs more present in ultra-long exons than others.

Good point, we have included k-mer analysis of the ultra-long exons bound by RBMX, and also more generally ultra-long exons in the human genome, in Figure 2H and 2I. We also add the following text:

K-mer analyses also showed that while ultra-long exons within mRNAs are rich in AT-rich sequences compared to shorter exons (Figure 2H), the ultra-long exons that are either regulated or bound by RBMX displayed enrichment of AG-rich sequences (Figure 2I), consistent with our identified RBMX-recognised sequences (Figure 2C).

(5) The authors should evaluate if RBMX-repressed 3' splice sites have similar or low splice site scores/strengths than natural 3' splice sites.

We have added splice site score analyses in Figure 1F and Figure 1 Supplement 1B. These show that the cryptic splice sites repressed by RBMX are not significantly different from those that are normally used. We add the following text to accompany these figure panels:

“Furthermore, analysis of splice site strength revealed that, unlike splice sites activated by RBMX (Figure 1 – Figure supplement 1B), alternative splice sites repressed by RBMX have comparable strength to more commonly used splice sites (Figure 1F). This means that RBMX operates as a splicing repressor in human somatic cells to prevent use of ‘decoy’ splice sites that could disrupt normal patterns of gene expression.”

(6) The section "RBMX protein-RNA interactions may insulate important splicing signals from the spliceosome." is a very preliminary look at possible mechanisms. Can you integrate the RNA Seq and CLIP datasets to generate "splicing maps" that would provide more generalized insights? In fact, where possible, it would be great to integrate the iCLIP data from the same cell types to generate RNA splicing maps (with the KD RNA-seq data)

We have added “RNA map-type” plots to integrate iCLIP data with splicing patterns (Figure 2 Figure supplement 1D and 1E), and made corresponding changes to the text.

Additional changes

We also made some extra changes to respond to the further points raised by reviewers.

(1) We have carried out gene ontology analysis of those genes that contain RBMX-regulated ultra-long exons versus all ultra-long exons (now Figure 3A, and also Figure 3- Figure supplement 1A and 1B).

(2) We have corrected the cartoon summarising the branch point analysis (now Figure 3 – Figure Supplement 2F).

Author response:

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

Reviewer #1 (Public Review):

Summary:

This work revealed an important finding that the blood-brain barrier (BBB) functionality changes with age and is more pronounced in males. The authors applied a non-invasive, contrast-agent-free approach of MRI called diffusion-prepared arterial spin labeling (DP-pCASL) to a large cohort of healthy human volunteers. DP-pCASL works by tracking the movement of magnetically labeled water (spins) in blood as it perfuses brain tissue. It probes the molecular diffusion of water, which is sensitive to microstructural barriers, and characterizes the signal coming from fast-moving spins as blood and slow-moving spins as tissue, using different diffusion gradients (b-values). This differentiation is then used to assess the water exchange rates (kw) across the BBB, which acts as a marker for BBB functionality. The main finding of the authors is that kw decreases with age, and in some brain regions, kw decreases faster in males. The neuroprotective role of the female sex hormone, estrogen, on BBB function is discussed as one of the explanations for this finding, supported by literature. The study also shows that BBB function remains stable until the early 60s and remarkably decreases thereafter.

Strengths:

The two main strengths of the study are the MRI method used and the amount of data. The authors employed a contrast-agent-free MRI method called ASL, which offers the opportunity to repeat such experiments multiple times without any health risk - a significant advantage of ASL. Since ASL is an emerging field that requires further exploration and testing, a study evaluating blood-brain barrier functionality is of great importance. The authors utilized a large dataset of healthy humans, where volunteer data from various studies were combined to create a substantial pool. This strategy is effective for statistically evaluating differences in age and gender.

Weaknesses:

R1.0: Gender-related differences are only present in some brain regions, not in the whole brain or gray matter - which is usually the assumption unless stated otherwise. From the title, this was not clear. Including simulations could increase readers' understanding related to model fitting and the interdependence of parameters, if present. The discussion follows a clear line of argument supported by literature; however, focusing solely on AQP4 channels and missing a critical consideration of other known/proven changes in transport mechanisms through the BBB and their effects substantially weakens the discussion. 

Thanks for your insightful feedback and suggestions. We have made the following changes to the manuscript:

(1) The title has been modified to highlight the sex differences in specific brain regions: “Age-Related Decline in Blood-Brain Barrier Function is More Pronounced in Males than Females in Parietal and Temporal Regions.”

(2) To study the potential impact of prolonged ATT seen in males on estimated kw, we simulated kw distribution for females by adjusting ATT by +60 ms to match males' ATT. This led to marginally higher kw values (Supplemental Figure S2), suggesting that the kw difference between males and females is not a direct result of prolonged ATT. Additionally, we have added a section titled “Data and Code Availability Statements” in the revised manuscript to indicate that we are willing to share the reconstruction toolbox with interested groups. The toolbox is a standalone MATLAB-based program (no license required) to generate kw, CBF, and ATT maps, which can run on Windows or Mac computers.

(3) We agree with the reviewer that BBB water exchange can be facilitated by other transport mechanisms, as we mentioned in the introduction: “Water exchange across the BBB occurs at a relatively high level and is mediated by passive diffusion, active co-transport through the endothelial membrane, and facilitated diffusion through the dedicated water channel, aquaporin-4 (AQP4), at the end-feet of astrocytes.” We emphasized our findings related to AQP4 based on the technical properties of DP-pCASL, which is more sensitive to the exchange occurring across astrocyte end-feet. We also acknowledge that different techniques can be helpful to study other components of BBB water exchange, and we have added the following discussion to the updated manuscript: “Mahroo et al., utilized a multi-echo ASL technique to measure BBB permeability to water and reported shorter intra-voxel transit time and lower BBB exchange time (Tex) in the older participants (≥50 years) compared to the younger group (≤20 years). In animal studies, reduced BBB Tex was also reported in the older mice compared to the younger group using multi-echo ASL and a multi-flip-angle, multi-echo dynamic contrast-enhanced (MFAME-DCE) MRI method. These findings contrast with the results presented in this study, likely due to the different components assessed by different techniques, and increased BBB permeability to water has been suggested to indicate a leakage of tight junctions in aging. In contrast, our recent study utilizing high resolution MCDW-pCASL scans with long averages reveals the potential existence of an intermediate stage of water exchange between vascular and tissue compartments (e.g., paravascular space or basal lamina). The DP module of the DP-pCASL is hypothesized to null the fast-flowing and pseudo-random oriented spins, which may include both vascular flow and less restricted water in paravascular space. The observed lower kw in older participants may be more related to the delayed exchange across the astrocyte end-feet into the tissue due to loss of AQP-4 water channel with older age. However, these hypotheses require further investigation to understand the exact mechanisms, especially under different physiological states. Future studies, particularly with animal models targeting specific BBB components under different physiological or diseased conditions, will be valuable for validating these measurements.”

Reviewer #1 (Recommendations For The Authors): 

R1.1 The manuscript is well-organized and presents arguments in a logical order. The visual representation of results in the form of figures is sufficient (see style suggestions below). 

Thanks for your suggestions on improving the figures, we have updated figures for better visualization (Please see our response to R1.5, R1.6, R1.7 and R1.8).

R1.2 It would be beneficial if the model/toolbox could be made publicly available so that fellow researchers from the community could apply and test it in their research. 

We have added a section “Data and code availability statements” in the revised manuscript to indicate we’re willing to share the toolbox to the interested groups (L529 in the annotated manuscript). The toolbox is a standalone MATLAB-based program (no license required) to generate kw, CBF and ATT maps, which can run on windows or MAC computers. Indeed, we have been sharing our reconstruction toolbox with over 50 collaboration sites. The following screenshots are examples of three steps performed by the toolbox (shared by one collaborator):

Author response image 1.

Step 1: Loading raw data and calculate T1 map

Author response image 2.

Step 2: Motion correction and skull stripping

Author response image 3.

Step 3: kw, CBF and ATT quantification (nii files will be saved)

R1.3 Line 46 states that the technique is novel, but it has been introduced and used before (Shao, et al. MRM 2019). It sure is innovative but the term novel is too strong and may confuse the readers that it is something new introduced in this manuscript.

Thanks for the suggestion, we agree the term ‘novel’ may cause confusion about the technique, we have removed it in the revised manuscript (L48, L50).

R1.4 Line 395, kw was generated using PLD = 1.8s with b = 0, 50 s/mm2. Is only one-time point enough for estimating kw? To me, it is not clear how robust is the kw estimation with only one PLD.

According to the single-pass approximation (SPA) model (1), kw can be accurately estimated when the PLD is longer than the ATT. We recruited cognitively normal participants in this study and found the longest ATT to be 1526.7±117.4 and 1468.1±166.9 ms in aged (62-92 years) males and females, respectively. A PLD of 1.8 s was chosen to balance the SNR of the data and the accuracy of the model fitting, which should be sufficient for this study. However, for future studies involving diseased populations with prolonged ATT, a longer PLD should be used, or a multi-PLD protocol could be helpful to improve the robustness of quantification accuracy.

We have added a limitation statement in the revised manuscript (L407): "A single PLD of 1800 ms was used in this study, which should be sufficient to allow all the labeled water to reach the tissue (i.e., the longest ATT was 1526.7±117.4 and 1468.1±166.9 ms in aged males and females, respectively) (1). However, a longer PLD should be used in participants with longer expected ATT, such as in stroke and cerebrovascular disorders. Additionally, a multi-PLD protocol can also be helpful to improve the robustness of quantification accuracy (2)."

R1.5 Suggestion: Figure 3A, colormap for kw appears suboptimal. Regional differences are hard to see.

Thanks for the suggestion, we have updated the range of color scale (from [0, 200], to [70, 160]) to highlight the regional differences in the updated Figure 3:

We prefer to use the same blue colormap that we and our collaborators have been using this for publications to maintain consistence. We also acknowledged the limitation of the spatial resolution of kw maps in the updated manuscript (L412): “To compensate for the half signal loss of the non-CPMG DP module, relatively low spatial resolution and TGV-regularized SPA modeling were employed. Our recently development of a motion-compensated diffusion weighted (MCDW)-pCASL can be utilized to improve the spatial resolution in the future studies (e.g. 3.5 mm3 isotropic maps in 10 mins) (2)”

R1.6 Suggestion: use same/similar colormaps for the same parameters (kw, ATT, CBF) to help the reader follow across Figures 3, 4, and 5.

Thanks for your suggestion, we agree that using the same color would be easier for readers to follow the context. However, figures 4 and 5 were created to show the age and sex dependent changes, so that we used warm and cold colors to indicate effects of decrease and increase, respectively. We clarified the choice of colormap in the figure captions (L260, L284): “The effects of decrease or increase were represented by warm colors (yellow to red) and cold (gray to blue) colors, respectively.”

R1.7 Suggestion: please be consistent with the ordering of parameters in Figures 3, 4, and 5.

Thanks for the suggestion, we have updated Figure 3 to consistently show kw, CBF and ATT results in order from left to right:

R1.8 Suggestion: use the same scaling (e.g.[|1.9|, |11 |] for Fig. 4, [|1.9|, |4|] for Figure 5) to enhance comparability across parameters in the subfigures.

Thanks for the suggestion, we agree that the same scaling would enhance the comparability across parameters. We have updated the color scales for Figure 5 using maximal |T| = 4:

However, range of maximal |T| was relatively large for Figure 4 (i.e. 5 for kw, 11 for CBF and 7 for ATT), and using the same color scale might oversaturate the regional responses or diminish the visibility of regional differences. Therefore, we prefer to keep the original color scale for Figure 4.

R1.9 In Figure 5, the interaction of age with sex in kw parameter seems to be more on one side of the brain. What could be the reasons for possible lateralization? 

We agree with the reviewer that the age and sex interaction effects emphasized on one side is an interesting finding. While we do not have a clear explanation now, we suspect it may relate to aging-related asymmetrical vascular burdens. Giannakopoulos et al. reported that vascular scores, indicating higher vascular burden, were significantly higher in the left hemisphere across all Clinical Dementia Rating scores. Moreover, the predominance of Alzheimer’s disease and vascular pathology in the right hemisphere correlated with significantly higher Clinical Dementia Rating scores  (3). We added the following to the updated manuscript to discuss this potential mechanism (L370): “… We also observed an asymmetric effect on left and right brain hemispheres, which might be associated with asymmetrically developed vascular burdens in aging (3)."

R1.10 A comparison between the present study and DCE MRI as well as other ASL methods evaluating BBB function with age is missing. ASL techniques probing transverse relaxation and DCE MRI have reported increased kw with age in humans as well as in animal models. What could be the reasons? 

We agree with the reviewer that BBB water exchange measured by other methods should be sufficiently discussed, especially regarding their age-related changes. We added the following discussion in the updated manuscript (L415): “Mahroo et al., utilized a multi-echo ASL technique to measure BBB permeability to water and reported shorter intra-voxel transit time and lower BBB exchange time (Tex) in the older participants (≥50 years) compared to the younger group (≤20 years) (4). In animal studies, reduced BBB Tex was also reported in the older mice compared to the younger group using multi-echo ASL (5) and a multi-flip-angle, multi-echo dynamic contrast-enhanced (MFAME-DCE) MRI method (6). These findings contrast with the results presented in this study, likely due to the different components assessed by different techniques, and increased BBB permeability to water has been suggested to indicate a leakage of tight junctions in aging (5, 6). In contrast, our recent study utilizing high resolution MCDW-pCASL scans with long averages reveals the potential existence of an intermediate stage of water exchange between vascular and tissue compartments (e.g., paravascular space or basal lamina) (2). The DP module of the DP-pCASL is hypothesized to null the fast-flowing and pseudo-random oriented spins, which may include both vascular flow and less restricted water in paravascular space. The observed lower kw in older participants may be more related to the delayed exchange across the astrocyte end-feet into the tissue due to loss of AQP-4 water channel with older age. However, these hypotheses require further investigation to understand the exact mechanisms, especially under different physiological states (7, 8). Future studies, particularly with animal models targeting specific BBB components under different physiological or diseased conditions, will be valuable for validating these measurements (9-13).”

R1.11 Line 163/164, a rapid decrease of CBF in males in the region of the hippocampus is reported. It would be beneficial to discuss this in discussion further (has this been reported before, possible reasons, etc). 

Thanks for the suggestion, we agree that the accelerated CBF decline in males in the hippocampus is an important finding, we have added discussion in the revised manuscript (L300): "Furthermore, we found a more pronounced age-related decline in CBF in the hippocampus of males compared to females (Fig. 2, Supplemental Table S2). To the best of our knowledge, no study has previously reported this accelerated hippocampal CBF decline in males. This finding may be linked to the accelerated hippocampal volume loss in males, as reported in a study analyzing 19,793 generally healthy UK Biobank participants (14). Lower hippocampal perfusion has been associated with poor memory performance (15, 16), suggesting that males might be more vulnerable to potential cognitive decline (17).

R1.12 Lines 198-202 describe a simulation done to test the dependence of kw on ATT. This is important and could be explained more in detail. Adding simulation results (numeric or figure) to supplementary materials would increase reproducibility and understanding for others. 

We apologize for not referencing to the simulation results in the main text. We simulated kw distribution for females by adjusting ATT by +60 ms to matching males’ ATT, leading to a marginally higher kw values. And these results were shown in the Supplemental Figure S2 C (yellow):

We have now referenced the simulation results in the updated manuscript (L206).

R1.13 No limitations of the presented work are mentioned. A critical perspective would increase the scientific impact on future research decisions and implementation of this method by others. 

Thanks for the suggestion, we agree the limitations need to be acknowledged. We have added a limitation paragraph in the revised manuscript (L406): "Limitations of the study and future directions: There are a few limitations of this study. A single PLD of 1800 ms was used in this study, which should be sufficient to allow all the labeled water to reach the tissue (i.e., the longest ATT was 1526.7±117.4 and 1468.1±166.9 ms in aged males and females, respectively) (1). However, a longer PLD should be used in participants with longer expected ATT, such as in stroke and cerebrovascular disorders. Additionally, a multi-PLD protocol can also be helpful to improve the robustness of quantification accuracy (2). To compensate for the half signal loss of the non-CPMG DP module, relatively low spatial resolution and TGV-regularized SPA modeling were employed. Our recently development of a motion-compensated diffusion weighted (MCDW)-pCASL can be utilized to improve the spatial resolution in the future studies (e.g. 3.5 mm3 isotropic maps in 10 mins) (2). Mahroo et al., utilized a multi-echo ASL technique to measure BBB permeability to water and reported shorter intra-voxel transit time and lower BBB exchange time (Tex) in the older participants (≥50 years) compared to the younger group (≤20 years) (4). In animal studies, reduced BBB Tex was also reported in the older mice compared to the younger group using multi-echo ASL (5) and a multi-flip-angle, multi-echo dynamic contrast-enhanced (MFAME-DCE) MRI method (6). These findings contrast with the results presented in this study, likely due to the different components assessed by different techniques, and increased BBB permeability to water has been suggested to indicate a leakage of tight junctions in aging (5, 6). In contrast, our recent study utilizing high resolution MCDW-pCASL scans with long averages reveals the potential existence of an intermediate stage of water exchange between vascular and tissue compartments (e.g., paravascular space or basal lamina) (2). The DP module of the DP-pCASL is hypothesized to null the fast-flowing and pseudo-random oriented spins, which may include both vascular flow and less restricted water in paravascular space. The observed lower kw in older participants may be more related to the delayed exchange across the astrocyte end-feet into the tissue due to loss of AQP-4 water channel with older age. However, these hypotheses require further investigation to understand the exact mechanisms, especially under different physiological stages (7, 8). Future studies, particularly with animal models targeting specific BBB components under different physiological or diseased conditions, will be valuable for validating these measurements (9-13). Including race as a covariate in our study aims to account for potential variations in brain perfusion observed in previous research (18, 19). However, it is important to recognize that these differences may not be solely attributable to race. They can be influenced by a complex interplay of factors such as education, environmental exposures, lifestyle, healthcare access, and other social determinants of health (20). For example, education has been shown to be highly relevant to regional CBF changes in AD (21, 22). Additionally, the potential influence of ancestry and mixed-race on perfusion and BBB function requires further investigation in future studies. Other factors such as hematocrit (23), menopausal status (24, 25), and vascular risk factors (26) should also be considered. These variables were not included in this study due to the unavailability or limited availability in some cohorts. We attempted to minimize the impact of these factors on our observations by including a relatively large and diverse sample. However, future studies examining the specific mechanism of each of these factors on BBB function in aging would be valuable.

Reviewer #2 (Public Review):

Summary: 

This study used a novel diffusion-weighted pseudo-continuous arterial spin labelling (pCASL) technique to simultaneously explore age- and sex-related differences in brain tissue perfusion (i.e., cerebral blood flow (CBF) & arterial transit time (ATT) - a measure of CBF delivery to brain tissue) and blood-brain barrier (BBB) function, measured as the water exchange (kw) across the BBB. While age- and sex-related effects on CBF are well known, this study provides new insights to support the growing evidence of these important factors in cerebrovascular health, particularly in BBB function. Across the brain, the decline in CBF and BBB function (kw) and elevation in ATT were reported in older adults, after the age of 60, and more so in males compared to females. This was also evident in key cognitive regions including the insular, prefrontal, and medial temporal regions, stressing the consideration of age and sex in these brain physiological assessments. 

Strengths: 

Simultaneous assessment of CBF with BBB along with transit time and at the voxel-level helped elucidate the brain's vulnerability to age and sex-effects. It is apparent that the investigators carefully designed this study to assess regional associations of age and sex with attention to exploring potential non-linear effects. 

Weaknesses: 

R2.0 It appears that no brain region showed concurrent CBF and BBB dysfunction (kw), based on the results reported in the main manuscript and supplemental information. Was an association analysis between CBF and kw performed? There is a potential effect of the level of formal education on CBF (PMID: 12633147; 15534055), which could have been considered and accounted for as well, especially for a cohort with stated diversity (age, race, sex). 

Thank you for your positive feedback and comments on the potential associations between BBB kw and other physiological parameters (e.g., CBF) and socioeconomic factors (e.g., education). We have made the following changes to the updated manuscript:

(1) We conducted additional linear regressions between regional kw and regional CBF or ATT, incorporating sex as a covariate, for participants aged 8-61 years and 62-92 years (when BBB kw starts declining). The results are summarized in Supplemental Table S6. We found that BBB kw was significantly negatively associated with CBF in the putamen, amygdala, hippocampus, parahippocampal gyrus, and medial temporal lobe in participants younger than 62 years, when kw was relatively consistent across ages. However, no significant correlations were found in any brain regions in the 62-92 years group. In contrast to CBF, kw was significantly negatively associated with ATT in the GM, temporal lobe, and precuneus in participants aged 8-61 years, and these correlations became significant in additional ROIs, including WM, frontal lobe, ACC, caudate, putamen, amygdala, hippocampus, PHG, and MTL in participants aged 62-92 years. These results suggest that BBB function may be influenced by different aspects of neurovascular function represented by CBF and ATT at different stages of aging.

(2) One limitation of this study is the lack of information on participants’ geographical, cultural, physical characteristics, and socioeconomic factors. While we included race as a covariate to account for potential variations observed in previous research, race is an imprecise proxy for the complex interplay of genetic, environmental, socioeconomic, and cultural factors that influence physiological outcomes. We have acknowledged this limitation by adding the following discussion in the updated manuscript: “Including race as a covariate in our study aims to account for potential variations in brain perfusion observed in previous research. However, it is important to recognize that these differences may not be solely attributable to race. They can be influenced by a complex interplay of factors such as education, environmental exposures, lifestyle, healthcare access, and other social determinants of health. For example, education has been shown to be highly relevant to regional CBF changes in AD. Additionally, the potential influence of ancestry and mixed-race on perfusion and BBB function requires further investigation in future studies.”

Reviewer #2 (Recommendations For The Authors): 

General comments: 

I commend the authors on a very well-written and laid-out study. General remarks have been provided in the short assessment and public review sections. 

We would like to thank the reviewer for the insightful suggestions and overall positive feedback. We have substantial revised and improved our manuscript, and point-to-point responses can be found in the following sections and in the annotated manuscript.

Specific comments: 

Results: 

R2.1 Line 127: "since race may influence the changes in perfusion and kw with aging, it was included as a covariate". It is not clear how race - a simplistic term for ethnicity or to be more specific ancestry has been shown to influence changes in perfusion? Is it known for a fact that for example, older Black people have lower/higher CBF or kw compared to Asians or Asians to Caucasian Americans? Can this be extrapolated to Japanese Brazilians having different patterns of regional CBF to Caucasian or Black Brazilians or similar patterns of CBF to Japanese people in Japan since they share similar race? Do Dutch people in the Netherlands share CBF characteristics to their descendants in the US or in South Africa? Would the geographical, cultural, and other physical characteristics of one's ethnicity or lineage impact CBF? Race is often used as a poor substitute for the complex interactions of physical, socioeconomic, and geopolitical factors that produce disparities that may have measurable biological effects including CBF. But it is not clear why being one race vs the other will impact CBF, without carefully parcelling out the many factors beyond biology, if any. Is any of the participants in the study mixed race? How about recently settled individuals who may identify for example as Black but have spent all their life up to adult years outside of the US and marked here in the study as simply African American? Not that I am saying this is the case. However this simplification may require more careful analysis. 

In our study, no participant indicated to be mixed-race, and unfortunately we do not have additional information about their specific ancestry or information about their geographical, cultural, and other physical characteristics. We acknowledge that race is an imprecise proxy for the complex interplay of genetic, environmental, socioeconomic, and cultural factors that influence physiological outcomes, including perfusion and BBB function. The use of race as a covariate in our study is intended to account for potential variations observed in previous research, rather than to imply a direct causal relationship.

Research has shown differences in blood flow among racial groups (18, 19). However, these differences are not solely attributable to race, and they are also shaped by environmental exposures, lifestyle factors, healthcare access, and other social determinants of health (20). We have added the following discussion in the updated manuscript (L436): “Including race as a covariate in our study aims to account for potential variations in brain perfusion observed in previous research (18, 19). However, it is important to recognize that these differences may not be solely attributable to race. They can be influenced by a complex interplay of factors such as education, environmental exposures, lifestyle, healthcare access, and other social determinants of health (20). For example, education has been shown to be highly relevant to regional CBF changes in AD (21, 22). Additionally, the potential influence of ancestry and mixed-race on perfusion and BBB function requires further investigation in future studies.”

R2.2 Figure 3: Could the standard deviation of the reported values be also stated so the variance can be appreciated? 

Thanks for the suggestion, we have added the standard deviation of the kw, CBF and ATT values on the updated Figure 3:

R2.3 Discussions: Line 280: .."observed distinct trajectory of kw changes with aging as compared with CBF and ATT. I presume this as compared to the earlier statements (line 268) of pervasive increase in ATT and decrease in CBF across the brain. Were there any brain regions that showed increased ATT, decreased CBF and kw as a function of age or even sex?? Was there any association between CBF and kw in any brain regions, across the participants after controlling for sex differences? If there is a suspicion of early BBB dysfunction (line 286) preceding cognitive decline that has been also suspected with CBF, is this concomitant with CBF in most people? This could maybe make CBF an easier and more straightforward biomarker since its effects mirror that of BBB? I suspect it generally does not, even in healthy aging. It would have been great to shed more light on this with your results and in your discussion.

Thank you for your comments. By 'distinct trajectory of kw changes with aging,' we refer to the ‘turning point’ in age at which kw starts declining. BBB kw remained relatively stable and began to decline in the early 60s, while CBF consistently decreased and ATT consistently increased with age, although the rates of change differed at 22 years and 36 years, respectively. Using linear regressions for voxel analysis, Figure 4 shows that age-dependent decreases in CBF and increases in ATT were observed in most of the brain. However, significant age-related decreases in kw were more localized to specific brain regions and were mostly accompanied by simultaneous decreases in CBF and increases in ATT. We highlighted this finding in the updated manuscript (L250): “In the brain regions showing significant age-related kw decreases (Fig. 4A), these decreases are mostly accompanied by CBF decreases (Fig. 4B) and ATT increases (Fig. 4C).”

Thank you for your suggestion regarding the relationship between kw and CBF. We further conducted linear regressions between regional kw and regional CBF or ATT, incorporating sex as a covariate, for participants aged 8-61 years and 62-92 years (when BBB kw starts declining). The results are summarized Supplemental Table S6.

This new supplemental tables shows many interesting results. BBB kw was significantly negatively associated with CBF in the putamen, amygdala, hippocampus, parahippocampal gyrus, and medial temporal lobe in participants younger than 62 years, when kw was relatively consistent across ages. However, no significant correlations were found in any brain regions in the 62-92 years group. In contrast to CBF, kw was significantly negatively associated with ATT in the GM, temporal lobe, and precuneus in participants aged 8-61 years, and these correlations became significant in additional ROIs, including WM, frontal lobe, ACC, caudate, putamen, amygdala, hippocampus, PHG, and MTL in participants aged 62-92 years.

We have added the following discussion to the updated manuscript (L307): 'We observed a distinct trajectory of kw changes with aging compared to CBF and ATT. To study the potential regional associations between kw and CBF and ATT, we conducted linear regressions between regional kw and regional CBF or ATT, incorporating sex as a covariate, for participants aged 8-61 years and 62-92 years (when BBB kw starts declining), respectively. The results are shown in Supplemental Table S6. BBB kw was significantly negatively associated with CBF in the putamen, amygdala, hippocampus, PHG, and MTL in participants aged 8-61 years (when kw was relatively consistent across ages), but no significant correlations were found in any brain regions in the 62-92 years group. In contrast to CBF, kw was significantly negatively associated with ATT in the GM, temporal lobe, and precuneus in participants aged 8-61 years, and these correlations became significant in additional brain regions, including WM, frontal lobe, ACC, caudate, putamen, amygdala, hippocampus, PHG, and MTL in participants aged 62-92 years. These results suggest that BBB function may be affected by different aspects of neurovascular function represented by CBF and ATT at different stages of aging."

Other notes: 

R2.4 While reading the results section, two things that jump out at me when I saw the sex differences: 1) hematocrit and 2) menopausal status. I saw in the discussion that these were touched on. I may have missed this in the methods, was hematocrit collected and included in the parameters estimates?? Was the menopausal status including ERT (estrogen replacement therapies) recorded and factored in? If not these could be included as limitations that may confound the results, especially when the age groups were split to include a group comprising or potentially both pre-and post-menopausal females (36-61). 

We do not have the information about hematocrit nor menopausal status and they were not included in data analysis. We agree this is a limitation of the current study and we discussed in the updated manuscript (L442): “Other factors such as hematocrit (23), menopausal status (24, 25), and vascular risk factors (26) should also be considered. These variables were not included in this study due to data unavailability or limited availability in some cohorts. We attempted to minimize the impact of these factors on our observations by including a relatively large and diverse sample. However, future studies examining the specific mechanism of each of these factors on BBB function in aging would be valuable.”

R2.5 The general vascular health of the cohort is not well described especially if some of the participants were from sickle cell study. While they are cognitively normal and free from major medical illnesses, or neurological disorders, did the sample also include individuals with considerable vascular risk factors and metabolic syndrome (known to affect CBF), especially in the older cohort?? 

We agree with the reviewer that vascular health can significantly impact perfusion and BBB function. Since the data presented in this study were collected from multiple cohorts, vascular risk factors were not available in all cohorts and thus were not included as covariates in the data analysis. To account for potential vascular variations across participants, we included CBF and ATT as covariates in our analysis on age related BBB kw changes. We have added discussion in the updated manuscript (L442, same as our response to the previous comment): “Other factors such as hematocrit (23), menopausal status (24, 25), and vascular risk factors (26) should also be considered. These variables were not included in this study due to data unavailability or limited availability in some cohorts. We attempted to minimize the impact of these factors on our observations by including a relatively large and diverse sample. However, future studies examining the specific mechanism of each of these factors on BBB function in aging would be valuable.”.

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Author response:

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

Reviewer #1 (Public Review):

In this manuscript, by using simulation, in vitro and in vivo electrophysiology, and behavioral tests, Peng et al. nicely showed a new approach for the treatment of neuropathic pain in mice. They found that terahertz (THz) waves increased Kv conductance and decreased the frequency of action potentials in pyramidal neurons in the ACC region. Behaviorally, terahertz (THz) waves alleviated neuropathic pain in the mouse model. Overall, this is an interesting study. The experimental design is clear, the data is presented well, and the paper is well-written. I have a few suggestions.

(1) The authors provide strong theoretical and experimental evidence for the impact of voltage-gated potassium channels by terahertz wave frequency. However, the modulation of action potential also relies on non-voltage-dependent ion channels. For example, I noticed that the RMP was affected by THz application (Figure 3F) as well. As the RMP is largely regulated by the leak potassium channels (Tandem-pore potassium channels), I would suggest testing whether terahertz wave photons have also any impact on the Kleak channels as well.

Thank you for your positive comment and for providing us with this valuable suggestion. After testing the leak K+ current with and without HFTS on the SNI model, we observed a notable increase in the leak K+ current with HFTS when the holding potential surpassed -40 mV (please see the revised Figs. 2m and n). This finding prompted us to delve deeper into the shifts in the resting membrane potential (RMP). The data, along with statistical analysis, are detailed in Tables S1-3.

(2) The activation curves of the Kv currents in Figure 2h seem to be not well-fitted. I would suggest testing a higher voltage (>100 mV) to collect more data to achieve a better fitting.

Thanks for your advice. We repeated the experiment while maintaining the voltage of patched neurons at a higher level (>100 mV) to collect ample data for better fitting. The outcomes are illustrated in the revised Figs. 2g-j. Clearly, the data reveals a significant increase in K+ conductance in the HFTS group as compared to the SNI group. We have integrated these discoveries into the revised manuscript, replacing the earlier results.

(3) In the part of behavior tests, the pain threshold increased after THz application and lasted within 60 mins. I suggest conducting prolonged tests to determine the end of the analgesic effect of terahertz waves.

Thank you for your insightful comment. We echo your curiosity about the duration of the HFTS effect. In the process of revising our work, we conducted a comparative analysis of the analgesic duration resulting from 10-minute and 15-minute applications of HFTS. The findings are visualized in the revised Fig. 5c. Our observations indicate that after 160 minutes, the PWMT value for the 15-minute HFTS group decreased to a level comparable to that of the SNI group. Meanwhile, the analgesic effects persisted for 140 minutes in the case of the 10-minute HFTS application. These results imply a direct correlation between the duration of HFTS application and the duration of analgesia.

(4) Regarding in vivo electrophysiological recordings, the post-HFTS recordings were acquired from a time window of up to 20 min. It seems that the HFTS effect lasted for minutes, but this was not tested in vitro where they looked at potassium currents. This long-lasting effect of HFTS is interesting. Can the authors discuss it and its possible mechanisms, or test it in slice electrophysiological experiments?

Thank you for your comment. Based on the results from in vivo electrophysiological recordings, it was observed that the effect of HFTS can endure for a minimum of 20 minutes, and this duration was even more extended in behavioral assessments. Taking your advice, we employed slice electrophysiological recording for further testing. Following a 15-minute application of HFTS, we evaluated the K+ current at 5 and 20 minutes after incubation. Our observations clearly indicated a substantial and lasting increase in K+ current, with the effect persisting for at least 20 minutes (refer to Fig. 2l). This provides confirmation of the long-lasting influence of HFTS. The relevant data and statistical analysis are documented in Table S1-2.

(5) How did the authors arrange the fiber for HFTS delivery and the electrode for in vivo multi-channel recordings? Providing a schematic illustration in Figure 4 would be useful.

Thank you for your comment. To enhance the reader's understanding of the HFTS delivery device during multi-channel recording, we have included a schematic illustration in Fig. 4a in the revised manuscript. The top portion of Fig. 4a depicts a quantum cascade laser (QCL) with a center frequency located at approximately 36 THz. This laser is then connected to the recording electrode via a PIR fiber. The left section illustrates the detailed structure of the recording electrode.

(6) Some grammatical errors should be corrected.

Thank you for your thorough review. We have carefully checked and corrected grammar errors we found throughout the entire text to ensure that readers can better comprehend the content of the article.

Reviewer #2 (Public Review):

In this manuscript, Peng et al., reported that 36 THz high-frequency terahertz stimulation (HFTS) can suppress the activity of pyramidal neurons by enhancing the conductance of voltage-gated potassium channel. The authors also demonstrated the effectiveness of using 36THz HFTS for treating neuropathic pain.

Strengths:

The manuscript is well written and the conclusions are supported by robust results. This study highlighted the potential of using 36 THz HFTS for neuromodulation.

Weaknesses:

More characterization of HFTS is needed, so the readers can have a better assessment of the potential usage of HFTS in their own applications.

Thank you for your suggestion. We have created schematic diagrams illustrating the HFTS delivery (Fig. 4a and Fig. 5a in the revised manuscript). Fig. 4a presents the structure designed for in vivo multi-channel recording. Fig. 5a shows the structure used in behavior test, the recording electrode is replaced by a metal hollow tube, allowing the PIR fiber to pass through the tube and target the ACC region of the mice.

(1) It would be very helpful to estimate the volume of tissue that can be influenced by HFTS. It is not clear how 15 mins HFTS was chosen for this functional study. Does a longer time have a stronger effect? A better characterization of the relationship between the stimulus duration of HFTS and its beneficial effects would be very useful.

Thank you for your feedback. The degree of tissue influence is directly related to the size of the spot emerging from the fiber outlet. In our experiment, we used a PIR fiber with a 630 nm inner core diameter to propagate high-frequency THz waves. This core features a refractive index of 2.15 and has an effective numerical aperture (NA) of 0.35 ± 0.05.

Our decision to apply HFTS for 15 minutes in the behavioral study was primarily based on observations from in vivo multi-channel recordings. Specifically, we noticed a considerable reduction in the average firing rate of PYR cells after 15 minutes of HFTS exposure. To further investigate the correlation between the duration of HFTS stimulation and its effects, we conducted a comparative study using a 10-minute HFTS session. The results, depicted in revised Fig. 5c, reveal that the PWMT value decreased to the level seen in the SNI group after approximately 160 minutes following 15 minutes of HFTS, and after about 140 minutes with 10 minutes of HFTS. This suggests a direct relationship between the length of HFTS application and its beneficial outcomes.

(2) How long does the behavioral effect last after 15 minutes of HFTS? Figure 5b only presents the behavioral effect for one hour, but the pain level is still effectively reduced at this time point. The behavioral measurement should last until pain sensitization drops back to pre-stim level.

Thank you for your feedback. Similar question is also mentioned by reviewer 1. As depicted in Fig. 5c, it was observed that the analgesic effects lasted for 140-160 min with 10-15 minutes application of HFTS. Based on these findings, we can conclude that in the SNI model, targeting the ACC brain region with HFTS for a duration of 10-15 minutes results in an analgesic effect that lasts for roughly 140-160 minutes. This provides valuable insights into the potential clinical applications and duration of relief that can be achieved through HFTS treatment.

(3) Although the manuscript only tested in ACC, it will also be useful to demonstrate the neural modulation effect on other brain regions. Would 36THz HFTS also robustly modulate activities in other brain regions? Or are different frequencies needed for different brain regions?

Thank you for your comment. We hypothesize that light waves at a frequency of approximately 36 THz effectively modulate neuronal activities in various brain regions, primarily due to their impact on K channels. Additionally, we speculate that the application of THz waves at different frequencies may influence other channels, such as Na and Ca channels, potentially facilitating or inhibiting neuronal activities. We believe this is a fascinating and significant area of research to explore in the future.

Reviewer #3 (Public Review):

Summary:

This manuscript by Peng et al. presents intriguing data indicating that high-frequency terahertz stimulation (HFTS) of the anterior cingulate cortex (ACC) can alleviate neuropathic pain behaviors in mice. Specifically, the investigators report that terahertz (THz) frequency stimulation widens the selectivity filter of potassium channels thereby increasing potassium conductance and leading to a reduction in the excitability of cortical neurons. In voltage clamp recordings from layer 5 ACC pyramidal neurons in acute brain slice, Peng et al. show that HFTS enhances K current while showing minimal effects on Na current. Current clamp recording analyses show that the spared nerve injury model of neuropathic pain decreases the current threshold for action potential (AP) generation and increases evoked AP frequency in layer 5 ACC pyramidal neurons, which is consistent with previous studies. Data are presented showing that ex-vivo treatment with HFTS in slice reduces these SNI-induced changes to excitability in layer 5 ACC pyramidal neurons. The authors also confirm that HFTS reduces the excitability of layer 5 ACC pyramidal neurons via in vivo multi-channel recordings from SNI mice. Lastly, the authors show that HFTS is effective at reducing mechanical allodynia in SNI using both the von Frey and Catwalk analyses. Overall, there is considerable enthusiasm for the findings presented in this manuscript given the need for non-pharmacological treatments for pain in the clinical setting.

Strengths:

The authors use a multifaceted approach that includes modeling, ex-vivo and in-vivo electrophysiological recordings, and behavioral analyses. Interpretation of the findings is consistent with the data presented. This preclinical work in mice provides new insight into the potential use of directed high-frequency stimulation to the cortex as a primary or adjunctive treatment for chronic pain.

Weaknesses:

There are a few concerns noted that if addressed, would significantly increase enthusiasm for the study.

(1) The left Na current trace for SNI + HFTS in Figure 2B looks to have a significant series resistance error. Time constants (tau) for the rate of activation and inactivation for Na currents would be informative.

Thank you for your feedback. We have carefully considered your comments and made several adjustments in the revised Figs. 2b-f to improve clarity and accuracy. Firstly, we have conducted a comparison of the time constants (tau) between the SNI group and the SNI+HFTS group. These time constants represent the latency of Na current activation or inactivation relative to the half-activated/inactivated voltage. Our analysis reveals that there is no statistically significant difference in tau between the two groups for both activation and deactivation curves. Secondly, we have updated the sample traces in Fig. 2b of the revised manuscript. These new traces illustrate that tau does not significantly differ between the SNI and SNI+HFTS groups, providing a visual representation of our findings. We believe that these modifications strengthen the presentation of our study's details and results, making the data more accessible and understandable for readers.

(2) It is unclear why an unpaired t-test was performed for paired data in Figure 2. Also, statistical methods and values for non-significant data should be presented.

Thank you for your comment. I think you mean the results in Fig. 3. We agree with you that we should use one-way ANOVA to analyze the data since there are more than 2 groups for comparison. We thus re-analyzed the data by using one-way ANOVA in Figs. 3g-k, and have included detailed statistical methods and P values in the revised manuscript.

(3) It would seem logical to perform HFTS on ACC-Pyr neurons in acute slices from sham mice (i.e. Figure 3 scenario). These experiments would be informative given the data presented in Figure 4.

Thank you for your valuable advice. During the revision process, we performed HFTS on ACC-PYR neurons in acute slices obtained from sham mice. The findings from this experiment have been integrated into the updated Fig. 3, where the sham group is represented by the green line and histogram (the revised Fig. 3 in the manuscript). It is noteworthy that a significant decrease in spike frequency was observed in the sham mice following HFTS.

(4) As the data are presented in Figure 4g, it does not seem as if SNI significantly increased the mean firing rate for ACC-Pyr neurons, which is observed in the slice. The data were analyzed using a paired t-test within each group (sham and SNI), but there is no indication that statistical comparisons across groups were performed. If the argument is that HFTS can restore normal activity of ACC-Pyr neurons following SNI, this is a bit concerning if no significant increase in ACC-Pyr activity is observed in in-vivo recordings from SNI mice.

Thank you for highlighting the inaccuracies in the analysis. After reviewing the data, we re-analyzed it using alternative statistical methods. In the revised version, since the data did not follow a normal distribution, we employed Wilcoxon matched-paired signed rank tests within the sham and SNI groups, and Mann-Whitney tests between the sham and SNI groups.

Upon comparing the statistical outcomes across the groups, we found that the mean firing rate of 130 ACC neurons in SNI mice was significantly higher compared to that of 108 ACC neurons in sham mice (P = 0.0447, Mann-Whitney test). Notably, the mean firing rate of ACC-PYR exhibited a more pronounced increase with a P value of 0.0274 in SNI pre-HFTS versus sham pre-HFTS, while the mean firing rate of ACC-INT did not display a significant change across the groups. These findings align with the observations we made in the slice, reinforcing the validity of our results.

(5) The authors indicate that the effects of HFTS are due to changes in Kv1.2. However, they do not directly test this. A blocking peptide or dendrotoxin could be used in voltage clamp recordings to eliminate Kv1.2 current and then test if this eliminates the effects of HFTS. If K current is completely blocked in VC recordings then the authors can claim that currents they are recording are Kv1.1 or 1.2.

Thank you for your kind suggestion. In our research, we employed the Kv1.2 structure as a model to determine the response frequency of terahertz waves. Through both in vitro and in vivo experiments, we were able to demonstrate that the frequency of approximately 36 THz affects the Kv channel and its corresponding spike frequency. Upon analyzing the action potential waveform, we observed a notable variance in the resting membrane potential (RMP). This RMP is predominantly controlled by leak potassium channels, specifically the Tandem-pore potassium channels. In accordance with the recommendation of reviewer 1, we have addressed this particular aspect of our experimentation in the revised manuscript.

We agree that we should use blocking peptides or dendrotoxin to eliminate Kv1.2 current. However, we meet problems in purchasing and delivery of the drugs. We thus added some explanation in the Discussion part to emphasize the value for this pharmacological experiment and can further confirm this in the future works.

(6) The ACC is implicated in modulating the aversive aspect of pain. It would be interesting to know whether HFTS could induce conditioned place preference in SNI mice via negative reinforcement (i.e. alleviation of spontaneous pain due to the injury). This would strengthen the clinical relevance of using HFTS in treating pain.

Thank you for this valuable advice. We share your intrigue regarding this experiment, and we fully recognize the importance and potential of further exploring this area. At present, however, our equipment and platform limitations prevent us from conducting the necessary tests. However, we remain committed to pursuing relevant research opportunities in the future.

Author response:

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

Public Reviews:

Reviewer #1:

(1) Study suggests that the effects of their tumor models of mouse behavioral are largely non-specific to the tumor as most behaviors are rescued by analgesic treatment. So, most of the changes were likely due to site-specific pain and not a unique signal from the tumor.

The tumor generates pain at the site it is implanted, and it is likely amplified by the oral activities tumor bearing mice have to engage in. As there is no pain in the absence of the tumor, the pain is, by definition, caused by the tumor, not by the site. Concerning the relationship between pain and behavior, the behavioral assays undertaken in our study (nesting, cookie test, wheel running) were very limited in scope.  Two of these assays (nesting, cookie test) require use of the oral cavity. Only nesting and wheel running were assessed in the context of treatment for pain. Nesting behavior was completely restored with carprofen and buprenorphine treatment suggesting that in the absence of pain, mice were able to make perfect nests. Consistent with this, carprofen and buprenorphine treated animals also gained weight indicating that eating (another activity dependent on the oral cavity) was also restored.  Wheel running, an activity that does not rely on the oral cavity, was only partially restored with drug treatment. While additional behavioral tests are necessary to confirm this finding, the data suggest that there is pain-independent information relayed to the brain which accounts for this decline in wheel running.

Reviewer #2:

(1) The main claim is that tumor-infiltrating nerves underlie cancer-induced behavioral alterations, but the experimental interventions are not specific enough to support this. For example, all TRPV1 neurons, including those innervating the skin and internal organs, are ablated to examine sensory innervation of the tumor. Within the context of cancer, behavioral changes may be due to systemic inflammation, which may alter TRPV1 afferents outside the local proximity of tumor cells. A direct test of the claims of this paper would be to selectively inhibit/ablate nerve fibers innervating the tumor or mouth region.

We agree with the reviewer that a direct test of the hypothesis would require selectively inhibiting the nerve fibers innervating the tumor and assessing the impact on behavior. Studies in the lab are on-going using pharmacological interventions to do this. These studies are beyond the scope of this current manuscript.

(2) Behavioral results from TRPV1 neuron ablation studies are in part confounded by differing tumor sizes in ablated versus control mice. Are the differences in behavior potentially explained by the ablated animals having significantly smaller tumors? The differences in tumor sizes are not negligible. One way to examine this possibility might be to correlate behavioral outcomes with tumor size.

As suggested by the reviewer, we have graphed nesting scores and time-to-interact (cookie test) relative to tumor volume.  In both cases, we used simple linear regression to fit the data and analyzed the slopes of the lines. In the case of nesting, there was no significant difference between the slopes. This is now included as Supplemental Figure 4A. In the case of the cookie test, there was a significant difference between the slopes. This is now included as Supplemental Figure 4B. Graphing the data in this way allows one to look at any given tumor volume and infer what the nesting score and the time-to-interact for the two groups of mice. The linear regression model fits the time to interact with the cookie reasonably well, thus from this graph, we can see that at any given tumor volume the time to interact with the cookie was generally shorter in TRPV1cre::DTAfl/wt animals as compared to C57BL/6 mice. Unfortunately, the linear regression does not fit the nesting data very well and thus it is more difficult to make the comparison of tumor volume and nesting score.

The following text has been added to the results section.

Given the impact of nociceptor neuron ablation on tumor growth, we wondered whether differences in tumor volume contributed to the behavioral differences we noted. Thus, the behavior data were graphed as a function of tumor volume (Supplemental Fig 4A, B). A simple linear regression model was used to fit the data. In the case of nesting scores, the linear regression did not fit the data points very well making it difficult to assess nesting scores at a given tumor volume (Supplemental Fig 4A). However, the linear regression model fit the time to interact data better. Here, the graph suggests that tumor volume did not influence behavior as at any given tumor volume the time to interact with the cookie is generally smaller in TRPV1-Cre::Floxed-DTA animals as compared to C57BL/6 animals (Supplemental Fig 4B).

Reviewer #3:

(1) The authors mention in their Discussion the need for additional experiments. Could they also include / comment on the potential impact on the anti-tumor immune system in their model?

The following text has been added to the discussion:

Neuro-immune interactions have been studied in the context of a variety of conditions including, but not limited to infection 109, inflammation 110,111, homeostasis in the gut 112-114, as well as neurological diseases115,116. Neuro-immune communications in the context of cancer and behavior have also been studied (e.g., sickness behavior, depression) 117-119 however, these studies did not assess these interactions at the tumor bed. Investigations into neuro-immune interactions occurring within primary malignancies which harbor nerves have shed light on these critical communications. In the context of melanoma, which is innervated by sensory nerves, we identified that release of the neuropeptide calcitonin gene related peptide (CGRP) induces immune suppression. This effect is mediated by CGRP binding to its receptor, RAMP1, which is expressed on CD8+ T cells 49. A study utilizing a different syngeneic model of oral cancer similarly found an immune suppressive role for CGRP 120-122. These studies demonstrate that neuro-immune interactions occur at the tumor bed. Our current findings indicating that tumor-infiltrating nerves connect to a circuit that includes regions within the brain suggest that neuro-immune interactions within the peripheral malignancy may contribute to the behavioral alterations we studied.

(2) The authors mention the importance of inflammation contributing to pain in cancer but do not clearly highlight how this may play a role in their model. Can this be clarified?

The following text has been added to the discussion section of the manuscript.

Moreover, given that carprofen and buprenorphine decrease inflammation 104, their ability to restore normal nesting and cookie test behaviors (which require the use of the oral cavity where the tumor is located) suggests that inflammation at the tumor site contributed to the decline in these behaviors in vehicle-treated animals. Since both drugs were given systemically and each only partially restored wheel running, it suggests that systemic inflammation alone cannot fully account for the decline in wheel running seen in vehicle-treated animals. We posit that the inflammation- and pain-independent component of this behavioral decline is mediated via the transcriptional and functional alterations in the cancer-brain circuit.

(3) The tumor model apparently requires isoflurane injection prior to tumor growth measurements. This is different from most other transplantable types of tumors used in the literature. Was this treatment also given to control (i.e., non-tumor) mice at the same time points? If not, can the authors comment on the impact of isoflurane (if any) in their model?

Mice in all groups (tumor and non-tumor) were treated with isoflurane. This important detail has been added to the methods section.

(4) The authors emphasize in several places that this is a male mouse model. They mention this as a limitation in the Discussion. Was there an original reason why they only tested male mice?

The following text has been added in the discussion section:

Head and neck cancer is predominantly a cancer in males; it occurs in males three times more often than in females 123, this disparity increases in certain parts of the world. While smoking cigarettes and drinking alcohol are risk factors for HPV negative head and neck squamous cell carcinoma, even males that do not smoke and drink are have a higher susceptibility for this cancer than females 124,125. Thus, our studies used only male mice. However, we do recognize that females also get this cancer. In fact, female patients with head and neck cancer, particularly oral cancer, report more pain than their male counterparts 126,127. These findings suggest that differences in tumor innervation exist in males and females.

Therefore, another project in the lab has been to compare disease characteristics (including innervation and behavior) in male and female mice. The findings from this second study are the topic of a separate manuscript.

Recommendations For The Authors:

Reviewing editor:

(1) Tumors can communicate with the brain via blood-borne agents from the tumor itself or immune cells that are activated by the tumor in addition to neurons that invade the tumor. The xia and malaise that accompanies some tumors can be mediated by direct innervation and/or the humoral factors because both can activate the same parabrachial pathway. This paper makes the case for the direct innervation being important but ignores the possibility of both being involved. The interesting observation that innervation supports tumor growth (perhaps via substance P) is troublesome because the slower appearance of behavioral consequences (Figures 4 & 5) could be attributed to the smaller tumor size. A nice control for humoral effects would be to implant the tumor cells someplace in the body where innervation does not occur (if possible) and then examine behavioral outcomes.

In the course of several projects, we have implanted different tumor cell lines in different locations in mice (oral cavity, hind limb, flank, peritoneal cavity). In each location, tumor innervation occurs. This is not a phenomenon found only in mice as we completed an immunohistological survey of human cancers from different sites and found they are all innervated (PMID 34944001). These data are consistent with tumor and locally-released factors that recruit nerves to the tumor bed (PMID: 30327461)(PMID: 32051587)(PMID: 27989802). Thus, an implantation site that does not result in tumor innervation is currently unknown and likely does not exist.

(2) The authors should address whether there is an inflammatory component in this tumor model.

MOC2-7 tumors have been characterized as non-inflamed and poorly immunogenic 129-131.

This information has been added to the methods section.

(3) The RTX experiment in Figure 5 would be more compelling if the drug was injected directly into the tumor rather than injecting it in the flank, thus ablating all TRPV1-exressing neurons as in the genetic approach.

While we agree with the reviewer that ablating the TRPV1-expressing neurons at the tumor site directly would be ideal, RTX treatment takes approximately one week for ablation to occur but a significant amount of inflammation is associated with this. Therefore, we wait a total of 4 weeks for the inflammation to resolve. By this time, tumors have generally reached sacrifice criteria. Thus, this approach would not enable the question to be answered Moreover, we are not aware of any studies in which RTX has been injected in the oral cavity or face. While RTX is utilized clinically to treat pain, it is typically administered intrathecally, epidurally or intra-ganglionically (PMID: 37894723).

(4) The authors address affective aspects of pain but do not adequately address the sensory aspects, e.g., sensitivity to touch, heat and/or cold. They attribute the decrease in food disappearance (consumption) and nest building to oral pain, but it could be due to anhedonia and anorexia that can accompany tumor progression.

Assaying for touch and heat/cold sensitivity in the oral cavity is a critical aspect of studying head and neck cancer that needs to be addressed. However, in rodents these assays are not trivial given that any touch/heat/cold in the area of the tumor (oral cavity) impacts the sensitive whiskers in that region which directly influence these assays. Thus, we have been refining assays (e.g., OPAD, facial von Frey) to address these important questions. The findings from these studies are beyond the scope of this manuscript.

The reviewer makes a good point about anhedonia and anorexia. The following text has been added to the results section:

Pain-induced anhedonia is mediated by changes in the reward pathway. Specifically, in the context of pain, dopaminergic neurons in the ventral tegmental area (VTA) become less responsive to pain and release less serotonin.  This decreased serotonin results in disinhibition of GABA release; the resulting increased GABA promotes an increased inhibitory drive leading to anhedonia  82 and, when extreme, anorexia. Carprofen and buprenorphine treatments completely reversed nesting behavior and significantly improved eating. Inflammation 83 and opioids 84 directly influence reward processing and though our tracing studies did not indicate that the tumor-brain circuit includes the VTA, this brain region may be indirectly impacted by tumor-induced pain in the oral cavity. Thus, an alternative interpretation of the data is that the effects of carprofen and buprenorphine treatments on nesting and food consumption may be due to inhibition of anhedonia (and anorexia) rather than, or in addition to, relieving oral pain.

(5) Comment on why only males were used in this study.

Please see response to public reviews.

Reviewer #1:

(1) Please provide a justification for the use of exclusively male mice and expand in the discussion if there is potential for these findings to be directly applicable to female mice as well.

Please see response to public reviews.

The following text has been added to the discussion:

Head and neck cancer is predominantly a cancer in males; it occurs in males three times more often than in females 123, this disparity increases in certain parts of the world. While smoking cigarettes and drinking alcohol are risk factors for HPV negative head and neck squamous cell carcinoma, even males that do not smoke and drink are have a higher susceptibility for this cancer than females 124,125. Thus, our studies used only male mice. However, we do recognize that females also get this cancer. In fact, female patients with head and neck cancer, particularly oral cancer, report more pain than their male counterparts 126,127. These findings suggest that differences in tumor innervation exist in males and females.

(2) When discussing the results shown in Figure 2, please include some mention of Fus, since it was the highest expressed transcript.

The following text has been added to the results section regarding Fus.

The gene demonstrating the highest increase in expression, Fus, was of particular interest; it increases in expression within DRG neurons following nerve injury and contributes to injury-induced pain 51,52. Of note, we purposefully used whole trigeminal ganglia rather than FACS-sorted tracer-positive dissociated neurons to avoid artificially imposing injury and altering the transcript levels of these cells 53,54. Thus, significantly elevated expression of Fus by ipsilateral TGM neurons from tumor-bearing animals suggests the presence of neuronal injury induced by the malignancy. This is consistent with our previous findings 55 and those of others 56 showing that tumor-infiltrating nerves harbor higher expression of nerve-injury transcripts and neuronal sensitization.

(3) In line 197 please clarify the mice used. Were all mice tumor-bearing and some had nociceptors ablated, or was there a control (no tumor) group as well?

Line 197 refers to Figure 4D. In this figure, panels B-D show quantification of cFos and DFosB in the spinal nucleus of the TGM (SpVc), The parabrachial nucleus (PBN) and the Central nucleus of the amygdala (CeA). These data are from C57BL/6 and TRPV1cre::DTAfl/wt animals all of whom had tumor. Supplementary Figure 3C also show quantification of cFos and DFosB but these are from control, non-tumor bearing animals. The fact that controls are non-tumor-bearing has been added to the supplemental figure legend and the text of the results section has been clarified as follows.

While Fos expression was similar between non-tumor bearing mice of the two genotypes (Supplemental Fig. 3C-E), the absence of nociceptor neurons in tumor-bearing animals decreases cFos and DFosB in the PBN, and DFosB in the SpVc (Fig. 4B, C).

(4) Overall it would improve the readability of the figures if the colors for the IHC channels were on the image itself and not exclusively in the figure legend.

The colors for all the staining have been added to each panel.

(5) It is not a problem that complete cartography was not done, but please include a justification for why the brain regions that were focused on were chosen.

In order to ensure that our neural tracing technique captured only nerves present within the tumor bed, we restricted the injection of tracer to only 2 µl. We demonstrated that this small volume did not leak out of the tumor (Figure 1) and thus any tracer labeled neurons we identified were deemed as being connected in a circuit to nerves in the tumor bed. While we acknowledged that this calculated technical approach restricted our ability to tracer label all neurons in the tumor bed (as well as those they share circuitry with), it ensured no tracer leakage and inadvertent labeling of non-tumoral nerves. In non-tumor animals injected with 10 µl of tracer, labeled regions in the brain included the spinal nucleus of the trigeminal, the parabrachial nucleus, the central amygdala, the facial nucleus and the motor nucleus of the trigeminal. The regions that were tracer positive when tumor was injected were limited to the spinal nucleus of the trigeminal, the parabrachial nucleus and the central amygdala. Thus, the regions in the brain that we focused on were the areas that became tracer-positive following injection of tracer into the tumor.

(6) Were the cells that were injected cultured in media with 10% fetal calf serum? If so was any inflammatory response seen? If not please state in the methods section the media that cells for injection were cultured in.

The cells injected into animals were cultured in media containing 10% fetal calf serum. When cells are harvested for tumor injections, they are first washed two times with PBS and then trypsinized to detach the cells from the plate. Cells are collected, washed again with PBS and resuspended with DMEM without serum; this is what is injected into animals. We harvest cells in this way in order to eliminate any serum being injected into mice. This information has been added to the Methods section.

(7) Would any of the differences in drug treatment (Carprofen vs Buprenorphine) be due to the differing routes of administration and metabolism of the drugs?

Since carprofen and buprenorphine each resulted in similar behavioral impacts (nesting and wheel running), their different routes of administration seem to play a minor or no role in the behaviors assessed.

(8) Please include in the methods section the specific approach and software that was used for processing calcium imaging data and calculating a relative change in fluorescence.

The specific approach used for processing calcium imaging data and calculating relative change in fluorescence as well as the software used are all included in the methods section. Please see below:

Ca2+ imaging. TGM neurons from non-tumor and tumor-bearing animals (n=4-6 mice/condition) were imaged on the same day. Neurons were incubated with the calcium indicator, Fluo-4AM, at 37°C for 20 min. After dye loading, the cells were washed, and Live Cell Imaging Solution (Thermo-Fisher) with 20 mM glucose was added. Calcium imaging was conducted at room temperature. Changes in intracellular Ca2+ were measured using a Nikon scanning confocal microscope with a 10x objective. Fluo-4AM was excited at 488 nm using an argon laser with intensity attenuated to 1%. The fluorescence images were acquired in the confocal frame (1024 × 1024 pixels) scan mode. After 1 min of baseline measure, capsaicin (300nM final concentration) was added. Ca2+ images were recorded before, during and after capsaicin application. Image acquisition and analysis were achieved using NIS-Elements imaging software. Fluo-4AM responses were standardized and shown as percent change from the initial frame. Data are presented as the relative change in fluorescence (DF/F0), where F0 is the basal fluorescence and DF=F-F0 with F being the measured intensity recorded during the experiment. Calcium responses were analyzed only for neurons responding to ionomycin (10 µM, positive control) to ensure neuronal health. Treatment with the cell permeable Ca2+ chelator, BAPTA (200 µM), served as a negative control.

(9) Suggestions for Figure 1:

- In Figures 1C, D, E, include labels for the days of tumor harvest.

- Please make the size of the labels the same for 1K an 1L and align them.

- Microscopy image in Figure 1L for SpVc looks like it may be at a different magnification.

- If possible, include (either in the figure or the supplement) IHC images staining for Dcx and tau, which would complement the western blot data.

The requested changes to the figures have been made. Unfortunately, we do not have Dcx and tau IHC staining of the day 4, 10 and 20 tumors.

(10) Suggestions for Figure 2:

- Include directly onto the graph in Figure 2a the legend for tumor-bearing (red) and non-tumor bearing (blue).

- Keep consistent between Figure 2G and 2H/I if the tumor/nontumor will be labeled as T/N or Tumor/Control.

The requested changes to the figures have been made.

(11) Suggestions for Figure 3:

- An example trace of calcium signal would complement Figure 3G, H well.

Example tracings of calcium signal are already provided in Supplementary Figure 3A and B.

Reviewer #2:

(1) While the use of male mice is acknowledged, there is not a rationale for why female mice were not included in the study.

Please see the response to Reviewer #1 (first question).

(2) Criteria for euthanasia should be described in the Methods. This is especially needed for interpreting the survival curve in Figure 4H.

Criteria for euthanasia in our IACUC approved protocol include:

- maximum tumor volume of 1000mm3

- edema

- extended period of weight loss progressing to emaciation

- impaired mobility or lesions interfering with eating, drinking or ambulation

- rapid weight loss (>20% in 1 week)

- weight loss at or more than 20% of baseline

In addition to tumor size and weight loss, we use the body condition score to evaluate the state of animals and to determine euthanasia.  These details have been added to the Methods section.

(3) At what stage in cancer progression were the Fos studies conducted for Figure 4A-D?

The brains used for Fos staining (Fig 4B-D) were harvested at week 5 post-tumor implantation.

(4) For Fos counts, what are the bregma coordinates for the sections that were quantified?

SpVc:  -7.56 to -8.24mm

PBN:  -4.96 to -5.52mm

CeA:  -0.82mm to -1.94mm

(5) Statistics are needed for the claim in Lines 171-173.

The statistical analysis of Fos staining from tumor-bearing and non-tumor bearing brains are included in Figure 3D-F. The statistical analysis of ex vivo Ca+2 imaging of brains from tumor-bearing and non-tumor bearing animals are included in Figure 3 I and J.

(6) How long was the baseline period for weight and food intake measurements? How long were the animals single-housed before taking the baseline measurements?  

Baseline weight and food intake measurements were 2 weeks and animals were singly housed before baseline measurements for 2 weeks (a total of 4 weeks).

Minor:

(7) The authors might consider rewording the sentence on lines 59-62, given that it is abundantly clear from rodent studies that both the tumor and chemotherapy are associated with adverse behavioral outcomes.

We have reworded the sentence as follows:  The association of cancer with impaired mental health is directly mediated by the disease, its treatment or both; these findings suggest that the development of a tumor alters brain functions.

(8) Line 212 needs a space between the two sentences.

This has been fixed.

(9) Font size in Figure 2 is not consistent with the other figures.

This has been fixed.

(10) "DAPI" is the more conventional than "DaPi".

This has been fixed.

Editorial Comments and Suggestions:

(1) The Abstract would be better if it were more concise, e.g. ~175 words.

The abstract has been shortened as requested and now reads:

Cancer patients often experience changes in mental health, prompting an exploration into whether nerves infiltrating tumors contribute to these alterations by impacting brain functions. Using a mouse model for head and neck cancer and neuronal tracing we show that tumor-infiltrating nerves connect to distinct brain areas. The activation of this neuronal circuitry altered behaviors (decreased nest-building, increased latency to eat a cookie, and reduced wheel running). Tumor-infiltrating nociceptor neurons exhibited heightened calcium activity and brain regions receiving these neural projections showed elevated cFos and delta FosB as well as increased calcium responses compared to non-tumor-bearing counterparts. The genetic elimination of nociceptor neurons decreased brain Fos expression and mitigated the behavioral alterations induced by the presence of the tumor. While analgesic treatment restored nesting and cookie test behaviors, it did not fully restore voluntary wheel running indicating that pain is not the exclusive driver of such behavioral shifts. Unraveling the interaction between the tumor, infiltrating nerves, and the brain is pivotal to developing targeted interventions to alleviate the mental health burdens associated with cancer.

(2) Lines 28, 104, 258, 486, 521, and many other places, "utilized" should be "used" because the former refers to an application for which it is not intended, e.g. a hammer was utilized as a doorstop.

The requested changes have been made.

(3) Lines 32 and 73, it is not clear whether the basal activity is heightened or whether excitability is increased. "manifest" might be better than "harbor" on line 73.

We have changed the wording in the abstract to be clearer. Moreover, our finding that TGM neurons from tumor-bearing animals have increased expression of the s1-Receptor and phosphorylated TRPV1 (Fig 2G-I) indicate that these neurons have increased excitability.

(4) Line 34 and elsewhere, it would be better to refer to Fos because the is no need to distinguish cellular, cFos, from viral, vFos, in this context.

The requested changes have been made.

(5) Line 38, It would be better to refer to what was actually measured rather than "oral movements".

The requested changes have been made. The sentence now reads: “While analgesic treatment restored nesting and cookie test behaviors, it did not fully restore voluntary wheel running.”

(6) Line 84, CXCR3-null mouse on a C57BL/6 background.

The requested change has been made.

(7) Lines 86,129 wild-type, male mice.

The requested change has been made.

(8) Lines114-115, the brackets are not necessary.

The requested change has been made.

(9) Lines 118, 384, 409, 527, 589, 971, 974 always leave a space between numbers and units. Use Greek u for micro.

The requested change has been made.

(10) Lines 123-124, it is not clear that there is meaningful labeling within the CeA.

We have replaced this image with a more representative one of the CeA from a tumor-bearing animal with clear tracer labeling.

(11) Lines 125, 138, and 246 transcription was not measured, only transcript levels were measured.

The requested changes have been made.

(12) Line 133, I think >4 fold is meant.

Thank you for catching that. I have fixed it to >4 fold.

(13) Line 165, single-time-point assessment (add hyphens).

The requested change has been made.

(14) Line 181 and elsewhere including figure, the superscripts refer to alleles of the genes; hence approved gene names should be used in italics (as in Methods), TRPV1-Cre:: Floxed-DTA (without italics) would be acceptable.

The requested changes have been made.

(15) Line 182, nociceptor-neuron-ablated mice (add hyphens).

The requested changes have been made.

(16) Line 197, It is not clear that the "speed" of food disappearance was measured or that it is due to oral pain vs loss of appetite.

The reviewer makes a good point. We have changed the sentence to read:

To evaluate the effects of this disruption on cancer-induced behavioral changes, we assessed the animals’ general well-being through nesting behavior 32 and anhedonia using the cookie test 76,77, as well as  body weight and food disappearance as surrogates for oral pain and/or loss of appetite.

(17) Line 199, The reduced tumor growth after ablation could account for most of the changes in the other parameters that were measured.

We have graphed the nesting scores and time-to-interact with the cookie as a function of tumor volume.  These data are now included as Supplemental Figure 4 and suggest that at the same tumor volume, nesting scores and times-to-interact with the cookie are different between the groups.

(18) Line 204 TPVP1 spelling. Is the TGN smaller after ablation of half of the neurons?

The requested change has been made.

(19) Line 235, "now" is not necessary.

The requested change has been made.

(20) Line 238-239 and elsewhere, a few references for to why the TGN-SpVc-PBN-CeA circuit is relevant would be helpful.

The following references have been added regarding the relevance of this circuit to behavior:

Molecular Brain 14: 94 (2021) (PMID 34167570)

Neuropharmacology 198: 108757 (2021) (PMID 34461068)

Frontiers in Cellular Neuroscience 16: 997360 (2022)  (PMID 36385947)

Neuropsychopharmacology  49(3): 508-520 (2024) (PMID 37542159)

(21) Lines 371, 434 and Figures, gm should be g or grams in scientific usage. Include JAX lab stock numbers for these mouse lines.

The requested changes have been made.

(22) Line 432, removing food for one hour is not a fast.

The sentence has been reworded as follows: One hour prior to testing, mouse food is removed and the animals are acclimated to the brightly lit testing room.

(23) Line 476, 5-um sections (add hyphen).

The hyphen has been added.

(24) Lines 988, and 1023, DAPI are usually shown this way.

The requested change has been made.

(25) Figure 1K, add Bregma levels to figures.

SpVc: -8.12 mm

PBN: -5.34 mm

CeA: -1.34 mm

(26) Figure 3 line 1033, "area under the curve" What curve was examined?

The curve examined was the change in fluorescence over time. This curve has been added as Supplemental Figure 3C.

(27) Figure 3B, the circled area is the lateral PBN. At first glance, I thought scp was meant as the label for the circled area.

Scp is noted in the figure legend as a landmark.

Author response:

Data replicability

There are no replicates contained in the manuscript. (Reviewer #1)

We respectfully disagree with this statement. In this manuscript, we included both cell and animal replicates. For cell replicates, we analyzed over 50.000 cells using RNAscope and over 10.000 cells using RNAseq, employing two independent methods on different animals. We believe this extensive analysis is sufficient by any standards. Regarding animal replicates, we generated four different transgenic lines (two knockin lines and two BAC transgenic lines), which is an uncommon and rigorous effort. We analyzed dozens of animals, consistently observing the expression pattern of Smim32 and its derived transgenes across multiple experiments, including crosses between transgenics and various reporter lines, which is again an uncommon and rigorous effort. These experiments were conducted on animals from different litters to ensure robustness. Additionally, our longitudinal study, which includes 13 animals harvested at two-day intervals from E16 to P20, provides further consistency of our data. 

However, to underscore the consistency of endogenous Smim32 expression, when submitting a revised manuscript, we will present Smim32 expression levels across individuals in single-cell RNA-seq data. Furthermore, we will pool data from different transgenic animals to demonstrate interindividual variability in the claustrum of adult animals. 

Additional examples of female mice should also be included and separately quantified. (Reviewer #1)

We initially analyzed both males and females for one line (the Smim32-Cre knock-in line). Since we observed no differences between males and females (which we will note in the revised manuscript), we subsequently limited our analyses to males to minimize the use of animals. 

Claustrum definition

Weaknesses lie in poor anatomical definitions of the claustrum (and endopiriform nucleus). (Reviewer #2)

No other orthogonal approaches were used to define the claustrum, such as retrograde neuroanatomical tracing from cortex. (Reviewer #3)

We share the reviewers’ opinion that the claustrum (CLA) and endopiriform nucleus (EN) are poorly defined anatomically in rodent brains due to the limited development of white matter tracts. This ambiguity has led to many conflicting descriptions of CLA/EN boundaries in various papers and atlases, including those by Paxinos and the Allen Brain Institute. Notably, the Allen Institute frequently updates the shape and anatomical location of the CLA/EN in their reference atlas, resulting in different websites displaying various versions (as illustrated in rebuttal figure 1 at comparable levels of the anteroposterior brain axis). It remains uncertain which version would most effectively satisfy the entire scientific community, if any. Indeed, after many years of working on these structures and surveying the literature, we regret to note that there is currently no consensus on the anatomical definition of the CLA and EN, even among expert laboratories using tracing or staining methods. At one end of the spectrum, some authors define the CLA as a small nucleus that could be, for example, characterized by the PVrich plexus. At the other end, other authors consider it part of a larger complex that includes the EN and extends dorsally to the S2 cortex. Additionally, differing definitions of the core and shell regions, as well as the precise anteroposterior extent of the nucleus, further complicate the issue.

Author response image 1.

Comparison of CLA and EN shapes in two recent versions of the Allen brain atlas

Given this lack of consensus, we deliberately opted for a molecular definition of the claustrum and its projection neurons. We used a set of well-documented canonical markers for the claustrum and neighboring neurons to determine the expression pattern of Smim32. The claustrum-specific markers we selected (Nr4a2, Lxn, Gnb4, Car3, etc.) have been extensively studied and allow us to distinguish claustrum projection neurons from neighboring and intermingled populations. Although none of these individual markers are exclusively specific to CLA and EN neurons, the combined expression of these markers provides greater confidence in identifying the different neuronal populations in space.

Smim32 expression is used to define claustrum anatomical boundaries, rather than first using several structural, molecular, and connectivity lines of evidence to define the claustrum anatomically and then to assess whether Smim32 expression fits within this anatomical definition. (Reviewer #2)

Contrary to the reviewer's suggestion, we do not define the claustrum based on Smim32 expression. Instead, Figures 1 and 2 demonstrate that Smim32 expression is highly correlated with the expression of known claustrum markers (Nr4a2, Lxn, Gnb4, Car3, etc.), both regionally and at the cellular level. As suggested by Peng et al. (2021, Fig. 4 and Extended Data Fig. 11), this population of cells, which includes the claustrum, a specific subset of cells in cortical layer 6, and the dorsal endopiriform nucleus, forms a discrete group of neurons sharing the same transcriptomic identity. Given what is known about the connectivity of claustrum and endopiriform nucleus projection neurons, this population obviously includes neurons projecting to various areas, likely fulfilling distinct functions. Whether these cells should be subdivided based on projection area, developmental origin, or structural features is beyond the scope of this article.

Specificity issues

Cre/Flp expression driven by the Smim32 promoter is present in non-claustrum regions, including the neighboring cortex, striatum, and endopiriform nucleus as well as the more distant thalamic reticular nucleus. (Reviewer #2)

The Smim32 gene is not specific to the claustrum. (Reviewer #3)

We do not claim that endogenous Smim32 expression is exclusive to the claustrum or that the knock-in lines, by themselves, are sufficient to isolate claustrum neurons without combined approaches based on the transgenic lines presented here. However, there are significant differences in the expression pattern between endogenous Smim32 and the expression of Cre in the various derived transgenic lines, which might not have been clear in the current manuscript. Notably, there is no expression of Cre in the striatum and the thalamic reticular nucleus, and only sparse expression in the endopiriform nucleus in Tg61(Smim32-cre). Each transgenic line provides different levels of overlap with the endogenous Smim32 expression, with the Tg61(Smim32-cre)  line allowing for the most specific genetic access to claustrum neurons. Again, for greater specificity, any of these lines could be used in combined approaches, such as viral targeting (as shown in Figure 6A and B) or using transgenic intersectional (dual recombinase) approaches based on Cre- and Flp-expressing mice with an overlap in the claustrum, leading to circuit-specific and/or claustrum-only labeling.

This means that our claims are supported by the observed data. However, we acknowledge that we may not have clearly explained the specificity of the random transgenes, which could have led some reviewers to believe that « the data do not support the claims ».

We will clarify these points in the revised manuscript and include additional examples and quantifications to highlight the differences between endogenous Smim32 expression and Cre expression in the transgenic Tg61(Smim32-cre)  line.

Regarding Cre-expressing cells in the neighboring cortex (layer 6 projection neurons), these cells are genetically distinct from other layer 6 cortical neurons and express the same canonical markers as claustrum projection neurons, likely sharing also the same transcriptomic identity. We will provide a more detailed characterization of these cells in the revised manuscript.

Since Smim32 driven recombinase (in 61 or 62lrod) is not exclusively expressed in the claustrum, it is not clear how Smim32 is an advantage over possible Nr4a2 or, the more selective, GNB4 Cre driver lines. (Reviewer #2)

Over the years, we have found a limited number of Cre lines used in the literature for targeting claustrum neurons. These include Gnb4-cre, Slc17a6-cre (also known as Vglut2-cre), Egr2-cre, Tg(Tbx21-cre), Ntng2-cre, Cux2-cre and Esr2-cre lines. We have not found any study describing and/or using an Nr4a2-cre line. Although a Nr4a2-Dre line exists (that we have studied in our laboratories), caution is warranted in its use, as it lacks the complete coding sequence of the Nr4a2 gene.

One problem with Nr4a2 is its documented expression in the adjacent Layer 6b cortical neurons, which discards it as a suitable candidate to selectively target the claustrum. Furthermore, Nr4a2 is also expressed in a majority of the endopiriform nucleus neurons, whereas endogenous Smim32 is expressed in a smaller proportion of these cells, and is restricted mainly to the dorsal endopiriform nucleus. These reasons led us to select Smim32 over Nr4a2.

Author response image 2.

(A) In situ hybridization for various CLA/EN marker genes. (B) Developmental recombination observed outside the CLA/EN in various cre lines (all data from the Allen brain databases)

What are the advantages of using the different Smim32-cre lines over the existing Cre lines mentioned above?

Let’s first consider the Gnb4-cre line, which is considered one of the best available. Although the endogenous Gnb4 gene appears to have a similar expression pattern to Nr4a2, Slc17a6, and Smim32 in the striato-claustro-insular region of adult mice (Rebuttal Figure 2A), the results observed with the Gnb4-cre line either shows otherwise, or indicate that the Cre line does not fully recapitulate Gnb4 endogenous expression (Rebuttal Figure 3). Indeed some neurons in the insular cortex, piriform cortex, and putamen express the Cre recombinase (possibly due to low Gnb4 expression not detected in the in situ hybridization data of the Allen brain institute or due to nonspecific transgene expression) and will recombine viral vectors injected in adult mice (Rebuttal Figure 3). Therefore, this Cre expression outside the CLA/EN neurons in the Gnb4-cre line presents complications for data interpretation, depending on the viral injection coordinates and the quantity of injected vectors. 

Author response image 3.

Specificity of the Gnb4-Cre line tested with viral transduction in adult mice (all data from the Allen Brain Institute database). The top and middle rows display the same data but with different scaling of the lookup tables to highlight either the patterns of axonal projections (top) or the infected neurons themselves (middle). The bottom row shows a higher magnification of the infection site. Note that individual neurons cannot be resolved in experiment 485903475 due to signal saturation.  

Cre expression in the CLA appears more specific in the various Smim32-cre transgenic lines than in many of the lines mentioned above. Although we have no doubt that the different existing transgenic lines can target CLA neurons, the selectivity of the targeting (for example, the fraction and types of CLA neurons versus potential non-CLA neurons) remains to be fully described for most of the lines. It is particularly true in the case of Tbx21 and Esr2 (used as drivers for the Tg(Tbx21-cre) and Esr2-cre transgenic lines). Tbx21 is not endogenously expressed in adult CLA neurons (evaluated by in situ and RNAseq data) and Egr2, if expressed in the claustrum, is not restricted to CLA neurons as it is an immediate early gene expressed in recently active neurons (Rebuttal Figure 2A). 

Cre expression in the EN is observed in all Cre-expressing transgenic lines used to target the claustrum (with the exception of Slc17a6-cre). This can naturally be problematic for some approaches. Luckily, the random integrant Tg61(Smim32-cre) we describe in our manuscript shows a strong expression in the claustrum, and very limited expression outside the CLA (a very weak activity in the EN), representing a novel tool with improved claustrum selectivity. An advantage of the Tg61(Smim32-cre) over the Slc17a6-cre is that more CLA neurons can be targeted with the Tg61(Smim32-cre) line. 

Another advantage of our four transgenic lines is their versatility; they can be used to recombine reporter lines as well as FRT-floxed and loxP-floxed knockouts in limited neuronal populations. They will be employed in the future for intersectional genetics to exclusively target CLA neurons. Existing transgenic lines cannot offer these possibilities because their marker genes are broadly expressed in the brain during embryogenesis, leading to the impact on a large number of non-CLA/EN neurons. This is evident in the Gnb4-cre and Slc17a6-cre lines crossed with the Ai14 reporter line expressing the fluorescent protein tomato (Rebuttal Figure 2B, right panels). Similar observations have been made for the Ntng2-Cre and Cux2-cre lines (see the Allen Brain Institute database for these data). Alternatively, inducible recombinase systems, such as the Gnb4-IRES2CreERT2-D line, could be used. However, the Gnb4-IRES2-CreERT2-D line requires tamoxifen to induce Cre recombination, which can be problematic depending on the research context, as well as recombinations in the absence of tamoxifen treatment (see experiments 560948627 and 560948194 in the Allen Brain database).

It is unclear how Smim32 relates to claustrum in other mammalian species (e.g. primates) (Reviewer #3)

As mentioned in the last paragraph of the introduction of the initial manuscript, Smim32 is specifically expressed in the claustrum of a primate species, Homo sapiens (reference 37 of the initially submitted manuscript).

Availability of the transgenic mice

These mice should be made available to the community through commercial vendors. (Reviewer #1 and #2 in private comments)

We are pleased to see that two of the three reviewers would like to see these mice available. These mice will not be kept for ourselves, and we will distribute them at some point in time, but this will naturally occur after the publication of the revised manuscript.

Critical comments on discussion and other topics

A clear description of the search in the Allen Mouse Brain Atlas is missing. A search for Smim32 in the ISH mouse atlas did not provide any hits and so it would be useful to include in the methods or results section the exact query used for examination of Smim32 expression as well as other genes identified in this process. (Reviewer #2)

Smim32 has been referred to by different names in various versions of the mouse genome. For the readers not versed in navigating genomes and annotations, before being officially named Smim32, this gene was originally called Gm6753 (as noted in the Allen Brain Institute database, see Rebuttal Figure 2A for an example of their in situ data) and later Gm45623.

Several sentences highlighting the shortfalls of other approaches are overstated and should be toned down. (Reviewer #1)

Very concerning is problematic language in the abstract and introduction sections that diminish the impact of several published studies (not cited) that have led to important findings regarding claustrum function. The authors create an argument that all the research performed thus far on the claustrum is unreliable because targeting the structure has been sub-optimal. (Reviewer #2)

A more balanced discussion of the strengths and weaknesses of these mice should be included. (Reviewer #1)

We regret if our choice of language inadvertently appeared to undermine the contributions of our colleagues; that was certainly not our intention. The paragraph in question was meant to address certain studies that we believe have led to inconsistent findings and unreliable data due to a lack of rigorous methodology in targeting claustrum projection neurons. To avoid singling out specific works, we chose not to cite them directly. We understand that some colleagues whose research does not fall under the “various cases” mentioned may feel unfairly targeted by this statement. We will revise this section to better clarify our intent and ensure it is respectful of all contributions. We will rephrase passages in the abstract, introduction, and discussion to provide a balanced view of the strengths and weaknesses of these mice.

Our main goal is to provide tools to specifically target claustrum cells based on their transcriptomic identity, which we believe is the best means to assess the function of any neuronal population. Due to the intermingling of claustrum neurons with neighboring populations, employing stereotaxic injections in the claustrum without genetic segregation will always infect and label physically adjacent cells that do not belong to the claustrum, ontologically and functionally speaking. 

Similarly, targeting claustrum neurons retrogradely by injecting into claustrum projection sites likely labels neurons from different populations. For instance, as reviewer 1 mentions Erwin et al. (2021), infecting retrosplenial projections without genetic specificity labels many claustrum Synpr+ neurons (considered the claustrum core), a small proportion of claustrum Nnat+ neurons (considered the claustrum shell by some, and non-claustrum neurons by others), and some neighboring cortical L6b neurons. These three populations have very different transcriptomic identities, connectivity patterns, and likely distinct functions.

Thus, we believe that genetic specificity provides an important added value for selectively targeting the claustrum or claustro-insular complex.

A better characterization of all data should be undertaken. (Reviewer #1)

Having generated hundreds of transgenic lines over the years, we have never performed a more thorough analysis of transgenic lines, nor have a recollection of reading a publication evaluating at such a precise level the expression pattern of transgenes in mice. We, therefore, do not see exactly what the reviewer means by this remark. It is possible, not being native English speakers, that we did not grasp a certain form of joke.

Author response:

Public Reviews:

Reviewer #1 (Public Review):

For the colony analysis, it is unclear from the methods and main text whether the initial individual sorted colonies were split and subject to different conditions to support the claim of bi-potency. The finding that 40% of colonies displayed tenogenic differentiation, may instead suggest heterogeneity of the sorted progenitor population. The methods as currently described, suggest that two different plates were subject to different induction conditions. It is therefore difficult to assess the strength of the claim of bi-potency.

Thanks for your valuable comment. We are sorry for the confusing illustration of colony assay. In fact, we first obtained CD29+/CD56+ cells by FACs. Then these freshly isolated cells were randomly seeded to 96-well plate with density of 1 cell/well. Subsequently, the single cell in each plate was cultured with growth medium to form colonies for ten days. Then myogenic induction was performed in three 96-well plates and tenogenic induction was performed in another three 96-well plates for subsequent analyses. Thus, we agree with your point that the sorted progenitor population could be heterogeneous. Almost all the cells highly expressed myogenic progenitor genes PAX7/MYOD1/MYF5 (Figure 1g) and over 95% colonies successfully differentiated into myotubes (Figure 2g). Thus, we believe these obtained CD29+/CD56+ cells were myogenic progenitor cells, while a subgroup of these cells obtained bi-potency.

This group uses the well-established CD56+/CD29+ sorting strategy to isolate muscle progenitor cells, however recent work has identified transcriptional heterogeneity within these human satellite cells (ie Barruet et al, eLife 2020). Given that they identify a tenocyte population in their human muscle biopsy in Figure 1a, it is critical to understand the heterogeneity contained within the population of human progenitors captured by the authors' FACS strategy and whether tenocytes contained within the muscle biopsy are also CD56+/CD29+.

Thanks for your constructive suggestion. We will include more samples to perform scRNA-seq and reanalyze the data.

The bulk RNA sequencing data presented in Figure 3 to contrast the expression of progenitor cells under different differentiation conditions are not sufficiently convincing. In particular, it is unclear whether more than one sample was used for the RNAseq analyses shown in Figure 3. The volcano plots have many genes aligned on distinct curves suggesting that there are few replicates or low expression. There is also a concern that the sorted cells may contain tenocytes as tendon genes SCX, MKX, and THBS4 were among the genes upregulated in the myogenic differentiation conditions (shown in Figure 3b).

Thanks for your comment. Each group consisted of three samples for RNAseq analyses. We are sorry there exist a minor analysis mistake in Figure 3b and Figure 3c, which will be reanalyzed in the revised version. As for contamination of tenocytes, almost all the obtained cells highly expressed myogenic progenitor marker PAX7/MYOD1/MYF5 (Figure 1g-h). Low expression levels of tendon markers were identified in these cells (Figure 2a-c). Furthermore, although tendon genes slightly upregulated in myogenic differentiation conditions, these markers dramatically upregulated in tenogenic differentiation conditions (Figure 2c). Thus, we believe the tenogenic differentiation ability of sorted cells were mainly ascribed to CD29+/CD56+ myogenic progenitor cells.

Reviewer #2 (Public Review):

scRNAseq assay using total mononuclear cell population did not provide meaningful insight that enriched knowledge on CD56+/CD29+ cell population. CD56+/CD29+ cells information may have been lost due to the minority identity of these cells in the total skeletal muscle mononuclear population, especially given the total cell number used for scRNAseq was very low and no information on participant number and repeat sample number used for this assay. Using this data to claim a stem cell lineage relationship for MuSCs and tenocytes may not convincing, as seeing both cell types in the total muscle mononuclear population does not establish a lineage connection between them.

Thanks for your constructive suggestion. We will include more samples to perform scRNA-seq and reanalyze the data.

The TGF-b pathway assay uses a small molecular inhibitor of TGF-b to probe Smad2/3. The assay conclusion regarding Smad2/3 pathway responsible for tenocyte differentiation may be overinterpretation without Smad2/3 specific inhibitors being applied in the experiments.

Thanks for your comment. We agree with your comment that we should revise it in the revision version.

Reviewer #3 (Public Review):

Comment: This dual differentiation capability was not observed in mouse muscle stem cells.

Thanks for your comment. We have explored the tenogenic differentiation potential of mouse MuSCs both in vivo and in vitro. However, low tenogenic differentiation ability was revealed (Figure 4), which might be due to species diversity. Maybe it is more demanding for humans to maintain the homeostasis of the locomotion system and the whole organism locomotion ability in much longer life span and bigger body size. Thus, the current study also indicated that anima studies may not clinically relevant when investigating human diseases.

Author response:

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

Public Reviews:

Reviewer #1:

Mehrdad Kashefi et al. investigated the availability of planning future reaches while simultaneously controlling the execution of the current reach. Through a series of experiments employing a novel sequential arm reaching paradigm they developed, the authors made several findings: 1) participants demonstrate the capability to plan future reaches in advance, thereby accelerating the execution of the reaching sequence, 2) planning processes for future movements are not independent one another, however, it's not a single chunk neither, 3) Interaction among these planning processes optimizes the current movement for the movement that comes after for it.

The question of this paper is very interesting, and the conclusions of this paper are well supported by data. However, certain aspects require further clarification and expansion.

We thank reviewer one for their evaluation of the work.

(1) The question of this study is whether future reach plans are available during an ongoing reach. In the abstract, the authors summarized that "participants plan at least two future reaches simultaneously with an ongoing reach and that the planning processes of the two future reaches are not independent of one another" and showed the evidence in the next sentences. However the evidence is about the relationship about ongoing reach and future plans but not about in between future plans (Line 52-55). But the last sentence (Line 55-58) mentioned about interactions between future plans only. There are some discrepancies between sentences. Could you make the abstract clear by mentioning interference between 1) ongoing movement and future plans and 2) in between future plans?

We thank Reviewer for their comment. We have separated the longer sentence in the original abstract into two shorter ones. This should clarify that the two pieces of evidence pertain to the interaction of planning processes.

(2) I understood the ongoing reach and future reaches are not independent from the results of first experiment (Figure 2). A target for the current reach is shown at Horizon 1, on the other hand, in Horizon 2, a current and a future target are shown on the screen. Inter-reach-interval was significantly reduced from H1 to H2 (Figure 2). The authors insist that "these results suggest that participants can plan two targets (I guess +1 and +2) ahead of the current reach (I guess +0)". But I think these results suggest that participants can plan a target (+1) ahead of the current reach (+0) because participants could see the current (+0) and a future target (+1) in H2. Could the authors please clarify this point?

We thank Reviewer for raising this point. Our conclusion that “participants can plan two targets ahead of the current reach” is supported by the reduction in Inter-Response Interval (IRI) observed when comparing H2 to H3 in the 75 ms Dwell time condition. Specifically, on average, participants were 16 ms faster when they could see two future targets on the screen (H3) than when they could see only one (H2). To clarify this in the paper, we have revised the wording in line 124 to explicitly state that the conclusion pertains to the 75 ms Dwell time condition. Additionally, we emphasize that the strongest evidence for planning two future targets comes from the experiment shown in Figure 3.

(3) Movement correction for jump of the +1 target takes longer time in H3 compared to H2 (Figure 4). Does this perturbation have any effect on reaching for +2 target? If the +1 jump doesn't affect reaching for +2 target, combined with the result that jump of the +2 target didn't affect the movement time of +1 target (Figure 3C), perturbation (target jump) only affects the movement directly perturbed. Is this implementation correct? If so, does these results support to decline future reaches are planned as motor chunk? I would like to know the author's thoughts about this.

In the experiment presented in Figure 4, once we jumped the +1 target, the reach to that target was changed and participants replaned a corrective movement to the new location of the +1 target. This usually was followed by a longer-than-usual pause at the new location of +1 target for resuming the sequence and finishing the trial. Consequently, in these jump trials, it was impossible to compare the +2 reach to no-jump trials, as the normal sequence of movement was disrupted, and the reach to the +2 target originated from a different starting location. Nevertheless, we addressed the possibility that the two future reaches were planned as a chunk by the analysis shown in figure 5: There we showed that a displacement of the +2 target did not influence the reach to the +1 target, indicating that the movement plans could be updated independently.

(4) Any discussion about Saccade position (Figure 7)?

We thank reviewer 1 for this important comment. The following discussion section is added for the gaze position results.

In our sequence task, participants switched their gaze location only once per reach, suggesting that information about the location of the next target is perceived parafoveally (Figure 7A). This observation aligns with previous studies (Clavagnier et al., 2007; González-Alvarez et al., 2007; Sivak and MacKenzie, 1990) that found participants keep their visual attention on the current sequence item and can perceive the location of spatial targets even when foveal vision is occluded. However, when comparing gaze locations for conditions Horizon >1, we observed that participants systematically biased their gaze location based on the sequence context. The gaze position shifted toward the next target, potentially allowing for more accurate location estimation (Figures 7C-D). Notably, changes in gaze location were observed even in Horizon 2, despite no changes in the curvature of hand movements in this horizon (Figure 6B). This suggests that information about the next target may first be available in the circuitry that controls eye movements and later in the cortical areas that control voluntary upper limb movements. Further control studies are required to investigate this hypothesis.

Reviewer #2:

Summary:

In this work, Kashefi et al. investigate the planning of sequential reaching movements and how the additional information about future reaches affects planning and execution. This study, carried out with human subjects, extends a body of research in sequential movements to ask important questions: How many future reaches can you plan in advance? And how do those future plans interact with each other?

The authors designed several experiments to address these questions, finding that information about future targets makes reaches more efficient in both timing and path curvature. Further, with some clever target jump manipulations, the authors show that plans for a distant future reach can influence plans for a near future reach, suggesting that the planning for multiple future reaches is not independent. Lastly, the authors show that information about future targets is acquired parafoveally--that is, subjects tend to fixate mainly on the target they are about to reach to, acquiring future target information by paying attention to targets outside the fixation point.

The study opens up exciting questions about how this kind of multi-target planning is implemented in the brain. As the authors note in the manuscript, previous work in monkeys showed that preparatory neural activity for a future reaching movement can occur simultaneously with a current reaching movement, but that study was limited to the monkey only knowing about two future targets. It would be quite interesting to see how neural activity partitions preparatory activity for a third future target, given that this study shows that the third target's planning may interact with the second target's planning.

Strengths:

A major strength of this study is that the experiments and analyses are designed to answer complementary questions, which together form a relatively complete picture of how subjects act on future target information. This complete description of a complex behavior will be a boon to future work in understanding the neural control of sequential, compound movements.

We thank the reviewer for their thorough reading of our work.

Weaknesses:

I found no real glaring weaknesses with the paper, though I do wish that there had been some more discussion of what happens to planning with longer dwell times in target. In the later parts of the manuscript, the authors mention that the co-articulation result (where reaches are curved to make future target acquisition more efficient) was less evident for longer dwell times, likely because for longer dwell times, the subject needs to fully stop in target before moving to the next one. This result made me wonder if the future plan interaction effect (tested with the target jumps) would have been affected by dwell time. As far as I can tell, the target jump portion only dealt with the shorter dwell times, but if the authors had longer dwell time data for these experiments, I would appreciate seeing the results and interpretations.

We thank the reviewer for raising this point. In our time (Figure 2) and curvature analysis (Figure 6), we collected data with five levels of the horizon and three levels of dwell time to explore the space of parameters and to see if there is any interaction between dwell time and the horizon of planning the future targets. Apriori, we expected that the full stop in each target imposed by the 400 ms dwell time would be long enough to remove any effect of future targets on how the current move is executed. In line with our initial hypothesis, the systematic curvature of reaches based on the future target was smaller in longer dwell times (Figure 6E). Nevertheless, we observed a significant curvature even in 400 ms dwell time. Based on this observation, we expect running the jump experiments (Figures 4 and 5) in longer dwell times will lead to the same pattern of results but with a smaller effect size since longer dwells break the interdependence of sequence elements (Kalidindi & Crevecoeur, 2023). In the end, for the jump experiments, we limited our experimental conditions to the fastest dwell time (75 ms dwell) since we were conceptually interested in situations where movements in the sequence are maximally dependent on each other.

Beyond this , the authors also mentioned in the results and discussion the idea of "neural resources" being assigned to replan movements, but it's not clear to me what this might actually mean concretely. I wonder if the authors have a toy model in mind for what this kind of resource reassignment could mean. I realize it would likely be quite speculative, but I would greatly appreciate a description or some sort of intuition if possible.

Our use of the term "neural resources" is inspired by classic psychology literature on how cognitive resources such as attention and working memory are divided between multiple sequence components. Early studies on working memory suggest that human participants can retain and manipulate a fixed number of abstract items in working memory (Miller, 1956). However, more recent literature postulates that a specific number of items does not limit working memory, rather, it is limited by a finite attentional resource that is softly allocated to task items.

Here we borrowed the same notion of soft distribution of resources for the preparation of multiple sequence items. A large portion of our observation in this paper and also previous work on sequence production can be explained by a simple model that assumes one central planning resource that is “softly” divided between sequence elements when participants see future items of the sequence (Author Response Image 1). The first sequence element receives the majority of the resources and is planned the most. The rest of the sequence receives the remaining planning resources in an exponentially decaying manner for preparation of the movement during the execution of the ongoing movement. Once the ongoing movement is over, the resource is then transferred to the next sequence item and this process is repeated until the sequence is over. Assignment of planning resources to future items explains why participants are faster when seeing future items (Figure 2). But this comes with a cost – if the ongoing movement is perturbed, the replanning process is delayed since some of the resources are occupied by future planning (Figure 4). This naturally leads to the question of how this resource allocation is implemented in neural tissue. To address this, we are conducting the same sequence task with the horizon in non-human primates (NHPs), and the investigation of these neural implementation questions will be the focus of future studies.

Author response image 1.

Basic diagram showing a soft distribution of a limited planning resource. The diagram shows a Horizon 3 condition in which two future reaches (+1 and +2) are planned while executing a movement (+0). The majority of resources is assigned to the execution of the ongoing movement while the reset is distributed for planning future movements. Once the movement is over, the chain of preparation and execution moves forward.

Recommendations for the author:

Reviewer #1

We thank reviewer one for these comments regarding the clarity and consistency of figures and terminology.

(1) Figure 3. Are "+1 Move" in Fig. 3B and "+ 1 Movement" in Fig. 3C as same as "E + 1" in Fig. 3A? Also does "Dwell" in Fig. 3B mean same as "+1 Dwell" in Fig. 3C? Consistent terminology would help readers to understand the figure.

“+1 Move” in Figure 3B is the same as +1 movement in Figure 3C. “Dwell” in Figure 3B is the same as +1 Dwell in Figure 3C. We changed the figure for more consistency.

(2) Figure 3. A type in the second last line in the legend, "pre-jump target for no-jump and jump and condition". The second "and" isn't necessary.

The typo is corrected. Thank you.

(3) Figure 4C. Is "Movement time" equivalent with "E + 1"?

“Movement time” is equivalent to E+1 only in no-jump conditions. When the jump occurs,

Movement time contains all the

(4) Figure 6B. Is the gray circle in between the graph and target positions there by mistake?

We fixed this typo. Thank you.

(5) Figure 6E. It's hard to distinguish H2-H5 from the color differences.

We changed the H5 to full white with a black stroke to improve the contrast. Thank you.

(6) Figure 7A. Blue dots are almost invisible.

We added a black stroke to blue circles for more visibility. Thank you.

Reviewer #2

I found this manuscript to be engaging and well written--many of the questions I had while reading were answered promptly in the next section. As such, my comments are mostly minor and primarily geared towards improving clarity in the manuscript.

(1) One major recurring confusion I had while reading the manuscript was how to think about H1, H2, and H3. It was clearly explained in the text, and the explanations of the results were generally clear once I read through it all, but I found it strangely confusing at times when trying to interpret the figures for myself (e.g., in H2, 2 targets are on screen, but the second target can only be planned during the reach toward the first target). This confusion may just be me reading the manuscript over two days, but I wonder if it could be made clearer with some semantic iconography associated with each horizon added to the later figures alongside the H labels. As one option, perhaps the planning timeline part of Fig 1D could be simplified and shrunk down to make an icon for each horizon that clearly shows when planning overlaps for each horizon.

(Please see the response to point #2 below)

(2) Regarding Fig 1D: I like this figure, but it's unclear to me how the exact preparation and execution times are determined. Is this more of a general schematic of overlaps, or is there specific information about timing in here?

We thank reviewer 2 for their important feedback. The role of Figure 1D was to summarize the timing of the experiments for different horizons. That is, to clarify the relative timing of the targets appearing on the screen (shown with a small circle above the horizontal line) and targets being captured by participants (the ticks and their associated number on the line). Execution is shown as the time interval that the hand is moving between the targets and planning is the potential planning time for participants from the target appearing on the screen until initiation of the reach to that target. We added the relevant parts of Figure 1D to the subplots for each subsequent experiment, to summarize the timing of other experiments and their analyses. For the experiments with target jump, a small vertical arrow shows the time of the target jump relative to other events.

However, this figure will be less useful, if the connection between the timing dots and ticks is not communicated. We agree that in the original manuscript, this important figure was only briefly explained in the caption of Figure 1. We expanded the explanation in the caption of Figure 1 and referenced the dots and ticks in the main text.

(3) Fig 6B - for some reason I got confused here: I thought the central target in this figure was the start target, and it took me embarrassingly long to figure out that the green target was the start target. This is likely because I'm used to seeing center-out behavioral figures. Incidentally, I wasn't confused by 7c (in fact, seeing 7c is what made me understand 6b), so maybe the solution is to clearly mark a directionality to the reach trajectories, or to point an arrow at the green target like in previous figures. Also, the bottom left gray target in the figure blends into the graph on the left--I didn't notice it until rereading. Because there's white space between that target and the green one, it might be good to introduce some white space to separate the graph from the targets more. The target arrangement makes more sense in panel C, but by the time I got there, I had already been a bit confused.

Thanks for raising this point. As shown in Figure 6C, we used the reach to the +1 target for the curvature analysis. The confusion about Figure 6B is probably due to continuing the reach trajectories after the +1 target. That also explains why Figure 7C seemed more straightforward. To solve this issue we modified Figure 6B such that the reaches are shown with full opacity right until the +1 target and then shown with more transparency. We believe this change focuses the reader's attention to the reach initiated from the +0 target to the +1 target.

As for the gray target in Figure 6B, we originally had the gray target as it is a potential start location for the reach to the +0 target, and for having similar visuals between the plots. The gray target is now removed from Figure 6B.

(4) Line 253 - I'm not sure I understand the advantage over simple averaging that the authors mention here--would be nice to get a bit more intuition.

Thanks for raising this point. We used a two-factor model in our analysis, with each factor representing the angle of the last and next target, respectively. Both factors had five levels: -120, -60, 0, 60, and 120 degrees relative to the +1 reach. In a balanced two-factor design, where each combination of factor levels has an equal number of trials, using a linear model and simple averaging would yield equivalent results. However, when the number of trials for the combinations of the two factors is unbalanced, simple averaging can lead to misleading differences in the levels of the second factor. Additionally, the linear model allows us to investigate potential interactions between the two factors, which is not possible with simple averaging.

(5) Fig 7a - I would have liked to see the traces labeled in figure (i.e. hand trajectory vs. eye trajectory)

Hand and eye trajectories are now labeled in the figure.

(6) Fig 7c - very minor, but the hexagon of targets is rotated 30 degrees from all previous hexagons shown (also, this hex grid target arrangement can't lead to the trajectory shown in 7a, so it can't be that this was a different experimental grid). I'm guessing this was a simple oversight.

We used the same grid in the eye-tracking experiment. The targets are to visually match the previous plots. Thank you for raising this point.

Reference

Clavagnier, S., Prado, J., Kennedy, H., & Perenin, M.-T. (2007). How humans reach: distinct cortical systems for central and peripheral vision. The Neuroscientist: A Review Journal Bringing Neurobiology, Neurology and Psychiatry, 13(1), 22–27.

González-Alvarez, C., Subramanian, A., & Pardhan, S. (2007). Reaching and grasping with restricted peripheral vision. Ophthalmic & Physiological Optics: The Journal of the British College of Ophthalmic Opticians , 27(3), 265–274.

Kalidindi, H. T., & Crevecoeur, F. (2023). Task dependent coarticulation of movement sequences (p.2023.12.15.571847). https://doi.org/10.1101/2023.12.15.571847

Miller, G. A. (1956). The magical number seven plus or minus two: some limits on our capacity for processing information. Psychological Review, 63(2), 81–97.

Sivak, B., & MacKenzie, C. L. (1990). Integration of visual information and motor output in reaching and grasping: the contributions of peripheral and central vision. Neuropsychologia, 28(10), 1095–1116.

Author response:

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In the manuscript entitled "Magnesium modulates phospholipid metabolism to promote bacterial phenotypic resistance to antibiotics", Li et al demonstrated the role of magnesium in promoting phenotypic resistance in V. alginolyticus. Using standard microbiological and metabolomic techniques, the authors have shown the significance of fatty acid biosynthesis pathway behind the resistance mechanism. This study is significant as it sheds light on the role of an exogenous factor in altering membrane composition, polarization, and fluidity which ultimately leads to antimicrobial resistance.

Strengths:

(1) The experiments were carried out methodically and logically.

(2) An adequate number of replicates were used for the experiments.

Weaknesses:

(1) The introduction section needs to be more informative and to the point.

(2) The weakest point of this paper is in the logistics through the results section. The way authors represented the figures and interpreted them in the results section (or the figure legends) does not match. The figures are difficult to interpret and are not at all self-explanatory.

(3) There are too many mislabeling of the figure panels in the main text which makes it difficult to find out which figures the authors are explaining. There should be more explanation on why and how they did the experiments and how the results were interpreted.

(1) We would like to extensive revise the introduction to make it more informative than the current version.

(2) We will check the description in the text and labeling in the figures to make it is logic.

(3) We will add the explanation of the experiments to make it clear that why we perform the assays.

Reviewer #2 (Public Review):

Summary:

In this study, the authors aimed to identify if and how magnesium affects the ability of two particular bacteria species to resist the action of antibiotics. In my view, the authors succeeded in their goals and presented a compelling study that will have important implications for the antibiotic resistance research community. Since metals like magnesium are present in all lab media compositions and are present in the host, the data presented in this study certainly will inspire additional research by the community. These could include research into whether other types of metals also induce multi-drug resistance, whether this phenomenon can be observed in other bacterial species, especially pathogenic species that cause clinical disease, and whether the underlying molecular determinants (i.e. enzymes) of metal-induced phenotypic resistance could be new antimicrobial drug targets themselves.

Strengths:

This study's strengths include that the authors used a variety of methodologies, all of which point to a clear effect of exogenous Mg2+ on drug resistance in the targeted species. I also commend the authors for carrying out a comprehensive study, spanning evaluation of whole cell phenotypes, metabolic pathways, genetic manipulation, to enzyme activity level evaluation. The fact that the authors uncovered a molecular mechanism underlying Mg2+-induced phenotypic resistance is particularly important as the key proteins should be studied further.

Weaknesses:

I believe there are weaknesses in the manuscript, however. The authors take for granted that the reader is familiar with all the assays utilized, and do not properly explain some experiments, and thus I highly suggest that the authors add a brief statement in each situation describing the rationale for each selected methodology (more details are in the private review to the authors). The Results section is also quite long and bogs down at times, and I suggest that the authors reduce its length by 10 to 20%. In contrast, the Introduction is sparse and lacks key aspects, for example, there should be mention of the study's main purpose and approaches, plus an introduction to the authors' choice of species and their known drug resistance properties, as well as the drug of choice (balofloxacin). Another notable weakness is that the authors evaluated Mg2+-induced phenotypic resistance only against two closely related species, and thus the generalizability of this mechanism of drug resistance is not known. The paper would be strengthened if the authors could demonstrate this type of phenotypic resistance in at least one more Gram-negative species and at least one Gram-positive species (antimicrobial susceptibility evaluations would suffice), each of which should be pathogenic to humans. Demonstrating magnesium-induced phenotypic drug resistance in the WHO Priority Bacterial Pathogens would be particularly important.

We will add the explanation of the experiments to make it clear that why we perform the assays. And we will revise the introduction and shorten the length of the manuscript. Expanding the bacterial species is very good idea and we will perform such experiment.

Author response:

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

Public Reviews: 

Reviewer #1 (Public Review): 

Summary: 

In this work, Odenwald and colleagues show that mutant biotin ligases used to perform proximity-dependent biotin identification (TurboID) can be used to amplify signal in fluorescence microscopy and to label phase-separated compartments that are refractory to many immunofluorescence approaches. Using the parasite Trypanosoma brucei, they show that fluorescent methods such as expansion microscopy and CLEM, which require bright signals for optimal detection, benefit from the elevated signal provided by TurboID fusion proteins when coupled with labeled streptavidin. Moreover, they show that phase-separated compartments, where many antibody epitopes are occluded due to limited diffusion and potential sequestration, are labeled reliably with biotin deposited by a TurboID fusion protein that localizes within the compartment. They show successful labeling of the nucleolus, likely phase-separated portions of the nuclear pore, and stress granules. Lastly, they use a panel of nuclear pore-TurboID fusion proteins to map the regions of the T. brucei nuclear pore that appear to be phase-separated by comparing antibody labeling of the protein, which is susceptible to blocking, to the degree of biotin deposition detected by streptavidin, which is not. 

Strengths: 

Overall, this study shows that TurboID labelling and fluorescent streptavidin can be used to boost signal compared to conventional immunofluorescence in a manner similar to tyramide amplification, but without having to use antibodies. TurboID could prove to be a viable general strategy for labeling phase-separated structures in cells, and perhaps as a means of identifying these structures, which could also be useful. 

Weaknesses: 

However, I think that this work would benefit from additional controls to address if the improved detection that is being observed is due to the increased affinity and smaller size of streptavidin/biotin compared to IgGs, or if it has to do with the increased amount of binding epitope (biotin) being deposited compared to the number of available antibody epitopes. I also think that using the biotinylation signal produced by the TurboID fusion to track the location of the fusion protein and/or binding partners in cells comes with significant caveats that are not well addressed here, mostly due to the inability to discern which proteins are contributing to the observed biotin signal. 

To dissect the contributions of the TurboID fusion to elevating signal, anti-biotin antibodies could be used to determine if the abundance of the biotin being deposited by the TurboID is what is increasing detection, or if streptavidin is essential for this.

We agree with the reviewer, that it would be very interesting to distinguish whether the increase in signal comes from the multiple biotinylation sites or from streptavidin being a very good binder, or perhaps from both. However, this question is very hard to answer, as antibodies differ massively in their affinity to the antigen which is further dependent on the respective IF-conditions, and are therefore not directly comparible. Even if anti-biotin gives a better signal then anti-HA, this can be either caused by the increase in antigen-number (more biotin than HA-tag) or by the higher binding affinity, or by a combination of both, thus hard to distinguish. Nevertheless, we have tested monoclonal mouse anti-biotin targeting the (non-phase-separated) NUP158. We found the signal from the biotin-antibody to be much weaker than from anti-HA, indicating that, at least this particular biotin antibody, is not a very good binder in IF. 

Alternatively, HaloTag or CLIP tagging could be used to see if diffusion of a small molecule tag other than biotin can overcome the labeling issue in phase-separated compartments. There are Halo-biotin substrates available that would allow the conjugation of 1 biotin per fusion protein, which would allow the authors to dissect the relative contributions of the high affinity of streptavidin from the increased amount of biotin that the TurboID introduces. 

This is a very good idea, as in this case, the signals are both from streptavidin and are directly comparable. We expressed NUP158 with HaloTag and added PEG-biotin as a Halo ligand. However, PEG-biotin is poorly cell-permeable, and is in general only used on lysates. In trypanosomes, cell permeability is particular restricted, and even Halo-ligands that are considered highly cell-penetrant give only a weak signal. Even after over-night incubation, we could not get any signal with PEG-biotin. Our control, the TMR-ligand 647, gave a weak nuclear pore staining, confirming the correct expression and function of the HaloTag-NUP158.

The idea of using the biotin signal from the TurboID fusion as a means to track the changing localization of the fusion protein or the location of interacting partners is an attractive idea, but the lack of certainty about what proteins are carrying the biotin signal makes it very difficult to make clear statements. For example, in the case of TurboID-PABP2, the appearance of a biotin signal at the cell posterior is proposed to be ALPH1, part of the mRNA decapping complex. However, because we are tracking biotin localization and biotin is being deposited on a variety of proteins, it is not formally possible to say that the posterior signal is ALPH1 or any other part of the decapping complex. For example, the posterior labeling could represent a localization of PABP2 that is not seen without the additional signal intensity provided by the TurboID fusion. There are also many cytoskeletal components present at the cell posterior that could be being biotinylated, not just the decapping complex. Similar arguments can be made for the localization data pertaining to MLP2 and NUP65/75. I would argue that the TurboID labeling allows you to enhance signal on structures, such as the NUPs, and effectively label compartments, but you lack the capacity to know precisely which proteins are being labeled.  

We fully agree with the reviewer, that tracking proteins by streptavidin imaging alone is problematic, because it cannot distinguish, which protein is biotinylated. We therefore used words like “likely”  in the description of the data. However, we still think, it is a valid method, as long as it is confirmed by an orthogonal method. We have added this paragraph to the end of this chapter:

“Importantly, tracking of proteins by streptavidin imaging requires orthogonal controls, as the imaging alone does not provide information about the nature of the biotinylated proteins. These can be proximity ligation assay, mass spectrometry or specific tagging visualisation of protein suspects by fluorescent tags. Once these orthogonal controls are established for a specific tracking, streptavidin imaging is an easy and cheap and highly versatile method to monitor protein interactions in a specific setting.”

Reviewer #2 (Public Review): 

Summary: 

The authors noticed that there was an enhanced ability to detect nuclear pore proteins in trypanosomes using a streptavidin-biotin-based detection approach in comparison to conventional antibody-based detection, and this seemed particularly acute for phase-separated proteins. They explored this in detail for both standard imaging but also expansion microscopy and CLEM, testing resolution, signal strength, and sensitivity. An additional innovative approach exploits the proximity element of biotin labelling to identify where interacting proteins have been as well as where they are. 

Strengths: 

The data is high quality and convincing and will have obvious application, not just in the trypanosome field but also more broadly where proteins are tricky to detect or inaccessible due to phase separation (or some other steric limitations). It will be of wide utility and value in many cell biological studies and is timely due to the focus of interest on phase separation, CLEM, and expansion microscopy. 

Thank you! We are glad you liked it.

Reviewer #3 (Public Review): 

Summary: 

The authors aimed to investigate the effectiveness of streptavidin imaging as an alternative to traditional antibody labeling for visualizing proteins within cellular contexts. They sought to address challenges associated with antibody accessibility and inconsistent localization by comparing the performance of streptavidin imaging with a TurboID-HA tandem tag across various protein localization scenarios, including phase-separated regions. They aimed to assess the reliability, signal enhancement, and potential advantages of streptavidin imaging over antibody labeling techniques. 

Overall, the study provides a convincing argument for the utility of streptavidin imaging in cellular protein visualization. By demonstrating the effectiveness of streptavidin imaging as an alternative to antibody labeling, the study offers a promising solution to issues of accessibility and localization variability. Furthermore, while streptavidin imaging shows significant advantages in signal enhancement and preservation of protein interactions, the authors must consider potential limitations and variations in its application. Factors such as the fact that tagging may sometimes impact protein function, background noise, non-specific binding, and the potential for off-target effects may impact the reliability and interpretation of results. Thus, careful validation and optimization of streptavidin imaging protocols are crucial to ensure reproducibility and accuracy across different experimental setups. 

Strengths: 

- Streptavidin imaging utilizes multiple biotinylation sites on both the target protein and adjacent proteins, resulting in a substantial signal boost. This enhancement is particularly beneficial for several applications with diluted antigens, such as expansion microscopy or correlative light and electron microscopy. 

- This biotinylation process enables the identification and characterization of interacting proteins, allowing for a comprehensive understanding of protein-protein interactions within cellular contexts. 

Weaknesses: 

- One of the key advantages of antibodies is that they label native, endogenous proteins, i.e. without introducing any genetic modifications or exogenously expressed proteins. This is a major difference from the approach in this manuscript, and it is surprising that this limitation is not really mentioned, let alone expanded upon, anywhere in the manuscript. Tagging proteins often impacts their function (if not their localization), and this is also not discussed.

- Given that BioID proximity labeling encompasses not only the protein of interest but also its entire interacting partner history, ensuring accurate localization of the protein of interest poses a challenge. 

- The title of the publication suggests that this imaging technique is widely applicable. However, the authors did not show the ability to track the localization of several distinct proteins on the same sample, which could be an additional factor demonstrating the outperformance of streptavidin imaging compared with antibody labeling. Similarly, the work focuses only on small 2D samples. It would have been interesting to be able to compare this with 3D samples (e.g. cells encapsulated in an extracellular matrix) or to tissues.  

Recommendations for the authors:

To enhance the assessment from 'incomplete' to 'solid', the reviewers recommend that the following major issues be addressed: 

Major issues: 

(1) Anti-biotin antibodies in combination with TurboID labeling should be used to compare the signal/labelling penetrance to streptavidin results. That would show if elevated biotin deposition matters, or if it is really the smaller size, more fluors, and higher affinity of streptavidin that's making the difference. 

We agree with the reviewer, that it would be very interesting to distinguish whether the increase in signal comes from the multiple biotinylation sites or from streptavidin being a very good binder, or perhaps from both, and whether the size matters (IgG versus streptavidin). However, this question is very hard to answer, as antibodies differ massively in their affinity to the antigen. Thus, even if antibiotin would give a better signal then anti-HA, this could be either caused by the increase in antigen-number (more biotin than HA-tag) or by the better binding affinity, or by a combination, and it would not allow to truly answer the question. We have now tested anti-biotin antibodies, also in repsonse to reviewer 1, and got a much poorer signal in comparison to anti-HA or streptavidin.

Please note that we made another attempt using nanobodies to target phase-separated proteins, to see, whether size matters (Fig. 2I). The nanobody did not stain Mex67 at the nuclear pores, but gave a weak nucelolar signal for NOG1, which may suggest that the nanobody can slightly better penetrate than IgG, but it does not rule out that the nanobody simply binds with higher affinity. Reviewer 1 has suggested to use the Halo Tag with PEG-biotin: this would indeed allow to directly compare the streptavidin signal caused by the TurboID with a single biotin added by the Halo tag. Unfortunately, the PEG-biotin does not  penetrate trypanosome cells. In conclusion, we are not aware of a method that would allow to establish why streptavidin but not IgGs can penetrate to phase separated areas. We therefore prefer to not overinterpret our data, but stick to what is supported by the data: “the inability to label phase-separated areas is not restricted to anti-HA but applies to other antibodies”.

(3) Figure 4 A-B. The validity of claiming the correct localization demonstrated by streptavidin imaging comes into question, especially when endogenous fluorescence, via the fusion protein, remains undetectable (as indicated by the yellow arrow at apex). 

In this figure, the streptavidin imaging does NOT show the correct localisation of the bait protein, but it does show proteins from historic interactions that have a distinct localisation to the bait. We had therefore introduced this chapter with the paragraph below, to make sure, the reader is aware of the limitations (which we also see as an opportunity, if properly controlled):

“We found that in most cases, streptavidin labelling faithfully reflects the steady state localisation of a bait protein, e.g., the localisation resembles those observed with immunofluorescence or direct fluorescence imaging of GFP-fusion proteins. For certain bait proteins, this is not the case, for example, if the bait protein or its interactors have a dynamic localisation to distinct compartments, or if interactions are highly transient. It is thus essential to control streptavidin-based de novo localisation data by either antibody labelling (if possible) or by direct fluorescence of fusion-proteins for each new bait protein.”

In particular, on lines 450-460, there's a fundamental issue with the argument put forward here. It is not possible to formally know that the posterior labeling is ALPH1 vs. another part of the decapping complex that was associated with PABP2-Turbo, or if the higher detection capacity of the Turbo-biotin label is uncovering a novel localization of the PABP2. While it is likely that it is ALPH1, it is not possible to rule out other possibilities with this approach. These issues should be discussed here and more generally the possibility of off-target labeling with this approach should be addressed in the discussion. 

We fully agree with the reviewer, that tracking proteins by streptavidin imaging alone is problematic, because it cannot distinguish, which protein is biotinylated. We therefore used words like “likely”  in the description of the data. However, we still think, it is a valid method, as long as it is back-uped by an orthogonal method. We have added this paragraph to the end of this chapter:

“Importantly, tracking of proteins by streptavidin imaging requires orthogonal controls, as the imaging alone does not provide information about the nature of the biotinylated proteins. These can be proximity ligation assay, mass spectrometry or specific tagging visualisation of protein suspects by fluorescent tags. Once these orthogonal controls are established for a specific tracking, streptavidin imaging is an easy and cheap and highly versatile method to monitor protein interactions in a specific setting.”

(4) More discussion and acknowledgment of the general limitations in using tagged proteins are needed to balance the manuscript, especially if the hope is to draw a comparison with antibody labeling, which works on endogenous proteins (not requiring a tag). For example: (a) tagging proteins requires genetic/molecular work ahead of time to engineer the constructs and/or cells if trying to tag endogenous proteins; (b) tagged proteins should technically be validated in rescue experiments to confirm the tag doesn't disrupt function in the cell/tissue/context of interest; and (c) exogenous tagged proteins compete with endogenous untagged proteins, which can complicate the interpretation of data.  

We have added this paragraph to the first paragraph of the discussion part:

“Like many methods that are frequently used in cell- and molecular biology, streptavidin imaging is based on the expression of a genetically engineered fusion protein: it is essential to validate both, function and localisation of the TurboID-HA tagged protein by orthogonal methods. If the fusion protein is non-functional or mis-localised, tagging at the other end may help, but if not, this protein cannot be imaged by streptavidin imaging. Likewise, target organisms not amenable to genetic manipulation, or those with restricted genetic tools,  are not or less suitable for this method.”

Also, we like to point out that for non-mainstream organisms like trypanosomes, antibodies are not commercially available and often genetic manipulation is more time-efficient and cheaper than the production of antiserum against the target protein.

Also, the introduction would ideally be more general in scope and introduce the pros and cons of antibody labeling vs biotin/streptavidin, which are mentioned briefly in the discussion. The fact that the biotin-streptavidin interaction is ~100-fold higher affinity than an IgG binding to its epitope is likely playing a key role in the results here. The difference in size between IgG and streptavidin, the likelihood that the tetrameric streptavidin carries more fluors than a IgG secondary, and the fact that biotin can likely diffuse into phase-separated environments should be clearly stated. The current introduction segues from a previous paper that a more general audience may not be familiar with. 

We have now included this paragraph to the introduction:

“It remains unclear, why streptavidin was able to stain biotinylated proteins within these antibody inaccessible regions, but possible reasons are: (i) tetrameric streptavidin is smaller and more compact than IgGs (60 kDa versus a tandem of two IgGs, each with 150 kDa) (ii) the interaction between streptavidin and biotin is ~100 fold stronger than a typical interaction between antibody and antigen and (iii) streptavidin contains four fluorophores, in contrast to only one per secondary IgG.”

Minor issues: 

The copy numbers of the HA and Ty1 epitope tags vary depending on the construct being used. For example, Ty1 is found as a single copy tag in the TurboID tag, but on the mNeonGreen tag there are 6 copies of the epitope. It makes it hard to know if differences in detection are due to variations in copies of the epitope tags. Line 372-374: can the authors explain why they chose to use nanobodies in this case? It would be great to show the innate mNeonGreen signal in 2K to compare to the Ty1 labeling. The presence of 6 copies of the Ty1 epitope could be essential to the labeling seen here.

We agree with the reviewer, that these data are a bit confusing. We have now removed Figure 3K, as it is the only construct with 6 Ty1 instead of one, and it does not add to the conclusions. (the mNeonsignal is entirely in the nucleolus, as shown by Tryptag). We have also added an explanation why we used nanobodies (“The absence of a nanobody signal rules out that its simply the size of IgGs that prevents the staining of Mex67 at the nuclear pores, as nanobodies are smaller than (tetrameric) streptavidin”). However, as stated above, we prefer not to overinterpret the data, as signals from different antibodies/nanobodies – antigen combinations are not comparable. Important to us was to stress that the absence of signal in phase-separated areas is NOT restricted to the anti-HA antibody, which is clearly supported by the data.

What is the innate streptavidin background labeling look like in cells that are not carrying a TurboID fusion, from the native proteins that are biotinylated? That should be discussed. 

We have now included the controls without the TurboID fusions for trypanosomes and HeLa cells: “Wild type cells of both Trypanosomes and human showed only a very low streptavidin signal, indicating that the signal from naturally biotinylated proteins is neglectable (Figure S8 in supplementary material).”

Line 328-331: This is likely to be dependent on whether or not the protein moves to different localizations within the cell. 

True, we agree, and we have added this paragraph:

“The one exception are very motile proteins that produce a “biotinylation trail” distinct to the steady state localisation; these exceptions, and how they can be exploited to understand protein interactions, are discussed in chapter 4 below. “

Line 304-305: Does biotin supplementation not matter at all? 

No, we never saw any increase in biotinylation when we added extra biotin to trypanosomes. The 0.8 µM biotin concentration in the medium were sufficient.

Line 326-327: Was the addition of biotin checked for enhancement in the case of the mammalian NUP98? I would argue that there is a significant number of puncta in Figure 1D that are either green or magenta, not both. The amount of extranuclear puncta in the HA channel is also difficult to explain. Biotin supplementation to 500 µM was used in mammalian TurboID experiments in the original Nature Biotech paper- perhaps nanomolar levels are too low. 

We now tested HeLa cells with 500 µM Biotin and saw an increase in signal, but also in background; due to the increased background  we conclude that low biotin concentrations are more suitable . We have also repeated the experiment using 4HA tags instead of 1HA, and we found a minor improvement in the antibody signal for NUP88 (while the phase separated NUP54 was still not detectable). We have replaced the images in Figure 1D  (NUP88) and also in Figure 2F (NUP54) with improved images and using 4HA tags. However, we like to note that single nuclear pore resolution is beyond what can be expected of light microscopy.

Line 371: In 2I, I see a signal that looks like the nucleus, similar to the Ty1 labeling in 2G, so I don't think it's accurate to say that that Mex67 was "undetectable". Does the serum work for blotting? 

Thank you, yes, “undetectable” was not the correct phrase here. Mex67 localises to the nuclear pores, to the nuceoplasm and to the nucleolus (GFP-tagging or streptavidin). Antibodies, either to the tag or to the endogenous proteins, fail to detect Mex67 at the nuclear pores and also don’t show any particular enrichment in the nucleolus. They do, however, detect Mex67 in the (not-phase-separated) area of the nucleoplasm. We have changed the text to make this clearer. The Mex67 antiserum works well on a western blot (see for example: Pozzi, B., Naguleswaran, A., Florini, F., Rezaei, Z. & Roditi, I. The RNA export factor TbMex67 connects transcription and RNA export in Trypanosoma brucei and sets boundaries for RNA polymerase I. Nucleic Acids Res. 51, 5177–5192 (2023))

Line 477: "lacked" should be "lagged".

Thank you, corrected.

Line 468-481: My previous argument holds here - how do you know that the difference in detection here is just a matter of much higher affinity/quantity of binding partner for the avidin?

See answer to the second point of (3), above.

483-491: Same issue - without certainty about what the biotin is on, this argument is difficult to make. 

See answer to the second point of (3), above.

Line 530: "bone-fine" should be "bonafide"

Thank you, corrected.

Line 602: biotin/streptavidin labeling has been used for expansion microscopy previously (Sun, Nature Biotech 2021; PMID: 33288959). 

Thank you, we had overlooked this! We have now included this reference and describe the differences to our approach clearer in the discussion part:

“Fluorescent streptavidin has been previously used in expansion microscopy to detect biotin residues in target proteins produced by click chemistry (Sun et al., 2021). However, to the best of our knowledge, this is the first report that employs fluorescent streptavidin as a signal enhancer in expansion microscopy and CLEM, by combining it with multiple biotinylation sites added by a biotin ligase. Importantly, for both CLEM and expansion, streptavidin imaging is the only alternative approach to immunofluorescence, as denaturing conditions associated with these methods rule out direct imaging of fluorescent tags.”

Author response:

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

eLife assessment:

This study presents valuable framework and findings to our understanding of the brain as a fractal object by observing the stability of its shape property within 11 primate species and by highlighting an application to the effects of aging on the human brain. The evidence provided is solid but the link between brain shape and the underlying anatomy remains unclear. This study will be of interest to neuroscientists interested in brain morphology, whether from an evolutionary, fundamental or pathological point of view, and to physicists and mathematicians interested in modeling the shapes of complex objects.

We now clarified the outstanding questions regarding if our model outputs can be related to actual primate brain anatomy, which we believe was mainly based on comments regarding the validity of our output of apparently thicker cortices than nature can produce.

We address this point in more detail in the point-by-point response below, but want to address this misunderstanding directly here: Our algorithm does not produce thicker cortices with increasing coarse-graining scales; in fact, the cortical thickness never exceeds the actual cortical thickness in our outputs, but rather thins with each coarse-graining scale. In other words, we believe that our outputs are fully in line with neuroanatomy across species.

Reviewer #2 (Public Review): 

In this manuscript, the authors analyze the shapes of cerebral cortices from several primate species, including subgroups of young and old humans, to characterize commonalities in patterns of gyrification, cortical thickness, and cortical surface area. The authors state that the observed scaling law shares properties with fractals, where shape properties are similar across several spatial scales. One way the authors assess this is to perform a "cortical melting" operation that they have devised on surface models obtained from several primate species. The authors also explore differences in shape properties between brains of young (~20 year old) and old (~80) humans. A challenge the authors acknowledge struggling with in reviewing the manuscript is merging "complex mathematical concepts and a perplexing biological phenomenon." This reviewer remains a bit skeptical about whether the complexity of the mathematical concepts being drawn from are justified by the advances made in our ability to infer new things about the shape of the cerebral cortex. 

To allow scientists from all backgrounds to adopt these complex ideas, we have made our code to “melt” the brains and for further downstream analysis publicly available. We have now also provided a graphical user interface, to allow users without substantial coding experience to run the analysis. We also believe that the algorithmic concepts are easy to understand due to the similarity to the coarse-graining procedures found in long-standing and well-accepted box-counting algorithms.

Beyond the theoretical insight of the fractal nature of cortices and providing an explicit and crucial link between vastly different brains that are gyrified and those that are not, we believe that the advance gained by our methods for future applications is clearly demonstrated in our proof-of-principle with a four-fold increase in effect size. For reference, an effect size of 8 would translate to an almost perfect separation of groups, i.e. an ideal biomarker with near 100% sensitivity and specificity.

(1) The series of operations to coarse-grain the cortex illustrated in Figure 1 produces image segmentations that do not resemble real brains.

As re-iterated in our Methods and Discussion: “Note, of course, that the coarse-grained brain surfaces are an output of our algorithm alone and are not to be directly/naively likened to actual brain surfaces, e.g. in terms of the location or shape of the folds. Our comparisons here between coarse-grained brains and actual brains is purely on the level of morphometrics across the whole cortex.”

Fig. 1 therefore serves as an explanation to the reader on the algorithmic outputs, but each melted brain is not supposed to be directly/visually compared to actual brains. Similar to algorithms measuring the fractal dimension, or the exposed surface area of a given brain, the intermediate outputs of these algorithms are not supposed to represent any biologically observed brain structures, but rather serve as an abstraction to obtain meaningful morphometrics.

We additionally added a note to the caption of Fig. 1 to clarify this point:

“Note that the actual size of the brains for analysis are rescaled (see Methods and Fig. 3); we display all brains scaled at an equal size here for the ease of visualisation of the method.”

Finally, we also edited the entire paper for terminology to clearly distinguish the terms of (1) the cortex as a 3D object, (2) coarse-grained and voxelised versions thereof, and (3) summary morphological measures derived from the former. When we invite comparisons in our paper between real brains and coarse-grained brains, this is always at the level of summary morphological measures, not at the level of the 3D objects/voxelisations themselves.

The process to assign voxels in downsampled images to cortex and white matter is biased towards the former, as only 4 corners of a given voxel are needed to intersect the original pial surface, but all 8 corners are needed to be assigned a white matter voxel. The reason for introducing this bias (and to the extent that it is present in the authors' implementation) is not provided.

This detail was in the Supplementary, and we have now added additional clarification on this specific point to our Supplementary:

“In detail, we assign all voxels in the grid with at least four corners inside the original pial surface to the pial voxelization. This process allows the exposed surface to remain approximately constant with increasing voxel sizes. A constant exposed surface is desirable, as we only want to gradually ‘melt’ and fuse the gyri, but not grow the bounding/exposed surface as well. We want the extrinsic area to remain approximately constant as we decrease the intrinsic area via coarse-graining; it is like generating iterates of a Koch curve in reverse, from more to less detailed, by increasing the length of smallest line segment.

We then assign voxels with all eight corners inside the original white matter surface to the white matter voxelization. This is to ensure integrity of the white matter, as otherwise white matter voxels in gyri may become detached from the core white matter, and thus artificially increase white matter surface area. Indeed, the main results of the paper are not very sensitive to this decision using all eight corners, vs. e.g. only four corners, as we do not directly use white matter surface area for the scaling law measurements. However, we still maintained this choice in case future work wants to make use of the white matter voxelisations or derivative measures.”

Note on the point of white matter integrity that if both grey and white matter voxelisations require all 8 corner to be inside the respective mesh, there will be voxels not assigned to either at the grey/white matter interface, causing potential downstream issues.

We further acknowledge:

“Of course, our proposed procedure is not the only conceivable way to erase shape details below a given scale; and we are actively working on related algorithms that are also computationally cheaper. Nevertheless, the current version requires no fine-tuning, is computationally feasible and conceptually simple, thus making it a natural choice for introducing the methodology and approach.”

The authors provide an intuitive explanation of why thickness relates to folding characteristics, but ultimately an issue for this reviewer is, e.g., for the right-most panel in Figure 2b, the cortex consists of several 4.9-sided voxels and thus a >2 cm thick cortex. A structure with these morphological properties is not consistent with the anatomical organization of typical mammalian neocortex. 

We assume the reviewer refers to Fig. 1B with the panel on scale=4.9mm. We would like to point out that Fig. 1 serves as an explanation of the voxelisation method. For the actual analysis and Results, we are using re-scaled brains (see Fig. 2 with the ever decreasing brain sizes). The rescaling procedure is now expanded as below:

“Morphological properties, such as cortical thicknesses measured in our ‘melted’ brains are to be understood as a thickness relative to the size of the brain. Therefore, to analyse the scaling behaviour of the different coarse-grained realisations of the same brain, we apply an isometric rescaling process that leaves all dimensionless shape properties unaffected (more details in Suppl. S3.1). Conceptually, this process fixes the voxel size, and instead resizes the surfaces relative to the voxel size, which ensures that we can compare the coarse-grained realisations to the original cortices, and test if the former, like the latter, also scale according to Eqn. (1). Resizing, or more precisely, shrinking the cortical surface is mathematically equivalent to increasing the box size in our coarse-graining method. Both achieved an erasure of folding details below a certain threshold. After rescaling, as an example, the cortical thickness also shrinks with increasing levels of coarse-graining, and never exceeds the thickness measured at native scale.”

We additionally added a note to the caption of Fig. 1 to clarify this point:

“Note that the actual size of the brains for analysis are rescaled (see Methods and Fig. 3); we display all brains scaled at an equal size here for the ease of visualisation of the method.”

Finally, we also edited the entire paper for terminology to clearly distinguish the terms of (1) the cortex as a 3D object, (2) coarse-grained versions thereof, and (3) summary morphological measures derived from the former. When we invite comparisons in our paper between real brains and coarse-grained brains, this is always at the level of summary morphological measures, not at the level of the 3D objects themselves and their detailed anatomical features.

(2) For the comparison between 20-year-old and 80-year-old brains, a well-documented difference is that the older age group possesses more cerebral spinal fluid due to tissue atrophy, and the distances between the walls of gyri becomes greater. This difference is born out in the left column of Figure 4b. It seems this additional spacing between gyri in 80 year olds requires more extensive down-sampling (larger scale values in Figure 4a) to achieve a similar shape parameter K as for the 20 year olds. The authors assert that K provides a more sensitive measure (associated with a large effect size) than currently used ones for distinguishing brains of young vs. old people. A more explicit, or elaborate, interpretation of the numbers produced in this manuscript, in terms of brain shape, might make this analysis more appealing to researchers in the aging field.

We have removed the main results relating to K and aging from our last revision already to avoid confusion. This is now only in the supplementary analysis, and our claim of K being a more sensitive measure for age and ageing – whilst still true – will be presented in more detail in a series of upcoming papers.

(3) In the Discussion, it is stated that self-similarity, operating on all length scales, should be used as a test for existing and future models of gyrification mechanisms. Given the lack of association between the abstract mathematical parameters described in this study and explicit properties of brain tissue and its constituents, it is difficult to envision how the coarse-graining operation can be used to guide development of "models of cortical gyrification."

We have clarified in more detail what we meant originally in Discussion:

“Finally, this dual universality is also a more stringent test for existing and future models of cortical gyrification mechanisms at relevant scales, and one that moreover is applicable to individual cortices. For example, any models that explicitly simulate a cortical surface as an output could be directly coarse-grained with our method and the morphological trajectories can be compared with those of actual human and primate cortices. The simulated cortices would only be ‘valid’ in terms of the dual universality, if it also produces the same morphological trajectories.”

However, we agree with the reviewer that our paper could be misread as demanding direct comparisons of each coarse-grained brain with an actual brain, and we have now added the following text to clarify that this is not our intention for the proposed method or outputs.

“Note, we do not suggest to directly compare coarse-grained brain surfaces with actual biological brain surfaces. As we noted earlier, the coarse-grained brain surfaces are an output of our algorithm alone and not to be directly/naively likened to actual brain surfaces, e.g. in terms of the location or shape of the folds. Our comparisons here between coarse-grained brains and actual brains is purely on the level of morphometrics across the whole cortex.”

Indeed, the dual universality imposes restrictive constraints on the possible shapes of real cortices, but do not fully specify them. Presumably, the location of individual folds in different individuals and species will depend on their respective evolutionary histories, so there is no reason to expect a match in fold location between the ‘melted’ cortices of more gyrified species, on one hand, and the cortex of a less-gyrified one, on the other,  even if their global morphological parameters and global mechanism of folding coincide.

(4) There are several who advocate for analyzing cortical mid-thickness surfaces, as the pial surface over-represents gyral tips compared to the bottoms of sulci in the surface area. The authors indicate that analyses of mid-thickness representations will be taken on in future work, but this seems to be a relevant control for accepting the conclusions of this manuscript.

In the context of some applications and methods, we agree that the mid-surface is a meaningful surface to analyse. However, in our work, the mid-surface is not. The fractal estimation rests on the assumption that the exposed area hugs the object of interest (hence convex hull of the pial surface), as the relationship between the extrinsic and intrinsic areas across scales determine the fractal relationship (Eq. 2). If we used the mid-surface instead of the pial surface for all estimation, this would not represent the actual object of interest, and it is separated from the convex hull. Estimating a new convex hull based on the mid surface would be the equivalent of asking for the fractal dimension of the mid-surface, not of the cortical ribbon. In other words, it would be a different question, bound to yield a different answer.

Hence, we indicated in our original response that we only have a provisional answer, but more work beyond the scope of this paper is required to answer this question, as it is a separate question. The mid-surface, as a morphological structure in its own right, will have its own scaling properties, and our provisional understanding is that these also yield a scaling law parallel to those of the cortical ribbon with the same or a similar fractal dimension. But more systematic work is required to investigate this question at native scale and across scales.

Reviewer #3 (Public Review):

Summary: Through a rigorous methodology, the authors demonstrated that within 11 different primates, the shape of the brain followed a universal scaling law with fractal properties. They enhanced the universality of this result by showing the concordance of their results with a previous study investigating 70 mammalian brains, and the discordance of their results with other folded objects that are not brains. They incidentally illustrated potential applications of this fractal property of the brain by observing a scale-dependant effect of aging on the human brain. 

Strengths: 

- New hierarchical way of expressing cortical shapes at different scales derived from previous report through implementation of a coarse-graining procedure 

- Investigation of 11 primate brains and contextualisation with other mammals based on prior literature 

- Proposition of tool to analyse cortical morphology requiring no fine tuning and computationally achievable 

- Positioning of results in comparison to previous works reinforcing the validity of the observation. 

- Illustration of scale-dependance of effects of brain aging in the human. 

Weaknesses: 

- The notion of cortical shape, while being central to the article, is not really defined, leaving some interpretation to the reader 

- The organization of the manuscript is unconventional, leading to mixed contents in different sections (sections mixing introduction and method, methods and results, results and discussion...). As a result, the reader discovers the content of the article along the way, it is not obvious at what stages the methods are introduced, and the results are sometimes presented and argued in the same section, hindering objectivity. 

To improve the document, I would suggest a modification and restructuring of the article such that: 1) by the end of the introduction the reader understands clearly what question is addressed and the value it holds for the community, 2) by the end of the methods the reader understands clearly all the tools that will be used to answer that question (not just the new method), 3) by the end of the results the reader holds the objective results obtained by applying these tools on the available data (without subjective interpretations and justifications), and 4) by the end of the discussion the reader understands the interpretation and contextualisation of the study, and clearly grasps the potential of the method depicted for the better understanding of brain folding mechanisms and properties. 

We thank this reviewer again for their attention to detail and constructive comments. We have followed the detailed suggestions provided by us in the Recommendations For The Authors, and summarise the main changes here:

- We have restructured all sections to be more clearly following Introduction, Methods, Results, and Discussion; by using subsections, we believe the structure is now more accessible to readers.

-  We have now clarified the concept of “cortical shape”, as we use it in our paper in several places, by distinguishing clearly the object of study, and the morphological properties measured from it.

Recommendations for the authors: 

Reviewer #2 (Recommendations For The Authors): None 

Reviewer #3 (Recommendations For The Authors): 

I once again compliment the authors for their elegant work. I am happy with the way they covered my first feedback. My second review takes into account some comments made by other reviewers with which I agree. 

We thank this reviewer again for their attention to detail and constructive comments.

Recommendations for clarifications: 

General comments: The purpose of the article could be made clearer in the introduction. When I differentiate results from discussion, I think of results as objective measures or observations, while discussion will relate to the interpretation of these results (including comparison with previous literature, in most cases). 

We have restructured all sections to be more clearly following Introduction, Methods, Results, and Discussion; by using subsection, we believe the structure is now more accessible to readers.

- l.39: define or discuss "cortical shape" 

We have gone through the entire paper and corrected for any ambiguities. We specifically distinguish between the cortex as a structure overall, shape measures derived from this structure, and coarse-grained versions of the structure.

- l.48-74: this would match either an introduction or a discussion rather than a methods section. 

Done

- l.98-106: this would match a discussion rather than a methods section. 

Done

- l.111: here could be a good spot to discuss the 4 vs 8 corners for inclusion of pial vs white matter voxelization 

We have discussed this in the more detailed Supplementary section now, as after restructuring, this appears to be the more suitable place.

- l.140-180: it feels that this section mixes methods, results and discussion of the results 

We agree and we have resolved this by removing sentences and re-arranging sections.

- l.183-217: mix of results and discussion 

We agree and we have resolved this by removing sentences and re-arranging sections.

Small cosmetic suggestions: 

- l.44: conservation of 'some' quantities: vague 

Changed to conservation of morphological relationships across evolution

- l.66: order of citations ([24, 22,23]) 

Will be fixed at proof stage depending on format of references.

- l.77: delete space between citation and period 

Done

- l.77: I would delete 'say' 

Done

- l.86: 'but to also analyse' -> 'to analyse' 

Done

- l.105: remove 'we are encouraged that' 

Done

- l.111: 'also see' -> 'see also' 

Done

- l.164: 'remarkable': subjective 

Done

- l.189: define approx. abbreviation 

Done

- l.190: 'approx' -> 'approx.' 

Revised

- l.195: 'dramatic': subjective 

removed

-l. 246: 'much' -> vague 

explained

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The authors report compound heterozygous deleterious variants in the kinase domains of the non-receptor tyrosine kinases (NRTK) TNK2/ACK1 in familial SLE. They suggest that ACK1 and BRK deficiencies are associated with human SLE and impair efferocytosis.

Strengths: 

The identification of similar mutations in non-receptor tyrosine kinases (NRTKs) in two different families with familial SLE is a significant finding in human disease. Furthermore, the paper provides a detailed analysis of the molecular mechanisms behind the impairment of efferocytosis caused by mutations in ACK1 and BRK.

Weaknesses: 

A critical point in this paper is whether the loss of function of ACK1 or BRK contributes to the onset of familial SLE. The authors emphasize that inhibitors of ACK1/BRK worsened IgG deposition in the kidneys in a pristane-induced SLE model, which contributes not to the onset but to the exacerbation of SLE, thus only partially supporting their claim.

The evidence supporting that the loss of function of ACK1 or BRK contributes to the onset of SLE in the patients from the 2 families mostly relies on the genetic analysis. As the reviewer states, the observation that inhibitors of ACK1/BRK worsened IgG deposition in the kidneys in a pristane-induced SLE model supports the genetic evidence.

To further address the possible role of ACK1 or BRK variants in the onset of autoimmunity in vivo, we treated wild-type (WT) BALB/cByJ female mice with inhibitors in the absence of pristane.

The results indicated that mice that had received a weekly injection of ACK1 or BRK inhibitors developed a large array of serum anti-nuclear IgG antibodies, including but not limited to autoantibodies associated with SLE such as anti-histones, anti-chromatin, anti U1-snRNP, anti-SSA, and anti-Ku in comparison to the control group inhibitor treated mice (Revised Fig 3A). However, they did not develop glomerular deposit of IgG after 12 weeks of treatment, in contrast to mice that have received Pristane (Revised Fig. 3B,C, Figure 3-figure supplement 1).

These additional data suggests that inhibition of ACK1 and BRK stimulates the production of serum autoantibodies, which strengthen the claim that ACK1 and BRK kinase deficiency contribute to autoimmunity in BALB/cByJ.

Reviewer #2 (Public Review):

Summary: 

In this manuscript, the authors revealed that genetic deficiencies of ACK1 and BRK are associated with human SLE. First, the authors found that compound heterozygous deleterious variants in the kinase domains of the non-receptor tyrosine kinases (NRTK) TNK2/ACK1 in one multiplex family and PTK6/BRK in another family. Then, by an experimental blockade of ACK1 or BRK in a mouse SLE model, they found an increase in glomerular IgG deposits and circulating autoantibodies. Furthermore, they reported that ACK and BRK variants from the SLE patients impaired the MERTK-mediated anti-inflammatory response to apoptotic cells in human induced pluripotent stem cells (hiPSC)-derived macrophages. This work identified new SLE-associated ACK and BRK variants and a role for the NRTK TNK2/ACK1 and PTK6/BRK in efferocytosis, providing a new molecular and cellular mechanism of SLE pathogenesis.

Strengths: 

This work identified new SLE-associated ACK and BRK variants and a role for the NRTK TNK2/ACK1 and PTK6/BRK in efferocytosis, providing a new molecular and cellular mechanism of SLE pathogenesis.

Weaknesses: 

Although the manuscript is well-organized and clearly stated, there are some points below that should be considered:

In this study, the authors used forward genetic analyses to identify novel gene mutations that may cause SLE, combined with GWAS studies of SLE. To further explore the importance of these variants, haplotype analysis of two candidate genes could be performed, to observe the evolution and selection relationship of candidate genes in the population (UK 1000 biobank, for example). 

To investigate whether ACK1/TNK2 or BRK/PTK6 were subject to selection, we gathered data using different metrics quantifying negative selection in the human genome. We collected the f parameter from SnIPRE1, lofTool2, and evoTol3, as well as intraspecies metrics from RVIS4, LOEUF5, and pLI6 (including pRec). We also used our in-house CoNeS metric7. None of these indicators suggest that the genes are under strong negative selection (Revised Figure 2-figure supplement 2). This is consistent with the deficiency being recessive. We also tested the variants with a MAF greater than 0.005. We found them to be neutral. We therefore did not test whether they were associated with any phenotype in the UK Biobank.

Although the authors focused on SLE and macrophage efferocytosis in their studies, direct evidence of how macrophage efferocytosis significantly affects SLE is lacking. This point should at least be explicitly introduced and discussed by citing appropriate literature.

We provide a more detailed description of the role of macrophage efferocytosis in autoimmunity and SLE in the revised manuscript. Specifically, we state (in the results section, paragraph: ACK1 and BRK kinase domain variants may lose the ability to link MERTK to RAC1, AKT and STAT3 activation for efferocytosis): “NRTKs such as ACK1 8 and PTK2/FAK 9 are also downstream targets of the TAM family receptor MERTK which is expressed on macrophages and controls the anti-inflammatory engulfment of apoptotic cells, a process known as efferocytosis 10-12. Efferocytosis allows for the clearance of apoptotic cells before they undergo necrosis and release intracellular inflammatory molecules, and simultaneously leads to increased production of anti-inflammatory molecules (TGFb, IL-10, and PGE2) and a decreased secretion of proinflammatory cytokines (TNF-alpha, IL-1b, IL-6) 10-14. In line with these findings, mice deficient in molecular components used by macrophages to efficiently perform efferocytosis, such as MFG-E8, MERTK, TIM4, and C1q, develop phenotypes associated with autoimmunity10,11,14-27. Furthermore, defects in efferocytosis are also observed in patients with SLE and glomerulonephritis14,28-31.“

It is still not clear how the target molecules identified in this paper may influence macrophage efferocytosis. More direct evidence should be established. 

Our studies show that wt -but not variants- of ACK1 and BRK are activated by MERTK, a key receptor that mediates the recognition of apoptotic cells. Our studies also show that wt -but not variants- activate RAC1 which is necessary for engulfment and phosphorylate AKT and STAT3 which are involved in the anti-inflammatory response to PtdSer recognition.

The TAM family receptor MERTK mediates recognition of PtdSer on apoptotic cells via GAS6 and Protein S 10,15,32 leading to their engulfment, which involves activation of RAC1 for actin reorganization and the formation of a phagocytic cup 9,33. Using IP kinase assays we show that MERTK and GAS6 can activate the kinase activity of wild-type ACK1 8 or BRK but not of the patient’s ACK1 or BRK variant alleles (Figure 4D). To further support the role of ACK1 and BRK downstream from PtdSer recognition and uptake of apoptotic cells, we show that reference ACK1 and BRK alleles, in contrast to the patient variant alleles, can activate RAC1 to generate RAC-GTP which is necessary for engulfment 9,33 (Figure 4C).

PtdSer recognition also typically stimulates an anti-inflammatory process mediated in part via AKT 34 and STAT3 and their target genes such as SOCS3 35-41 and results in the inhibition of LPS-mediated production of inflammatory mediators such as TNF and IL-1b, and the production of cytokines such as IL-10, TGFb 11,25-27,42. Consistent with this literature and the findings of the paper, we show that reference ACK1 and BRK, unlike the patient’s variant alleles, can phosphorylate AKT and STAT3 (Figure 4A, B). The role of ACK1 and BRK in these signaling pathways is further supported by our transcriptomics data comparing the response of controls, patients, and inhibitor-treated iPSC-derived macrophages to apoptotic thymocytes by RNA-seq. Specifically, we show Transcriptional repressors including the AKT targets ATF3, TGIF1, NFIL3, and KLF4, the STAT3 targets SOCS3 and DUSP5, as well as CEBPD and the inhibitor of E-BOX DNA Binding ID3 were among the top-ten genes which expression is induced by apoptotic cells in WT macrophages (Figure 4F), but this regulation was lost in mutant and inhibitor-treated macrophages (Figure 4F).

For some transcriptional repressors mentioned in their studies, the authors should check whether there is clear experimental evidence. If not, it is recommended to supplement the experimental verifications for clarity.

Transcriptional repressors including the AKT targets ATF3, TGIF1, NFIL3, and KLF4, the STAT3 targets SOCS3 and DUSP5, as well as CEBPD and the inhibitor of E-BOX DNA Binding ID3 were among the top-ten genes which expression is induced by apoptotic cells in WT macrophages (Figure 4F), but this regulation was lost in mutant and inhibitor-treated macrophages (Figure 4F).

In the manuscript we cited published evidence, to the best of our knowledge, for the role of these genes in the regulation of inflammatory responses. Specifically we state: “ATF3, TGIF1, NFIL3, and KLF4 are involved in the negative regulation of inflammation in macrophages 35-38, SOCS3 is an inhibitor of the macrophage inflammatory response and DUSP5 is a negative regulator of ERK activation 39,40,43. These data suggest that the kinase domain of ACK1 and BRK contribute to the macrophage anti-inflammatory gene expression program driven by apoptotic cells.”

In Figures 4C and 4D, it is seen that the usage of inhibitors causes cytoskeletal changes, however this reviewer would not have expected such large change. Did the authors check whether the cells die after heavy treatment by the inhibitors?

We carefully examine the viability of Isogenic WT, BRK and ACK1 mutant macrophages (left panel) and of WT macrophages treated with ACK1 or BRK inhibitors and we did not observed changes in viability (Figure 4-figure supplement 2).

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

A crucial step in the development of SLE is the production of autoantibodies. It is shown in Figure 2F that inhibitors of ACK1/BRK enhanced the production of autoantibodies against histones and SSA in a pristane-induced SLE model, which is a significant result that could support the authors' claim. Strangely, this autoantigen panel does not include double-stranded DNA, RNP, or Sm, which should be presented regarding antibody production.

We thank the reviewer for this comment. In the revised manuscript (Revised Figure 3 – Supplement 1) we added the remainder of the autoantibody panel, which includes double-stranded DNA, RNP, and Sm autoantibody levels. We also added the results for serum IgG autoantibody levels in BALB/cByJ mice treated for three months with DMSO, ACK1, or BRK inhibitors but did not receive a pristane injection (Revised Figure 3A). This data shows that mice which received ACK1 or BRK inhibitors had increased serum IgG autoantibodies in comparison to DMSO treated controls.

Additionally, if there is information that inhibitors of ACK1/BRK promote the differentiation of follicular helper T cells, memory B cells, and plasma cells in a pristane-induced SLE model, it could be considered indirect evidence supporting the authors' claims.

These are not available at present to the best of our knowledge.

Reviewer #2 (Recommendations For The Authors):

Minor points:

* In the literature, unpaired t-tests and ordinary one-way ANOVA (Tukey's multiple comparisons test) were used for statistical analysis, which requires data to be normally distributed. This part of the proposal is reflected in the text, and the non-conforming results need to be statistically analyzed using the non-parametric test of graphpad prism.

We would like to thank the reviewer for pointing out this oversight. In the revised manuscript, for all applicable datasets, we tested whether the data was normally distributed using a Shapiro-Wilk normality test. For datasets that were normally distributed statistical significance was determined by a Student t test or ordinary one-way ANOVA with Tukey’s multiple comparisons test depending on the number of conditions being compared and the experimental setup. In contrast, for datasets that were not normally distributed statistical significance was determined using a Mann-Whitney, Kruskal-Wallis multiple comparisons tests, or Wilcoxon matched-pairs signed rank test depending on the experimental setup. P values below 0.05 were considered significant for all statistical tests.

The authors used different methods to represent the level of significant difference. Therefore, it is suggested that the significance level should be expressed by letters. 

As suggested by the reviewer, in the revised manuscript we have designated the significance level throughout all figures using letters (p, or q values).

For RNA-seq, more information should be provided in the paper. For example, the correlation between sample biological replicates, the total number of differentially expressed genes, and randomly selected genes for qRT-PCR results verification.

We would like to thank the reviewer for pointing out this oversight. In the revised manuscript we provided more information regarding the RNA-seq dataset, including a Principal Component Analysis (PCA) showing correlation between sample replicates (Revised Figure 4-figure supplement 1A), as well as a table indicating the number of upregulated and downregulated genes between relevant datasets (Revised Figure 4-figure supplement 1B).

The results of the RNA-seq analysis indicated that ACK1 and BRK contribute to the macrophage anti-inflammatory gene expression program driven by apoptotic cells. MERTK-dependent anti-inflammatory program elicited by apoptotic cells on macrophages is best evidenced by the reduction of LPS-mediated production of inflammatory mediators such as TNF or IL1b 25-27,34,44. Therefore, to validate the RNA-seq results in a functional manner we tested the decrease of LPS-induced production of TNF and IL1b by apoptotic cells in isogenic WT, ACK1 deficient, and BRK deficient macrophages. Consistent with the RNA-seq data, the functional assays indicated that ACK1 and BRK kinase activities are required for the decrease of TNF and IL1b production induced by LPS in response to apoptotic cells (Revised Figure 4H,I).

The raw data files for the RNA-seq analysis have been deposited in the NCBI Gene Expression Omnibus under accession number GEO: GSE118730.

The authors did not have the formats for some of the citations correct. This should be fixed. 

References were reformatted.

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Author response:

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

Response to reviewer’s comments

Reviewer #2 (Public Review):

Summary: 

The manuscript focuses on comparison of two PLP-dependent enzyme classes that perform amino acyl decarboxylations. The goal of the work is to understand the substrate specificity and factors that influence catalytic rate in an enzyme linked to theanine production in tea plants.

Strengths: 

The work includes x-ray crystal structures of modest resolution of the enzymes of interest. These structures provide the basis for design of mutagenesis experiments to test hypotheses about substrate specificity and the factors that control catalytic rate. These ideas are tested via mutagenesis and activity assays, in some cases both in vitro and in plants. 

Weaknesses:

Although improved in a revision, the manuscript could be more clear in explaining the contents of the x-ray structures and how the complexes studied relate to the reactant and product complexes. The manuscript could also be more concise, with a discussion section that is largely redundant with the results and lacking in providing scholarly context from the literature to help the reader understand how the current findings fit in with work to characterize other PLP-dependent enzymes or protein engineering efforts. Some of the figures lack sufficient clarity and description. Some of the claims about the health benefits of tea are not well supported by literature citations.

Thank you for your insightful comments on our manuscript and your recognition of the strengths of our study. We understand your concerns about the weaknesses mentioned, and we have addressed them appropriately in the revised manuscript. We acknowledge that the discussion section needs to be improved for conciseness and context. We have revised this part by removing the redundant content. We also acknowledge your comments concerning the clarity and description of some figures. We have revisited these figures and revised them, ensuring they are clear and adequately described. Lastly, concerning the claims about the health benefits of tea, we understand your concern about the lack of supporting citations. We ensure to back such claims with valid literature or, if necessary, omit these statements.

Reviewer #2 (Recommendations For The Authors):

(1) Line 21: Alanine Decarboxylase should not be capitalized.

Thank you very much for your careful reading of the manuscript. We have corrected it in the revised manuscript.

(2) Line 31: Grammatical error. Also not clear what "evolution analysis" means here. Revise to "Structural comparisons led us to..."

Thank you very much for your careful reading of the manuscript. We have corrected it in the revised manuscript.

(3) Line 34: Revise to "Combining a double mutant of CsAlaDC"

Thank you very much for your careful reading of the manuscript. We have corrected it in the revised manuscript.

(4) Line 35: Change word order to "increased theanine production 672%"

Thank you very much for your careful reading of the manuscript. We have corrected it in the revised manuscript.

(5) Line 37: meaning unclear. Revise to "provides a route to more efficient biosynthesis of theanine."

Thank you very much for your careful reading of the manuscript. We have corrected it in the revised manuscript.

(6) Line 44: I'm not sure that the "health effects" of tea have been proven in placebo controlled studies. And the references provided (2-4 and 5) do not describe original research articles supporting these claims. I would suggest removing these statements from the introduction and at later points in the manuscript.

Thank you for your thoughtful feedback and suggestions. Based on your suggestion, we have removed these statements: "The popularity of tea is determined by its favorable flavor and numerous health benefits (2-4). The flavor and health-beneficial effects of tea are conferred by the abundant secondary metabolites, including catechins, caffeine, theanine, volatiles, etc (5). " As for the subsequent statement: " It has also many health-promoting functions, including neuroprotective effects, enhancement of immune functions, and potential anti-obesity capabilities, among others. " the referenced literature cited can substantiate this conclusion.

(7) Line 58: insert "the" between provided and basis

Thank you very much for your careful reading of the manuscript. We have corrected it in the revised manuscript.

(8) Line 100: Not clear what this phrase means, "As expected, CsSerDC was closer to AtSerDC" Please clarify - closer to what?

We apologize for any confusion caused by the unclear phrasing. When referring to "CsSerDC was closer to AtSerDC," we intended to convey that CsSerDC exhibits a higher degree of sequence homology with AtSerDC than it does with the other enzymes evaluated in our investigation. However, a 1.29% difference between 86.21% and 84.92% in amino acid similarity is not statistically significant (Figure 1B and Supplementary table 1 in the original manuscript), we have deleted the relevant descriptions in the revised manuscript.

(9) Line 112: "were constructed into" makes no sense. It would be better to say the genes for the proteins of interest were inserted into the overexpression plasmid.

Thank you very much for your careful reading of the manuscript. We have corrected it in the revised manuscript.

(10) Line 115: missing the word "the" between generated and recombinant

Thank you very much for your careful reading of the manuscript. We have corrected it in the revised manuscript.

(11) Line 121: catalyze not catalyzed

Thank you very much for your careful reading of the manuscript. We have corrected it in the revised manuscript.

(12) Lines 129 and 130: The reported Km values are really large - in the mM range. Do these values make sense in terms of the available concentrations of the substrates inside the cell?

The content of alanine in tea plant roots ranges from 0.28 to 4.18 mg/g DW (Yu et al., 2021; Cheng et al., 2017). Correspondingly, the physiological concentration of alanine is 3.14 mM to 46.92 mM, in tea plant roots. The content of serine in plants ranges from 0.014 to 17.6 mg/g DW (Kumar et al., 2017). Correspondingly, the physiological concentration of serine is 0.13 mM to 167.48 mM in plants. Therefore, in this study, the Km values are within the range of available substrate concentrations inside the cell.

Yu, Y. et al. (2021) Glutamine synthetases play a vital role in high accumulation of theanine in tender shoots of albino tea germplasm "Huabai 1". J. Agric. Food Chem. 69 (46),13904-13915.

Cheng, S. et al. (2017) Studies on the biochemical formation pathway of the amino acid L-theanine in tea (Camellia sinensis) and other plants. J. Agric. Food Chem. 65 (33), 7210-7216.

Kumar, V. et al. (2017) Differential distribution of amino acids in plants. Amino Acids. 49(5), 821-869.

(13) Line 211: it is unclear what the phrase "as opposed to wild-type" means. Please clarify.

Thank you very much for your careful reading of the manuscript and valuable suggestions. We intend to communicate that the wild-type CsAlaDC and AtSerDC demonstrate decarboxylase activity, while the mutated proteins have experienced a loss of decarboxylation activity. We have already modified this concern in the revised version of the manuscript.

(14) Line 222: residues not residue

Thank you very much for your careful reading of the manuscript. We have corrected it in the revised manuscript.

(15) Line 227 and Figure 4B: It is not clear what the different sequence logos mean in this part of the figure. The caption is too brief and not helpful. And the sentences describing this figure panel are also not sufficiently clear.

Thank you very much for your careful reading of the manuscript and valuable suggestions. We have provided a more detailed explanation of this section in the revised manuscript and added additional annotations in the figure caption to provide further clarity.

(16) Lines 233 and 234: "in the substrate specificity" is awkwardly worded. I would revise to "in selective binding of the appropriate substrate."

Thank you very much for your careful reading of the manuscript and valuable suggestions. We have meticulously revised the description of this section.

(17) Line 243: a word is missing in this sentence - but I can't figure out the intended meaning or what the missing word is. Rephrase to improve clarity.

Thank you very much for your careful reading of the manuscript and valuable suggestions. We have revised this sentence to: " These findings indicate the essential role of Phe106 in the selective binding of alanine for CsAlaDC. "

(18) Line 255: The "expression system...was carried out" is not correct. I would say the expression system was used - but you probably also want to rearrange the sentences to more directly say what it was used for. Later, the word "the" is also missing.

Thank you very much for your careful reading of the manuscript and valuable suggestions. We have revised this sentence to: "To further verify that Phe106 of CsAlaDC and Tyr111 of AtSerDC were key amino acid residues determining its substrate recognition in planta, we employed the Nicotiana benthamiana transient expression system. "

(19) Line 273: use "understand" instead of "elucidate" and instead of "we proposed a prediction test:" say "we designed a test of the prediction that..."

Thank you very much for your careful reading of the manuscript. We have revised this sentence to: “In light of this observation, we postulated a hypothesis:”

(20) Line 301: I don't think "effectuate" is a word. Replace with something else.

Thank you very much for your careful reading of the manuscript. We have revised the sentence as: " The biosynthetic pathway of theanine in tea plants comprises two consecutive enzymatic steps: alanine decarboxylase facilitates the decarboxylation of alanine to generate EA, while theanine synthetase catalyzes the condensation reaction between EA and Glu to synthesize theanine. "

(21) Line 307: replace "activity" with "ability"

Thank you very much for your careful reading of the manuscript. We have corrected it in the revised manuscript.

(22) Line 322: I didn't find the discussion very useful. Much of it is simply a recap of the results - which is not necessary. The structural comparisons are overly descriptive without providing appropriate rationale or topic sentence structure so that the reader understands why certain details are emphasized. I think the manuscript would be much stronger if this section were not included or integreted more concisely into the results section where appropriate.

Thank you for your constructive comments. We understand your concerns about the discussion section of our manuscript. We acknowledge that the discussion section has redundancies with the result. In response to this, we have revised this section to eliminate unnecessary repetition of the results.

(23) Line 369: "an amino acid devoid of the hydroxyl moiety present in Lys" - what does this mean? Lys does not have a hydroxyl functional group. Please correct so that the sentence makes sense.

Thank you very much for your careful reading of the manuscript. This sentence states that the amino acid occupying the corresponding position in CsAlaDC is Phe, which lacks one hydroxyl functional group as compared to Lys. We have made modifications to the sentence as follows: "In contrast, the equivalent position in CsAlaDC is occupied by Phe, an amino acid lacking the hydroxyl group. This substitution enhances the hydrophobic nature of the substrate-binding pocket. "

(24) Line 370: "This structural nuance portends a predisposition for CsAlaDC to select the comparatively hydrophobic amino acid alanine as its suitable substrate." This sentence also makes no sense - please revise to use simpler language so the meaning is more clear.

Thank you very much for your careful reading of the manuscript and valuable suggestions. We have revised the sentence as follows: " Consequently, CsAlaDC demonstrates a unique predilection, selectively binding Ala (an amino acid with comparatively hydrophobic properties) as its preferred substrate."

(25) Lines 376-384: This section makes several references to "catalytic rings." I have no idea what this term means? If the authors mean a loop structure in the enzyme - please use the term "loop"

Thank you very much for your careful reading of the manuscript and valuable suggestions. We have corrected it in the revised manuscript.

(26) Line 396-397: The authors reference data that is not shown in the manuscript. Either show the data in the results section or do not mention.

Thank you for your insightful comment regarding the unshown data referenced in the manuscript. We have included Supplementary figure 9 in the revised manuscript to display this data.

(27) Line 445-446: what is "mutation technology" - if the authors mean site-directed mutagenesis - please use the simpler and more recognizable terminology.

Thank you very much for your careful reading of the manuscript and valuable suggestions. We have revised the sentence as follows: "Based on the findings of this study, site-directed mutagenesis can be employed to modify enzymes involved in theanine synthesis. This modification enhances the capacity of bacteria, yeast, model plants, and other organisms to synthesize theanine, thereby facilitating its application in industrial theanine production."

Reviewer #3 (Public Review):

In the manuscript titled "Structure and Evolution of Alanine/Serine Decarboxylases and the Engineering of Theanine Production," Wang et al. solved and compared the crystal structures of Alanine Decarboxylase (AlaDC) from Camellia sinensis and Serine Decarboxylase (SerDC) from Arabidopsis thaliana. Based on this structural information, the authors conducted both in vitro and in vivo functional studies to compare enzyme activities using site-directed mutagenesis and subsequent evolutionary analyses. This research has the potential to enhance our understanding of amino acid decarboxylase evolution and the biosynthetic pathway of the plant specialized metabolite theanine, as well as to further its potential applications in the tea industry.

Thank you very much for taking the time to review this manuscript. We appreciate all your insightful comments.

Reviewer #3 (Recommendations For The Authors):

The additional material added by the authors addresses some of the previously raised questions and enhances the manuscript's quality. However, certain critical issues we pointed out earlier remain unaddressed. Some of the new data also raises new questions. To provide readers with more comprehensive data, the authors should include additional quantitative data and convert the data presented in the reviewer's comments into supplemental figure format.

Thank you for acknowledging the improvements in the revised manuscript and providing further valuable feedback. We understand your concern about the critical issues that have not been fully addressed and the new questions raised by some of the newly added data. We have strived to address these issues with additional analysis and clarification in our subsequent revision. Regarding your suggestion for more quantitative data and converting the data mentioned in the reviewer's comments into a supplemental figure format, we agree that this would provide a more comprehensive view of the results. We have reformatted the relevant data into supplemental figures to enhance the clarity and accessibility of information. We are grateful for the time and effort you have dedicated to improving our manuscript.

* Page 5 & Figure 1B

"As expected, CsSerDC was most closed to AtSerDC, which implies that they shared similar functions. However, CsAlaDC is relatively distant from CsSerDC."

: In Figure 1B, CsSerDC and AtSerDC are in different clades, and this figure does not show that the two enzymes are closest. To provide another quantitative comparison, please provide a matrix table showing amino acid sequence similarities as a supplemental table. 

Comment: I don't believe that a 1.29% difference between 86.21% and 84.92% in amino acid similarity is statistically significant. Although the authors have rephrased the original sentence, it's improbable that this small 1.29% difference can explain the observed distinction.

Many thanks. We have carefully considered your comments. Indeed, the 1.29% difference in amino acid similarity cannot reflect the functional difference between the AlaDC and SerDC proteins. We have deleted the relevant descriptions in the revised manuscript.

* Page 6, Figure 2, Page 23 (Methods)

"The supernatants were purified with a Ni-Agarose resin column followed by size-exclusion chromatography."

: What kind of SEC column did the authors use? Can the authors provide the SEC elution profile comparison results and size standard curve?

Comment: The authors should include the SEC elution profiles as a supplemental figure or incorporate them as a panel in Figure 2. Furthermore, they should provide a description of the oligomeric state of each protein in this experiment. Additionally, there is a significant difference between CsSerDC (65.38 mL) and CsAlaDC (74.37 mL) elution volumes. Can this difference be explained structurally? In comparison to the standard curve of molecular weight provided by the authors, it appears that these proteins are at least homo-tetramers, which contradicts the description in the text. This should be re-evaluated and clarified.  

Thank you very much for your careful reading of the manuscript and valuable suggestions. We have included the SEC elution profile in Supplemental figure 1A and added descriptions of the oligomeric states of proteins in the revised manuscript. CsSerDC was eluted at 65.38 mL, corresponding to a molecular weight of 292 kDa, which is five times the monomeric protein (54.7 kDa). However, due to the absence of CsSerDC crystal structure, it remains uncertain whether the protein forms a pentamer. AtSerDC was eluted at 72.25 mL, with a corresponding molecular weight of 155 kDa, which is 3.3 times the monomer (47.3 kDa). CsAlaDC was eluted at 74.37 mL, with a corresponding molecular weight of 127 kDa, which is 2.7 times the monomer (47.3 kDa). The elution profiles suggest that AtSerDC and CsAlaDC potentially exist in homotrimeric form. This observation stands in contradiction to our subsequent findings where the protein manifests in a dimeric structure. A plausible explanation could be the non-ideal spherical shape of the protein. Under such circumstances, the hydrodynamic radius of the protein could supersede its actual size, potentially leading to an overestimation of the molecular weight on the size-exclusion chromatography [ref].

References:

Burgess, R. R. (2018) A brief practical review of size exclusion chromatography: Rules of thumb, limitations, and troubleshooting. Protein Expression and Purification. 150, 81-85.

Erdner J. M., et al. (2006) Size-Exclusion Chromatography Using Deuterated Mobile Phases. Journal of Chromatography A. 1129(1):41–46.

* Page 6 & Page 24 (Methods)

"The 100 μL reaction mixture, containing 20 mM substrate (Ala or Ser), 100 mM potassium phosphate, 0.1 mM PLP, and 0.025 mM purified enzyme, was prepared and incubated at standard conditions (45 {degree sign}C and pH 8.0 for CsAlaDC, 40 {degree sign}C and pH 8.0 for AtSerDC for 30 min)."

(1) The enzymatic activities of CsAldDC and AtSerDC were measured at two different temperatures (45 and 40 {degree sign}C), but their activities were directly compared. Is there a reason for experimenting at different temperatures?

(2) Enzyme activities were measured at temperatures above 40{degree sign}C, which is not a physiologically relevant temperature and may affect the stability or activity of the proteins. At the very least, the authors should provide temperature-dependent protein stability data (e.g., CD spectra analysis) or, if possible, temperature-dependent enzyme activities, to show that their experimental conditions are suitable for studying the activities of these enzymes.

Comment: I appreciate the authors for including temperature-dependent enzyme activity data in their study. However, it remains puzzling that plant enzymes were tested at a physiologically irrelevant temperature of 40 and 45 degrees Celsius. Additionally, it may not be appropriate to directly compare enzyme activity measurements at different temperatures. Furthermore, the data at 45 degrees in panel A appears to be an outlier, which contrasts with the overall trend observed in the graph.

We appreciate your point regarding the testing temperatures for plant enzymes. We fully appreciate the importance of conducting experiments under physiologically relevant conditions. But the intent behind operating at these elevated temperatures was to assess the thermal stability of the enzymes, which can be a valuable characteristic in certain applications, such as industrial production processes, and does not necessarily reflect their physiological conditions. Our findings indicate that CsAlaDC exhibits its peak activity at 45 °C. This result aligns with previously reported data in the literature [Bai, P. et al. (2021) figure 4e], thus bolstering our confidence in the reliability of our experimental outcomes.

Author response image 1.

Relative activity of CsAlaDC at different temperatures.

* Pages 6-7 & Table 1

(1) Use the correct notation for Km and Vmax. Also, the authors show kinetic parameters and use multiple units (e.g., mmol/L or mM for Km).

(2) When comparing the catalytic efficiency of enzymes, kcat/Km (or Vmax/Km) is generally used. The authors present a comparison of catalytic activity from results to conclusion. A clarification of what results are being compared is needed.

Comment: The authors are still comparing catalytic efficiency solely based on the Vmax values. As previously suggested, it would be advisable to calculate kcat/Km and employ it for comparing catalytic efficiencies. Furthermore, based on the data provided by the authors, I conducted a rough calculation of these catalytic efficiencies and did not observe a significant difference, which contrasts with the authors' statement, "These findings indicated that the catalytic efficiency of CsAlaDC is considerably lower than that of both CsSerDC and AtSerDC." This discrepancy requires clarification.  

We want to express our sincere appreciation for your meticulous review and constructive suggestions. We understand the importance of accurately comparing catalytic efficiencies using Kcat/Km values, rather than solely relying on Vmax values. Following your suggestion, we recalculated Kcat/Km to reanalyze our results. The computed Kcat/Km for CsSerDC and AtSerDC are 152.7 s-1 M-1 and 184.6 s-1 M-1, respectively. For CsAlaDC, the calculated Kcat/Km is 55.7 s-1 M-1. Therefore, the catalytic efficiency of CsSerDC and AtSerDC is approximately three times that of CsAlaDC.  What we intended to convey was that the Vmax of CsAlaDC is lower than that of CsSerDC and AtSerDC.  Our description in the manuscript was not accurate, and we have addressed this in the revised version.

* Pages 9 & 10

"This result suggested this Tyr is required for the catalytic activity of CsAlaDC and AtSerDC."

: The author's results are interesting, but it is recommended to perform the experiments in a specific order. First, experiments should determine whether mutagenesis affects the protein's stability (e.g., CD, as discussed earlier), and second, whether mutagenesis affects ligand binding (e.g., ITC, SPR, etc.), before describing how site-directed mutagenesis alters enzyme activity. In particular, the authors' hypothesis would be much more convincing if they could show that the ligand binding affinity is similar between WT and mutants.

Comments: While it is appreciated that you have included CD and UV-vis absorption spectra data, it would be more beneficial to provide quantitative data to address the previously proposed binding affinity. I also recommend presenting the data mentioned in the reviewer's comments as a supplementary figure for better clarity and reference.  

Thank you for your valuable feedback and suggestions. I agree that providing quantitative data would lend more support to our findings and better address the proposed binding affinity.

It is generally acknowledged that proteins complexed with PLP exhibit a yellow hue, and the ligand PLP forms a Schiff base structure with the ε-amino group of a lysine residue in the protein, with maximum absorbance around 420 nm. However, during our protein purification process, we observed that the purified protein retained its yellow coloration, even when PLP wasn't introduced into the purification buffer. Subsequent absorbance measurements revealed that the protein exhibited absorbance within the aforementioned wavelength (420 nm) (the experimental results are shown in the following figures), implying an inherent presence of the PLP ligand within the protein. This could have resulted from binding with PLP during the protein's expression in E. coli. Consequently, due to this inseparability between the protein and the ligand, obtaining quantitative data through experimental means becomes unfeasible.

Author response image 2.

(A) Absorption Spectra of CsAlaDC (WT) and CsAlaDC (Y336F). (B) Absorption Spectra of AtSerDC (WT) and AtSerDC (Y341F).

Regarding your suggestion about presenting the data mentioned in the reviewer's comments as a supplementary figure, we agree that it is an excellent idea. We have prepared supplementary figure 7 and supplementary figure 8 accordingly, ensuring that they present the required data.

* Page 10

"The results showed that 5 mM L-DTT reduced the relative activity of CsAlaDC and AtSerDC to 22.0% and 35.2%, respectively"

: The authors primarily use relative activity to compare WT and mutants. Can the authors specify the exact experiments, units, and experimental conditions? Is it Vmax or catalytic efficiency? If so, under what specific experimental conditions?

Response: "However, due to the unknown mechanism of DTT inhibition on protein activity, we have removed this part of the content in the revised manuscript."

Comment: I believe this requires a more comprehensive explanation rather than simply removing it from the text.  

Although we have observed that DTT is capable of inhibiting enzyme activity, at present, we are unable to offer a comprehensive explanation for the inhibitory effect of DTT on enzyme activity in terms of its structural and catalytic mechanisms. Further research is required to elucidate the mechanism of action of DTT. It is worth noting, however, that our study does not emphasize investigating the specific inhibitory mechanisms of DTT on enzyme activity. Furthermore, the existing findings do not provide an adequate explanation for the observed phenomenon, leading us to exclude this particular aspect from the content.

* Pages 10-12

: The identification of 'Phe106 in CsAlaDC' and 'Tyr111 in AtSerDC,' along with the subsequent mutagenesis and enzymatic activity assays, is intriguing. However, the current manuscript lacks an explanation and discussion of the underlying reasons for these results. As previously mentioned, it would be helpful to gain insights and analysis from WT-ligand and mutant-ligand binding studies (e.g., ITC, SPR, etc.). Furthermore, the authors' analysis would be more convincing with accompanying structural analysis, such as steric hindrance analysis.

Comment: While it is appreciated that you have included UV-vis absorption spectra data, it would be more beneficial to provide quantitative data to address the previously proposed binding affinity. I also recommend presenting the data mentioned in the reviewer's comments as a supplementary figure for better clarity and reference.  

Response: Thank you for your valuable feedback and suggestions. Given that the protein forms a complex with PLP during its expression in E. coli and cannot be dissociated from it, obtaining quantitative data via experimental protocols is rendered impracticable.

Author response image 3.

(A) Absorption Spectra of CsAlaDC (WT) and CsAlaDC (F106Y). (B) Absorption Spectra of AtSerDC (WT) and AtSerDC (Y111F).

Mutant proteins and wild-type proteins exhibited absorption bands at 420 nm, suggesting the formation of a Schiff base between PLP and the active-site lysine residue.

Regarding your suggestion about presenting the data mentioned in the reviewer's comments as a supplementary figure, we have prepared supplementary figure 7 and supplementary figure 8 accordingly, ensuring that they present the required data.

Author response:

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

Public Reviews:

Reviewer #1:  

Overall, the conclusions appear appropriately supported by the data, and the data appear of high quality.

Strengths:

The particular strengths of the paper include an impressive combination of genomic and imaging-based approaches and insightful genetically engineered cell systems. The manuscript reports interesting and potentially important findings. The text is generally very well written, the ideas are clearly explained, and the reasoning is easy to follow.

Weaknesses:

The main weakness seems to be that the heat and ethanol shock approaches likely elicit pleiotropic effects, and therefore it is a challenge to test the causal relationship between various observations. Nevertheless, even as indirect effects might contribute to some of the authors' observations, the results are definitively worth reporting.  

We agree that these two proteotoxic stresses can impact cell physiology in multiple ways and discuss this on lines 132-143 and 500-519. Moreover, in this revision we have more rigorously quantified the extent of proteotoxic stress elicited by the 39°C heat shock and 8.5% ethanol stress (Figure 1E; see response 1 to Reviewer 2). We have additionally added new Figure 2 that reveals an important difference in the way Hsf1 and its negative regulator, the Hsp70 co-chaperone Sis1, respond to HS and ES. This difference is evident at two different intensities for each stress as described in more detail below (see response 1 to Reviewer 2).

Presentation of some of the data could be improved.

We agree and have made improvements/data additions to multiple figures: Figure 1E; Figures 3A, B; Figures 4A, B; Figure 7 (data drawn from original Fig. 6 and Fig. 6 – fig. suppl. 1 and reorganized); Fig. 8B; Figure 9; Figure 10. Corresponding enhancements to the supplemental figures have been made as well. 

Reviewer #2:  

(1) The central finding of the study highlights the different dynamics of Hsf1, Pol II, and gene organization in response to heat shock versus ethanol stress. However, one important limitation to consider is that the two chosen conditions may not be directly comparable. For a balanced assessment, the authors should ideally expose yeast to various ethanol concentrations and different heat shock temperatures, ensuring the observed differences stem from the nature of the stressor rather than suboptimal stress intensity. At the very least, an additional single ethanol concentration point on each side of 8.5% should be investigated to ensure that 8.5% is near the optimum. In fact, comparing the number of Hsp104 foci in the two conditions in Fig. 1E and F suggests that the yeast is likely experiencing different intensities of stress for the chosen heat shock condition and ethanol concentration used in this study.

We thank the reviewer for this important suggestion. In this revision, we have included an enhanced analysis of the yeast cellular response to each of these stresses. As illustrated in revised Figure 1, the two stresses used throughout this study – 39°C heat shock and 8.5% ethanol stress – both elicit a proteotoxic response, as assayed by the de novo formation of Hsp104 clusters. While 10 min exposure to 8.5% ethanol results in the formation of multiple discrete (spherical) foci, a 10 min exposure to the elevated temperature leads the appearance of multiple, largely diffuse Hsp104 clusters, some of which are spherical (new Fig. 1D). The difference in morphology notwithstanding, we have attempted to quantify these clusters using Imaris v. 10.0.1 image analysis software; the results are depicted in Fig. 1E. Such quantification suggests that 8.5% ethanol elicits a more intense stress than exposure to 39°C. A caveat is that it is unclear whether diffuse Hsp104 clusters are comparable to compact Hsp104 foci (see response 3 below).

Beyond the apparent difference in intensity, a new analysis presented in new Figure 2 reveals that heat shock, elicited by temperature upshift to either 39°C or 42°C, induces relocalization of the J-protein Sis1 – a key negative regulator of Hsf1 – from the nucleoplasm to the nucleolar periphery. Sis1’s perinucleolar ring localization agrees with previous findings of 39°C heat-shocked cells (Feder et al., 2021). Ethanol stress, whether 5% or 8.5%, initially causes Sis1 to relocalize diffusely throughout the nucleus and cytosol. At 10 min, Sis1 localizes to the periphery of the nucleus, thereby providing a marked contrast to what is observed in response to heat shock. These new results are described on lines 174-191.

Taking these two observations together, we asked whether a less severe ethanol stress (5%) would induce Hsf1 puncta. It does, and as rapidly as 8.5% ethanol (data are presented in revised Figure 8-figure supplement 1). Interestingly, in the presence of 5% ethanol, Hsf1 puncta begin to dissolve at 30 min. This strongly contrasts with the case when cells are exposed to 8.5% ethanol (Figure 8; Figure 8-figure supplement 1). As we state in this revision (lines 414-424), the sustained presence of condensates that we originally observed is likely the consequence of the intensity of the proteotoxic stress elicited by exposure to 8.5% ethanol; analogous responses to these two stress conditions have been observed before (lines 495-501). 

(2) A second significant concern is the use of the term "Hsf1 condensate". Chowdhary et al.'s 2022 Molecular Cell study highlighted an inhomogeneous distribution and rapid dynamics of Hsf1 clustering upon heat shock, with sensitivity to 1,6-hexandiol, which is interpreted as evidence for condensation by LLPS. However this interpretation has been criticized severely by McSwiggen et al. Genes Dev 2019 and Mussacchio EMBO J 2022. It is important to mention that 1,6-hexandiol is known to affect chromatin organization (Itoh et al. Life Science Alliance 2021). Describing such clusters as 'condensates' without further experimental evidence is premature.  

While we appreciate and largely agree with the point made by this reviewer, we prefer to maintain the term “condensate”. Banani et al (2017) originally defined “biomolecular condensate” to mean selforganized membrane-free compartments that concentrate specific biomolecules. It was never meant to imply LLPS although its widespread use in the literature has led to that implication. We clarify our use of this term on lines 99-104.   

(3) Figure 1: Why does ethanol stress at 0 min display a larger number of Hsp104 foci per cell than heat shock at the same time? How are foci defined by the authors? In Fig. 1D, there are many smaller puncta. A comparative assessment of the number and size of foci for heat shock and ethanol stress would be beneficial.

We thank the reviewer for raising this point and have addressed it as follows.  First, we repeated the assay with a different strain (DPY1561) and increased the number of cells assayed from 40 to 200. This larger sample size created the same T=0 baseline for both stresses (Figure 1E). Second, we define Hsp104 foci as diffraction-limited structures with a diameter of ~0.4 µm (lines 747-749).  Third, employing Imaris v. 10.0.1, we quantified foci size (= volume) and a summary graph has been added to Figure 1E that also displays the number of foci per cell. In the legend to this figure, we point out that to conduct this analysis we assumed that the diffuse Hsp104 clusters seen in HS cells are comparable to the compact Hsp104 foci in ES cells (lines 1169-1171). 

(4) Figure 2: Selecting a housekeeping gene with consistent expression levels is crucial for meaningful qPCR analysis. Do SCR1 mRNA levels fluctuate during heat shock or ethanol stress?  

We thank the reviewer for this question. In revised Figure 3 – figure supplement 1C we provide a new graph (reproduced here) revealing that the levels of SCR1 do not significantly change under either heat shock or ethanol stress relative to the non-stressed control (0 min). One-way ANOVA analysis was performed for both HS and ES and p values were 0.094 and 0.083, respectively (calculated using GraphPad Prism 8).

(5) Additionally, certain genes, such as TMA10 and SSA4, lack visible bars at time 0. Are these levels undetectable? The varying y-axis scales are confusing; presenting data as relative fold changes could offer a clearer perspective.

Transcript levels for all genes evaluated here are detectable, even in the basal unstressed state. They are not visible on the histogram for certain genes at T= 0 due to the prodigious fold-increase in RNA elicited by heat shock.  However, to address this concern, we have added a bar graph inset displaying basal transcript levels for each gene in revised Figure 3. We reproduce data for SSA4 and TMA10 in the graphs below. In addition, we present transcript levels in new Figure 3 - figure supplement 1 for cells subjected to ethanol stress to allow a better appreciation of their increase over time. 

Author response image 1.

(6) Line 239: The evidence for chromatin compaction is unconvincing. An increase in H3 occupancy by ChIP might indicate a reduction in histone exchange dynamics but may not relate to overall chromatin compaction. The authors use H2A-mCherry to suggest a decrease in chromatin volume, but this data is not persuasive. Did the authors observe any changes in nuclear size? Perhaps quantifying chromatin compaction more directly, using signal intensity per volume, would be informative.

To address this concern, we attempted to quantify integrated density for H2A-mCherry using Image J software. While the volume decreased for both stresses, the integrated density only increased for ethanol stress. We speculate that this may be due to photobleaching which has been reported for heat shock. The combination of heat and acidic pH contribute to loss of fluorescence signal (Alkaabi et al., 2005). While the integrated density supports the idea of global chromatin compaction in the ethanol stress condition, given the above concerns with the HS sample we elected to not present these data.

(7) Line 340: The claim of a "strong spatiotemporal correlation" isn't evident from the data. Could correlation coefficients be provided? There is potential anti-correlation in Fig. 6 - Figure Supplement 1C.

We thank the reviewer for this excellent suggestion. We now present an analysis of the correlation between HSP104 – HSP12 coalescence and HSP104 transcription for both HS and ES time courses, using single cell data of Figures 7D, 7E and Figure 7- suppl. 1D.  This analysis is presented in new Figure 7F.

(8) Figure 8: The WT data in Fig 8 seem inconsistent with Fig. 4 (e.g. the interaction frequency for HSP104 and SSA2). Are these fluctuations between experiments, or are they side effects of IAA treatment? The use of ethanol as an IAA solvent vehicle raises concerns. It would be beneficial if the authors could demonstrate that 1.7% ethanol in the control does not induce ethanol stress.

We acknowledge that there existed an inconsistency in the magnitude of intergenic interaction frequencies reported in the two experiments for HSP104 and SSA2. Some of this might be attributed to the fact that different strains were used, W303-1B in Figure 4 and LRY016 (W303-1B; LEU2::pGPD1osTIR1) in Figure 8. Nonetheless, in each experiment there was a prodigious fold-increase in interaction frequency over the no stress (T= 0 min) control for both HS and ES conditions and moreover, in each experiment the magnitude of this interaction was greater for the 2.5 min HS sample vs. the 10 min ES sample. However, to obviate this concern, we have removed the HSP104-SSA2 analysis from Figure 9 (corresponds to original Fig. 8).

Regarding the second point, we cannot entirely rule out the concern that the 1.7% ethanol vehicle might impact 3C interaction frequencies. It is unlikely to be significant, however, given that most other pairwise tests evaluated in the two experiments (Figs. 5 and 9) resulted in similar 3C values. In particular, there was no consistent trend towards higher (or lower) interaction frequencies in the IAA experiment of Fig. 9.  

Reviewer #3:  

This is an interesting manuscript that builds off of this group's previous work focused on the interface between Hsf1, heat shock protein (HSP) mRNA production, and 3D genome topology. Here the group subjects the yeast Saccharomyces cerevisiae to either heat stress (HS) or ethanol stress (ES) and examines Hsf1 and Pol II chromatin binding, Histone occupancy, Hsf1 condensates, HSP gene coalescence (by 3C and live cell imaging), and HSP mRNA expression (by RT-qPCR and live cell imaging). The manuscript is well written, and the experiments seem well done, and generally rigorous, with orthogonal approaches performed to support conclusions…While identifying a mechanistic basis for the results [presented here] would be a tough task perhaps beyond the scope of this study, it would nevertheless be helpful to place these results in context with a series of other studies…importantly, this work left out PMID: 32015439 (HSF1 phase transition mediates stress adaptation and cell fate decisions) which is particularly relevant considering that it shows that it is human HSF1 condensate resolution rather than simple condensate formation that is associated with HSF1 transcriptional activity - which is similar to the findings here with this particular dose of HS resulting in resolution and high transcriptional activity versus ES resulting in resolution failure and lower activity. 

We thank the Reviewer for pointing out this oversight. In this revision, we cite Gaglia et al., 2020 and several others reporting HSF1 foci formation in human cells exposed to heat shock. The single cell analysis of Gaglia et al argued that dissolution of large HSF1 foci (aka “nuclear stress bodies”), typically several µm in diameter and localized over satellite III DNA repeats (Jolly et al., 1997, 2002), correlates with HSP gene activation. Importantly, these condensates are postulated to act as reservoirs of HSF1, sequestered away from HSP genes (Gaglia et al., 2020).  In contrast, Zhang et al., 2022 has shown that human HSF1 inducibly forms small condensates (~300 nm) that localize over HSP genes and whose formation directly correlates with HSP gene activation (we discuss the Jolly, Gaglia and Zhang findings on lines 382-394). Likewise, our work shows that in yeast, Hsf1 inducibly forms small, dynamic clusters that colocalize with HSR genes within 2.5 min of exposure to elevated temperature; these dissolve ~20-60 min later (Figure 8 and Figure 8-supp. 1). In concert with Hsf1 condensate formation, HSR gene repositioning and transcription/ Pol II recruitment are likewise evident within 2.5 min. Therefore, in HS cells there exists coordinate induction of condensate formation, Pol II recruitment, transcription and intergenic interactions (for a detailed kinetic analysis of HSR gene interactions, see Figures 5 and 6 of Chowdhary et al, 2017).  This tight temporal relationship is absent in ethanol stressed cells (Figures 3, 4, 5, 6, 7, 8; summarized in Figure 10 and Table 1).

It is also worth noting that the stresses themselves are quite different - ethanol can be used as a carbon source and so beyond inducing proteotoxic stress, the yeast are presumably adapting to this distinct metabolic state. Basically, it is not clear whether these differences are due to the dose of stress, versus we are looking at an early timepoint as ES initiates a genome-wide chromatin restructuring and gene expression reprogramming that goes beyond a response to proteotoxic stress. This reviewer is not suggesting a barrage of new experiments, but perhaps discussion points to contextualize results.

We thank the reviewer for this suggestion and in our revised manuscript discuss these issues (lines 414424 and 486-498 [5% vs. 8.5% ethanol]; lines 500-519 [ethanol as a metabolite]).

Recommendations for the authors:

Reviewer #1:

(1) In Figure 1E, the number of foci in control (0 min) cells is very different for the two conditions. Could the authors clarify/check this? Based on the mean numbers at time point 0, the control cells for the ethanol treatment already contain about 10-20 Hsp104 foci, compared to around 5 foci per cell in the control for heat shock.

We thank the reviewer for raising this point and have repeated the assay with a different strain (DPY1561).  And as shown in Figure 1E, have confirmed that the control samples have similar number of foci.  

(2) In the same Figure 1E, is the P-value relative to the control or the same time point in the other treatment? A comparison across treatments would be necessary to support the claim in lines 168-171 of the text.

The statistical analysis (Mann Whitney test) was performed by comparing each stress timepoint to the no stress control. We clarify this in the figure legend. 

(3) In Figure 1D, the heat-shock condition shows the same cells that are used in the control, but the cells in the ethanol-shock condition are different. This is a bit visually misleading compared to the experimental setup shown in panel 1C. The authors could show the control cells for the ethanol condition as well.

We thank the reviewer for this excellent suggestion and have added the 0 min image for the ethanol stress conditions.

(4) In Figure 7B adding images at 60min would help underscore the point that the condensates are stable in ethanol shocked cells.

We appreciate this suggestion as well and have included a 60 min timepoint for both stresses (Figure 8B). 

Reviewer #2:

(1) Line 113: Has it not been established that yeast Hsf1 is constitutively trimeric?

In yeast, only a fraction of Hsf1 is thought to be constitutively trimeric and it is this species that binds high-affinity HSEs even under non-stressful conditions (Giardina & Lis, 1995; Pincus et al., 2018). We have added this clarification to the text (lines 121-123). 

(2) Ethanol can precipitate proteins, especially in rich media like YPD. Did the authors notice any protein precipitation? If yes, how do they account for effects due to nutrient loss by precipitation?

This is an interesting point, but we did not notice any precipitates in either rich or synthetic liquid media containing 8.5% (v/v) ethanol for any of the time points used in the experiments.

(3) Figure 3: The figure appears incomplete. Can enhancer, promoter, coding region, and 3'UTR be shown consistently for all genes examined?

In response to this point, we have simplified this figure (new Fig. 4) by uniform presentation of factor occupancy at enhancer, promoter, and coding region loci for all but one of the genes evaluated. For HSP12 (330 bp), we were unable to distinguish promoter from coding region since the average sonicated chromatin fragment obtained using a Bioruptor is ~300 bp. Therefore, we evaluated only the HSP12 coding region for Pol II and histone H3 occupancy. 

(4) Figure 4: The comparison between heat shock at 2.5 min and ethanol stress at later points is puzzling. Why not use consistent time points as in Fig. 3?

Time points for the two stresses examined in this figure (new Fig. 5) were selected to represent times of peak intergenic interaction between HSR genes. These times were derived from our earlier analysis of 3C interactions during a heat shock time course (Figs. 5, 6 of Chowdhary et al., 2017) and ES data presented in this study, including Fig. 4 (Pol II ChIP time course) and Fig. 6 (3C time course). Data presented in Figs. 5 and 6 are consistent with the notion that intergenic interactions in cells subjected to ethanol stress are delayed relative to those observed in heat shocked cells, peaking in most cases at ~10 min (vs. ~2.5 min for heat stress (Chowdhary et al., 2017)).  

(5) Figure 5: Fig. 5B top panel seems to show color inconsistencies for bars at 0 and 120 min. Also, the xaxis on the top left panel seems to have a typo; should it read "10," not "0?"

We thank the reviewer for the observation. We changed the graphs in new Figure 6 to display the same color for all time points.  We also fixed the typo. 

(6) Line 302: The evidence presented supports maximal mRNA levels, but the claim of "maximal transcription" requires support from nascent RNA analysis.

We agree that RT-qPCR measures mRNA abundance, not nascent transcription. We have changed the text to refer to “transcript levels” where pertinent (lines 301-302; 1331-1332).

(7) How long do loci remain coalescent during heat shock versus ethanol stress? Both 3C and imaging analyses do not differentiate between frequency and duration, which seems essential for understanding interaction dynamics.

We thank the reviewer for this excellent question. In new Fig. 7D,E (data drawn from Fig. 6 – fig. suppl. 1), HSR gene coalescence detected in single cells over a HS or ES time course is charted.  Interpretable data exist for a small number of cells. Moreover, for both HS and ES states, in certain cells coalescence between the representative Hsf1 target genes HSP104 and HSP12 dissolves and then reappears. With this caveat in mind, the data suggest that HSP104-HSP12 coalescence can last at least 15 min in HS cells and up to 30 min in ES cells. We have not emphasized this point in the manuscript since a far more comprehensive analysis – beyond the scope of this study – is required.

(8) For longer analyses, how do the authors accommodate potential ethanol concentration changes due to evaporation?

For liquid cultures, we relied on maintaining minimal changes in the vapor pressure within the experimental vessel; to facilitate that, flasks were tightly covered to minimize evaporation and temperature was kept at 25°C. For most molecular analyses (RT-qPCR, ChIP, 3C), we confined our analysis to the first 60 min. For microscopy, the samples were encased within a concave slide, covered by a coverslip, as illustrated below. In addition, to tightly seal the coverslip on the slide we used petrolatum.  This arrangement minimized evaporation.

Author response image 2.

(9) Figure 9: This legend seems to have an incomplete sentence: "(represented using ...)."

We have substituted an entirely new model in this revised manuscript (new Figure 10) that omits the use of an ellipsis. (We had used it to symbolize a delay in the appearance of HSR gene transcription in ES cells.)

References  

Alkaabi, K. M., Yafea, A., & Ashraf, S. S. (2005). Effect of pH on thermal- and chemical-induced denaturation of GFP. Applied Biochemistry and Biotechnology, 126(2), 149–156. https://doi.org/10.1385/ABAB:126:2:149

Chowdhary, S., Kainth, A. S., & Gross, D. S. (2017). Heat Shock Protein Genes Undergo Dynamic Alteration in Their Three-Dimensional Structure and Genome Organization in Response to Thermal Stress. Molecular and Cellular Biology, 37(24), 1–23. https://doi.org/10.1128/mcb.00292-17

Feder, Z. A., Ali, A., Singh, A., Krakowiak, J., Zheng, X., Bindokas, V. P., Wolfgeher, D., Kron, S. J., & Pincus, D. (2021). Subcellular localization of the J-protein Sis1 regulates the heat shock response. Journal of Cell Biology, 220(1), e202005165. https://doi.org/10.1083/JCB.202005165

Gaglia, G., Rashid, R., Yapp, C., Joshi, G. N., Li, C. G., Lindquist, S. L., Sarosiek, K. A., Whitesell, L., Sorger, P. K., & Santagata, S. (2020). HSF1 phase transition mediates stress adaptation and cell fate decisions. Nature Cell Biology, 22(2), 151–158. https://doi.org/10.1038/s41556-019-0458-3

Giardina, C., & Lis, J. T. (1995). Dynamic protein-DNA architecture of a yeast heat shock promoter. Molecular and Cellular Biology, 15(5), 2737–2744. https://doi.org/10.1128/mcb.15.5.2737

Jolly, C., Konecny, L., Grady, D. L., Kutskova, Y. A., Cotto, J. J., Morimoto, R. I., & Vourc’h, C. (2002). In vivo binding of active heat shock transcription factor 1 to human chromosome 9 heterochromatin during stress. Journal of Cell Biology, 156(5), 775–781. https://doi.org/10.1083/jcb.200109018

Jolly, C., Morimoto, R. I., Robert-Nicoud, M., & Vourc’h, C. (1997). HSF1 transcription factor concentrates in nuclear foci during heat shock: Relationship with transcription sites. Journal of Cell Science, 110(23), 2935–2941. https://doi.org/10.1242/jcs.110.23.2935

Pincus, D., Anandhakumar, J., Thiru, P., Guertin, M. J., Erkine, A. M., & Gross, D. S. (2018). Genetic and epigenetic determinants establish a continuum of Hsf1 occupancy and activity across the yeast genome. Molecular Biology of the Cell, 29(26), 3168–3182. https://doi.org/10.1091/mbc.E18-060353

Zhang, H., Shao, S., Zeng, Y., Wang, X., Qin, Y., Ren, Q., Xiang, S., Wang, Y., Xiao, J., & Sun, Y. (2022). Reversible phase separation of HSF1 is required for an acute transcriptional response during heat shock. Nature Cell Biology, 24(3), 340–352. https://doi.org/10.1038/s41556-022-00846-7

Author response:

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

Public Reviews:

Reviewer #1:

(1) In cardiac and renal transplantation, cold preservation in ice remains a common practice for transporting explanted hearts to donors which remains a cheap and easily accessible way of preserving organs. While ex-vivo mechanical circulatory platforms have been developed and are increasingly being utilized to prolong organ viability, cold preservation remains widely used. The authors perfused explanted hearts with oxygenated perfusion preservation devices at subnormothermic temperatures (20-23C) which is even much lower than routinely used in clinical cardiopulmonary bypass scenarios (28-32C) (in the discussion, the authors allude to SNC80's possible "protective effect" in cardiac bypass). It is unclear how much of the hypometabolic state is related to WB3 administration versus hypothermia. The study will benefit from a comparison of WB3 administration and hypothermia in Xenopus, explanted porcine organs versus cold preservation alone to show distinction in biostasis parameters.

Indeed, we expect that both pharmaceutical interventions and cooling could contribute to a hypometabolic state. To assess this, the controls and the treated groups were exposed to the same temperatures for both the Xenopus (18C) and porcine heart experiments (20-23C). Therefore, we can conclude that any changes in the treatment group relative to control can be attributed to the introduction of SNC80 or WB3 and not from cooling alone.  

(2) The authors selected SNC80 based on a literature survey where it was identified based on its ability to induce hypothermia and protect against the effects of spinal cord ischemia in rodents. While this makes sense, were other drugs (eg. Puerarin) considered? The induction of hypothermia and spinal cord protective effect of SNC80 may be multifactorial and not necessarily related to its biostatic effects as the authors describe. Please provide some more context into the background of SNC80.

During our research program, we considered and tested other drugs (>100 existing compounds in Xenopus screens). Although the published hypothermic and tissue protective effects suggested to us that SNC80 should be included in screening, it was not until we observed effects across multiple test parameters, systems, and species that we honed in on SNC80 as a lead compound. We have added additional information to further clarify the background of SNC80 on pgs. 3-4. 

(3) In most of the models, the primary metric that the authors utilize to characterize metabolic activity is oxygen consumption, which is a somewhat limited indicator. For instance, this does not provide any information, however, on anaerobic metabolic activity. In addition, the ATP/ADP ratio was found to decrease in the organ chips where SNC80 was utilized, but similar findings were not presented for the other models. 

We thank reviewers for their important point. We have therefore added additional experiments, including the Seahorse Mitostress assay for the four human cell types (Caco-2, Huh7, LSEC and HUVEC) used in the Organ Chip systems. We have added a description and an interpretation of the results in the section, Stasis induction in cultured human cells and tissues and mention the role of glycolysis and cytosolic reductive carboxylation as compensatory mechanisms.  Although the ATP/ADP ratio gave us useful insight into Huh-7 cells and chips metabolic activity, this method requires transfection and live imaging which does not suit other models such as Xenopus, or whole organs. Additionally, in animal models there may be other confounding factors that might influence ATP/ADP.

(4) The authors should provide a more detailed explanation of SNC80's mechanisms of interaction with proteins related to transmembrane transport, mitochondrial activity, and metabolic processes. What is the impact of SNC80 on mitochondrial function, particularly ATP production and mitochondrial respiration? Are there changes in mitochondrial membrane potential, electron transport chain activity, or oxidative phosphorylation? In this context, the authors discuss the potential role of NCX1 as a binding target for SNC80 and its various mechanisms in slowing metabolism. However, no experiments have been done to confirm this binding in the present study. Coimmunoprecipitation studies using appropriate antibodies against SNC80 and NCX1 should be considered to demonstrate their direct binding. Additionally, surface plasmon resonance (SPR) or isothermal titration calorimetry (ITC) experiments could be employed to quantify the binding affinity between SNC80 and NCX1, providing further evidence of their interaction. These experiments would elucidate the binding mechanism between SNC80 and NCX1 and reveal more information on the mechanism of action for SNC80. 

We agree that further definition of the mechanism of action is an important next step for this work; however, it is far beyond the scope of the present study.

(5) The manuscript notes that histological analysis was conducted, but it seems that only example images are provided, such as Figure 4f. Quantified histological data would provide a more thorough understanding of tissue integrity. 

We have added quantified histological data to the manuscript that was performed by a clinician blinded to the groups and interventions (Figure 4f).

(6) Some of the points mentioned in the discussion and conclusion are rather strong and based on possible associations such as SNC80's potential vasodilatory capacity conferring a cardioprotective effect, and ability to reversibly suppress metabolism across different temperatures and species. Please tone this down and stay limited to the organs studied. Further, the reversibility of the findings may be more objectively assessed by biomarkers with decreased immunofluorescence in response to ischemia such as troponin I for the heart and albumin for the liver. Additionally, an investigation of proteins involved in inflammation, hypoxia, and key cell death pathways using immunohistochemistry analysis can better describe the impact of treatment on apoptosis/necroptosis. 

We have revised aspects of the Discussion and Conclusion to focus on the organs studied in the present work (pgs. 14-17). We agree that markers of inflammation, hypoxia, and cell death are critical for assessing tissue health post-treatment. We performed PCR to assess such markers (Figure 4e) and found reductions in inflammatory cytokine and injury biomarker levels. Although we agree that immunohistochemistry may be useful, such as for looking at any spatial patterns of injury, PCR offers broader dynamic range and higher sensitivity and therefore was chosen for this assay.

(7) What could be the underlying cause of the observed increase in intercellular spacing after SNC80 administration in porcine limbs which also seems to be evident in the heart histology samples? This seems to be more prominent in the SNC80 compared to the vehicle group. 

Since the muscle bundle areas of baseline and treated tissues were essentially the same, the increase in intracellular space in the SNC80-treated tissue suggests a compensatory reduction in muscle fiber diameter.  Intracellular metabolite concentrations have been shown to be quite stable over a large range of metabolic activities (Hochachka et al. 1998). As such, a reduction in metabolic activity induced by SNC80 may suggest reduction in the accumulation of intracellular metabolites. In order to maintain a stable intracellular metabolite concentration, water would have to be expelled accounting for the increased intracellular space.

P W Hochachka, G B McClelland, G P Burness, J F Staples, R K Suarez Comp Biochem Physiol B Biochem Mol Biol 120, 17–26 (1998).

(8) In the Discussion section, it would be valuable to provide a concise interpretation of the lipidomic data, particularly explaining how changes in acylcarnitine and cholesterol ester levels may relate to tadpole metabolism, hibernation, or other biological processes. 

An interpretation of the lipidomics data has been summarized in the Discussion (pg. 14).

(9) What are the limitations or disadvantages of the study? Does SNC80 possess any immunomodulatory properties that might affect the outcomes of organ transplantation? Are there specific organs for which SNC80 may not be a suitable preservation agent, and if so, what are the reasons behind this? 

This study is limited in two ways. The first is that we characterized the function of the donor pig heart outside of the body, and therefore future work will be required to verify the function and quality of the hearts after they have been transplanted. Secondly, SNC80 is not currently approved for use in clinical settings and during earlier pre-clinical trials of the drug, side effects including seizures were noted and its development was halted. It is hypothesized that these seizures are related to SNC80’s delta opioid activity, so we developed a new, non-opioid analog called WB3, which will be used in future work. We have added a description of the prior seizure findings to the text (pg. 5).

Based on assessment of tissue biomarkers by PCR, it seems that SNC80 does exhibit immunomodulating properties. Because organ transplant recipients are treated with strong immunosuppressants to prevent organ rejection, we anticipate that SNC80 would either further support this goal, have little additional effect, or reduce the amount of additional immunosuppressive drugs that would need to be administered. To date, our data does not suggest that there are specific organs for which SNC80 may not be a suitable preservation agent.     

Reviewer #2:

(1) The authors developed an analog of a known delta opioid receptor activator SNC80 with three orders of magnitude lesser binding with the delta opioid receptor WB3. This will likely reduce the undesirable effects of SNC80 while preserving the metabolic slowing needed for organ preservation. Yet, most experiments were done with SNC80, not the superior modification, WB3, shown in only a limited set of experiments, Figure 3.  

We included the WB3 studies in Xenopus to confirm that the biostatic activity is not mediated through the delta opioid receptor. We have only performed a limited number of experiments with WB3 because we are focused on improving its solubility so that it can be easily dissolved in common organ perfusates without DMSO, which we were able to use in the Xenopus experiments. 

(2) The heart is one of the most challenging organs to preserve, and some experiments are done to establish the metabolic effects of SNC80. However, the biodistribution study, shown in Figure 2, conspicuously omitted the heart. 

Thank you for this suggestion. We returned to the biodistribution study dataset and were able to measure uptake by the heart at the 1-hour time point. We observe an increase in uptake above levels observed for other tissues at 1 hour and at levels similar to the skeletal muscle at 2 hours (plot below). Unfortunately, the heart was not visible in a sufficient number of Xenopus tissue sections to reevaluate uptake at the 2-hour time point. We were also able to re-evaluate the lipidomics data for the heart. Acylcarnitine and cholesterol ester were not significantly different between vehicle and SNC80-treated groups. The lack of change in acylcarnitine is particularly important since its upregulation has been shown to be a marker for cardiovascular disease in humans (Deda et al. 2022). The expanded lipidomics data have been added to Figure 2.

Deda O, Panteris E, Meikopoulos T, Begou O, Mouskeftara T, Karagiannidis E, Papazoglou AS, Sianos G, Theodoridis G, Gika H. Correlation of serum acylcarnitines with clinical presentation and severity of coronary artery disease. Biomolecules. 2022 Feb 23;12(3):354.

Author response image 1.

(3) I do not understand the design of the electrophysiology and contractility experiments with the porcine hearts. How did you defibrillate the hearts after removal and establishing perfusion? Lines 173-175 on Page 7 state: "After defibrillation with epinephrine, the P and QRS waveforms were visible in ECGs from 3 of 4 SNC80-treated hearts (Table S1), suggesting that those hearts regain atrial and ventricular polarization." Please clarify.

Defibrillation is done with an electric shock. Also, please show the ECG recordings to support your conclusions about "polarization." What did you mean by "polarization"? Depolarization? Repolarization? Or resting potential. To establish a normal physiological state, please show ECG waveforms and present data on basic ECG characteristics: heart rate, PQ and QT intervals, and P and QRS durations. I recommend perfusion of the porcine heart with WB3, not only SNC80.  

Hearts were defibrillated by the application of a 10 to 30 Joule electrical shock delivered from internal paddles positioned at the right atrium (negative) across to the left ventricle (positive). Once rhythm was established, 0.5 ml of 1:1000 epinephrine was administered via the aortic inflow. Electrocardiogram (ECG) showed that both vehicle and SNC80-treated hearts exhibited irregular contractions after perfusate flush and during rewarming prior to defibrillation. After defibrillation (10-30 J electrical shock) followed by epinephrine, a regular heartbeat was established in 3 of 4 SNC80-treated hearts, exhibiting normal P and QRS waveforms (Table S1). That observation suggested that the intrinsic atrial and ventricular muscle fiber contractility was preserved, and the overall conduction system of the heart was viable. The pulse rates of SNC80-treated hearts were at or near normal for porcine hearts (70-120 beats/min) after defibrillation. Vehicle-treated hearts exhibited tachycardia following defibrillation, with all exhibiting pulse rates above the normal range for porcine hearts. We have added clarifying text and definitions (pg. 8). We have only performed a limited number of experiments with WB3 because we are focused on improving its solubility so that it can be easily dissolved in common organ perfusates without DMSO, which we were able to use in the Xenopus experiments.

(4) Pathology data also raises concerns. The histology images shown in Figure 4f are not quantified, and they show apparently higher levels of tissue disruption in SNC80-treated tissue vs vehicle-treated. The test (lines 169-171) confirms this concern: "In some hearts treated with SNC80, greater waviness of muscle fibers was observed, possibly indicating a state of muscle contraction."  

The histology images shown in Figure 4f were quantified and the myocardial injury score quantification show comparable histology between the groups.

(5) The apparent state of contracture suggests a higher degree of myocardial damage and a high intracellular calcium level in SNC80-treated hearts. 

The authors suggested that the sodium-calcium exchanger NCX is a possible target of SNC80 and could be responsible for the "hypometabolic state." However, NCX1 is critically important in the extrusion of cytosolic Ca2+ during the diastolic phase. Failure to remove excessive calcium and restore ionic homeostasis would lead to calcium overload and heart failure. 

The histological assessment doesn’t indicate a higher degree of myocardial damage in SNC80 treated hearts. Our data are not suggestive of high intracellular calcium buildup in SNC80treated hearts. If that were the case, we would have had challenges restoring the rhythm of the hearts on the Langendorff post-preservation, which was not observed.

(6) I am surprised the authors did not consider using the gold standard assay for measuring mitochondrial function in cells by the Seahorse Cell Mito Stress Test. 

Thank you for this important point. We have added data from the Seahorse Mitostress assay for the four human cell types (Caco-2, Huh7, LSEC and HUVEC) included in the Organ Chip experiments. We have added a description and an interpretation of the results in the section Stasis induction in cultured human cells and tissues. We now mention the role of glycolysis and cytosolic reductive carboxylation as compensatory mechanisms.   

Reviewer #3:

(1) The authors perform a literature search to identify SNC80 as a promising hit. However, the details of the literature search, a list of other potential hits, and the criteria for identification of SNC80 are not described. The hypometabolic effect of SNC80 exposure is well-characterized in the Xenopus model. Furthermore, the authors show that SNC80 localises to the brain, but do not discuss several studies that have pointed to convulsions induced by exposure to high doses of SCN80, and whether this would be apparent in the Xenopus studies. The authors have promising data on the WB3 morpholino that retains or even improves on the hypometabolism phenotype of SCN80 while likely not retaining delta opioid activity. However, this is not functionally demonstrated. Moreover, WB3 is not used in any of the other assays and models used in the study. In the setting of cardiac transplant surgery, co-administration of SNC80 reduces metabolic activity and inflammation, although it is unclear if there is an improvement in recovery of organ function due to SCN80.

Thank you for raising these important points. We have added details of the process to identify SNC80 (pgs. 3-4) and a discussion of the studies pointing to convulsions with high doses of SNC80 (pg. 5) (which were not observed in Xenopus studies). We have also incorporated measurements of oxygen consumption during WB3 treatment in Xenopus (Figure 3d).

(2) The reversible induction of hypometabolic status is also demonstrated in two different organ chips. These models could identify the differential response of epithelial cells and vascular cells to drug perfusion, but the authors have mostly focused on the former. Finally, the authors identify specific targets for the hypometabolic effect of SNC80, which is a valuable resource for other screening studies and can form the basis for future work. 

In the revised manuscript, we have also added data from the Seahorse Mitostress assay for the four human cell types (Caco-2, Huh7, LSEC and HUVEC). We have added a description and an interpretation of the results in the section Stasis induction in cultured human cells and tissues. We highlight the differences in metabolic response from the four cell types to SNC80 treatment. It is important to note that the metabolism-suppressing effects of SNC80 were most potent in the epithelial cells that were originally derived from highly metabolic tumors (Caco-2 and Huh7) versus primary normal endothelial cells (HUVEC and LSEC), which is also consistent with past work suggesting that targeting of the NCX1 channel might offer a way to slow tumor growth (Wan et al. 2022). Because we observed more prominent effects in epithelial cells in 2D assays, we decided to focus the 3D organ chips assays on epithelial cells.

Wan, H. et al. NCX1 coupled with TRPC1 to promote gastric cancer via Ca2+/AKT/β-catenin pathway. Oncogene (2022) doi:10.1038/s41388-022-02412-9.

Recommendations for the authors:

Reviewer #1:

(1) Line 136, "Based on these intriguing findings with human Organ Chips". No mention of human organ chips was made in the text at this point, suggest rewording.  

Thank you for identifying this error. We have revised this line (pg. 6).

(2) Please provide more information on previous studies that have explored other drugs for organ protection, the novelty of the findings of this study, and how the findings of this study compare to prior data. 

Building on the background of organ preservation drugs provided in the Introduction, we have added details to compare our outcomes to other drugs explored for organ protection (pg. 15).

(3) The dosing study in Supplemental Figure S1 provides some context on why the authors utilized the 100 uM SNC80 concentration. It would be helpful if the authors could elaborate in the Discussion on the mechanistic rationale for this concentration. 

This dose was chosen to maximize suppression of metabolic and activity parameters, while ensuring reversibility of biostasis. We have clarified this in the Discussion (pg. 14).

(4) In Supplement Figure S2a, the y-axis measures the relative metabolic rate. It seems from the text that this is a relative measure of oxygen consumption, but it should be clarified accordingly. 

We have clarified this point in the Methods section.  

(5) What is the specific time or time frame when the reversed effect of SNC80 is most pronounced or at its peak? 

When Xenopus are moved to fresh medium after SNC80 treatment, we observe a 15-minute period during which no reversal is evident from motion measurements. After that period, we observe a gradual, linear recovery over 2 hours. We cannot designate a specific period in which the reversal effect is most pronounced from these data.

(6) WB3 seems to show a faster and stronger impact on swimming in comparison to SNC80. What could be the potential reasons for this difference, and could this have any clinical implications? 

From our current data, we understand the key difference to be that SNC80 has greater affinity for the delta opioid receptor compared to WB3. Therefore, we hypothesize that by not interacting with the opioid system, WB3 induces faster and stronger impacts on swimming. In mice, it has been shown that SNC80 directly inhibits forebrain GABAergic neurons via activity at their delta opioid receptors, which leads to convulsions (Chung et al. 2015). Although we do not observe seizure-like behavior in Xenopus, drugs that inhibit GABAergic neurons can produce stimulant effects in vivo. Since WB3 has a lower affinity for the delta opioid receptor, it likely produces less stimulation, leading to faster and stronger suppression of swimming behaviors. Additionally, it is possible that WB3 interacts with additional targets we have not yet identified.

Chung PC, Boehrer A, Stephan A, Matifas A, Scherrer G, Darcq E, Befort K, Kieffer BL. Delta opioid receptors expressed in forebrain GABAergic neurons are responsible for SNC80-induced seizures. Behavioural brain research. 2015 Feb 1;278:429-34.

(7) Elaborate on the potential significance of SNC80's distribution in the GI tract, gill region, and skeletal muscle. How might this distribution relate to the observed physiological effects? 

In Xenopus tadpoles, we observe SNC80 uptake in the gill region and GI tract within 1 hour. The multiple possible routes of uptake in Xenopus (skin, gills, and mouth) may account for the relatively rapid physiological effects observed in our experiments. The uptake observed in the muscle may be specifically responsible for the slowed motion observed in Xenopus activity assays. This has been elaborated upon in the text (pg. 5).

(8) Please use italics where needed, e.g., in vitro, in vivo, etc. 

This has been updated throughout the article.

(9) Supplemental Figure S1 - Is there any reason for having 3 replicates for the 100uM compared to the 4 replicates in the other groups? 

Each group had 4 replicates; however, a review of the replicates for the 100 µM group suggested the presence of a leak or air bubble in one oxygen measurement vial, which, therefore, had to be excluded from the analysis.

(10) Figure 3 description - 'c' should be bold. 

Figure 3 has been updated.

Reviewer #3:

Title: The title suggests that several candidate compounds are identified but the study focuses primarily on SCN80. Please consider rephrasing to make it more specific to this molecule. Alternatively, the manuscript would be significantly strengthened if more data is provided for WB3. 

Although the study focuses on SNC80, we introduce an entirely novel molecule, WB3, and therefore, we feel it is more appropriate to indicate that multiple molecules were studied.

Line 58-59: please cite additional primary literature papers for the different therapeutics discussed. As an example, the authors do not cite or discuss Massen et al PMID: 31743376 which suggests that H2S is able to induce similar hypometabolic effects even at 37C. 

Thank you for this suggestion. We have expanded our discussion of primary literature paper for the therapeutics discussed (pg. 15).

Line 76 - 77: The authors do not provide any data on the other possible hits from their literature search or methods details on how this was done. No relevant literature has been cited. What criteria were used to finalise SNC80? 

During our research program, we considered and tested other drugs (>100 existing compounds in Xenopus screens). Although the published hypothermic and tissue-protective effects suggested that SNC80 should be included in screening, it was not until we observed effects across multiple test parameters, systems, and species that we honed in on SNC80 as a lead compound. We have added additional information to further clarify the background of SNC80 on pgs. 3-4.  

Line 85 and Lines 342-345 in the Discussion: SNC80 is reported to induce convulsions at high doses in rodents and primates - was this also evident in the Xenopus studies? How does the dose used in the Xenopus studies compare with the high dose (ca. 10 mg/kg) used in primate studies Danielson et al., PMID: 17112570? 

We did not observe convulsions in SNC80-treated Xenopus. However, we have updated the manuscript to include previous observations of convulsions in rodents and primates treated with SNC80 (pg. 5). Due to a number of differences, it is challenging to directly compare the dosing in Xenopus studies to those in the primate. In the present study, groups of 10 Xenopus were exposed to a 10 mL pool of 100 µM SNC80, which may be absorbed via oral, gill, and skin routes. Primates were dosed with 10 mg/kg delivered intramuscularly. Because these models may result in different drug biodistributions, any direct comparisons would be speculative. Further work in rodent models may help clarify the relevant dosing differences.

Line 117: what does 'double the concentration' mean? Is this with reference to the dose of SNC80? If so, is this sufficient to completely block opioid receptor activity? 

Yes, we meant that naltrindole was dosed at double the concentration of SNC80. We have clarified this in the text (pg. 5). Prior work in rodent brain tissue has shown that radiolabeled naltrindole binds to saturation at picomolar to nanomolar concentrations (Yamamura et al. 1992). To confirm our initial observations with naltrindole and SNC80, we also tested a SNC80 analog (WB3) with very low delta opioid activity (Figure 3), which showed similar effects.

Yamamura MS, Horvath R, Toth G, Otvos F, Malatynska E, Knapp RJ, Porreca F, Hruby VJ, Yamamura HI.

Characterization of [3H] naltrindole binding to delta opioid receptors in rat brain. Life sciences. 1992 Jan 1;50(16):PL119-24.

Figure 3c, d: It appears that WB3 is even more effective at rapidly reducing motion and inducing faster recovery which is an exciting result. However, in 3d it appears that longterm exposure of 8h has detrimental effects since the heart rate remains depressed. Please clarify. 

Yes, at 8 hours, we observe slow recovery and, in some cases, maintenance of depressed heart rates. This could be because the drug is more lipophilic and might remain in fat tissue for longer times. Although our current goal is to lengthen the time window for heart transplant surgery to 6 hours, we are working on formulating WB3 to optimize safety for longer applications (8+ hours).

Figure 4: the experiments with the heart transplants are well done, but do not demonstrate an additional protective effect over the current standard of care except for the reduced metabolism. Could the authors discuss this further in the discussion or provide data with WB83, which may show a stronger effect? Scale bars are missing in panel f.  

In addition to reduced metabolism, we also demonstrate reduced expression of inflammation, hypoxia, and cell death-related markers compared to machine perfusion alone (Figure 4e). The potential protective effect of the biostasis-inducing compounds will be further investigated in a planned orthotopic porcine transplant study where pigs will be followed up for 6 hours post weaning off a bypass machine allowing enough time to assess potential benefit of biostasisinducing drugs. Additionally, we have added scale bars (Figure 4f).

Order of manuscript: Line 136 already refers to the organ-chip data, which is only presented at the end. Please edit. I feel the manuscript would flow better with the organchip data presented before the heart transplant data. 

Organ-chip data: this is an important component of the story but is only shown in supplementary figures. Consider showing this data in the main figures, as eLife has no space restrictions. Furthermore, it is unclear if the effluent collected and analysed is from apical or vascular, or both. In any case, the analysis via microscopy-based methods appears restricted to the epithelium. The manuscript would be significantly strengthened by providing some data on the effect of SNC80 on vascular cells. 

As requested, we have moved the Organ Chips results to a main figure (new Fig. 5). We have added additional experiments, including the Seahorse Mitostress assay for the four human cell types (Caco-2, Huh7, LSEC and HUVEC). We have added a description and an interpretation of the results in the section Stasis induction in cultured human cells and tissues. The 2D assays showed that metabolism-suppressing effects of SNC80 were most potent in the epithelial cells that were originally derived from highly metabolic tumors (Caco-2 and Huh7) versus endothelial cells (HUVEC and LSEC). Based on these results, we decided to focus the 3D organ chips assays on epithelial cells only, and hence only analyzed effluents from the epithelial (apical) channel.

Methods section for fabrication of oxygen sensors: Please refer to prior papers from your lab (Grant et al., PMID: 35274118) with regards to details of the fabrication of the devices with inbuilt oxygen sensors. 

The methods used for the fabrication of oxygen sensors will be included in a separate manuscript currently in preparation.  

Figure S3 and Line 243-244: Please provide the data for untreated control organ chips in panels d and e a mean value for which is quoted in the main text. The images in panel f are too small for the reader to appreciate the point, please provide zooms. Scalebars are also missing from these images. Please increase the number of replicates for S3f - the liver-chip data has only two replicates which has very low power for statistical testing. In general, the number of organ chips used for the data for each panel is missing. 

As mentioned in the captions, Figure S3 (now Figure S5) panels d and e show average albumin production of Liver Chips at day 7-10 of culture. These measurements were performed before any treatment with SNC80 to characterize the chip’s functional metabolism. In panel g, although we only show biological N=2-3, each datapoint corresponds to an average of multiple fields of view (multiple technical replicates). We have now clarified this in the figure legend.

Figure S4 - I do not quite understand why the perfusion with the vehicle only also affects oxygen release in the liver chip. Is it possible to use a different vehicle? 

The liver and gut oxygen levels are not on the same y-axis (gut on the left and liver on the right). The oxygen fold change of the liver control chip is below 0.5, which is in the same range as the gut control chip (0 +/- 0.25). There is a natural variation in oxygen consumption over the lifetime of the chips (now Figure 5c), and untreated cells are metabolically active and consuming oxygen. The small drop observed suggests that liver chips may not have reached a stable oxygen consumption rate at the time of the experiment, whereas the gut chips have stabilized.  

Figure S5c-f: The units on the Y-axis are missing. 

Panels S5c-d (now Figure S6c-d) depict the percent cytotoxicity and are thus unitless. Panels S5e-h (now Figure S6e-h) show the effluent levels relative to baseline and are also unitless. We have updated the figure caption to clarify this.

Author response:

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

Critical synopsis of the articles cited by referee 2:

(1) ‘Generalized workflow for characterization of Nernstian dyes and their effects on bacterial physiology’, L.Mancini et al, Biophysical Journal, 2020, 118, 1, 4-14.

This is the central article used by referee 2 to argue that there are issues with the calibration of ThT for the measurement of membrane potentials. The authors use a simple Nernstian battery (SNB) model and unfortunately it is wrong when voltage-gated ion channels occur. Huge oscillations occur in the membrane potentials of E. coli that cannot be described by the SNB model. Instead a Hodgkin Huxley model is needed, as shown in our eLife manuscript and multiple other studies (see above). Arrhenius kinetics are assumed in the SNB model for pumping with no real evidence and the generalized workflow involves ripping the flagella off the bacteria! The authors construct an elaborate ‘work flow’ to insure their ThT results can be interpreted using their erroneous SNB model over a limited range of parameters.

(2) ‘Non-equivalence of membrane voltage and ion-gradient as driving forces for the bacterial flagellar motor at low load’, C.J.Lo, et al, Biophysical Journal, 2007, 93, 1, 294.

An odd de novo chimeric species is developed using an E. coli  chassis which uses Na+ instead of H+ for the motility of its flagellar motor. It is not clear the relevance to wild type E. coli, due to the massive physiological perturbations involved. A SNB model is using to fit the data over a very limited parameter range with all the concomitant errors.

(3) Single-cell bacterial electrophysiology reveals mechanisms of stress-induced damage’, E.Krasnopeeva, et al, Biophysical Journal, 2019, 116, 2390.

The abstract says ‘PMF defines the physiological state of the cell’. This statement is hyperbolic. An extremely wide range of molecules contribute to the physiological state of a cell. PMF does not even define the electrophysiology of the cell e.g. via the membrane potential. There are 0.2 M of K+ compared with 0.0000001 M of H+ in E. coli, so K+ is arguably a million times more important for the membrane potential than H+ and thus the electrophysiology!

Equation (1) in the manuscript assumes no other ions are exchanged during the experiments other than H+. This is a very bad approximation when voltage-gated potassium ion channels move the majority ion (K+) around!

In our model Figure 4A is better explained by depolarisation due to K+ channels closing than direct irreversible photodamage. Why does the THT fluorescence increase again for the second hyperpolarization event if the THT is supposed to be damaged? It does not make sense.

(4) ‘The proton motive force determines E. coli robustness to extracellular pH’, G.Terradot et al, 2024, preprint.

This article expounds the SNB model once more. It still ignores the voltage-gated ion channels. Furthermore, it ignores the effect of the dominant ion in E. coli, K+. The manuscript is incorrect as a result and I would not recommend publication.

In general, an important problem is being researched i.e. how the membrane potential of E. coli is related to motility, but there are serious flaws in the SNB approach and the experimental methodology appears tenuous.

Answers to specific questions raised by the referees

Reviewer #1 (Public Review):

Summary:

Cell-to-cell communication is essential for higher functions in bacterial biofilms. Electrical signals have proven effective in transmitting signals across biofilms. These signals are then used to coordinate cellular metabolisms or to increase antibiotic tolerance. Here, the authors have reported for the first time coordinated oscillation of membrane potential in E. coli biofilms that may have a functional role in photoprotection.

Strengths:

- The authors report original data.

- For the first time, they showed that coordinated oscillations in membrane potential occur in E. Coli biofilms.

- The authors revealed a complex two-phase dynamic involving distinct molecular response mechanisms.

- The authors developed two rigorous models inspired by 1) Hodgkin-Huxley model for the temporal dynamics of membrane potential and 2) Fire-Diffuse-Fire model for the propagation of the electric signal.

- Since its discovery by comparative genomics, the Kch ion channel has not been associated with any specific phenotype in E. coli. Here, the authors proposed a functional role for the putative K+ Kch channel : enhancing survival under photo-toxic conditions.

We thank the referee for their positive evaluations and agree with these statements.

Weaknesses:

- Since the flow of fresh medium is stopped at the beginning of the acquisition, environmental parameters such as pH and RedOx potential are likely to vary significantly during the experiment. It is therefore important to exclude the contributions of these variations to ensure that the electrical response is only induced by light stimulation. Unfortunately, no control experiments were carried out to address this issue.

The electrical responses occur almost instantaneously when the stimulation with blue light begins i.e. it is too fast to be a build of pH. We are not sure what the referee means by Redox potential since it is an attribute of all chemicals that are able to donate/receive electrons. The electrical response to stress appears to be caused by ROS, since when ROS scavengers are added the electrical response is removed i.e. pH plays a very small minority role if any.

- Furthermore, the control parameter of the experiment (light stimulation) is the same as that used to measure the electrical response, i.e. through fluorescence excitation. The use of the PROPS system could solve this problem.

>>We were enthusiastic at the start of the project to use the PROPs system in E. coli as presented by J.M.Krajl et al, ‘Electrical spiking in E. coli probed with a fluorescent voltage-indicating protein’, Science, 2011, 333, 6040, 345. However, the people we contacted in the microbiology community said that it had some technical issues and there have been no subsequent studies using PROPs in bacteria after the initial promising study. The fluorescent protein system recently presented in PNAS seems more promising, ‘Sensitive bacterial Vm sensors revealed the excitability of bacterial Vm and its role in antibiotic tolerance’, X.Jin et al, PNAS, 120, 3, e2208348120.

- Electrical signal propagation is an important aspect of the manuscript. However, a detailed quantitative analysis of the spatial dynamics within the biofilm is lacking. In addition, it is unclear if the electrical signal propagates within the biofilm during the second peak regime, which is mediated by the Kch channel. This is an important question, given that the fire-diffuse-fire model is presented with emphasis on the role of K+ ions.

We have presented a more detailed account of the electrical wavefront modelling work and it is currently under review in a physical journal, ‘Electrical signalling in three dimensional bacterial biofilms using an agent based fire-diffuse-fire model’, V.Martorelli, et al, 2024 https://www.biorxiv.org/content/10.1101/2023.11.17.567515v1

- Since deletion of the kch gene inhibits the long-term electrical response to light stimulation (regime II), the authors concluded that K+ ions play a role in the habituation response. However, Kch is a putative K+ ion channel. The use of specific drugs could help to clarify the role of K+ ions.

Our recent electrical impedance spectroscopy publication provides further evidence that Kch is associated with large changes in conductivity as expected for a voltage-gated ion channel (https://pubs.acs.org/doi/10.1021/acs.nanolett.3c04446, 'Electrical impedance spectroscopy with bacterial biofilms: neuronal-like behavior', E.Akabuogu et al, ACS Nanoletters, 2024, in print.

- The manuscript as such does not allow us to properly conclude on the photo-protective role of the Kch ion channel.

That Kch has a photoprotective role is our current working hypothesis. The hypothesis fits with the data, but we are not saying we have proven it beyond all possible doubt.

- The link between membrane potential dynamics and mechanosensitivity is not captured in the equation for the Q-channel opening dynamics in the Hodgkin-Huxley model (Supp Eq 2).

Our model is agnostic with respect to the mechanosensitivity of the ion channels, although we deduce that mechanosensitive ion channels contribute to ion channel Q.

- Given the large number of parameters used in the models, it is hard to distinguish between prediction and fitting.

This is always an issue with electrophysiological modelling (compared with most heart and brain modelling studies we are very conservative in the choice of parameters for the bacteria). In terms of predicting the different phenomena observed, we believe the model is very successful.

Reviewer #2 (Public Review):

Summary of what the authors were trying to achieve:

The authors thought they studied membrane potential dynamics in E.coli biofilms. They thought so because they were unaware that the dye they used to report that membrane potential in E.coli, has been previously shown not to report it. Because of this, the interpretation of the authors' results is not accurate.

We believe the Pilizota work is scientifically flawed.

Major strengths and weaknesses of the methods and results:

The strength of this work is that all the data is presented clearly, and accurately, as far as I can tell.

The major critical weakness of this paper is the use of ThT dye as a membrane potential dye in E.coli. The work is unaware of a publication from 2020 https://www.sciencedirect.com/science/article/pii/S0006349519308793 [sciencedirect.com] that demonstrates that ThT is not a membrane potential dye in E. coli. Therefore I think the results of this paper are misinterpreted. The same publication I reference above presents a protocol on how to carefully calibrate any candidate membrane potential dye in any given condition.

We are aware of this study, but believe it to be scientifically flawed. We do not cite the article because we do not think it is a particularly useful contribution to the literature.

I now go over each results section in the manuscript.

Result section 1: Blue light triggers electrical spiking in single E. coli cells

I do not think the title of the result section is correct for the following reasons. The above-referenced work demonstrates the loading profile one should expect from a Nernstian dye (Figure 1). It also demonstrates that ThT does not show that profile and explains why is this so. ThT only permeates the membrane under light exposure (Figure 5). This finding is consistent with blue light peroxidising the membrane (see also following work Figure 4 https://www.sciencedirect.com/science/article/pii/S0006349519303923 [sciencedirect.com] on light-induced damage to the electrochemical gradient of protons-I am sure there are more references for this).

The Pilizota group invokes some elaborate artefacts to explain the lack of agreement with a simple Nernstian battery model. The model is incorrect not the fluorophore.

Please note that the loading profile (only observed under light) in the current manuscript in Figure 1B as well as in the video S1 is identical to that in Figure 3 from the above-referenced paper (i.e. https://www.sciencedirect.com/science/article/pii/S0006349519308793 [sciencedirect.com]), and corresponding videos S3 and S4. This kind of profile is exactly what one would expect theoretically if the light is simultaneously lowering the membrane potential as the ThT is equilibrating, see Figure S12 of that previous work. There, it is also demonstrated by the means of monitoring the speed of bacterial flagellar motor that the electrochemical gradient of protons is being lowered by the light. The authors state that applying the blue light for different time periods and over different time scales did not change the peak profile. This is expected if the light is lowering the electrochemical gradient of protons. But, in Figure S1, it is clear that it affected the timing of the peak, which is again expected, because the light affects the timing of the decay, and thus of the decay profile of the electrochemical gradient of protons (Figure 4 https://www.sciencedirect.com/science/article/pii/S0006349519303923 [sciencedirect.com]).

We think the proton effect is a million times weaker than that due to potasium i.e. 0.2 M K+ versus 10-7 M H+. We can comfortably neglect the influx of H+ in our experiments.

If find Figure S1D interesting. There authors load TMRM, which is a membrane voltage dye that has been used extensively (as far as I am aware this is the first reference for that and it has not been cited https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1914430 [ncbi.nlm.nih.gov]/). As visible from the last TMRM reference I give, TMRM will only load the cells in Potassium Phosphate buffer with NaCl (and often we used EDTA to permeabilise the membrane). It is not fully clear (to me) whether here TMRM was prepared in rich media (it explicitly says so for ThT in Methods but not for TMRM), but it seems so. If this is the case, it likely also loads because of the damage to the membrane done with light, and therefore I am not surprised that the profiles are similar.

The vast majority of cells continue to be viable. We do not think membrane damage is dominating.

The authors then use CCCP. First, a small correction, as the authors state that it quenches membrane potential. CCCP is a protonophore (https://pubmed.ncbi.nlm.nih.gov/4962086 [pubmed.ncbi.nlm.nih.gov]/), so it collapses electrochemical gradient of protons. This means that it is possible, and this will depend on the type of pumps present in the cell, that CCCP collapses electrochemical gradient of protons, but the membrane potential is equal and opposite in sign to the DeltapH. So using CCCP does not automatically mean membrane potential will collapse (e.g. in some mammalian cells it does not need to be the case, but in E.coli it is https://www.biorxiv.org/content/10.1101/2021.11.19.469321v2 [biorxiv.org]). CCCP has also been recently found to be a substrate for TolC (https://journals.asm.org/doi/10.1128/mbio.00676-21 [journals.asm.org]), but at the concentrations the authors are using CCCP (100uM) that should not affect the results. However, the authors then state because they observed, in Figure S1E, a fast efflux of ions in all cells and no spiking dynamics this confirms that observed dynamics are membrane potential related. I do not agree that it does. First, Figure S1E, does not appear to show transients, instead, it is visible that after 50min treatment with 100uM CCCP, ThT dye shows no dynamics. The action of a Nernstian dye is defined. It is not sufficient that a charged molecule is affected in some way by electrical potential, this needs to be in a very specific way to be a Nernstian dye. Part of the profile of ThT loading observed in https://www.sciencedirect.com/science/article/pii/S0006349519308793 [sciencedirect.com] is membrane potential related, but not in a way that is characteristic of Nernstian dye.

Our understanding of the literature is CCCP poisons the whole metabolism of the bacterial cells. The ATP driven K+ channels will stop functioning and this is the dominant contributor to membrane potential.

Result section 2: Membrane potential dynamics depend on the intercellular distance

In this chapter, the authors report that the time to reach the first intensity peak during ThT loading is different when cells are in microclusters. They interpret this as electrical signalling in clusters because the peak is reached faster in microclusters (as opposed to slower because intuitively in these clusters cells could be shielded from light). However, shielding is one possibility. The other is that the membrane has changed in composition and/or the effective light power the cells can tolerate (with mechanisms to handle light-induced damage, some of which authors mention later in the paper) is lower. Given that these cells were left in a microfluidic chamber for 2h hours to attach in growth media according to Methods, there is sufficient time for that to happen. In Figure S12 C and D of that same paper from my group (https://ars.els-cdn.com/content/image/1-s2.0-S0006349519308793-mmc6.pdf [ars.els-cdn.com]) one can see the effects of peak intensity and timing of the peak on the permeability of the membrane. Therefore I do not think the distance is the explanation for what authors observe.

Shielding would provide the reverse effect, since hyperpolarization begins in the dense centres of the biofilms. For the initial 2 hours the cells receive negligible blue light. Neither of the referee’s comments thus seem tenable.

Result section 3: Emergence of synchronized global wavefronts in E. coli biofilms

In this section, the authors exposed a mature biofilm to blue light. They observe that the intensity peak is reached faster in the cells in the middle. They interpret this as the ion-channel-mediated wavefronts moved from the center of the biofilm. As above, cells in the middle can have different membrane permeability to those at the periphery, and probably even more importantly, there is no light profile shown anywhere in SI/Methods. I could be wrong, but the SI3 A profile is consistent with a potential Gaussian beam profile visible in the field of view. In Methods, I find the light source for the blue light and the type of microscope but no comments on how 'flat' the illumination is across their field of view. This is critical to assess what they are observing in this result section. I do find it interesting that the ThT intensity collapsed from the edges of the biofilms. In the publication I mentioned https://www.sciencedirect.com/science/article/pii/S0006349519308793#app2 [sciencedirect.com], the collapse of fluorescence was not understood (other than it is not membrane potential related). It was observed in Figure 5A, C, and F, that at the point of peak, electrochemical gradient of protons is already collapsed, and that at the point of peak cell expands and cytoplasmic content leaks out. This means that this part of the ThT curve is not membrane potential related. The authors see that after the first peak collapsed there is a period of time where ThT does not stain the cells and then it starts again. If after the first peak the cellular content leaks, as we have observed, then staining that occurs much later could be simply staining of cytoplasmic positively charged content, and the timing of that depends on the dynamics of cytoplasmic content leakage (we observed this to be happening over 2h in individual cells). ThT is also a non-specific amyloid dye, and in starving E. coli cells formation of protein clusters has been observed (https://pubmed.ncbi.nlm.nih.gov/30472191 [pubmed.ncbi.nlm.nih.gov]/), so such cytoplasmic staining seems possible.

>>It is very easy to see if the illumination is flat (Köhler illumination) by comparing the intensity of background pixels on the detector. It was flat in our case. Protons have little to do with our work for reasons highlighted before. Differential membrane permittivity is a speculative phenomenon not well supported by any evidence and with no clear molecular mechanism.

Finally, I note that authors observe biofilms of different shapes and sizes and state that they observe similar intensity profiles, which could mean that my comment on 'flatness' of the field of view above is not a concern. However, the scale bar in Figure 2A is not legible, so I can't compare it to the variation of sizes of the biofilms in Figure 2C (67 to 280um). Based on this, I think that the illumination profile is still a concern.

The referee now contradicts themselves and wants a scale bar to be more visible. We have changed the scale bar.

Result section 4: Voltage-gated Kch potassium channels mediate ion-channel electrical oscillations in E. coli

First I note at this point, given that I disagree that the data presented thus 'suggest that E. coli biofilms use electrical signaling to coordinate long-range responses to light stress' as the authors state, it gets harder to comment on the rest of the results.

In this result section the authors look at the effect of Kch, a putative voltage-gated potassium channel, on ThT profile in E. coli cells. And they see a difference. It is worth noting that in the publication https://www.sciencedirect.com/science/article/pii/S0006349519308793 [sciencedirect.com] it is found that ThT is also likely a substrate for TolC (Figure 4), but that scenario could not be distinguished from the one where TolC mutant has a different membrane permeability (and there is a publication that suggests the latter is happening https://onlinelibrary.wiley.com/doi/10.1111/j.1365-2958.2010.07245.x [onlinelibrary.wiley.com]). Given this, it is also possible that Kch deletion affects the membrane permeability. I do note that in video S4 I seem to see more of, what appear to be, plasmolysed cells. The authors do not see the ThT intensity with this mutant that appears long after the initial peak has disappeared, as they see in WT. It is not clear how long they waited for this, as from Figure S3C it could simply be that the dynamics of this is a lot slower, e.g. Kch deletion changes membrane permeability.

The work that TolC provides a possible passive pathway for ThT to leave cells seems slightly niche. It just demonstrates another mechanism for the cells to equilibriate the concentrations of ThT in a Nernstian manner i.e. driven by the membrane voltage.

The authors themselves state that the evidence for Kch being a voltage-gated channel is indirect (line 54). I do not think there is a need to claim function from a ThT profile of E. coli mutants (nor do I believe it's good practice), given how accurate single-channel recordings are currently. To know the exact dependency on the membrane potential, ion channel recordings on this protein are needed first.

We have good evidence form electrical impedance spectroscopy experiments that Kch increases the conductivity of biofilms  (https://pubs.acs.org/doi/10.1021/acs.nanolett.3c04446, 'Electrical impedance spectroscopy with bacterial biofilms: neuronal-like behavior', E.Akabuogu et al, ACS Nanoletters, 2024, in print.

Result section 5: Blue light influences ion-channel mediated membrane potential events in E. coli

In this chapter the authors vary the light intensity and stain the cells with PI (this dye gets into the cells when the membrane becomes very permeable), and the extracellular environment with K+ dye (I have not yet worked carefully with this dye). They find that different amounts of light influence ThT dynamics. This is in line with previous literature (both papers I have been mentioning: Figure 4 https://www.sciencedirect.com/science/article/pii/S0006349519303923 [sciencedirect.com] and https://ars.els-cdn.com/content/image/1-s2.0-S0006349519308793-mmc6.pdf [ars.els-cdn.com] especially SI12), but does not add anything new. I think the results presented here can be explained with previously published theory and do not indicate that the ion-channel mediated membrane potential dynamics is a light stress relief process.

The simple Nernstian battery model proposed by Pilizota et al is erroneous in our opinion for reasons outlined above. We believe it will prove to be a dead end for bacterial electrophysiology studies.

Result section 6: Development of a Hodgkin-Huxley model for the observed membrane potential dynamics

This results section starts with the authors stating: 'our data provide evidence that E. coli manages light stress through well-controlled modulation of its membrane potential dynamics'. As stated above, I think they are instead observing the process of ThT loading while the light is damaging the membrane and thus simultaneously collapsing the electrochemical gradient of protons. As stated above, this has been modelled before. And then, they observe a ThT staining that is independent from membrane potential.

This is an erroneous niche opinion. Protons have little say in the membrane potential since there are so few of them. The membrane potential is mostly determined by K+.

I will briefly comment on the Hodgkin Huxley (HH) based model. First, I think there is no evidence for two channels with different activation profiles as authors propose. But also, the HH model has been developed for neurons. There, the leakage and the pumping fluxes are both described by a constant representing conductivity, times the difference between the membrane potential and Nernst potential for the given ion. The conductivity in the model is given as gK*n^4 for potassium, gNa*m^3*h sodium, and gL for leakage, where gK, gNa and gL were measured experimentally for neurons. And, n, m, and h are variables that describe the experimentally observed voltage-gated mechanism of neuronal sodium and potassium channels. (Please see Hodgkin AL, Huxley AF. 1952. Currents carried by sodium and potassium ions through the membrane of the giant axon of Loligo. J. Physiol. 116:449-72 and Hodgkin AL, Huxley AF. 1952. A quantitative description of membrane current and its application to conduction and excitation in nerve. J. Physiol. 117:500-44).

In the 70 years since Hodgkin and Huxley first presented their model, a huge number of similar models have been proposed to describe cellular electrophysiology. We are not being hyperbolic when we state that the HH models for excitable cells are like the Schrödinger equation for molecules. We carefully adapted our HH model to reflect the currently understood electrophysiology of E. coli.

Thus, in applying the model to describe bacterial electrophysiology one should ensure near equilibrium requirement holds (so that (V-VQ) etc terms in authors' equation Figure 5 B hold), and potassium and other channels in a given bacterium have similar gating properties to those found in neurons. I am not aware of such measurements in any bacteria, and therefore think the pump leak model of the electrophysiology of bacteria needs to start with fluxes that are more general (for example Keener JP, Sneyd J. 2009. Mathematical physiology: I: Cellular physiology. New York: Springer or https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0000144 [journals.plos.org])

The reference is to a slightly more modern version of a simple Nernstian battery model. The model will not oscillate and thus will not help modelling membrane potentials in bacteria. We are unsure where the equilibrium requirement comes from (inadequate modelling of the dynamics?)

Result section 7: Mechanosensitive ion channels (MS) are vital for the first hyperpolarization event in E. coli.

The results that Mcs channels affect the profile of ThT dye are interesting. It is again possible that the membrane permeability of these mutants has changed and therefore the dynamics have changed, so this needs to be checked first. I also note that our results show that the peak of ThT coincides with cell expansion. For this to be understood a model is needed that also takes into account the link between maintenance of electrochemical gradients of ions in the cell and osmotic pressure.

The evidence for permeability changes in the membranes seems to be tenuous.

A side note is that the authors state that the Msc responds to stress-related voltage changes. I think this is an overstatement. Mscs respond to predominantly membrane tension and are mostly nonspecific (see how their action recovers cellular volume in this publication https://www.pnas.org/doi/full/10.1073/pnas.1522185113 [pnas.org]). Authors cite references 35-39 to support this statement. These publications still state that these channels are predominantly membrane tension-gated. Some of the references state that the presence of external ions is important for tension-related gating but sometimes they gate spontaneously in the presence of certain ions. Other publications cited don't really look at gating with respect to ions (39 is on clustering). This is why I think the statement is somewhat misleading.

We have reworded the discussion of Mscs since the literature appears to be ambiguous. We will try to run some electrical impedance spectroscopy experiments on the Msc mutants in the future to attempt to remove the ambiguity.

Result section 8: Anomalous ion-channel-mediated wavefronts propagate light stress signals in 3D E. coli biofilms.

I am not commenting on this result section, as it would only be applicable if ThT was membrane potential dye in E. coli.

Ok, but we disagree on the use of ThT.

Aims achieved/results support their conclusions:

The authors clearly present their data. I am convinced that they have accurately presented everything they observed. However, I think their interpretation of the data and conclusions is inaccurate in line with the discussion I provided above.

Likely impact of the work on the field, and the utility of the methods and data to the community:

I do not think this publication should be published in its current format. It should be revised in light of the previous literature as discussed in detail above. I believe presenting it in it's current form on eLife pages would create unnecessary confusion.

We believe many of the Pilizota group articles are scientifically flawed and are causing the confusion in the literature.

Any other comments:

I note, that while this work studies E. coli, it references papers in other bacteria using ThT. For example, in lines 35-36 authors state that bacteria (Bacillus subtilis in this case) in biofilms have been recently found to modulate membrane potential citing the relevant literature from 2015. It is worth noting that the most recent paper https://journals.asm.org/doi/10.1128/mbio.02220-23 [journals.asm.org] found that ThT binds to one or more proteins in the spore coat, suggesting that it does not act as a membrane potential in Bacillus spores. It is possible that it still reports membrane potential in Bacillus cells and the recent results are strictly spore-specific, but these should be kept in mind when using ThT with Bacillus.

>>ThT was used successfully in previous studies of normal B. subtilis cells (by our own group and A.Prindle, ‘Spatial propagation of electrical signal in circular biofilms’, J.A.Blee et al, Physical Review E, 2019, 100, 052401, J.A.Blee et al, ‘Membrane potentials, oxidative stress and the dispersal response of bacterial biofilms to 405 nm light’, Physical Biology, 2020, 17, 2, 036001, A.Prindle et al, ‘Ion channels enable electrical communication in bacterial communities’, Nature, 2015, 527, 59-63). The connection to low metabolism pore research seems speculative.

Reviewer #3 (Public Review):

It has recently been demonstrated that bacteria in biofilms show changes in membrane potential in response to changes in their environment, and that these can propagate signals through the biofilm to coordinate bacterial behavior. Akabuogu et al. contribute to this exciting research area with a study of blue light-induced membrane potential dynamics in E. coli biofilms. They demonstrate that Thioflavin-T (ThT) intensity (a proxy for membrane potential) displays multiphasic dynamics in response to blue light treatment. They additionally use genetic manipulations to implicate the potassium channel Kch in the latter part of these dynamics. Mechanosensitive ion channels may also be involved, although these channels seem to have blue light-independent effects on membrane potential as well. In addition, there are challenges to the quantitative interpretation of ThT microscopy data which require consideration. The authors then explore whether these dynamics are involved in signaling at the community level. The authors suggest that cell firing is both more coordinated when cells are clustered and happens in waves in larger, 3D biofilms; however, in both cases evidence for these claims is incomplete. The authors present two simulations to describe the ThT data. The first of these simulations, a Hodgkin-Huxley model, indicates that the data are consistent with the activity of two ion channels with different kinetics; the Kch channel mutant, which ablates a specific portion of the response curve, is consistent with this. The second model is a fire-diffuse-fire model to describe wavefront propagation of membrane potential changes in a 3D biofilm; because the wavefront data are not presented clearly, the results of this model are difficult to interpret. Finally, the authors discuss whether these membrane potential changes could be involved in generating a protective response to blue light exposure; increased death in a Kch ion channel mutant upon blue light exposure suggests that this may be the case, but a no-light control is needed to clarify this.

In a few instances, the paper is missing key control experiments that are important to the interpretation of the data. This makes it difficult to judge the meaning of some of the presented experiments.

(1) An additional control for the effects of autofluorescence is very important. The authors conduct an experiment where they treat cells with CCCP and see that Thioflavin-T (ThT) dynamics do not change over the course of the experiment. They suggest that this demonstrates that autofluorescence does not impact their measurements. However, cellular autofluorescence depends on the physiological state of the cell, which is impacted by CCCP treatment. A much simpler and more direct experiment would be to repeat the measurement in the absence of ThT or any other stain. This experiment should be performed both in the wild-type strain and in the ∆kch mutant.

ThT is a very bright fluorophore (much brighter than a GFP). It is clear from the images of non-stained samples that autofluorescence provides a negligible contribution to the fluorescence intensity in an image.

(2) The effects of photobleaching should be considered. Of course, the intensity varies a lot over the course of the experiment in a way that photobleaching alone cannot explain. However, photobleaching can still contribute to the kinetics observed. Photobleaching can be assessed by changing the intensity, duration, or frequency of exposure to excitation light during the experiment. Considerations about photobleaching become particularly important when considering the effect of catalase on ThT intensity. The authors find that the decrease in ThT signal after the initial "spike" is attenuated by the addition of catalase; this is what would be predicted by catalase protecting ThT from photobleaching (indeed, catalase can be used to reduce photobleaching in time lapse imaging).

Photobleaching was negligible over the course of the experiments. We employed techniques such as reducing sample exposure time and using the appropriate light intensity to minimize photobleaching.

(3) It would be helpful to have a baseline of membrane potential fluctuations in the absence of the proposed stimulus (in this case, blue light). Including traces of membrane potential recorded without light present would help support the claim that these changes in membrane potential represent a blue light-specific stress response, as the authors suggest. Of course, ThT is blue, so if the excitation light for ThT is problematic for this experiment the alternative dye tetramethylrhodamine methyl ester perchlorate (TMRM) can be used instead.

Unfortunately the fluorescent baseline is too weak to measure cleanly in this experiment. It appears the collective response of all the bacteria hyperpolarization at the same time appears to dominate the signal (measurements in the eLife article and new potentiometry measurements).

(4) The effects of ThT in combination with blue light should be more carefully considered. In mitochondria, a combination of high concentrations of blue light and ThT leads to disruption of the PMF (Skates et al. 2021 BioRXiv), and similarly, ThT treatment enhances the photodynamic effects of blue light in E. coli (Bondia et al. 2021 Chemical Communications). If present in this experiment, this effect could confound the interpretation of the PMF dynamics reported in the paper.

We think the PMF plays a minority role in determining the membrane potential in E. coli. For reasons outlined before (H+ is a minority ion in E. coli compared with K+).

(5) Figures 4D - E indicate that a ∆kch mutant has increased propidium iodide (PI) staining in the presence of blue light; this is interpreted to mean that Kch-mediated membrane potential dynamics help protect cells from blue light. However, Live/Dead staining results in these strains in the absence of blue light are not reported. This means that the possibility that the ∆kch mutant has a general decrease in survival (independent of any effects of blue light) cannot be ruled out.

>>Both strains of bacterial has similar growth curve and also engaged in membrane potential dynamics for the duration of the experiment. We were interested in bacterial cells that observed membrane potential dynamics in the presence of the stress. Bacterial cells need to be alive to engage in membrane potential  dynamics (hyperpolarize) under stress conditions. Cells that engaged in membrane potential dynamics and later stained red were only counted after the entire duration. We believe that the wildtype handles the light stress better than the ∆kch mutant as measured with the PI.

(6) Additionally in Figures 4D - E, the interpretation of this experiment can be confounded by the fact that PI uptake can sometimes be seen in bacterial cells with high membrane potential (Kirchhoff & Cypionka 2017 J Microbial Methods); the interpretation is that high membrane potential can lead to increased PI permeability. Because the membrane potential is largely higher throughout blue light treatment in the ∆kch mutant (Fig. 3AB), this complicates the interpretation of this experiment.

Kirchhoff & Cypionka 2017 J Microbial Methods, using fluorescence microscopy, suggested that changes in membrane potential dynamics can introduce experimental bias when propidium iodide is used to confirm the viability of tge bacterial strains, B subtilis (DSM-10) and Dinoroseobacter shibae, that are starved of oxygen (via N2 gassing) for 2 hours. They attempted to support their findings by using CCCP in stopping the membrane potential dynamics (but never showed any pictoral or plotted data for this confirmatory experiment). In our experiment methodology, cell death was not forced on the cells by introducing an extra burden or via anoxia. We believe that the accumulation of PI in ∆kch mutant is not due to high membrane potential dynamics but is attributed to the PI, unbiasedly showing damaged/dead cells. We think that propidium iodide is good for this experiment. Propidium iodide is a dye that is extensively used in life sciences. PI has also been used in the study of bacterial electrophysiology (https://pubmed.ncbi.nlm.nih.gov/32343961/, ) and no membrane potential related bias was reported.

Throughout the paper, many ThT intensity traces are compared, and described as "similar" or "dissimilar", without detailed discussion or a clear standard for comparison. For example, the two membrane potential curves in Fig. S1C are described as "similar" although they have very different shapes, whereas the curves in Fig. 1B and 1D are discussed in terms of their differences although they are evidently much more similar to one another. Without metrics or statistics to compare these curves, it is hard to interpret these claims. These comparative interpretations are additionally challenging because many of the figures in which average trace data are presented do not indicate standard deviation.

Comparison of small changes in the absolute intensities is problematic in such fluorescence experiments. We mean the shape of the traces is similar and they can be modelled using a HH model with similar parameters.

The differences between the TMRM and ThT curves that the authors show in Fig. S1C warrant further consideration. Some of the key features of the response in the ThT curve (on which much of the modeling work in the paper relies) are not very apparent in the TMRM data. It is not obvious to me which of these traces will be more representative of the actual underlying membrane potential dynamics.

In our experiment, TMRM was used to confirm the dynamics observed using ThT. However, ThT appear to be more photostable than TMRM (especially towars the 2nd peak). The most interesting observation is that with both dyes, all phases of the membrane potential dynamics were conspicuous (the first peak, the quiescent period and the second peak). The time periods for these three episodes were also similar.

A key claim in this paper (that dynamics of firing differ depending on whether cells are alone or in a colony) is underpinned by "time-to-first peak" analysis, but there are some challenges in interpreting these results. The authors report an average time-to-first peak of 7.34 min for the data in Figure 1B, but the average curve in Figure 1B peaks earlier than this. In Figure 1E, it appears that there are a handful of outliers in the "sparse cell" condition that likely explain this discrepancy. Either an outlier analysis should be done and the mean recomputed accordingly, or a more outlier-robust method like the median should be used instead. Then, a statistical comparison of these results will indicate whether there is a significant difference between them.

The key point is the comparison of standard errors on the standard deviation.

In two different 3D biofilm experiments, the authors report the propagation of wavefronts of membrane potential; I am unable to discern these wavefronts in the imaging data, and they are not clearly demonstrated by analysis.

The first data set is presented in Figures 2A, 2B, and Video S3. The images and video are very difficult to interpret because of how the images have been scaled: the center of the biofilm is highly saturated, and the zero value has also been set too high to consistently observe the single cells surrounding the biofilm. With the images scaled this way, it is very difficult to assess dynamics. The time stamps in Video S3 and on the panels in Figure 2A also do not correspond to one another although the same biofilm is shown (and the time course in 2B is also different from what is indicated in 2B). In either case, it appears that the center of the biofilm is consistently brighter than the edges, and the intensity of all cells in the biofilm increases in tandem; by eye, propagating wavefronts (either directed toward the edge or the center) are not evident to me. Increased brightness at the center of the biofilm could be explained by increased cell thickness there (as is typical in this type of biofilm). From the image legend, it is not clear whether the image presented is a single confocal slice or a projection. Even if this is a single confocal slice, in both Video S3 and Figure 2A there are regions of "haze" from out-of-focus light evident, suggesting that light from other focal planes is nonetheless present. This seems to me to be a simpler explanation for the fluorescence dynamics observed in this experiment: cells are all following the same trajectory that corresponds to that seen for single cells, and the center is brighter because of increased biofilm thickness.

We appreciate the reviewer for this important observation. We have made changes to the figures to address this confusion. The cell cover has no influence on the observed membrane potential dynamics. The entire biofilm was exposed to the same blue light at each time. Therefore all parts of the biofilm received equal amounts of the blue light intensity. The membrane potential dynamics was not influenced by cell density (see Fig 2C). 

The second data set is presented in Video S6B; I am similarly unable to see any wave propagation in this video. I observe only a consistent decrease in fluorescence intensity throughout the experiment that is spatially uniform (except for the bright, dynamic cells near the top; these presumably represent cells that are floating in the microfluidic and have newly arrived to the imaging region).

A visual inspection of Video S6B shows a fast rise, a decrease in fluorescence and a second rise (supplementary figure 4B). The data for the fluorescence was carefully obtained using the imaris software. We created a curved geometry on each slice of the confocal stack. We analyzed the surfaces of this curved plane along the z-axis. This was carried out in imaris.

3D imaging data can be difficult to interpret by eye, so it would perhaps be more helpful to demonstrate these propagating wavefronts by analysis; however, such analysis is not presented in a clear way. The legend in Figure 2B mentions a "wavefront trace", but there is no position information included - this trace instead seems to represent the average intensity trace of all cells. To demonstrate the propagation of a wavefront, this analysis should be shown for different subpopulations of cells at different positions from the center of the biofilm. Data is shown in Figure 8 that reflects the velocity of the wavefront as a function of biofilm position; however, because the wavefronts themselves are not evident in the data, it is difficult to interpret this analysis. The methods section additionally does not contain sufficient information about what these velocities represent and how they are calculated. Because of this, it is difficult for me to evaluate the section of the paper pertaining to wave propagation and the predicted biofilm critical size.

The analysis is considered in more detail in a more expansive modelling article, currently under peer review in a physics journal, ‘Electrical signalling in three dimensional bacterial biofilms using an agent based fire-diffuse-fire model’, V.Martorelli, et al, 2024 https://www.biorxiv.org/content/10.1101/2023.11.17.567515v1

There are some instances in the paper where claims are made that do not have data shown or are not evident in the cited data:

(1) In the first results section, "When CCCP was added, we observed a fast efflux of ions in all cells"- the data figure pertaining to this experiment is in Fig. S1E, which does not show any ion efflux. The methods section does not mention how ion efflux was measured during CCCP treatment.

We have worded this differently to properly convey our results.

(2) In the discussion of voltage-gated calcium channels, the authors refer to "spiking events", but these are not obvious in Figure S3E. Although the fluorescence intensity changes over time, it's hard to distinguish these fluctuations from measurement noise; a no-light control could help clarify this.

The calcium transients observed were not due to noise or artefacts.

(3) The authors state that the membrane potential dynamics simulated in Figure 7B are similar to those observed in 3D biofilms in Fig. S4B; however, the second peak is not clearly evident in Fig. S4B and it looks very different for the mature biofilm data reported in Fig. 2. I have some additional confusion about this data specifically: in the intensity trace shown in Fig. S4B, the intensity in the second frame is much higher than the first; this is not evident in Video S6B, in which the highest intensity is in the first frame at time 0. Similarly, the graph indicates that the intensity at 60 minutes is higher than the intensity at 4 minutes, but this is not the case in Fig. S4A or Video S6B.

The confusion stated here has now been addressed. Also it should be noted that while Fig 2.1 was obtained with LED light source, Fig S4A was obtained using a laser light source. While obtaining the confocal images (for Fig S4A ), the light intensity was controlled to further minimize photobleaching. Most importantly, there is an evidence of slow rise to the 2nd peak in Fig S4B. The first peak, quiescence and slow rise to second peak are evident.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Scientific recommendations:

- Although Fig 4A clearly shows that light stimulation has an influence on the dynamics of cell membrane potential in the biofilm, it is important to rule out the contribution of variations in environmental parameters. I understand that for technical reasons, the flow of fresh medium must be stopped during image acquisition. Therefore, I suggest performing control experiments, where the flow is stopped before image acquisition (15min, 30min, 45min, and 1h before). If there is no significant contribution from environmental variations (pH, RedOx), the dynamics of the electrical response should be superimposed whatever the delay between stopping the flow stop and switching on the light.

In this current research study, we were focused on studying how E. coli cells and biofilms react to blue light stress via their membrane potential dynamics. This involved growing the cells and biofilms, stopping the media flow and obtaining data immediately. We believe that stopping the flow not only helped us to manage data acquisition, it also helped us reduce the effect of environmental factors. In our future study we will expand the work to include how the membrane potential dynamics evolve in the presence of changing environmental factors for example such induced by stopping the flow at varied times.

- Since TMRM signal exhibits a linear increase after the first response peak (Supplementary Figure 1D), I recommend mitigating the statement at line 78.

- To improve the spatial analysis of the electrical response, I suggest plotting kymographs of the intensity profiles across the biofilm. I have plotted this kymograph for Video S3 and it appears that there is no electrical propagation for the second peak. In addition, the authors should provide technical details of how R^2(t) is measured in the first regime (Figure 7E).

See the dedicated simulation article for more details. https://www.biorxiv.org/content/10.1101/2023.11.17.567515v1

- Line 152: To assess the variability of the latency, the authors should consider measuring the variance divided by the mean instead of SD, which may depend on the average value.

We are happy with our current use of standard error on the standard deviation. It shows what we claim to be true.

- Line 154-155: To truly determine whether the amplitude of the "action potential" is independent of biofilm size, the authors should not normalise the signals.

Good point. We qualitatively compared both normalized and unnormalized data. Recent electrical impedance spectroscopy measurements (unpublished) indicate that the electrical activity is an extensive quantity i.e. it scales with the size of the biofilms.

- To precise the role of K+ in the habituation response, I suggest using valinomycin at sub-inhibitory concentrations (10µM). Besides, the high concentration of CCCP used in this study completely inhibits cell activity. Not surprisingly, no electrical response to light stimulation was observed in the presence of CCCP. Finally, the Kch complementation experiment exhibits a "drop after the first peak" on a single point. It would be more convincing to increase the temporal resolution (1min->10s) to show that there is indeed a first and a second peak.

An interesting experiment for the future.

- Line 237-238: There are only two points suggesting that the dynamics of hyperpolarization are faster at higher irradiance(Fig 4A). The authors should consider adding a third intermediate point at 17µW/mm^2 to confirm the statement made in this sentence.

Multiple repeats were performed. We are confident of the robustness of our data.

- Line 249 + Fig 4E: It seems that the data reported on Fig 4E are extracted from Fig 4D. If this is indeed the case, the data should be normalised by the total population size to compare survival probabilities under the two conditions. It would also be great to measure these probabilities (for WT and ∆kch) in the presence of ROS scavengers.

- To distinguish between model fitting and model predictions, the authors should clearly state which parameters are taken from the literature and which parameters are adjusted to fit the experimental data.

- Supplementary Figure 4A: why can't we see any wavefront in this series of images?

For the experimental data, the wavefront was analyzed by employing the imaris software. We systematically created a ROI with a curved geometry within the confocal stack (the biofilm). The fluorescence of ThT was traced along the surface of the curved geometry was analyzed along the z-axis.

- Fig 7B: Could the authors explain why the plateau is higher in the simulations than in the biofilm experiments? Could they add noise on the firing activities?

See the dedicated Martorelli modelling article. In general we would need to approach stochastic Hodgkin-Huxley modelling and the fluorescence data (and electrical impedance spectroscopy data) presented does not have extensive noise (due to collective averaging over many bacteria cells).

- Supplementary Figure 4B: Why can't we see the second peak in confocal images?

The second peak is present although not as robust as in Fig 2B. The confocal images were obtained with a laser source. Therefore we tried to create a balance between applying sufficient light stress on the bacterial cells and mitigating photobleaching.

Editing recommendations:

The editing recommendations below has been applied where appropriate

- Many important technical details are missing (e.g. R^2, curvature, and 445nm irradiance measurements). Error bars are missing from most graphs. The captions should clearly indicate if these are single-cell or biofilm experiments, strain name, illumination conditions, number of experiments, SD, or SE. Please indicate on all panels of all figures in the main text and in the supplements, which are the conditions: single cell vs. biofilm, strains, medium, centrifugal vs centripetal etc..., where relevant. Please also draw error bars everywhere.

We have now made appropriate changes. We specifically use cells when we were dealing with single cells and biofilms when we worked on biofilms. We decided to describe the strain name either on the panel or the image description.

- Line 47-51: The way the paragraph is written suggests that no coordinated electrical oscillations have been observed in Gram-negative biofilms. However, Hennes et al (referenced as 57 in this manuscript) have shown that a wave of hyperpolarized cells propagates in Neisseria gonorrhoea colony, which is a Gram-negative bacterium.

We are now aware of this work. It was not published when we first submitted our work and the authors claim the waves of activity are due to ROS diffusion NOT propagating waves of ions (coordinated electrical wavefronts).

- Line 59: "stressor" -> "stress" or "perturbation".

The correction has been made.

- Line 153: Please indicate in the Material&Methods how the size of the biofilm is measured.

The biofilm size was obtained using BiofilmQ and the step by step guide for using BiofilmQ were stated..

- Figure 2A: Please provide associated brightfield images to locate bacteria.

- Line 186: Please remove "wavefront" from the caption. Fig2B only shows the average signal as a function of time.

This correction has been implemented.

- Fig 3B,C: Please indicate single cell and biofilm on the panels and also WT and ∆kch.

- Line 289: I suggest adding "in single cell experiments" to the title of this section.

- Fig 5A: blue light is always present at regular time intervals during regime I and II. The presence of blue light only in regime I could be misleading.

- Fig 5C: The curve in Fig 5D seems to correspond to the biofilm case. The curve given by the model, should be compared with the average curve presented in Fig 1D.

- Fig 6A, B, and C: These figures could be moved to supplements.

- Line 392: Replace "turgidity" with "turgor pressure".

- Fig 7C,E: Please use a log-log scale to represent these data and indicate the line of slope 1.

- Fig 7E: The x-axis has been cropped.

- Please provide a supplementary movie for the data presented in Fig 7E.

- Line 455: E. Coli biofilms do not express ThT.

- Line 466: "\gamma is the anomalous exponent". Please remove anomalous (\gamma can equal 1 at this stage).

- Line 475: Please replace "section" with "projection".

- Line 476: Please replace "spatiotemporal" with "temporal". There is no spatial dependency in either figure.

- Line 500: Please define Eikonal approximation.

- Fig 8 could be moved to supplements.

- Line 553: "predicted" -> "predict".

- Line 593: Could the authors explain why their model offers much better quantitative agreement?

- Line 669: What does "universal" mean in that context?

- Line 671: A volume can be pipetted but not a concentration.

- Line 676: Are triplicates technical or biological replicates?

- Sup Fig1: Please use minutes instead of seconds in panel A.

- Model for membrane dynamics: "The fraction of time the Q+ channel is open" -> "The dynamics of Q+ channel activity can be written". Ditto for K+ channel...

- Model for membrane dynamics: "the term ... is a threshold-linear". This function is not linear at all. Why is it called linear? Also, please describe what \sigma is.

- ABFDF model: "releasing a given concentration" -> "releasing a local concentration" or "a given number" but it's not \sigma anymore. Besides, this \sigma is unlikely related to the previous \sigma used in the model of membrane potential dynamics in single cells. Please consider renaming one or the other. Also, ions are referred to as C+ in the text and C in equation 8. Am I missing something?

Reviewer #2 (Recommendations For The Authors):

I have included all my comments as one review. I have done so, despite the fact that some minor comments could have gone into this section, because I decided to review each Result section. I thus felt that not writing it as one review might be harder to follow. I have however highlighted which comments are minor suggestions or where I felt corrections.

However, while I am happy with all my comments being public, given their nature I think they should be shown to authors first. Perhaps the authors want to go over them and think about it before deciding if they are happy for their manuscript to be published along with these comments, or not. I will highlight this in an email to the editor. I question whether in this case, given that I am raising major issues, publishing both the manuscript and the comments is the way to go as I think it might just generate confusion among the audience.

Reviewer #3 (Recommendations For The Authors):

I was unable to find any legends for any of the supplemental videos in my review materials, and I could not open supplemental video 5.

I made some comments in the public review about the analysis and interpretation of the time-to-fire data. One of the other challenges in this data set is that the time resolution is limited- it seems that a large proportion of cells have already fired after a single acquisition frame. It would be ideal to increase the time resolution on this measurement to improve precision. This could be done by imaging more quickly, but that would perhaps necessitate more blue light exposure; an alternative is to do this experiment under lower blue light irradiance where the first spike time is increased (Figure 4A).

In the public review, I mentioned the possible impact of high membrane potential on PI permeability. To address this, the experiment could be repeated with other stains, or the viability of blue light-treated cells could be addressed more directly by outgrowth or colony-forming unit assays.

In the public review, I mentioned the possible combined toxicity of ThT and blue light. Live/dead experiments after blue light exposure with and without ThT could be used to test for such effects, and/or the growth curve experiment in Figure 1F could be repeated with blue light exposure at a comparable irradiance used in the experiment.

Throughout the paper and figure legends, it would help to have more methodological details in the main text, especially those that are critical for the interpretation of the experiment. The experimental details in the methods section are nicely described, but the data analysis section should be expanded significantly.

At the end of the results section, the authors suggest a critical biofilm size of only 4 µm for wavefront propagation (not much larger than a single cell!). The authors show responses for various biofilm sizes in Fig. 2C, but these are all substantially larger. Are there data for cell clusters above and below this size that could support this claim more directly?

The authors mention image registration as part of their analysis pipeline, but the 3D data sets in Video S6B and Fig. S4A do not appear to be registered- were these registered prior to the velocity analysis reported in Fig. 8?

One of the most challenging claims to demonstrate in this paper is that these membrane potential wavefronts are involved in coordinating a large, biofilm-scale response to blue light. One possible way to test this might be to repeat the Live/Dead experiment in planktonic culture or the single-cell condition. If the protection from blue light specifically emerges due to coordinated activity of the biofilm, the Kch mutant would not be expected to show a change in Live/Dead staining in non-biofilm conditions.

Line 140: How is "mature biofilm" defined? Also on this same line, what does "spontaneous" mean here?

Line 151: "much smaller": Given that the reported time for 3D biofilms is 2.73 {plus minus} 0.85 min and in microclusters is 3.27 {plus minus} 1.77 min, this seems overly strong.

Line 155: How is "biofilm density" characterized? Additionally, the data in Figure 2C are presented in distance units (µm), but the text refers to "areal coverage"- please define the meaning of these distance units in the legend and/or here in the text (is this the average radius?).

Lines 161-162: These claims seem strong given the data presented before, and the logic is not very explicit. For example, in the second sentence, the idea that this signaling is used to "coordinate long-range responses to light stress" does not seem strongly evidenced at this point in the paper. What is meant by a long-range response to light stress- are there processes to respond to light that occur at long-length scales (rather than on the single-cell scale)? If so, is there evidence that these membrane potential changes could induce these responses? Please clarify the logic behind these conclusions.

Lines 235-236: In the lower irradiance conditions, the responses are slower overall, and it looks like the ThT intensity is beginning to rise at the end of the measurement. Could a more prominent second peak be observed in these cases if the measurement time was extended?

Line 242-243: The overall trajectories of extracellular potassium are indeed similar, but the kinetics of the second peak of potassium are different than those observed by ThT (it rises some minutes earlier)- is this consistent with the idea that Kch is responsible for that peak? Additionally, the potassium dynamics also reflect the first peak- is this surprising given that the Kch channel has no effect on this peak?

Line 255-256: Again, this seems like a very strong claim. There are several possible interpretations of the catalase experiment (which should be discussed); this experiment perhaps suggests that ROS impacts membrane potential, but does not obviously indicate that these membrane potential fluctuations mitigate ROS levels or help the cells respond to ROS stress. The loss of viability in the ∆kch mutant might indicate a link between these membrane potential experiments and viability, but it is hard to interpret without the no-light control I mention in the public review.

Lines 313-315: "The model predicts... the external light stress". Please clarify this section. Where this prediction arises from in the modeling work? Second, I am not sure what is meant by "modulates the light stress" or "keeps the cell dynamics robust to the intensity of external light stress" (especially since the dynamics clearly vary with irradiance, as seen in Figure 4A).

Line 322: I am not sure what "handles the ROS by adjusting the profile of the membrane potential dynamics" means. What is meant by "handling" ROS? Is the hypothesis that membrane potential dynamics themselves are protective against ROS, or that they induce a ROS-protective response downstream, or something else? Later in lines 327-8 the authors write that changes in the response to ROS in the model agree with the hypothesis, but just showing that ROS impacts the membrane potential does not seem to demonstrate that this has a protective effect against ROS.

Line 365-366: This section title seems confusing- mechanosensitive ion channels totally ablate membrane potential dynamics, they don't have a specific effect on the first hyperpolarization event. The claim that mechanonsensitive ion channels are specifically involved in the first event also appears in the abstract.

Also, the apparent membrane potential is much lower even at the start of the experiment in these mutants- is this expected? This seems to imply that these ion channels also have a blue light independent effect.

Lines 368, 371: Should be VGCCs rather than VGGCs.

Line 477: I believe the figure reference here should be to Figure 7B, not 6B.

Line 567-568: "The initial spike is key to registering the presence of the light stress." What is the evidence for this claim?

Line 592-594: "We have presented much better quantitative agreement..." This is a strong claim; it is not immediately evident to me that the agreement between model and prediction is "much better" in this work than in the cited work. The model in Figure 4 of reference 57 seems to capture the key features of their data. Clarification is needed about this claim.

Line 613: "...strains did not have any additional mutations." This seems to imply that whole genome sequencing was performed- is this the case?

Line 627: I believe this should refer to Figure S2A-B rather than S1.

Line 719: What percentage of cells did not hyperpolarize in these experiments?

Lines 751-754: As I mentioned above, significant detail is missing here about how these measurements were made. How is "radius" defined in 3D biofilms like the one shown in Video S6B, which looks very flat? What is meant by the distance from the substrate to the core, since usually in this biofilm geometry, the core is directly on the substrate? Most importantly, this only describes the process of sectioning the data- how were these sections used to compute the velocity of ThT signal propagation?

I also have some comments specifically on the figure presentation:

Normalization from 0 to 1 has been done in some of the ThT traces in the paper, but not all. The claims in the paper would be easiest to evaluate if the non-normalized data were shown- this is important for the interpretation of some of the claims.

Some indication of standard deviation (error bars or shading) should be added to all figures where mean traces are plotted.

Throughout the paper, I am a bit confused by the time axis; the data consistently starts at 1 minute. This is not intuitive to me, because it seems that the blue light being applied to the cells is also the excitation laser for ThT- in that case, shouldn't the first imaging frame be at time 0 (when the blue light is first applied)? Or is there an additional exposure of blue light 1 minute before imaging starts? This is consequential because it impacts the measured time to the first spike. (Additionally, all of the video time stamps start at 0).

Please increase the size of the scale bars and bar labels throughout, especially in Figure 2A and S4A.

In Figure 1B and D, it would help to decrease the opacity on the individual traces so that more of them can be discerned. It would also improve clarity to have data from the different experiments shown with different colored lines, so that variability between experiments can be clearly visualized.

Results in Figure 1E would be easier to interpret if the frequency were normalized to total N. It is hard to tell from this graph whether the edges and bin widths are the same between the data sets, but if not, they should be. Also, it would help to reduce the opacity of the sparse cell data set so that the full microcluster data set can be seen as well.

Biofilm images are shown in Figures 2A, S3A, and Video S3- these are all of the same biofilm. Why not take the opportunity to show different experimental replicates in these different figures? The same goes for Figure S4A and Video S6B, which again are of the same biofilm.

Figure 2C would be much easier to read if the curves were colored in order of their size; the same is true for Figure 4A and irradiance.

The complementation data in Figure S3D should be moved to the main text figure 3 alongside the data about the corresponding knockout to make it easier to compare the curves.

Fig.ure S3E: Is the Y-axis in this graph mislabeled? It is labeled as ThT fluorescence, but it seems that it is reporting fluorescence from the calcium indicator?

Video S6B is very confusing- why does the video play first forwards and then backwards? Unless I am looking very carefully at the time stamps it is easy to misinterpret this as a rise in the intensity at the end of the experiment. Without a video legend, it's hard to understand this, but I think it would be much more straightforward to interpret if it only played forward. (Also, why is this video labeled 6B when there is no video 6A?)

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Plasmacytoid dendritic cells (pDCs) represent a specialized subset of dendritic cells (DCs) known for their role in producing type I interferons (IFN-I) in response to viral infections. It was believed that pDCs originated from common DC progenitors (CDP). However, recent studies by Rodrigues et al. (Nature Immunology, 2018) and Dress et al. (Nature Immunology, 2019) have challenged this perspective, proposing that pDCs predominantly develop from lymphoid progenitors expressing IL-7R and Ly6D. A minor subset of pDCs arising from CDP has also been identified as functionally distinct, exhibiting reduced IFN-I production but a strong capability to activate T-cell responses. On the other hand, clonal lineage tracing experiments, as recently reported by Feng et al. (Immunity, 2022), have demonstrated a shared origin between pDCs and conventional DCs (cDCs), suggesting a contribution of common DC precursors to the pDC lineage.

In this context, Araujo et al. investigated the heterogeneity of pDCs in terms of both development and function. Their findings revealed that approximately 20% of pDCs originate from lymphoid progenitors common to B cells. Using Mb1-Cre x Bcl11a floxed mice, the authors demonstrated that the development of this subset of pDCs, referred to as "B-pDCs," relied on the transcription factor BCL11a. Functionally, B-pDCs exhibited a diminished capacity to produce IFN-I in response to TLR9 agonists but secreted more IL-12 compared to conventional pDCs. Moreover, B-pDCs, either spontaneously or upon activation, exhibited increased expression of activation markers (CD80/CD86/MHC-II) and a heightened ability to activate T-cell responses in vitro compared to conventional pDCs. Finally, Araujo et al. characterized these B-pDCs at the transcriptomic level using bulk and single-cell RNA sequencing, revealing them as a unique subset of pDCs expressing certain B cell markers such as Mb1, as well as specific markers (Axl) associated with cells recently described as transitional DCs.

Thus, in contrast to previous findings, this study posits that a small proportion of pDCs derive from B cell-committed lymphoid progenitors, and this subset of B-pDCs exhibits distinct functional characteristics, being less specialized in IFN-I production but rather in T cell activation.

Strengths:

Previously, the same research group delineated the significance of BCL11a as a critical transcription factor in pDC development (Ippolito et al., PNAS, 2014). This study elucidates the precise stage during hematopoiesis at which BCL11a expression becomes essential for the emergence of a distinct subset of pDCs, substantiated by robust genetic evidence in vivo. Furthermore, it underscores the shared developmental origin between pDCs and B cells, reinforcing prior research in the field that suggests a lymphoid origin of pDCs. Finally, this work attributes specific functional properties to pDCs originating from these lymphoid progenitors shared with B cells, emphasizing the early imprinting of functional heterogeneity during their development.

Weaknesses:

The authors delineate a subset of pDCs dependent on the BCL11a transcription factor, originating from lymphoid progenitors, and compare it to conventional pDCs, which they suggest differentiate from common DC progenitors of myeloid origin. However, this interpretation lacks support from the authors' data. Their single-cell RNA sequencing data identifies cells corresponding to progenitors (Prog2), from which the majority of pDCs, termed conventional pDCs, likely originate. This progenitor cell population expresses Il7r, Siglech, and Ly6D, but not Csfr1. The authors describe this progenitor as resembling a "pro-pDC myeloid precursor," yet these cells align more closely with lymphoid (Il7r+) progenitors described by Rodrigues et al. (Nature Immunology, 2018) and Dress et al. (Nature Immunology, 2019). Furthermore, analysis of their Mb1 reporter mice reveals that only a fraction of common lymphoid progenitors (CLP) express YFP, giving rise to a fraction of YFP+ pDCs. However, this does not exclude the possibility that YFP- CLP could also give rise to pDCs. The authors could address this caveat by attempting to differentiate pDCs from both YFP+ and YFP- CLPs in vitro in the presence of FLT3L. Additionally, transfer experiments using these lymphoid progenitors could be conducted in vivo to assess their differentiation potential in competitive settings.

Dear Reviewer 1, we appreciate your thoughtful comments. We made the decision to address the Prog2 cluster as “pro-pDC myeloid precursor” because despite its lack of CSFR-1, its CIPR similarity score showed highest transcriptional similarity to the population “SC.CDP.BM” (GEO accession number: GSM791114), which is shown to be Sca1- Flt3+ cKitlo.

A similar population identified as “common dendritic cell progenitor” is shown by Onai and colleagues (Onai et al. 2013, Immunity) to be capable of differentiating into pDCs by upregulating E2-2 and subsequently downregulating M-CSFR. In addition, we were unable to infer a developmental trajectory between Prog2 and B-pDCs using SimplePPT on Monocle3 (Figure 5B). Since we know our B-pDCs are CLP derived and most likely share a B cell progenitor population, we feel this lack of connectivity to the UMAP myeloid partition corroborates our assignment of Prog2 as a myeloid pDC progenitor (not CLP derived). Of note, recent work by Medina and colleagues has shown that while IL-7Rα knockout mice exhibit a block in B cell development at the all-lymphoid progenitor (ALP) stage, PDCA-1+ pDCs identified within the initially gated BLP population persisted (PLoS One, 2013), suggesting the IL7R chain is not required for the development of PDCA1+ cells. 

Using their Mb1-reporter mice, the authors demonstrate that YFP pDCs originating from lymphoid progenitors are functionally distinct from conventional pDCs, mostly in vitro, but their in vivo relevance remains unknown. It is crucial to investigate how Bcl11a conditional deficiency in Mb1-expressing cells affects the anti-viral immune response, for example, using the M-CoV infection model as described by Sulczewski et al. in Nature Immunology, 2023. Particularly, the authors suggest that their B-pDCs act as antigen-presenting cells involved in T-cell activation compared to conventional pDCs. However, these findings contrast with those of Rodrigues et al., who have shown that pDCs of myeloid origin are more effective than pDCs of lymphoid origin in activating T-cell responses. The authors should discuss these discrepancies in greater detail. It is also notable that B-PDCs acquire the expression of ID2 (Figure S3A), commonly a marker of conventional/myeloid DCs. The authors could analyze in more detail the acquisition of specific myeloid features (CD11c, CX3CR1) by this B-PDCs subset and discuss how the expression of ID2 may impair classical pDC features, as ID2 is a repressor of E2-2, a master regulator of pDC fate.

Both reviewers expressed the need to further investigate how Bcl11a conditional deficiency in Mb1-expressing cells affects anti-viral responses of B-pDCs. While the functional characterization of B-pDC in the context of infection could be highly informative, it is really outside the scope of the present study. Our discovery that B-pDCs expand robustly upon TLR-9 agonist challenges in vivo and can prime T cells in vitro efficiently, however, suggests that these cells might play an important role during viral infections or anti-cancer immunity.

Finally, through the analysis of their single-cell RNA sequencing data, the authors show that the subset of B-pDCs they identified expresses Axl, confirmed at the protein level. Given this specific expression profile, the authors suggest that B-pDCs are related to a previously described subset of transitional DCs, which were reported to share a common developmental path with pDCs, (Sulczewski et al. in Nature Immunology, 2023). While intriguing, this observation requires further phenotypic and functional characterization to substantiate this claim.

We agree with the reviewer’s comments. We are currently preparing a separate manuscript addressing the commonalities between human transitional DCs and murine non-conventional pDCs.

Reviewer #2 (Public Review):

Summary:

The origin of plasmatoid dendritic cells and their subclasses continues to be a debated field, akin to any immune cell field that is determined through the expression of surface markers (relative to clear subclass separation based on functional biology and experimentation). In this context, in this manuscript by Araujo et al, the authors attempt to demonstrate that a subtype of pDCs comes from lymphoid origin due to the presence of some B cell gene expression markers. They nomenclature these cells as B-pDCs. Strikingly, pDCs function via expression of IFNa where as B-pDCs do not express IFNa - thereby raising the question of what are their physiological or pathophysiological properties. B-pDCs also express AXL, a marker not seen in mouse pDCs but observed in human pDCs. Overall, using a combination of gene expression profiling of immune cells isolated from mice via RNA-seq and single-cell profiling the authors propose that B-pDCs are a novel subtype of pDCs in mice that were not previously identified and characterized.

Weaknesses:

My two points of discussion about this manuscript are as follows.

(1) How new are these observations that pDCs could also originate from common lymphoid progenitors. This fact has been previously outlined by many laboratories including Shigematsu et al, Immunity 2004. These studies in the manuscript can be considered new based on the single-cell profiling presented, only if the further characterization of the isolated B-pDCs is performed at the functional biology level. Overlapping gene expression profiles are often seen in developing immune cell types- especially when only evaluated at the RNA expression level- and can lead to cell type complexity (and identification of new cell types) that are not biologically and functionally relevant.

Dear reviewer 2, we appreciate your thoughtful comments. We believe our single cell seq analysis adds new information to the studies mentioned because of our broader approach to BM profiling. By using only one marker (PDCA1+), scRNA-seq allowed us to dissect not only several subpopulations of pDCs that to our knowledge were not previously dissected in mice, but also linked the transcriptional similarity of B-pDCs to myeloid derived pDCs (and even other myeloid cell types), as well as B cells.

(2) The authors hardly perform any experiments to interrogate the function of these B-pDCs. The discussion on this topic can be enhanced. Ideally, some biological experiments would confirm that B-pDCs are important.

Dear reviewer 2, we appreciate your thoughtful comment and agree about the need for further functional characterization of B-pDCs (please see comments directed to reviewer 1 above).

(1) Considering that Bcl11a conditional deficiency severely impacts the B cell lineage, there is a possibility that such an effect on B cells may indirectly influence pDC development. To address this, the authors could repeat their bone marrow transfer experiments in a competitive setting by mixing both Bcl11a WT and CKO BM cells (using congenic markers to track the origin of the BM cells) and then specifically assess whether BM cells originating from Bcl11a CKO donors have impaired pDC output.

Dear reviewer 2, while the comment above is valid (that the reduced number of mature B cells in our Bcl11a conditional knockout might indirectly impact B-pDC development), we and many others have previously shown that lack of transcriptional regulation of E2-2 and other pDC differentiation modulators by Bcl11a  (including ID2 and MTG16) intrinsically and selectively disrupts the pDC lineage. At the current stage, we feel rederiving Bcl11a cKOs and performing bone marrow transfers (which usually take several months) only to investigate indirect effects of B cells on pDC developments is outside the scope of this publication.

(2) As mentioned earlier, it is important to assess the potential of CLP, whether YFP- or YFP+, in their ability to give rise to pDCs both in vitro and in vivo. This is also crucial since the authors previously demonstrated that Bcl11a deficiency in all hematopoietic cells had a more drastic impact on pDC development than mb1-cre specific deficiency.

We agree the manuscript could be strengthened by differentiation experiments. However, in our previous publication (mentioned above by the reviewer), we specifically show that although fewer overall LSK progenitors were detected in Vav-Cre+ F/F mice, both MDP and CDP progenitor populations persisted within the Flt3+ compartment in cKO mice at percentages similar to controls. MDP (Lin– Flt3+ Sca-1− CD115+ c-kithi); CDP (Lin– Flt3+ Sca-1− CD115+ c-kitlo). This data confirms that CLPs give rise to a substantial pool of pDC subpopulations. Other works have shown this as well, both in vivo and in vitro (Wang et al. Immunity 2004;  Karsunky et al, JEM 2003, etc). We therefore feel that confirming the previous observations that CLPs can give rise to pDCs is unnecessary, as our main goal in this manuscript was to describe a new pDC subpopulation that emerges primarily from CD79a+ B cell biased progenitors.

(3) The authors show a more severe impact of Bcl11a CKO on pDC depletion in the spleen than in the BM. Is this effect specific to the spleen, or can it also be observed in lymph nodes? What is the overall impact of Bcl11a conditional deficiency on pDC distribution in tissues such as the liver and lung? These questions are important to address to understand whether the heterogeneity of pDCs is differentially affected by their localization.

We agree heterogeneity of pDCs can be affected by their microenvironment. Although phenotyping of lymph nodes in Bcl11a cKOs would greatly add to our manuscript, the genetically altered strains required are no longer being bred in our facility and resurrecting them from frozen sperm is outside the realm of this publication.

(4) Regarding the functional study of pDCs, as emphasized previously, it is important to assess the in vivo relevance of B-pDCs in infectious settings.

Dear reviewer 2, we appreciate your thoughtful comment. Please see our response directed to reviewer 1 above.

(5) The authors injected CpG-ODN into mice and analyzed pDC phenotype upon activation. It is important to note that upon activation, especially upon induction of IFN-I production in vivo, mPDCA1 expression is no longer specific to pDCs  (Blasius et al, Journal of Immunology, 2006). Therefore, to specifically characterize pDC phenotype upon activation, a differential gating strategy is required (CD11c, B220, Ly6C, and Siglec H) to ensure that bona fide pDCs are analyzed.

We agree with the reviewer that this would be a more appropriate characterization. Regarding PDCA1 promiscuity in activated states, we are not aware of any cell types that express very high levels of B220 and PDCA1 simultaneously other than pDCs. We therefore firmly believe that our assignment is valid. Interestingly, gating B220+ cells of Cpg challenged mice that show intermediate expression of PDCA1 results in an increase in the frequency of CD19+ B cells, which we were careful to avoid by gating only the cells that most strongly express PDCA1.

(6) How does pDC activation regulate their mb1 expression? Could conventional pDCs, upon activation, become B-PDCs? Could activation and induction of IFN-I production in vivo also affect CLP and increase the amount of YFP+ lymphoid progenitors and thus B-pDC output?

Dear reviewer, we agree with your concern, albeit beyond the scope of the present study. While changes in YFP MFI via flow cytometry upon vaccination was not substantial, we have included the following comment in the manuscript discussion, acknowledging the aforementioned possibility: “Of note, whether induction of IFN-I production in vivo could also affect CLP and increase the amount of YFP+ lymphoid progenitors and thus B-pDC output is unclear. Further research is required to answer this question.”

(7) If pDCs are preferentially expanding upon in vivo stimulation, it would be informative to assess their Ki67 profile. This is a surprising observation since pDCs are generally considered quiescent cells that were previously described to die in response to activation and IFN-I (Swiecki et al, Journal of Experimental Medicine, 2011).

We agree and have entered the following statement to address this concern: “Functionally, they expand more readily after TLR9 engagement than classical pDCs (either through increased proliferation or differentiation of other cell types) and excel at activating T cells in culture.”

(8) How does the conditional deficiency of BCL11a affect the production of IFN-I and IL-12 in vivo (serum) upon CpG-ODN stimulation?

Dear reviewer 2, we are currently unable to rederive the conditional knockout mouse strain in a timely fashion. However, our ELISA experiments performed under controlled in vitro activation conditions, along with the in vivo findings of Zhang et al.(PNAS 2017) warrants the hypothesis that B-pDCs most likely exhibit a similar cytokine secreting profile under inflammatory conditions.

(9) Given that B-PDCs show downregulation of pDC canonical markers, including IRF8 and TLR7, could the authors address how B-PDCs respond to TLR7 stimulation in vitro and assess a broader spectrum of cytokines produced by pDCs in response to such stimulation (IL-6, TNFa, CXCL10...)?

Dear reviewer 2, although expanding our findings to include B-pDC responses to TLR-7 stimulation would greatly enhance our manuscript, a technical deterrent stands in our way. As mentioned prior, sorting B-pDCs for new experiments using reporter YFP mice is currently not possible, as we have retired this mouse strain. Sorting of live CD79a+ BpDCs via FACS is also not feasible, as CD79a staining with most antibody clones requires permeabilization of cells for easier access to the intra-membrane portion of CD79a.

(10) It would be informative to compare scRNA sequencing data between control and Bcl11a CKO mice to ascertain their contribution to B-PDCs and whether this deficiency may affect other pDC clusters and/or progenitors.

We are unable to sort B-pDCs for new experiments, as we unfortunately retired the transgenic colony.

(11) Transitional DCs were reported to give rise to a subset of cDC2. Given that the authors claim that B-PDCs are related to this subset of transitional DCs, could the authors observe any YFP staining in cDC2 upon the generation of their BM chimeras?

We saw no YFP positivity in CD11c hi cells (cDCs) via flow or through scRNA-seq, indicating CD79a expression is unique in mature B cells and B-pDCs.

(12) Most of the statistical analysis is done with a student test. This requires a normal distribution of the sample which is highly unlikely given the size of the sample. Therefore, the authors shall rather use a non-parametric test (Mann Whitney) to compare their samples.

We agree and have redone our statistical analyses using non-parametric test (Mann Whitney).

Author response:

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

Reviewer #1:

(1)  In the subsets of the γδ T cells that exhibit reduced BLK expression in B6. SAP KO mice, have the authors examined the expression of Lck and/or Fyn? 

The reviewer raises an excellent point. We have included in the revised manuscript additional data on Lck and Fyn expression in our scRNAseq dataset in (new Suppl. Fig. 1 and new Suppl. Fig. 4). These data revealed that in contrast to Blk, which appears primarily restricted to the γδT17 clusters, Lck and Fyn exhibit a much broader distribution and lack restriction to specific clusters. We did note that, like Blk, Lck and Fyn transcripts were abundant in SAP-dependent C2 cluster cells. Pseudobulk analysis on the immature clusters revealed that, neither Fyn nor Lck expression level differences reached our cut-off of 0.5 log2 FC (log2 FC Blk = 1.06), leading us to conclude that Blk is particularly dependent on SAP. We did note, however, that the magnitude of Lck differential expression was close to the 0.5 log2 FC cut-off and that its expression was increased in B6.SAP-/- γδ T cells (Suppl. Fig. 4). These results have been added to lines 202-212 in the Results section and lines 491-499 in the Discussion section.

(2)  Does BLK directly associate with SLAM F1 and or SLAM F6 receptors? 

The reviewer raises an interesting question given previous reports that BLK, LCK, and FYN have all been implicated in γδ T cell development. While SAP has a well-known ability to recruit FYN to SLAMF1 and there is evidence of a similar SAP-mediated recruitment of LCK to SLAMF6, we are not aware of any evidence a SAP-BLK interaction or of a direct binding of BLK to SLAM family receptors. Future experiments to investigate this possiibility are certainly warranted. In the revised ms, we have included additional discussion of these possibilities (lines 491- 499).  

(3)  Given the emerging role of γδ T cells in host immunity, it would be useful if the authors could add a discussion of how their findings are relevant in disease conditions such as cancer. 

We agree and have included new text in the Introduction (lines 37-45). 

(4)  Delete repeated words in lines 546 and line 553. 

Thank you—this has been corrected in the revised manuscript.

Reviewer #2:

This is a very complete study and requires no additional experimentation. One thing to keep in mind in assessing the ultimate fate of the "ab wannabe cells" is that mechanisms exist to silence the gd TCR as cells differentiate to the DP stage and so their presence as diverted DP cells may not be evident by staining for gdTCR expression - and will only be evident transcriptomically. 

We appreciate this helpful comment from the reviewer which we will take into consideration in our future experimental design.

There are a couple of minor points to raise: 

(1)  Figure 3C is not called out in the text. 

Thank you—this has been corrected in the revised manuscript.

(2)  Line 546 - "dependent" is repeated.

Thank you—this has been corrected in the revised manuscript.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

This article by Navratna et al. reports the first structure of human HGSNAT in an acetyl-CoA-bound state. Through careful structural analysis, the authors propose potential reasons why certain human mutations lead to lysosomal storage disorders and outline a catalytic mechanism. The structural data are of good quality, and the manuscript is clearly written. This study represents an important step toward understanding the mechanism of HGSNAT and is valuable to the field. I have the following suggestions:

(1) The authors should characterize whether the purified protein is active. Otherwise, how does one know if the detergent used maintains the protein in a biologically relevant state? The authors should at least attempt to do so. If these prove to be challenging, at the very least, the authors should try a cell-based assay to demonstrate that the GFP tag does not interfere with the function.

We have addressed these concerns in the revised version and mentioned these efforts in our previous response letter. We’re briefly mentioning them here again. We attempted measuring HGSNAT catalyzed reaction by monitoring the decrease in acetyl-CoA in the presence of D-glucosamine (acetyl group acceptor) using a coupled enzyme acetyl-CoA assay kit from SIGMA (MAK039) that converts acetyl-CoA to a fluorescent product measurable at Ex/Em of 535/587 nm. We noticed a decrease in the level of acetyl-CoA (gray) upon the addition of HGSNAT (red) (Rebuttal figure 1).

Author response image 1.

Acetyl-CoA levels in absence and presence of HGSNAT purified in digitonin. Decrease in the levels of 10 M acetyl-CoA was measured in presence of 10 M D-glucosamine and 30 nM HGSNAT at pH 7.5.

While optimizing the assay, Xu et al. (2024, Nat Struct Mol Biol) published structural and biochemical characterization of HGSNAT, showing that detergent-purified HGSNAT is active. In addition, we have shown by cryo-EM that GFP-tagged HGSNAT that we purified in detergent was already bound to the endogenous substrate ACO, an observation that has been observed by Xu et al., as well. Finally, we performed LC-MS on GFP-tagged HGSNAT purified in detergent to detect bound ACO, which could be further removed by dialysis. These results have been included in Figure S9. The endogenous binding of ACO to HGSNAT in detergent suggests that neither the tag nor detergent are detrimental to the function.

(2) In Figure 5, the authors present a detailed schematic of the catalytic cycle, which I find to be too speculative. There is no evidence to suggest that this enzyme undergoes isomerization, similar to a transporter, between open-to-lumen and open-to-cytosol states. Could it not simply involve some movements of side chains to complete the acetyl transfer?

We have already changed this figure in our latest submission. Perhaps the changes made were not obvious while reviewing. We agreed with this reviewer that the enzyme could likely achieve catalysis by simple side chain movements without undergoing extensive isomerization steps, as depicted in Figure 5. In the absence of data supporting large movements during the acetyl transfer reaction, old Figure 5 appeared speculative. Hence, we have edited Figure 5 in the revised version of the manuscript based on the observations we made in this study, and different states shown in the figure do not show any conformational changes and only depict acetyl transfer.

Reviewer #2 (Public Review):

Summary:

This work describes the structure of Heparan-alpha-glucosaminide N-acetyltransferase (HGSNAT), a lysosomal membrane protein that catalyzes the acetylation reaction of the terminal alpha-D-glucosamine group required for degradation of heparan sulfate (HS). HS degradation takes place during the degradation of the extracellular matrix, a process required for restructuring tissue architecture, regulation of cellular function and differentiation. During this process, HS is degraded into monosaccharides and free sulfate in lysosomes.

HGSNAT catalyzes the transfer of the acetyl group from acetyl-CoA to the terminal non-reducing amino group of alpha-D-glucosamine. The molecular mechanism by which this process occur has not been described so far. One of the main reasons to study the mechanism of HGSNAT is that multiple mutations spanning the entire sequence of the protein, such as, nonsense mutations, splice-site variants, and missense mutations lead to dysfunction that causes abnormal accumulation of HS within the lysosomes. This accumulation is a cause of mucopolysaccharidosis IIIC (MPS IIIC), an autosomal recessive neurodegenerative lysosomal storage disorder, for which there are no approved drugs or treatment strategies.

This paper provides a 3.26A structure of HGSNAT, determined by single-particle cryo-EM. The structure reveals that HGSNAT is a dimer in detergent micelles, and a density assigned to acetyl-CoA. The authors speculate about the molecular mechanism of the acetylation reaction, map the mutations known to cause MPS IIIC on the structure and speculate about the nature of the HGSNAT disfunction caused by such mutations.

Strengths:

The paper describes a structure of HGSNAT a member of the transmembrane acyl transferase (TmAT) superfamily. The high-resolution of a HGSNAT bound to acetyl-CoA is important for our understanding of HGSNAT mechanism. The density map is of high-quality, except for the luminal domain. The location of the acetyl-CoA allows speculation about the mechanistic role of multiple residues surrounding this molecule. The authors thoroughly describe the architecture of HGSNAT and map the mutations leading to MPS IIIC.

Reviewer #3 (Public Review):

Summary:

Navratna et al. have solved the first structure of a transmembrane N-acetyltransferase (TNAT), resolving the architecture of human heparan-alpha-glucosaminide N-acetyltransferase (HGSNAT) in the acetyl-CoA bound state using single particle cryo-electron microscopy (cryoEM). They show that the protein is a dimer, and define the architecture of the alpha- and beta-GSNAT fragments, as well as convincingly characterizing the binding site of acetyl-CoA.

Strengths:

This is the first structure of any member of the transmembrane acyl transferase superfamily, and as such it provides important insights into the architecture and acetyl-CoA binding site of this class of enzymes.

The structural data is of a high quality, with an isotropic cryoEM density map at 3.3Å facilitating building of a high-confidence atomic model. Importantly, the density for the acetyl-CoA ligand is particularly well-defined, as are the contacting residues within the transmembrane domain.

The structure of HSGNAT presented here will undoubtedly lay the groundwork for future structural and functional characterization of the reaction cycle of this class of enzymes.

Weaknesses:

While the structural data for the state presented in this work is very convincing, and clearly defines the binding site of acetyl-CoA, to get a complete picture of the enzymatic mechanism of this family, additional structures of other states will be required.

A weakness of the study is the lack of functional validation. The enzymatic activity of the enzyme characterized was not measured, and the enzyme lacks native proteolytic processing, so it is a little unclear whether the structure represents an active enzyme.

Recommendations for the authors:

Reviewer #3 (Recommendations For The Authors):

In the response to reviewers, the authors mention revised coordinates, but the revised coordinates provided to this reviewer do not reflect the stated changes (I assume a technical error somewhere)

Perhaps, the old coordinates in the deposition system were resubmitted with the revised draft. Nevertheless, we have made the changes suggested by this reviewer to structure in the previous round and have released the new coordinates (PDB ID: 8TU9).

Is there any evidence for the interprotomer disulfide except for the map? e.g. if it is a disulfide-linked dimer, one should see a shift in mobility on non-reducing vs reducing SDS-PAGE. Without this, the evidence from the map is not conclusive - while the symmetry-related cysteines are nearby to one another, based on the map I could argue that they could just as well be modeled with the cys sidechains reduced and pointing away from one another.

In addition to building the density based on cryo-EM maps, we have performed FSEC-based thermal melt analysis of the Ala mutation of C334 that is involved in disulfide at the dimer interface. C334A is still expressed as a dimer, suggesting that C334A is not the only residue stabilizing the dimer. Upon heating the detergent-solubilized protein, we noticed that the FSEC peak for C334A shows a monomeric HGSNAT (Figure 4-Figure supplement 1 in main manuscript). We hypothesize that in the absence of C334 disulfide, the extensive hydrophobic side-chain interaction network displayed in Figure 2C is responsible for maintaining the integrity of the dimer. Heating disturbs these non-disulfide interactions, thereby rendering the protein monomer. We have also performed PAGE analysis as suggested by this reviewer and noticed that reducing conditions result in a monomeric protein band (Rebuttal figure 2). While we were revising this manuscript, two other groups published structures of HGSNAT (Xu et al., 2024, Nat. Struct Mol Biol, and Zhao et al., 2024, Nat. Comm). These groups have also identified this disulfide at the dimer interface in their HGSNAT structures. Zhao et al. showed that this disulfide is not crucial for dimerization and also suggested that it can break depending on the conformation of HGSNAT. Our FSEC results agree with this observation.

Author response image 2.

Comparison of purified HGSNAT on native and reducing SDS-PAGE. The arrows on both the gels indicate N-GFP-HGSNAT. The two bands on the SDS PAGE are, perhaps, two differentially glycosylated forms of HGSNAT.

---

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

(1) The authors should characterize whether the purified protein is active. Otherwise, how does one know if the detergent used maintains the protein in a biologically relevant state? The authors should at least attempt to do so. If these prove to be challenging, at the very least, the authors should try a cell-based assay to demonstrate that the GFP tag does not interfere with the function. The authors would need to establish an in vitro assay using purified protein and assess the level of Acetyl-CoA in the reaction (there are commercial kits and a long list of literature showing how to measure this). They could also follow the HS acetylation reaction by e.g. HPLC-MS or NMR (among other methods).

The cryo-EM sample was prepared without the exogenous addition of ligand, as noted in the manuscript. However, we see that acetyl-CoA was intrinsically bound to the protein, indicating the ability of GFP-tagged HGSNAT protein to bind the ligand. Upon dialysis, we see release of acetyl-CoA from the protein, which we have confirmed by LC-MS analysis (Fig S9). We purified the protein at a pH optimal for acetyl-CoA binding, as suggested by Bame, K. J. and Rome, L. H. (1985) and Meikle, P. J. et al., (1995). Because we see acetyl-CoA in a structure obtained using a GFP fusion, we argue that GFP does not interfere with protein stability and ability to bind to the co-substrate. As demonstrated by existing literature HGSNAT catalyzed reaction is compartmentalized spatially and conditionally. The binding of acetyl-CoA happens towards the cytosol and is optimal at pH 7-0.8.0, while the transfer of the acetyl group to heparan sulfate occurs towards the luminal side and is optimal at pH 5.0-6.0. We attempted measuring HGSNAT catalyzed reaction by monitoring decrease in acetyl-CoA in presence of D-glucosamine (acetyl group acceptor) using a coupled enzyme acetyl-CoA assay kit from SIGMA (MAK039) that converts acetyl-CoA to a fluorescent product measurable at Ex/Em of 535/587 nm. We noticed a decrease in the level of acetyl-CoA in the presence of HGSNAT-ACO complex (blue) and apo HGSNAT (red); the difference compared to the ACO standard (gray) was not significant. While optimizing the assay, Xu et al. (2024, Nat Struct Mol Biol) published structural and biochemical characterization of HGSNAT, showing that detergent-purified HGSNAT is active.

Author response image 3.

Acetyl-CoA levels in absence and presence of HGSNAT purified in digitonin. Decrease in the levels of 10 mM acetyl-CoA was measured in presence of 10 mM D-glucosamine and 30 nM HGSNAT at pH 7.5.

(2) In Figure 5, the authors present a detailed schematic of the catalytic cycle, which I find to be too speculative. There is no evidence to suggest that this enzyme undergoes isomerization, similar to a transporter, between open-to-lumen and open-to-cytosol states. Could it not simply involve some movements of side chains to complete the acetyl transfer? The speculative nature of this assumption needs to be clearly acknowledged throughout the manuscript and discussed in more detail. The authors could use HDX-MS or introduce cysteine residues in the hypothetical inward- and outward-facing cavities and test accessibility by incubating the purified protein with maleimides or other agents reacting with free cysteine.

We thank the reviewers for this insightful critique. Yes, the enzyme could likely achieve catalysis by simple side chain movements without undergoing extensive isomerization steps, as depicted in Figure 5. We also agree with the reviewer that HDX-MS could be the best way to monitor the substrate-induced conformational dynamics within HGSNAT experimentally. In the absence of data supporting large movements during the acetyl transfer reaction, figure 5 is speculative. We have now edited Figure 5 in the revised version of the manuscript based on the observations we made in this study.

(3) The acetyl-CoA-bound state is described as the open-to-lumen state. Indeed, from Figure 1C, the lumen opening appears much larger than the cytosol opening. Is there any small tunnel that connects the substrate site to the cytosol? In other words, is this state accessible to both the lumen and the cytosol, albeit with a larger opening toward the lumen? This question arises because, in Figure S5, the tunnel calculated by MOLE seems to also connect to the cytosol.

Yes, it is likely that the ACOS is accessible via lumen and cytosol to varying degrees, as evidenced by MOLE prediction. However, binding of the bulky nucleoside head group of acetyl-CoA at ACOS blocks the cytosolic entrance in the confirmation discussed in this manuscript. MOLE prediction was performed on a structure devoid of acetyl-CoA, and it is possible that the protein doesn’t essentially undergo isomerization between open-to-lumen and open-to-cytosol confirmations during acetyl transfer. Likely, ACOS is always accessible from both the lumen and cytosol, but depending on the substrates or products bound, the accessibility could be limited to either the lysosomal lumen or cytosol. We have rewritten all the statements mentioning an open-to-lumen confirmation to reflect this argument.

(4) The authors state, "Interestingly, in most of the detergent conditions we tested, HGSNAT was predominantly dimeric (Fig S1C-H)," and also mention, "In all the detergents we tested, HGSNAT eluted as a dimer, a testament to the extensive side-chain interaction network." The dimerization is said to be mediated by a disulfide bond. I would be surprised if the detergents the authors tested could break a disulfide bond. Therefore, can this observation truly serve as a testament to an "extensive" side-chain interaction network?

We agree with the reviewer that detergents are unlikely to break a disulfide bond. To address this comment, we generated a C334A mutant of HGSNAT and extracted it from cells in 1% digitonin. It is still expressed as a dimer (Fig S8E). However, upon heating the detergent solubilized protein, we noticed that the FSEC peak for C334A shows a monomeric HGSNAT (Fig S8I and S8K). We hypothesize that in the absence of C334 disulfide, the extensive hydrophobic side-chain interaction network displayed in Figure 2C is responsible for maintaining the integrity of the dimer. Heating disturbs these non-disulfide interactions, thereby rendering the protein monomer.

(5) Apart from the cryo-EM structure, the article does not provide any other experimental evidence to support or explain a molecular mechanism. Due to the complete absence of functional assays, mutagenesis analysis, or other structures such as a ternary complex or an acetylated enzyme intermediate, the mechanistic model depicted in Figure 5 should be taken with caution. This uncertainty needs to be clearly described in the manuscript text. Performing additional mutagenesis experiments to test key hypotheses, or further discussing relevant data from the literature, would strengthen the manuscript.

We agree with the reviewer on the lack of supporting evidence for the mechanistic models proposed in Fig 5. They were made based on previously reported biochemical characterization of HGSNAT by Rome & Crain (1981), Rome et al. (1983), Miekle et al. (1995), and Fan et al. (2011). However, we agree with the reviewer that this schematic is not experimentally proven and is speculative at best. We have edited Figure 5 in the revised version of the manuscript. In addition, we have also performed mutagenesis analysis to study the stability of mutants (Fig S8) and performed LC-MS analysis to identify endogenously bound acetyl-CoA (Fig S9) to strengthen parts of the manuscript. We have discussed our findings in the results and modified the discussion according to these suggestions.

(6) It is discussed that H269 is an essential residue that participates in the acetylation reaction, possibly becoming acetylated during the process. However, there is no solid experimental evidence, e.g. mutagenesis analysis or structural analysis, in this or previous articles, that demonstrates this to be the case. Providing more information, ideally involving additional experimental work, would strengthen this aspect of the mechanism that is proposed. This would require establishing an in vitro assay, as described in 1).

H269, as a crucial catalytic residue, was suggested by monitoring the effect of chemical modifications of amino acids on acetylation of HGSNAT membranes by Bame, K. J. and Rome, L. H. (1986). We generated N258I and H269A mutants of HGSNAT and analyzed their stability. We noticed a greater destabilization in N258I compared to H269A (Fig S8). We believe this is because of the loss of ability to bind acetyl-CoA, as the TMs around a catalytic core of the protein in our cryo-EM structure were stabilized by interactions with acetyl-CoA. Recently, Xu et al. (2024, Nat Struct Mol Biol) suggested that they do not observe acetylated histidine in their structure. However, our structure and that reported by Xu et al. (2024) are obtained at cytosolic pH. Perhaps, acetylation of H269 occurs at acidic lysosomal pH. Extensive structural and catalytic investigation of HGSNAT at low pH is required to rule out H269 acetylation as a step in the HGSNAT catalyzed reaction.

(7) In the discussion part, the authors mention previous studies in which it was postulated that the catalytic reaction can be described by a random order mechanistic model or a Ping Pong Bi Bi model. However, the authors leave open the question of which of these mechanisms best describes the acetylation reaction. The structure presented here does not provide evidence that could support one mechanism or the other. The authors could explore if an in vitro experimental measurement of protein activity would provide any information in this regard.

We agree with the reviewer that a more detailed kinetic analysis is necessary to define the bisubstrate reaction mechanism of HGSNAT. All the existing structural data on two isoforms of HGSNAT is obtained at basic pH. As a result, the existing structures do not unambiguously demonstrate the bisusbtrate mechanism of HGSNAT. We believe low pH structural characterization and a detailed kinetic and structural characterization of HGSNAT in membrane mimetics like nanodiscs could provide more insights into the mechanism. However, these studies are a future undertaking and are not a part of this manuscript.

(8) Although the authors map the mutations leading to MPS IIIC on the structure and use FoldX software to predict the impact of these mutations on folding and fold stability, there is no experimental evidence to support FoldX's predictions. It would be ideal if an additional test for these predictions were included in the manuscript. The authors could follow the unfolding of purified mutants by SEC, FSEC, or changes in intrinsic fluorescence to assess protein stability.

As suggested here, we prepared HGSNAT MPSIIIC variants and tested their expression and stability (please see Fig S8). These results have been included in the revised version of the manuscript.

(9) Some sidechains that have quite strong sidechain density are missing atoms. I would be particularly careful with omitting sidechains that pack in the hydrophobic core, as this can tend to artificially reduce the clash score. Check F81, L62, P91 and V87, for example.

We have revisited the modeling of these regions and deposited new coordinates.

(10) W316 seems to have the wrong rotamer.

This has been corrected in the new coordinate file that has been released.

(11) N134 and N433 seem to have extra density. Are these known glycosylation sites?

As per Hrebicek M. et al., 2006 and Feldhammer M. et al., 2009, there are five predicted glycosylation sites: N66, N114, N134, N433, and N602. However, we see evidence for NAG density at N114, N134, and N433. These have now been modeled in the structure.

(12) At the C-terminal residue (Ile-635), the very C-terminal carboxylate is modeled pointing to a hydrophobic environment. It seems more likely to me that the Ile sidechain is packing here, with the C-terminal carboxylate facing the solvent.

Thank you for pointing this out. We have edited the orientation of the Ile sidechain accordingly.

Presentation and wording of results/methods:

- Figure S3 legend "At places with missing density, the side chains were trimmed to C- alpha" - this is incorrect, I think the authors mean C-beta.

We have corrected this error in the revised version of the manuscript.

- Figure S3 legend - the authors refer to a gray mesh, where a transparent surface is displayed.

Thanks for pointing this error out. We have corrected this in the revised version.

- Some colloquial/vague wording in the main text (a lot of sentences starting with "Interestingly, ...". Making the wording more specific would help the reader I think.

We have edited out ‘interestingly’ from the document and have re-written parts of the manuscript, per reviewers’ suggestion, for brevity.

- Figure S2 legend, "throughout the processing workflow the resolution of luminal domain was used as a guidepost" - it is not entirely clear to me what this means in this context, perhaps revise the wording?

We have rephrased this line in the revised draft of the manuscript.

- Figure S2 and methods, Local refinements of LD and TMD are mentioned, but not indicated on the processing workflow.

We have included a new Fig S2 & edited the legend, including these changes, per the reviewers’ suggestions.

Author response:

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

Response to reviewers (minor points):

We thank all reviewers for their very helpful suggestions and greatly appreciate their positive evaluation of our work.

Reviewer #1:

Ad 1) The reviewer states: Fig 5 While the data very nicely show that CPX and Syt1 have interdependent interactions in the chromaffin neurons, this seems to be not the case in neurons, where the loss of complexins and synaptotagmins have additive effects, suggesting independent mechanisms (eg Xue et al., 2010). This would be a good opportunity to discuss some possible differences between secretion in endocrine cells vs neurons.

We greatly appreciate the insightful suggestion by the reviewer. To accommodate the reviewer’s suggestion, we now discuss this issue on page 21, line 486-491: “In murine hippocampal neurons, loss of CpxI and Syt1 has additive effects on fast synchronous release, suggesting independent mechanisms (Xue et al., 2010). On the other hand, the same study also showed that Syt1 heterozygosity fails to reduce release probability in wild-type neurons, but does so in the absence of Cpx, again suggesting that Cpx and Syt1 may functionally interact in Ca2+-triggered release.”

Ad 2) The reviewer states: Fig 8 Shows an apparent shift in Ca sensitivity in N-terminal mutants suggesting a modification of Ca sensitivity of Syt1. Could there be also an alternative mechanism, that explains this phenotype which is based on a role of the n-term lowering the energy barrier for fusion, that in turn shifts corresponding fusion rates to take place at lower Ca saturation levels?

We fully agree with the reviewer. While our data indicate that Cpx and Syt1 act in a dependent manner in accelerating exocytosis, they do not provide decisive evidence that the NTD of CpxII directly modulates the Ca2+ affinity of Syt1, an issue that we discuss on page 23 , line 523529: ”The results favor a model wherein the CpxII NTD either directly regulates the biophysical properties of the Ca2+-sensor by increasing the apparent forward rate of Ca2+-binding or indirectly affects SytI-SNARE or SytI-membrane interactions, thereby, lowering the energy barrier of Ca2+triggered fusion.”

Reviewer #2:

Ad 1) The reviewer states: The authors provide a "chromaffin cell-centric" view of the function of mammalian Cplx in vesicle fusion. With the exception of mammalian renal ribbon synapses (and some earlier RNAi knockdown studies that had off-target effects), there is very little evidence for a "fusion-clamp"-like function of Cplxs in mammalian synapses. At conventional mammalian synapses, genetic loss of Cplx (i.e. KO) consistently decreases AP-evoked release, and generally either also decreases spontaneous release rates or does not affect spontaneous release, which is inconsistent with a "fusion-clamp" theory. This is in stark contrast to invertebrate (D. m. and C. e.) synapses where genetic Cplx loss is generally associated with strong upregulation of spontaneous release, providing support for Cplx acting as a "fusion-clamp".

We agree with the reviewer that it is difficult to reconcile contradictory findings regarding the role of Cpx in membrane fusion in vertebrates and invertebrates or between murine hippocampal neurons and neuroendocrine cells. On the other hand, we respectfully disagree with the statement of providing a "chromaffin cell-centric" view of the function of mammalian Cplx in vesicle fusion. In fact, a large number of model systems (in vitro and in vivo studies) support a scenario where complexin takes center stage in clamping of premature vesicle release. For example, in vitro analyses using a liposome fusion assay (Schaub et al., 2006, Nat Struct Mol Biol 13, 748; Schupp et al., 2016) or Hela cells that ectopically express “flipped” SNAREs on their cell surface (Giraudo et al., 2008, JBC 283, 21211) showed that complexin can inhibit the SNARE-driven fusion machinery. Likewise, several studies boosting complexin action by either genetic overexpression or peptide supplementation have provided evidence for the complexin clamp function in neuronal and nonneuronal cells (e.g. Itakura et al., 1999, BBRC 265, 691; Liu et al., 2007, Biochemistry 72, 439; Abderrahmani et al., 2004, J Cell Sci 117, 2239; Archer et al., 2002, JBC 277, 18249; Tang et al, 2006,

Cell 126, 1175; Vaithianathan et al., 2013, J Neurosci 33, 8216; Roggero et al., 2007, JBC, 282, 26335.)

In addition, chromaffin cells enable the investigation of secretion on the background of a well-defined intracellular calcium concentration. Indeed, CplxII knock-out in chromaffin cells demonstrated an enhanced tonic release which is evident at elevated levels of [Ca]i (>100nM), but absent at low resting [Ca]i (Dhara et al., 2014). Given this observation, it is tempting to speculate that variations in [Ca]i among the different preparations may contribute to the deviating expression of the complexin null phenotype in different preparations.

Ad 2) The reviewer states: The authors use a Semliki Forest virus-based approach to express mutant proteins in chromaffin cells. This strategy leads to a strong protein overexpression (~7-8 fold, Figure 3 Suppl. 1). Therefore, experimental findings under these conditions may not necessarily be identical to findings with normal protein expression levels.

As shown in Fig. 4, we use the secretion response of wt cells as a control so that we can assess the specificity and quality of the rescue approach in our experiments. In addition, the comparative analysis of the CpxII mutants was performed with respect to the equally overexpressed CpxII wt protein (Fig. 3 Suppl. 1), which we used as a control to determine the standard response under these conditions.

Ad 3) The reviewer states: Measurements of delta Cm in response to Ca2+ uncaging by ramping [Ca2+ ] from resting levels up to several µM over a me period of several seconds were used to establish changes in the release rate vs [Ca2+ ]i relationship. It is not clear to this reviewer if and how concurrently occurring vesicle endocytosis together with a possibly Ca2+-dependent kinetics of endocytosis may affect these measurements.

By infusing bovine chromaffin cells with 50µM free Ca2+, Smith and Betz have shown that the total capacitance increase is dominated by exocytosis and that significant endocytosis only sets in after 3 minutes (Smith and Betz, 1996, Nature, 380, 531). In the same line, we previously showed that mouse chromaffin cells (infused with 19µM free calcium over 2 minutes) responded with robust increase in membrane capacitance which strongly correlated with the number of simultaneously recorded amperometric events monitoring fusion of single vesicles (Dhara et al., 2014, Fig. 5B). Thus, capacitance alterations recorded under tonic intracellular Ca2+ increase in chromaffin cells are solely due to exocytosis and are not contaminated by significant endocytosis. As our Ca2+ ramp experiments were carried out for 6 seconds and the intracellular free [Ca]i did not exceed 19 µM the observed phenotypical differences between the experimental groups are most likely due to changes in exocytosis rather than endocytosis.

Ad 4) The reviewer states: It should be pointed out that an altered "apparent Ca2+ affinity" or "apparent Ca2+ binding rate" does not necessarily reflect changes at Ca2+-binding sites (e.g. Syt1).

We fully agree with the reviewer’s comment. As pointed out also in the response to reviewer 1, our experiments do not provide decisive evidence that the NTD of CpxII directly modulates the Ca2+ affinity of Syt1, an issue that we discuss on page 23 , line 523-529: ” The results favor a model wherein the CpxII NTD either directly regulates the biophysical properties of the Ca2+sensor by increasing the apparent forward rate of Ca2+-binding or indirectly affects SytI-SNARE or SytI-membrane interactions, thereby, lowering the energy barrier of Ca2+-triggered fusion.” 

AD 5) There are alternative models on how Cplx may "clamp" vesicle fusion (see Bera et al. 2022, eLife) or how Cplx may achieve its regulation of transmitter release without mechanistically "clamping" fusion (Neher 2010, Neuron). Since the data presented here cannot rule out such alternative models (in this reviewer's opinion), the authors may want to mention and briefly discuss such alternative models.

The study by Bara et al reiterates the model proposed by the Rothman group which attributes the clamping function of Cpx to its accessory alpha helix by hindering the progressive SNARE complex assembly. We have explicitly stated this issue in the original version of the manuscript (page 19, line 425) “As the accessory helix of Cpx has been found to bind to membrane proximal cytoplasmic regions of SNAP-25 and SybII (Malsam et al., 2012; Bykhovskaia et al., 2013; Vasin et al., 2016), an attractive scenario could be that both domains of CpxII, the CTD and the accessory helix, synergistically cooperate to stall final SNARE assembly”. In this context, we will now cite also the study by Bera et al.. 

A related view of the function of complexin suggested that it may act as an allosteric adaptor for sytI (Neher 2010, Neuron). Here, rather than postulang independent "clamp" and "trigger" functions for the dual action of complexin, these were explained as facets of a simple allosteric mechanism by which complexin modulates the Ca2+ dependence of release. Yet, this interpretation appears to be difficult to reconcile with the observation of our and other laboratories, showing that the fusion-promoting and clamping effects are separable (e.g. Dhara et al., 2014; Lai et al., 2014; Makke et al., 2018; Bera et al., 2022).  

Some parts of the Discussion are quite general and not specifically related to the results of the present study. The authors may want to consider shortening those parts.

Considering the contrary findings in the field of SNARE-regulating proteins, the authors hope that the reviewer will agree that it is necessary to discuss the new observations in a broader context, as also acknowledged by the first reviewer.

Last but not least, the presentation of the results could be improved to make the data more accessible to non-specialists, this concerns providing necessary background information, choice of colors, and labeling of diagrams.

Done

Recommendations for the authors:

Reviewer #2 (Recommendations For The Authors): 

Regarding figures: 

(1) Please use clearly distinct colors in diagrams. For example, in Figure 2 Suppl. 3, four different shades of red (or reddish) are used to color the traces and the respective bars. These different shades of red are difficult to discriminate. In Figure 5 Suppl. 1, the two greens are nearly indistinguishable.  

Done

(2) RRP size and SRP size on the one hand, and SR rate on the other represent different quantities which are measured in different units. Please use a separate y-axis for the SR (a rate measured in fF/s) and do not combine with RRP and SRP (pool sizes measured in fF). This would also automatically alleviate the need for axis breaks in the plots of RRP size and SRP size. In general, please do not use axis breaks which make interpretation of data unnecessarily more complicated.  

In order to clarify the display, we now define the different units together with the quantified parameter (e.g. RRP [fF], SRP [fF], SR [fF/s]) allowing us to omit a second axis in those subpanels.

(3) When plotting bar graphs showing mean tau_RRP, mean tau_SRP, and mean delay, please always use the correct y-axis labels, i.e. use "tau_RRP", "tau_SRP" and "delay" as y-axis labels as it was done for example in Figure 4D, and do not use "tau_RRP", "tau_SRP" and "delay" as x-axis labels as it was done for example in Figure 1D and many other figure panels.  

We have standardized the figure display. Yet, we would prefer to keep our way subpanel labelling which states the parameter underneath the bar graph and thereby makes the results more accessible.  

(4) Are the asterisks indicating statistical significance perhaps missing in Figure 4D, middle panel (tau_SRP)?

There was not a statistically significant difference (wt vs cpxIIko+CpxII EA, P=0.0826, Kruskal-Wallis with Dunn’ post hoc test).  

(5) According to the Results section (pages 12 to 13), I assume that in Figures 6 and 7 the labels "+Cplx XYZ" are used by the authors to identify an overexpression of Cplx XYZ in a Cplx WT background. The legend text reads however " ... cells expressing either Cplx2 wt or the mutant ...", which would not be correct. Please check.

We have changed the formulations to “overexpression” accordingly.

(6) The x-axis unit in Figure 8C is likely "µM" and not "M".

Done.

(7) The abbreviations "CplxII LL-EE" and "CplxII LL-WW", and "CplxII LLEE" and "CplxII LLWW" are very similar but refer to different mutants. Could you please think of a more specific and unambiguous abbreviation? Perhaps "CplxII L124E-L128E"?  

We have changed the abbreviations, accordingly (i.e. CpxII L124E-L128E).  

Regarding the manuscript text:  

Line 65: "prevents" instead of "impairs"? 

done

Line 67: why "in vivo"? 

We changed the formulation to ‘Several’

Line 83: "in addition to the clamping function ..." This is misleading. Many of the studies listed here did not provide evidence for enhanced spontaneous release following Cplx loss and often observed the opposite, reduced spontaneous release. The enhanced delayed release was observed by Strenzke et al 2009 J.Neurosci. and by Chang et al. 2015 J.Neurosci. (which the authors may want to cite). However, that enhanced delayed release occurred despite reduced spontaneous release indicating that it is not simply the result of a missing "fusion clamp". 

To accommodate the reviewer’s suggestion, we have changed the formulation to “Independent of the clamping function of Cpx….”

Line 104: "speeds up exocytosis that is controlled by the forward rate of Ca2+ binding" This is difficult to understand without context.  

We have now added the corresponding citations (Voets et al., 2001; Sorensen et al., 2003), which showed that exocytosis timing in chromaffin cells is largely determined by the kinetics of Ca2+-binding to SytI.

Line 116: "Cplx2 knock out ..." Please provide (here or earlier in the manuscript) information to the reader about which Cplx paralogs are expressed in chromaffin cells.  

We now state on line 111 that “CpxII is the only Cpx isoform expressed in chromaffin cells (Cai et al., 2008)”

Line 118: "=~" either "=" or "~". 

done

Line 120: "instead" seems superfluous.

done

Line 272: "calcium binding rates" should perhaps better read "apparent calcium binding rates". 

done

Line 290: "enhancing SytI's Ca2+ affinity" should perhaps better be "enhancing the apparent Ca2+ affinity of the release machinery". Ca2+ binding kinetics is never directly assayed here.

We agree and have phrased the sentence accordingly.

Line 300: "Expression of Cplx ... in Syt1 R233Q ki cells, ..." Perhaps better "Overexpression of Cplx ... in Syt1 R233Q ki/Cplx2 wt cells, ..." for clarification?

done

Lines 313ff: What is assayed here is the apparent Ca2+ binding kinetics and apparent KD values of the release machinery. Ca2+ binding to Syt1 is never directly measured!  

We agree and have changed the wording accordingly to “CpxII NTD supports the forward rate of calcium binding to SytI in accelerating exocytosis”

Line 347: "Complexin plays a dual role ..." This is partially misleading. It does so in chromaffin cells and D.m. and C.e. NMJs but not at conventional mammalian synapses. 

We agree and have changed the formulation to “In many secretory systems, Complexin plays a dual role in the regulation of SNARE-mediated vesicle fusion”

Author response:

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

Introduction to the revised manuscript:

We thank all three reviewers for their time and insightful comments on our original submission. We are submitting a substantially revised manuscript that includes several new experiments, analyses, discussion points, and clarifications that we believe address all of the main concerns of the reviewers.

To address the request of Reviewers 2 and 3 to reinforce key findings in a more physiologically intact preparation, we performed recordings of YH-HET SST neurons in brain slices and found that these neurons show impairments in AP generation similar to those observed in YH-HET SST cultured neurons. These data are summarized in a new figure (Fig. 9). Along these lines, we performed additional recordings in cultured neurons at room temperature compared with physiological temperature and found that WT and YH-HET PV neuronal properties were similarly altered by temperature increases, suggesting that our YH variant-induced neuronal phenotypes are not temperature dependent. These data are shown in a new supplemental figure (Supplemental Fig. 4-3). To address concerns of Reviewer 1 regarding our KNa and NaP current recordings, we performed new experiments to further assess the specificity of the VU170 blocker in KNa KO neurons (summarized in Supplemental Fig. 5-2) and to better characterize the time course over which TTX blocks the persistent Na+ current and the KNa current (summarized in Supplemental Fig. 7-1). These latter two experiments provide further clarity and confidence in the accuracy of our measurements of both KNa and NaP currents. Lastly, to address the concern of Reviewer 3 regarding statistical analyses of the modeling data, we’ve added a new table with the results of a repeated measures ANOVA analysis (Supplemental Table 6), and two new figures illustrating the relative changes in each neuron group compared to their controls (Supplemental Figures 6-2 and 7-2). 

In addition to the new experiments and analyses, we’ve added three new paragraphs to the Discussion section. As the hyperexcitability phenotype in YH-HET PV neurons is somewhat unexpected, we’ve added a paragraph comparing our findings with those found in PV neurons in another KCNT1 GOF model. We’ve also added a paragraph to speculate on the contribution of YH-HET variant-induced alterations in SST and PV neurons to network behavior and seizure propensity. Lastly, we’ve added a paragraph to include the additional limitations and caveats of our study requested by the reviewers.  

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This manuscript reports the effects of a heterozygous mutation in the KCNT1 potassium channels on the properties of ion currents and the firing behavior of excitatory and inhibitory neurons in the cortex of mice expressing KCNT1-Y777H. In humans, this mutation as well as multiple other heterozygotic mutations produce very severe early-onset seizures and produce a major disruption of all intellectual function. In contrast, in mice, this heterozygous mutation appears to have no behavioral phenotype or any increased propensity to seizures.

Regarding the last sentence above, we wanted to clarify a point that we neglected to emphasize in the initial submission. In the Results section from our previous paper (Shore et al., 2020), we failed to observe seizures in 14 heterozygous mice, whereas 23/25 homozygous mice showed seizures by video-EEG. However, in the fifth paragraph of the Discussion section from that paper, we further stated that “during the preparation and review of [that] article, we observed seizures in two Kcnt1-Y777H heterozygous mice, one during a widefield Ca2+ imaging experiment and the other during a video-EEG experiment”. Thus, we concluded that “heterozygous expression can result in seizures in a rodent model, but apparently at a much lower frequency than that observed with homozygous expression”. To emphasize these findings, we’ve added a sentence to the Introduction in this manuscript about the occurrence of infrequent seizures in Kcnt1-Y777H heterozygous mice, along with a reference to the Discussion of our previous paper.

A relevant phenotype is, however, evident in mice with the homozygous mutation, and the authors have previously published the results of similar experiments with the homozygotes. As perhaps expected, the neuronal effects of the heterozygous mutation presented in this manuscript are generally similar but markedly smaller than the previously published findings on homozygotes. There are, however, some interesting differences, particularly on PV+ interneurons, which appear to be more excitable than wild type in the heterozygotes but more excitable in the heterozygotes. This raises the interesting question (which could be more explicitly discussed by the authors) as to whether the reported changes represent homeostatic events that suppress the seizure phenotype in the mouse heterozygotes or simply changes in excitability that do not reach the threshold for behavioral outcomes.

That is an interesting question. We have added a new paragraph to the Discussion speculating about whether the alterations in SST and PV excitability suppress seizures or do not reach the threshold for behavioral outcomes. This seems to be requested by the second reviewer as well in Weaknesses point #2.

Strengths and Weaknesses:

(1) The authors find that the heterozygous mutation in PV+ interneurons increases their excitability, a result that is opposite from their previous observation in neurons with the corresponding homozygous mutation.

We would like to provide a minor clarification to the above statement that, in this manuscript, we show that “the heterozygous mutation in PV+ interneurons increases their excitability, a result that is opposite from their previous observation in neurons with the corresponding homozygous mutation”. In our previous manuscript, we assessed YH-HOM phenotypes in NFS and FS GABAergic neurons, but did not specifically mark PV neurons. Although the YH-HOM FS neurons showed an increase in rheobase and a decrease in AP firing, the magnitudes of these effects were far less than those observed in the NFS population. More importantly, the FS GABAergic population likely consists of PV- and SST-expressing neurons; thus, we can not directly compare the results from the NFS and FS groups to the PV and SST groups, respectively (please see our response to Weaknesses point #3, Reviewer #2). We apologize for the confusion.

They propose that this results from the selective upregulation of a persistent sodium current INaP in the PV+ interneurons. While the observations are very interesting, there are three issues concerning this interpretation that should be addressed:

A) The protocol for measuring the INaP current could potentially lead to results that could be (mis)interpreted in different ways in different cells. First, neither K currents nor Ca currents are blocked in these experiments. Instead, TTX is applied to the cells relatively rapidly (within 1 second) and the ramp protocol is applied immediately thereafter. It is stated that, at this time, Na currents and INaP are fully blocked but that any effects on Na-activated K currents are minimal. In theory, this would allow the pre- to post-difference current to represent a relatively uncontaminated INaP. This would, however, only work if activation of KNa currents following Na entry is very slow, taking many seconds. A good deal of literature has suggested that the kinetics of activation of KNa currents by Na influx vary substantially between cell types, such that single action potentials and single excitatory synaptic events rapidly evoke KNa currents in some cell types. This is, of course, much faster than the time of TTX application. Most importantly, the kinetics of KNa activation may be different in different neuronal types, which would lead to errors that could produce different estimates of INaP in PV+ interneurons vs other cell types.

First, we’d like to point out that we did not want to block K+ currents (which would also block KNa) when measuring INaP for these experiments, because our hypothesis was that the increased KNa current in YH-HET PV neurons was somehow causing an increase in INaP, and it is possible that this increase depends on an intact KNa. Thus, we decided to use a method based on the observation in our experiments, and previously made by others (Budelli et al., 2009), that the reduction of outward current after TTX addition is slow relative to the rapid reduction in Na+ current. We understand and agree with the reviewer that, if KNa currents were blocked more quickly by TTX in some neuron types than others, then our estimate of INaP using this method would be contaminated in these neuron types, which would lead to inaccurate measurements. To assess this possibility among the main neuron types used in this study, we performed new experiments in which we monitored the time course of INaP block and subsequent IKNa loss following TTX application in PV and SST neurons during slow voltage ramps. We note that action potentials are not present in the slow voltage ramps due to inactivation of the transient Na+ current. These new experiments show that, in SST and PV (both WT and Het) neurons, the block of INaP is nearly complete at the 6s time point, whereas the decay in IKNa is far slower (V50 of ≈ 25s), and importantly, these results do not differ substantially by cell type or genotype. These data suggest that our measurements of INaP are not significantly contaminated by IKNa, and that this method allows for the effective separation of these two currents. These data have been added as a supplemental figure (Supplemental Fig. 7-1) and are briefly described and referenced in the Results section.

B) As the authors recognize, INaP current provides a major source of cytoplasmic sodium ions for the activation. An expected outcome of increased INaP is, therefore, further activation of KNa currents, rather than a compensatory increase in an inward current that counteracts the increase in KNa currents, as is suggested in the discussion.

We agree that the increase in INaP could theoretically further increase IKNa, as veratridine was previously shown to increase IKNa (Hage & Salkoff, 2012). However, we do not believe that this would necessarily be the case, because as the reviewer notes in their next comment, there is insufficient information on the relative locations of the INaP and KCNT1 channels, as well as the kinetics of sodium transfer to KCNT1 channels, and even less is known in the context of KCNT GOF neurons. Thus, there are a couple of plausible reasons that increased INaP may not alter KNa currents in YH-HET PV neurons: (1) In YH-HET PV neurons, the particular sodium channels that are responsible for the increased INaP may not be located within close proximity to the KCNT1 channels. (2) Homeostatic mechanisms that alter the AIS length, or move the AIS further from the soma, in response to altered neuronal excitability are well described (Grubb & Burrone, 2010; Kuba et al., 2010); thus, it is possible that in YH-HET PV neurons, the length or location of the AIS is altered, leading to uncoupling of the sodium channels that are responsible for the increased INaP to the KCNT1 channels.

C) Numerical simulations, in general, provide a very useful way to evaluate the significance of experimental findings. Nevertheless, while the in-silico modeling suggests that increases in INaP can increase firing rate in models of PV+ neurons, there is as yet insufficient information on the relative locations of the INaP channels and the kinetics of sodium transfer to KNa channels to evaluate the validity of this specific model.

We completely agree; thus, we have described each of these limitations in the Discussion. We state that the model neurons may “lack more detailed features of ion channels, such as post-translational modifications and subcellular localizations”, and that our KCNT1 model conductance is “hampered by an incomplete understanding of the relationship between Na+ influx, membrane voltage, and channel gating in neurons”.  

(2) The greatest effect of TTX application would be expected to be the elimination of large transient inward sodium currents. Why are no such currents visible in the control (pre-TTX) or the difference currents (Fig. 2)? Is it possible I missed something in the methods?

We apologize for the confusion and our mistake in failing to mention this important feature of the displayed traces. To include all of the representative traces in the figures, and prevent overlap of the traces, we removed the large inward sodium currents using the masking tool in Adobe Illustrator in Figure 2 and Supplemental Figure 5-1. We have added that information to the relevant figure legends. We have also provided unmasked images of the representative traces from Figure 2 and Supplemental Figure 5-1 to illustrate the large transient inward sodium currents, and the significant reduction of these currents with TTX treatment.

(3) As expected, the changes in many of the measured parameters are smaller in the present study with heterozygotes than those previously reported for the homozygous mutation. Some of the statements on the significance of some of the present findings need to be stated more clearly. For example, in the results section describing Fig. 2, it is stated that "In glutamatergic and NFS GABAergic YH-HET neurons, the overall KNa current was increased ...as measured by a significant effect of genotype ...." Later in the same paragraph it is stated that the increases in KNa current are not significant. Apparently, different tests lead to different conclusions. Both for the purpose of understanding the pathophysiological effects of changes in KNa current and for making further numerical simulations, more explicit clarifying statements should be made.

We apologize for the confusion on the description of these statistics. The results come from the same test, which is a Generalized Linear Mixed Model (GLMM). The factors in our GLMM were voltage step, genotype, and a voltage step x genotype interaction term. The overall effect of genotype is significant in glutamatergic neurons, but pairwise tests at each voltage step show no significant effect of genotype at any given voltage. This is somewhat analogous to running a traditional ANOVA on multiple groups and finding a significant ANOVA p-value but no significant post-hoc multiple comparisons tests, and is not uncommon. Our interpretation of this is that heterozygous expression of the YH variant in glutamatergic neurons likely increases KNa currents across positive potentials (as was seen with the YH-HOM glutamatergic neurons), but only a small amount at each positive step; thus, we lack the statistical power to determine any particular voltage step where this occurs.

(4) The effects of the KCNT1 channel blocker VU170 on potassium currents are somewhat larger and different from those of TTX, suggesting that additional sources of sodium may contribute to activating KCNT1, as suggested by the authors. Because VU170 is, however, a novel pharmacological agent, it may be appropriate to make more careful statements on this. While the original published description of this compound reported no effect on a variety of other channels, there are many that were not tested, including Na and cation channels that are known to activate KCNT1, raising the possibility of off-target effects.

We agree and thank the reviewer for making this point. To address this question, we measured KNa currents in WT vs. Kcnt1/Kcnt2-dKO neurons using VU170 to illustrate the extent of outward current due to off-target effects of the drug. These data have been included as a supplemental figure (Supplemental Fig. 5-2). We have also added several sentences to the Results section referencing this figure. Interestingly, in Kcnt1/Kcnt2-dKO neurons, VU170 seems to be quite specific across the negative potentials, as no outward currents are apparent until approximately -10 mV onward, whereas across positive potentials, there is a VU170-senstive outward current reaching ~1 nA by +50 mV. We have also included a note of caution in interpreting these data and added the possibility of off-target effects of VU170 as an alternative explanation for the differences observed on KNa currents between TTX and VU170 to the Discussion section.

(5) The experiments were carried out at room temperature. Is it possible that different effects on firing patterns in heterozygotes and homozygotes would be observed at more physiological temperatures?

Yes, it is reasonable to assume that an increased temperature would affect neuronal firing patterns in cultured neurons, as temperature differences have been shown to alter synaptic transmission and neuronal function, as assessed in both cultured neuron and slice recordings. All of our recordings were performed at room temperature in this study, and although they are valid with regard to between-group comparisons, this additional caveat is worth mentioning. We have added this to the paragraph describing study limitations in the Discussion section.

To better understand the effects of temperature in our recordings, we have now compared membrane and AP generation parameters at room temperature (~22°C) and at a more physiological temperature (35°C) in a before-after study of 16 WT neurons, including both glutamatergic and GABAergic neuron types. Not surprisingly, we found robust alterations in all parameters assessed, excluding resting membrane potential and capacitance. We further assessed the effect of temperature on WT and YH-HET PV neurons, as the PV neurons expressing the YH variant showed the most unexpected phenotypes in our study. In our room temperature recordings, we showed that the YH-HET variant decreased the rheobase current, increased the AP amplitude, and increased the AP firing. In our before-after comparison (22°C-35°C) of PV neurons (WT; n=11, YH-Het; n=10), the WT and YH-HET neurons showed the same temperature-dependent effects on these parameters, including increased rheobase, decreased AP amplitude, and a higher maximal firing rate, at 35°C compared to those at 22°C. These data have been added to the manuscript as a supplemental figure (Supplemental Fig. 4-3) and are briefly referenced and described in the Results section.     

Moreover, in our original manuscript, we showed that the effects of the homozygous YH variant on glutamatergic and NFS GABAergic neuron excitability were highly similar between cultured recordings at room temperature (~22°C) and slice recordings at 32°C. Taken together, these data suggest that the reported neurophysiological phenotypes downstream of the YH variant are likely not temperature dependent. 

Reviewer #2 (Public Review):

Summary:

In this manuscript, Shore et al. investigate the consequent changes in excitability and synaptic efficacy of diverse neuronal populations in an animal model of juvenile epilepsy. Using electrophysiological patch-clamp recordings from dissociated neuronal cultures, the authors find diverging changes in two major populations of inhibitory cell types, namely somatostatin (SST)- and parvalbumin (PV)-positive interneurons, in mice expressing a variant of the KCNT1 potassium channel. They further suggest that the differential effects are due to a compensatory increase in the persistent sodium current in PV interneurons in pharmacological and in silico experiments.

Strengths:

(1) Heterozygous KCNT1 gain of function variant was used which more accurately models the human disorder.

(2) The manuscript is clearly written, and the flow is easy to follow. The authors explicitly state the similarities and differences between the current findings and the previously published results in the homozygous KCNT1 gain of function variant.

(3) This study uses a variety of approaches including patch clamp recording, in silico modeling, and pharmacology that together make the claims stronger.

(4) Pharmacological experiments are fraught with off-target effects and thus it bolsters the authors' claims when multiple channel blockers (TTX and VU170) are used to reconstruct the sodium-activated potassium current. Having said that, it would be helpful to see the two drug manipulations used in the same experiment. Notably, does the more selective blocker VU170 mimic the results of TTX for NFS GABAergic cells in Figure 2? And does it unmask a genotype difference for FS GABAergic cells like the one seen in PV interneurons in Figure 5C3.

To illustrate the two drug manipulations in the same experiment, we recorded from WT SST and PV neurons (5 neurons/group) and blocked KNa currents first using TTX and then VU170, following wash out between the two drugs, in the same neurons. Below, we have plotted the points at each voltage step for each SST and PV neuron, and for each drug treatment, on the same graph to show how they vary directly. At each voltage step, lines connect the points representing the TTX-sensitive and VU-sensitive currents for each neuron to show the individual effects (left-most graphs). Summary data are shown across all voltages (middle graphs) and across negative voltages (right-most graphs).

Author response image 1.

We have not used VU170 on FS and NFS populations of GABAergic neurons. However, for reasons that are explained more extensively below in response to Weaknesses #3, we would not predict KNa currents recorded from SST- and PV-GABAergic neurons to mimic those of NFS- and FS-GABAergic neurons, respectively.

Weaknesses:

(1) This study relies on recordings in dissociated cortical neurons. Although specific WT interneurons showed intrinsic membrane properties like those reported for acute brain slices, it is unclear whether the same will be true for those cells expressing KCNT1 variants. This reviewer highly recommends confirming some of the key findings using an ex vivo slice preparation. This is especially important given the discrepant result of reduced excitability of PV cells reported by Gertler et al., 2022 (cited here in the manuscript but not discussed in this context) in acute hippocampal slices for a different KCNT1 gain of function variant.

We thank the reviewer for this suggestion. To test whether SST-expressing YH-HET neurons show similar impairments to those observed in culture, we crossed the FVB-Tg(GadGFP)45704Swn/J transgenic mouse line (Jackson Labs #003718), also known as the GIN line, to the Kcnt1-YH line. Mice from the GIN line express eGFP in a subpopulation of SST-expressing neurons in the hippocampus and cortex. We performed slice recordings of cortical layer 2/3, GFP-expressing neurons from P21-30, WT and YH-HET GIN mice. Although the input resistance was not significantly decreased, the rheobase was higher in the YH-HET neurons, and they fired fewer APs across increasing current steps, than WT neurons, supporting the main findings from the SST-expressing neurons in culture. These data have been added to the manuscript in a new figure (Fig. 9).

Regarding the previously published results on the effect of KCNT1 GOF on PV neuron excitability by Gertler et al., we have written a new paragraph in the Discussion section (last paragraph of the section, “Neuron-type-dependent KCNT1 GOF effects”) that discusses the differences between the findings by Gertler et al. and the current study. 

To further investigate the effects of heterozygous YH variant expression on SST- vs. PV-expressing neuron excitability in ex vivo slice recordings, we are now crossing a cre-inducible, Td-Tomato Red reporter line (Ai9) to the Kcnt1-YH line. After obtaining Ai9Tg/Tg; Kcnt1m/+ mice, we will cross these to Sst-Cre and Pvalb-Cre lines to be able to record from marked SST and PV, WT and YH-HET neurons in slice. We plan on submitting results from these recordings as an eLife Research Advances article linked to this article.

(2) It is unclear how different pieces of results fit together to form a story about the disease pathophysiology.

We have added a paragraph to the Discussion to speculate on how these various GABAergic subtype-specific effects downstream of the YH variant may contribute to overall network/brain pathology and seizure propensity in heterozygous mice.

For example, hyperexcitability of PV cells would suggest more inhibition which would counter seizure propensity. However, spontaneous inhibitory postsynaptic currents show no change in pyramidal neurons. Moreover, how do the authors reconcile that the reductions in synaptic inputs onto interneurons in Figure 3B with the increases in Figure 8? This should be discussed.

Generally, network and synaptic alterations downstream of the heterozygous variant were quite minimal compared with those of the homozygous variant. Although there were reductions in the frequency of synaptic inputs onto inhibitory neurons, the changes were relatively small. Thus, we concluded that the neuronal effects downstream of the heterozygous YH variant were below some threshold to result in broader network effects on synaptic activity and connectivity similar to those in the homozygous YH model. The discrepancies between our GABAergic vs. FS/NFS vs. VIP/SST/PV data will be discussed in more detail in response to Weakness #3.   

(3) Similarly, the results in this work are not entirely internally consistent. For example, given the good correspondence between FS and NFS GABAergic cells with PV and SST expression, why are FS GABAergic cells hyperexcitable in Figure 1? If anything, there is a tendency to show reduced excitability like the NFS GABAergic cells.

In our neuron cultures, 76-80% of Neu-N-expressing neurons are GFP+ (from the CamKII-eGFP virus used to mark glutamatergic neurons), and of the remaining ~20-24%, the majority are GABAergic (verified using the Dlx5/6-mRuby virus to mark GABAergic neurons and using electrophysiology to assess AP parameters and analyze evoked responses). In our original experiments, recordings sampled from this larger GABAergic population were used (Fig. 3), or this population was sorted almost equally into FS and NFS (Figs. 1 and 2).

In later experiments, we isolated and cultured neurons from VIP-Cre, SST-Cre, and PV-Cre mouse lines and marked these neuron types in vitro with a Cre-inducible mCherry virus. In the VIP-Cre cultures, ~6% of the GFP- population, or 1.2% of the Neu-N-population, was mCherry+. In the SST-Cre cultures, ~20.5% of the GFP- population, or 4.7% of the Neu-N-population, was mCherry+. In the PV-Cre cultures, less than 1% of the Neu-N-population was mCherry+, which is not surprising considering the relatively late onset of PV expression compared with those of VIP and SST. Thus, we would estimate that we are marking and recording from less than 30% of the total GABAergic population in these in vitro experiments, rather than the 80-90% that these three populations would sum to in vivo.  

Furthermore, using our original criteria for sorting GABAergic neurons into FS and NFS subtypes, all VIP recorded neurons were of the NFS type, PV of the FS type, whereas SST were of the FS (38%) and NFS (62%) types, which is not far off from the significant fraction of SST neurons that have been shown to be fast-spiking in slice experiments (Kvitsiani et al., 2013; Urban-Ciecko & Barth, 2016). Therefore, the FS group consists of both PV and SST neurons, and the NFS group consists of both VIP and SST neurons, and likely also contains immature PV neurons that have not yet developed a fast-spiking phenotype. Taken together, this suggests that the data from these two sets of experiments (FS/NFS vs. VIP/SST/PV) are not directly comparable.

Also, why do the WT I-V curves look so different between Figures 2 and 5? This reviewer suggests at least a brief explanation in the discussion.

As to the differences in appearance between the WT I-V curves in Figures 2 and 5, those plots are from different neuron types (Fig. 2: Glutamatergic, FS GABAergic, and NFS GABAergic vs. Fig. 5: VIP-, SST-, and PV-expressing), and the KNa currents are isolated using different methods (Fig. 2: TTX-subtraction vs. Fig. 5: VU170-subtraction). TTX blocks an inward Na+ current, which is apparent across subthreshold voltages in Fig. 2C1-3, whereas VU170 does not block this current, making it not apparent in Fig. 5C1-3. Also, the bottom three panels in Fig. 2C1-3 show the KNa current from -80 to 0 mV, whereas those in Fig. 5C1-3 show from -80 to -30 mV, to better illustrate the areas spanning KNa current increases, so their appearance is not directly comparable.

(4) Given the authors' claim that the KCNT1 activation curve is a major contributor to the observed excitability differences in specific GABA cell subtypes, it would be helpful to directly measure the activation curve in the variants experimentally as was done for WT KCNT1 in Figure 6A and use the derived kinetics in the compartmental model.

We apologize for the confusion. Although the activation curves among different GABAergic subtypes from WT KCNT1 are distinct, and we believe that these varying kinetics contribute to the neuron-type-specific phenotypes of KCNT1 GOF, we didn’t intend to suggest that the heterozygous Y777H variant itself causes neuron-type-specific alterations to the activation curves of the GABAergic subtypes. To clarify this point, below, we show the high similarity of the activation curves between WT KCNT1 and YH-HET KCNT1 in each of the GABAergic subtypes.

Author response image 2.

Reviewer #3 (Public Review):

Summary:

The present manuscript by Shore et al. entitled Reduced GABAergic Neuron Excitability, Altered Synaptic Connectivity, and Seizures in a KCNT1 Gain-of-Function Mouse Model of Childhood Epilepsy" describes in vitro and in silico results obtained in cortical neurons from mice carrying the KCNT1-Y777H gain-of-function (GOF) variant in the KCNT1 gene encoding for a subunit of the Na+-activated K+ (KNa) channel. This variant corresponds to the human Y796H variant found in a family with Autosomal Dominant Nocturnal Frontal lobe epilepsy. The occurrence of GOF variants in potassium channel encoding genes is well known, and among potential pathophysiological mechanisms, impaired inhibition has been documented as responsible for KCNT1-related DEEs. Therefore, building on a previous study by the same group performed in homozygous KI animals, and considering that the largest majority of pathogenic KCNT1 variants in humans occur in heterozygosis, the Authors have investigated the effects of heterozygous Kcnt1-Y777H expression on KNa currents and neuronal physiology among cortical glutamatergic and the 3 main classes of GABAergic neurons, namely those expressing vasoactive intestinal polypeptide (VIP), somatostatin (SST), and parvalbumin (PV), crossing KCNT1-Y777H mice with PV-, SST- and PV-cre mouse lines, and recording from GABAergic neurons identified by their expression of mCherry (but negative for GFP used to mark excitatory neurons).

The results obtained revealed heterogeneous effects of the variant on KNa and action potential firing rates in distinct neuronal subpopulations, ranging from no change (glutamatergic and VIP GABAergic) to decreased excitability (SST GABAergic) to increased excitability (PV GABAergic). In particular, modelling and in vitro data revealed that an increase in persistent Na current occurring in PV neurons was sufficient to overcome the effects of KCNT1 GOF and cause an overall increase in AP generation.

Strengths:

The paper is very well written, the results clearly presented and interpreted, and the discussion focuses on the most relevant points.

The recordings performed in distinct neuronal subpopulations are a clear strength of the paper. The finding that the same variant can cause opposite effects and trigger specific homeostatic mechanisms in distinct neuronal populations is very relevant for the field, as it narrows the existing gap between experimental models and clinical evidence.

Weaknesses:

My main concern is in the epileptic phenotype of the heterozygous mice investigated. In fact, in their previous paper the Authors state that "...Kcnt1-Y777H heterozygous mice did not exhibit any detectable epileptiform activity" (first sentence on page 4). However, in the present manuscript, they indicate twice in the discussion section that these mice exhibit "infrequent seizures". This relevant difference needs to be clarified to correctly attribute to the novel pathophysiological mechanism a role in seizure occurrence. Were such infrequent seizures clearly identified on the EEG, or were behavioral seizures? Could the authors quantify this "infrequent" value? This is crucial also to place in the proper perspective the Discussion statement regarding "... the increased INaP contribution to ... network hyperexcitability and seizures".

We apologize for the confusion. Indeed, in the Results section from our previous paper, we failed to observe seizures in 14 heterozygous mice, whereas 23/25 homozygous mice showed seizures by video-EEG. However, in the fifth paragraph of the Discussion section from that paper, we further stated that “during the preparation and review of [that] article, we observed seizures in two Kcnt1-Y777H heterozygous mice, one during a widefield Ca2+ imaging experiment and the other during a video-EEG experiment”. Thus, we concluded that “heterozygous expression can result in seizures in a rodent model, but apparently at a much lower frequency than that observed with homozygous expression”. To emphasize these findings, we’ve added a sentence to the Introduction in this manuscript about the occurrence of infrequent seizures in Kcnt1-Y777H heterozygous mice, along with a reference to the Discussion of our previous paper.

Of the two observed seizures, one seizure was captured in the Weston Lab at the University of Vermont from a Kcnt1-Y777H heterozygous mouse expressing a calcium indicator (after it was bred to the Snap25-GCaMP6s line) during a Ca2+ widefield imaging experiment, and it was accompanied by a time-locked video of the seizure event. The other seizure was recorded as a control during a drug study using video-EEG. This Kcnt1-Y777H heterozygous mouse had multiple tonic seizures, as evidenced by EEG traces and the accompanying video, which were recorded and analyzed in the Frankel Lab at Columbia University. The seizures from heterozygous mice have not been officially quantified, as they have only been rarely observed across multiple different experiments using heterozygous mice at multiple institutions, making quantification quite difficult.

Lastly, regarding attributing the role of the identified pathological mechanisms to seizure occurrence mentioned by the reviewer, we have added a paragraph to the Discussion to speculate on how the various GABAergic subtype-specific effects downstream of the YH variant may contribute to the general lack of network/brain pathology and seizure generation in heterozygous mice.  

Also, some statistical analysis seems to be missing. For example, I could not find any for the data shown in Fig. 6. Thus, the following statement: "the model PV neurons responded to KCNT1 GOF with decreased AP firing and an increased rheobase" requires proper statistical evaluation.

We thank the reviewer for this suggestion. We were initially hesitant to apply a formal statistical analysis to the modeling data because it differs in important ways from the experimental data. However, we have now provided statistical analyses of these data, with some caveats. Because we applied each KCNT1 GOF level (40, 35, and 30 mM) to the same set of neurons for each data set, we performed repeated measures ANOVA analyses to assess differences due to GOF in each subtype. We note that some changes are statistically significant, but may not be physiologically relevant. For example, there are changes in input resistance and rheobase in VIP neurons only at the higher GOF level (30 mM), but the magnitude of each change is quite small relative to those in SST neurons (Rin: 1.7 MΩ in VIP vs. 23.2 MΩ in SST, rheo: 1.7 pA in VIP vs. 52.5 pA in SST), and likely as a consequence, there are no downstream effects on the AP firing rate at either GOF level in VIP neurons. It is important to examine the magnitude of the effects and interpret them in the context of the changes in other neuron types and in the experimental data, thus, we’ve provided two new figures to better illustrate the relative changes in each neuron type (Supplemental Figures 6-2 and 7-2). We have also added these statistical results to Figures 6E2, 6F2, 6G2, and 7E, and Supplemental Fig. 6-1, and we have described them in the Results section. A summary of the statistics has also been added in Supplemental Table 6.

Recommendations for the authors:

Reviewer #2 (Recommendations For The Authors):

In addition to addressing the weaknesses highlighted in the public review, this reviewer recommends using a KCNT1 agonist such as loxapine to see if activating the potassium channel mimics the KCNT1 GOF in SST and PV cells.

Although we appreciate this suggestion, we’re not sure whether treating GABAergic subtypes with loxapine would provide much clarity in the absence of many supporting experiments. First, the amount of channel activation and any changes in kinetics caused by loxapine would need to be measured and compared to the YH-HET GOF effects in order to interpret the results. In addition, the aforementioned caveat about off-target effects of small molecules would also have to be considered, as loxapine inhibits many other channels at nanomolar concentrations.

More importantly, we hypothesize that several of the GABAergic subtype-specific effects of KCNT1 GOF result from homeostatic or adaptive mechanisms due to long-term increases in KNa currents. For instance, PV-expressing YH-HET neurons had a lower rheobase, increased AP amplitude, and increased AP firing frequency, effects that we believe are due, not to increased KNa currents themselves, but to a compensatory increase in a persistent Na+ current. For the SST neurons, we hypothesize that the increased capacitance and soma size, together with the increased electrical coupling, exacerbate the hypoexcitability phenotype downstream of the YH variant. Thus, we would not necessarily expect that opening KCNT1 channels by acute loxapine treatment would mimic many of these effects.

Indeed, in a previous study using a different KCNT1 GOF mouse model, loxapine treatment mimics KCNT1 GOF effects in some neuron types (reduced AP firing frequency in loxapine-treated, WT PV neurons mimics that observed in heterozygous KCNT1 GOF PV neurons), but not in others (reduced AP firing frequency in loxapine-treated, WT pyramidal neurons does not mimic the unaltered AP firing frequency observed in heterozygous and homozygous KCNT1 GOF pyramidal neurons) (Gertler et al., 2022).  

Related to this suggestion by the reviewer, we are currently performing studies using a KCNT1 blocker in WT and Kcnt1-KO neurons to better understand the role of KCNT1 among cortical neuronal subtypes that will be published in a future manuscript.

Reviewer #3 (Recommendations For The Authors):

Though I realize that primary cultures allow for efficient identification of neuronal subclasses, it would have been useful to show that similar changes also occur in neurons with conserved in vivo connectivity, such as those recorded from brain slices.

We thank the reviewer for this suggestion. We have added an additional figure (Fig. 9) showing that the hypoexcitability phenotype observed in SST neurons in culture recordings is conserved in SST neurons in slice recordings from GIN mice, which express GFP predominately in SST-expressing neurons.

In addition, further experiments in PV neurons from Kcnt1-Y777H homozygous mice would provide evidence for a gene-dosage role in the changes found in heteros.

For this manuscript, we chose to focus our efforts on understanding the effects of heterozygous Kcnt1 variant expression in various neuronal subtypes with the goal of better modeling GOF variant effects in human disease. However, we’re very interested in investigating the effects of homozygous expression of the YH variant on each of the GABAergic subtypes to compare with those found in this study, but this requires more rounds of breeding to get homozygous mice with GABAergic subtype-specific expression of cre recombinase. We look forward to reporting the results from these experiments in a future manuscript.

Also, when addressing the issue regarding the different effects of the same GOF variant on the excitability of distinct neuronal populations in the Discussion or Introduction sections, the authors may want to cite the recent work on KCNQ2 and KCNQ3 by the Tzingounis group (https://pubmed.ncbi.nlm.nih.gov/37607817/).

We thank the reviewer for bringing this manuscript to our attention. We have added this citation to a new paragraph in the Discussion section regarding neuron-type specific effects of ion channel variants (the last paragraph focusing on the effects in PV neurons).

Budelli, G., Hage, T. A., Wei, A., Rojas, P., Jong, Y. J., O'Malley, K., & Salkoff, L. (2009). Na+-activated K+ channels express a large delayed outward current in neurons during normal physiology. Nat Neurosci, 12(6), 745-750. https://doi.org/10.1038/nn.2313

Gertler, T. S., Cherian, S., DeKeyser, J. M., Kearney, J. A., & George, A. L., Jr. (2022). K(Na)1.1 gain-of-function preferentially dampens excitability of murine parvalbumin-positive interneurons. Neurobiol Dis, 168, 105713. https://doi.org/10.1016/j.nbd.2022.105713

Grubb, M. S., & Burrone, J. (2010). Activity-dependent relocation of the axon initial segment fine-tunes neuronal excitability. Nature, 465(7301), 1070-1074. https://doi.org/10.1038/nature09160

Hage, T. A., & Salkoff, L. (2012). Sodium-activated potassium channels are functionally coupled to persistent sodium currents. J Neurosci, 32(8), 2714-2721. https://doi.org/10.1523/JNEUROSCI.5088-11.2012

Kuba, H., Oichi, Y., & Ohmori, H. (2010). Presynaptic activity regulates Na(+) channel distribution at the axon initial segment. Nature, 465(7301), 1075-1078. https://doi.org/10.1038/nature09087

Kvitsiani, D., Ranade, S., Hangya, B., Taniguchi, H., Huang, J. Z., & Kepecs, A. (2013). Distinct behavioural and network correlates of two interneuron types in prefrontal cortex. Nature, 498(7454), 363-366. https://doi.org/10.1038/nature12176

Shore, A. N., Colombo, S., Tobin, W. F., Petri, S., Cullen, E. R., Dominguez, S., Bostick, C. D., Beaumont, M. A., Williams, D., Khodagholy, D., Yang, M., Lutz, C. M., Peng, Y., Gelinas, J. N., Goldstein, D. B., Boland, M. J., Frankel, W. N., & Weston, M. C. (2020). Reduced GABAergic neuron excitability, altered synaptic connectivity, and seizures in a KCNT1 gain-of-function mouse model of childhood epilepsy. Cell Rep.

Urban-Ciecko, J., & Barth, A. L. (2016). Somatostatin-expressing neurons in cortical networks. Nat Rev Neurosci, 17(7), 401-409. https://doi.org/10.1038/nrn.2016.53

Author response:

We thank the reviewers for their constructive comments that will help us clarify and strengthen the paper. We will be happy to address all the comments and adjust the text accordingly. Regarding the suggestion in the assessment to include a “more thorough comparison with with human behavior”, we believe this comment reflects one of the reviewer’s comments to compare with order effects (primacy and recency); we did not see any other comments that would reflect this (our existing simulations do make contact with other human behavior regarding error distributions, including probability of recall, precision, sensitivity to reinforcement history, and dopamine manipulation effects on human WM). We thank the reviewers for this comment and we will conduct the appropriate simulations and analysis to compare with sequential effects in working memory.

Author response:

Reviewer #1 (Recommendations For The Authors): 

This paper represents a huge amount of work on a condition whose patients' health and well-being have not always been prioritized, and only relatively recently has the immune dysregulation seen in patients with Down Syndrome (DS) been garnering major research interest. 

This paper provides an unparalleled examination of immune disorder in patients with DS. In a truly herculean effort, the authors provided the cumulative examination of over 440 patients with DS, confirmed the alterations in immune cell subsets (n=292, 96 controls) and multi-organ autoimmunity seen in these patients as they age, and identified autoantibody production that could contribute to conditions co-occurring in patients with DS. They also sought to look at whether the early immunosenescence seen in DS was due to the inflammatory profile by comparing age-associated markers in DS patients and euploid controls separately, finding that several markers are regulated with age regardless of group, while comparing the effect of age versus DS status on cytokine status identified inflammatory markers elevated in DS patients across the lifespan that do not increase with age or that increase with age only in the DS cohort. This is very interesting in the context of DS in particular, and immunity during aging in general. 

The second part of the manuscript presents the results from a clinical trial with the JAK inhibitor tofacitinib in DS patients. While the number of DS patients treated with tofacitinib was small, the results were often quite striking. Treatment was well-tolerated and the improvement of dermatological conditions was clear. The less responsive patients AA4 and AA2 provide a very clear illustration that these patients are sensitive to immune triggers during treatment. Additionally, the demonstration that patients' IFN scores and cytokine levels decreased without clear immunosuppression with tofacitinib treatment is encouraging, since treatment with this drug would need to be continuous. I would be curious to see if the patients added past the cutoff for interim analysis follow a similar trajectory. I would not ask the authors to add any data; the paper is well-written and logically constructed. 

I only have a small comment: I really did not like how Figure 2 a, d, and g tethered the coloring to the magnitude of fold change to show the effect of DS particularly for 2a and 2g. Given that these fold changes are quite modest, the coloring is very light and hard to distinguish. The clear takeaway is that the effect on T cells is greatest, but there must be a better way to illustrate this. Perhaps displaying this graph on a non-white background could help with contrast. 

We are grateful for the Reviewer’s very positive assessment of the manuscript and constructive feedback. We want to assure the Reviewer that similar analyses will be completed in the future for the entire cohort recruited into the trial to determine if similar trajectories and results are observed with the larger sample size. Additionally, following Reviewer’s guidance, we will explore alternative ways to present the data in Figure 2 for greater clarity in a revised version of the manuscript.

Reviewer #2 (Recommendations For The Authors): 

• Although the focus of the patients in the first part of the paper is on autoimmune/inflammatory conditions, it will be useful to also list the non-autoimmune infectious manifestations for reference with prevalence data. For example, otitis media, or lung infections (mentioned within the paper), or mucosal candidiasis. Same for other manifestations such as cardiac or malignant conditions. Given the impressive number of patients, it will be useful to the readers to have prevalence data for these as well, even in brief statements within the results. 

We appreciate this inquiry by the Reviewer and will present additional data on the co-occurring conditions mentioned by the Reviewer in a revised version of the manuscript.

• Have the authors looked at DN T cells and whether they may be enriched in DS patients, given their enrichment in some autoimmune conditions? 

Thanks for this inquiry. We did examine DN T cells (double negative T cells), which we referred to in our Figure 2 and Figure 2 – figure supplement 1 as non-CD4+ CD8+ T cells. Although this T cell subset is mildly elevated (in terms of frequency among T cells) in individuals with Down syndrome, the result did not reach statistical significance after multiple hypothesis correction. This negative result is shown in the heatmap in Figure 2 – figure supplement 1d.

• It would be useful to move the segment of the discussion that discusses the interim predefined analysis of the phase 2 trial to the corresponding segment of the results. As this reviewer was reading the paper, it was unclear why the interim analysis was done, whether it was predefined and it was not until the discussion that it became apparent. I believe it will help the readers to have a brief mention that this interim analysis was predefined and set to occur at the first 10 DS enrollees. Also, it would be helpful to state what is the total number of DS patients planned for enrollment in the Phase 2 trial which is continuing recruitment. 

We appreciate this comment and will modify the text following the Reviewer’s guidance in the revised manuscript. The trial will be considered complete once a total of 40 participants undergo 16-weeks of treatment with good medicine compliance (less that 15% missed doses).

• Although the authors present data on TPO autoantibodies before and after tofacitinib, it remains unclear whether the other non-TPO autoantibodies were altered during treatment or whether this was a TPO autoantibody-specific phenomenon. Was there an alteration in mature B cells or plasmablast populations after tofacitinib? If these data are available, they would further enhance the manuscript. If they are not available, it would be useful for the authors to discuss those in the discussion of the manuscript. 

We are grateful for this comment, which strongly aligns with our future research interests and plans for the analysis of the full cohort once the trial is completed. In the interim analysis, we analyzed only auto-antibodies related to autoimmune thyroid disease and celiac disease, as shown in the manuscript. However, we plan to complete a more comprehensive analysis of the effects of JAK inhibition on autoantibody production once the full sample set is available at the end of the trial. Likewise, the clinical trial protocol contemplates collection and processing of blood samples for immune mapping using mass cytometry, which will enable us to answer the question from the Reviewer about potential changes in B cells or plasmablasts populations. Following Reviewer’s guidance, we will discuss these planned analyses in the Discussion of the revised manuscript.

Reviewer #3 (Recommendations For The Authors): 

(1) Cellular immune phenotyping data in Figure 2 presents a large number of patients with DS versus euploid controls (292 and 96 respectively). Given the relatively large cohort there would seem to be an opportunity to determine whether age or sex alters the immune phenotype shown, for example, TEMRAs, etc. Was the data analyzed in this way? 

We welcome this comment, which clearly aligns with our research interests and planned additional analyses of these datasets generated by the Human Trisome Project. We can share with the Reviewer that although sex as a biological variable has minimal impacts on the strong immune dysregulation observed in Down syndrome, there are clear age-dependent effects, with some immune changes occurring early during childhood versus others taking place later in adult life. A manuscript describing a complete analysis of age-dependent effects on the multi-omics datasets in the Human Trisome Project is currently under preparation.

(2) The authors should strongly consider incorporating/discussing the findings from Gansa et al, Journal of Clinical Immunology May 2024 - where they reviewed the immune phenotype of 1299 patients with Down syndrome. 

Thanks for this suggestion, we will surely cite and discuss this recent paper in the revised manuscript.

(3) It is difficult to differentiate patients Hs2 and Ps1 in Figure 5d. 

Thanks for this observation, we will modify the labels for greater clarity in the revised manuscript.

(4) Given their finding of no correlation between cytokine levels/immune phenotype and autoimmunity, some additional discussion of the relevance of hypercytokinemia in the pathogenesis of autoimmunity would seem relevant (given that this was the basis for the clinical trial). The authors mention that cytokine levels may not be appropriate measures of disease in the patients. 

We welcome this opportunity to expand the discussion of the relevance of hypercytokinemia in the pathogenesis of autoimmunity and will do so in the revised manuscript.

(5) Data availability statement: appropriate.

Author response:

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

Reviewer #1 (Recommendations For The Authors): 

The authors should perform experiments to answer this question: does Cav3 transcription increase in the G369i-KI, or is there instead some post-transcriptional modulation that permits surface expression of functional Cav3-containing channels in the absence of typical HVA Ca conductances? Also, the authors should determine whether G369i-KI can mediate Ca2+ release from intracellular stores and whether release from stores is upregulated as Cav3-containing channel expression (or function) is increased. 

We performed transcriptomic (drop-seq) analysis to test whether a Cav3 subtype is upregulated in cones of G369i KI mice. These experiments show that, consistent with previous studies (PMID 35803735, 26000488), Cacna1h appears to be the primary Cav3 subtype expressed mouse cones. However, as shown in new Supp.Fig.S3, there was no significant difference in the levels of Cacna1h transcripts in WT and G369i KI cones. Therefore, we propose that there may be some post-transcriptional modification, or alteration in a pathway that regulates channel availability, that enables the contribution Cav3 channels to the whole-cell Ca2+ current in the absence of functional Cav1.4 channels cones.

We also performed Ca2+ imaging experiments in WT vs G369i KI cone terminals to assess whether the diminutive Cav3 current in G369i KI cone terminals may be compensated by upregulation of a Ca2+ signal such as from intracellular stores. Arguing against this possibility, depolarization-evoked Ca2+ signals in G369i KI cones were dramatically reduced compared to WT cones (new Fig.9). 

Reviewer #2 (Recommendations For The Authors): 

Major points- 

(1) It is stated in too many places that cone features in the Cav1.4 knock-in are "intact", preserved, or spared, but this representation is not accurate. There are two instances in this study that qualify as intact when comparing KI to WT: 1) the photopic a-waves in the Cav1.4 knock-in (also demonstrated in Maddox et al 2020) and 2) latency to the platform (current MS, Figure 7f). However, in the numerous instances listed below, the authors compared the Cav1.4 knock-in to the Cav1.4 knock-out, and then referred to the KI as exhibiting intact responses. The reference point for intactness needs to be wildtype, as appropriately done for Figures 2 and 3, and when comparing the KI to the KO the phrasing should be altered; for example: "the KI was spared from the extensive degeneration witnessed in the KO....". 

In most cases, we clearly note that there are key differences in the WT and the G369i KI cone synapses, which highlight the importance of Cav1.4-specific Ca2+ signals for certain aspects of the cone synapse. We disagree with the reviewer on the point that we did not often use the WT as a reference since most of our experiments involved comparisons of only WT and G369i KI (Figs. 3-6) or WT, G369i KI, and Cav1.4 KO (Figs.1,7—and in these cases comparisons specifically between WT and G369i KI mice were included). We used “intact” as a descriptor for G369i KI cone synapses since these are actually present, albeit abnormal in the G369i KI retina, whereas cone synapses are completely absent in the Cav1.4 KO retina. To avoid confusion, we modified our use of “intact” and “preserved” where appropriate.

A. Abstract, line 34 to 35: ".......preserved in KI but not in KO.". 

Abstract was rewritten and this line was removed.

B. Line 36: "....synaptogenesis remains intact". The MS documents many differences in the morphology of KI and WT cones (immunofluorescence and electron microscopy data), which is counter to an intact phenotype. 

The sentence was: “In CSNB2, we propose that Cav3 channels maintain cone synaptic output provided that the Ca2+-independent role of Cav1.4 in cone synaptogenesis remains intact.”

Here the meaning of “intact” refers to the Ca2+ -independent role of Cav1.4, not synapses. Thus, we have left the sentence unchanged.

C. This strikes the right balance, lines 67 to 68: "....although greatly impaired.....". 

D. Line 149, "Cone signaling to a postsynaptic partner is intact in G369i KI mice". This description is inaccurate. Here there is only WT and KI, and the text reads as follows in line 162: "terminals (Figure 6b). The ON and OFF components of EPSCs in G369i KI HCs were measurable, although lower in amplitude than in WT (Figure 6a,b)." Neither "measurable" nor "lower in amplitude" meet the definition of "intact", and actual numerical values are lacking in the text. 

We have added results showing that there are no light responses in the Cav1.4 KO horizontal cells and have modified the sentence to: “Cone synaptic responses are present in horizontal cells of G369i KI but not Cav1.4 KO mice”. 

We have modified discussion of these results as (line 210-213): “Consistent with the lack of mature ribbons and abnormal cone pedicles (Fig.1), HC light responses were negligible in Cav1.4 KO mice (Fig.8a,b). In contrast, the ON and OFF responses were present in G369i KI HCs although significantly lower in amplitude than in WT HCs (Fig. 8a,b).”

E. Please add a legend to Figure 6a to indicate the intensities. The shape of the KI responses is different from the control which is worthy of discussion: i) there is no clear cessation of HC EPSCs in the KI during the light ON period (when release stops, Im fluctuations should be minimal), and ii) the "peaked" appearances of the initial 500ms of the On and Off periods are very similar in shape for the KI (hard to interpret in the same fashion as a control response). How were the On and Off amplitudes analyzed? Furthermore, the OFF current is not summarized in Figure 6D, but should not this be when Cav3 should be opening and triggering release: Off response-EPSC? Lastly, Figure 6b,d shows a ~70% reduction in On-current in the KI, and the KI example of 6b an 80% reduction in Off current compared to WT. Yet, the only place asterisks are used to indicate sig diff is the DNQX data within each genotype in Fig 6d. These data cannot be described as showing "intact" KI responses, and the absence of numerical and statistical values needs to be addressed. 

New Fig.8a depicting the horizontal cell light responses has been modified to include the legend indicating light intensities. The ON and OFF amplitudes were analyzed as the peak current amplitudes. This information has been added to the legend.

The reviewer is correct in that the OFF response represents the EPSC whereas the ON response represents the decrease in the EPSC with light. To avoid confusion, we changed the y axis label for the averaged data to read ON or OFF “response” rather than “current” in new Fig.8b.

As the reviewer suggests, the more transient nature of the KI response during the light ON period could result from aberrant continuation of vesicular release during the light-induced hyperpolarization of cones in the KI mice, in contrast to the prolonged suppression of release by light which is evident in the WT responses. We speculated on this difference as follows (lines 237-241):

“In addition to its smaller amplitude, the transient nature of the ON response in G369i KI HCs suggested inadequate cessation of cone glutamate release by light (Fig.8b). Slow deactivation of Cav3 channels and/or their activation at negative voltages20 could give rise to Ca2+ signals that support release following light-induced hyperpolarization of G369i KI cones.”

We added astericks to new Fig.8b,d indicating statistical differences and description of the tests in the legend.

F. line 168 the section titled "Light responses of bipolar cells and visual behavior is spared in G369i KI but not Cav1.4 KO mice". 

Changed to: “Light responses of bipolar cells and visual behavior are present in G369i KI but not Cav1.4 KO mice”

Last sentence of erg results, 189-190: "These results suggest that cone-to-CBC signaling is intact in G369i KI mice.". "Spared and intact" are not accurate descriptions. The ERG data presented here shows massive differences between WT and the KI, except in the instance of awaves. 

This sentence was removed.

As for Figure 6, the results text related to Figure 7a-d does not present real numbers for ERG responses, and there is no indication of significant differences there or in the Figure panels. For instance, in Figure 7b, b-waves are KI are comparable to KO, except at the two highest-intensity flashes that show KI responses ~20% the amplitude of WT. Presentation of KI and KO data on a 6- to 10-fold expanded scale higher than WT can be misleading: a quick read of these Figure panels might make one incorrectly conclude that the KI is intact while the KO is impaired when compared to WT. The Methods section needs more details on the ERG analysis (e.g. any filtering out of oscillatory potentials when measuring b-wave, and what was the allowable range of time-to-peak for b-wave amplitude, etc..). 

The vertical scaling of the ERG results in new Fig.10c,d has been changed so as to reflect clearly diminished responses of the KO and KI vs the WT. Further details regarding the ERG analysis was added to the Methods section.

G. Can you point to other studies that have used the "visible platform swim test" used in Figure 7e, f, and specify further how mice were dark/light adapted prior to the recordings? 

As referenced in the Methods, original line 674, the methods we used for the swim test were described in our previous study (PMID 29875267). Other studies that have used this assay include PMIDs: 28262416, 26402607.

(2) The Maddox et al 2020 study does not safely address whether rods have a residual T-type Ca2+ current in the Cav 1.4 KO or KI. The study showed that membrane currents measured from rods in the KI and KO retina were distinct from WT, supporting their claim that L-type Ca2+ current is absent in the KI and KO. However, the recordings had shortcomings that challenge the analysis of Ca2+ currents: i) collected at room temp (22-24{degree sign}C), ii) at an unknown distance from the terminal (uncertain voltage clamp), iii) with a very slow voltage ramp rate that is not suitable for probing T-type currents (Figure 1d Maddox 2020, 140 mV over 1 sec: 7msec/1mV), and iv) at a signal-to-noise that does not allow to resolve a membrane current under 1 pA (avg wt rod Ca2+ current was -3.5 pA, and line noise ~1pA peak-to-peak in Maddox 2020). Suggestion: say T-type currents were not probed in Maddox et al 2020, but Davison et al 2022 did not find PCR signal for Cav3.2 in rods. 

We disagree that recordings in the Maddox 2020 study were not sufficient to uncover a T-type current. The voltage ramps in that study were not much slower than that of the Davison et al. 2022 study (they used 0.19 mV/ms). Moreover, in new Supp. Fig.S1, we show that like the slower voltage ramp (0.15 mV/ms) used in the prior study of G369i KI rods, the voltage ramps we used in the present study (0.5 mV/ms), which clearly evoke currents with T-type properties in G369i KI cones (Fig.2a,b, Fig.3a,b) do not evoke currents in WT or G369i KI rods.  

Minor comments. 

(1) Suggestion: add an overview panel to Figure 1 that shows the rod terminals in the KI. The problem is that cropping out the ribbon and active zone signals from rods, to highlight cones, can give the impression that the cones are partially spared in the KI, and the rods are not spared at all. (yet you nicely clarify this in Figure 4 and in the legend and text, etc.). 

We chose to modify the legend with this information as in Fig.4 rather than modify the figure.

(2) Mouse wt cone Ca2+ currents look like L-type currents, as do your monkey and squirrel cone recordings, and also much like those of mouse rods (see Figure S5, Hagiwara et al., 2018 or Grabner and Moser 2021). Your pharm data from mice and squirrels further supports your conclusion, and certainly took much effort. Davison et al 2022 J Neurosci showed PCR results that support their claim that a Cav3 current exists in wt cones. Questions: 1) have you tried PCR? 2) Can you offer more details on what Cav3 KO you tried and what antibodies failed to confirm the KO? As the authors know, one complication is that the deletion of one Cav can be compensated for by the expression of a new Cav. There are 3 types of Cav3s and removal of one type may be compensated for by another Cav3. 

We have included drop-seq data (new Supp.Fig.S3) implicating Cav3.2 as the main Cav3 subtype in cones and have modified our discussion of these results accordingly. These experiments did not reveal any changes in Cav3 subtype expression in G369i KI vs WT cones.

(3) Lines 95/96- onward, spend more time telling the story. When working out the biophysical and pharmacological behavior of the Ca2+ currents, you might want to initially refer to the membrane current as a membrane current, and then state how your voltage protocols, intra- and extra-cell solutions, and drugs helped you verify 1) L-type and 2) T-type Ca2+ currents. 

We have modified the text with more detail.

(4) If data is in hand, add a ramp I-V to Figure S2, which shows the response of the ground squirrel cone. The steps in S2a are excellent for making your point that a transient current is missing, and the bipolar is a great control to illustrate ML218 works. However, a comparison of a squirrel cone ramp to a bipolar ramp response could complete the figure. 

See Reponse to #5 below.

(5) Consider moving Supplementary Figures S2 and S3 to the main text; these are highly relevant to the story, novel, and well-executed. 

Fig.S2 and S3 were added as new Figs.4,5. The new Fig.4 includes voltage ramps in ground squirrel cones (panel a) to compare with the bipolar data (panel f).

(6) The nice electron microscopy reconstructions are not elaborated on in any detail, and there is no mention of ribbon size. Is the resolution sufficient to estimate ribbon size, the number of synaptic vesicles around the ribbon and in the adjacent cytosol? The images indicate major changes in the morphology of the terminals. Is the glial envelope similar in WT and KI? 

Since ribbons were quantified extensively in the confocal analyses in Fig.6, we felt it unnecessary to add this to the EM analysis which focused mainly on aspects of 3D structure (i.e., arrangement of ribbons, postsynaptic wiring, cone pedicle morphology). We added further discussion of the change in morphology of the G369i KI cone pedicle (lines 200-203): “Compared to WT, ribbons in G369i KI pedicles appeared disorganized and were often parallel rather than perpendicular to the presynaptic membrane (Fig.7a-c). Consistent with our confocal analyses (Fig.1), G369i KI cone pedicles extended telodendria in multiple directions rather than just apically (Fig. 7a).”

While we did not opt to characterize the glial envelope in WT cones, we did add an analysis of synaptic vesicles around ribbons to Table 2.

(7) Discussion line 250: "we found no evidence for a functional contribution of Cav3 in our recordings of cones in WT mice (Figures. 2,3), ground squirrels, or macaque (Supplementary Figures S2 and S3).". I would not use "functional" in this context because when comparing your work to Davison et al 2022, they defined functional as a separate response component driven by Cav3. For instance, they examined the influence of their T-type current on exocytosis (by membrane capacitance) and other features like spiking Ca2+ transients. Suggestion: substitute functional with "detectable", and say "we found no detectable Cav currents". Or if you had Ttype staining, but not T-type Ca2+ currents, then say "no functional current even though there is staining...". 

We have modified the text as (lines 336-338): “However, in contrast to recordings of WT mouse cone pedicles in a previous study21, we found no evidence for Cav3-mediated currents in somatic recordings of cones in WT mice (Figs.2,3).”

We propose an alternative interpretation of the results in the Davison et al study concerning the conclusion that Cav3 channels contribute to Ca2+ spikes and exocytosis. That study used 100 µM Ni2+ to block a “T-type” contribution to spike activity in cones. In their Figs.4,5, the spikes are suppressed by 100 µM Ni2+ and 10 µM nifedipine, a Cav1 antagonist, and spared by the T-type selective drug Z944. This is problematic for several reasons. First, as shown by the authors

(their Fig.2A1,A2) and others (PMID: 15541900), 100 µM Ni2+ inhibits Cav1-type currents in photoreceptors. Second, Z944 potentiates Cav1 current in their mouse cones (their Fig.2C1,C2). Thus, both reagents are suboptimal for dissecting the contribution of either Cav subtype to spiking activity. With respect to Cav3 channels and exocytosis, these authors interpreted a reduction in exocytosis upon holding at -39 mV compared to at -69 mV as indicating a loss of a T-type driven component of release. However, Cav1 channel inactivation (PMID: 12473074) could lead to the observed reduction in exocytosis at -30 mV.

(8) Additional literature related to your Intro and Discussion. Regarding CSNB2, related mutations of active zone proteins, and what happens to Ca2+ currents when ribbons are deleted, you might want to consider the following studies that measure Ca2+ currents from rods: conditional KO of RIM1/2 (Grabner et al 2015 JN), KO of ELKS1/2 (Hagiwara et al, 2018 JCB), and KO of Ribeye (Grabner and Moser eLife 2021). In these studies, the Cav currents were absent in rods of the ELKS1/2 DKO, strongly reduced (80%) in the RIM1/2DKO, but altered in more subtle ways (activation-inactivation) without significantly changing steady-state Ca2+ current in the Ribeye KO. This does not seem to support some of the arguments you have made in the Introduction and Discussion regarding ribbon size and Ca2+ currents, yet the suggested literature is related to the topic at hand. 

A description of these synaptic proteins as potential mediators of the effect of Cav1.4 on ribbon morphogenesis was added to the Discussion, lines 325-327.

(9) Line 129: "Along with the major constituents of the ribbon, CtBP2, and RIBEYE", for clarity Ribeye has two domains, one that is identical to CtBP2 (B-domain) and the unique Ribeye domain (A-domain) that is only expressed at ribbon synapses. And, Piccolino is also embedded in the ribbon (Brandstaetter lab, Wichmann/Moser labs). In other words, Ribeye and Piccolino are the major constituents of the ribbon. 

To avoid confusion, we simply mention Ctbp2 and RIBEYE in the context of the corresponding antibodies that were used to label ribbons.

(10) Abstract: consider to rephrase "Ca2+-independent role of Cav1.4" by "Ca2+-permeationindependent role of Cav1.4" or alike 

Sentence changed to: “In CSNB2, we propose that Cav3 channels maintain cone synaptic output provided that the nonconducting role of Cav1.4 in cone synaptogenesis remains intact.”

Reviewer #3 (Recommendations For The Authors): 

Cav1.4 voltage-gated calcium channels play an important role in neurotransmission at mammalian photoreceptor synapses. Mutations in the CACNA1f gene lead to congenital stationary night blindness that particularly affects the rod pathway. Mouse Cav1.4 knockout and Cav1.4 knockin models suggest that Cav1.4 is also important for the cone pathway. Deletion of Cav1.4 in the knockout models leads to signaling malfunctions and to abundant morphological re-arrangements of the synapse suggesting that the channel not only has a role in the influx of Ca2+ but also in the morphological organization of the photoreceptor synapse. Of note, also additional Cav-channels have been previously detected in cone synapses by different groups, including L-type Cav1.3 (Wu et al., 2007; pmid; Kersten et al., 2020; pmid), and also T-type Cav3.2 (Davison et al., 2021; pmid 35803735). 

In order to study a conductivity-independent role of Cav1.4 in the morphological organization of photoreceptor synapses, the authors generated the knockin (KI) mouse Cav1.4 G369i in a previous study (Maddox et al., eLife 2020; pmid 32940604). The Cav1.4 G369i KI channel no longer works as a Ca2+-conducting channel due to the insertion of a glycine in the pore-forming unit (Madox et al. elife 2020; pmid 32940604). In this previous study (Madox et al. elife 2020; pmid 32940604), the authors analyzed Cav1.4 G369i in rod photoreceptor synapses. In the present study, the authors analyzed cone synapses in this KI mouse. 

For this purpose, the authors performed a comprehensive set of experimental methods

including immunohistochemistry with antibodies (also with quantitative analyses), electrophysiological measurements of presynaptic Ca2+ currents from cone photoreceptors in the presence/absence of inhibitors of L-type- and T-type- calcium channels, electron microscopy (FIB-SEM), ERG recordings and visual behavior tests of the Cav G369i KI in comparison to the Cav1.4 knockout and wild-type control mice. 

The authors found that the non-conducting Cav channel is properly localized in cone synapses and demonstrated that there are no gross morphological alterations (e.g., sprouting of postsynaptic components that are typically observed in the Cav1.4 knockout). These findings demonstrate that cone synaptogenesis relies on the presence of Cav1.4 protein but not on its Ca2+ conductivity. This result, obtained at cone synapses in the present study, is similar to the previously reported results observed for rod synapses (Maddox et al., eLife 2020, pmid 32940604). No further mechanistic insights or molecular mechanisms were provided that demonstrated how the presence of the Cav channels could orchestrate the building of the cone synapse. 

We respectfully disagree regarding the mechanistic advance of our study. As indicated by Reviewer 2, a major advance of our study is in providing a mechanism that can explain the longstanding conundrum that congenital stationary night blindness type 2 mutations that would be expected to severely compromise Cav1.4 function do not produce complete blindness. Our study provides an important contrast to the Maddox et al 2020 study in showing that rods and cones respond differentially to loss of Cav1.4 function, which is also relevant to the visual phenotypes of CSNB2. How the presence of Cav1.4 orchestrates cone synaptogenesis is an important topic that is outside the scope of our present study.

In the present study, the authors also propose a homeostatic switch from L-type to (newly occurring) T-type calcium channels in the Cav1.4 G369i KI mouse as a consequence of the deficient calcium channel conductivity in the Cav1.4 G369i Cav1.4 KI mouse. In cones of the Cav1.4 G369i, the high-voltage activated, L-type Ca2+-entry was abolished, in agreement with their previous paper (Maddox et al., eLife 2020, pmid 32940604). The authors found a lowvoltage activated Ca2+ current instead that they assigned to T-type Ca2+-currents based on pharmacological inhibitor experiments. T-type Ca2+-currents/channels were already previously identified in other studies by independent groups and independent techniques

(electrophysiology, RT-PCR, single-cell sequencing) in cones of wild-type mice (Davison et al.,

2021, pmid 35803735; Macosko et al., 2015, pmid 26000488; Williams et al., 2022, pmid 35650675). In the present manuscript (Figures 3a/b), the authors also observed a low-voltage activated, T-type like current in cones of wild-type mice, that is isradipine-resistant and affected by the T-type inhibitor ML218. This finding appears compatible with a T-type-like current in wildtype cones and is consistent with the published data mentioned above, although the authors interpret this data in a different way in the discussion. 

Due to the noise inherent in whole cell voltage clamp measurements and some crossover effects in the pharmacology, we cannot completely exclude the presence of a T-type current in WT mouse cones. However, our results very clearly support a conclusion opposite to that stated by the reviewer. Namely, if WT mouse cones have T-type Ca currents, then they are far smaller than those in the Cav1.4 G369i KI and KO cones. In particular, while we identified message for Cav3.2 in WT mouse cones, we were unable to identify a functional T-type current by either voltage clamp measurements or pharmacology. See below for a detailed rebuttal.

This proposal of a homeostatic switch is not convincingly supported in this reviewer's opinion

(for further details, please see below). Furthermore, no data on possible molecular mechanisms were provided that would support such a proposal of a homeostatic switch of calcium channels. No mechanistic/molecular insights were provided for a proposed homeostatic switch between Ltype to T-type channels that the authors propose to occur between wild-type and Cav1.4 G369i as a consequence of conduction-deficient Cav1.4 G369i channels. Is this e.g. based on posttranslational modifications that switch on T-type channels or regulation at the transcriptional level inducing expression of T-type calcium channel or on other mechanisms? The authors remain descriptive with their central hypotheses. No molecular mechanisms/signaling pathways were provided that would support the idea of such a homeostatic switch. 

Homeostatic plasticity refers to the maintenance of neuronal function in response to some perturbation in neuronal activity and can result from changes in the expression of ion channel genes (PMID: 36377048, 32747440, 19778903) or regulatory pathways that modulate ion channels (PMID: 15051886, 32492405). We present multiple lines of evidence showing that Cav3 currents appear in cones upon genetically induced Cav1.4 loss of function and can support cone synaptic responses and visual behavior if cone synapse structure is maintained. Our new transcriptomic studies show no difference between levels of Cav3 channel transcripts in WT and G369i KI cones, suggesting that the appearance of the Cav3 currents in G369i KI cones does not result from an increase in Cav3 gene expression. We are currently investigating our transcriptomic dataset to determine if Cav3 regulatory pathways are upregulated in G369i KI cones and will present this in a follow-up study.

The authors show residual photopic signaling in the non-conducting Cav1.4 G369i KI mouse as judged by the recording of postsynaptic currents, ERG recordings and visual behavior tests though in a reduced manner. The residual cone-based signaling could be based on the nonaffected T-type Ca2+ channel conductivity in cone synapses. Given that the L-type current through Cav1.4 is gone in the Cav1.4 G369i KI as previously shown (Maddox et al., 2020, pmid 32940604), the T-type calcium current will remain. However as discussed above, this does not necessarily support the idea of a homeostatic switch. 

A major point which we highlighted with new results is that despite the expression of Cav3 transcripts in WT mouse cones, Cav3 channels do not contribute to the cone Ca2+ current. This is at odds with the Davison et al study (PMID: 35803735, see our response to Reviewer 2, pt 7 for caveats of this study), but our results convincingly show that the Cav3 current appears only when Cav1.4 is genetically inactivated. Pharmacological or electrophysiological methods that should reveal the presence of Cav3 currents do not change the properties of the Ca2+ current in cones of WT mice, ground squirrel, or macaque:

• Figs.2-4: Voltage steps to -40 mV (Fig 2e) that activate a sizeable T-current in G369i KI mouse cones produce a negligible transient at pulse onset in WT mouse cones. Similarly, transient currents that are obvious in G369i KI mouse cones during the final step to -30 mV are absent in WT cones.  When we block Cav1.4 with isradipine either in cones of WT mice or ground squirrel, the current that remains does not resemble a Cav3 current but rather a scaled down version of the L-type current. ML218, which readily blocks Cav3 channels in HEK293T cells and in G369i KI cones, has only minor effects in cones of WT mice and ground squirrel; these effects of ML218 can be attributed to non-specific actions on Cav1.4 (new Supp.Fig.S2). New Fig.4 (moved from the supplementary data to the main article) clearly shows that the ML218-sensitive current in ground squirrel cones exhibits properties of Cav1.4 not Cav3 channels. 

• Figs.2,5: Holding voltages that inactivate Cav3 channels have no effect on the Ca2+ current in cones of WT mice or macaque (recordings of macaque cones were moved from the supplement to the main article as new Fig.5).

In Figure 4 the authors measured an increase in the size of the active zone (as judged by the size of the bassoon cluster) and of the synaptic ribbons in the Cav1.4 G369i. A mechanistic explanation for this phenomenon was not provided and the underlying molecular mechanisms were not unraveled. 

The FIB-SEM data uncover some ultrastructural alteration/misalignments of the synaptic ribbons and misalignments of the regular arrangement of the postsynaptic dendrites in the G369i KI mice. Also concerning this observation, the study remains descriptive and does not reveal the underlying mechanisms as it would be expected for eLife. 

We respectfully disagree on the descriptive nature of our study and the need for a full characterization of the molecular mechanism underlying the cone synaptic defects in the G369i KI mouse.   

An important study in the field (Zanetti et al., Sci. Rep. 2021; pmid 33526839) should be also cited that used a gain-of-function mutation of Cav1.4 to analyze its functional and structural role in the cone pathway. 

We have added citation of this paper to the Discussion (lines 354-356).

In conclusion, the study has been expertly performed but remains descriptive without deciphering the underlying molecular mechanisms of the observed phenomena, including the proposed homeostatic switch of synaptic calcium channels. Furthermore, a relevant part of the data in the present paper (presence of T-type calcium channels in cone photoreceptors) has already been identified/presented by previous studies of different groups (Macosko et al., 2015; pmid 26000488; Davison et al., 2021; pmid 35803735; Williams et al., 2022; pmid 35650675). The degree of novelty of the present paper thus appears limited. I think that the study might be better suited in a more specialized journal than eLife. 

We thank the reviewer for acknowledging the rigor of our study but disagree with their evaluation regarding the novelty of our work as outlined in our responses above.

Author response:

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

Reviewer #1 (Recommendations For The Authors):

My comments are largely limited to suggestions to make the manuscript easier to read and digest.

In the abstract they say RNA sequencing highlights changes in innate...

Could they be more specific? Innate immune system up or down? They do not indicate actual findings in the abstract.

We thank the reviewer for the comment and we have revised the abstract accordingly.  

Their use of non‐intuitive abbreviations is often confusing. Perhaps they can add a table in methods listing all the abbreviations so that the reader can follow the data better. mNGA, vmHT....etc.

As suggested, we have now included a list of the abbreviations used in the paper.

There are mis‐spellings in the manuscript.

We have gone through the manuscript and corrected the mis-spellings.   

Has the SPR RNAi line been validated?

The SPR RNAi line that we used has been extensively validated by Yapici et al., 2007 and several subsequent publications. Importantly, the effectiveness of SPR knockdown is evident in female flies as they exhibit dramatically reduced egg laying and, importantly, lack the typical post-mating behaviors (such as rejection of male flies after initial mating) observed in the wild type mated female flies. In fact, female flies with RNAi-mediated SPR knockdown behave identically to females mated with SP-null male flies, confirming the effective disruption of the SP-SPR signaling pathway. We have revised the manuscript and added these statements in the results section concerning SPR RNAi.  

In the figures showing the Climbing Index vs time, can they abbreviate seconds as sec vs s? At least I think it is seconds. At first, I thought it was Time or Times, and was confused about what they were indicating on those types of graphs (Figures 1D‐F).

We have revised the figure as suggested by the reviewer.

In Figure 3F, they have a significance indicated in an unclear manner. It looks like they are comparing neuropil to the cortex, but I think they really mean to compare the cortex of sham to cortex of D31?

The reviewer was correct. We have revised figure 3F to make this clear.     

In Figure 4B, what is the y‐axis? Percentage of what? Is that percentage of total flies?

The reviewer was correct. We have revised the figure to make this clear. 

In a figure like SF3 B, what is the y‐axis? "Norm. Accum. CI" Can they explain the abbreviation?

We have revised the Y-axis label to be “Normalized accumulative CI”.  We have also made this clear in the legend.   

In the methods, what does this mean: "Regions devoid of Hoechst and phalloidin signal in non‐physiologically appropriate areas were considered vacuoles"? What are non‐physiologically appropriate areas? To me, that would mean outside of the brain. I would have thought the areas should be physiologically appropriate (aka neuropil and cortex)? This is confusing.

We have revised the method section to be more specific.  In the Drosophila brain, there are structures such as esophagus that are devoid of both Hoechst and phalloidin staining, which were excluded from our vacuole quantification.    

Reviewer #2 (Recommendations For The Authors):

Since I use mammalian systems, my comment about the confirmation of siRNA should be removed if this is not possible in the Drosophila system.

We have revised the figures to include total N values when appropriate. Including individual n values for each experimental assay and condition will inevitably crowd the figure legends, so specific values are available upon request. 

Regarding RNAi knockdown of sex peptide receptors (SPRs), we agree that confirmation of the knockdown by IHC or qRT-PCR will further strengthen our findings. It should be noted, however, that the RNAi line we used has been extensively validated by Yapici et al., 2007 and several subsequent publications. Importantly, the effectiveness of SPR knockdown is evident in female flies as they exhibit dramatically reduced egg laying and, importantly, lack the typical post-mating behaviors (such as rejection of male flies after initial mating) observed in the wild type mated female flies. In fact, female flies with RNAi-mediated SPR knockdown behave identically to females mated with SP-null male flies, confirming the effective disruption of the SP-SPR signaling pathway. We have revised the manuscript to include these statements in the results concerning the SPR RNAi knockdown.    

Reviewer #3 (Recommendations For The Authors):

(1) In Figures 1 and 2, the authors found that females have a lower climbing index in the acute phase in D17 injury, not due to neurodegeneration as shown no significant changes of brain vacuolation and other markers. However, in Figure 3, the authors found that female flies have a lower climbing index, more brain vacuolation, and neurodegeneration in the late phase. It's not very convincing that having a lower climbing index at the late phase is due to neurodegeneration. Is it possible that females suffered from more severe acute effects, at least in D17 injury?

We thank the reviewer for this point. Female flies injured on D17 displayed acute climbing deficits at 90 minutes post-injury. Since we did not observe significant structural changes in the brain at this time, we believe that this short-term functional deficit is not due to acute neuronal death. Here it is important to note that males did not display any acute climbing deficits when injured on D17, which suggests that the females suffered from more severe acute effects than males. However, these injured female flies recovered fully at 24 hours post-injury and displayed no climbing deficits. At two weeks post-injury, we observe climbing deficits and increased vacuole formation as a direct result of the injuries on D17 (see Supplemental Figure 3). When we assessed sensorimotor behavior and brain vacuolation on D45, we found that the injured females had significantly lower climbing indices and more brain vacuolation than the non-injured females of the same age. In this case, the concurrent observance of decreased climbing ability and increased brain vacuolation suggests chronic neurodegeneration in aged, injured females. This is not to be confused with the acute neuronal death observed by other groups using injury models of stronger severity. Overall, our data are consistent with the current view that in many neurodegenerative diseases, functional deficits often precede observable brain degeneration, which may take years to manifest.

(2) The authors determined late‐life brain deficits and neurodegeneration purely based on climbing index and vacuole formation. These phenotypes are not really specific to TBI‐related neurodegeneration and the significance and mechanisms of vacuole formation are not clear. Indeed, in Figures 3 A and B, male flies especially D31inj tend to have a much larger variation than any other groups. What could be the reasons? The authors should perform additional analyses on TBI‐related neurodegeneration in flies, which have been shown before, such as retinal degeneration and loss, neuronal degeneration, and loss, neuromuscular junction abnormalities, etc (Genetics. 2015 Oct; 201(2): 377‐402).

We thank the reviewer for the thorough evaluation of our manuscript. The reviewer raised a very important question: whether the neurodegeneration observed in our model is specific to TBI. As the reviewer rightly pointed out, the neurodegenerative phenotypes are unlikely to be specific to TBI-related neurodegeneration. Throughout the manuscript, we have tried to convey the notion that the mild physical impacts to the head represent one form of environmental insults, which in combination with other risk factors such as aging can lead to the emergence of neurodegenerative conditions. It should be noted that the negative geotaxis assay and vacuolation quantification are two well-established approaches to assess sensorimotor deficits and frank brain degeneration in fly brains. 

It is important to emphasize that the head-specific impacts delivered to the flies in our study are much milder than those used in previous studies. As we showed in our figure 1, this very mild form of head trauma (referred to as vmHT) did not cause any death, nor affected the lifespan of the injured flies. Our supplemental data also show very minimal structural neuronal damage and no acute and chronic apoptosis induced by vmHT exposure. Consistently, we did not observe any exoskeletal or eye damage immediately following injuries, nor did we observe any retinal degeneration and pseudopupil loss at the chronic stage of these flies. We have incorporated these important points in the revised manuscript.  

(3) In Figure 4, it would be important to perform the behavior test fly speed and directional movement in the acute phase as well to determine whether the females have reduced performance at the acute phase.

We thank the reviewer for this suggestion. Please note that our modified NGA has already improved the spatiotemporal resolution over the classic NGA.  The data presented in Fig.3 show that there are no acute deficits for young cohorts.  Therefore, we do not believe that the detailed analysis of the direction and speed of these flies is essential.  

Unfortunately, the current setup for the AI-based analysis requires manual corrections of tracking errors, which are time-consuming and tedious.  We are building a newly designed AI-based NGA (NGA.ai) that will allow automatic tracking and quantification with minimal manual interventions. Once it is completed, we will perform some of the analyses that the reviewer suggested.  

(4) In Figure 8, the authors performed an RNA‐seq analysis and identified some dysregulated gene expressions. However, it is really surprising to see so few DEGs even in wild‐type males and mated females, and to see that none of DEGs overlap among groups or related to the SP‐signaling. This raises questions about the validity of the RNAseq analysis. It is critical to independently verify their RNA‐sequencing results and to add some more molecular evidence to support their conclusion.

We agree that future studies are needed to independently validate our RNA sequencing results. We believe that the small number of DEGs are likely due to two unique features of our study: (1) the very mild nature of our injury paradigm and (2) the chronic examination timepoint that was long after the head injury and SP exposure, which distinguish our study from previous fly TBI studies.  As pointed out in the manuscript, our study was aimed to understand how early life exposure to repetitive head traumatic insults could lead to the latelife onset of neurodegenerative conditions. We hope to further validate our results in our next phase of experiments using single-cell RNA sequencing and RT-qPCR. 

(5) The current results raise a series of interesting questions: what implication of female fly mating and its associated Sex Peptide signaling would be to mammalians or humans? Would mammalian female animals mating with wild‐type or sex hormone‐null male animals have different effects on their post‐injury behavior tests or neuropathological changes? What are the mechanisms underlying the sexual dimorphism?

As the reviewer pointed out, it would be very interesting to explore the possible roles of sex peptide-signaling in other animals and humans. As far as we know, there is no known mammalian ortholog to the insect sex peptide, so it would be difficult to study SP or an SPlike molecule in mammalian models. However, we believe that prolonged post-mating changes associated with reproduction in female fruit flies contribute to their elevated vulnerability to neurodegeneration.  In this regard, drastic changes within the biology of female mammals associated with reproduction can potentially lead to vulnerability to neurodegeneration. We agree that this demands further study, which may be done with future collaborators using rodent or large animal models.  We have discussed this point in the manuscript.

Author response:

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

We would like to thank you very much for reviewing our manuscript and express our sincere appreciation for the valuable and thoughtful comments that led us to significantly improve the manuscript on Fshr-ZsGreen reporter mice. We have seriously taken your comments to make a major revision of the manuscript, and here is a summary of the revision:

(1) New data on Fshr expression are input to the revised Manuscript:

a. Fshr expression in the testis and adipose tissues (WAT and BAT) of B6 mice;

b. Fshr expression in the testis of B6 by RNA-smFISH;

c. Comparison of Fshr expression in the testis and ovary between Fshr-ZsGreen and B8 mice by ddRT-PCR to prove Fshr expression without interruptions by insertion of P2A-ZsGreen vector;

d. Reduction of Fshr expression in osteocytes within the femoral sections from DMP1-CreERT2:Fshrfl/fl mice;

e. Fshr expression in an established Leydig cell line-TM3 by immunofluorescence and ddRT-PCR, also show Fshr located in the nuclei of TM3 cells;

f. Fshr expression at scRNA-seq level from 5 public single cell portals as Supplementary Data 3 to support our findings of the widespread expression pattern of Fshr, particularly in Leydig cells.

(2) Re-organization of Figure 2 with a new legend.

(3) A new paragraph is added to the Discussion Section of the revised MS to explain the function of P2A peptide in generation of GFP reporter mice and why Fshr express is not interrupted by the P2A-ZsGreen insertion in Fshr-ZsGreen reporter.

(4) Deletion of Figure 1-D-c, as it is not necessary.

(5) Replace of Figure 8-A (the left panel) with a reduced exposure time image.

(6) Amended parts of the revised MS are labeled in red.

A point by point response to the Reviewers’ comments:

Reviewer 1:

One of the shocking observations in this manuscript is the expression of FSHR in Leydig cells. Other observations are in the osteoblasts and endothelial cells as well as epithelial cells in different organs. The expression of ZsGreen in these tissues seems high and one shall start questioning if there are other mechanisms at play here.

First, the turnover of fluorescent proteins is long, longer than 48h, which means that they accumulate at a different speed than the endogenous FSHR This means that ZsGreen will accumulate in time while the FSHR receptor might be degraded almost immediately. This correlated with mRNA expression (by the authors) but does not with the results of other studies in single-cell sequencing (see below).

The expression of ZsGreen in Leydig cells seems much higher than in Sertoli cells, this is "disturbing" to put it mildly. This is visible in both the ZsGreen expression and the FISH assay (Figure 2 B-D).

Thank you for this valuable comments. We added new data on Fshr expression to prove the presence of Fshr in Leydig cells in B6 detected by immunofluorescence staining, RNA-smFISH and ddRT-PCR, as well as in TM3 cells-isolated Leydig cells from a male mice in the revise MS (Fig 2E, F and G), that demonstrate no interruptions of normal Fshr expression by insertion of P2A-ZsGreen vector into a locus located between exon10 and stop code. We use ZsGreen as an indicator for active Fshr promoter status, rather than a method to measure Fshr expression, which is done by ddRT-PCR. These data are shown in Figure 2G of the revised MS

In addition, we provide scRNA-seq based evidence on Fshr expression in human Leydig cells from two single cell portals (DISCO and BioGPS) as shown in Supplementary Data 3 in the revised MS. We also cited a recent report on scRNA-seq analysis of Fshr expression in Hu sheep in the revised MS as Reference 65 (PMID: 37541020) 1, which also clearly showed Fshr expression in Leydig cells at single cell level in Hu Sheep.

We believe that the lack of Fshr expression in some single cell databases may be due to the degradation of Fshr transcript in cells during the process of single cell populations. In our laboratory, we spent more than 6 months to optimize methods and reagents to perverse mRNA integrity more than 8 for RAN-seq.

The expression in WAT and BAT is also questionable as the expression of ZsGreen is high everywhere. That makes it difficult to believe that the images are truly informative. For example, the stainings of aorta show the ZsGreen expression where elastin and collagen fibres are - these are not "cells" and therefore are not expressing ZsGreen.

FISH expression (for FSHR) in WT mice is missing.

Also, the tissue sections were stained with the IgG only (neg control) but in practice both the KI and the WT tissues should be stained with the primary and secondary antibodies. The only control that I could think of to truly get a sense of this would be a tagged receptor (N-terminal) that could then be analysed by immunohistochemistry.

Reply 2 and 3: Thank you for these comments. New data on Fshr expression in WAT and BAT of B6 mice by immunofluorescence staining and in the testis of B6 mice by immunofluorescence staining and RNA-smFISH are added to the revised MS (Fig.2D and E, and Fig. 4G), showing similar patterns to that of Fshr-ZsGreen mice. Furthermore, we provide more evidences as Supplementary Data 3 on Fshr expression obtained from 4 public single cell portables, showing FSHR expression in a widespread organs and tissues (including different fractions of adipose cells) of human, mice and rat at single cell levels. Please also check Fshr expression pattern in adipose tissues by immunostaining for Fshr in previous reports (Fig. 3a of PMID: 28538730 and Fig. 2 of PMID: 25754247) 2 3, which showed a similar expression pattern to our finding. These data should address your concerns on Fshr expression in WAT and BAT and other organs/tissues.

Regard of “For example, the stainings of aorta show the ZsGreen expression where elastin and collagen fibres are - these are not "cells" and therefore are not expressing ZsGreen.” We believe that you referred to the image of the aorta in Supplementary Data2. However, Please take a look at the images of the aorta in Figure 5-C, which shows positively stained the layer of ‘elastin and collagen fibres’ for EMCN and a-SMA colocalized with Fshr expression with stained DAPI at a 1000X magnification, indicating endothelial cells and the cellular membrane presented in this layer, not just ‘elastin and collagen’.

The authors also claim:

To functionally prove the presence of FSHR in osteoblasts/osteocytes, we also deleted FSHR in osteocytes using an inducible model. The conditional knockout of FSHR triggered a much more profound increase in bone mass and decrease in fat mass than blockade by FSHR antibodies (unpublished data).

This would be a good control for all their images. I think it is necessary to make the large claim of extragonadal expression, as well as intragonadal such as Leydig cells.

Thank you for this very encouraging comment. As you suggested, we did add a result of reduced Fshr expression in osteocytes from DMP1-CreERT2+:Fshrfl/fl mice treated with tamoxifen to the revise MS, as shown in Figure 3D, demonstrating Fshr present in osteocytes and the specificity of Fshr antibody. Furthermore, we incorporated your advice on making ‘ large claim of extrogonadal and intragonadal expression of Fshr’ into the revised MS in red.

Claiming that the under-developed Leydig cells in FSHR KO animals are due to a direct effect of the FSHR, and not via a cross-talk between Sertoli and Leydig cells, is too much of a claim. It might be speculated to some degree but as written at the moment it suggests this is "proven".

Thank you for pointing out this incorrect claim and we apologized for it. In the revised MS, we deleted this claim.

We also do not know if this FSHR expressed is a spliced form that would also result in the expression of ZsGreen but in a non-functional FSHR, or whether the FSHR is immediately degraded after expression. The insertion of the ZsGreen might have disturbed the epigenetics, transcription, or biosynthesis of the mRNA regulation.

Thanks for this comment. In the revised MS, we added a new section to explain the function of P2A peptide in generation of a GFP reporter by sgRNA-guilded site specific knockin of P2A ZsGreen vector through CRISPRA/cas9 and provided a new result on comparison of Fshr expression in the testes and ovaries from Fshr-ZsGreen and B6 mice, showing equivalent Fshr expression between Fshr-ZsGreen and B6 mice (Figure 2G), which indicates no interruptions of Fshr expression by the insertion of P2A vector.

The authors should go through single-cell data of WT mice to show the existence of the FSHR transcript(s).<br /> For example here:<br /> https://www.nature.com/articles/sdata2018192

Thank you so much for the valuable comment. Yes, we took you critical advice to check Fshr expression through 4 single cell portals, including DISCO, GTEx, BioGPS and Human single cell portal, and present the collected data as Supplementary Data 3 in the revised MS, that strongly support our findings of the wider Fshr expression. Particularly, Fshr expression in Leydig cells is proved by scRNA-seq studies of human cells from DISCO and BioGPS, as well as a recent study in Hu sheep (PMID: 37541020) 1 and we cited it in the revised MS.

Reviewer 2:

Is the FSHR expression pattern affected by the knockin mice (no side-by-side comparison between wt and GSGreen mice, using in situ hybridization and ddRTPCR, at least in the gonads, is provided)?

Thanks for the comment. In the revised MS, we provided a set of new data on Fshr expression in the testis, ovary, WAT and BAT of B6 mice by immunofluorescence staining and by RNA-smFISH for Fshr expression, showing similar expression patterns. Additionally, we also performed ddRT-PCT to compare Fshr expression in the testes and ovaries between Fshr-ZsGreen and B6 mice, demonstrating equivalent expression of Fshr expression between Fshr-ZsGreen and B6 mice. Interestingly, we also observed an significantly higher Fshr expression in the testis than that in the ovary (more than 30 folds).

Is the splicing pattern of the FSHR affected in the knockin compared to wt mice, at least in the gonads?

Thanks for the question. Please see our reply to the Reviewer 1 for the function of P2A peptide used for generation of GFP reporters.  Although we didn’t directly assess the splicing pattern, we provide a result of comparison of Fshr expression in Figure 2F in the revised MS, indirectly showing no changes of the splicing pattern. We will assess the splicing pattern of Fshr in the future that has been neglected in the field.

Are there any additional off-target insertions of GSGreen in these mice?” and “Are similar results observed in separate founder mice?

Thanks for the questions. As we describe it in the method section  in detail in the MS, Fshr-ZsGreen reporter was produced by the a site-specific long ssDNA recombination of the P2A-ZsGreen targeting vector to the locus between Exon10 and stop code by CRIPRA/cas9, which was guided by site-specific single guide RNA (sgRNA). We showed the results of Southern blot, DNA sequencing and site-specific PCR, proving the site-specific insertion of P2A-ZsGreen as shown in Figure 1. Because of the site-specific recombination, professionally, only one funder line is required for the study and there are no additional off-target insertions.

How long is GSGreen half-life? Could a very long half-life be a major reason for the extremely large expression pattern observed?

Thanks for the question. The half life of ZsGreen, also called ZsGreen1, is at least 26 h in mammalian cells or slightly longer due to its tetrameric structure, in contrast with the monomeric configuration of other well-known fluorescent proteins (PMID: 17510373) 4. The rationale for using this GFP protein is that ZsGreen is an exceptionally bright green fluorescent protein, which is up to 4X brighter than EGFP—and is ideally suited for whole-cell labelling, promoter-reporter studies, considering of the higher turnover and rapid degradation of Fshr transcript. In this study, we used ZsGreen as a monitor or an indicator of the active Fshr endogenous promoter, rather than a means for measuring the promoter activity. Therefore, regardless of its accumulation or not, ZsGreen driven by Fshr promoter, indicates the presence of active Fshr promoter in the defined cells. In stead, we used ddRT-PCR to measure Fshr expression degrees in this study. In addition, we also provide single cell sequence-based evidence from 4 public single cell portables to support our findings of the wide Fshr expression. Please see Supplementary Data 3 in the revised MS.

References:

(1) Su J, Song Y, Yang Y, et al. Study on the changes of LHR, FSHR and AR with the development of testis cells in Hu sheep. Anim Reprod Sci. Sep 2023;256:107306. doi:10.1016/j.anireprosci.2023.107306

(2) Liu P, Ji Y, Yuen T, et al. Blocking FSH induces thermogenic adipose tissue and reduces body fat. Nature. Jun 1 2017;546(7656):107-112. doi:10.1038/nature22342

(3) Liu XM, Chan HC, Ding GL, et al. FSH regulates fat accumulation and redistribution in aging through the Galphai/Ca(2+)/CREB pathway. Aging Cell. Jun 2015;14(3):409-20. doi:10.1111/acel.12331

(4) Bell P, Vandenberghe LH, Wu D, Johnston J, Limberis M, Wilson JM. A comparative analysis of novel fluorescent proteins as reporters for gene transfer studies. J Histochem Cytochem. Sep 2007;55(9):931-9. doi:10.1369/jhc.7A7180.2007

Author response:

eLife assessment 

This important study identifies a novel gastrointestinal enhancer of Ctnnb1. The authors present convincing evidence to support their claim that the dosage of Wnt/β-catenin signaling controlled by this enhancer is critical to intestinal epithelia homeostasis and the progression of colorectal cancers. The study will be of interest to biomedical researchers interested in Wnt signaling, tissue-specific enhancers, intestinal homeostasis, and colon cancer. 

We greatly appreciate editors’ and reviewers’ extensive and constructive comments and suggestions. We will do our utmost to revise the manuscript accordingly.

Public Reviews: 

Reviewer #1 (Public Review): 

Summary: 

Ctnnb1 encodes β-catenin, an essential component of the canonical Wnt signaling pathway. In this study, the authors identify an upstream enhancer of Ctnnb1 responsible for the specific expression level of β-catenin in the gastrointestinal tract. Deletion of this promoter in mice and analyses of its association with human colorectal tumors support that it controls the dosage of Wnt signaling critical to the homeostasis in intestinal epithelia and colorectal cancers. 

Strengths: 

This study has provided convincing evidence to demonstrate the functions of a gastrointestinal enhancer of Ctnnb1 using combined approaches of bioinformatics, genomics, in vitro cell culture models, mouse genetics, and human genetics. The results support the idea that the dosage of Wnt/β-catenin signaling plays an important role in the pathophysiological functions of intestinal epithelia. The experimental designs are solid and the data presented are of high quality. This study significantly contributes to the research fields of Wnt signaling, tissue-specific enhancers, and intestinal homeostasis. 

Weaknesses: 

One weakness of this manuscript is an insufficient discussion on the Ctnnb1 enhancers for different tissues. For example, do specific DNA motifs and transcriptional factors contribute to the tissue-specificity of the neocortical and gastrointestinal enhancers? It is also worth discussing the potential molecular mechanisms controlling the gastrointestinal expression of Ctnnb1 in different species since the identified human and mouse enhancers don't seem to share significant similarities in primary sequences. 

We agree with the reviewer that the manuscript lacks sufficient discussions on how enhancers control cell-type-specific expressions of target genes, which is one of the most important questions in the field of transcription regulation. Equally important are the common and species-specific features of this regulation. In general, motif composition, location, order, and affinity with trans-factors within enhancers are four key elements. We will elaborate the point in follow-up revision.

Reviewer #2 (Public Review): 

Wnt signaling is the name given to a cell-communication mechanism that cells employ to inform on each other's position and identity during development. In cells that receive the Wnt signal from the extracellular environment, intracellular changes are triggered that cause the stabilization and nuclear translocation of β-catenin, a protein that can turn on groups of genes referred to as Wnt targets. Typically these are genes involved in cell proliferation. Genetic mutations that affect Wnt signaling components can therefore affect tissue expansion. Loss of function of APC is a drastic example: APC is part of the β-catenin destruction complex, and in its absence, β-catenin protein is not degraded and constitutively turns on proliferation genes, causing cancers in the colon and rectum. And here lies the importance of the finding: β-catenin has for long been considered to be regulated almost exclusively by tuning its protein turnover. In this article, a new aspect is revealed: Ctnnb1, the gene encoding for β-catenin, possesses tissue-specific regulation with transcriptional enhancers in its vicinity that drive its upregulation in intestinal stem cells. The observation that there is more active β-catenin in colorectal tumors not only because the broken APC cannot degrade it, but also because transcription of the Ctnnb1 gene occurs at higher rates, is novel and potentially game-changing. As genomic regulatory regions can be targeted, one could now envision that mutational approaches aimed at dampening Ctnnb1 transcription could be a viable additional strategy to treat Wnt-driven tumors. 

We appreciate the reviewer for acknowledging the potential significance represented by the manuscript. We also recognize that targeting genomic regulatory regions to dampen Ctnnb1 transcription could be a promising strategy for treating Wnt-driven tumors, including many colorectal carcinomas. However, we would like to point out that three are significant technical challenges associated with AAV delivery to the GI epithelium, including the hostile environment, immune response, and low delivery efficiency.

Reviewer #3 (Public Review): 

The authors of this paper identify an enhancer upstream of the Ctnnb1 gene that selectively enhances expression in intestinal cells. This enhancer sequence drives expression of a reporter gene in the intestine and knockout of this enhancer attenuates Ctnnb1 expression in the intestine while protecting mice from intestinal cancers. The human counterpart of this enhancer sequence is functional and involved in tumorigenesis. Overall, this is an excellent example of how to fully characterize a cell-specific enhancer. The strength of the study is the thorough nature of the analysis and the relevance of the data to the development of intestinal tumors in both mice and humans. A minor weakness is that the loss of this enhancer does not completely compromise the expression of the Ctnnb1 gene in the intestine, suggesting that other elements are likely involved. Adding some discussion on that point would be helpful.

We are quite encouraged by the reviewer’s positive comments. We agree with the reviewer that other cis-regulatory elements may be involved in the transcription of Ctnnb1 within the GI epithelium. It is also possible that the basal transcription of Ctnnb1 within the GI epithelium is relatively high, and that enhancers can only boost transcription within a certain range. We will discuss these possibilities in the revision.

Author response:

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

eLife assessment

The manuscript presents a machine-learning method to predict protein hotspot residues. The validation is incomplete, along with the misinterpretation of the results with other current methods like FTMap.

We believe that validation is complete: The two most common techniques for testing and validating machine-learning methods are to split the dataset into either (1) a training set and a test set with a fixed ratio (e.g., 70% for training and 30% for testing) or (2) multiple subsets/folds; i.e., cross-validation. We did not employ a training set to train the model and a separate test set to evaluate its performance, as Reviewer 2 assumed. Instead, we employed cross-validation, as it helps reduce the variability in performance estimates compared to a single training/test split, and utilizes the entire dataset for training and testing, making efficient use of the limited data. Each fold was used once as a test set and the remaining folds as the training set - this process was repeated for each fold and the model's performance was measured using the F1 score. We had listed the mean validation F1 score in Table 1.

We have clarified our comparison with FTMAP  - see reply to point 1 of reviewer 1 below. 

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The paper describes a program developed to identify PPI-hot spots using the free protein structure and compares it to FTMap and SPOTONE, two webservers that they consider as competitive approaches to the problem. On the positive side, I appreciate the effort in providing a new webserver that can be tested by the community but have two major concerns as follows.

(1) The comparison to the FTMap program is wrong. The authors misinterpret the article they refer to, i.e., Zerbe et al. "Relationship between hot spot residues and ligand binding hot spots in protein-protein interfaces" J. Chem. Inf. Model. 52, 2236-2244, (2012). FTMap identifies hot spots that bind small molecular ligands. The Zerbe et al. article shows that such hot spots tend to interact with hot spot residues on the partner protein in a protein-protein complex (emphasis on "partner"). Thus, the hot spots identified by FTMap are not the hot spots defined by the authors. In fact, because the Zerbe paper considers the partner protein in a complex, the results cannot be compared to the results of Chen et al. This difference is missed by the authors, and hence the comparison of the FTMap is invalid. I did not investigate the comparison to SPOTONE, and hence have no opinion.

Brenke et al. (Bioinformatics 2009 25: 621-627), who developed FTMAP, defined hot spots as regions of the binding surface that “contribute a disproportionate amount to the binding free energy”. Kozakov et al. (Proc. Natl. Acad. Sci. 2011:108, 13528-1353) used unbound protein structures as input to FTMap to predict binding hot spots for protein-protein interactions (PPIs), which are defined as regions (so-called consensus sites) on a protein surface that bind multiple probe clusters − the main hot spot is the largest consensus site binding the largest number of probe clusters. 

Zerbe et al. (J. Chem. Inf. Model. 2012:52, 2236) noted that a consensus “site is expected to be important in any interaction that involves that region of the target independent of any partner protein.” They showed that for hot spot residues found by Ala scanning not only overlapped with the probe ligands but also form consensus sites, as shown in Figure 4. They stated that “A residue can also be identified as a hot spot by alanine scanning if it contributes to creating such a favorable binding environment by being among the residues forming a consensus site on the protein to which it belongs.”

To clarify the comparison with FTmap in the revised version, we have added the following sentence in the Abstract on p. 3:

“We explored the possibility of detecting PPI-hot spots using (i) FTMap in the PPI mode, which identifies hot spots on protein-protein interfaces from the free protein structure, and (ii) the interface residues predicted by AlphaFold-Multimer.”

We have added the following sentences in the Introduction section on p. 4:

“We explored the possibility of detecting PPI-hot spots using the FTMap server in the PPI mode, which identifies hot spots on protein-protein interfaces from free protein structures.45 These hot spots are identified by consensus sites − regions that bind multiple probe clusters.42,45,59 Such regions are deemed to be important for any interaction involving that region of the target, independent of partner protein.42 PPIhot spots were identified as residues in van der Waals (vdW) contact with probe ligands within the largest consensus site containing the most probe clusters.”

and in the Results section on p. 5:

“Given the free protein structure, PPI-HotspotID and SPOTONE53 predict PPI-hot spots based on a probability threshold (> 0.5). FTMap, in the PPI mode, detects PPIhot spots as consensus sites/regions on the protein surface that bind multiple probe clusters.59 Residues in vdW contact with probe molecules within the largest consensus site were compared with PPI-hotspotID/SPOTONE predictions.”

(2) Chen et al. use a number of usual features in a variety of simple machine-learning methods to identify hot spot residues. This approach has been used in the literature for more than a decade. Although the authors say that they were able to find only FTMap and SPOTONE as servers, there are dozens of papers that describe such a methodology. Some examples are given here: (Higa and Tozzi, 2009; Keskin, et al., 2005; Lise, et al., 2011; Tuncbag, et al., 2009; Xia, et al., 2010). There are certainly more papers. Thus, while I consider the web server as a potentially useful contribution, the paper does not provide a fundamentally novel approach.

Our paper introduces several novel elements in our approach: 

(1) Most PPI-hot spot prediction methods employ PPI-hotspots where mutations decrease protein binding free energy by > 2 kcal/mol (J. Chem. Inf. Model. 2022, 62, 1052). In contrast, our method incorporates not only PPI-hot spots with such binding free energy changes, but also those whose mutations have been curated in UniProtKB to significantly impair/disrupt PPIs. Because our method employs the largest collection of experimentally determined PPI-hot spots, it could uncover elusive PPI-hot spots not within binding interfaces, as well as potential PPI-hot spots for other protein partners (see point 3 below). 

(2) Whereas most machine-learning methods for PPI-hot spot prediction focus on features derived from (i) primary sequences or (ii) protein-protein complexes, we introduce novel features such as per-residue free energy contributions derived from unbound protein structures. We further revealed the importance of one of our novel features, namely, the gas-phase energy of the target protein relative to its unfolded state and provided the physical basis for its importance. For example, PPI-hot spots can enhance favorable enthalpic contributions to the binding free energy through hydrogen bonds or van der Waals contacts across the protein’s interface. This makes them energetically unstable in the absence of the protein’s binding partner and solvent; hence providing a rationale for the importance of the gas-phase energy of the target protein relative to its unfolded state.

(3) As a result of these novel elements, our approach, PPI-HotspotID,  could identify many true positives that were not detected by FTMap or SPOTONE (see Results and Figure 1). Previous methods generally predict residues that make multiple contacts across the proteinprotein interface as PPI-hot spots. In contrast, PPI-HotspotID can detect not only PPI-hot spots that make multiple contacts across the protein-protein interface, but also those lacking direct contact with the partner protein (see Discussion).

(4) Unlike most machine-learning methods which require feature customization, data preprocessing, and model optimization, our use of AutoGluon’s AutoTabular module automates data preprocessing, model selection, hyperparameter optimization, and model evaluation. This automation reduces the need for manual intervention.

We have revised and added the following sentences on p. 9 in the Discussion section to highlight the novelty of our approach: 

“Here, we have introduced two novel elements that have helped to identify PPI-hot spots using the unbound structure. First, we have constructed a dataset comprising 414 experimentally known PPI-hot spots and 504 nonhot spots, and carefully checked that PPI-hot spots have no mutations resulting in ΔΔGbind < 0.5 kcal/mol, whereas nonhot spots have no mutations resulting in ΔΔGbind ≥ 0.5 kcal/mol or impact binding in immunoprecipitation or GST pull-down assays (see Methods). In contrast, SPOTONE53 employed nonhot spots defined as residues that upon alanine mutation resulted in ΔΔGbind < 2.0 kcal/mol. Notably, previous PPI-hot spot prediction methods did not employ PPIhot spots whose mutations have been curated to significantly impair/disrupt PPIs in UniProtKB (see Introduction). Second, we have introduced novel features derived from unbound protein structures such as the gas-phase energy of the target protein relative to its unfolded state.”

Strengths:

A new web server was developed for detecting protein-protein interaction hot spots.

Weaknesses:

The comparison to FTMap results is wrong. The method is not novel.

See reply to points 1 and 2 above.

Reviewer #2 (Public Review):

Summary:

The paper presents PPI-hotspot a method to predict PPI-hotspots. Overall, it could be useful but serious concerns about the validation and benchmarking of the methodology make it difficult to predict its reliability.

Strengths:

Develops an extended benchmark of hot-spots.

Weaknesses:

(1) Novelty seems to be just in the extended training set. Features and approaches have been used before.

The novelty of our approach extends beyond just the expanded training set, as summarized in our reply to Reviewer #1, point 2 above. To our knowledge, previous studies did not leverage the gas-phase energy of the target protein relative to its unfolded state for detecting PPI-hot spots from unbound structures. Previous studies did not automate the training and validation process. In contrast, we used AutoGluon’s AutoTabular module to automate the training  of (i) individual “base” models, including LightGBM, CatBoost, XGBoost, random forests, extremely randomized trees, neural networks, and K-nearest neighbours, then (ii) multiple “stacker” models. The predictions of multiple “stacker” models were fed as inputs to additional higher layer stacker models in an iterative process called multi-layer stacking. The output layer used ensemble selection to aggregate the predictions of the stacker models. To improve stacking performance, AutoGluon used all the data for both training and validation through repeated k-fold bagging of all models at all layers of the stack, where k is determined by best precision. This comprehensive approach, including repeated k-fold bagging of all models at all layers of the stack, sets our methodology apart from previous studies, including SPOTONE (see Methods). 

(2) As far as I can tell the training and testing sets are the same. If I am correct, it is a fatal flaw.

The two most common techniques for testing and validating machine-learning methods are to split the dataset into either (1) a training set and a test set with a fixed ratio (e.g., 70% for training and 30% for testing) or (2) multiple subsets/folds; i.e., cross-validation. We did not employ a training set to train the model and a separate test set to evaluate its performance. Instead, we employed cross-validation, where the model was trained and evaluated multiple times. Each fold was used once as a test set and the remaining folds serve as the training set - this process was repeated for each fold. For each test set, we assessed  the model's performance using the F1 score. We had listed the mean validation F1 score in Table 1 in the original manuscript. Cross-validation helps reduce the variability in performance estimates compared to a single training/test split. It also utilizes the entire dataset for training and testing, making efficient use of the limited data. We have clarified this on p. 14 in the revised version:

“AutoGluon was chosen for model training and validation due to its robustness and userfriendly interface, allowing for the simultaneous and automated exploration of various machine-learning approaches and their combinations. Instead of using a single training set to train the model and a separate test set to evaluate its performance, we employed cross-validation, as it utilizes the entire dataset for both training and testing, making efficient use of the limited data on PPI-hot spots and PPI-nonhot spots. AutoGluonTabular automatically chose a random partitioning of our dataset into multiple subsets/folds for training and validation. Notably, the training and validation data share insignificant homology, as the average pairwise sequence identity in our dataset is 26%. Each fold was used once as a test set, while the remaining folds served as the training set. For each test set, the model's performance was measured using the F1 score.”

(3) Comparisons should state that: SPOTONE is a sequence (only) based ML method that uses similar features but is trained on a smaller dataset. FTmap I think predicts binding sites, I don't understand how it can be compared with hot spots. Suggesting superiority by comparing with these methods is an overreach.

In the Introduction on page 3, we had already stated that:

“SPOTONE53 predicts PPI-hot spots from the protein sequence using residue-specific features such as atom type, amino acid (aa) properties, secondary structure propensity, and mass-associated values to train an ensemble of extremely randomized trees. The PPIhot spot prediction methods have mostly been trained, validated, and tested on data from the Alanine Scanning Energetics database (ASEdb)55 and/or the Structural Kinetic and Energetic database of Mutant Protein Interactions (SKEMPI) 2.0 database.56”

On p. 4, we have clarified how we used FTMAP to detect hot spots - see reply to Reviewer #1, point 1. 

“We explored the possibility of detecting PPI-hot spots using the FTMap server in the PPI mode, which identifies hot spots on protein-protein interfaces from free protein structures.45 These hot spots are identified by consensus sites − regions that bind multiple probe clusters.42,45,59 Such regions are deemed to be important for any interaction involving that region of the target, independent of partner protein.42 PPI-hot spots were identified as residues in van der Waals (vdW) contact with probe ligands within the largest consensus site containing the most probe clusters.”

(4) Training in the same dataset as SPOTONE, and then comparing results in targets without structure could be valuable.

We think that the dataset used by SPOTONE is not as “clean” as ours since SPOTONE employed nonhot spots defined as aa residues that upon alanine mutation resulted in ΔΔGbind < 2.0 kcal/mol.  In contrast, we define nonhot spots as residues whose mutations resulted in protein  ΔΔGbind changes < 0.5 kcal/mol. Moreover, we carefully checked that the nonhot spots have no mutations resulting in ΔΔGbind changes ≥ 0.5 kcal/mol or impact binding in immunoprecipitation or GST pull-down assays (see Methods). We cannot compare results in targets without structure because we require the free protein structure to compute the perresidue free energy contributions. 

(5) The paper presents as validation of the prediction and experimental validation of hotspots in human eEF2. Several predictions were made but only one was confirmed, what was the overall success rate of this exercise?

We did not test all predicted PPI-hot spots but only the PPI-hot spot with the highest probability of 0.67 (F794) and 7 other predicted PPI-hot spots that were > 12 Å from F794 as well as 4 predicted PPI-nonhot spots. Among the 13 predictions tested, F794 and the 4 predicted nonhot spots were confirmed to be correct. 

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Remove the comparison to FTMap, and find a more appropriate reference method, even if it requires installing programs rather than using the available web servers.

We have clarified comparison to FTMap in the revised ms - see our reply above.

Author response:

eLife assessment

This useful study examines the neural activity in the motor cortex as a monkey reaches to intercept moving targets, focusing on how tuned single neurons contribute to an interesting overall population geometry. The presented results and analyses are solid, though the investigation of this novel task could be strengthened by clarifying the assumptions behind the single neuron analyses, and further analyses of the neural population activity and its relation to different features of behaviour.

Thanks for recognizing the content of our research, and please stay tuned for our follow-up studies on neural dynamics during interception.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This study addresses the question of how task-relevant sensory information affects activity in the motor cortex. The authors use various approaches to address this question, looking at single units and population activity. They find that there are three subtypes of modulation by sensory information at the single unit level. Population analyses reveal that sensory information affects the neural activity orthogonally to motor output. The authors then compare both single unit and population activity to computational models to investigate how encoding of sensory information at the single unit level is coordinated in a network. They find that an RNN that displays similar orbital dynamics and sensory modulation to the motor cortex also contains nodes that are modulated similarly to the three subtypes identified by the single unit analysis.

Strengths:

The strengths of this study lie in the population analyses and the approach of comparing single-unit encoding to population dynamics. In particular, the analysis in Figure 3 is very elegant and informative about the effect of sensory information on motor cortical activity. The task is also well designed to suit the questions being asked and well controlled.

We appreciate these kind comments.

It is commendable that the authors compare single units to population modulation. The addition of the RNN model and perturbations strengthen the conclusion that the subtypes of individual units all contribute to the population dynamics. However, the subtypes (PD shift, gain, and addition) are not sufficiently justified. The authors also do not address that single units exhibit mixed modulation, but RNN units are not treated as such.

We’re sorry for not providing sufficient grounds to introduce the subtypes. We determined the PD shift, gain, and addition as pertinent subtypes based on classical cosine tuning model (Georgopoulos et al., 1982) and referred to some gain modulation studies (e.g. Pesaran et al. 2010, Bremner and Andersen, 2012). Here, we applied this subtype analysis as a criteria to identify the modulation in neuronal population rather than to sort neuron into distinct cell types. We will update Methods in the revised version of manuscript.

Weaknesses:

The main weaknesses of the study lie in the categorization of the single units into PD shift, gain, and addition types. The single units exhibit clear mixed selectivity, as the authors highlight. Therefore, the subsequent analyses looking only at the individual classes in the RNN are a little limited. Another weakness of the paper is that the choice of windows for analyses is not properly justified and the dependence of the results on the time windows chosen for single-unit analyses is not assessed. This is particularly pertinent because tuning curves are known to rotate during movements (Sergio et al. 2005 Journal of Neurophysiology).

The mixed selectivity or precisely the mixed modulation is indeed a significant feature of neuronal population in the present study. The purpose of the subtype analysis was to serve as a criterion for the potential modulation mechanisms. However, the results appear to be a spectrum than clusters. It still through some insights to understand the modulation distribution and we will refine the description in the next version. In the current version, we observed single-unit tuning and population neural state with sliding windows, focusing on the period around movement onset (MO) due to the emergence of a ring-like structure. We will clarify the choice of windows and the dependence assessment in the next version. It’s a great suggestion to consider the role of rotating tuning curves in neural dynamics during interception.

This paper shows sensory information can affect motor cortical activity whilst not affecting motor output. However, it is not the first to do so and fails to cite other papers that have investigated sensory modulation of the motor cortex (Stavinksy et al. 2017 Neuron, Pruszynski et al. 2011 Nature, Omrani et al. 2016 eLife). These studies should be mentioned in the Introduction to capture better the context around the present study. It would also be beneficial to add a discussion of how the results compare to the findings from these other works.

Thanks for the reminder. We will introduce the relevant research in the next version of manuscript.

This study also uses insights from single-unit analysis to inform mechanistic models of these population dynamics, which is a powerful approach, but is dependent on the validity of the single-cell analysis, which I have expanded on below.

I have clarified some of the areas that would benefit from further analysis below:

(1) Task:

The task is well designed, although it would have benefited from perhaps one more target speed (for each direction). One monkey appears to have experienced one more target speed than the others (seen in Figure 3C). It would have been nice to have this data for all monkeys.

Great suggestion! However, it’s hard to implement as the implanted arrays have been removed.

(2) Single unit analyses:

In some analyses, the effects of target speed look more driven by target movement direction (e.g. Figures 1D and E). To confirm target speed is the main modulator, it would be good to compare how much more variance is explained by models including speed rather than just direction. More target speeds may have been helpful here too.

Nice suggestion! The fitting goodness of the simple model (just motor direction) is much less than the complex model (including target speed). We will update the results in the next version.

The choice of the three categories (PD shift, gain addition) is not completely justified in a satisfactory way. It would be nice to see whether these three main categories are confirmed by unsupervised methods.

A good point. We will have a try with unsupervised methods. 

The decoder analyses in Figure 2 provide evidence that target speed modulation may change over the trial. Therefore, it is important to see how the window considered for the firing rate in Figure 1 (currently 100ms pre - 100ms post movement onset) affects the results.

Thanks for the suggestion and close reading. We will test the decoder in other epochs.

(3) Decoder:

One feature of the task is that the reach endpoints tile the entire perimeter of the target circle (Figure 1B). However, this feature is not exploited for much of the single-unit analyses. This is most notable in Figure 2, where the use of a SVM limits the decoding to discrete values (the endpoints are divided into 8 categories). Using continuous decoding of hand kinematics would be more appropriate for this task.

This is a very reasonable suggestion. In this study, we discrete the reach-direction as the previous studies (Li et al., 2018&2022) and thought that the discrete decoding was already enough to show the interaction of sensory and motor variables. In future studies, we will try continuous decoding of hand kinematics.

(4) RNN:

Mixed selectivity is not analysed in the RNN, which would help to compare the model to the real data where mixed selectivity is common. Furthermore, it would be informative to compare the neural data to the RNN activity using canonical correlation or Procrustes analyses. These would help validate the claim of similarity between RNN and neural dynamics, rather than allowing comparisons to be dominated by geometric similarities that may be features of the task. There is also an absence of alternate models to compare the perturbation model results to.

Thank you for these helpful suggestions. We will perform decoding analysis on RNN units to verify if there is interaction of sensory and motor variables as in real data, as well as the canonical correlation or Procrustes analysis.

Reviewer #2 (Public Review):

Summary:

In this manuscript, Zhang et al. examine neural activity in the motor cortex as monkeys make reaches in a novel target interception task. Zhang et al. begin by examining the single neuron tuning properties across different moving target conditions, finding several classes of neurons: those that shift their preferred direction, those that change their modulation gain, and those that shift their baseline firing rates. The authors go on to find an interesting, tilted ring structure of the neural population activity, depending on the target speed, and find that (1) the reach direction has consistent positioning around the ring, and (2) the tilt of the ring is highly predictive of the target movement speed. The authors then model the neural activity with a single neuron representational model and a recurrent neural network model, concluding that this population structure requires a mixture of the three types of single neurons described at the beginning of the manuscript.

Strengths:

I find the task the authors present here to be novel and exciting. It slots nicely into an overall trend to break away from a simple reach-to-static-target task to better characterize the breadth of how the motor cortex generates movements. I also appreciate the movement from single neuron characterization to population activity exploration, which generally serves to anchor the results and make them concrete. Further, the orbital ring structure of population activity is fascinating, and the modeling work at the end serves as a useful baseline control to see how it might arise.

Thank you for recognizing our work.

Weaknesses:

While I find the behavioral task presented here to be excitingly novel, I find the presented analyses and results to be far less interesting than they could be. Key to this, I think, is that the authors are examining this task and related neural activity primarily with a single-neuron representational lens. This would be fine as an initial analysis since the population activity is of course composed of individual neurons, but the field seems to have largely moved towards a more abstract "computation through dynamics" framework that has, in the last several years, provided much more understanding of motor control than the representational framework has. As the manuscript stands now, I'm not entirely sure what interpretation to take away from the representational conclusions the authors made (i.e. the fact that the orbital population geometry arises from a mixture of different tuning types). As such, by the end of the manuscript, I'm not sure I understand any better how the motor cortex or its neural geometry might be contributing to the execution of this novel task.

The present study shows the sensory modulation on motor tuning in single units and neural state during motor execution period. It’s a pity that the findings were constrained in certain time windows. We are still working this topic, and hopefully will address related questions in our follow-up studies.

Main Comments:

My main suggestions to the authors revolve around bringing in the computation through a dynamics framework to strengthen their population results. The authors cite the Vyas et al. review paper on the subject, so I believe they are aware of this framework. I have three suggestions for improving or adding to the population results:

(1) Examination of delay period activity: one of the most interesting aspects of the task was the fact that the monkey had a random-length delay period before he could move to intercept the target. Presumably, the monkey had to prepare to intercept at any time between 400 and 800 ms, which means that there may be some interesting preparatory activity dynamics during this period. For example, after 400ms, does the preparatory activity rotate with the target such that once the go cue happens, the correct interception can be executed? There is some analysis of the delay period population activity in the supplement, but it doesn't quite get at the question of how the interception movement is prepared. This is perhaps the most interesting question that can be asked with this experiment, and it's one that I think may be quite novel for the field--it is a shame that it isn't discussed.

Great idea! We are on the way, and close to complete the puzzle.

(2) Supervised examination of population structure via potent and null spaces: simply examining the first three principal components revealed an orbital structure, with a seemingly conserved motor output space and a dimension orthogonal to it that relates to the visual input. However, the authors don't push this insight any further. One way to do that would be to find the "potent space" of motor cortical activity by regression to the arm movement and examine how the tilted rings look in that space (this is actually fairly easy to see in the reach direction components of the dPCA plot in the supplement--the rings will be highly aligned in this space). Presumably, then, the null space should contain information about the target movement. dPCA shows that there's not a single dimension that clearly delineates target speed, but the ring tilt is likely evident if the authors look at the highest variance neural dimension orthogonal to the potent space (the "null space")--this is akin to PC3 in the current figures, but it would be nice to see what comes out when you look in the data for it.

Nice suggestion. Target-speed modulation mainly influences PC3, which is consistent with ‘null space’ hypothesis. We will try other methods of dimensionality reduction (e.g. dPCA, Manopt) to determine the potent and null space.

(3) RNN perturbations: as it's currently written, the RNN modeling has promise, but the perturbations performed don't provide me with much insight. I think this is because the authors are trying to use the RNN to interpret the single neuron tuning, but it's unclear to me what was learned from perturbing the connectivity between what seems to me almost arbitrary groups of neurons (especially considering that 43% of nodes were unclassifiable). It seems to me that a better perturbation might be to move the neural state before the movement onset to see how it changes the output. For example, the authors could move the neural state from one tilted ring to another to see if the virtual hand then reaches a completely different (yet predictable) target. Moreover, if the authors can more clearly characterize the preparatory movement, perhaps perturbations in the delay period would provide even more insight into how the interception might be prepared.

We are sorry that we didn’t clarify the definition of “none” type, which can be misleading. The 43% unclassified nodes include those inactive ones, when only activate (task-related) nodes included, the ratio of unclassified nodes would be much lower. By perturbing the connectivity, we intended to explore the interaction between different modulations.

Thank you for the great advice. We tried moving neural states from one ring to another without changing the directional cluster, but this perturbation didn’t have a significant influence on network performance as expected. We will check this result again and try perturbations in the delay period.

Reviewer #3 (Public Review):

Summary:

This experimental study investigates the influence of sensory information on neural population activity in M1 during a delayed reaching task. In the experiment, monkeys are trained to perform a delayed interception reach task, in which the goal is to intercept a potentially moving target.

This paradigm allows the authors to investigate how, given a fixed reach endpoint (which is assumed to correspond to a fixed motor output), the sensory information regarding the target motion is encoded in neural activity.

At the level of single neurons, the authors found that target motion modulates the activity in three main ways: gain modulation (scaling of the neural activity depending on the target direction), shift (shift of the preferred direction of neurons tuned to reach direction), or addition (offset to the neural activity).

At the level of the neural population, target motion information was largely encoded along the 3rd PC of the neural activity, leading to a tilt of the manifold along which reach direction was encoded that was proportional to the target speed. The tilt of the neural manifold was found to be largely driven by the variation of activity of the population of gain-modulated neurons.

Finally, the authors studied the behaviour of an RNN trained to generate the correct hand velocity given the sensory input and reach direction. The RNN units were found to similarly exhibit mixed selectivity to the sensory information, and the geometry of the « neural population » resembled that observed in the monkeys.

Strengths:

- The experiment is well set up to address the question of how sensory information that is directly relevant to the behaviour but does not lead to a direct change in behavioural output modulates motor cortical activity.

- The finding that sensory information modulates the neural activity in M1 during motor preparation and execution is non trivial, given that this modulation of the activity must occur in the nullspace of the movement.

- The paper gives a complete picture of the effect of the target motion on neural activity, by including analyses at the single neuron level as well as at the population level. Additionally, the authors link those two levels of representation by highlighting how gain modulation contributes to shaping the population representation.

Thanks for your recognition.

Weaknesses:

- One of the main premises of the paper is the fact that the motor output for a given reach point is preserved across different target motions. However, as the authors briefly mention in the conclusion, they did not record muscle activity during the task, but only hand velocity, making it impossible to directly verify how preserved muscle patterns were across movements. While the authors highlight that they did not see any difference in their results when resampling the data to control for similar hand velocities across conditions, this seems like an important potential caveat of the paper whose implications should be discussed further or highlighted earlier in the paper.

Thanks for the suggestion. We will highlight the resampling results as important control in the next version of manuscript.

- The main takeaway of the RNN analysis is not fully clear. The authors find that an RNN trained given a sensory input representing a moving target displays modulation to target motion that resembles what is seen in real data. This is interesting, but the authors do not dissect why this representation arises, and how robust it is to various task design choices. For instance, it appears that the network should be able to solve the task using only the motion intention input, which contains the reach endpoint information. If the target motion input is not used for the task, it is not obvious why the RNN units would be modulated by this input (especially as this modulation must lie in the nullspace of the movement hand velocity if the velocity depends only on the reach endpoint). It would thus be important to see alternative models compared to true neural activity, in addition to the model currently included in the paper. Besides, for the model in the paper, it would therefore be interesting to study further how the details of the network setup (eg initial spectral radius of the connectivity, weight regularization, or using only the target position input) affect the modulation by the motion input, as well as the trained population geometry and the relative ratios of modulated cells after training.

Great suggestions. It’s a considerable pity that we didn’t dissect the formation reason and influence factor of the representation in the current version. We’ve tried several combinations of inputs before: in the network which received only motor intention and GO inputs, there were rings but not tilting related to target-speed; in the network which received only target location and GO inputs, there were ring-like structures but not clear directional clusters. We will check these results and try alternative models in the next version. In future studies, we will examine the influence of network setup details.

- Additionally, it is unclear what insights are gained from the perturbations to the network connectivity the authors perform, as it is generally expected that modulating the connectivity will degrade task performance and the geometry of the responses. If the authors wish the make claims about the role of the subpopulations, it could be interesting to test whether similar connectivity patterns develop in networks that are not initialized with an all-to-all random connectivity or to use ablation experiments to investigate whether the presence of multiple types of modulations confers any sort of robustness to the network.

Thank you for the great suggestions. By perturbations, we intended to explore the contribution of interaction between certain subpopulations. We tried ablation experiments, but the result was not significant. Probably because the most units were of mixed selectivity, the units of only modulations were not enough for bootstrapping, or the random sampling from single subpopulation (bearing mixed selectivity) could be repeated. We will consider these suggestions carefully in the revised version.

- The results suggest that the observed changes in motor cortical activity with target velocity result from M1 activity receiving an input that encodes the velocity information. This also appears to be the assumption in the RNN model. However, even though the input shown to the animal during preparation is indeed a continuously moving target, it appears that the only relevant quantity to the actual movement is the final endpoint of the reach. While this would have to be a function of the target velocity, one could imagine that the computation of where the monkeys should reach might be performed upstream of the motor cortex, in which case the actual target velocity would become irrelevant to the final motor output. This makes the results of the paper very interesting, but it would be nice if the authors could discuss further when one might expect to see modulation by sensory information that does not directly affect motor output in M1, and where those inputs may come from. It may also be interesting to discuss how the findings relate to previous work that has found behaviourally irrelevant information is being filtered out from M1 (for instance, Russo et al, Neuron 2020 found that in monkeys performing a cycling task, context can be decoded from SMA but not from M1, and Wang et al, Nature Communications 2019 found that perceptual information could not be decoded from PMd)?

How and where sensory information modulates M1 are very interesting and open questions. We will discuss further about this topic in the next version.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Semenova et al. have studied a large cross-sectional cohort of people living with HIV on suppressive ART, N=115, and performed high dimensional flow cytometry to then search for associations between immunological and clinical parameters and intact/total HIV DNA levels.

A number of interesting data science/ML approaches were explored on the data and the project seems a serious undertaking. However, like many other studies that have looked for these kinds of associations, there was not a very strong signal. Of course, the goal of unsupervised learning is to find new hypotheses that aren't obvious to human eyes, but I felt in that context, there were (1) results slightly oversold, (2) some questions about methodology in terms mostly of reservoir levels, and (3) results were not sufficiently translated back into meaning in terms of clinical outcomes.

We appreciate the reviewer’s perspective.  In our revised version of the manuscript, we have attempted to address these concerns by more adequately explaining the limitations of the study and by more thoroughly discussing the context of the findings.  We are not able to associate the findings with specific clinical outcomes for individual study participants but we speculate about the overall biological meaning of these associations across the cohort.  We cannot disagree with the reviewer, but we find the associations statistically significant, potentially reflecting real biological associations, and forming the basis for future hypothesis testing research. 

Strengths:

The study is evidently a large and impressive undertaking and combines many cutting-edge statistical techniques with a comprehensive experimental cohort of people living with HIV, notably inclusive of populations underrepresented in HIV science. A number of intriguing hypotheses are put forward that could be explored further. Sharing the data could create a useful repository for more specific analyses.

We thank the reviewer for this assessment.

Weaknesses:

Despite the detailed experiments and methods, there was not a very strong signal for the variable(s) predicting HIV reservoir size. The Spearman coefficients are ~0.3, (somewhat weak, and acknowledged as such) and predictive models reach 70-80% prediction levels, though sometimes categorical variables are challenging to interpret.

We agree with the reviewer that individual parameters are only weakly correlated with the HIV reservoir, likely reflecting the complex and multi-factorial nature of reservoir/immune cell interactions.  Nevertheless, these associations are statistically significant and form the basis for functional testing in viral persistence.

There are some questions about methodology, as well as some conclusions that are not completely supported by results, or at minimum not sufficiently contextualized in terms of clinical significance.  On associations: the false discovery rate correction was set at 5%, but data appear underdetermined with fewer observations than variables (144vars > 115ppts), and it isn't always clear if/when variables are related (e.g inverses of one another, for instance, %CD4 and %CD8).

When deriving a list of cell populations whose frequency would be correlated with the reservoir, we focused on well-defined cell types for which functional validation exists in the literature to consider them as distinct cell types.  For many of the populations, gating based on combinations of multiple markers leads to recovery of very few cells, and so we excluded some potential combinations from the analysis.  We are also making our raw data available for others to examine and find associations not considered by our manuscript.

The modeling of reservoir size was unusual, typically intact and defective HIV DNA are analyzed on a log10 scale (both for decays and predicting rebound). Also, sometimes in this analysis levels are normalized (presumably to max/min?, e.g. S5), and given the large within-host variation of level we see in other works, it is not trivial to predict any downstream impact of normalization across population vs within-person.

We have repeated the analysis using log10 transformed data and the new figures are shown in Figure 1 and S2-S5.

Also, the qualitative characterization of low/high reservoir is not standard and naturally will split by early/later ART if done as above/below median. Given the continuous nature of these data, it seems throughout that predicting above/below median is a little hard to translate into clinical meaning.

Our ML models included time before ART as a variable in the analysis, and this was not found to be a significant driver of the reservoir size associations, except for the percentage of intact proviruses (see Figure 2C). Furthermore, we analyzed whether any of the reservoir correlated immune variables were associated with time on ART and found that, although some immune variables are associated with time on therapy, this was not the case for most of them (Table S4). We agree that it is challenging to translate above or below median into clinical meaning for this cohort, but we emphasize that this study is primarily a hypothesis generating approach requiring additional validation for the associations observed.  We attempted to predict reservoir size as a continuous variable using the data and this approach was not successful (Figure S13). We believe that a significantly larger cohort will likely be required to generate a ML model that can accurately predict the reservoir as a continuous variable.  We have added additional discussion of this to the manuscript.

Lastly, the work is comprehensive and appears solid, but the code was not shared to see how calculations were performed.

We now provide a link to the code used to perform the analyses in the manuscript, https://github.com/lesiasemenova/ML_HIV_reservoir.

Reviewer #2 (Public Review):

Summary:

Semenova et. al., performed a cross-sectional analysis of host immunophenotypes (using flow cytometry) and the peripheral CD4+ T cell HIV reservoir size (using the Intact Proviral DNA Assay, IPDA) from 115 people with HIV (PWH) on ART. The study mostly highlights the machine learning methods applied to these host and viral reservoir datasets but fails to interpret these complex analyses into (clinically, biologically) interpretable findings. For these reasons, the direct translational take-home message from this work is lost amidst a large list of findings (shown as clusters of associated markers) and sentences such as "this study highlights the utility of machine learning approaches to identify otherwise imperceptible global patterns" - lead to overinterpretation of their data.

We have addressed the reviewer’s concern by modifications to the manuscript that enhance the interpretation of the findings in a clinical and biological context.

Strengths:

Measurement of host immunophenotyping measures (multiparameter flow cytometry) and peripheral HIV reservoir size (IPDA) from 115 PWH on ART.

Major Weaknesses:

(1) Overall, there is little to no interpretability of their machine learning analyses; findings appear as a "laundry list" of parameters with no interpretation of the estimated effect size and directionality of the observed associations. For example, Figure 2 might actually give an interpretation of each X increase in immunophenotyping parameter, we saw a Y increase/decrease in HIV reservoir measure.

We have added additional text to the manuscript in which we attempt to provide more immunological and clinical interpretation of the associations.  We also have emphasized that these associations are still speculative and will require additional validation.  Nevertheless, our data should provide a rich source of new hypotheses regarding immune system/reservoir interaction that could be tested in future work.

(2) The correlations all appear to be relatively weak, with most Spearman R in the 0.30 range or so.

We agree with the review that the associations are mostly weak, consistent with previous studies in this area.  This likely is an inherent feature of the underlying biology – the reservoir is likely associated with the immune system in complex ways and involves stochastic processes that will limit the predictability of reservoir size using any single immune parameter. We have added additional text to the manuscript to make this point clearer.

(3) The Discussion needs further work to help guide the reader. The sentence: "The correlative results from this present study corroborate many of these studies, and provide additional insights" is broad. The authors should spend some time here to clearly describe the prior literature (e.g., describe the strength and direction of the association observed in prior work linking PD-1 and HIV reservoir size, as well as specify which type of HIV reservoir measures were analyzed in these earlier studies, etc.) and how the current findings add to or are in contrast to those prior findings.

We have added additional text to the manuscript to help guide the readers through the possible biological significance of the findings and the context with respect to prior literature.

(4) The most interesting finding is buried on page 12 in the Discussion: "Uniquely, however, CD127 expression on CD4 T cells was significantly inversely associated with intact reservoir frequency." The authors should highlight this in the abstract, and title, and move this up in the Discussion. The paper describes a very high dimensional analysis and the key takeaways are not clear; the more the author can point the reader to the take-home points, the better their findings can have translatability to future follow-up mechanistic and/or validation studies.

We appreciate the reviewer’s comment.  We have increased the emphasis on this finding in the revised version of the manuscript.

(5) The authors should avoid overinterpretation of these results. For example in the Discussion on page 13 "The existence of two distinct clusters of PWH with different immune features and reservoir characteristics could have important implications for HIV cure strategies - these two groups may respond differently to a given approach, and cluster membership may need to be considered to optimize a given strategy." It is highly unlikely that future studies will be performing the breadth of parameters resulting here and then use these directly for optimizing therapy.

Our analyses indicate that membership of study participants in cluster1 or cluster 2 can be fairly accurately determined by a small number of individual parameters (KLRG1 etc, Figure 4F), and measuring the cells of PWH with the degree of breadth used in this paper would not be necessary to classify PWH into these clusters.  As such, we feel that it is not unrealistic to speculate that this finding could turn out to be clinically useful, if it becomes clear that the clusters are biologically meaningful.

(6) There are only TWO limitations listed here: cross-sectional study design and the use of peripheral blood samples. (The subsequent paragraph notes an additional weakness which is misclassification of intact sequences by IPDA). This is a very limited discussion and highlights the need to more critically evaluate their study for potential weaknesses.

We have expanded on the list of limitations discussed in the manuscript. In particular, we now address the size of the cohort, the composition with respect to different genders and demographics, lack of information for the timing of ART and the lack of information regarding intracellular transcriptional pathways.

(7) A major clinical predictor of HIV reservoir size and decay is the timing of ART initiation. The authors should include these (as well as other clinical covariate data - see #12 below) in their analyses and/or describe as limitations of their study.

All of the participants that make up our cohort were treated during chronic infection, and the precise timing of ART initiation is unclear in most of these cases.  We have added additional information to explain this in the manuscript and include this in the list of limitations.

Reviewer #3 (Public Review):

Summary:

This valuable study by Semenova and colleagues describes a large cross-sectional cohort of 115 individuals on ART. Participants contributed a single blood sample which underwent IPDA, and 25-color flow with various markers (pre and post-stimulation). The authors then used clustering, decision tree analyses, and machine learning to look for correlations between these immunophenotypic markers and several measures of HIV reservoir volume. They identified two distinct clusters that can be somewhat differentiated based on total HIV DNA level, intact HIV DNA level, and multiple T cell cellular markers of activation and exhaustion.

The conclusions of the paper are supported by the data but the relationships between independent and dependent variables in the models are correlative with no mechanistic work to determine causality. It is unclear in most cases whether confounding variables could explain these correlations. If there is causality, then the data is not sufficient to infer directionality (ie does the immune environment impact the HIV reservoir or vice versa or both?). In addition, even with sophisticated and appropriate machine learning approaches, the models are not terribly predictive or highly correlated. For these reasons, the study is very much hypothesis-generating and will not impact cure strategies or HIV reservoir measurement strategies in the short term.

We appreciate the reviewer’s comments regarding the value of our study.  We fully acknowledge that the causal nature and directionality of these associations are not yet clear and agree that the study is primarily hypothesis generating in nature.  Nevertheless, we feel that the hypotheses generated will be valuable to the field.  We have added additional text to the manuscript to emphasize the hypothesis generating nature of this paper.

Strengths:

The study cohort is large and diverse in terms of key input variables such as age, gender, and duration of ART. Selection of immune assays is appropriate. The authors used a wide array of bioinformatic approaches to examine correlations in the data. The paper was generally well-written and appropriately referenced.

Weaknesses:

(1) The major limitation of this work is that it is highly exploratory and not hypothesis-driven. While some interesting correlations are identified, these are clearly hypothesis-generating based on the observational study design.

We agree that the major goal of this study was hypothesis generating and that our work is exploratory in nature. Performing experiments with mechanism testing goals in human participants with HIV is challenging.  Additionally, before such mechanistic studies can be undertaken, one must have hypotheses to test. As such we feel our study will be useful for the field in helping to identify hypotheses that could potentially be tested.

(2) The study's cross-sectional nature limits the ability to make mechanistic inferences about reservoir persistence. For instance, it would be very interesting to know whether the reservoir cluster is a feature of an individual throughout ART, or whether this outcome is dynamic over time.

We agree with the reviewer’s comment. Longitudinal studies are challenging to carry out with a study cohort of this size, and addressing questions such as the one raised by the reviewer would be of great interest. We believe our study nevertheless has value in identifying hypotheses that could be tested in a longitudinal study.

(3) A fundamental issue is that I am concerned that binarizing the 3 reservoir metrics in a 50/50 fashion is for statistical convenience. First, by converting a continuous outcome into a simple binary outcome, the authors lose significant amounts of quantitative information. Second, the low and high reservoir outcomes are not actually demonstrated to be clinically meaningful: I presume that both contain many (?all) data points above levels where rebound would be expected soon after interruption of ART. Reservoir levels would also have no apparent outcome on the selection of cure approaches. Overall, dividing at the median seems biologically arbitrary to me.

The reviewer raises a valid point that the clinical significance of above or below median reservoir metrics is unclear, and that the size of the reservoir has potentially little relation to rebound and cure approaches.  In the manuscript, we attempted to generate models that can predict reservoir size as a continuous variable in Figure S13 and find that this approach performs poorly, while a binarized approach was more successful. As such we have included both approaches in the manuscript.  It is possible that future studies with larger sample sizes and more detailed measurements will perform better for continuous variable prediction.  While this is a fairly large study (n=115) by the standards of HIV reservoir analyses, it is a small study by the standards of the machine learning field, and accurate predictive ML models for reservoir size as a continuous variable will likely require a much larger set of samples/participants.  Nevertheless, we feel our work has value as a template for ML approaches that may be informative for understanding HIV/immune interactions and generates novel hypotheses that could be validated by subsequent studies.

(4) The two reservoir clusters are of potential interest as high total and intact with low % intact are discriminated somewhat by immune activation and exhaustion. This was the most interesting finding to me, but it is difficult to know whether this clustering is due to age, time on ART, other co-morbidity, ART adherence, or other possible unmeasured confounding variables.

We agree that this finding is one of the more interesting outcomes of the study. We examined a number of these variables for association with cluster membership, and these data are reported in Figure S8A-D.  Age, years of ART and CD4 Nadir were all clearly different between the clusters.   The striking feature of this clustering, however, is the clear separation between the two groups of participants, as opposed to a continuous gradient of phenotypes.  This could reflect a bifurcation of outcomes for people with HIV, dynamic changes in the reservoir immune interactions over time, or different levels of untreated infection.  It is certainly possible that some other unmeasured confounding variables contribute to this outcome and we have attempted to make this limitation clearer.

(5) At the individual level, there is substantial overlap between clusters according to total, intact, and % intact between the clusters. Therefore, the claim in the discussion that these 2 cluster phenotypes may require different therapeutic approaches seems rather speculative. That said, the discussion is very thoughtful about how these 2 clusters may develop with consideration of the initial insult of untreated infection and / or differences in immune recovery.

We agree with the reviewer that this claim is speculative, and we have attempted to moderate the language of the text in the revised version.

(6) The authors state that the machine learning algorithms allow for reasonable prediction of reservoir volume. It is subjective, but to me, 70% accuracy is very low. This is not a disappointing finding per se. The authors did their best with the available data. It is informative that the machine learning algorithms cannot reliably discriminate reservoir volume despite substantial amounts of input data. This implies that either key explanatory variables were not included in the models (such as viral genotype, host immune phenotype, and comorbidities) or that the outcome for testing the models is not meaningful (which may be possible with an arbitrary 50/50 split in the data relative to median HIV DNA volumes: see above).

We acknowledge that the predictive power of the models generated from these data is modest and we have clarified this point in the revised manuscript. As the reviewer indicates, this may result from the influence of unmeasured variables and possible stochastic processes.  The data may thus demonstrate a limit to the predictability of reservoir size which may be inherent to the underlying biology.  As we mention above, this study size (n-115) is fairly small for the application of ML methods, and an increased sample size will likely improve the accuracy of the models. At this stage, the models we describe are not yet useful as predictive clinical tools, but are still nonetheless useful as tools to describe the structure of the data and identify reservoir associated immune cell types.

(7) The decision tree is innovative and a useful addition, but does not provide enough discriminatory information to imply causality, mechanism, or directionality in terms of whether the immune phenotype is impacting the reservoir or vice versa or both. Tree accuracy of 80% is marginal for a decision tool.

The reviewer is correct about these points.  In the revised manuscript, we have attempted to make it clear that we are not yet advocating using this approach as a decision tool, but simply a way to visualize the data and understand the structure of the dataset.  As we discuss above, the models will likely need to be trained on a larger dataset and achieve higher accuracy before use as a decision tool.

(8) Figure 2: this is not a weakness of the analysis but I have a question about interpretation. If total HIV DNA is more predictive of immune phenotype than intact HIV DNA, does this potentially implicate a prior high burden of viral replication (high viral load &/or more prolonged time off ART) rather than ongoing reservoir stimulation as a contributor to immune phenotype? A similar thought could be applied to the fact that clustering could only be detected when applied to total HIV DNA-associated features. Many investigators do not consider defective HIV DNA to be "part of the reservoir" so it is interesting to speculate why these defective viruses appear to have more correlation with immunophenotype than intact viruses.

We agree with the reviewer that this observation could reflect prior viral burden and we have added additional text to make this clearer.  Even so, we cannot rule out a model in which defective viral DNA is engaged in ongoing stimulation of the immune system during ART, leading to the stronger association between total DNA and the immune cell phenotypes. We hypothesize that the defective proviruses could potentially be triggering innate immune pattern recognition receptors via viral RNA or DNA, and a higher burden of the total reservoir leads to a stronger apparent association with the immune phenotype.  We have included text in the discussion about this hypothesis.

(9) Overall, the authors need to do an even more careful job of emphasizing that these are all just correlations. For instance, HIV DNA cannot be proven to have a causal effect on the immunophenotype of the host with this study design. Similarly, immunophenotype may be affecting HIV DNA or the correlations between the two variables could be entirely due to a separate confounding variable

We have revised the text of the manuscript to emphasize this point, and we acknowledge that any causal relationships are, at this point, simply speculation. 

(10) In general, in the intro, when the authors refer to the immune system, they do not consistently differentiate whether they are referring to the anti-HIV immune response, the reservoir itself, or both. More specifically, the sentence in the introduction listing various causes of immune activation should have citations. (To my knowledge, there is no study to date that definitively links proviral expression from reservoir cells in vivo to immune activation as it is next to impossible to remove the confounding possible imprint of previous HIV replication.) Similarly, it is worth mentioning that the depletion of intact proviruses is quite slow such that provial expression can only be stimulating the immune system at a low level. Similarly, the statement "Viral protein expression during therapy likely maintains antigen-specific cells of the adaptive immune system" seems hard to dissociate from the persistence of immune cells that were reactive to viremia.

We updated the text of the manuscript to address these points and have added additional citations as per the reviewer’s suggestion.

(11) Given the many limitations of the study design and the inability of the models to discriminate reservoir volume and phenotype, the limitations section of the discussion seems rather brief.

We have now expanded the limitations section of the discussion and added additional considerations. We now include a discussion of the study cohort size, composition and the detail provided by the assays.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

A few specific comments:

"This pattern is likely indicative of a more profound association of total HIV DNA with host immunophenotype relative to intact HIV DNA."

Most studies I have seen (e.g. single cell from Lictherfeld/Yu group) show intact proviruses are generally more activated/detectable/susceptible to immune selection, so I have a hard time thinking defective proviruses are actually more affected by immunotype.

We hypothesize that this association is actually occurring in the opposite direction – that the defective provirus are having a greater impact on the immune phenotype, due to their greater number and potential ability to engage innate or adaptive immune receptors. We have clarified this point in the manuscript

"The existence of two distinct clusters of PWH with different immune features and reservoir characteristics could have important implications for HIV cure strategies - these two groups may respond differently to a given approach, and cluster membership may need to be considered to optimize a given strategy."

I find this a bit of a reach, given that the definition of 2 categories depended on the total size.

We have modified the language of this section to reduce the level of speculation.

"This study is cross-sectional in nature and is primarily observational, so caution should be used interpreting findings associated with time on therapy".

I found this an interesting statement because ultimately time on ART shows up throughout the analysis as a significant predictor, do you mean something about how time on ART could indicate other confounding variables like ART regimen or something?

We have rephrased this comment to avoid confusion.  We were simply trying to make the point that we should avoid speculating about longitudinal dynamics from cross sectional data.

"As expected, the plots showed no significant correlation for intact HIV DNA versus years of ART (Figure 1B), while total reservoir size was positively correlated with the time of ART (Figure 1A, Spearman r = 0.31)."<br />  Is this expected? Studies with longitudinal data almost uniformly show intact decay, at least for the first 10 or so years of ART, and defective/total stability (or slight decay). Also probably "time on ART" to not confuse with the duration of infection before ART.

We have updated the language of this section to address this comment.  We have avoided comparing our data with respect to time on ART to longitudinal studies for reasons given above.

On dimensionality reduction, as this PaCMAP seems a relatively new technique (vs tSNE and UMAP which are more standard, but absolutely have their weaknesses), it does seem important to contextualize. I think it would still be useful to show PCA and asses the % variance of each additional dimension to assess the effective dimensionality, it would be helpful to show a plot of % variance by # components to see if there is a cutoff somewhere, and if PaCMAP is really picking this up to determine the 2 dimensions/2 clusters is ideal. Figure 4B ultimately shows a lot of low/high across those clusters, and since low/high is defined categorically it's hard to know which of those dots are very close to the other categories.

We have added this analysis to the manuscript – found in Figure S9. The PCA plot indicates that members of the two clusters also separate on PCA although this separation is not as clear as for the PaCMAP plot.

Minor comments on writing etc:

Intro

-Needs some references on immune activation sequelae paragraph.

We have added some additional references to this section.

-"promote the entry of recently infected cells into the reservoir" -- that is only one possible mechanistic explanation, it's not unreasonable but it seems important to keep options open until we have more precise data that can illuminate the mechanism of the overabundance.

We have modified the text to discuss additional hypotheses.

-You might also reference Pankau et al Ppath for viral seeding near the time of ART.

We have added this reference.

-"Viral protein expression during therapy likely maintains antigen-specific cells of the adaptive immune system" - this was unclear to me, do you mean HIV-specific cells that act against HIV during ART? I think most studies show immunity against HIV (CD8 and CD4) wanes over time during ART.

The Goonetilleke lab has recently generated data indicating that antiviral T cell responses are remarkably stable over time on ART, but we agree with the reviewer that the idea that ongoing antigen expression in the reservoir maintains these cells is speculative.  We have modified the text to make this point clearer.

-Overall I think the introduction lacked a little bit of definitional precision: i.e. is the reservoir intact vs replication competent vs all HIV DNA and whether we are talking about PWH on long-term ART and how long we should be imagining? The first years of ART are certainly different than later, in terms of dynamics. The ultimate implications are likely specific for some of these categorizations.

-"persistent sequelae of the massive disruptions to T cell homeostasis and lymphoid structures that occur during untreated HIV infection" needs a lot more context/referencing. For instance, Peter Hunt showed a decrease in activation after ART a long time ago.

-Heather Best et al show T cell clonality stays perturbed after ART.

We have updated the text of the introduction and added references to address the reviewer’s comments.

Results

-It would be important to mention the race of participants and any information about expected clades of acquired viruses, this gets mentioned eventually with reference to the Table but the breakdown would be helpful right away.

We have added this information to the results section.

-"performed Spearman correlations", may be calculated or tested?

We have corrected the language for this sentence.

Comments on figures:

-Figure 1 data on linear scale (re discussion above) -- hard to even tell if there is a decay (to match with all we know from various long-term ART studies).

-Figure 4 data is shown on ln (log_e) scale, which is hard to interpret for most people.

-Figures 4 C,D, and E should have box plots to visually assess the significance.

-Figure 4B legend says purple/pink but I think the colors are different in the plot, could be about transparency

-Figure 5 it is now not clear if log_e(?).

-Figure 6 "HIV reservoir characteristics" might be better to make this more explicit. Do you mean for instance in the 6B title Total HIV DNA per million CD4+ T cells I think?

We have made these modifications.

Reviewer #2 (Recommendations For The Authors):

Minor Weaknesses:

(1) The Introduction is too long and much of the text is not directly related to the study's research question and design.

We have streamlined the introduction in the revised manuscript.

(2) While no differences were seen by age or race, according to the authors, this is unlikely to be useful since the numbers are so small in some of these subcategories. Results from sensitivity analyses (e.g., excluding these individuals) may be more informative/useful.

We agree that the lower numbers of participants for some subgroupings makes it challenging to know for sure if there are any differences based on these variables.  Have added text to clarify this. We have added age, race and gender to the LOCO analysis and to the variable inflation importance analysis (Table S5).

(3) For Figure 4, based on what was described in the Results section of the manuscript, the authors should clarify that the figures show results for TOTAL HIV DNA only (not intact DNA): "Dimension reduction machine learning approaches identified two robust clusters of PWH when using total HIV DNA reservoir-associated immune cell frequencies (Figure 4A), but not for intact or percentage intact HIV DNA (Figure 4B and 4C)".

We have added this information.

(4) The statement on page 5, first paragraph, "Interestingly, when we examined a plot of percent intact proviruses versus time on therapy (Figure 1C), we observed a biphasic decay pattern," is not new (Peluso JCI Insight 2020, Gandhi JID 2023, McMyn JCI 2023). Prior studies have clearly demonstrated this biphasic pattern and should be cited here, and the sentence should be reworded with something like "consistent with prior work", etc.

We have added citations to these studies and rephrased this comment.

(5) The Cohort and sample collection sections are somewhat thin. Further details on the cohort details should include at the very minimum some description of the timing of ART initiation (is this mostly a chronic-treated cohort?) and important covariate data such as nadir CD4+ T cell count, pre-ART viral load, duration of ART suppression, etc.

The cohort was treated during chronic infection, and we have clarified this in the manuscript.  Information regarding CD4 nadir and years on ART are included in Table 1.  Unfortunately, pre-ART viral load was not available for most members of this cohort, so we did not use it for analyses. The partial pre-ART viral load data is included with the dataset we are making publicly available.

Reviewer #3 (Recommendations For The Authors):

Minor points:

(1) What is meant by CD4 nadir? Is this during primary infection or the time before ART initiation?

We have clarified this description in the manuscript.  This term refers to the lowest CD4 count recorded during untreated infection.

(2) The authors claim that determinants of reservoir size are starting to emerge but other than the timing of ART, I am not sure what studies they are referring to.

We have updated the language of this section.  We intended to refer to studies looking at correlates of reservoir size, and feel that this is a more appropriate term that ‘determinants’

(3) The discussion does not tie in the model-generated hypotheses with the known mechanisms that sustain the reservoir: clonal proliferation balanced by death and subset differentiation. It would be interesting to tie in the proposed reservoir clusters with these known mechanisms.

We have added additional text to the manuscript to address these mechanisms.

(4) Figure 1: Total should be listed as total HIV DNA.

We have updated this in the manuscript.

(5) Figure 1C: Worth mentioning the paper by Reeves et al which raises the possibility that the flattening of intact HIV DNA at 9 years may be spurious due to small levels of misclassification of defective as intact.

We have added this reference.

(6) "Total reservoir frequency" should be "total HIV DNA concentration"

We respectfully feel that “frequency” is a more accurate term than “concentration”, since we are expressing the reservoir as a fraction of the CD4 T cells, while “concentration” suggests a denominator of volume.

(7) Figure S2-5: label y-axis total HIV DNA.

We have updated this figure.