la balise br est utilisée pour insérer un saut de ligne dans le texte. C'est une balise auto-fermante, ce qui signifie qu'elle n'a pas besoin d'une balise de fermeture. Elle est utile lorsque vous souhaitez forcer un passage à la ligne sans commencer un nouveau paragraphe, ce qui serait le cas avec la balise p

Reviewer #1 (Public Review):

In this study, Deng et al. investigate the antibody response against HA antigen following repeated vaccination with the H1N1 2009 pandemic influenza vaccine strain, using in silico modeling. The proposed model provides valuable mechanistic insights into how the broadening of the antibody response takes place upon repeated vaccination.

Overall, the authors' model effectively explains the mechanistic principles underlying antibody responses against the viral antigens harboring epitope immunodominancy.

Reviewer #2 (Public Review):

The authors have been studying the mechanism of breadth expansion in antibody responses with repeated vaccinations using their own mathematical model. In this study, they applied this mathematical model to a cohort data analyzing anti-HA antibody responses after multiple influenza virus vaccination and investigated the mechanism of antibody breadth expansion to diversified target viral strains.<br /> The manuscript is well written, and the mathematical model is well built that incorporates various parameters related to B cell activation in GC and EGC based on experimental data.

Strengths:

By carefully reanalyzing the published cohort data (Nunez IA et al 2017 PLoS One), they have clearly demonstrated that the repeated influenza virus vaccinations result in an expansion of the breadth to unmatched viral strains.

Using their mathematical model, they have determined the major factors for the breadth expansion following multiple immunizations.

Weaknesses:

The overall concept of their model has already been published (Yang L et al 2023 Cell Reports) with a SRAS-CoV-2 vaccine model, and they have applied it to influenza virus vaccine in this study, with the conclusions being largely the same.

It is unclear how the re-evaluation of public data in the first half part is related to the validation of their model in the later part.

Other points:

In the original data by Nurez LA et al., HAI (the inhibitory effect of anti-HA antibodies on the binding of HA to sialic acid on erythrocytes) was used as the lead-out. The authors conclude that the breadth expansion with repeated vaccinations is primarily due to the activation of B cells with BCRs that recognize minor common epitopes, induced by covering up of strain specific major epitopes by pre-existing antibodies. However, as they themselves show in Fig 1, once the sialic acid-binding region is covered, it seems difficult for another BCR to bind to this region. When the target epitope is limited like this, the effect of increasing antigen supply to DCs by pre-existing antibodies and the effect of increasing the presentation of minor epitopes appears to compete with each other. Could the author please explain this point? In relation to this point, please explain the meaning of analysis of the entire ectodomain when the original data's lead-out is HAI.

Minor point:

The description "The purpose of this model is ...." starting at line 171 and the description of "we obtain results in harmony with the clinical findings ...." starting at line 478 sound to be contradictory. As the authors themselves state at line 171, if the purpose of this model is not to fit the data but to demonstrate the principle, then the prudent sampling and reanalyzing data itself seems to have less meaning.

Author response:

Reviewer #1 (Public Review):

In this study, Deng et al. investigate the antibody response against HA antigen following repeated vaccination with the H1N1 2009 pandemic influenza vaccine strain, using in silico modeling. The proposed model provides valuable mechanistic insights into how the broadening of the antibody response takes place upon repeated vaccination.

Overall, the authors' model effectively explains the mechanistic principles underlying antibody responses against the viral antigens harboring epitope immunodominancy.

We thank the Reviewer for their positive and thoughtful assessment of the work. We address issues raised in the revised manuscript and in the point-by-point responses below.

Reviewer #2 (Public Review):

The authors have been studying the mechanism of breadth expansion in antibody responses with repeated vaccinations using their own mathematical model. In this study, they applied this mathematical model to a cohort data analyzing anti-HA antibody responses after multiple influenza virus vaccination and investigated the mechanism of antibody breadth expansion to diversified target viral strains.

The manuscript is well written, and the mathematical model is well built that incorporates various parameters related to B cell activation in GC and EGC based on experimental data.

We thank the reviewer for their positive and thoughtful review and address issues raised in a revised version of the manuscript and in the point-by-point below.

Strengths:

By carefully reanalyzing the published cohort data (Nunez IA et al 2017 PLoS One), they have clearly demonstrated that the repeated influenza virus vaccinations result in an expansion of the breadth to unmatched viral strains.

Using their mathematical model, they have determined the major factors for the breadth expansion following multiple immunizations.

We thank the reviewer for pointing out the strengths of our study.

Weaknesses

The overall concept of their model has already been published (Yang L et al 2023 Cell Reports) with a SARS-CoV-2 vaccine model, and they have applied it to influenza virus vaccine in this study, with the conclusions being largely the same.

It is unclear how the re-evaluation of public data in the first half part is related to the validation of their model in the later part.

The reviewer is correct in that we build directly on our model published previously to study related phenomena for SARS-CoV-2. However, a critical advance of the work was to now ask whether antibody broadening following repeated homologous antigen exposure is a general feature of human humoral immunity. As we point out in the introduction of our manuscript, repeated exposure to the same antigen has long been assumed to predominantly boost strain limited humoral immunity, necessitating rational design of vaccines that re-orient antibody responses to target otherwise immune-subdominant targets. Hence, antibody broadening in response to homologous SARS-CoV-2 antigen points to reconsideration of that basic premise in immunology; and if we are to now define this as general feature of human antibody responses, then evaluation of the principle using a different vaccine protocol and antigen is necessitated. Accordingly, we took advantage of the influenza vaccine space where, within the immediate years following the 2009 H1N1 pandemic, the 2009 H1N1 strain was repeatedly applied as the seasonal vaccine strain. This HA was also novel (as it was from a pandemic virus pHA), meaning that traditional back-boosting to historical strains would be limited. We then re-evaluated the longitudinal HAI data of Nurez et al. to define whether a broadening to increasingly divergent vaccine-unmatched strains is observed upon repeated exposure to pHA. This was not done before and was enabled by incorporating our amino acid relatedness parameter and our structure-based definition of the RBS patch. To then query mechanistic origins of the broadening effect, we adapted and extended our previous computational model to: (1) better reflect HA epitope diversity and overlap within the RBS patch; and (2) to better reflect the influenza immunization regimens that are used clinically. The differences between the modeling done in this paper and that in Yang et al. 2023 are described in the Methods section separately. Taken together, our analyses of data in Nunez et al and our simulations strengthen the emerging view that repeated boosting with the same antigen enables the humoral immune system to diversify immune responses because of feedback regulation which leads to enhanced antigen on FDCs, persistent GCs, and epitope masking. This, in turn, enables the immune system to generalize to recognize and respond to unseen variant antigens that harbor mutations in the immunodominant epitopes. Our results point to a new and emerging paradigm regarding booster immunizations and fundamental features of the humoral immune system.

Other points:

In the original data by Nurez LA et al., HAI (the inhibitory effect of anti-HA antibodies on the binding of HA to sialic acid on erythrocytes) was used as the lead-out. The authors conclude that the breadth expansion with repeated vaccinations is primarily due to the activation of B cells with BCRs that recognize minor common epitopes, induced by covering up of strain specific major epitopes by pre-existing antibodies. However, as they themselves show in Fig 1, once the sialic acid-binding region is covered, it seems difficult for another BCR to bind to this region. When the target epitope is limited like this, the effect of increasing antigen supply to DCs by pre-existing antibodies and the effect of increasing the presentation of minor epitopes appears to compete with each other. Could the author please explain this point?

We agree that accounting for epitope overlap is important when the target is limited, as the reviewer indicates. In Figure 6C vs 6D we assess steric effects of possible spatial overlap between dominant and subdominant epitopes. Under overlapping conditions, we find evidence for steric-based constrainment of broadening, as predicted by the reviewer. Depending upon the degree of overlap between the epitopes and differences in germline characteristics in the B cells targeting dominant and subdominant epitopes, this effect could be compensated during subsequent shots, as described by our results (see lines 392-406).

We also now incorporate the following sentence into our discussion (lines 448-453):

“Epitope masking will also be constrained by the dimensions of the RBS and our simulations do report attenuation of titers against historical influenza strains when we introduce epitope overlap. Depending upon the degree of overlap between the epitopes and differences in germline characteristics in the B cells targeting dominant and subdominant epitopes, this effect could be compensated during subsequent shots.”

In relation to this point, please explain the meaning of analysis of the entire ectodomain when the original data's lead-out is HAI.

We include side-by-side full length ectodomain versus RBS patch (sialic acid binding residues + antibody epitope ring) to demonstrate relatedness differences in the lead-out data. But it is precisely because of the point raised by the reviewer that we focus on using the RBS patch as the relatedness values to assess antibody broadening as defined by HAI activity (see Figure 3 and S2). 

Minor point:

The description "The purpose of this model is ...." starting at line 171 and the description of "we obtain results in harmony with the clinical findings ...." starting at line 478 sound to be contradictory. As the authors themselves state at line 171, if the purpose of this model is not to fit the data but to demonstrate the principle, then the prudent sampling and reanalyzing data itself seems to have less meaning.

We respectfully disagree. Please see above point as to how the clinical data is more than just “reanalyzing” but to first discover the previously unreported broadening effect across highly divergent strains following sequential immunization with homologous antigen in the influenza vaccine space; we then extended and adapted our computational model for the influenza vaccination paradigm to gain mechanistic insight on how such antibody broadening may occur. The word “harmony” was not meant to imply quantitative agreement, and apologize if it caused confusion.

DOI: 10.3389/fcell.2025.1642006

Resource: (RRID:CVCL_1222)

Curator: @sonofthor

SciCrunch record: RRID:CVCL_1222

What is this?

DOI: 10.1093/hmg/ddz063

Resource: RRID:BDSC_33549

Curator: @maulamb

SciCrunch record: RRID:BDSC_33549

What is this?

DOI: 10.3389/fimmu.2023.1293766

Resource: (Jackson ImmunoResearch Labs Cat# 115-177-003, RRID:AB_2338719)

Curator: @scibot

SciCrunch record: RRID:AB_2338719

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DOI: 10.1534/g3.118.200578

Resource: RRID:BDSC_35759

Curator: @scibot

SciCrunch record: RRID:BDSC_35759

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DOI: 10.1101/2025.08.01.668089

Resource: RRID:BDSC_3605

Curator: @scibot

SciCrunch record: RRID:BDSC_3605

What is this?

DOI: 10.1096/fj.202501613RR

Resource: (Vector Laboratories Cat# BA-1000, RRID:AB_2313606)

Curator: @scibot

SciCrunch record: RRID:AB_2313606

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DOI: 10.1073/pnas.1901183116

Resource: RRID:BDSC_1560

Curator: @scibot

SciCrunch record: RRID:BDSC_1560

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DOI: 10.1016/j.xcrm.2025.102263

Resource: (Vector Laboratories Cat# BA-1000, RRID:AB_2313606)

Curator: @scibot

SciCrunch record: RRID:AB_2313606

What is this?

DOI: 10.1016/j.cell.2025.07.018

Resource: (R and D Systems Cat# MAB4470, RRID:AB_1293549)

Curator: @scibot

SciCrunch record: RRID:AB_1293549

What is this?

"unverbesserliche Optimisten"? ja... zu schwach zum auswandern...

Author response:

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

Reviewer #1 (Public review):

Summary:

The study by Wu et al presents interesting data on bacterial cell organization, a field that is progressing now, mainly due to the advances in microscopy. Based mainly on fluorescence microscopy images, the authors aim to demonstrate that the two structures that account for bacterial motility, the chemotaxis complex and the flagella, colocalize to the same pole in Pseudomonas aeruginosa cells and to expose the regulation underlying their spatial organization and functioning.

Comments on revisions:

The authors have addressed all major and minor points that I raised in a satisfying way during the revision process. The work can now be regarded as complete, the assumptions were clarified, the results are convincing, the conclusions are justified, and the novelty has been made clear.

This manuscript will be of interest to cell biologists, mainly those studying bacteria, but not only.

Reviewer #2 (Public review):

Summary:

Here, the authors studied the molecular mechanisms by which the chemoreceptor cluster and flagella motor of Pseudomonas aeruginosa (PA) are spatially organized in the cell. They argue that FlhF is involved in localizing the receptors-motor to the cell pole, and even without FlhF, the two are colocalized. Finally, the authors argue that the functional reason for this colocalization is to insulate chemotactic signaling from other signaling pathways, such as cyclic-di-GMP signaling.

Strength:

The experiments and data are high quality. It is clear that the motor and receptors co-localize, and that elevated CheY levels lead to elevated c-di-GMP.

Weakness:

The explanation for the functional importance of receptor-motor colocalization is plausible but is still not conclusively demonstrated. Colocalization might reduce CheY levels throughout the cell in order to reduce cross-talk with c-di-GMP. This would mean that if physiologically-relevant levels of CheYp near the pole were present throughout the cell, c-di-GMP levels would be elevated to a point that is problematic for the cell. Clearly demonstrating this seems challenging.

We acknowledge that directly proving the necessity of colocalization to prevent problematic c-di-GMP elevation is experimentally challenging, as it would require creating a system where CheY-P is artificially distributed throughout the cell at physiologically relevant concentrations while maintaining normal chemotaxis function.

However, our data provide several lines of evidence supporting this model. First, we show that CheY overexpression leads to substantial c-di-GMP elevation (71.8% increase) and cell aggregation, demonstrating that elevated CheY levels can indeed cause problematic cross-pathway interference. Second, previous work has shown that CheY-P levels near the pole are an order of magnitude higher than in the rest of the cell (ref. 46). If this elevated CheY-P concentration near the pole were present throughout the cell, our data suggest that c-di-GMP levels would be elevated sufficiently to cause cell aggregation (Fig. 4A), thereby disabling normal motility and chemotaxis. Third, the dose-dependent relationship between CheY concentration and aggregation phenotype supports the idea that precise spatial regulation of CheY levels is functionally important for avoiding cross-pathway interference.

Reviewer #3 (Public review):

Summary:

The authors investigated the assembly and polar localization of the chemosensory cluster in P. aeruginosa. They discovered that a certain protein (FlhF) is required for the polar localization of the chemosensory cluster while a fully-assembled motor is necessary for the assembly of the cluster. They found that flagella and chemosensory clusters always co-localize in the cell; either at the cell pole in wild type cells or randomly-located in the cell in FlhF mutant cells. They hypothesize that this co-localization is required to keep the level of another protein (CheY-P), which controls motor switching, at low levels as the presence of high-levels of this protein (if the flagella and chemosensory clusters were not co-localized) is associated with high-levels of c-di-GMP and cell aggregations.

Strengths:

The manuscript is clearly written and straightforward. The authors applied multiple techniques to study the bacterial motility system including fluorescence light microscopy and gene editing. In general, the work enhances our understanding of the subtlety of interaction between the chemosensory cluster and the flagellar motor to regulate cell motility.

Weaknesses:

The major weakness for me in this paper is that the authors never discussed how the flagellar genes expression is controlled in P. aeruginosa. For example, in E. coli there is a transcriptional hierarchy for the flagellar genes (early, middle, and late genes, see Chilcott and Hughes, 2000). Similarly, Campylobacter and Helicobacter have a different regulatory cascade for their flagellar genes (See Lertsethtakarn, Ottemann, and Hendrixson, 2011). How does the expression of flagellar genes in P. aeruginosa compare to other species? how many classes are there for these genes? is there a hierarchy in their expression and how does this affect the results of the FliF and FliG mutants? In other words, if FliF and FliG are in class I (as in E. coli) then their absence might affect the expression of other later flagellar genes in subsequent classes (i.e., chemosensory genes). Also, in both FliF and FliG mutants no assembly intermediates of the flagellar motor are present in the cell as FliG is required for the assembly of FliF (see Hiroyuki Terashima et al. 2020, Kaplan et al. 2019, Kaplan et al. 2022). It could be argued that when the motor is not assembled then this will affect the expression of the other genes (e.g., those of the chemosensory cluster) which might play a role in the decreased level of chemosensory clusters the authors find in these mutants.

We thank the reviewer for the valuable suggestions. In the revised manuscript, we have further elaborated on the regulatory control of flagellar genes expression in P. aeruginosa (see our response to comment #4).

Comments on revisions:

I believe the authors have performed additional experiments that improved their manuscript and they have answered many of my comments and those of the other reviewers. I am supportive of publishing this manuscript, but I still find the following points that are not clear to me (probably I am misunderstanding some points; the authors can clarify).

(1) In response to reviewer 1, the authors say that they "analyzed and categorized the distribution of the chemotaxis complex in both wild-type and flhF mutant strains into three patterns: precise-polar, near-polar, and mid-cell localization." I can see what they mean by polar and mid-cell, but near-polar sounds a bit elusive? Can they provide examples of this stage and mention how accurately they can identify it? Also, do the pie charts they show in Figure S4 really show "significant alterations"? There is a difference between 98% and 85% as they mention in their response to reviewer 1, but I am not sure that this is significant? Probably they can explain/change the language in the text? Also, the number of cells they counted for FlhF mutant is more than the double of other strains (WT and FlhF FliF mutant)?

We thank the reviewer for the valuable suggestions. To clarify, we divided the intracellular area along the cell's long axis into three domains: the two ends each representing 10% of the length as the precise-polar domain, the central 50% as the mid-cell domain, and the remaining regions between these as the near-polar domain. The localization pattern of the chemotaxis complex was assigned based on the position of the fluorescence intensity centroid within these domains.

Regarding the significance of the changes, you are correct to question our language. When flhF was knocked out, the proportion of chemotaxis complexes with precise-polar distribution decreased from 98% to 85% - a 13% reduction. While this represents a measurable shift in localization pattern, describing this as "significant alterations" was probably imprecise. We have revised this language to more accurately reflect the magnitude of the change (lines 169-177).

For the cell counting, we increased the sample size for the flhF mutant because this strain exhibited the appearance of mid-cell localization (approximately 5% of cells), which was not observed in wild-type or flhF fliF double mutant strains. To accurately quantify this rare phenotype and ensure statistical reliability, we analyzed more cells for this particular strain. This explains why the flhF mutant dataset contains approximately double the number of cells compared to the other strains.

We have redrawn Figure S4 to include a clear schematic diagram of the cell partitioning method and provided representative examples of each localization pattern (precise-polar, near-polar, and mid-cell) to better illustrate how we distinguished between these categories.

(2) One thing that also confused me is the following: One point that the authors stress is that FlhF localizes both the flagellum and the chemoreceptors to the pole. However, if I look at Figure 2B, the flagellum and the chemoreceptors still co-localize together (although not at the pole). If FlhF was responsible for co-localizing both of them to the pole, then wouldn't one expect them to be randomly localized in this mutant and by that I mean that they do not co-localize but that each of them (the flagellum and the chemoreceptors) are located in a different random location of the cell (not co-localized). The fact that they are still co-localized together in this mutant could also be interpreted by, for example, that FlhF localizes the flagellum to the pole and another mechanism localizes the chemoreceptors to the flagellum, hence, they still co-localize in this mutant because the chemoreceptors follow the flagellum by another mechanism to wherever it goes?

Thank you for this insightful observation. You are correct that our current experimental results do not definitively establish that FlhF directly localizes both the flagellum and chemoreceptors to the pole independently. The persistent colocalization of flagella and chemoreceptors in the DflhF mutant, even when both are mislocalized away from the pole, actually suggests a more complex regulatory mechanism than we initially proposed.

This observation highlights an important distinction between polar targeting and colocalization maintenance. Our data suggest that FlhF influences the polar targeting of the flagellum-chemoreceptor assembly, but the colocalization itself appears to be governed by a different mechanism that operates independently of FlhF. This could involve direct protein-protein interactions between flagellar and chemotaxis components, or shared assembly machinery that we have yet to identify.

To better reflect this interpretation, we have revised the subsection title (line 150). We have also modified the relevant discussion (line 180) to more accurately describe FlhF’s role in polar targeting rather than claiming it directly controls chemoreceptor localization.

(3) In the response to reviewers, the authors mention "suggesting that the assembly of the receptor complex is likely influenced mainly by the C-ring and MS-ring structures rather than by the P ring". However, in the article, they still write "The complete assembly of the motor serves as a partial prerequisite for the assembly of the chemotaxis complex, and its assembly site is also regulated by the polar anchor protein FlhF" despite their FlgI results which is not in accordance with this statement? Also, As I mentioned in my previous report, in FliG and FliF mutant the motor does not assemble (see Hiroyuki Terashima et al. 2020., and Kaplan et al., 2022).

We thank the reviewer for the suggestions and acknowledge the contradictions in our original text. You are correct that in DfliF and DfliG mutants, the flagellar motor does not assemble, while the P ring (FlgI) functions as a bushing for the peptidoglycan layer and its absence does not prevent motor assembly.

Our DflgI results, which showed normal chemotaxis complex assembly similar to wild-type, clearly demonstrate that the P ring is not required for chemoreceptor complex formation. This contradicts our original statement that "complete assembly of the motor serves as a partial prerequisite for the assembly of the chemotaxis complex."

We have corrected this inconsistency by: 1) Revising the subsection title (line 186) to more accurately reflect that core motor structures, rather than complete motor assembly, influences chemoreceptor complex formation. 2) Modifying sentences in the introduction (lines 97-98) to better align with our experimental findings.

(4) The authors have said in their response to my point "and currently, there is no evidence that FliA activity is influenced by proteins like FliG". I just want to clarify what I meant in my previous report: In E. coli, FliA binds to FlgM, and when the hook is assembled FlgM is secreted outside the cell allowing FliA to trigger the transcription of class III genes, which include the chemosensory genes (see Figure 5 in Beeby et al, 2020 in FEMS Microbiology, and Chilcott and Hughes, 2000). This implies that if the hook is not built, then late genes (including the chemoreceptors) should not be present. However, in Kaplan et al., 2019, the authors imaged a FliF mutant in Shewanella oneidensis (Figure S3) and still saw that chemoreceptors are present (I believe the authors must highlight this). This suggests that species such as Shewanella and Pseudomonas have a different assembly process than that E. coli, and although the authors say that in the text, I believe they still can refine this part more in the spirit of what I wrote here.

We thank the reviewer for the important clarification regarding the differences in transcriptional regulation among bacterial species. We agree that the observation of chemoreceptors in Shewanella oneidensis DfliF mutants (Kaplan et al., 2019) represents a significant deviation from the well-characterized E. coli model and merits stronger emphasis. In response, we have expanded the discussion to more clearly highlight the critical distinctions in the transcriptional regulatory circuits governing flagellar and chemoreceptor biogenesis between E. coli and species such as Shewanella oneidensis and Pseudomonas aeruginosa (lines 351-363).

I do not like to ask for additional experiments in the second round of review, so for me if the authors modify the text to tackle these points and allow for probable alternative explanations/ highlight gaps/ modify language used for some claims, then that is fine with me.

Reviewer #2 (Recommendations for the authors):

It is plausible that colocalization reduces CheY levels throughout the cell in order to reduce cross-talk with c-di-GMP. This would mean that if physiologically-relevant levels of CheYp near the pole were present throughout the cell, c-di-GMP levels would be elevated to a point that is problematic for the cell. Clearly demonstrating this seems challenging.

We acknowledge that directly proving the necessity of colocalization to prevent problematic c-di-GMP elevation is experimentally challenging, as it would require creating a system where CheY-P is artificially distributed throughout the cell at physiologically relevant concentrations while maintaining normal chemotaxis function.

However, our data provide several lines of evidence supporting this model. First, we show that CheY overexpression leads to substantial c-di-GMP elevation (71.8% increase) and cell aggregation, demonstrating that elevated CheY levels can indeed cause problematic cross-pathway interference. Second, previous work has shown that CheY-P levels near the pole are an order of magnitude higher than in the rest of the cell (ref. 46). If this elevated CheY-P concentration near the pole were present throughout the cell, our data suggest that c-di-GMP levels would be elevated sufficiently to cause cell aggregation (Fig. 4A), thereby disabling normal motility and chemotaxis. Third, the dose-dependent relationship between CheY concentration and aggregation phenotype supports the idea that precise spatial regulation of CheY levels is functionally important for avoiding cross-pathway interference.

Here, or in general?

I do think about this a lot. This is a nice, succinct way to put it. (Critique, though: "classism" is not the best way to put it. For better or worse, "privilege" is probably one of the best words we have for this. Separately: Since "privilege" became a staple of common rhetoric, I've mused a lot about trying to convince people to minimize the focus on "privilege" (to avoid the familiar kneejerk reactions from those hearing it who have associated it with overuse), with the intent to be to sway people instead by speaking about privilege without actually using the word "privilege" and speaking exclusively in terms of affordances*.)

See: https://hypothes.is/a/TCB5zClKEeyrIOu9mp-5TA and tag:"privilege vs affordance". (NB: Hypothes.is doesn't linkify the tag in the preceding annotation correctly.)

Isomerasas

Author response:

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

Reviewer #1 (Public review):

Major comments:

(1) The main issue that I have with this study is the lack of exploration of "why" the model produces the results it does. Considering this is a model, it should be possible to find out why the three timescales of half-act/inact parameter modifications lead to different sets of results. Without this, it is simply an exploratory exercise. (The model does this, but we do not know the mechanism.) Perhaps this is enough as an interesting finding, but it remains unconvincing and (clearly) does not have the impact of describing a potential mechanism that could be potentially explored experimentally.

This is now addressed in a new section in Results (“Potential Mechanism”):

“To explore why the properties of the resulting bursters depend on the timescale of half-(in)activation adjustments, we examined what happens when SP1 is assembled under different half-(in)activation timescales: (1) fast, (2) intermediate (matching the timescale of ion channel density changes), and (3) infinitely slow (i.e., effectively turned off). The effects of these timescales can be seen by comparing the zoomed-in views of the SP1 activity profiles under each condition (Figure 4).

When half-(in)activations are fast, the time evolution of — which tracks how far the activity pattern is from its targets (see Methods)—shows an abrupt jump as it searches for a voltage-dependence configuration that meets calcium targets (Figure 4A). As this happens, the channel densities are slightly altered, and this process continues again. Slowing the half-(in)activations alterations reduces these abrupt fluctuations (Figure 4B). Making the alterations infinitely slow effectively removes half-(in)activation changes altogether, leaving the system reliant solely on slower alterations in maximal conductances (Figure 4C). Because each timescale of half-(in)activation produces a different channel repertoire at each time step, different timescales of half-(in)activation alteration led the model through a different path in the space of activity profiles and intrinsic properties. Ultimately, this resulted in distinct final activity patterns – all of which were consistent with the Ca²⁺ targets [22].

(2) A related issue is the use of bootstrapping to do statistics for a family of models, especially when the question is in fact the width of the distribution of output attributes. I don't buy this. One can run enough models to find say N number of models within a tight range (say 2% cycle period) and the same N number within a loose range (say 20%) and compare the statistics within the two groups with the same N.

We appreciate the reviewer’s skepticism regarding our statistical approach with the “Group of 5” and “Group of 20.” These groups arose from historical aspects of our analysis and this analysis does not directly advance the main point—that changes in the timescale of channel voltage-dependence alterations impact the properties of bursters to which the homeostatic mechanism converges. Therefore, we removed the references to the Group of 5 and focus on how the Group of 20 responds to variations in the timescale of voltage-dependent alterations.

(3) The third issue is that many of the results that are presented (but not the main one) are completely expected. If one starts with gmax values that would never work (say all of them 0), then it doesn't matter how much one moves the act/inact curves one probably won't get the desired activity. Alternately, if one starts with gmax values that are known to work and randomizes the act/inact midpoints, then the expectation would be that it converges to something that works. This is Figure 1 B and C, no surprise. But it should work the other way around too. If one starts with random act/inact curves that would never work and fixes those, then why would one expect any set of gmax values would produce the desired response? I can easily imagine setting the half-act/inact values to values that never produce any activity with any gmax.

We appreciate this observation and agree that it highlights a limitation of our initial condition sampling. Our claim that the half-(in)activation mechanism is subordinate to the maximal conductance mechanism is not intended as a general statement. Rather, we make this observation only within the specific range of initial conditions we explored. Within this restricted set, we found that the conductance mechanism was sufficient for successful assembly, while the half-(in)activation mechanism alone was not. We have revised the manuscript to limit the claim.

“The results shown in Figure 1A require activity-dependent regulation of the maximal conductances. When activity-dependent regulation of the maximal conductances is turned off, the model failed to assemble SP1 into a burster (Figure 1B). This was seen in the other 19 Starting Parameters (SP2-SP20), as well [22].

(4) A potential response to my previous criticism would be that you put reasonable constraints on gmax's or half-act/inact values or tie the half-act to half-inact. But that is simply arbitrary ad hoc decisions made to make the model work, much like the L8-norm used to amplify some errors. There is absolutely no reason to believe this is tied to the biology of the system.

Here the reviewer highlights that model choices (e.g., constraints on maximal conductance and half-(in)activation, use of the L8 norm) are not necessarily justified by biology. A discussion of the constraints on maximal conductance and half-(in)activation are in the Model Assumptions section at the end of Methods. The Methods also contains a longer discussion of the use of the L8 norm:

“To compute this match score, we adapted a formulation from Alonso et al (2023),  who originally used a root-mean-square (RMS) or  norm to combine the sensor mismatches. In that approach, each error (, , and ) is divided by its allowable tolerance (, , and ) to produce a normalized error. These normalized errors are then squared, summed, and square-rooted to produce a single scalar score that reflects how well the model matches the target activity pattern.

In our version, we instead used an  norm, which raises each normalized error to the 8th power before summing and taking the 1/8th root. This formulation emphasizes large deviations in any one sensor, making it easier to pinpoint which feature of the activity is limiting convergence. By amplifying outlier mismatches, this approach provided a clearer view of which sensor was driving model mismatch, helping us both interpret failure modes and tune the model’s sensitivity by adjusting the tolerances for individual sensor errors.

Although the  norm emphasizes large deviations more strongly than the  norm, the choice of norm does not fundamentally alter which models can converge—a model that performs well under one norm can also be made to perform well under another by adjusting the allowable tolerances. The biophysical mechanisms by which neurons detect deviations from target activity and convert them into changes in ion channel properties are still not well understood. Given this uncertainty, and the fact that using different norms ultimately shouldn’t affect the convergence of a given model, the use of different norms to combine sensor errors is consistent with the broader basic premise of the model: that intrinsic homeostatic regulation is calcium mediated [22].

(5) The discussion of this manuscript is at once too long and not adequate. It goes into excruciating detail about things that are simply not explored in this study, such as phosphorylation mechanisms, justification of model assumptions of how these alterations occur, or even the biological relevance. (The whole model is an oversimplification - lack of anatomical structure, three calcium sensors, arbitrary assumptions, and how parameter bounds are implemented.) Lengthy justifications for why channel density & half-act/inact of all currents are obeying the same time constant are answering a question that no one asked. It is a simplified model to make an important point. The authors should make these parts concise and to the point. More importantly, the authors should discuss the mechanism through which these differences may arise. Even if it is not clear, they should speculate.

We agree. A long discussion on Model Assumptions and potential biological mechanisms that implement alteration in channel voltage-dependence obscure this. The former is relocated to the Methods section. The latter discussion is shortened. A discussion of a potential mechanism is included in the Results (Figure 4).

(6) There should be some justification or discussion of the arbitrary assumptions made in the model/methods. I understand some of this is to resolve issues that had come up in previous iterations of this approach and in fact the Alonso et al, 2023 paper was mainly to deal with these issues. However, some level of explanation is needed, especially when assumptions are made simply because of the intuition of the modeler rather than the existence of a biological constraint or any other objective measure.

A discussion of Model Assumptions is included in the Methods.

Reviewer #2 (Public review):

Summary:

In this study, Mondal and co-authors present the development of a computational model of homeostatic plasticity incorporating activity-dependent regulation of gating properties (activation, inactivation) of ion channels. The authors show that, similar to what has been observed for activity-dependent regulation of ion channel conductances, implementing activity-dependent regulation of voltage sensitivity participates in the achievement of a target phenotype (bursting or spiking). The results however suggest that activity-dependent regulation of voltage sensitivity is not sufficient to allow this and needs to be associated with the regulation of ion channel conductances in order to reliably reach the target phenotype. Although the implementation of this biologically relevant phenomenon is undeniably relevant, the main conclusions of the paper and the insights brought by this computational work are difficult to grasp.

Strengths:

(1) Implementing activity-dependent regulation of gating properties of ion channels is biologically relevant.

(2) The modeling work appears to be well performed and provides results that are consistent with previous work performed by the same group.

Weaknesses:

(1) The writing is rather confusing, and the state of the art explaining the need for the study is unclear.

We reorganized the manuscript to make its focus clearer.

Introduction: We clarified our explanation of the state-of-the-art. Briefly, prior work on activity-dependent homeostasis has focused on regulating ion channel density. Neurons have also been documented to homeostatically regulate channel voltage-dependence. However, the consequences of channel voltage-dependence alterations on homeostatic regulation remain underexplored. To study this, we extend a computational model of activity-dependent homeostasis — originally developed to only alter channel density— to alter channel voltage-dependence.

Results: We reorganized this section to underscore the main point: that the timescale of half-(in)activation alterations influences the intrinsic properties and activity patterns targeted by a homeostatic mechanism. Figures 1A and 1B were retained to provide context—Figure 1A illustrates how activity can emerge from random initial conditions, while Figure 1B suggests that in these simulations, modulation of half-(in)activation played a specific limited role. Figure 2 builds on Figure 1A by summarizing how intrinsic properties and activity characteristics vary across a population of 20 bursters. Figure 3 then demonstrates that despite playing this specific limited role, altering the timescale of half-(in)activation in these simulations significantly impacted the intrinsic properties and activity characteristics of the bursters targeted by the homeostatic mechanism. Figure 4 supports this by offering a possible mechanistic explanation. Finally, Figure 5 reinforces the central message by showing how the same population responds to perturbation when the timescale of half-(in)activation alterations is varied—essentially extending the analysis of Figure 3 to a perturbed regime.

Discussion: The Discussion concentrates on more specifically on how the timescale of half-(in)activation alterations shape bursters targeted he homeostatic mechanism. Extended content on model assumptions is moved to Methods. The discussion of biological pathways that implement channel voltage-dependence is shortened to avoid distracting from the main message.

Methods: Aside from moving model assumptions here, we removed discussion of the “Group of 5” and explained in more detail why we chose the L8 norm.

(2) The main outcomes and conclusions of the study are difficult to grasp. What is predicted or explained by this new version of homeostatic regulation of neuronal activity?

Our message is general: the timescale of half-(in)activation alterations influences the intrinsic properties and activity characteristics of bursters targeted by a homeostatic mechanism. As such, the implications are general. Their value lies in circumscribing a conceptual framework from which experimentalists may devise and test new hypotheses. We do not aim to predict or explain any specific phenomenon in this work. To address this concern the Discussion highlights two potential implications of our findings—one to neuronal development and another to pathologies that may arise from disruptions to homeostatic processes:

“One application for the simulations involving the self-assembly of activity may be to model the initial phases of neural development, when a neuron transitions from having little or no electrical activity to possessing it (Baccaglini & Spitzer 1977). As shown in Figure 6, the timescale of (in)activation curve alterations define a neuron's activity characteristics and intrinsic properties. As such, neurons may actively adjust these timescales to achieve a specific electrical activity aligned with a developmental phase’s activity targets. Indeed, developmental phases are marked by changes in ion channel density and voltage-dependence, leading to distinct electrical activity at each stage (Baccaglini & Spitzer 1977, Gao & Ziskind-Conhaim 1998, Goldberg et al 2011, Hunsberger & Mynlieff 2020, McCormick & Prince 1987, Moody & Bosma 2005, O'Leary et al 2014, Picken Bahrey & Moody 2003).

Additionally, our results show that activity-dependent regulation of channel voltage-dependence can play a critical role in restoring neuronal activity during perturbations (Figure 5). Specifically, the presence and timing of half-(in)activation modulation influenced whether the model neuron could successfully return to its target activity pattern. Many model neurons only achieved recovery when a half-(in)activation mechanism was present. Moreover, the speed of this modulation shaped recovery outcomes in nuanced ways: some model neurons reached their targets only when voltage-dependence was adjusted rapidly, while others did so only when these changes occurred slowly. These observations all suggest that impairments in a neuron’s ability to modulate the voltage-dependence of its channels may lead to disruptions in activity-dependent homeostasis. This may have implications for conditions such as addiction (Kourrich et al 2015) and Alzheimer’s disease (Styr & Slutsky 2018), where disruptions in homeostatic processes are thought to contribute to pathogenesis.”

Reviewer #3 (Public review):

Mondal et al. use computational modeling to investigate how activity-dependent shifts in voltage-dependent (in)activation curves can complement activity-dependent changes in ion channel conductance to support homeostatic plasticity. While changes in the voltage-dependent properties of ion channels are known to modulate neuronal excitability, their role as a homeostatic plasticity mechanism interacting with channel conductance has been largely unexplored. The results presented here demonstrate that activity-dependent regulation of voltage-dependent properties can interact with plasticity in channel conductance to allow neurons to attain and maintain target activity patterns, in this case, intrinsic bursting. These results also show that the rate of channel voltage-dependent shifts can influence steady-state parameters reached as the model stabilizes into a stable intrinsic bursting state. That is, the rate of these modifications shapes the range of channel conductances and half-(in)activation parameters as well as activity characteristics such as burst period and duration. A major conclusion of the study is that altering the timescale of channel voltage dependence can seamlessly shift a neuron's activity characteristics, a mechanism that the authors argue may be employed by neurons to adapt to perturbations. While the study's conclusions are mostly well-supported, additional analyses, and simulations are needed.

(1) A main conclusion of this study is that the speed at which (in)activation dynamics change determines the range of possible electrical patterns. The authors propose that neurons may dynamically regulate the timescale of these changes (a) to achieve alterations in electrical activity patterns, for example, to preserve the relative phase of neuronal firing in a rhythmic network, and (b) to adapt to perturbations. The results presented in Figure 4 clearly demonstrate that the timescale of (in)activation modifications impacts the range of activity patterns generated by the model as it transitions from an initial state of no activity to a final steady-state intrinsic burster. This may have important implications for neuronal development, as discussed by the authors.

However, the authors also argue that the model neuron's dynamics - such as period, and burst duration, etc - could be dynamically modified by altering the timescale of (in)activation changes (Figure 6 and related text). The simulations presented here, however, do not test whether modifications in this timescale can shift the model's activity features once it reaches steady state. In fact, it is unlikely that this would be the case since, at steady-state, calcium targets are already satisfied. It is likely, however, as the authors suggest, that the rate at which (in)activation dynamics change may be important for neuronal adaptation to perturbations, such as changes in temperature or extracellular potassium. Yet, the results presented here do not examine how modifying this timescale influences the model's response to perturbations. Adding simulations to characterize how alterations in the rate of (in)activation dynamics affect the model's response to perturbations-such as transiently elevated extracellular potassium (Figure 5) - would strengthen this conclusion.

The reviewer suggests that our core message — namely, that the timescale of half-(in)activation alterations influences the intrinsic properties and activity patterns targeted by a homeostatic mechanism — should also hold during perturbations. We agree that this extension strengthens the central message and have incorporated it into the subsection of the Results (“Half-(in)activation Alterations Contribute to Activity Homeostasis”) and Figure 5.

(2) Another key argument in this study is that small, coordinated changes in channel (in)activation contribute to shaping neuronal activity patterns, but that, these subtle effects may be obscured when averaging across a population of neurons. This may be the case; however, the results presented don't clearly demonstrate this point. This point would be strengthened by identifying correlations, if they exist, between (in)activation curves, conductance, and the resulting bursting patterns of the models for the simulations presented in Figure 2 and Figure 4, for example. Alternatively, or additionally, relationships between (in)activation curves could be probed by perturbing individual (in)activation curves and quantifying how the other model parameters compensate, which could clearly illustrate this point.

In part of the Discussion, we noted that small, coordinated shifts in half-(in)activation curves could be obscured when averaging across a population of neurons. Our intention was not to present this as a primary result, but to highlight an emergent consequence of the model: that distinct initial maximal conductances may converge to activity targets via different small shifts in half-(in)activation, making such changes difficult to detect at the population level. However, we did not systematically examine correlations between (in)activation parameters, conductances, and activity features, nor how these correlations might vary with the timescale of (in)activation modulation. While this observation is consistent with model behavior, it does not directly advance the study’s main point — that the timescale of half-(in)activation modulation influences the types of bursting patterns that satisfy the activity target. To keep the focus clear, we have removed this remark from the Discussion, though we agree that a more detailed analysis of these correlations may offer a fruitful direction for future work.

Reviewer #1 (Recommendations for the authors):

Minor comments:

(1) Page 5: remove "an" from "achieve a given an activity..."

The sentence containing this error has been removed.

(2) Page 7, bottom of page. Explain what prespecifying means here. This requires a conceptual explanation, even if the equations are given in the methods. Was one working ad hoc model built from which the three sensor values were chosen? What was this model and how was it benchmarked? The sensors are never shown. In any figure, but presumably they have different kinetics. What is meant by "average value"? What was the window of averaging and why?

The intention of this passage was to provide a broad overview of the homeostatic mechanism, with the rationale for using sensor “averages” as homeostatic targets explained in detail in the Methods. We have replaced the word “average” with “target” to maintain this focus.

(3) Page 9: add "the" in "electrical activity of the neuron as [the] model seeks...".

Done

(4) Page 9: say briefly what alpha is before using it. Also, please be consistent in either using the symbol for alpha or spelling it out across the manuscript and the figures.

Done

(5) Page 10: the paragraph "In general, ..." is confusing although it becomes clear later on what this is all about. Please rewrite and expand this to clarify some points. For instance, the word "degenerate" is first used here and it is unclear in what sense these models are degenerate. Then it is unclear why the first 5 models were chosen and then 15 more added. What was the point of doing this? What is the intent? Set this up properly before saying that you just did it. This also would clarify the weird terminology used later on of Group of 20 vs. Group of 5. The 20 and 5 are arbitrary. Say what the purpose is. Finally, is the "mean" at the very end the same 416 ms? If not, what do you mean by "the mean"? In fact, I find these 2% and 20% to be imprecise substitutes of (say) two distinct values of CV which are an order of magnitude different. Is that the intent?

This comment refers to a passage that was removed during revision.

(6) Page 10: this may be clear to you, but it took me a while to understand that in Figure 1C, you took the working model at the end of 1A, fixed the gmax values and randomized just the half-act/inact values to run it. Perhaps rewrite this to clarify?

This comment refers to a figure that was removed during revision.

(7) Page 13: why do channel densities not change much after the perturbation?

This comment refers to a figure that has since been reworked during revision. In particular, we only study what happens during perturbation. This question is interesting and is the subject of ongoing work.

Reviewer #2 (Recommendations for the authors):

The article should be carefully corrected, because the current quality of writing might obscure the interest of the study. Particular attention should be paid to the state-of-the-art section and to the discussion, but even the writing of the results should be carefully reworked. The current state of the article makes it very difficult to understand the motivation behind the study but also what the main result provided by this work is.

The Introduction, Results, and Discussion have been reworked to build on the central premise of the work: the timescale of half-(in)activation alterations influences the intrinsic properties and activity patterns targeted by the neuron’s homeostatic mechanism. These changes are detailed in Public Comment #1.

Reviewer #3 (Recommendations for the authors):

The manuscript presents an interesting computational study exploring how activity-dependent regulation of (in)activation dynamics interacts with conductance plasticity to shape neuronal activity patterns. While the study provides valuable insights, some aspects would benefit from clarification, further analyses, and/or additional simulations to strengthen the conclusions. Below, I outline concerns and comments related to specific details of the model and results presentation that were not included in the public review.

(1) The results presented in Figure 5 show that adaptation occurs in both channel conductances and (in)activation dynamics; however, the changes in conductance remain relatively permanent after the model recovers from the transient elevation in extracellular potassium. It therefore seems likely that the model would recover bursting more quickly in response to a subsequent exposure to simulated elevated extracellular potassium since large modifications in the slowly changing conductances would not be required. If this is the case, it could provide a plausible mechanism for adaptation to repeated high-potassium exposure, as demonstrated experimentally in Cancer borealis by this group (PMID: 36060056).

This is an astute observation and the subject of our present follow-up investigation.

(2) In the text relating to Figure 5, it is argued that the resulting shifts in (in)activation curves may be conceptualized as alterations in window currents. It would be helpful to illustrate this by plotting and comparing changes in window currents of these channels alongside the changes in their (in)activation curves.

This comment refers to a passage that was removed during revision.

(3) Some discussion of the role these homeostatic mechanisms may play when the neuron is synaptically integrated into a rhythmically active network could be informative. Surely, phasic and tonic inputs to the neuron would alter its conductance and voltage-dependent properties. Therefore, the model's parameters in an intact network could be very different from those in the synaptically isolated case.

This is an excellent point. We agree that synaptic context—particularly tonic and phasic inputs—would likely influence a neuron’s conductances and voltage-dependent properties, potentially leading to different homeostatic outcomes than in the isolated case. While our current study focuses on synaptically isolated neurons, the Marder lab has considered how homeostatically stabilized neurons might interact in network settings. For example, O'Leary et al (2014) presents an example network of three such neurons operating under homeostatic regulation. However, systematically exploring this question remains a challenge. We are currently developing ideas to study this in the context of a simplified half-center oscillator model, where network-level dynamics can be more tractably analyzed.

(4) Why are the transitions of alpha typically so abrupt, essentially either 1 or 0? Similarly, what happens in the model when there are transient transitions from what appears to be a steady-state alpha that abruptly shifts from 0 to 1 or 1 to 0? For example, what is occurring in Figure 1A at ~150s and ~180s when alpha jumps between 1 and 0, or in Figure 1B when the model transiently jumps up from 0 to 1 at ~400s and ~830s? In Figure 1A, does the bursting pattern change at all after ~250s, or is it identical to the pattern at c?

This is addressed in the revision (Lines 141 – 150).

(5) Are the final steady-state parameters of the 25 (sic) models consistent with experimental observations?

It is difficult to assess — it is hard to design an experiment to do what the reviewer is suggesting.

(6) Why isn't gL allowed to change dynamically? This seems like the most straightforward way to allow a neuron to adjust its excitability (aside from tonic synaptic inputs).

Passive currents could, in principle, be subject to homeostatic regulation. However, our study focused on active intrinsic currents. This focus stems from earlier investigations, which showed that active currents are dynamically regulated during homeostasis – for instance Turrigiano et al (1995) and (Desai et al 1999).

Alonso LM, Rue MCP, Marder E. 2023. Gating of homeostatic regulation of intrinsic excitability produces cryptic long-term storage of prior perturbations. Proc Natl Acad Sci U S A 120: e2222016120

Baccaglini PI, Spitzer NC. 1977. Developmental changes in the inward current of the action potential of Rohon-Beard neurones. J Physiol 271: 93-117

Desai NS, Rutherford LC, Turrigiano GG. 1999. Plasticity in the intrinsic excitability of cortical pyramidal neurons. Nature Neuroscience 2: 515-20

Gao BX, Ziskind-Conhaim L. 1998. Development of ionic currents underlying changes in action potential waveforms in rat spinal motoneurons. J Neurophysiol 80: 3047-61

Goldberg EM, Jeong HY, Kruglikov I, Tremblay R, Lazarenko RM, Rudy B. 2011. Rapid developmental maturation of neocortical FS cell intrinsic excitability. Cereb Cortex 21: 666-82

Hunsberger MS, Mynlieff M. 2020. BK potassium currents contribute differently to action potential waveform and firing rate as rat hippocampal neurons mature in the first postnatal week. J Neurophysiol 124: 703-14

Kourrich S, Calu DJ, Bonci A. 2015. Intrinsic plasticity: an emerging player in addiction. Nature Reviews Neuroscience 16: 173-84

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Moody WJ, Bosma MM. 2005. Ion channel development, spontaneous activity, and activity-dependent development in nerve and muscle cells. Physiol Rev 85: 883-941

O'Leary T, Williams AH, Franci A, Marder E. 2014. Cell types, network homeostasis, and pathological compensation from a biologically plausible ion channel expression model. Neuron 82: 809-21

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Turrigiano G, LeMasson G, Marder E. 1995. Selective regulation of current densities underlies spontaneous changes in the activity of cultured neurons. J Neurosci 15: 3640-52

Reviewer #2 (Public review):

Summary:

Klug et al. use monosynaptic rabies tracing of inputs to D1- vs D2-SPNs in the striatum to study how separate populations of cortical neurons project to D1- and D2-SPNs. They use rabies to express ChR2, then patch D1-or D2-SPNs to measure synaptic input. They report that cortical neurons labeled as D1-SPN-projecting preferentially project to D1-SPNs over D2-SPNs. In contrast, cortical neurons labeled as D2-SPN-projecting project equally to D1- and D2-SPNs. They go on to conduct pathway-specific behavioral stimulation experiments. They compare direct optogenetic stimulation of D1- or D2-SPNs to stimulation of MCC inputs to DMS and M1 inputs to DLS. In three different behavioral assays (open field, intra-cranial self-stimulation, and a fixed ratio 8 task), they show that stimulating MCC or M1 cortical inputs to D1-SPNs is similar to D1-SPN stimulation, but that stimulating MCC or M1 cortical inputs to D2-SPNs does not recapitulate the effects of D2-SPN stimulation (presumably because both D1- and D2-SPNs are being activated by these cortical inputs).

Strengths:

Showing these same effects in three distinct behaviors is strong. Overall, the functional verification of the consequences of the anatomy is very nice to see. It is a good choice to patch only from mCherry-negative non-starter cells in the striatum. This study adds to our understanding of the logic of corticostriatal connections, suggesting a previously unappreciated structure.

Weaknesses:

One limitation is that all inputs to SPNs are expressing ChR2, so they cannot distinguish between different cortical subregions during patching experiments. Their results could arise because the same innervation patterns are repeated in many cortical subregions or because some subregions have preferential D1-SPN input while others do not. There are also some caveats with respect to the efficacy of rabies tracing. Although they only patch non-starter cells in the striatum, only 63% of D1-SPNs receive input from D1-SPN-projecting cortical neurons. It's hard to say whether this is "high" or "low," but one question is how far from the starter cell region they are patching. Without this spatial indication of where the cells that are being patched are relative to the starter population, it is difficult to interpret if the cells being patched are receiving cortical inputs from the same neurons that are projecting to the starter population. The authors indicate they are patching from mCherry-negative neurons within the region of the mCherry-positive neurons, but since the mCherry population will include both true starter cells and monosynaptically connected cells, this is not perfectly precise. Convergence of cortical inputs onto SPNs may vary with distance from the starter cell region quite dramatically, as other mapping studies of corticostriatal inputs have shown specialized local input regions can be defined based on cortical input patterns (Hintiryan et al., Nat Neurosci, 2016, Hunnicutt et al., eLife 2016, Peters et al., Nature, 2021). A caveat for the optogenetic behavioral experiments is that these optogenetic experiments did not include fluorophore-only controls, although a different control (with light delivered in M1) is provided in Supplementary Figure 3. Another point of confusion is that other studies (Cui et al, J Neurosci, 2021) have reported that stimulation of D1-SPNs in DLS inhibits rather than promotes movement. This study may have given different results due to subtly different experimental parameters, including fiber optic placement and NA.

Ja, aber wenn möglich ohne on-demand

A bejövő SWIFT instrukció beérkezési határidejét figyeli a beállított cut off szabály. Ha későn érkezett akkor a Settlement modul instruction felületén egy jelölő (checkbox) töltve lesz, mint Late instruction, későn beérkező ügyfél megbízás

Cut off, ha esetleg akarjuk majd** megbízási határidő kezelés **

eLife Assessment

Cryptovaranoides, an end-Triassic animal (just over 200 Ma old), was originally described as a possibly anguimorph squamate, i.e., more closely related to snakes and some extant lizards than to other extant lizards, making Squamata much older than previously thought and providing a new calibration date inside it. Following a rebuttal and a defense, this fourth important contribution to the debate makes a meticulous and solid argument that Cryptovaranoides is not a squamate. However, further comparisons to potentially closely related animals would greatly benefit this study, and parts of the text require clarification.

Reviewer #1 (Public review):

In the Late Triassic and Early Jurassic (around 230 to 180 Ma ago), southern Wales and adjacent parts of England were a karst landscape. The caves and crevices accumulated remains of small vertebrates. These fossil-rich fissure fills are being exposed in limestone quarrying. In 2022 (reference 13 of the article), a partial articulated skeleton and numerous isolated bones from one fissure fill of end-Triassic age (just over 200 Ma) were named Cryptovaranoides microlanius and described as the oldest known squamate - the oldest known animal, by some 20 to 30 Ma, that is more closely related to snakes and some extant lizards than to other extant lizards. This would have considerable consequences for our understanding of the evolution of squamates and their closest relatives, especially for their speed and absolute timing, and was supported in the same paper by phylogenetic analyses based on different datasets.

In 2023, the present authors published a rebuttal (reference 18) to the 2022 paper, challenging anatomical interpretations and the irreproducible referral of some of the isolated bones to Cryptovaranoides. Modifying the datasets accordingly, they found Cryptovaranoides outside Squamata and presented evidence that it is far outside. In 2024 (reference 19), the original authors defended most of their original interpretation and presented some new data, some of it from newly referred isolated bones. The present article discusses anatomical features and the referral of isolated bones in more detail, documents some clear misinterpretations, argues against the widespread but not justifiable practice of referring isolated bones to the same species as long as there is merely no known evidence to the contrary, further argues against comparing newly recognized fossils to lists of diagnostic characters from the literature as opposed to performing phylogenetic analyses and interpreting the results, and finds Cryptovaranoides outside Squamata again.

Although a few of the character discussions and the discussion of at least one of the isolated bones can probably still be improved (and two characters are addressed twice), I see no sign that the discussion is going in circles or otherwise becoming unproductive. I can even imagine that the present contribution will end it.

Author response:

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

Reviewer #1 (Public review):

This work focuses on the connection strength of the corticostriatal projections, without considering the involvement of synaptic plasticity in sensory integration.

Thank you for raising this point. Indeed, sensory integration is a complex process with a multitude of factors beyond connectivity patterns and synaptic strength. In addition, it is true that both connectivity levels and synaptic strength can be modified by plasticity. 

We modified our conclusion as follows, line 354: 

“Since the inputs to a single SPN represent only a limited subset of whisker columns, a complete representation of whiskers could emerge at the population level, with each SPN’s representation complementing those of its neighbors (Fig. 7). These observations raise the hypothesis of a selective or competitive process underlying the formation of corticostriatal synapses. The degree of input convergence onto SPNs could be modulated by plasticity, potentially enabling experience-driven reconfiguration of S1 corticostriatal coupling. “

Reviewer #2 (Public review):

A few minor changes to the figures and text could be made to improve clarity.

We thank you for having taken the time to indicate where changes could benefit the paper. We followed your recommendations. 

Reviewer #3 (Public review):

(1) Several factors may contribute to an underestimation of barrel cortex inputs to SPNs (and thus an overestimate of the input heterogeneity among SPNs). First, by virtue of the experiments being performed in an acute slice prep, it is probable that portions of recorded SPN dendritic trees have been dissected (in an operationally consistent anatomical orientation). If afferents happen to systematically target the rostral/caudal projections of SPN dendritic fields, these inputs could be missed. Similarly, the dendritic locations of presynaptic cortical inputs remain unknown (e.g., do some inputs preferentially target distal vs proximal dendritic positions?). As synaptic connectivity was inferred from somatic recordings, it's likely that inputs targeting the proximal dendritic arbor are the ones most efficiently detected. Mapping the dendritic organization of synapses is beyond the scope of this work, but these points could be broached in the text.

Thank you for this analysis. The positions of S1 spines have been mapped on the SPN dendritic arbor by the group of Margolis (B.D. Sanabria et al., ENeuro 2024,10.1523/ENEURO.0503-23.2023). They observed that S1 spines were at 80 % on dendrites but with a specific distribution, on average rather close to the soma.  In this study, S1 spines did not exhibit a specific distribution that would systematically hinder their detection in a slice. But, it remains that the position in the dendritic arbor where an S1 input is received does indeed impact its detection in somatic recordings. We modified the discussion as follows, line 275:

“The LSPS combined with glutamate uncaging mapped projections contained in the slice, intact from the presynaptic cell bodies to the SPN dendrites. Some cortical inputs targeting distal SPN dendrites may have gone undetected, either due to attenuation of synaptic events recorded at the soma or because distal dendritic branches were lost during slice preparation. Indeed, about 80 % of S1 synaptic contacts are distributed along dendrites (Sanabria et al., 2024). However, synapses located distally are proportionally rare (Sanabria et al., 2024), and our estimates suggest that the loss of S1 input was minimal (see Methods). More significantly, our mapping only included projections from neuronal somata located within the S1 barrel field in the slice: projections from cortical columns outside the slice were not stimulated. For this reason, our study characterized connectivity patterns rather than the full extent of connectivity with the barrel cortex.”

We explain our estimation of truncated S1 contacts in the Methods, line 434:

“To estimate the loss of S1 synaptic contacts caused by slice preparation, we modeled the SPN dendritic field as a sphere centered on the soma. S1 synapses were at 80 % distributed radially along dendrites, according to the specific distribution described by Sanabria et al. (2024). The simulation also incorporated the known distribution of SPN dendritic length as a function of distance from the soma (Gertler et al., 2008). Finally, it assumed that synapse placement was isotropic, with equal probability in all directions from the soma. Truncation was simulated by removing a spherical cap at one pole of the sphere, reflecting the depth of our recordings (beyond 80 μm). Based on this simulation, the loss of S1 inputs was < 10 %.”

(2) In general, how specific (or generalizable) is the observed SPN-specific convergence of cortical barrel cortex projections in the dorsolateral striatum? In other words, does a similar cortical stimulation protocol targeted to a non-barrel sensory (or motor) cortex region produce similar SPN-specific innervation patterns in the dorsolateral striatum?

This is an interesting question that could be addressed using the LSPS approach in areas for which ex vivo preparations have been designed to maintain the integrity of the corticostriatal projections, such as A1, M1 and S2.  

We included this point in the discussion, line 299: 

” The speckled connectivity pattern of individual SPNs, arising from the abundant and diffuse cortical innervation in the DLS, suggests that somatosensory corticostriatal synapses are established through a selective and/or competitive process. It is important to determine whether this sparse innervation of SPNs by S1 is a characteristic shared with other projections. In particular, it will be interesting to test this hypothesis on the auditory projections targeting the posterior striatum, where neurons exhibit clear tone frequency selectivity (Guo et al., 2018).”

(3) In general, some of the figure legends are extremely brief, making many details difficult to infer. Similarly, some statistical analyses were either not carried out or not consistently reported.

We thank you for having taken the time to indicate where changes could benefit the paper. We have followed your recommendations. 

Reviewer #1 (Recommendations for the authors):

A few limitations should be discussed in the manuscript:

(1) The manuscript should mention that most corticostriatal synapses are formed at the dendritic spines of the SPNs, not their cell bodies. This is particularly important regarding the analysis and interpretation of the data in Figure 4.

Thank you for this comment. This characteristic is important with regards to a limitation of electrophysiological recordings. This is now discussed:

Line 275:

“The LSPS combined with glutamate uncaging mapped projections contained in the slice, intact from the presynaptic cell bodies to the SPN dendrites. Some cortical inputs targeting distal SPN dendrites may have gone undetected, either due to attenuation of synaptic events recorded at the soma or because distal dendritic branches were lost during slice preparation. Indeed, about 80 % of S1 synaptic contacts are distributed along dendrites (Sanabria et al., 2024). However, synapses located distally are proportionally rare (Sanabria et al., 2024), and our estimates suggest that the loss of S1 input was minimal (see Methods).“

Line 313:

[...],, we found that overlaps between the connectivity maps of SPNs were rare and, when present, involved only a small fraction of the connected sites. This indicates that neighboring SPNs predominantly integrated distinct inputs from the barrel cortex, although it is possible that overlapping inputs received in distal dendrites were not all detected”

(1) SPNs show up- and down-states in vivo, which were not mimicked by the present study since all cells were held at - 80 mV (Line 364) and recorded at room temperature (Line 368). It should be discussed how the conclusion of the present work may be affected by the up/down states of SPNs in vivo.

Thank you for raising this point. Indeed, our experimental conditions were not designed to capture the effects of network oscillatory activity. Instead, LSPS conditions were optimized to reveal monosynaptic connectivity between neurons in S1 and their postsynaptic targets. These optimizations include the use of a high concentration of extracellular divalents (4 mM Ca²⁺ and Mg²⁺) to generate robust yet moderate and spatially-restricted stimulations of cortical cells and reliable neurotransmitter release (Shepherd, Pologruto and Svoboda, Neuron 2003; 10.1016/s0896-6273(03)00152-1; in our study, see Fig. 1D  and Suppl Fig. 2). Investigating the pre- and postsynaptic modulations of the corticostriatal coupling by up- and down-states would require specific conditions. 

The conclusion now acknowledges that functional connectivity is subject to plasticity in general, line 358:

“The degree of input convergence onto SPNs could be modulated by plasticity, potentially enabling experience-driven reconfiguration of S1 corticostriatal coupling.”

(2) In addition to population-level integration (Line 337), sensory integration is likely to involve synaptic plasticity (like via NMDARs), which was not studied in the present work

Thank you for raising this point. Indeed, we agree that sensory integration is a complex process with a multitude of factors beyond connectivity patterns and synaptic strength. We also agree that both connectivity levels and synaptic strength can be modified by plasticity. 

We modified our conclusion as follows, line 354:

“Since the inputs to a single SPN represent only a limited subset of whisker columns, a complete representation of whiskers could emerge at the population level, with each SPN’s representation complementing those of its neighbors (Fig. 7). These observations raise the hypothesis of a selective or competitive process underlying the formation of corticostriatal synapses. The degree of input convergence onto SPNs could be modulated by plasticity, potentially enabling experience-driven reconfiguration of S1 corticostriatal coupling. “

(3) The potential corticostriatal connectivity may be underestimated due to loss of axonal branches during slice resection, and this might contribute to the conclusion of "sparse connectivity". Whether the author has considered performing LSPS studies within the striatum (i.e., stimulating ChR2-expressing cortical axon terminals) and whether this experiment may consolidate the conclusion of the present work.

We appreciate the suggestion to employ Subcellular Channelrhodopsin-2-Assisted Circuit Mapping (sCRACM) to study the density of S1 spines on SPNs dendritic arbor. If ChR2 is broadly expressed in S1, this approach would likely increase spine detection, as spines contacted by presynaptic neurons located inside and outside the slice would now be activated. If ChR2 expression could be restricted to the whisker columns present in our preparation, enhanced detection could still occur, but in this case, it would reflect the activation of spines contacted by specific ChR2⁺ axonal branches that exit and re-enter the slice to form synapses on the recorded SPN. The anatomy of corticostriatal axonal arbors suggest convoluted axonal trajectories could be relatively rare (T. Zheng and C.J. Wilson, J Neurophysiol. 2001; 10.1152/jn.00519.2001; M. Lévesque et al., Brain Res. 1996; 10.1016/0006-8993(95)01333-4).  

Moreover, it is important to remember that sCRACM does not generate connectivity maps between 2 structures, but maps of spines on dendritic arbors (Petreanu L.T. et al., Nature 2009; 10.1038/nature07709.). Precise localization of presynaptic cell bodies was key for the present study, as it enabled distinguishing between different connectivity patterns and between different degrees of convergence of inputs from adjacent S1 cortical columns present in the slice (schematized in Fig. 1). Distinguishing these inputs using the stimulation of axon terminals would require the possibility to express one distinct opsin in each whisker column (or each cortical layer, depending on the axis of investigation). This is an exciting perspective but the technology is not yet available to our knowledge. 

To emphasize our reasons for using LSPS, we revised the final paragraph of the Introduction, line 69: 

“LSPS enabled precise mapping of corticostriatal functional connectivity by identifying cortical sites where stimulation evoked synaptic currents in the recorded SPNs, thereby localizing the cell bodies of their presynaptic neurons. This approach allowed us to determine both the cortical column and layer of origin within the barrel field in the slice for each SPN input.”

Reviewer #2 (Recommendations for the authors):

(1)  Figure 2F: SPN and cortical regions - both are shown in green. The distinction between the two would be clearer if SPNs were made a different color.

Done

(2)  Figure 2H: Based on their data, the authors conclude that since EPSCs in SPNs had small amplitudes (~40pA), only one or a few presynaptic cortical neurons (< 5) were activated by uncaging. It is not clear how this number was estimated. Either this statement should be qualified with data or citations provided to support it.

We thank you for noticing it. We modified this part as follows, line 105:

“Based on known amplitudes of spontaneous and miniature EPSCs in SPNs (10-20 pA on average; Kreitzer and Malenka, 2007; Cepeda et al., 2008; Dehorter et al., 2011; Peixoto et al., 2016), this finding is consistent with the presence of only one or a few presynaptic cells (≤ 5) at each connected site of the map.”

(3) Figure 2I: The top graph is difficult to understand without already seeing the lower plot. Moving it below or to the side would help the reader follow the data more easily.

done

(4) Figure 3D: In Line 162, the authors state, " Furthermore, SPNs receiving input from a single column were often located near others receiving input from multiple ones (Figure 3D), reinforcing that the low functional connectivity with barrel columns in the slice was genuine in these cases." However, Figure 3D does not show spatial information about SPNs relative to each other. This data should be added or the statement adjusted to reflect what is shown in the panel.

Corrected as follows, line 167:

“Furthermore, SPNs receiving input from a single column were often located in slices where other cells received input from multiple ones (Fig. 3D), reinforcing that the low functional connectivity with barrel columns in the slice was genuine in these cases.”

(5) Figure 3F: Are the authors attempting to show how cluster number, cluster width, and connectivity gaps contribute to input field width? If so, this could be clarified by flipping the x- and y-axes so that the input field width is the y-axis in each case. Additionally, the difference between black and white points should be stated (or, if there is no difference, made to be the same). The significance of the dotted red line vs. the solid red lines should also be stated in the figure legend.

These plots illustrate how cluster number, cluster width, and ratio of connectivity gaps over total length vary as a function of input field width. As expected, wider input fields contain more clusters (top). However, the overall density of connected sites does not increase with input field width, as indicated by a higher ratio of connectivity gaps over total length (bottom).

This suggests the presence of a mechanism that regulates the connectivity level of individual SPNs (mentioned in the discussion). We prefer this orientation because the flipped one makes a cluttered panel due to different X axis labels. Symbols and lines were corrected. The correlation coefficients and statistics are now indicated in the panels and in the legend.

(6) Figure 3H: The schematic is very useful for highlighting the core conclusions and is greatly appreciated. The pie charts are a bit hard to see and could be replaced with the percentages stated simply as text within the figure. It would also help to label the panel as "Summary," so readers can quickly identify its purpose.

Done

(7) Figures 4B-D: To clarify the overall percentage, the maximum for the y-axis should be set to 100% in each panel.

Done

Reviewer #3 (Recommendations for the authors):

(1) Though mostly minor, several sentences/statements in the manuscript are confusing or overstated. For example:

a. Lines 62-63: "Studies have found that inputs received by D1 SPNs were stronger than those received by D2 SPNs" is a broad statement that should be qualified.

We changed this sentence for: 

“Electrophysiological studies have found that inputs received by D1 SPNs were stronger than those received by D2 SPNs, both in vivo and ex vivo (Reig and Silberberg, 2014 ; Filipović et al., 2019 ; Kress et al., 2013 ; Parker et al., 2016).”

b. Lines 118-119: "EPSCs evoked with stimulations in L2/3 to L5b had similar amplitudes (Figure 2H), suggesting that L5a dominated these other layers thanks to a greater connectivity with SPNs principally." Here, the word "connectivity" is vague and could easily be misunderstood. Connectivity could refer to the amplitude of corticostriatal EPSCs, which the authors stated are not different between L2/3-L5b. Presumably, connectivity here refers to % of connected SPNs, but for the sake of clarity, the authors should be more explicit, e.g,. "...L5a dominated the other layers because a larger fraction of SPNs received connections from L5a, rather than because L5a synapses were stronger."

We changed the sentence for (line 122): 

“EPSCs evoked with stimulations in L2/3 to L5b had similar amplitudes (Fig. 2H), suggesting that L5a dominance over these other layers is primarily due to a higher likelihood of SPNs being connected to it, rather than to stronger synaptic inputs.”

c. In the Figure 4 legend, (A) says "Four example slices with 2 to 4 recordings. Same as in Figure 2A." Did the authors mean Figure 3A?

Done

d.Line 184: Should Figure 4B, C actually be Figure 4D?

Done

(2) Line 32: typo in Sippy et al. reference.

Done

(3) In Figure 2I, the label "dSPN" is confusing, as in the literature, dSPN often refers to the direct pathway SPN.

Done

(4) The y-axes in Figure 3C should be better labeled/explained.

Fig.3C. Median (red) and 25-75th percentiles (box) of cluster width and spacing, expressed in µm (left Y axis) and number of cortical columns (right Y axis). Labels have been changed in the figure.

(5)  Lines 150-152: "...45 % of the input fields with several clusters produced no synaptic response upon stimulation." This wording is confusing. It can be inferred that the authors mean "no synaptic response in the gaps between clusters." However, their phrasing omits this crucial detail and reads as though those input fields produce no response at all.

We changed this sentence for (line 154):

“Strikingly, regions lacking evoked synaptic responses (i.e., connectivity gaps) made up an average of 45 % of the length of input fields with multiple clusters (maps collapsed along the vertical axis; Fig. 3F, bottom). “

(6)  Lines 184-186: "DLS SPNs could receive inputs from the same domain in the barrel cortex and yet have patterns of cortical innervation without or little redundancy." This should be rephrased to "with little to no redundancy."

Done

(7)  Lines 186-187: "They support a connectivity model in which synaptic connections on each SPNs..." should be revised to "connections to each SPN...".

Done

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Reply to the reviewers

Manuscript number: RC-2025-03031

Corresponding author(s): Lara-Pezzi, Enrique and Gómez-Gaviro, María Victoria

1. General Statements [optional]

Dear Editors,

Following the review of our article entitled "Loss of the alternative calcineurin variant CnAβ1 enhances brown adipocyte differentiation and drives metabolic overactivation through FoxO1 activation", we propose below a number of experiments to be performed in order to address the issues raised by the reviewers.

While we acknowledge the limitations of the full CnAβ1 knockout mouse and we unfortunately lack a tissue-specific knockout mouse, we believe that the proposed new experiments together with the (abundant) existing information in the paper will help clarify the concerns raised by the reviewers.

2. Description of the planned revisions

Insert here a point-by-point reply that explains what revisions, additional experimentations and analyses are planned to address the points raised by the referees.

Reviewer #1 (Evidence, reproducibility and clarity (Required)):

*The current study examines the metabolic phenotype of mice lack the calcineurin variant CnAb1 (CnAb1KO). On a high fat diet, CnAb1KO mice gain less weight compared to WT controls, which is accompanied by improvements in obesity-related metabolic dysfunction, such as glucose/insulin intolerance and hyperlipidemia. The authors attribute most of the observed phenotypes to enhanced brown fat function, notably fatty acid catabolism and the thermogenic capacity. Mechanistically, the authors propose that CnAb1KO increases FoxO1 transcriptional activity, as a result of reduced mTOR/Akt signaling, which in turn mediates the hyper-catabolism of BAT in CnAb1KO mice. *

* Major comments: *

*Q1. The main issue of the study is it's not hypothesis driven. Based on high fat diet-induced metabolic phenotype of the whole body CnAb1KO mice, the authors put together a mechanism focusing on potential roles of CnAb1 in BAT functions that affect systemic metabolic homeostasis. However, the rationales to establish this link were based largely on correlative results and at times incorrect data interpretation (for instance, using the expression of Myf5 and Pax7 as markers for brown adipocyte differentiation). The sequential event from CnAb1 loss of function to reduced mTOR signaling and increased FoxO1 activity (or conversely, how CnAb1 increases mTOR signaling to reduce FoxO1 activity) has not been mechanistically characterized. There are also no studies to explain how FoxO1 is involved in brown fat differentiation and hyper-catabolism of BAT downstream of the CnAb1-mTOR pathway. In addition, the UCP-1 FoxO1KO experiment in Fig. 6 fails to provide strong evidence to support the claim. Thus, there are many gaps between the observed phenotype and the proposed mechanism. *

A1. We thank the reviewer for the insightful comments. We agree with the reviewer that, historically, this project did not originally focus on the BAT. Instead, we arrived at the BAT after ruling out other possibilities to explain the reduced body weight observed in these animals, together with the reduced body temperature after starvation, which was our first observation. While the BAT involvement was not our first hypothesis a priori, we do not agree that this would invalidate or reduce the interest of our work. While our initial evidence may have been correlative at first, the FoxO1 BAT-specific knockout experiments and the AAV/Ucp1-Cre CnAβ1 expression restoration experiments prove that the BAT is indeed involved in the phenotype observed in CnAβ1Δi12 (KO) mice. It is likely that other organs may be also involved (since the phenotype is not fully prevented by the BAT-specific approaches) but the BAT is definitely involved.

To further substantiate the involvement of the BAT in the improved metabolic phenotype observed in CnAβ1Δi12 mice, we propose to perform BAT transplantation, monitoring body weight over 8 weeks following transplantation. If successful, BAT transplantation from CnAβ1Δi12 mice into WT mice should improve their metabolic response to high-fat diet (HFD), thereby reinforcing the role of the BAT in these mice.

        In addition, we propose to measure the __*levels of so-called batokines*__ FGF21, VEGFA, IL6, and also of 12,13-diHOME in BAT and serum from 12-week-old chow and HFD mice.

        With regards to Pax7 and Myf5, while we agree that these are common precursors to other lineages (skeletal muscle), we show in Fig. S1E additional differentiation markers such as Cox2 and Cpt1b. __*The 5 markers assessed showed an increase in *____*CnAβ1Δi12 mice, pointing towards a cell-autonomous effect of the absence of CnAβ1 on the BAT*__. Nevertheless, to further substantiate the accelerated differentiation of brown preadipocytes in the absence of CnAβ1, we propose to __*measure the expression of additional BAT markers*__ (although they are not exclusive of BAT), such as Ucp1, Prdm16, PPARγ, and AdipoQ in brown preadipocytes isolated from 6–8-week-old mice.

        With regards to the activation of mTOR (specifically mTORC2) by CnAβ1, we published this in previous papers from our group: Gómez-Salinero et al (Cell Chem Biol, 2016), Felkin et al (Circulation, 2011), Lara-Pezzi et al (J Cell Biol 2007), Padrón-Barthe et al (J Am Coll Cardiol 2018). The mechanism involves the interaction between CnAβ1 and mTORC2 in cellular membranes. Knockdown of CnAβ1 results in mTORC2 mislocalisation and Akt inhibition. In addition, we show in Fig. 6C in this paper that PTEN inhibition reduces the improved differentiation of BAT adipocytes from CnAβ1Δi12 mice, further involving the Akt pathway in the observed phenotype. Furthermore, Fig. 6 shows a significant increase in body weight and BAT weight in BAT-specific FoxO1 knockout CnAβ1Δi12 mice, together with a significant decrease in different Pnpla1, Irf4, and Bcat2 expression. While we agree that the reversal of the phenotype is only partial, the effect of knocking out FoxO1 in the BAT of CnAβ1Δi12 mice is both statistically significant and biologically relevant. We would be happy to provide additional information at the Editors’ request. In addition, we propose to carry out __BAT preadipocyte differentiation experiments comparing cells isolated from CnAβ1Δi12 mice to those isolated from CnAβ1Δi12 mice with BAT-specific FoxO1 knockout__.

Q2. A second issue is that most of the phenotypes can be explained by the difference in weight gain. With the available data, it's difficult to pinpoint the tissue origin(s) mediating the weight gain/loss phenotype. The authors would first need to generate a BAT-CnAb1KO mouse line to convincingly show a main role for BAT CnAb1 in systemic metabolic homeostasis. There are also many problems with data presentations/interpretations of the metabolic phenotyping studies. For example, Fig. 1A shows that CnAb1KO mice are about 5 g lighter than controls. However, Fig. 1G indicates a 10 g difference in fat mass. The EM images in Fig. 3B are of poor quality, which seems to suggest that HFD fed CnAb1KO mice have the highest mitochondrial density. Lastly, in Fig. 4C/D, the authors interpret the reduced FFA and glycerol levels in CnAb1KO after b3-agonist injection as increased fatty acid burning by BAT, which is incorrect. If anything, the reduced glycerol release in the KO mice would suggest a reduction in lipolysis. However, the most likely explanation is that WT mice have more fat mass and as such, more fat hydrolysis.

A2. While we agree with the reviewer that some of the features may be explained by reduced body weight gain (reduced WAT weight, for instance), many other changes showed by CnAβ1Δi12 mice cannot be explained by reduced body weight gain alone, including higher expression of differentiation markers in BAT, higher number of mitochondria in BAT, or improved cold-tolerance, among others. Therefore, we respectfully disagree with the reviewer’s opinion.

Unfortunately, we do not have a tissue-specific CnAβ1 knockout mouse and we cannot commit to having one in the short term. While we acknowledge the limitations of using a full knockout mouse, we provided several pieces of evidence that the BAT is involved in the observed phenotype, as pointed out in the discussion: 1) Placing CnAβ1Δi12 mice in thermoneutral conditions mitigated the weight loss. 2) Reintroducing CnAβ1 in BAT with a CnAβ1-overexpressing virus partially prevented the weight loss. 3) Minimal changes in mitochondrial gene expression were observed in skeletal muscle and liver, suggesting that the phenotype is primarily driven by alterations in BAT. 4) BAT adipocytes from CnAβ1Δi12 mice differentiated more effectively than those from wild type mice, suggesting a cell-autonomous effect. While a direct effect of CnAβ1 on WAT cannot be entirely ruled out, our results strongly suggest that loss of CnAβ1 in BAT is a major contributor to the observed metabolic changes.

        With regards to Fig. 1E, this is an estimation of fat weight from __MRI__ images. We agree with the reviewer that this is obviously wrong and we will __revise this quantification__. We propose to __add measurements of subcutaneous WAT__, which we also have, to further support the difference observed in eWAT.

        With regards to Fig. 3B, we agree that some of the individual figures may have been poorly chosen, but the graph in Fig. 3C (which quantifies the electron microscopy pictures) clearly shows that the reduction in mitochondria in WT mice as a result of HFD feeding is prevented in CnAβ1Δi12 mice. Fig. 3C does not show an increase in mitochondria with HFD, as implied by the reviewer based on Fig. 3B. We propose to __provide adequate panels for Fig. 3B that better reflect the averages shown in Fig. 3C__.

        Regarding Fig. 4C and D, we thank the reviewer for this correction, which we agree with. We still believe that the BAT of CnAβ1Δi12 mice is burning fat more effectively than that of WT mice, but we agree that these experiments are not the proof of this claim. We will__ move or remove panels C and D from Fig. 4__ and focus this figure on thermogenic capacity.

        To assess systemic lipolysis, we will __measure in vivo serum levels of NEFA__ (non-esterified fatty acids) __and glycerol__ in 12-week-old mice fed a HFD. Additionally, to evaluate BAT lipolytic activation, we will perform __BAT explant and *ex vivo* experiments__ to determine the lipolysis rate. This should provide valuable information supporting the role of the BAT in the observed phenotype in CnAβ1Δi12 mice.

*Q3. The authors should take a fresh, unbiased look at existing data, form a testable hypothesis and design a series of new experiments (including new tissue-specific KO mice) to assess the function of CnAb1 in BAT or other tissues responsible for the metabolic phenotype. If BAT is indeed involved, the authors need to mechanistically determine the role of CnAb1 in brown adipocyte differentiation vs BAT function and explain why the ratio of CnAb1/CnAb2 ratio matters in this context, as this is the basis for the entire study. A revision addressing main issues of the manuscript will not likely to be completed in a typical revision time (e.g. 3 months). *

A3. As explained above, unfortunately we do not have tissue-specific CnAβ1 knockout mice. If the Editors consider that this is essential for resubmission of a revised article, we are afraid that we cannot comply. This said, we believe that our manuscript contains relevant data about metabolic regulation by the CnAβ1 calcineurin isoform that are new and relevant to the field.

        Our data provide clear evidence that the BAT is indeed involved in the phenotype observed in CnAβ1Δi12 mice, as explained in our previous answers above. It may not be the *only* tissue involved, but it is most definitely involved. The BAT transplant experiments will add further evidence of this.

        We already show evidence of the role of CnAβ1 (or rather, its absence) in the differentiation of BAT pre-adipocytes (Fig. S1E and Fig. 6C) and we will __provide additional evidence through the proposed new experiments__. Similarly, we provide evidence of the role of CnAβ1 in BAT weight, transcriptional profile, lipid content, and number of mitochondria. Also here, we believe that __the proposed experiments will reinforce this aspect of the paper__.

Reviewer #1 (Significance (Required)):

*Q4. The thermogenic capacity of brown and beige adipocytes has shown promise as a means to reduce fat burden to treat obesity and related metabolic diseases. Identification of brown/beige adipocyte promoting mechanisms may provide druggable targets for therapeutic development. As such, the topic and findings of the current study would be of interest to researchers in the metabolism and drug development fields. The weakness of the study is that it's descriptive and the authors jump to conclusions without strong supporting evidence. Most of the metabolic phenotypes associated with CnAb1KO mice are likely secondary to the weight difference. The rationale to focus on BAT is not well justified. A well-thought-out approach would be needed to identify the tissue origins mediating the metabolic phenotypes of CnAb1KO mice and to dissect the underlying mechanisms. *

*Reviewer's field of expertise: adipose tissue biology, systemic metabolic regulation, immunometabolism *

A4. We agree with the reviewer about the potential relevance of our findings. The shortcomings pointed out in this comment have been addressed above. Overall, we thank the reviewer for their thorough review of our ms.

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

*The manuscript entitled « Loss of the alternative calcineurin variant CnAβ1 enhances brown adipocyte differentiation and drives metabolic overactivation through FoxO1 activation » by Dr Lara-Pezzi and colleagues describes the role of the calcium/calmodulin dependent serine/threonine phosphatase catalytic subunit calcineurin variant CnAß1 in brown adipose tissue physiology and function. Through the use of global CnAß1 KO mice, the authors show that these mice are resistant to diet-induced obesity, have increased thermogenesis due to increased mitochondrial activity, decreased body weight, improved glucose homeostasis, increased fatty acid oxidation. The authors also demonstrate that these effect are mostly mediated through improved brown adipose tissue (BAT) function, through increased Foxo1 activation in BAT. Genetic deletion of Foxo1 in BAT resulted in increased body weight and impaired mitochondrial gene expression. In addition, the authors also correlate their findings to potential CNAß1 polymorphism from the UK biobank associated to improved metabolic traits in humans (blood glucose mainly). *

Although interesting, the conclusion are not always supported by the data. The manuscript requires additional experiments to further consolidate their claims.

*Q1. It should be mentioned that all experiments are performed in global CnAβ1 KO mice. Thus, it is difficult to assess the cell-autonomous role if this protein in BAT function (even if an AAV9 driving CnAβ1 expression is used; or if other tissues have been studied). This should be discussed at least as a limitation of the study, except if floxed mice are available. *

A1. We thank the reviewer for the positive comments about our work.

Unfortunately, we do not have a tissue-specific CnAβ1 knockout mouse. However, we believe we provide abundant evidence of the involvement of the BAT in the phenotype observed in CnAβ1Δi12 mice, including the following: 1) Placing CnAβ1Δi12 mice in thermoneutral conditions mitigated the weight loss. 2) Reintroducing CnAβ1 in BAT with a CnAβ1-overexpressing virus partially prevented the weight loss. 3) Minimal changes in mitochondrial gene expression were observed in skeletal muscle and liver, suggesting that the phenotype is primarily driven by alterations in BAT. 4) BAT adipocytes from CnAβ1Δi12 mice differentiated more effectively than those from wild type mice, suggesting a cell-autonomous effect. While a direct effect of CnAβ1 on WAT cannot be entirely ruled out, our results strongly suggest that loss of CnAβ1 in BAT is a major contributor to the observed metabolic changes.

This said, we fully agree with the reviewer to acknowledge in the discussion the limitation of using a full knockout mouse for this study.

Q2. Is there good antibodies for CnAβ1? The protein levels of the protein should be shown in, at least, adipose tissues of WT and KO mice under chow and HFD.

A2. There is no good antibody against CnAβ1. The main reason is that the C-ter domain of this isoform is not very immunogenic. We did try to generate an antibody, but we got no immune response against the unique C-ter domain. We do have an old antibody generated against CnAβ1 years ago. We propose to try to perform WB and immunohistochemistry in WT and ____CnAβ1Δi12 mice. However, we need to be clear that we cannot make any commitments towards these results, since the antibody may not work. In any case, we believe that the RT-PCR results, which clearly discriminate both isoforms, are very clear.

*Q3. A general comment is that most of the conclusions are drawn from qRT-PCR data. It lacks functional experiments that may reinforce the conclusion. For example, did the authors measure mitochondrial function in BAT of WT and KO mice using different substrate (fatty acids, glucose, ...)? *

A3. We thank the reviewer for this suggestion and we therefore propose to include in the revised paper measurements of mitochondrial activity with different substrates in WT and ____CnAβ1Δi12 mice.

*Q4. Lack of validation of the mouse model used (CnAβ1 expression in BAT upon AAV9 over expression confirmed? What about the other tissues?). *

A4. We showed in Fig. 5E the increase in CnAβ1 expression in the BAT of Ucp1-Cre mice infected with the floxed AAV-CnAβ1 virus. We propose to include similar expression analyses in other tissues.

Reviewer #2 (Significance (Required)):

Q5. This is a novel study addressing the role of CnAβ1 in energy homeostasis, more specifically in BAT function. This study reports for the first time the role of CnAβ1 in energy homeostasis, with new mechanistic insights related to the crosstalk between CnAβ1 and Foxo1.

The authors have previously described the role of this protein in cardiac function. There are not a lot of publications describing the function of this protein, thus this study may be interested for the community working on diabetes/obesity/cardio-metabolic field.

*Limitations : see below (lack of functional data, ...). *

A5. We thank the reviewer for these comments, with which we agree.

3. Description of the revisions that have already been incorporated in the transferred manuscript

• *

• *

4. Description of analyses that authors prefer not to carry out

As much as we would like to have a tissue-specific CnAβ1 knockout mouse, the reality is that we do not have it. In any case, we believe that our paper provides a considerable amount of data that is relevant to the field.

We remain open to incorporating the suggested experiments, or others, should they be considered necessary to further strengthen the manuscript.

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Referee #1

Evidence, reproducibility and clarity

The current study examines the metabolic phenotype of mice lack the calcineurin variant CnAb1 (CnAb1KO). On a high fat diet, CnAb1KO mice gain less weight compared to WT controls, which is accompanied by improvements in obesity-related metabolic dysfunction, such as glucose/insulin intolerance and hyperlipidemia. The authors attribute most of the observed phenotypes to enhanced brown fat function, notably fatty acid catabolism and the thermogenic capacity. Mechanistically, the authors propose that CnAb1KO increases FoxO1 transcriptional activity, as a result of reduced mTOR/Akt signaling, which in turn mediates the hyper-catabolism of BAT in CnAb1KO mice.

Major comments:

1. The main issue of the study is it's not hypothesis driven. Based on high fat diet-induced metabolic phenotype of the whole body CnAb1KO mice, the authors put together a mechanism focusing on potential roles of CnAb1 in BAT functions that affect systemic metabolic homeostasis. However, the rationales to establish this link were based largely on correlative results and at times incorrect data interpretation (for instance, using the expression of Myf5 and Pax7 as markers for brown adipocyte differentiation). The sequential event from CnAb1 loss of function to reduced mTOR signaling and increased FoxO1 activity (or conversely, how CnAb1 increases mTOR signaling to reduce FoxO1 activity) has not been mechanistically characterized. There are also no studies to explain how FoxO1 is involved in brown fat differentiation and hyper-catabolism of BAT downstream of the CnAb1-mTOR pathway. In addition, the UCP-1 FoxO1KO experiment in Fig. 6 fails to provide strong evidence to support the claim. Thus, there are many gaps between the observed phenotype and the proposed mechanism.
2. A second issue is that most of the phenotypes can be explained by the difference in weight gain. With the available data, it's difficult to pinpoint the tissue origin(s) mediating the weight gain/loss phenotype. The authors would first need to generate a BAT-CnAb1KO mouse line to convincingly show a main role for BAT CnAb1 in systemic metabolic homeostasis. There are also many problems with data presentations/interpretations of the metabolic phenotyping studies. For example, Fig. 1A shows that CnAb1KO mice are about 5 g lighter than controls. However, Fig. 1G indicates a 10 g difference in fat mass. The EM images in Fig. 3B are of poor quality, which seems to suggest that HFD fed CnAb1KO mice have the highest mitochondrial density. Lastly, in Fig. 4C/D, the authors interpret the reduced FFA and glycerol levels in CnAb1KO after b3-agonist injection as increased fatty acid burning by BAT, which is incorrect. If anything, the reduced glycerol release in the KO mice would suggest a reduction in lipolysis. However, the most likely explanation is that WT mice have more fat mass and as such, more fat hydrolysis.
3. The authors should take a fresh, unbiased look at existing data, form a testable hypothesis and design a series of new experiments (including new tissue-specific KO mice) to assess the function of CnAb1 in BAT or other tissues responsible for the metabolic phenotype. If BAT is indeed involved, the authors need to mechanistically determine the role of CnAb1 in brown adipocyte differentiation vs BAT function and explain why the ratio of CnAb1/CnAb2 ratio matters in this context, as this is the basis for the entire study. A revision addressing main issues of the manuscript will not likely to be completed in a typical revision time (e.g. 3 months).

Significance

The thermogenic capacity of brown and beige adipocytes has shown promise as a means to reduce fat burden to treat obesity and related metabolic diseases. Identification of brown/beige adipocyte promoting mechanisms may provide druggable targets for therapeutic development. As such, the topic and findings of the current study would be of interest to researchers in the metabolism and drug development fields. The weakness of the study is that it's descriptive and the authors jump to conclusions without strong supporting evidence. Most of the metabolic phenotypes associated with CnAb1KO mice are likely secondary to the weight difference. The rationale to focus on BAT is not well justified. A well-thought-out approach would be needed to identify the tissue origins mediating the metabolic phenotypes of CnAb1KO mice and to dissect the underlying mechanisms.

Reviewer's field of expertise: adipose tissue biology, systemic metabolic regulation, immunometabolism

Reviewer #2 (Public review):

Summary:

In this paper, entitled "SpikeMAP: An unsupervised spike sorting pipeline for cortical excitatory and inhibitory 2 neurons in high-density multielectrode arrays with ground-truth validation", the authors are presenting spikeMAP, a pipeline for the analysis of large-scale recordings of in vitro cortical activity. According to the authors, spikeMAP not only allows for the detection of spikes produced by single neurons (spike sorting), but also allows for the reliable distinction between genetically determined cell types by utilizing viral and optogenetic strategies as ground-truth validation. While I find that the paper is nicely written, and easy to follow, I find that the algorithmic part of the paper is not really new and should have been more carefully compared to existing solutions. While the GT recordings to assess the possibilities of a spike sorting tool to distinguish properly between excitatory and inhibitory neurons is interesting, spikeMAP does not seem to bring anything new to state of the art solutions, and/or, at least, it would deserve to be properly benchmarked. This is why I would suggest the authors to perform a more intensive comparison with existing spike sorters.

Strengths:

The GT recordings with optogenetic activation of the cells, based on the opsins is interesting and might provide useful data to quantify how good spike sorting pipelines are, in vitro, to discriminate between excitatory and inhibitory neurons. Such an approach can be quite complementary with artificially generated ground truth.

Weaknesses:

The global workflow of spikeMAP, described in Figure 1, seems to be very similar to the one of [Hilgen et al, 2020, 10.1016/j.celrep.2017.02.038.]. Therefore, the first question is what is the rationale of reinventing the wheel, and not using tools that are doing something very similar (as mentioned by the authors themselves). I have a hard time, in general, believing that spikeMAP has something particularly special, given its Methods, compared to state-of-the-art spike sorters. This is why at the very least, the title of the paper is misleading, because it let the reader think that the core of the paper will be about a new spike sorting pipeline. If this is the main message the authors want to convey, then I think that numerous validations/benchmarks are missing to assess first how good spikeMAP is, w.r.t. spike sorting in general, before deciding if this is indeed the right tool to discriminate excitatory vs inhibitory cells. The GT validation, while interesting, is not enough to entirely validate the paper. The details are a bit too scarce to me, or would deserve to be better explained (see other comments after)

Regarding the putative location of the spikes, it has been shown that center of mass, while easy to compute, is not the most accurate solution [Scopin et al, 2024, 10.1016/j.jneumeth.2024.110297]. For example, it has an intrinsic bias for finding positions within the boundaries of the electrodes, while some other methods such as monopolar triangulation or grid-based convolution might have better performances. Can the authors comment on the choice of Center of Mass as a unique way to triangulate the sources?

Still in Figure 1, I am not sure to really see the point of Spline Interpolation. I see the point of such a smoothing, but the authors should demonstrate that it has a key impact on the distinction of Excitatory vs. Inhibitory cells. What's special with the value of 90kHz for a signal recorded at 18kHz? What is the gain with spline enhancement compared to without? Does such a value depend on the sampling rate, or is it a global optimum found by the authors?

Figure 2 is not really clear, especially panel B. The choice of the time scale for the B panel might not be the most appropriate, and the legend filtered/unfiltered with a dot is not clear to me in Bii. In panel E, the authors are making two clusters with PCA projections on single waveforms. Does this mean that the PCA is only applied to the main waveforms, i.e. the ones obtained where the amplitudes are peaking the most? This is not really clear from the methods, but if this is the case, then this approach is a bit simplistic and not really matching state-of-the-art solutions. Spike waveforms are quite often, especially with such high-density arrays, covering multiple channels at once and thus the extracellular patterns triggered by the single units on the MEA are spatio-temporal motifs occurring on several channels. This is why, in modern spike sorters, the information in a local neighbourhood is often kept to be projected, via PCA, on the lower dimensional space before clustering. Information on a single channel only might not be informative enough to disambiguate sources. Can the authors comment on that, and what is the exact spatial resolution of the 3Brain device? The way the authors are performing the SVD should be clarified in the methods section. Is it on a single channel, and/or on multiple channels in a local neighbourhood?

About the isolation of the single units, here again, I think the manuscript lacks some technical details. The authors are saying that they are using a k-means cluster analysis with k=2. This means that the authors are explicitly looking for 2 clusters per electrodes. If so, this is a really strong assumption that should not be held in the context of spike sorting, because since it is a blind source separation technique, one cannot pre-determine in advance how many sources are present in the vicinity of a given electrode. While the illustration on Figure 2E is ok, there is no guarantee that one cannot find more clusters, so why this choice of k=2? Again, this is why most modern spike sorting pipelines are not relying on k-means, to avoid any hard coded number of clusters. Can the authors comment on that?

I'm surprised by the linear decay of the maximal amplitude as a function of the distance from soma, as shown in Figure 2H. Is it really what should be expected? Based on the properties of the extracellular media, shouldn't we expect a power law for the decay of the amplitude? This is strange that up to 100um away from the some, the max amplitude only dropped from 260 to 240 uV. Can the authors comment on that? It would be interesting to plot that for all neurons recorded, in a normed manner V/max(V) as function of distances, to see what the curve looks like

In Figure 3A, it seems that the total number of cells is rather low for such a large number of electrodes. What are the quality criteria that are used to keep these cells? Did the authors exclude some cells from the analysis, and if yes, what are the quality criteria that are used to keep cells? If no criteria are used (because none is mentioned in the Methods), then how come so few cells are detected, and can the authors convince us that these neurons are indeed "clean" units (RPVs, SNRs, ...)

Still in Figure 3A, it looks like there is a bias to find inhibitory cells at the borders, since they do not appear to be uniformly distributed over the MEA. Can the authors comment on that? What would be the explanation for such a behaviour? It would be interesting to see some macroscopic quantities on Excitatory/Inhibitory cells, such as mean firing rates, averaged SNRs, ... Because again, in Figure 3C, it is not clear to me that the firing rates of inhibitory cells is higher than Excitatory ones, while it should be in theory.

For Figure 3 in general, I would have performed an exhaustive comparison of putative cells found by spikeMAP and other sorters. More precisely, I think that to prove the point that spikeMAP is indeed bringing something new to the field of spike sorting, the authors should have compared the performances of various spike sorters to discriminate Exc vs Inh cells based on their ground truth recordings. For example, either using Kilosort [Pachitariu et al, 2024, 10.1038/s41592-024-02232-7], or some other sorters that might be working with such large high-density data [Yger et al, 2018, 10.7554/eLife.34518]

Figure 4 has a big issue, and I guess the panels A and B should be redrawn. I don't understand what the red rectangle is displaying.

I understand that Figure 4 is only one example, but I have a hard time understanding from the manuscript how many slices/mice were used to obtain the GT data? I guess the manuscript could be enhanced by turning the data into an open access dataset, but then some clarification is needed. How many flashes/animals/slices are we talking about. Maybe this should be illustrated in Figure 4, if this figure is devoted to the introduction of the GT data.

While there is no doubt that GT data as the ones recorded here by the authors are the most interesting data from a validation point of view, the pretty low yield of such experiments should not discourage the use of artificially generated recordings such as the ones made in [Buccino et al, 2020, 10.1007/s12021-020-09467-7] or even recently in [Laquitaine et al, 2024, 10.1101/2024.12.04.626805v1]. In these papers, the authors have putative waveforms/firing rates patterns for excitatory and inhibitory cells, and thus the authors could test how good they are in discriminating the two subtypes

Comments on revised version:

While I must thank the authors for their answers, I still think that they miss an important one, and only partially answering some of my concerns.

I truly think that SpikeMAP would benefit with a comparison with a state-of-the-art spike sorting pipeline, for example Kilosort. The authors said that they made the sorter modular enough such that only the E/I classification step can be compared. I think this would be worth it, just to be sure that SpikeMAP spike sorting, which might be more simple than other recent solution (with template matching), is not missing some cells, and thus degrading the E/I classification performances. I know that such a comparison is not straightforward, because there is no clear ground truth, but I would still need to be convinced that the sorting pipelines is bringing something, on its own. While there is no doubt that the E/I classification layer can be interesting, especially given the recordings shared by the authors, I'm still a bit puzzled by the sorting step. Thus maybe either a Table, a figure, or even as Supplementary one. Or the authors could try to generate fake GT data with MEArec for example, with putative E/I cells (discriminated via waveforms and firing rates) and show on such (oversimplified) data that SpikeMAP is performing similarly to modern spike sorters. Otherwise, this is a bit hard to judge...

Author response:

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

Reviewer #1 (Public review)

As this code was developed for use with a 4096 electrode array, it is important to be aware of double-counting neurons across the many electrodes. I understand that there are ways within the code to ensure that this does not happen, but care must be taken in two key areas. Firstly, action potentials traveling down axons will exhibit a triphasic waveform that is different from the biphasic waveform that appears near the cell body, but these two signals will still be from the same neuron (for example, see Litke et al., 2004 "What does the eye tell the brain: Development of a System for the Large-Scale Recording of Retinal Output Activity"; figure 14). I did not see anything that would directly address this situation, so it might be something for you to consider in updated versions of the code.

Thank you for this comment. We have added a routine to the SpikeMAP to remove highly correlated spikes detected within a given spatial radius of each other. The following was added to the main text (line 149):

“As an additional verification step, SpikeMAP allows the computation of spike-count correlations between putative neurons located within a user-defined radius. Signals that exceed a defined threshold of correlation can be rejected as they likely reflect the same underlying cell.”

Secondly, spike shapes are known to change when firing rates are high, like in bursting neurons (Harris, K.D., Hirase, H., Leinekugel, X., Henze, D.A. & Buzsáki, G. Temporal interaction between single spikes and complex spike bursts in hippocampal pyramidal cells. Neuron 32, 141-149 (2001)). I did not see this addressed in the present version of the manuscript.

We have added a routine to SpikeMAP that computes population spike rates to verify stationarity over time. We have also added a routine to identify putative bursting neurons through a Hartigan statistical dip test applied to the inter-spike distribution of individual cells.

We added the following (line 204):

“Further, SpikeMAP contains a routine to perform a Hartigan statistical dip test on the inter-spike distribution of individual cells to detect putative bursting neurons.”

Another area for possible improvement would be to build on the excellent validation experiments you have already conducted with parvalbumin interneurons. Although it would take more work, similar experiments could be conducted for somatostatin and vasoactive intestinal peptide neurons against a background of excitatory neurons. These may have different spike profiles, but your success in distinguishing them can only be known if you validate against ground truth, like you did for the PV interneurons.

We have added the following (line 326):

“future work could include different inhibitory interneurons such as somatostatin (SOM) and vasoactive intestinal polypeptide (VIP) neurons to improve the classification of inhibitory cell types. Another avenue could involve applying SpikeMAP on artificially generated spike data (Buccino & Einevoll 2021; Laquitaine et al., 2024).”

Reviewer #2 (Public review)

Summary:

While I find that the paper is nicely written and easy to follow, I find that the algorithmic part of the paper is not really new and should have been more carefully compared to existing solutions. While the GT recordings to assess the possibilities of a spike sorting tool to distinguish properly between excitatory and inhibitory neurons are interesting, spikeMAP does not seem to bring anything new to state-of-the-art solutions, and/or, at least, it would deserve to be properly benchmarked. I would suggest that the authors perform a more intensive comparison with existing spike sorters.

Thank you for your insightful comment. A full comparison between SpikeMAP and related methods is provided in Table. 1. As can be seen, SpikeMAP is the only method listed that performs E/I sorting on large-scale multielectrodes. Nonetheless, several aspects of SpikeMAP included in the spike sorting pipeline do overlap with existing methods, as these constitute necessary steps prior to performing E/I identification. These steps are not novel to the current work, nor do they constitute rigid options that cannot be substituted by the user. Rather, we aim to offer SpikeMAP users the option to combine E/I identification with preliminary steps performed either through our software or through another package of their choosing. For instance, preliminary spike sorting could be done through Kilosort before importing the spike data into SpikeMAP for E/I identification. To allow greater flexibility, we have now modularized our suite so that E/I identification can be performed as a stand-alone module. We have clarified the text accordingly (line 317):

“While SpikeMAP is the only known method to enable the identification of putative excitatory and inhibitory neurons on high-density multielectrode arrays (Table 1), several aspects of SpikeMAP included in the spike sorting pipeline (Figure 1) overlap with existing methods, as these constitute required steps prior to performing E/I identification. To enable users the ability to integrate SpikeMAP with existing toolboxes, we provide a modularized suite of protocols so that E/I identification can be performed separately from preliminary spike sorting steps. In this way, a user could carry out spike sorting through Kilosort or another package before importing their data to SpikeMAP for E/I identification.”

Weaknesses:

(1) The global workflow of spikeMAP, described in Figure 1, seems to be very similar to that of Hilgen et al. 2020 (10.1016/j.celrep.2017.02.038). Therefore, the first question is what is the rationale of reinventing the wheel, and not using tools that are doing something very similar (as mentioned by the authors themselves). I have a hard time, in general, believing that spikeMAP has something particularly special, given its Methods, compared to state-of-the-art spike sorters.

The paper by Hilgen et al. is reported in Table 1. As seen, while this paper employs optogenetics, it does not target inhibitory (e.g., PV) cells. We have added the following clarification (line 82):

“Despite evidence showing differences in action potential kinetics for distinct cell-types as well as the use of optogenetics (Hilgen et al., 2017), there exists no large-scale validation efforts, to our knowledge, showing that extracellular waveforms can be used to reliably distinguish cell-types.”

This is why, at the very least, the title of the paper is misleading, because it lets the reader think that the core of the paper will be about a new spike sorting pipeline. If this is the main message the authors want to convey, then I think that numerous validations/benchmarks are missing to assess first how good spikeMAP is, with reference to spike sorting in general, before deciding if this is indeed the right tool to discriminate excitatory vs inhibitory cells. The GT validation, while interesting, is not enough to entirely validate the paper. The details are a bit too scarce for me, or would deserve to be better explained (see other comments after).

We thank the reviewer for this comment, and have amended the title as follows:

“SpikeMAP: An unsupervised pipeline for the identification of cortical excitatory and inhibitory neurons in high-density multielectrode arrays with ground-truth validation”

(2) Regarding the putative location of the spikes, it has been shown that the center of mass, while easy to compute, is not the most accurate solution [Scopin et al, 2024, 10.1016/j.jneumeth.2024.110297]. For example, it has an intrinsic bias for finding positions within the boundaries of the electrodes, while some other methods, such as monopolar triangulation or grid-based convolution,n might have better performances. Can the authors comment on the choice of the Center of Mass as a unique way to triangulate the sources?

We agree with the reviewer that the center-of-mass algorithm carries limitations that are addressed by other methods. To address this issue, we have included two additional protocols in SpikeMAP to perform monopolar triangulation and grid-based convolution, offering additional options for users of the package. The text has been clarified as follows (line 429):

“In addition to center-of-mass triangulation, SpikeMAP includes protocols to perform monopolar triangulation and grid-based convolution, offering additional options to estimate putative soma locations based on waveform amplitudes.”

(3) Still in Figure 1, I am not sure I really see the point of Spline Interpolation. I see the point of such a smoothing, but the authors should demonstrate that it has a key impact on the distinction of Excitatory vs. Inhibitory cells. What is special about the value of 90kHz for a signal recorded at 18kHz? What is the gain with spline enhancement compared to without? Does such a value depend on the sampling rate, or is it a global optimum found by the authors?

We clarified the text as follows (line 183):

“While we found that a resolution of 90 kHZ provided a reasonable estimate of spike waveforms, this value can be adjusted as a parameter in SpikeMAP.”

(4) Figure 2 is not really clear, especially panel B. The choice of the time scale for the B panel might not be the most appropriate, and the legend filtered/unfiltered with a dot is not clear to me in Bii.

We apologize for the rendering issues in the Figures that occurred during conversion into PDF format. We have now ensured that all figures are properly displayed.

In panel E, the authors are making two clusters with PCA projections on single waveforms. Does this mean that the PCA is only applied to the main waveforms, i.e. the ones obtained where the amplitudes are peaking the most? This is not really clear from the methods, but if this is the case, then this approach is a bit simplistic and does not really match state-of-the-art solutions. Spike waveforms are quite often, especially with such high-density arrays, covering multiple channels at once, and thus the extracellular patterns triggered by the single units on the MEA are spatio-temporal motifs occurring on several channels. This is why, in modern spike sorters, the information in a local neighbourhood is often kept to be projected, via PCA, on the lower-dimensional space before clustering. Information on a single channel only might not be informative enough to disambiguate sources. Can the authors comment on that, and what is the exact spatial resolution of the 3Brain device? The way the authors are performing the SVD should be clarified in the methods section. Is it on a single channel, and/or on multiple channels in a local neighbourhood?

We agree with the reviewer that it would be useful to have the option of performing PCA on several channels at once, since spikes can occur at several channels at the same time. We have now added a routine to SpikeMAP that allows users to define a radius around individual channels prior to performing PCA. The text was clarified as follows (line 131):

“The SpikeMAP suite also offers a routine to select a radius around individual channels in order to enter groups of adjacent channels in PCA.”

(5) About the isolation of the single units, here again, I think the manuscript lacks some technical details. The authors are saying that they are using a k-means cluster analysis with k=2. This means that the authors are explicitly looking for 2 clusters per electrode? If so, this is a really strong assumption that should not be held in the context of spike sorting, because, since it is a blind source separation technique, one can not pre-determine in advance how many sources are present in the vicinity of a given electrode. While the illustration in Figure 2E is ok, there is no guarantee that one can not find more clusters, so why this choice of k=2? Again, this is why most modern spike sorting pipelines do not rely on k-means, to avoid any hard-coded number of clusters. Can the authors comment on that?

We clarified the text as follows (line 135):

“In SpikeMAP, the optimal number of k-means clusters can be chosen by a Calinski-Harabasz criterion (Calinski and Harabasz, 1974) or pre-selected by the user.”

(6) I'm surprised by the linear decay of the maximal amplitude as a function of the distance from the soma, as shown in Figure 2H. Is it really what should be expected? Based on the properties of the extracellular media, shouldn't we expect a power law for the decay of the amplitude? This is strange that up to 100um away from the soma, the max amplitude only dropped from 260 to 240 uV. Can the authors comment on that? It would be interesting to plot that for all neurons recorded, in a normed manner V/max(V) as function of distances, to see what the curve looks like.

We added Supplemental Figure 1 showing the drop in voltage over all putative somas (N=1,950) of one recording, after excluding somas with an increase voltage away from electrode peak and computing normed values V/max(V). We see a distribution of slopes as well as intercepts across somas, showing some variability across recordings sites. As the reviewer suggests, it is possible that a power-law describes these data better than a linear function, and this would need to be investigated further by quantitatively comparing the fit of these functions.

(7) In Figure 3A, it seems that the total number of cells is rather low for such a large number of electrodes. What are the quality criteria that are used to keep these cells? Did the authors exclude some cells from the analysis, and if yes, what are the quality criteria that are used to keep cells? If no criteria are used (because none are mentioned in the Methods), then how come so few cells are detected, and can the authors convince us that these neurons are indeed "clean" units (RPVs, SNRs, ...)?

The reviewer is correct to point out that a number of stringent criteria were employed to exclude some putative cells. We now outline these criteria directly in the text (line 161):

“ At different steps in the process, conditions for rejecting spikes can be tailored by applying: (1) a stringent threshold to filtered voltages; (2) a minimal cut-off on the signal-to-noise ratio of voltages (see Supplemental Figure 2); (3) an LDA for cluster separability; (4) a minimal spike rate to putative neurons; (5) a Hartigan statistical dip test to detect spike bursting; (6) a decrease in voltage away from putative somas; and (7) a maximum spike-count correlation for nearby channels. Together, these criteria allow SpikeMAP users the ability to precisely control parameters relevant to automated spike sorting.”

Further, we provide SNRs of individual channels (Supplemental Figure 2), and added to the SpikeMAP software the ability to apply a minimal criterion based on SNR.

(8) Still in Figure 3A, it looks like there is a bias to find inhibitory cells at the borders, since they do not appear to be uniformly distributed over the MEA. Can the authors comment on that? What would be the explanation for such a behaviour? It would be interesting to see some macroscopic quantities on Excitatory/Inhibitory cells, such as mean firing rates, averaged SNRs... Because again, in Figure 3C, it is not clear to me that the firing rates of inhibitory cells are higher than Excitatory ones, whilst they should be in theory.

We have added figures showing the distribution of E and I firing rates across a population of N=1,950 putative cells (Supplemental Figure 3). Firing rates of inhibitory neurons are marginally higher than excitatory neurons, and both E and I follow an approximately exponential distribution of rates.

Reviewer may be right that there are more I neurons at borders in Fig.3B because injections were done in medial prefrontal cortex, so this may reflect an experimental artefact related to a high probability of activating I neurons in locations where the opsin was activated. We added a sentence to the text to clarify this point (line 201):

“It is possible that the spatial location of putative I cells reflects the site of injection of the opsin in medial prefrontal cortex.”

(9) For Figure 3 in general, I would have performed an exhaustive comparison of putative cells found by spikeMAP and other sorters. More precisely, I think that to prove the point that spikeMAP is indeed bringing something new to the field of spike sorting, the authors should have compared the performances of various spike sorters to discriminate Exc vs Inh cells based on their ground truth recordings. For example, either using Kilosort [Pachitariu et al, 2024, 10.1038/s41592-024-02232-7], or some other sorters that might be working with such large high-density data [Yger et al, 2018, 10.7554/eLife.34518].

The reviewer is correct to point out that our the spike-sorting portion of our pipeline shares similarities with related approaches. Other aspects, however, are unique to SpikeMAP. We have clarified the text accordingly:

“In sum, SpikeMAP provides an end-to-end pipeline to perform spike-sorting on high-density multielectrode arrays. Some elements of this pipeline are similar to related approaches (Table 1), including the use of voltage filtering, PCA, and k-means clustering. Other elements are novel, including the use of spline interpolation, LDA, and the ability to identify putative excitatory and inhibitory cells.”

(10) Figure 4 has a big issue, and I guess the panels A and B should be redrawn. I don't understand what the red rectangle is displaying.

Again, we apologize for the rendering issues in the Figures that occurred during conversion into PDF format. We have now ensured that all figures are properly displayed.

(11) I understand that Figure 4 is only one example, but I have a hard time understanding from the manuscript how many slices/mices were used to obtain the GT data? I guess the manuscript could be enhanced by turning the data into an open-access dataset, but then some clarification is needed. How many flashes/animals/slices are we talking about? Maybe this should be illustrated in Figure 4, if this figure is devoted to the introduction of the GT data.

Details of the open access data are now provided in Supplemental Table 1. We also clarified Figure 5B:

“Quantification of change in firing rate following optogenetic stimulation. Average firing rates are taken over four recordings obtained from 3 mice.”

(12) While there is no doubt that GT data as the ones recorded here by the authors are the most interesting data from a validation point of view, the pretty low yield of such experiments should not discourage the use of artificially generated recordings such as the ones made in [Buccino et al, 2020, 10.1007/s12021-020-09467-7] or even recently in [Laquitaine et al, 2024, 10.1101/2024.12.04.626805v1]. In these papers, the authors have putative waveforms/firing rate patterns for excitatory and inhibitory cells, and thus, the authors could test how good they are in discriminating the two subtypes.

We agree with the reviewer that it would be worthwhile for future work to apply SpikeMAP to artificially generated spike trains, and have added the following (line 328):

“Another avenue could involve applying SpikeMAP on artificially generated spike data (Buccino & Einevoll 2021; Laquitaine et al., 2024).”

Reviewer #1 (Recommendations for the authors):

(1) Line 154 seems to include a parenthetical expression left over from editing: "sensitive to noise (contamination? Better than noise?) generated by the signal of proximal units." See also line 186: "use (reliance?) of light-sensitive" and line 245: "In the absence of synaptic blockers (right?)," and line 270: "the size of the data prevents manual intervention (curation?)." Check carefully for all parentheses like that, which should be removed.

Thank you for pointing this out. We have revised the text and removed parenthetical expressions left over from editing.

(2) In lines 285-286, you state that: "k-mean clustering of spike waveform properties best differentiated the two principal classes of cells..." But I could not find where you compared k-means clustering to other methods. I think you just argued that k-means seemed to work well, but not better than, another method. If that is so, then you should probably rephrase those lines.

The reviewer is correct that direct comparisons are not performed here, hence we removed this sentence.

(3) Methods section, E/I classification, lines 396-405: You give us figures on what fraction was E and I (PV subtype) (94.75% and 5.25%), but there is more that you could have said. First of all, what is the expected fraction of parvalbumin-sensitive interneurons in the cortex - is it near 5%?

We clarified the text as follows (line 444): “This number is close to the expected percentage of PV interneurons in cortex (4-6%) (Markram et al. 2004).”

Second, how would these percentages change if you altered the threshold from 3 s.d. to something lower, like 2 s.d.? Giving us some idea of how the threshold affects the fraction of PV interneurons could give us an idea of whether this method agrees with our expectations or not.

While SpikeMAP offers the flexibility to set the voltage threshold manually, we opted for a stringent threshold to demonstrate the capabilities of the software. As seen in Figure 2D, at 2 and 3 s.d., the signal is largely accounted for by Gaussian noise, while deviation from noise arises around 4 s.d. We clarified the text as follows (line 120):

“At a threshold of -3 , the signal could be largely accounted for by Gaussian noise, while a separation between signal and noise began around a threshold of -4 ”

Third, did the inhibitory neurons identified by this optogenetic method also have narrow spike widths at half amplitude? Could you do a scatterplot of all the spike widths and inter-peak distances that had color-coded dots for E and I based on your optogenetic method?

We have added a scatterplot (Supplemental Figure 5).

(4) Can you compare your methods with others now widely in use, like, for example, Spiking Circus or Kilosort? You do that in Table 1 in terms of features, but not in terms of performance. For example, you could have applied Kilosort4 to your data from the 4096 electrode array and seen how often it sorted the same neurons that SpikeMAP did. I realize this could not give you a comparison of how many were E/I, but it could tell you how close your numbers of neurons agreed with their numbers. Were your numbers within 5% of each other? This would be helpful for groups who are already using Kilosort4.

As mentioned ealier, packages listed in Table 1 do not provide an identification of putative E/I neurons on high-density electrode arrays. To facilitation the integration of SpikeMAP with other spike sorting packages, our suite now provides a stand-alone module to perform E/I identification. This is now mentioned in the text (see earlier comment).

Reviewer #2 (Recommendations for the authors):

I would encourage the authors to decide what the paper is about: is it about a new sorting method (and if yes, more tests/benchmarks are needed to explain the pros and the cons of the pipelines, and the Methods need to be expanded). Or is it about the new data for Ground Truth validation, and again, if yes, then maybe explain more what they are, how many slices/mice/cells, ... Maybe also consider making the data available online as an open dataset.

We agree with the reviewer that the paper is best slated toward ground truth validation of E/I identification. We now specify how many slices/mice/cells etc. (see Supplemental Table 1) and make the data available online as open source.

Reviewer #3 (Public review):

Summary:

In their study, Shen et al. examine how first- and second-order neurons of early olfactory circuits among invertebrates and vertebrates alike respond to and encode odor identity and concentration. Previously published electrophysiological and imaging data are re-analyzed and complemented with computational simulations. The authors explore multiple potential circuit computations by which odor concentration-dependent increases in first-order neuron responses transform into concentration-invariant responses on average across the second-order neuron population, and report that divisive normalization exceeds subtractive normalization and intraglomerular gain control in accounting for this transformation. The authors then explore how either rate- or timing-based schemes in third-order neurons may decode odor identity and concentration information from such concentration-invariant mean responses across the second-order neuron population. Finally, the results of their study of second-order neurons (invertebrate projection neurons and vertebrate mitral cells) are contrasted with the concentration-variant responses of second-order projection tufted cells in mammals. Overall, through a combination of neural data re-analysis, computational simulation, and conceptual theory, this study provides important new understanding of how aspects of sensory information are encoded through the actions of distinct components of early olfactory circuits.

Strengths:

Consideration of multiple evolutionarily disparate olfactory circuits, as well as re-analysis of previously published neural data sets combined with novel simulations guided by those sets, lends considerable robustness to some key findings of this study. In particular, the finding that divisive normalization - with direct inspiration from established circuit components in the form of glomerular layer short-axon cells - accounts more thoroughly for the average concentration invariance of second-order olfactory neurons at a population level than other forms of normalization is compelling. Likewise, demonstration of the required 'crossover' of first-order neuron concentration sensitivity for divisive normalization to achieve such flattening of concentration variance across the second-order population is notable, with simulations providing important insight into experimentally observed patterns of first-order neuron responses. Limited clarity in other aspects of the study, in particular related to the consideration of neural response latencies and enumerated below, temper the overall strength of the study.

Weaknesses:

(1) While the authors focus on concentration-dependent increases in first-order neuron activity, reflecting the majority of observed responses, recent work from the Imai group shows that odorants can also lead to direct first-order neuron inhibition (i.e., reduction in spontaneous activity), and within this subset, increasing odorant concentration tends to increase the degree of inhibition. Some discussion of these findings and how they may complement divisive normalization to contribute to the diverse second-order neuron concentration-dependence would be of interest and help expand the context of the current results.

(2) Related to the above point, odorant-evoked inhibition of second-order neurons is widespread in mammalian mitral cells and significantly contributes to the flattened concentration-dependence of mitral cells at the population level. Such responses are clearly seen in Figure 1D. Some discussion of how odorant-evoked mitral cell inhibition may complement divisive normalization, and likewise relate to comparatively lower levels of odorant-evoked inhibition among tufted cells, would further expand the context of the current results. Toward this end, replication of analyses in Figures 1D and E following exclusion of mitral cell inhibitory responses would provide insight into the contribution of such inhibition to the flattening of the mitral cell population concentration dependence.

(3) The idea of concentration-dependent crossover responses across the first-order population being required for divisive normalization to generate individually diverse concentration response functions across the second-order population is notable. The intuition of the crossover responses is that first-order neurons that respond most sensitively to any particular odorant (i.e., at the lowest concentration) respond with overall lower activity at higher concentrations than other first-order neurons less sensitively tuned to the odorant. Whether this is a consistent, generalizable property of odorant binding and first-order neuron responsiveness is not addressed by the authors, however. Biologically, one mechanism that may support such crossover events is intraglomerular presynaptic/feedback inhibition, which would be expected to increase with increasing first-order neuron activation such that the most-sensitively responding first-order neurons would also recruit the strongest inhibition as concentration increases, enabling other first-order neurons to begin to respond more strongly. Discussion of this and/or other biological mechanisms (e.g., first-order neuron depolarization block) supporting such crossover responses would strengthen these results.

(4) It is unclear to what degree the latency analysis considered in Figures 4D-H works with the overall framework of divisive normalization, which in Figure 3 we see depends on first-order neuron crossover in concentration response functions. Figure 4D suggests that all first-order neurons respond with the same response amplitude (R in eq. 3), even though this is supposed to be pulled from a distribution. It's possible that Figure 4D is plotting normalized response functions to highlight the difference in latency, but this is not clear from the plot or caption. If response amplitudes are all the same, and the response curves are, as plotted in Figure 4D, identical except for their time to half-max, then it seems somewhat trivial that the resulting second-order neuron activation will follow the same latency ranking, regardless of whether divisive normalization exists or not. However, there is some small jitter in these rankings across concentrations (Figure 4G), suggesting there is some randomness to the simulations. It would be helpful if this were clarified (e.g., by showing a non-normalized Figure 4D, with different response amplitudes), and more broadly, it would be extremely helpful in evaluating the latency coding within the broader framework proposed if the authors clarified whether the simulated first-order neuron response timecourses, when factoring in potentially different amplitudes (R) and averaging across the entire response window, reproduces the concentration response crossovers observed experimentally. In summary, in the present manuscript, it remains unclear if concentration crossovers are captured in the latency simulations, and if not, the authors do not clearly address what impact such variation in response amplitudes across concentrations may have on the latency results. It is further unclear to what degree divisive normalization is necessary for the second-order neurons to establish and maintain their latency ranks across concentrations, or to exhibit concentration-dependent changes in latency.

(5) How the authors get from Figure 4G to 4H is not clear. Figure 4G shows second-order neuron response latencies across all latencies, with ordering based on their sorted latency to low concentration. This shows that very few neurons appear to change latency ranks going from low to high concentration, with a change in rank appearing as any deviation in a monotonically increasing trend. Focusing on the high concentration points, there appear to be 2 latency ranks switched in the first 10 responding neurons (reflecting the 1 downward dip in the points around neuron 8), rather than the 7 stated in the text. Across the first 50 responding neurons, I see only ~14 potential switches (reflecting the ~7 downward dips in the points around neurons 8, 20, 32, 33, 41, 44, 50), rather than the 32 stated in the text. It is possible that the unaccounted rank changes reflect fairly minute differences in latencies that are not visible in the plot in Figure 4G. This may be clarified by plotting each neuron's latency at low concentration vs. high concentration (i.e., similar to Figure 4H, but plotting absolute latency, not latency rank) to allow assessment of the absolute changes. If such minute differences are not driving latency rank changes in Fig. 4G, then a trend much closer to the unity line would be expected in Figure 4H. Instead, however, there are many massive deviations from unity, even within the first 50 responding neurons plotted in Figure 4G. These deviations include a jump in latency rank from 2 at low concentration to ~48 at high concentration. Such a jump is simply not seen in Figure 4G.

(6) In the text, the authors state that "Odor identity can be encoded by the set of highest-affinity neurons (which remains invariant across concentrations)." Presumably, this is a restatement of the primacy model and refers to invariance in latency rank (since the authors have not shown that the highest-affinity neurons have invariant response amplitudes across concentration). To what degree this statement holds given the results in Figure 4H, however, which appear to show that some neurons with the earliest latency rank at low concentration jump to much later latency ranks at high concentration, remains unclear. Such changes in latency rank for only a few of the first responding neurons may be negligible for classifying odor identity among a small handful of odorants, but not among 1-2 orders of magnitude more odors, which may feasibly occur in a natural setting. Collectively, these issues with the execution and presentation of the latency analysis make it unclear how robust the latency results are.

(7) Analysis in Figures 4A-C shows that concentration can be decoded from first-order neurons, second-order neurons, or first-order neurons with divisive normalization imposed (i.e., simulating second-order responses). This does not say that divisive normalization is necessary to encode concentration, however. Therefore, for the authors to say that divisive normalization is "a potential mechanism for generating odor-specific subsets of second-order neurons whose combinatorial activity or whose response latencies represent concentration information" seems too strong a conclusion. Divisive normalization is not generating the concentration information, since that can be decoded just as well from the first-order neurons. Rather, divisive normalization can account for the different population patterns in concentration response functions between first- and second-order neurons without discarding concentration-dependent information.

(8) Performing the same polar histogram analysis of tufted vs. mitral cell concentration response functions (Figure 5B) provides a compelling new visualization of how these two cell types differ in their concentration variance. The projected importance of tufted cells to navigation, emerging directly through the inverse relationship between average concentration and distance (Figure 5C), is not surprising, and is largely a conceptual analysis rather than new quantitative analysis per se, but nevertheless, this is an important point to make. Another important consideration absent from this section, however, is whether and how divisive normalization may impact tufted cell activity. Previous work from the authors, as well as from Schoppa, Shipley, and Westbrook labs, has compellingly demonstrated that a major circuit mediating divisive normalization of mitral cells (GABA/DAergic short-axon cells) directly targets external tufted cells, and is thus very likely to also influence projection tufted cells. Such analysis would additionally provide substantially more justification for the Discussion statement "we analyzed an additional type of second-order neuron (tufted cells)", which at present instead reflects fairly minimal analysis.

Author response:

(1) Explore the temporal component of neural responses (instead of collapsing responses to a single number, i.e., the average response over 4s), and determine which of the three models can recapitulate the observed dynamics.

(2) Expand the polar plot visualization to show all three slopes (changes in responses across all three successive concentrations) instead of only two slopes.

(3) Attempt to collect and analyze, from published papers, data of: (a) first-order neuron responses to odors to determine the role of first-order inhibition towards generating non-monotonic responses, and (b) PN responses in Drosophila to properly compare with corresponding first-order neuron responses.

(4) Further discuss: (a) why the brain may need to encode absolute concentration, (b) the distinction between non-monotonic responses and cross-over responses, and (c) potential limitations of the primacy model.

(5) Expand the divisive normalization model by evaluating different values of k and R, and study the effects of divisive normalization on tufted cells.

(6) Add discussion of other potential inhibitory mechanisms that could contribute towards the observed effects.

Reviewer #1:

The article starts from the premise that animals need to know the absolute concentration of an odor over many log units, but the need for this isn't obvious. The introduction cites an analogy to vision and audition. These are cases where we know for a fact that the absolute intensity of the stimulus is not relevant. Instead, sensory perception relies on processing small differences in intensity across space or time. And to maintain that sensitivity to small differences, the system discards the stimulus baseline. Humans are notoriously bad at judging the absolute light level. That information gets discarded even before light reaches the retina, namely through contraction of the pupil. Similarly, it seems plausible that a behavior like olfactory tracking relies on sensing small gradients across time (when weaving back and forth across the track) or space (across nostrils). It is important that the system function over many log units of concentration (e.g., far and close to a source) but not that it accurately represents what that current concentration is [see e.g., Wachowiak et al, 2025 Recalibrating Olfactory Neuroscience..].

We thank the Reviewer for the insightful input and agree that gradients across time and space are important for various olfactory behaviors, such as tracking. At the same time, we think that absolute concentration is also needed for two reasons. First, in order to extract changes in concentration, the absolute concentration needs to be normalized out; i.e., change needs to be encoded with respect to some baseline, which is what divisive normalization computes. Second, while it is true that representing the exact number of odor molecules present is not important, this number directly relates to distance from the odor source, which does provide ethological value (e.g., is the tiger 100m or 1000m away?). Indeed, our decoding experiments focused on discriminating relative, and not on absolute, concentrations by classifying between each pair of concentrations (i.e., relative distances), which is effectively an assessment of the gradient. In our revision, we will make all of these points clearer.

Still, many experiments in olfactory research have delivered square pulses of odor at concentrations spanning many log units, rather than the sorts of stimuli an animal might encounter during tracking. Even within that framework, though, it doesn't seem mysterious anymore how odor identity and odor concentration are represented differently. For example, Stopfer et al 2003 showed that the population response of locust PNs traces a dynamic trajectory. Trajectories for a given odor form a manifold, within which trajectories for different concentrations are distinct by their excursions on the manifold. To see this, one must recognize that the PN responds to an odor pulse with a time-varying firing rate, that different PNs have different dynamics, and that the dynamics can change with concentration. This is also well recognized in the mammalian systems. Much has been written about the topic of dynamic coding of identity and intensity - see the reviews of Laurent (2002) and Uchida (2014).

Given the above comments on the dynamics of odor responses in first- and second-order neurons, it seems insufficient to capture the response of a neuron with a single number. Even if one somehow had to use a single number, the mean firing rate during the odor pulse may not be the best choice. For example, the rodent mitral cells fire in rhythm with the animal's sniffing cycle, and certain odors will just shift the phase of the rhythm without changing the total number of spikes (see e.g., Fantana et al, 2008). During olfactory search or tracking, the sub-second movements of the animal in the odor landscape get superposed on the sniffing cycle. Given all this, it seems unlikely that the total number of spikes from a neuron in a 4-second period is going to be a relevant variable for neural processing downstream.

To our knowledge, it is not well understood how downstream brain regions read out mitral cell responses to guide olfactory behavior. The olfactory bulb projects to more than a dozen brain regions, and different regions could decode signals in different ways. We focused on the mean response because it is a simple, natural construct.

The datasets we analyzed may not include all relevant timing information; for example, the mouse data is from calcium imaging studies that did not track sniff timing. Nonetheless, we plan to address this comment within our framework by binning time into smaller-sized windows (e.g., 0-0.2s, 0.2-0.4s, etc.) and repeating our analysis for each of these windows. Specifically, we will determine how each normalization method fares in recapitulating statistics of the population responses of each window, beyond simply assessing the population mean.

Much of the analysis focuses on the mean activity of the entire population. Why is this an interesting quantity? Apparently, the mean stays similar because some neurons increase and others decrease their firing rate. It would be more revealing, perhaps, to show the distribution of firing rates at different concentrations and see how that distribution is predicted by different models of normalization. This could provide a stronger test than just the mean.

We agree that mean activity is only one measure to summarize a rich data set and will perform the suggested analysis.

The question "if concentration information is discarded in second-order neurons, which exclusively transmit odor information to the rest of the brain, how does the brain support olfactory behaviors, such as tracking and navigation?" is really not an open question anymore. For example, reference 23 reports in the abstract that "Odorant concentration had no systematic effect on spike counts, indicating that rate cannot encode intensity. Instead, odor intensity can be encoded by temporal features of the population response. We found a subpopulation of rapid, largely concentration-invariant responses was followed by another population of responses whose latencies systematically decreased at higher concentrations."

Primacy coding does provide one plausible mechanism to decode concentration. Our manuscript demonstrated how such a code could emerge in second-order neurons with the help of divisive normalization, though it does require maintaining at least partial rank invariance across concentrations, which may not be robust. We also showed how concentration could be decoded via spike rates, even if average rates are constant, which provides an alternative hypothesis to that of ref 23.

Further, ref 23 only considers the piriform cortex, which, as mentioned above, is one of many targets of the olfactory bulb, and it remains unclear what the decoding mechanisms are of each of these targets. In addition, work from the same authors of ref 23 found multiple potential decoding strategies in the piriform cortex itself, including changes in firing rate (see Fig. 2E of ref. 23 - Bolding & Franks, 2017; as well as Fig. 4 in Roland et al., 2017).

It would be useful to state early in the manuscript what kinds of stimuli are being considered and how the response of a neuron is summarized by one number. There are many alternative ways to treat both stimuli and responses.

We will add this explanation to the manuscript.

"The change in response across consecutive concentration levels may not be robust due to experimental noise and the somewhat limited range of concentrations sampled": Yes, a number of the curves just look like "no response". It would help the reader to show some examples of raw data, e.g. the time course of one neuron's firing rate to 4 concentrations, and for the authors to illustrate how they compress those responses into single numbers.

We agree and will add this information to the manuscript.

"We then calculated the angle between these two slopes for each neuron and plotted a polar histogram of these angles." The methods suggest that this angle is the arctan of the ratio of the two slopes in the response curve. A ratio of 2 would result from a slope change from 0.0001 to 0.0002 (i.e., virtually no change in slope) or from 1 to 2 (a huge change). Those are completely different response curves. Is it reasonable to lump them into the same bin of the polar plot? This seems an unusual way to illustrate the diversity of response curve shapes.

We agree that the two changes in the reviewer’s example will be categorized in the same quadrant in our analysis. We did not focus on the absolute changes because our analysis covers many log ratios of concentrations. Instead, we focused on the relative shapes of the concentration response curves, and more specifically, the direction of the change (i.e., the sign of the slope). We will better motivate this style of analysis in the revision. Moreover, in response to comments by Reviewer 2, we will compare response shapes between all three successive levels of concentration changes, as opposed to only two levels.

The Drosophila OSN data are passed through normalization models and then compared to locust PN data. This seems dangerous, as flies and locusts are separated by about 300 M years of evolution, and we don't know that fly PNs act like locust PNs. Their antennal lobe anatomy differs in many ways, as does the olfactory physiology. To draw any conclusions about a change in neural representation, it would be preferable to have OSN and PN data from the same species.

We are in the process of requesting PN response data in Drosophila from groups that have collected such data and will repeat the analysis once we get access to the data.

One conclusion is that divisive normalization could account for some of the change in responses from receptors to 2nd order neurons. This seems to be well appreciated already [e.g., Olsen 2010, Papadopoulou 2011, minireview in Hong & Wilson 2013].

While we agree that these manuscripts do study the effects of divisive normalization in insects and fish, here we show that this computation also generalizes to rodents. In addition, these previous studies do not focus on divisive normalization’s role towards concentration encoding/decoding, which is our focus. We will clarify this difference in the revision.

Another claim is that subtractive normalization cannot perform that function. What model was used for subtractive normalization is unclear (there is an error in the Methods). It would be interesting if there were a categorical difference between divisive and subtractive normalization.

We apologize for the mistake in the subtractive normalization equation and will correct it. Thank you for catching it.

Looking closer at the divisive normalization model, it really has two components: (a) the "lateral inhibition" by which a neuron gets suppressed if other neurons fire (here scaled by the parameter k) , and (b) a nonlinear sigmoid transformation (determined by the parameters n and sigma). Both lateral inhibition and nonlinearity are known to contribute to decorrelation in a neural population (e.g., Pitkow 2012). The "intraglomerular gain control" contains only the nonlinearity. The "subtractive normalization" we don't know. But if one wanted to put divisive and subtractive inhibition on the same footing, one should add a sigmoid nonlinearity in both cases.

Our intent was not to place all the methods on the “same footing” but rather to isolate the two primary components of normalization methods – non-linearity and lateral inhibition – and determine which of these, and in which combination, could generate the desired effects. Divisive normalization incorporates both components, whereas intraglomerular gain control and subtractive normalization only incorporate one of these components. We will clarify this reasoning in the revision.

The response models could be made more realistic in other ways. For example, in both locusts and fish, the 2nd order neurons get inputs from multiple receptor types; presumably, that will affect their response functions. Also, lateral inhibition can take quite different forms. In locusts, the inhibitory neurons seem to collect from many glomeruli. But in rats, the inhibition by short axon cells may originate from just a few sparse glomeruli, and those might be different for every mitral cell (Fantana 2008).

We thank the Reviewer for the input. Instead of fixing k for all second-order neurons, we will apply different k values for different neurons. We will also systematically vary the percentage of neurons used for the divisive normalization calculation in the denominator, and determine the regime under which the effects experimentally observed are reproducible. This approach takes into account the scenario that inter-glomerular inhibitory interactions are sparse.

There are questions raised by the following statements: "traded-off energy for faster and finer concentration discrimination" and "an additional type of second-order neuron (tufted cells) that has evolved in land vertebrates and that outperforms mitral cells in concentration encoding" and later "These results suggest a trade-off between concentration decoding and normalization processes, which prevent saturation and reduce energy consumption.". Are the tufted cells inferior to the mitral cells in any respect? Do they suffer from saturation at high concentration? And do they then fail in their postulated role for odor tracking? If not, then what was the evolutionary driver for normalization in the mitral cell pathway? Certainly not lower energy consumption (50,000 mitral cells = 1% of rod photoreceptors, each of which consumes way more energy than a mitral cell).

The question of what mitral cells are “good for”, compared to tufted cells, remains unclear in our view. We speculate that mitral cells provide superior context-dependent processing and are better for determining stimuli-reward contingencies, but this remains far from settled experimentally.

We believe the mitral cell pathway evolved earlier than tufted cells, since the former appear akin to projection neurons in insects. Nonetheless, we agree that differences in energy consumption are unlikely to be the primary distinguishing factor, and in the revision, we will drop this argument.

Reviewer #2:

The main premise that divisive normalization generates this diversity of dose-response curves in the second-order neurons is a little problematic. … The analysis in [Figure 3] indicates that divisive normalization does what it is supposed to do, i.e., compresses concentration information and not alter the rank-order of neurons or the combinatorial patterns. Changes in the combinations of neurons activated with intensity arise directly from the fact that the first-order neurons did not have monotonic responses with odor intensity (i.e., crossovers). This was the necessary condition, and not the divisive normalization for changes in the combinatorial code. There seems to be a confusion/urge to attribute all coding properties found in the second-order neurons to 'divisive normalization.' If the input from sensory neurons is monotonic (i.e., no crossovers), then divisive normalization did not change the rank order, and the same combinations of neurons are activated in a similar fashion (same vector direction or combinatorial profile) to encode for different odor intensities. Concentration invariance is achieved, and concentration information is lost. However, when the first-order neurons are non-monotonic (i.e., with crossovers), that causes the second-order neurons to have different rank orders with different concentrations. Divisive normalization compresses information about concentrations, and rank-order differences preserve information about the odor concentration. Does this not mean that the non-monotonicity of sensory neuron response is vital for robustly maintaining information about odor concentration? Naturally, the question that arises is whether many of the important features of the second-order neuron's response simply seem to follow the input. Or is my understanding of the figures and the write-up flawed, and are there more ways in which divisive normalization contributes to reshaping the second-order neural response? This must be clarified. Lastly, the tufted cells in the mouse OB are also driven by this sensory input with crossovers. How does the OB circuit convert the input with crossovers into one that is monotonic with concentration? I think that is an important question that this computational effort could clarify.

It appears that there is confusion about the definitions of “non-monotonicity” and “crossovers”.  These are two independent concepts – one does not necessarily lead to the other. Non-monotonicity concerns the response of a single neuron to different concentration levels. A neuron’s response is considered non-monotonic if its response goes up then down, or down then up, across increasing concentrations. A “cross-over” is defined based on the responses of multiple neurons. A cross-over occurs when the response of one neuron is lower than another neuron at one concentration, but higher than the other at a different concentration. For example, the responses of both neurons could increase monotonically with increasing concentration, but one neuron might start lower and grow faster, hence creating a cross-over. We will clarify this in the manuscript, which we believe will resolve the questions raised above.

The way the decoding results and analysis are presented does not add a lot of information to what has already been presented. For example, based on the differences in rank-order with concentration, I would expect the combinatorial code to be different. Hence, a very simple classifier based on cosine or correlation distance would work well. However, since divisive normalization (DN) is applied, I would expect a simple classification scheme that uses the Euclidean distance metric to work equally as well after DN. Is this the case?

Yes, we used a simple classification scheme, logistic regression with a linear kernel, which is essentially a Euclidean distance-based classification. This scheme works better for tufted cells because they are more monotonic; i.e., if neuron A and B both increase their responsiveness with concentration, then Euclidean distance would be fine. But if neuron A’s response amplitude goes up and neuron B’s response goes down – as often happens for mitral cells – then Euclidean distance does not work as well. We will add intuition about this in the manuscript.

Leave-one-trial/sample-out seems too conservative. How robust are the combinatorial patterns across trials? Would just one or two training trials suffice for creating templates for robust classification? Based on my prior experience (https://elifesciences.org/reviewed-preprints/89330https://elifesciences.org/reviewed-preprints/89330), I do expect that the combinatorial patterns would be more robust to adaptation and hence also allow robust recognition of odor intensity across repeated encounters.

As suggested, we will compute the correlation coefficient of the similarity of neural responses for each odor (across trials). We will repeat this analysis for both mitral and tufted cells. To determine the effect of adaptation, we will compute correlation coefficients of responses between the 1st and 2nd trials vs the 1st and final trial.

Lastly, in the simulated data, since the affinity of the first-order sensory neurons to odorants is expected to be constant across concentration, and "Jaccard similarity between the sets of highest-affinity neurons for each pair of concentration levels was > 0.96," why would the rank-order change across concentration? DN should not alter the rank order.

We agree that divisive normalization should not alter the rank order, but the rank order may change in first-order neurons, which carries through to second-order neurons. This confusion may be related to the one mentioned above re: cross-overs vs non-monotonicity. Moreover, in the simulated data (Fig. 4D-H), the Jaccard similarity was calculated based on only the 50 neurons with the highest affinity, not the entire population of neurons. As shown in Fig. 4H, most of the rank-order change happens in the remaining 150 neurons.

Note that in response to a comment by Reviewer 3, we will change the presentation of Fig. 4H in the revision.

If the set of early responders does change, how will the decoder need to change, and what precise predictions can be made that can be tested experimentally? The lack of exploration of this aspect of the results seems like a missed opportunity.

In the Discussion, we wrote about how downstream circuits will need to learn which set of neurons are to be associated with each distinct concentration level. We will expand upon this point and include experimentally testable predictions.

Based on the methods, for Figures 1 and 2, it appears the responses across time, trials, and odorants were averaged to get a single data point per neuron for each concentration. Would this averaging not severely dilute trends in the data? The one that particularly concerns me is the averaging across different odorants. If you do odor-by-odor analysis, is the flattening of second-order neural responses still observable? Because some odorants activate more globally and some locally, I would expect a wide variety of dose-response relationships that vary with odor identity (more compressed in second-order neurons, of course). It would be good to show some representative neural responses and show how the extracted values for each neuron are a faithful/good representation of its response variation across intensities.

It appears there is some confusion here; we will clarify in the text and figure captions that we did not average across different odors in our analysis. We will also add figure panels showing some representative neural responses as suggested by the Reviewer.

A lot of neurons seem to have responses that flat line closer to zero (both firing rate and dF/F in Figure 1). Are these responsive neurons? The mean dF/F also seems to hover not significantly above zero. Hence, I was wondering if the number of neurons is reducing the trend in the data significantly.

Yes, if a neuron responds to at least one concentration level in at least 50% of the trials, it is considered responsive. So it is possible that some neurons respond to one concentration level and otherwise flatline near zero.  We will highlight a few example neurons to visualize this scenario.

I did not fully understand the need to show the increase in the odor response across concentrations as a polar plot. I see potential issues with the same. For example, the following dose-response trend at four intensities (C4 being the highest concentration and C1 the lowest): response at C3 > response at C1 and response at C4 > response at C2. But response at C3 < response at C2. Hence, it will be in the top right segment of the polar plot. However, the responses are not monotonic with concentrations. So, I am not convinced that the polar plot is the right way to characterize the dose-response curves. Just my 2 cents.

Your 2 cents are valuable! Thank you for raising this point. Instead of computing two slopes (C1-C3 and C2-C4), we will expand our analysis to include all three slopes (C1-C2, C2-C3, C3-C4). Consequently, there are 2^3 = 8 different response shapes, and we will list them and quantify the fraction of the responses that fall into each shape category.

In many analyses, simulated data were used (Figures 3 and 4). However, there is no comparison of how well the simulated data fit the experimental data. For example, the Simulated 1st order neuron in Figure 3D does not show a change in rank-order for the first-order neuron. In Figure 3E, temporal response patterns in second-order neurons look unrealistic. Some objective comparison of simulated and experimental data would help bolster confidence in these results.

We believe the Reviewer is referring to Figs. 4D and 4E, since Fig. 3D does not show a first-order neuron simulation, and there is no Fig 3E. In Fig. 4D there is no change of rank order because the simulation is for a single odor and single concentration level, and the change of rank-order (i.e., cross-overs) as we define occurs between concentration levels. We will clarify this in the manuscript.

Reviewer #3:

While the authors focus on concentration-dependent increases in first-order neuron activity, reflecting the majority of observed responses, recent work from the Imai group shows that odorants can also lead to direct first-order neuron inhibition (i.e., reduction in spontaneous activity), and within this subset, increasing odorant concentration tends to increase the degree of inhibition. Some discussion of these findings and how they may complement divisive normalization to contribute to the diverse second-order neuron concentration-dependence would be of interest and help expand the context of the current results.

We thank the Reviewer for the suggestion. We will request datasets of first-order neuron responses from the groups who acquired them. We will analyze this data to determine the role of inhibition or antagonistic binding and quantify what percentage of first-order neurons respond less strongly with larger concentrations.

Related to the above point, odorant-evoked inhibition of second-order neurons is widespread in mammalian mitral cells and significantly contributes to the flattened concentration-dependence of mitral cells at the population level. Such responses are clearly seen in Figure 1D. Some discussion of how odorant-evoked mitral cell inhibition may complement divisive normalization, and likewise relate to comparatively lower levels of odorant-evoked inhibition among tufted cells, would further expand the context of the current results. Toward this end, replication of analyses in Figures 1D and E following exclusion of mitral cell inhibitory responses would provide insight into the contribution of such inhibition to the flattening of the mitral cell population concentration dependence.

We will perform the analysis suggested, specifically, we will set the negative mitral cell responses to 0 and assess whether the population mean remains flat.

The idea of concentration-dependent crossover responses across the first-order population being required for divisive normalization to generate individually diverse concentration response functions across the second-order population is notable. The intuition of the crossover responses is that first-order neurons that respond most sensitively to any particular odorant (i.e., at the lowest concentration) respond with overall lower activity at higher concentrations than other first-order neurons less sensitively tuned to the odorant. Whether this is a consistent, generalizable property of odorant binding and first-order neuron responsiveness is not addressed by the authors, however. Biologically, one mechanism that may support such crossover events is intraglomerular presynaptic/feedback inhibition, which would be expected to increase with increasing first-order neuron activation such that the most-sensitively responding first-order neurons would also recruit the strongest inhibition as concentration increases, enabling other first-order neurons to begin to respond more strongly. Discussion of this and/or other biological mechanisms (e.g., first-order neuron depolarization block) supporting such crossover responses would strengthen these results.

We thank the reviewer for providing additional mechanisms to consider. As suggested, we will add discussion of these alternatives to divisive normalization.

It is unclear to what degree the latency analysis considered in Figures 4D-H works with the overall framework of divisive normalization, which in Figure 3 we see depends on first-order neuron crossover in concentration response functions. Figure 4D suggests that all first-order neurons respond with the same response amplitude (R in eq. 3), even though this is supposed to be pulled from a distribution. It's possible that Figure 4D is plotting normalized response functions to highlight the difference in latency, but this is not clear from the plot or caption. If response amplitudes are all the same, and the response curves are, as plotted in Figure 4D, identical except for their time to half-max, then it seems somewhat trivial that the resulting second-order neuron activation will follow the same latency ranking, regardless of whether divisive normalization exists or not. However, there is some small jitter in these rankings across concentrations (Figure 4G), suggesting there is some randomness to the simulations. It would be helpful if this were clarified (e.g., by showing a non-normalized Figure 4D, with different response amplitudes), and more broadly, it would be extremely helpful in evaluating the latency coding within the broader framework proposed if the authors clarified whether the simulated first-order neuron response timecourses, when factoring in potentially different amplitudes (R) and averaging across the entire response window, reproduces the concentration response crossovers observed experimentally. In summary, in the present manuscript, it remains unclear if concentration crossovers are captured in the latency simulations, and if not, the authors do not clearly address what impact such variation in response amplitudes across concentrations may have on the latency results. It is further unclear to what degree divisive normalization is necessary for the second-order neurons to establish and maintain their latency ranks across concentrations, or to exhibit concentration-dependent changes in latency.

As suggested by the Reviewer, we will add another simulation scenario where the response amplitudes (R) are different for different neurons. For each concentration, we will then average each neuron’s response across the entire response window and determine if the simulation reproduces the cross-overs as observed experimentally.

How the authors get from Figure 4G to 4H is not clear. Figure 4G shows second-order neuron response latencies across all latencies, with ordering based on their sorted latency to low concentration. This shows that very few neurons appear to change latency ranks going from low to high concentration, with a change in rank appearing as any deviation in a monotonically increasing trend. Focusing on the high concentration points, there appear to be 2 latency ranks switched in the first 10 responding neurons (reflecting the 1 downward dip in the points around neuron 8), rather than the 7 stated in the text. Across the first 50 responding neurons, I see only ~14 potential switches (reflecting the ~7 downward dips in the points around neurons 8, 20, 32, 33, 41, 44, 50), rather than the 32 stated in the text. It is possible that the unaccounted rank changes reflect fairly minute differences in latencies that are not visible in the plot in Figure 4G. This may be clarified by plotting each neuron's latency at low concentration vs. high concentration (i.e., similar to Figure 4H, but plotting absolute latency, not latency rank) to allow assessment of the absolute changes. If such minute differences are not driving latency rank changes in Fig. 4G, then a trend much closer to the unity line would be expected in Figure 4H. Instead, however, there are many massive deviations from unity, even within the first 50 responding neurons plotted in Figure 4G. These deviations include a jump in latency rank from 2 at low concentration to ~48 at high concentration. Such a jump is simply not seen in Figure 4G.

We apologize that Fig. 4H was a poor choice for visualization. What is plotted in Fig. 4H is the sorted identity of neurons under low and high concentrations, and points on the y=x line indicate that the two corresponding neurons have the same rank under the two concentrations. We will replace this panel with a more intuitive visualization, where the x and y axes are the ranks of the neurons; and deviation from the y=x line indicates how different the ranks are of a neuron to the two concentrations.

In the text, the authors state that "Odor identity can be encoded by the set of highest-affinity neurons (which remains invariant across concentrations)." Presumably, this is a restatement of the primacy model and refers to invariance in latency rank (since the authors have not shown that the highest-affinity neurons have invariant response amplitudes across concentration). To what degree this statement holds given the results in Figure 4H, however, which appear to show that some neurons with the earliest latency rank at low concentration jump to much later latency ranks at high concentration, remains unclear. Such changes in latency rank for only a few of the first responding neurons may be negligible for classifying odor identity among a small handful of odorants, but not among 1-2 orders of magnitude more odors, which may feasibly occur in a natural setting. Collectively, these issues with the execution and presentation of the latency analysis make it unclear how robust the latency results are.

The original primacy model states that the latency of a neuron decreases with increasing concentration, while the ranks of neurons remain unaltered. Our results, on the other hand, suggest that the ranks do at least partially change across concentrations. This leads to two possible decoding mechanisms. First, if the top K responding neurons remain invariant across concentrations (even if their individual ranks change within the top K), then the brain could learn to associate a population of K neurons with a response latency; lower response latency means higher concentration. Second, if the top K responding neurons do not remain invariant across concentrations, then the brain would need to learn to associate a different set of neurons with each concentration level. The latter imposes additional constraints on the robustness of the primacy model and the corresponding read-out mechanism. We will include more discussion of these possibilities in the revision.

Analysis in Figures 4A-C shows that concentration can be decoded from first-order neurons, second-order neurons, or first-order neurons with divisive normalization imposed (i.e., simulating second-order responses). This does not say that divisive normalization is necessary to encode concentration, however. Therefore, for the authors to say that divisive normalization is "a potential mechanism for generating odor-specific subsets of second-order neurons whose combinatorial activity or whose response latencies represent concentration information" seems too strong a conclusion. Divisive normalization is not generating the concentration information, since that can be decoded just as well from the first-order neurons. Rather, divisive normalization can account for the different population patterns in concentration response functions between first- and second-order neurons without discarding concentration-dependent information.

We agree that the word “generating” is faulty. We thank the reviewer for their more precise wording, which we will adopt.

Performing the same polar histogram analysis of tufted vs. mitral cell concentration response functions (Figure 5B) provides a compelling new visualization of how these two cell types differ in their concentration variance. The projected importance of tufted cells to navigation, emerging directly through the inverse relationship between average concentration and distance (Figure 5C), is not surprising, and is largely a conceptual analysis rather than new quantitative analysis per se, but nevertheless, this is an important point to make. Another important consideration absent from this section, however, is whether and how divisive normalization may impact tufted cell activity. Previous work from the authors, as well as from Schoppa, Shipley, and Westbrook labs, has compellingly demonstrated that a major circuit mediating divisive normalization of mitral cells (GABA/DAergic short-axon cells) directly targets external tufted cells, and is thus very likely to also influence projection tufted cells. Such analysis would additionally provide substantially more justification for the Discussion statement "we analyzed an additional type of second-order neuron (tufted cells)", which at present instead reflects fairly minimal analysis.

We agree that tufted cells are subject to divisive normalization as well, albeit probably to a less degree than mitral cells. To determine the effect of this, we will alter the strength (and degree of sparseness of interglomerular interactions) of divisive normalization and determine if there is a regime where response features of tufted cells match those observed experimentally.

Très incomplet tout de même, ce qui invite une fois de plus à questionner la façon dont Eugène Gallois se situe par rapport au colonialisme

Où sont les Etats-Unis ? On aimerait une analyse de l'européocentrisme de ce classement et plus encore, une réflexion sur les façons de la désamorcer sans la faire disparaître ?

Le "domaine colonial français" n'existe pas en dehors des territoires qui le constituent. Mieux vaut parler d'empire colonial avec toute la charge de violence que cette formule colporte.

Cette généralisation fondée sur une catégorisation a priori n'est pas convaincante. Elle va à l'encontre des recherches sur les publics de la propagande coloniale, par exemple celles de la très riche collection "Studies In Imperialism" fondée par John MacKenzie (https://manchesteruniversitypress.co.uk/series/studies-in-imperialism/). Dans quelle mesure est-il possible de faire une étude des publics des conférences et des oeuvres d'Eugène Gallois ?

Est-ce pour cela qu'il privilégie les territoires colonisés et parmi eux, ceux qui sont colonisés par la France ? La remarque est intéressante, mais elle n'est pas assez étayée.

Dimension peu abordée au final, sans doute pour des raisons de taille du texte. La question de ce qu'il faudrait faire pour mettre en évidence le caractère européocentrique du classement des aquarelles sans le faire disparaître - c'est en soi une archive - serait sans doute un bon point d'entrée.

Comment ? Sur quel plan ?

La transition des voyages en général au savoir colonial, en quel sens d'ailleurs, n'est pas expliquée. Elle pose la question importante des différences éventuelles entre voyages dans des pays reconnus comme des Etats et voyages dans les territoires colonisés. Y a-t-il une spécificité des seconds ? Cette question est d'autant plus incontournable qu'elle est au coeur des recherches historiques sur les situations coloniales.

Formule forte, à justifier en passant par la bibliographie abondante sur les enquêtes sociales et en situation coloniale.

Je réitère ma remarque sur l'utilisation des termes au contenu plutôt consensuel : "transmission" ; "vulgarisation" et de la formule "propagande coloniale" qui renvoie à des pratiques autoritaires. Les commenter enrichirait la problématique adoptée.

Même si le soutien à la cause coloniale est mentionné, la phrase donne l'impression de contourner le terme classique de propagande et sa dimension autoritaire. Peut-être faudrait-il poser d'emblée la question de la qualification de cette transmission, dans toutes ses nuances et en évitant bien sûr toute catégorisation a priori.

Reviewer #1 (Public review):

Summary:

Zhang et al. used a conditional knockout mouse model to re-examine the role of the RNA-binding protein PTBP1 in the transdifferentiation of astroglial cells into neurons. Several earlier studies reported that PTBP1 knockdown can efficiently induce the transdifferentiation of rodent glial cells into neurons, suggesting potential therapeutic applications for neurodegenerative diseases. However, these findings have been contested by subsequent studies, which in turn have been challenged by more recent publications. In their current work, Zhang et al. deleted exon 2 of the Ptbp1 gene using an astrocyte-specific, tamoxifen-inducible Cre line and investigated, using fluorescence imaging and bulk and single-cell RNA-sequencing, whether this manipulation promotes the transdifferentiation of astrocytes into neurons across various brain regions. The data strongly indicate that genetic ablation of PTBP1 is not sufficient to drive efficient conversion of astrocytes into neurons. Interestingly, while PTBP1 loss alters splicing patterns in numerous genes, these changes do not shift the astroglial transcriptome toward a neuronal profile.

Strengths:

Although this is not the first report of PTBP1 ablation in mouse astrocytes in vivo, this study utilizes a distinct knockout strategy and provides novel insights into PTBP1-regulated splicing events in astrocytes. The manuscript is well written, and the experiments are technically sound and properly controlled. I believe this study will be of considerable interest to a broad readership.

Weaknesses:

(1) The primary point that needs to be addressed is a better understanding of the effect of exon 2 deletion on PTBP1 expression. Figure 4D shows successful deletion of exon 2 in knockout astrocytes. However, assuming that the coverage plots are CPM-normalized, the overall PTBP1 mRNA expression level appears unchanged. Figure 6A further supports this observation. This is surprising, as one would expect that the loss of exon 2 would shift the open reading frame and trigger nonsense-mediated decay of the PTBP1 transcript. Given this uncertainty, the authors should confirm the successful elimination of PTBP1 protein in cKO astrocytes using an orthogonal approach, such as Western blotting, in addition to immunofluorescence. They should also discuss possible reasons why PTBP1 mRNA abundance is not detectably affected by the frameshift.

(2) The authors should analyze PTBP1 expression in WT and cKO substantia nigra samples shown in Figure 3 or justify why this analysis is not necessary.

(3) Lines 236-238 and Figure 4E: The authors report an enrichment of CU-rich sequences near PTBP1-regulated exons. To better compare this with previous studies on position-specific splicing regulation by PTBP1, it would be helpful to assess whether the position of such motifs differs between PTBP1-activated and PTBP1-repressed exons.

(4) The analyses in Figure 5 and its supplement strongly suggest that the splicing changes in PTBP1-depleted astrocytes are distinct from those occurring during neuronal differentiation. However, the authors should ensure that these comparisons are not confounded by transcriptome-wide differences in gene expression levels between astrocytes and developing neurons. One way to address this concern would be to compare the new PTBP1 cKO data with publicly available RNA-seq datasets of astrocytes induced to transdifferentiate into neurons using proneural transcription factors (e.g., PMID: 38956165).

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

Zhang et al. used a conditional knockout mouse model to re-examine the role of the RNA-binding protein PTBP1 in the transdifferentiation of astroglial cells into neurons. Several earlier studies reported that PTBP1 knockdown can efficiently induce the transdifferentiation of rodent glial cells into neurons, suggesting potential therapeutic applications for neurodegenerative diseases. However, these findings have been contested by subsequent studies, which in turn have been challenged by more recent publications. In their current work, Zhang et al. deleted exon 2 of the Ptbp1 gene using an astrocyte-specific, tamoxifen-inducible Cre line and investigated, using fluorescence imaging and bulk and single-cell RNA-sequencing, whether this manipulation promotes the transdifferentiation of astrocytes into neurons across various brain regions. The data strongly indicate that genetic ablation of PTBP1 is not sufficient to drive efficient conversion of astrocytes into neurons. Interestingly, while PTBP1 loss alters splicing patterns in numerous genes, these changes do not shift the astroglial transcriptome toward a neuronal profile.

Strengths:

Although this is not the first report of PTBP1 ablation in mouse astrocytes in vivo, this study utilizes a distinct knockout strategy and provides novel insights into PTBP1-regulated splicing events in astrocytes. The manuscript is well written, and the experiments are technically sound and properly controlled. I believe this study will be of considerable interest to a broad readership.

Weaknesses:

(1) The primary point that needs to be addressed is a better understanding of the effect of exon 2 deletion on PTBP1 expression. Figure 4D shows successful deletion of exon 2 in knockout astrocytes. However, assuming that the coverage plots are CPM-normalized, the overall PTBP1 mRNA expression level appears unchanged. Figure 6A further supports this observation. This is surprising, as one would expect that the loss of exon 2 would shift the open reading frame and trigger nonsense-mediated decay of the PTBP1 transcript. Given this uncertainty, the authors should confirm the successful elimination of PTBP1 protein in cKO astrocytes using an orthogonal approach, such as Western blotting, in addition to immunofluorescence. They should also discuss possible reasons why PTBP1 mRNA abundance is not detectably affected by the frameshift.

We thank the reviewer for raising this important point. Indeed, the deletion of exon 2 introduces a frameshift that is predicted to disrupt the PTBP1 open reading frame and trigger nonsensemediated decay (NMD). While our CPM-normalized coverage plots (Figure 4D) and gene-level expression analysis (Figure 6A) suggest that PTBP1 mRNA levels remain largely unchanged in cKO astrocytes, we acknowledge that this observation is counterintuitive and merits further clarification.

We suspect that the process of brain tissue dissociation and FACS sorting for bulk or single cell RNA-seq may enrich for nucleic material and thus dilute the NMD signal, which occurs in the cytoplasm. Alternatively, the transcripts (like other genes) may escape NMD for unknown mechanisms. Although a frameshift is a strong indicator for triggering NMD, it does not guarantee NMD will occur in every case. We will include this discussion in the revised manuscript to provide additional context for the apparent discrepancy between mRNA abundance and protein loss.

Regarding the validation of PTBP1 protein depletion in cKO astrocytes by Western blotting, we acknowledge that orthogonal approaches to confirm PTBP1 elimination would address uncertainty around the effect of exon 2 deletion on PTBP1 expression. The low cell yield of cKO astrocytes poses a significant burden on obtaining sufficient samples for immunoblotting detection of PTBP1 depletion. On average 3-5 adult animals per genotype are needed for each biological replicate. Our characterization of this Ptbp1 deletion allele in other contexts show the loss of full length PTBP1 proteins in ESCs and NPCs using Western blotting. Furthermore, germline homozygous mutant mice do not survive beyond embryonic day 6, supporting that it is  a loss of function allele.

(2) The authors should analyze PTBP1 expression in WT and cKO substantia nigra samples shown in Figure 3 or justify why this analysis is not necessary.

We thank the reviewer for pointing out this important question. We used Aldh1l1-CreERT2, which is designed to be active in all the astrocyte throughout mouse brain. Although we have systematically verified PTBP1 elimination in different mouse brain regions (cortex and striatum) at multiple time points (from 4w to 12w after tamoxifen administration), we agree that it remains necessary and important to demonstrate whether the observed lack of astrocyte-to-neuron conversion is indeed associated with sufficient PTBP1 depletion. We will analyze the PTBP1 expression in the substantia nigra, as we did in the cortex and striatum. 

(3) Lines 236-238 and Figure 4E: The authors report an enrichment of CU-rich sequences near PTBP1-regulated exons. To better compare this with previous studies on position-specific splicing regulation by PTBP1, it would be helpful to assess whether the position of such motifs differs between PTBP1-activated and PTBP1-repressed exons.

We thank the reviewer for this insightful comment. We agree that assessing the positional distribution of CU-rich motifs between PTBP1-activated and PTBP1-repressed exons would provide valuable insight into the position-specific regulatory mechanisms of PTBP1. In response, we will perform separate motif enrichment analyses for PTBP1-activated and PTBP1-repressed exons and examine whether their positional patterns differ. This will help clarify whether these exons are differentially regulated by PTBP1 through distinct motif positioning in mature astrocytes.

(4) The analyses in Figure 5 and its supplement strongly suggest that the splicing changes in PTBP1-depleted astrocytes are distinct from those occurring during neuronal differentiation. However, the authors should ensure that these comparisons are not confounded by transcriptome-wide differences in gene expression levels between astrocytes and developing neurons. One way to address this concern would be to compare the new PTBP1 cKO data with publicly available RNA-seq datasets of astrocytes induced to transdifferentiate into neurons using proneural transcription factors (e.g., PMID: 38956165).

We would like to express our gratitude for the thoughtful feedback. We agree that transcriptomewide differences in gene expression between astrocytes and developing neurons could confound the interpretation of splicing differences. To address this concern, we will incorporate publicly available RNA-seq datasets from studies in which astrocytes are reprogrammed into neurons using proneural transcription factors (PMID: 38956165). 

Reviewer #2 (Public review):

Summary:

The manuscript by Zhang and colleagues describes a study that investigated whether the deletion of PTBP1 in adult astrocytes in mice led to an astrocyte-to-neuron conversion. The study revisited the hypothesis that reduced PTBP1 expression reprogrammed astrocytes to neurons. More than 10 studies have been published on this subject, with contradicting results. Half of the studies supported the hypothesis while the other half did not. The question being addressed is an important one because if the hypothesis is correct, it can lead to exciting therapeutic applications for treating neurodegenerative diseases such as Parkinson's disease.

In this study, Zhang and colleagues conducted a conditional mouse knockout study to address the question. They used the Cre-LoxP system to specifically delete PTBP1 in adult astrocytes. Through a series of carefully controlled experiments, including cell lineage tracing, the authors found no evidence for the astrocyte-to-neuron conversion.

The authors then carried out a key experiment that none of the previous studies on the subject did: investigating alternative splicing pattern changes in PTBP1-depleted cells using RNA-seq analysis. The idea is to compare the splicing pattern change caused by PTBP1 deletion in astrocytes to what occurs during neurodevelopment. This is an important experiment that will help illuminate whether the astrocyte-to-neuron transition occurred in the system. The result was consistent with that of the cell staining experiments: no significant transition was detected.

These experiments demonstrate that, in this experimental setting, PTBT1 deletion in adult astrocytes did not convert the cells to neurons.

Strengths:

This is a well-designed, elegantly conducted, and clearly described study that addresses an important question. The conclusions provide important information to the field.

To this reviewer, this study provided convincing and solid experimental evidence to support the authors' conclusions.

Weaknesses:

The Discussion in this manuscript is short and can be expanded. Can the authors speculate what led to the contradictory results in the published studies? The current study, in combination with the study published in Cell in 2021 by Wang and colleagues, suggests that observed difference is not caused by the difference of knockdown vs. knockout. Is it possible that other glial cell types are responsible for the transition? If so, what cells? Oligodendrocytes?

We are grateful for the reviewer’s careful reading and valuable suggestions. These will help us improve the manuscript. We will expand the Discussion. The contradictory results in the previously published studies can be due to the stringency and neuronal leakage of the astrocytespecific GFAP promoter that some investigators chose. Other possibilities include alternative cell origin, increased neuronal resilience, or combinations of as yet unidentified factors.

DOI: 10.1111/jnc.70178

Resource: (Abcam Cat# ab183591, RRID:AB_2857345)

Curator: @scibot

SciCrunch record: RRID:AB_2857345

What is this?

DOI: 10.1038/s42003-025-08620-9

Resource: (Vector Laboratories Cat# BA-1000, RRID:AB_2313606)

Curator: @scibot

SciCrunch record: RRID:AB_2313606

What is this?

DOI: 10.1016/j.cub.2025.07.041

Resource: (DSHB Cat# anti-Scr 6H4.1, RRID:AB_528462)

Curator: @scibot

SciCrunch record: RRID:AB_528462

What is this?

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Referee #3

Evidence, reproducibility and clarity

Hamadou, Alunno et al. have found evidence for the notion that although translational regulation plays a key role in determining cell behavior, few studies have explored how single nucleotide polymorphisms (SNPs) affect mRNA translation. They developed a method to analyze allele-specific expression in both total and polysome-associated mRNA using RNA-seq data from HCT116 cells. This approach revealed 40 potential "tranSNPs"-SNPs linked to differences in translation between alleles. One SNP, rs1053639 (T/A) in the 3' untranslated region of the DDIT4 gene, was found to influence translation: the T allele was more often associated with polysomes. Cells engineered to carry the TT genotype produced more DDIT4 protein than those with the AA genotype, especially when exposed to stressors like Thapsigargin or Nutlin that boost DDIT4 transcription. The authors found that the RNA-binding protein RBMX mediates this allele-specific protein expression. Knocking down RBMX in TT cells lowered DDIT4 protein levels to those seen in AA cells. Functionally, TT cells suppressed mTORC1 activity more effectively under ER stress, whereas AA cells had a growth advantage in cell culture and in zebrafish models. In human cancer data from TCGA, individuals with the AA genotype had poorer outcomes under a recessive genetic model.

The manuscript needs major revision due to additional data interpretation, lack of statistical analysis, and lack of mechanistic and causal insights. The paper is overall correlative and descriptive and has not enough data to claim a translation regulation aspect of DDIT4 and the protein product to cause the observed genotypic differences stemming from a SNP in the 3' UTR. The paper reads as a collection of individual findings that do not seem to be very cohesive and ranges from polysome-seq, RBP binding, ER stress, mTOR activity, cellular co-culture tumor models and zebrafish tumor models. I wish the authors would have focused on one aspect and described one finding well. Without addressing these fundamental concerns, the study's core claims regarding p53-dependent responses in cancer remain unsubstantiated. Overall, this reviewer supports the publication in a Review Commons journal dependent on that the points of criticism are adequately addressed in the course of a major revision.

Major comments:

1. Fig.1: The presentation of the location of the tranSNPs in the target mRNAs from polysome data should be presented in a schematic in Fig.1. It should be emphasized; what fold change was considered relevant to select mRNA targets. Do SNPs overlap other regulatory element in the 3' UTRs of the mRNA targets?
2. Fig.2: If mRNA steady-state levels and protein levels are not affected by the SNP, what mechanism can be assumed for translation? Can you perform luciferase reporter mRNA experiments with the different SNPS under ER/thapsigargin stress conditions? Can you isolate the region that has the SNP and show that the effect on translation is local?
3. Fig.3: Given the subtle differences in polysome association of mRNA distributions in the mutants, the polysomes need quantifications of the area under the curve in 3 categories: sub-polysomal, light and heavy polysomes. The overall decreased translation of all 3 mRNAs in tg-stress cells of the AA SNPs needs to be explained. This effect is not specific to DDIT4.
4. Fig.4: The cherry-picking based on CLIP data of RBMX needs to be addressed more. A pulldown of all 3 identified RBPs needs to be done to determine if RBMX is the strongest regulator of DDIT4 via the 3' UTR. The EMSA in (A) needs to be quantified to determine the Kd. In (C) the RBMX is mainly nuclear which does not align with the translation effect on DDIT4 mRNA. Please explain. The effect on localization upon RBMX on DDIT4 protein seems subtle. Are there more dominant mechanisms at play for translation regulation other than via RBMX?
5. Fig.5: How do you interpret the TT-specific effect on mTOR activity? Is there a link between RBMX binding, DDIT4 protein levels/activity and mTOR? The stats in (F) are missing.
6. Fig.6: The rationale for these sets of experiments is not clear. Is it expected that the DDIT4 protein alone and its regulation through the AA phenotype is affecting global translation? Thapsigargin is a global ER stress but the expectation is not that DDIT4 itself is such a strong global regulator. This figure can move to the supplement.
7. Fig.7: The data in (A) is very clear, can you expand a bit on that how translation regulation of the genotypes in co-culture can have such a strong effect? The data in (C) needs to be reevaluated with stats as there does not seem to be a strong difference.
8. Fig.8: How much is the AA-induced tumor growth in zebrafish comparable to a co-culture tumour model? Again, how are the DDIT4 proteins levels derived from AA related and responsible for this?

Minor comment:

1. The manuscript is littered with non-intuitive abbreviations that make the figures less accessible without reading all main text. Please simplify and reduce abbreviations.

Significance

The manuscript needs major revision due to additional data interpretation, lack of statistical analysis, and lack of mechanistic and causal insights. The paper is overall correlative and descriptive and has not enough data to claim a translation regulation aspect of DDIT4 and the protein product to cause the observed genotypic differences stemming from a SNP in the 3' UTR. The paper reads as a collection of individual findings that do not seem to be very cohesive and ranges from polysome-seq, RBP binding, ER stress, mTOR activity, cellular co-culture tumor models and zebrafish tumor models. I wish the authors would have focused on one aspect and described one finding well. Without addressing these fundamental concerns, the study's core claims regarding p53-dependent responses in cancer remain unsubstantiated. Overall, this reviewer supports the publication in a Review Commons journal dependent on that the points of criticism are adequately addressed in the course of a major revision.

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Referee #2

Evidence, reproducibility and clarity

Summary:

In this manuscript, Hamadou et al. describe the functional characterization of a 3'UTR SNP (rs1053639) in the DDIT4 gene that influences mRNA localization and translation. The authors use polysome profiling, isogenic HCT116 clones, and molecular assays to link the SNP to allele-specific protein expression, proposing a mechanistic role for RBMX and potentially m6A. The manuscript is clearly written and presents compelling evidence to support the authors conclusion.

Major Comments:

1. The comparison between TT and AA clones relies on a very limited number of HCT116-derived edited lines. The possibility that the observed differences in DDIT4 translation are due to clonal artifacts cannot be excluded. The authors could partially address this by transfecting the luciferase reporters carrying the A or T allele into both AA and TT clones to assess whether genotype-specific effects persist independently of clone background.
2. All functional assays are restricted to HCT116 cells. It is essential that key findings, such as especially allele-specific effects on protein levels and mRNA localization, are validated in at least one additional cell line to generalize the findings.
3. While TT and AA clones show differences in DDIT4 protein levels, the downstream biological effects (e.g., in co-culture or zebrafish xenografts) are modest and not clearly attributable to DDIT4 expression. The authors should strengthen this connection by manipulating DDIT4 expression (e.g., knockdown or overexpression) in both genotypic backgrounds to determine whether the observed growth or localization phenotypes are DDIT4-dependent.

Minor Comments:

1. Fig4B: IgG controls for the RIP-qPCR are missing.
2. Figure 7C is not properly aligned and the total proportion of cells is not 100%.
3. The discussion section, while informative, is overly long and could be more concise and focused to improve readability and impact.

Significance

The authors present a novel and sound pipeline to identify SNPs that regulate mRNA translation using allelic differences in polysome association. Using this approach, they focus on rs1053639 in the 3'UTR of DDIT4 and provide convincing evidence of its impact on mRNA localization and protein expression in HCT116 cells. While the molecular findings are robust, the biological consequences appear relatively modest, and the proposed clinical relevance remains speculative at this stage.

Overall, the study will be of primary interest to a specialized audience of researchers in the fields of post-transcriptional regulation, RNA biology, and functional genomics. The proof-of-concept framework may also attract broader interest for its potential applications in understanding non-coding genetic variation in cancer biology.

Reviewer expertise: p53 biology, molecular cancer biology

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Referee #1

Evidence, reproducibility and clarity

This study investigates the role of a 3'UTR SNP variant in DDIT4 mRNA on allele specific expression at post transcriptional level. The authors have previously developed an experimental approach to identify differences in allele specific transcript distribution in polysomes vs. This was done using polysome profiling combined with RNA-seq analysis of polysome associated and total RNA fractions. This systematic approach identified 40 candidate transcripts exhibiting differential polysome association between reference and variant alleles, indicating post transcriptional effects. Focusing on DDIT4, the study demonstrated that the SNP variant alters subcellular mRNA localization patterns between cytoplasm and nucleus through an impaired interaction with a specific RNA binding protein. Since DDIT4 functions as a negative regulator of mTORC1 signalling, the study examined the mTOR pathway status in homozygous reference and variant genotypes. Using genome-edited cell lines revealed enhanced proliferative capacity of the homozygous AA variant in both co-culture assays and zebrafish xenograft models. I agree with the authors that we don't know much about allele specific effects on mRNA translation mechanisms. However this study doesn't provide much evidence for translational effects either because the differences appear to be mostly due to the impaired export of the variant RNA from the nucleus. Irrespective, the findings are very important as they show how genetic variants in non-coding regions can result in changes of expression at posttranscriptional level.<br /> A comprehensive suite of experimental approaches was utilized to systematically assess both the SNP's impact on mRNA translation and the gene specific functional consequences for DDIT4. The manuscript is well written and presents the work with great clarity.

Major comments

"HCT116, about 11% of genes with analyzable heterozygous SNPs show a difference in AF between paired total and polysome-bound mRNAs, suggesting allele-specific post-transcriptional and translational control." For the remaining candidate transcripts that did not undergo targeted experimental validation like DDIT4, it remains possible that the observed allele specific translational effects could be attributed to other SNPs located elsewhere within these transcripts or to combinatorial effects involving multiple variants. Have the authors considered this possibility? The authors employed RNA probes designed to mimic the secondary structures of the T and A alleles of endogenous DDIT4 mRNA. Could you clarify the exact composition of these probes, do they contain a partial DDIT4 3'UTR sequence? Is it possible that the probes lack critical sequences required for complete protein recognition? Figure 3A - the authors suggest that "in the mock condition, AA cells showed a slight reduction in translation efficiency for the DDIT4 mRNA, as revealed by higher relative abundance in lighter polysomes (fraction 9)" I am not convinced that this is the case, first because the number of ribosomes per mRNA doesn't necessarily reflect translation efficiency and also the TT seems to have increased monosome fraction, and overall to me the profile suggests of slightly reduced translation for TT. Was the nucleotide sequence of the binding site of RBMX determined and if so is this sequence present within the DDIT4 3'UTR?

Minor

Could the authors maybe define what is meant by "analyzable" SNPs or genes? What was the rationale for the selection of HCT116 cells, from a quick search it appears that DDIT4 effects on mTORC1 inhibition could be cell type specific ("mediates mTORC1 inhibition in fibroblasts and thymocytes, but not in hepatocytes"), have the authors considered other cell types Results section 2: Editing of HCT116 cell... I appreciate the clear methodological explanations provided in this section; however, the manuscript might benefit from more concise organization with substantial portions of this descriptive content relocated to the Methods section. Regarding statistical presentation, I recommend reporting exact significance values rather than using threshold indicators (ns, , *, etc.). This approach provides more informative and transparent statistical reporting as differences between "non-significant" and "significant" designations can be minimal neighbouring p-values that fall on opposite sides of arbitrary thresholds and may be misleadingly interpreted. For instance in Figure 2D, the comparison between TT and AA genotypes may approach statistical significance, and displaying the actual p-values would allow readers to better assess the strength of evidence. Fig 3 What is the significance of the control mRNAs? According to the plots it seems as if these also have variants TT/AA? Figure 5A why does AA clone 6 look so different on the gel? "rs1053639 genotype, a relatively common SNP" - what is the estimated frequency of the SNP?

Significance

It is a substantial study and a very interesting story. The findings will be of interest for a broad audience, because it combines elements of basic research and clinical significance. The work allows for interpretation of an allele specific genomic variant outside of the coding region and it reveals the importance of similar characterisation of other SNPs.

O livro didático de história não é só uma lista de datas e nomes. Ele é uma ferramenta que ajuda a fixar uma versão da história na nossa mente, escolhendo alguns fatos como os mais importantes. Mas o autor, Valentine, avisa que o livro não faz isso sozinho. Na verdade, ele pega as ideias dos historiadores e as repete, reforçando as mesmas histórias e explicações que já eram aceitas. Assim, o livro continua a usar os mesmos eventos principais como base para contar toda a história.

Algo que conseguimos ver no Livro aqui de Floripa. Defasagens, clivagens entre a produção acadêmica e a escolar ou ausências ou estereótipos de grupos étnicos ou minoritários da sociedade brasileira.

O livro didático é um objeto de intensos debates e críticas. Sua complexidade o coloca no centro de discussões que vão além da escola, envolvendo educadores, pais e alunos, e se estendem a jornais, revistas e encontros acadêmicos. Autores, editores, políticos e intelectuais de diversas áreas participam dessas polêmicas, que têm seu alcance exemplificado pelo processo de avaliação do MEC. Apesar de sua importância cultural e social no Brasil, o livro didático também tem um papel econômico crucial, sustentando um vasto setor da indústria editorial do país.

Questionar e saber analisar o conteúdo que contém naquele livro, julgando se faz sentido com o que você acredita ou até na veracidade das informações ali contidas.

Livro didático como suporte de métodos pedagógicos, ao conter exercícios, atividades, etc. Essa característica de associar conteúdo e método de ensino explica a sua importância na constituição da disciplina ou do saber escolar. Dimensões técnicas e pedagógicas: livro didático como um veículo de um sistema de valores, de ideologias, de uma cultura de determinada época e de determinada sociedade.

Livro didático como um suporte de conhecimentos escolares propostos pelos currículos educacionais. Essa característica faz que o estado esteja sempre presente na existência do livro didático: interfere indiretamente na elaboração dos conteúdos escolares veiculados por ele e posteriormente estabelece critérios para avaliá-lo, seguindo, na maior parte das vezes, os pressupostos dos currículos escolares institucionais. Como os conteúdos propostos pelos currículos são expressos pelos textos didáticos, o livro torna-se um instrumento fundamental na própria constituição dos saberes escolares.

Livro didático: produto cultural fabricado por técnicos que determinam seus aspectos materiais, o livro didático caracteriza-se nessa dimensão material, por ser uma mercadoria ligada ao mundo editorial e à lógica da indústria cultural do sistema capitalista.

críticas aos livros didáticos apontam para muitas de suas deficiências de conteúdo, suas lacunas e erros conceituais ou informativos e mostra que o problema dessas análises reside na concepção de que seja possível existir um livro didático ideal.

Livros didáticos de História como um tema polêmico, onde diversas pesquisas tem revelado que são um instrumento a serviço da ideologia e da perpetuação de um "ensino tradicional". Porém, ainda continuam sendo usados no trabalho diário das escolas em todo o País. Ao serem maior analisados dentro de uma perspectiva histórica, demonstram ter sofrido mudanças em seus aspectos formais e de um ganho de possibilidades de uso diferenciado por parte de professores e estudantes.

Livro didático I. instrumento de trabalho; II. Faz parte do cotidiano escolar há pelo menos dois séculos; III. Objeto cultural

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Reply to the reviewers

Response to the Reviews

We thank the reviewers for their input and detailed feedback, which has helped us improve both the manuscript and the Microscopy Nodes software. Based on the comments, we have implemented new features, currently available as version 2.2.1 of Microscopy Nodes. We have edited the text and figures of the manuscript to reflect these changes and add clarification where needed.

Reviewer #1

Evidence, reproducibility and clarity

*The work by Gros et al. presents a paper introducing Microscopy Nodes, a new plugin for Blender 3D visualization software designed to import and visualize multi-dimensional (up to 5D) light and electron microscopy datasets. Given that Blender is not directly suited for such tasks, this plugin significantly simplifies the process, making its visualization engine accessible to a wide range of researchers without prior knowledge of Blender. The plugin supports importing volumes and labels from generic TIF or modern OME-Zarr image formats and includes supplementary video tutorials on YouTube to facilitate basic understanding of the visualization workflows.

Major comments: - The manuscript suggests that Microscopy Nodes can easily handle large datasets, as evidenced by the showcases. However, in my personal tests, I was unable to import a moderate TIF stack of about 5GB, which is considerably smaller than the showcased datasets. Post-import, a data cube was displayed, but the Blender interface became unresponsive. The manuscript should include a section stating limitations and addressing issues and providing suggestions for visualization of large datasets.*

We want to thank the reviewer for this valuable comment, which led us to find a core issue in Blender’s large data handling. Specifically, Blender’s rasterized pipeline causes issues with > 4 GiB of data loaded. This issue does not occur in the raytraced (Cycles) renderer, which is why we had not previously encountered it.

To address this, we have extended the reloading workflow of Microscopy Nodes to provide a workaround for this. If the data is larger than 4 Gibibytes (GiB) (per timepoint, or per timepoint per channel), Microscopy Nodes now automatically downsamples these data during import. While using these downsampled options is recommended for adjusting the visualization settings, the user can then still make their animation and reload their data to the largest scale for the final render by using the raytraced (Cycles) renderer. Additionally, we have raised this bug with the core Blender developers, and hope to work this out in the long term (blender/blender#136263).

We reflect these changes in the manuscript in the segment:

“Blender currently has a notable limitation that its default ‘quick’ rasterized rendering engines (such as ‘EEVEE’, but also the viewport ‘Surface’ and ‘Wireframe’ modes) do not support more than 4 Gibibytes (GiB) of volumetric data. The raytracing render mode ‘Cycles’, however, can handle large volumetric data. To allow users with large data to flexibly use Microscopy Nodes, we implemented a reloading scheme, where one first loads a smaller version of the data (under 4 GiB per timeframe for all loaded channels combined) - and only upon final render in Cycles, exchange it for the full/larger scale copy (Fig 3A). This downscaling of data offers additional benefits as it allows for fast adjustment of the render settings on e.g. a personal computer which can eventually be transferred to a larger workstation or HPC cluster for the final render at full resolution. This feature is critical as working in Cycles with larger files requires sufficient RAM to fit the (temporary) VDB files comfortably. For example, multiple figures in this manuscript were made on a 32GB RAM M1 Macbook Pro (Fig 1A, Video SV1, Fig 1D, Figure 2A-D, Fig S2A-B), but for larger data or long movies the movies were made on workstations or prepared on a laptop and then transferred to an HPC cluster for final rendering.”

* - The feature of importing Zarr-datasets over HTTP is great, but the import process was very slow in my tests, even on a robust network. For reference, loading 1.8 GB of the PRPE1_4x dataset at s1 level took 52 minutes. This raises concerns about potential code issues and general usability of the suggested workflow.*

We believe that this loading time may have been caused by the same issue that plagued all of our datasets of >4GB outside of the raytraced mode, as we have not seen loading issues like that. Moreover, Microscopy Nodes now supports Zarr version to Zarr 3/OME-Zarr 0.5, which allows ‘sharded’ Zarr datasets, which should be even faster at loading large blocks of data at the same time, as Microscopy Nodes does.

- The onsite documentation is a bit outdated and fails to fully describe the plugin settings.

We have updated our documentation to offer new written tutorials, which include full start-up tutorials, but also for some key extra instructions.

- The YouTube tutorials feature an outdated version of the plugin, which could confuse the general microscopy audience. These should be updated to better align with the current plugin functionality. Additionally, using smaller, easily accessible datasets for these tutorials would improve user testing experiences. Hosting complete (downsampled) demo project folder on platforms like zenodo.org could also enhance usability of such tutorials.

We have made a new series of YouTube tutorials that align with the current interface of Microscopy Nodes. These tutorials include public datasets, allowing users to follow along easily. We have chosen to also retain the older tutorials for users running legacy versions of the plugin, as they cover different workflows.

- The manuscript describes a novel dataset used in Fig. 2, but no reference is provided. Additionally, practical implementation of the coloring description for Fig. 2D can be unclear for inexperienced users, necessitating either step-by-step instructions or the provision of downsampled Blender files to aid understanding.

We have now shared the OME-Zarr address in the text (https://uk1s3.embassy.ebi.ac.uk/idr/share/microscopynodes/FIBSEM_dino_masks.zarr), and included this both in the manuscript and the tutorials. Additionally, to guide the implementation and explain the logic behind the coloring we introduced additional panels in Fig S1 and Fig S2 to showcase the shader setups used for this image.

[OPTIONAL] When importing labels, they can be assigned to individual materials only if initially split into multiple color channels. It would be great if the same logic is implemented when those materials are provided as indices within a single color channel. There can be a switch to define the logic used during the import process: e.g. the current one, when the objects are just colored based on a color map, or when they are arranged as individual materials as done when labels are imported from multiple color channels.

We agree with the reviewer and to address this concern with the update to version 2.2, we have implemented a new colorpicking system (See Fig 3B, inset 3, Fig 3C), this allows users to choose between a single color, various continuous, or categorical color maps.

Minor comments: - The manuscript shows nice visualizations of time series, light, and electron microscopy datasets, but in its current state, it is targeted more for light microscopy, where the signal is white. On the other hand, many EM datasets are rendered in inverted contrast (TEM-like), where the signal is black. To render such volume properly, it is needed to go into the Shading tab and flip the color ramp. Would it be possible to perhaps define the data type during import to accommodate various data types or perhaps select the flipped color ramp when the emission mode is switched off? It could make it easier for inexperienced EM users to use the plugin.

To address this, we include new default settings, with ‘invert colormaps on load’ option in the preferences, and default colors per channel (See Fig S4). We have also implemented a new color picking system in version 2.2 (See Fig 3B, inset 3, Fig 3C) that hopefully makes it easier before and after load to change colors.

- It was not completely clear to me whether it is possible to render a single/multiple EM slices using the inverted (TEM-like) contrast. For example, XY, XZ, YZ ortho slices across the volume. The manuscript contains: "This visualization is also supported in Blender, allowing for arbitrary selections of viewing angles (Fig 2B).", but it is not clear how to achieve that.

We introduced an additional explanation in Fig S1A and added a separate density window in the default shader to make this opaque view easier. To get a single slicing plane, users can reduce the scale of the slicing cube in one axis, at it is now also explained in Fig S2B.

- In 3D microscopy, it is quite common to have data with anisotropic voxels. As a result, the surfaces may require smoothing. I was not able to quickly find a way to smooth the surfaces (at least smooth modifiers for surfaces did not work for me). Is it possible to apply smoothing during the import of labels, or alternatively, smoothing of the generated surfaces can be a topic for an additional YouTube video.

The smoothness of the loaded masks can be indirectly affected in the preferences by changing the mesh resolution (changing the relative amount of vertices per pixel), but can be further affected by operations such as the Blender “Smooth” or e.g. the “Smooth by Laplacian” modifiers. To guide the users in doing so, we have included instructions for smoothing in the written tutorials on the website https://aafkegros.github.io/MicroscopyNodes/tutorials/surface_smoothing/ .

- It is also typical to have somewhat custom color maps for materials. It would be great if the plugin remembers the previously used color map for labels.

We have implemented new Preference settings, which include default colors and colormaps per channel, improving customization and reproducibility. This new option is described in Figure S4.

* - The pixel size edit box rounds up the values to 2 digits after the dot. Could it be changed to accommodate 3 or 4 digits as the units are um.*

Blender’s interface truncates the display, but stores higher-precision values internally, and become visible when users click or edit the values. We have added support for alternative pixel units to reduce the impact of the truncation.

- Import is not working when: - Start Blender - Select Data storage: with project - Overwrite files: on, set env: on, chunked: on - Select a file to import - Save Blender file - Pressing the Load button gives an error: "Empty data directory - please save the project first before using With Project saving."

We thank the reviewer for finding this bug which is now fixed in version 2.2.

- I was not able to play the downloaded supplementary video 3 using my VLC media player, while it was working fine in a browser. The video can be opened but looks distorted and heavily zoomed in. It may need to be re-saved from a video editor.

We have recompiled this video.

- References 12 and 16 are URL links instead of proper references to articles.

Thanks for catching this mistake in our bibliography. We have corrected this.

Significance

*This work effectively bridges a gap in the availability of tools for 3D microscopy dataset visualization. While many visualization programs exist, the high-quality ones are often expensive and thus not accessible to all researchers. The integration of Blender with Microscopy Nodes democratizes access to high-quality 3D visualization, enabling researchers to explore datasets and models from multiple perspectives, potentially leading to new discoveries and enhancing the understanding of key study findings. Despite its limitations, my experience with the plugin was engaging and useful. I would like to thank the authors for such useful work!

Limitations: - There remains a steep learning curve associated with using Microscopy Nodes, primarily due to Blender's complexity. More comprehensive tutorials could help mitigate this. - The conversion of imported images to Blender's internal 32-bit format results in a 4x increase in data size for 8-bit datasets. - Managing moderate-sized volumes (5-10 GB) can be challenging without clear strategies for effective handling. - The import of Zarr-datasets over the net is notably slow.

Audience: The plugin is suitable for a broad audience with a basic understanding of 3D visualization concepts, providing a solid foundation for exploring Blender's extensive features and options for optimal visualizations.

Reviewer expertise: Light microscopy, electron microscopy, image segmentation and analysis, software development, no experience with Blender*

Reviewer #2

*Evidence, reproducibility and clarity *

*Summary:

The article introduces Microscopy Nodes, a Blender add-on designed to simplify the loading and visualization of 3D microscopy data. It supports TIF and OME-Zarr images, handling datasets with up to five dimensions. The authors present different visualization modes, including volumetric rendering, isosurfaces, and label masks, demonstrating the application in light and electron microscopy. They provide examples using expansion microscopy, electron microscopy, and real-time imaging, highlighting how the tool enhances scientific communication and interactive visualization.

Comments:

However, some key aspects could be improved to enhance usability and reproducibility:

Example datasets: The images used in the YouTube tutorials were not accessible, making it difficult to reproduce the workflows shown in the figures and tutorials. It would be helpful if the authors provided direct links to the datasets or ensured that the same examples used in the tutorials were readily available for replication.*

We created new and updated tutorials and for all new tutorials, the data is now easily available from an S3 server.

Input file specifications: The article does not clearly detail how input files should be formatted. Many users will pre-visualize images in Fiji to convert their original images to a compatible format. It would be beneficial to specify which formats are supported for hyperstack creation, including details on bit depth, dimension ordering, label formats, and metadata compatibility, if applicable.

We have added new documentation on this on the website and in the manuscript. The addon can take 8, 16, and 32 bit data, and any dimension order (with the letters tzcyx) and pixel size. Dimension order and pixel size can be edited in the GUI. This is reflected in the manuscript in the rewritten section in Design and Implementation:

“It can handle 8bit to 32bit integer and floating point data, although all data types will be resaved into 32bit floating point VDB files, which can cause temporary files to take up more space than the original. Microscopy Nodes loads 2D to 5D files of containing data across time, z, y, x and channels, in arbitrary order (can be remapped in the user interface as well, Fig 3B, inset 2). To focus on relevant data, users can clip the time axis, which can be useful for long videos.”

* Hardware requirements: The article does not discuss RAM or hardware constraints in detail. In testing, attempting to load two images into the same project caused the program to freeze (tested on Mac M1). Specifying hardware requirements and limitations would help users manage expectations when working with large datasets.*

We have since found a limitation in the Blender engine that indeed limits the amount of data loaded (see also comment by Reviewer 1). Currently, rasterized engines are capped at 4 GiB, and only the raytraced engine can handle larger data. As such, the Microscopy Nodes pipeline, where one works with small images until it is time to render a final version, and the data is only exchanged for the final render, is still viable. To make this easier, we now also included optional downscaling for Tif images. This is described in the rewritten section on Design and Implementation:

“Blender currently has a notable limitation that its default ‘quick’ rasterized rendering engines (such as ‘EEVEE’, but also the viewport ‘Surface’ and ‘Wireframe’ modes) do not support more than 4 Gibibytes (GiB) of volumetric data. The raytracing render mode ‘Cycles’, however, can handle large volumetric data. To allow users with large data to flexibly use Microscopy Nodes, we implemented a reloading scheme, where one first loads a smaller version of the data (under 4 GiB per timeframe for all loaded channels combined) - and only upon final render in Cycles, exchange it for the full/larger scale copy (Fig 3A). This downscaling of data offers additional benefits as it allows for fast adjustment of the render settings on e.g. a personal computer which can eventually be transferred to a larger workstation or HPC cluster for the final render at full resolution. This feature is critical as working in Cycles with larger files requires sufficient RAM to fit the (temporary) VDB files comfortably. For example, multiple figures in this manuscript were made on a 32GB RAM M1 Macbook Pro (Fig 1A, Video SV1, Fig 1D, Figure 2A-D, Fig S2A-B), but for larger data or long movies the movies were made on workstations or prepared on a laptop and then transferred to an HPC cluster for final rendering.”

Significance

*General Assessment:

One of the major strengths of this work is its seamless compatibility with Blender, a powerful and widely used animation and 3D rendering tool. Integrating advanced visualization techniques from the animation and graphics industry into scientific imaging opens new possibilities for presenting complex microscopy data in an intuitive and accessible way. Additionally, the support for OME-Zarr is particularly valuable, as this format represents a major shift in bioimaging towards scalable, cloud-compatible, and standardized data storage solutions. The adoption of OME-Zarr facilitates large-scale data handling and improves interoperability across imaging platforms, making this integration a significant step forward for the field. Overall, the greatest strength of the tool lies in its flexibility for rendering microscopy data, but its accessibility for users without Blender experience might be a challenge.

Advance in the Field This work introduces a novel solution to the visualization challenges in microscopy by leveraging Blender's advanced rendering capabilities.

Audience This paper will be of interest to: Bioimage researchers seeking to enhance their microscopy data visualization. Image analysis tool developers interested in integrating advanced visualization into their workflows.

Field of Expertise This review is based on expertise in image analysis, segmentation, and 3D biological data visualization.*

*Reviewer #3 (Evidence, reproducibility and clarity (Required)):

The paper "Microscopy Nodes: Versatile 3D Microscopy Visualization with Blender" presents an easy and accessible approach for microscopists and microscopy users to visualize their data in a different and more controlled way. The authors have developed a plug-in script that enables the integration of complex 3D datasets into Blender, a widely used software for 3D visualization and illustration. By leveraging Blender's advanced rendering engine, the plug-in provides greater control over the scene, enviromint and presentation of the 3D data.

I believe that this development, especially when combined with additional analysis tools can be of a great value for microscopist and advanced users to presenting their 3D data sets.

However, at this stage, the paper does not seem to fully demonstrate the benefits of using Microscopy Nodes. To enhance the paper impact, it would be helpful for the authors to further emphasize and provide examples of how Blender's rendering specifically improves data presentation and, in turn, enhances the understanding of the data compared to existing solutions. Specifically, the authors claim at the end of the introduction that their development provides powerful tools for high-quality, visually compelling presentations, enabling "more effective communication of 3D biological data." I believe this statement should be supported by a figure comparing currently available visualization methods and demonstrating how using Blender enhances data presentation and by which enhances the communication of the results. *

*Additionally, at the end of the first paragraph of the results, the authors say: "These options allow us to combine the data and its analyzed interpretation in the same representation with Microscopy Nodes." However, this capability already exists in currently available software. Aside from now being able to achieve this in Blender, what additional benefits does it offer? *

We now include a new Table 1, to showcases which requirements for visualizing complex biological data are available in different visualization software, and discuss this in the text:

“Although several tools for 3D visualization of bioimages already exist and offer essential features for microscopy data (Table 1), many are proprietary, and open-source alternatives often struggle to deliver a comprehensive user experience, such as advanced animation and annotation controls. Proprietary solutions may offer some of these capabilities, but they are frequently limited by licensing costs, platform restrictions, and a lack of customizability. In contrast, Blender is a mature, well-supported open-source platform with a large community of developers that excels in both animation and visualization. By integrating microscopy-specific functionality through Microscopy Nodes, Blender becomes a uniquely powerful solution that bridges the gap between high-end graphics capabilities and the specialized needs of bioimage visualization.”

Additionally, we attempted to remake Figure 2C and 2D in the EM-field standard software Amira, but were not able to. This is because without an advanced light scattering algorithm, it is very hard to see the depth in the nucleus, and the semi-transparent masks do show each other behind them, but cannot interact with the volume.

We chose not to include this in the actual manuscript, as we are not experts at the Amira software, and will, by the nature of this manuscript, present a challenge that Blender is especially good at, such as here the combination of scattering light and semitransparent masks.

* In the last sentence of the second paragraph of the results, it is stated: "Blender powered by Microscopy Nodes: the ability to combine microscopy data with any 3D illustration in the same 3D environment." Could you please elaborate on the accuracy of the models that can be built and provide guidelines for achieving this using the data coordinates imported by Microscopy Nodes? If the illustrations are purely freehand and do not require specific accuracy, it would be helpful to clarify the advantages of creating them within the same environment rather than separately, as many scientists currently do. Additionally, if the inclusion of 3D model illustrations is one of the key advantages of using Blender, I believe it would be beneficial to present this in a figure rather than only in the supplementary video. *

We thank the reviewer for this comment and agree that in the previously submitted version of Microscopy Nodes, it was very difficult to align objects accurately, as the coordinate space was not transparent. A hurdle in this was the fact that Blender only works well with the unit ‘meters’. To address this issue, we now provide a choice of mapping the physical size to meters, as shown in the new interface (See Fig 3B, inset 5). Here the user can choose from the default ‘px -> cm’ (this will always look fine for a quick look) to options such as ‘nm -> m’ or ‘µm -> m’, which, combined with the new choice for adjusting the object origin upon load, allow users to treat the Blender coordinate space as based on the actual physical scales. Additionally, other Blender addons, such as Molecular Nodes (Reference 25 of the manuscript), also allow for accurate localization for cryo-EM datasets.

We appreciate the note that we should more clearly display the ability to show our illustrations and the data together in the figure and have added a visualization to show this in Figure 1C.

* Reviewer #3 (Significance (Required)):

The significance of the paper at this stage is primarily technical and mainly relevant to the field of microscopy

My field of expertise is microscopy and 3D visualization of models using mainly Maya3D and AMIRA.*

@ozzie does your tool have explicit background instructions talking about "EA organizations", or is it getting this from the context of the post? Obviously the former would make it hard to apply in a more general range of settings.

Reviewer #1 (Public review):

This study aims to elucidate the mechanisms by which stress-induced α2A-adrenergic receptor (α2A-AR) internalization leads to cytosolic noradrenaline (NA) accumulation and subsequent neuronal dysfunction in the locus coeruleus (LC). While the manuscript presents an interesting but ambitious model involving calcium dynamics, GIRK channel rundown, and autocrine NA signaling, several key limitations undermine the strength of the conclusions.

First, the revision does not include new experiments requested by reviewers to validate core aspects of the mechanism. Specifically, there is no direct measurement of cytosolic NA levels or MAO-A enzymatic activity to support the link between receptor internalization and neurochemical changes. The authors argue that such measurements are either not feasible or beyond the scope of the study, leaving a significant gap in the mechanistic chain of evidence.

Second, the behavioral analysis remains insufficient to support claims of cognitive impairment. The use of a single working memory test following an anxiety test is inadequate to verify memory dysfunction behaviors. Additional cognitive assays, such as the Morris Water Maze or Novel Object Recognition, are recommended but not performed.

Third, concerns regarding the lack of rigor in differential MAO-A expression in fluorescence imaging were not addressed experimentally. Instead of clarifying the issue, the authors moved the figure to supplementary data without providing further evidence (e.g., an enzymatic assay or quantitative reanalysis of Western blot, or re-staining of IF for MAO-A) to support their interpretation.

Fourth, concerns regarding TH staining remain unresolved. In Figure S7, the α2A-AR signal appears to resemble TH staining, and vice versa, raising the possibility of labeling errors. It is recommended that the authors re-examine this issue by either double-checking the raw data or repeating the immunostaining to validate the staining.

Overall, the manuscript offers a potentially interesting framework but falls short in providing the experimental rigor necessary to establish causality. The reliance on indirect reasoning and reorganizing of existing data, rather than generating new evidence, limits the overall impact and interpretability of the study.

Reviewer #2 (Public review):

Summary:

This manuscript investigates the mechanism by which chronic stress induces degeneration of locus coeruleus (LC) neurons. The authors demonstrate that chronic stress leads to the internalization of α2A-adrenergic receptors (α2A-ARs) on LC neurons, causing increased cytosolic noradrenaline (NA) accumulation and subsequent production of the neurotoxic metabolite DOPEGAL via monoamine oxidase A (MAO-A). The study suggests a mechanistic link between stress-induced α2A-AR internalization, disrupted autoinhibition, elevated NA metabolism, activation of asparagine endopeptidase (AEP), and Tau pathology relevant to Alzheimer's disease (AD). The conclusions of this paper are largely well-supported by the data, but some aspects of image acquisition require further examination.

Strengths:

This study clearly demonstrates the effects of chronic stimulation on the excitability of LC neurons using electrophysiological techniques. It also elucidates the role of α2-adrenergic receptor (α2-AR) internalization and the associated upstream and downstream signaling pathways of GIRK-1, using a range of pharmacological agents, highlighting the innovative nature of the work. Additionally, the study identifies the involvement of the MAO-A-DOPEGAL-AEP pathway in this process. The topic is timely, the proposed mechanistic pathway is compelling, and the findings have translational relevance, particularly about therapeutic strategies targeting α2A-AR internalization in neurodegenerative diseases.

Weaknesses:

(1) The manuscript reports that chronic stress for 5 days increases MAO-A levels in LC neurons, leading to the production of DOPEGAL, activation of AEP, and subsequent tau cleavage into the tau N368 fragment, ultimately contributing to neuronal damage. However, the authors used wild-type C57BL/6 mice, and previous literature has indicated that AEP-mediated tau cleavage in wild-type mice is minimal and generally insufficient to cause significant behavioral alterations. Please clarify and discuss this apparent discrepancy.

(2) It is recommended that the authors include additional experiments to examine the effects of different durations and intensities of stress on MAO-A expression and AEP activity. This would strengthen the understanding of stress-induced biochemical changes and their thresholds.

(3) Please clarify the rationale for the inconsistent stress durations used across Figures 3, 4, and 5. In some cases, a 3-day stress protocol is used, while in others, a 5-day protocol is applied. This discrepancy should be addressed to ensure clarity and experimental consistency.

(4) The abbreviation "vMAT2" is incorrectly formatted. It should be "VMAT2," and the full name (vesicular monoamine transporter 2) should be provided at first mention.

Comments on revisions:

The authors have addressed all of the reviewers' comments.

Author response:

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

Reviewer #1 (Public review):

Weaknesses:

(1) The manuscript's logical flow is challenging and hard to follow, and key arguments could be more clearly structured, particularly in transitions between mechanistic components.

We have revised our manuscript so as to make it easy for readers to follow the logical flow in transitions between mechanistic components by adding the descriptions of Figure S1E-J, Figure S2F-K, Figure S3A-H, Figure S4A-F, Figure S5, and Figure S6 in the revised manuscript.

(2) The causality between stress-induced α2A-AR internalization and the enhanced MAO-A remains unclear. Direct experimental evidence is needed to determine whether α2A-AR internalization itself or Ca2+ drives MAO-A activation, and how they activate MAO-A should be considered.

We believe that the causality between stress-induced α2A-AR internalization and the enhancement of MAO-A is clearly demonstrated by our current experiments, while our explanations may be improved by making them easier to understand especially for those who are not expert on electrophysiology.

Firstly, it is well established that autoinhibition in LC neurons is mediated by α2A-AR coupled-GIRK (Arima et al., 1998, J Physiol; Williams et al., 1985, Neuroscience). We found that spike frequency adaptation in LC neurons was also mediated by α2A-AR coupled GIRK-I (Figure 1A-I), and that α2A-AR coupled GIRK-I underwent [Ca²⁺]_i dependent rundown (Figures 2, S1, S2), leading to an abolishment of spike-frequency adaptation (Figures S4). [Ca²⁺]_i dependent rundown of α2A-AR coupled GIRK-I was prevented by barbadin (Figure 2G-J), which prevents the internalization of G-protein coupled receptor (GPCR) channels.

Abolishment of spike frequency adaptation itself, i.e., “increased spike activity” can increase [Ca²⁺]_i because [Ca²⁺]_i is entirely dependent on the spike activity as shown by [Ca²⁺]_i imaging method in Figure S3.

Thus, α2A-AR internalization can increase [Ca²⁺]_i through the abolishment of autoinhibition or spike frequency adaptation, and a [Ca²⁺]_i increase drives MAO-A activation as reported previously (Cao et al., 2007, BMC Neurosci). The mechanism how Ca²⁺ activates MAO-A is beyond the scope of the current study.

Our study just focused on the mechanism how chronic or sever stress can cause persistent overexcitation and how it results in LC degeneration.

(3) The connection between α2A-AR internalization and increased cytosolic NA levels lacks direct quantification, which is necessary to validate the proposed mechanism.

Direct quantification of the relationship between α2A-AR internalization and increased cytosolic NA levels may not be possible, and may not be necessarily needed to be demonstrated as explained below.

The internalization of α2A-AR can increase [Ca²⁺]_i through the abolishment of autoinhibition or spike frequency adaptation, and [Ca²⁺]_i increases can facilitate NA autocrine (Huang et al., 2007), similar to the transmitter release from nerve terminals (Kaeser & Regehr, 2014, Annu Rev Physiol).

Autocrine released NA must be re-uptaken by NAT (NA transporter), which is firmly established (Torres et al., 2003, Nat Rev Neurosci). Re-uptake of NA by NAT is the only source of intracellular NA, and NA re-uptake by NAT should be increased as the internalization of NA biding site (α2A-AR) progresses in association with [Ca²⁺]_i increases (see page 11, lines 334-336).

Thus, the connection between α2A-AR internalization and increased cytosolic NA levels is logically compelling, and the quantification of such connection may not be possible at present (see the response to the comment made by the Reviewer #1 as Recommendations for the authors (2) and beyond the scope of our current study.

(4) The chronic stress model needs further validation, including measurements of stress-induced physiological changes (e.g., corticosterone levels) to rule out systemic effects that may influence LC activity. Additional behavioral assays for spatial memory impairment should also be included, as a single behavioral test is insufficient to confirm memory dysfunction.

It is well established that restraint stress (RS) increases corticosterone levels depending on the period of RS (García-Iglesias et al., 2014, Neuropharmacology), although we are not reluctant to measure the corticosterone levels. In addition, there are numerous reports that showed the increased activity of LC neurons in response to various stresses (Valentino et al., 1983; Valentino and Foote, 1988; Valentino et al., 2001; McCall et al., 2015), as described in the text (page 4, lines 96-98). Measurement of cortisol levels may not be able to rule out systemic effects of CRS on the whole brain.

We had already done another behavioral test using elevated plus maze (EPM) test.By combining the two tests, it may be possible to more accurately evaluate the results of Y-maze test by differentiating the memory impairment from anxiety. However, the results obtained by these behavioral tests are just supplementary to our current aim to elucidate the cellular mechanisms for the accumulation of cytosolic free NA. Therefore, we have softened the implication of anxiety and memory impairment (page 13, lines 397-400 in the revised manuscript).

(5) Beyond b-arrestin binding, the role of alternative internalization pathways (e.g., phosphorylation, ubiquitination) in α2A-AR desensitization should be considered, as current evidence is insufficient to establish a purely Ca²⁺ -dependent mechanism.

We can hardly agree with this comment. 

It was clearly demonstrated that repeated application of NA itself did not cause desensitization of α2A-AR (Figure S1A-D), and that the blockade of b-arrestin binding by barbadin completely suppressed the Ca^2a-dependent downregulation of GIRK (Figure 2G-K). These observations can clearly rule out the possible involvement of phosphorylation or ubiquitination for the desensitization.

Not only the barbadin experiment, but also the immunohistochemistry and western blot method clearly demonstrated the decrease of α2A-AR expression on the cell membrane (Figure 3).

Ca²⁺-dependent mechanism of the rundown of GIRK was convincingly demonstrated by a set of different protocols of voltage-clamp study, in which Ca²⁺ influx was differentially increased. The rundown of GIRK-I was orderly potentiated or accelerated by increasing the number of positive command pulses each of which induces Ca²⁺ influx (compare Figure S1E-J, Figure S2A-E and Figure S2F-K along with Figure 2A-F). The presence or absence of Ca²⁺ currents and the amount of Ca²⁺ currents determined the trend of the rundown of GIRK-I (Figures 2, S1 and S2). Because the same voltage protocol hardly caused the rundown when it did not induce Ca²⁺ currents in the absence of TEA (Figure S1F; compare with Figure 2B), blockade of Ca²⁺ currents by nifedipine would not be so beneficial.

We believe the series of voltage-clamp protocols convincingly demonstrated the orderly involvement of [Ca²⁺]_i in accelerating the rundown of GIRK-I.

(6) NA leakage for free NA accumulation is also influenced by NAT or VMAT2. Please discuss the potential role of VMAT2 in NA accumulation within the LC in AD. 

It has been demonstrated that reduced VMAT2 levels increased susceptibility to neuronal damage: VMAT2 heterozygote mice displayed increased vulnerability to MPTP as evidenced by reductions in nigral dopamine cell counts (Takahashi et al, 1997, PNAS). Thus, when the activity of VMAT2 in LC neurons were impaired by chronic restraint stress, cytosolic NA levels in LC neurons would increase. We have added such discussion in the revised manuscript (page 12, lines 381-384).

(7) Since the LC is a small brain region, proper staining is required to differentiate it from surrounding areas. Please provide a detailed explanation of the methodology used to define LC regions and how LC neurons were selected among different cell types in brain slices for whole-cell recordings.

LC neurons were identified immunohistochemically and electrophysiologically as we previously reported (see Fig. 2 in Front. Cell. Neurosci. 16:841239. doi: 10.3389/fncel.2022.841239). We have added this explanation in the method section of the revised manuscript (page 15, lines 474-475). A delayed spiking pattern in response to depolarizing pulses (Figure S10 in the revised manuscript) applied at a hyperpolarized membrane potential was commonly observed in LC neurons in many studies (Masuko et al., 1986; van den Pol et al., 2002; Wagner-Altendorf et al., 2019).

Reviewer #2 (Public review):

Weaknesses:

(1) The manuscript reports that chronic stress for 5 days increases MAO-A levels in LC neurons, leading to the production of DOPEGAL, activation of AEP, and subsequent tau cleavage into the tau N368 fragment, ultimately contributing to neuronal damage. However, the authors used wild-type C57BL/6 mice, and previous literature has indicated that AEP-mediated tau cleavage in wild-type mice is minimal and generally insufficient to cause significant behavioral alterations. Please clarify and discuss this apparent discrepancy.

In our study, normalized relative value of AEP-mediated tau cleavage (Tau N368) was much higher in CRS mice than non-stress wild-type mice. It is not possible to compare AEP-mediated tau cleavage between our non-stress wild type mice and those observed in previous study (Zhang et al., 2014, Nat Med), because band intensity is largely dependent on the exposure time and its numerical value is the normalized relative value. In view of such differences, our apparent band expression might have been intensified to detect small changes.

(2) It is recommended that the authors include additional experiments to examine the effects of different durations and intensities of stress on MAO-A expression and AEP activity. This would strengthen the understanding of stress-induced biochemical changes and their thresholds.

GIRK rundown was almost saturated after 3-day RS and remained the same in 5-day RS mice (Fig. 4A-G), which is consistent with the downregulation of α2A-AR and GIRK1 expression by 3-day RS (Fig. 3C, F and G; Fig. 4J and K). However, we examined the protein levels of MAO-A, pro/active-AEP and Tau N368 only in 5-day RS mice without examining in 3-day RS mice. This is because we considered the possibility that a high [Ca²⁺]_i condition may have to be sustained for some period of time to induce changes in MAO-A, AEP and Tau N368, and therefore 3-day RS may be insufficient to induce such changes. We have added this in the revised manuscript (page 17, lines 521-525).

(3) Please clarify the rationale for the inconsistent stress durations used across Figures 3, 4, and 5. In some cases, a 3-day stress protocol is used, while in others, a 5-day protocol is applied. This discrepancy should be addressed to ensure clarity and experimental consistency.

Please see our response to the comment (2).

(4) The abbreviation "vMAT2" is incorrectly formatted. It should be "VMAT2," and the full name (vesicular monoamine transporter 2) should be provided at first mention.

Thank you for your suggestion. We have revised accordingly.

Reviewer #3 (Public review):

Weaknesses:

Nevertheless, the manuscript currently reads as a sequence of discrete experiments rather than a single causal chain. Below, I outline the key points that should be addressed to make the model convincing.

Please see the responses to the recommendation for the authors made by reviewer #3.

Reviewer #1 (Recommendations for the authors):

(1) Improve the clarity and organization of the manuscript, ensuring smoother transitions between concepts and mechanisms.

Please see the response to the comment raised by Reviewer #1 as Weakness

(2) Adjust any quantifying method for cytosolic NA levels under different conditions to support the link between receptor internalization and NA accumulation.

If fluorescent indicator of cytosolic free NA is available, it would be possible to measure changes in cytosolic NA levels. However, at present, there appeared to be no fluorescence probe to label cytosolic NA. For example, NS521 labels both dopamine and norepinephrine inside neurosecretory vesicles (Hettie & Glass et al., 2014, Chemistry), and BPS3 fluorescence sensor labels NA around cell membrane by anchoring on the cell membrane (Mao et al., 2023, Nat Comm). Furthermore, the method reported in “A Genetically Encoded Fluorescent Sensor for Rapid and Specific In Vivo Detection of Norepinephrine” is limited to detect NA only when α2AR is expressed. In the present study, increases in cytosolic NA levels are caused by internalization of α2AR. Cytosolic NA measurements with GRAB NE photometry may not be applicable in the present study. However, we have discussed the availability of such fluorescent methods to directly prove the increase in cytosolic NA as a limitation of our study (page 14, lines 429-436 in the revised manuscript).

(3) Include validation of the chronic stress model with physiological and behavioral measures (e.g., corticosterone levels and another behavioral test).

Please see the response to the comment raised by Reviewer #1 as Weakness (4).

(4) All supplemental figures should be explicitly explained in the Results section. Specifically, clarify and describe the details of Figure S1G-K, Figure S2F-K, Figure S3A-H, Figure S4A-F, Figure S5, and Figure S6 to ensure all supplementary data are fully integrated into the main text.

We have more explicitly and clearly described the details of Figure S1E-J, Figure S2F-K, Figure S3A-H, Figure S4A-F, Figure S5, and Figure S6 and fully integrated those explanations into the main text in the revised manuscript.

(5) In Figure 3, the morphology of TH-positive cells differs between panels D and E. Additionally, TH is typically expressed in the cytosol, but in the provided images, it appears to be localized only to the membrane. Please clarify this discrepancy and provide a lower-magnification image to display a larger area, not one cell.

In a confocal image, TH is not necessarily expressed homogenously in the cytosol, but is expressed in a ring-shaped pattern inside the plasma membrane, avoiding the cell nucleus and its surrounding Golgi apparatus and endoplasmic reticulum (ER) (Henrich et al., 2018, Acta Neuropathol Commun; see Fig. 4a and 6e), especially when the number of z-stack of confocal images is small. This is presumably because LC neurons are especially enriched with numerous Golgi apparatus and ER (Groves & Wilson, 1980, J Comp Neurol).

In Figure S7, we showed a lower-magnification image of LC and its adjacent area (mesencephalic trigeminal nucleus). In the LC area, there are a variety of LC neurons, which include oval shaped neurons (open arrowhead; similar to Figure 3D) and also rhombus-like shaped neurons (open double arrowheads, similar to Figure 3E). A much lower-magnification image of LC neurons constituting LC nucleus was shown in Figure 5A.

(6) In Figure 5, the difference in MAO-A expression is not clearly visible in the fluorescence images. Enzymatic assays for AEP and MAO-A should be included to demonstrate the increased activity better.

In the current study, we did not elaborate to detect the changes in TH, MAO-A and AEP in terms of immunohistochemical method. Instead, we elaborated to detect such changes in terms of western blot method. The main conclusions in the current study were drawn primarily by electrophysiological techniques as we have expended much effort on electrophysiological experiments. Because the relative quantification of active AEP and Tau N368 proteins by western blotting analysis may accurately reflect changes in those enzyme activities, enzymatic assay may not be necessarily required but is helpful to better demonstrate AEP and MAO-A activity. We have described the necessity of enzymatic assay to better demonstrate the AEP and MAO-A activities (page 10, lines 314-315).

Reviewer #3 (Recommendations for the authors):

(1) Causality across the pathway

Each step (α2A internalisation, GIRK rundown, Ca²⁺ rise, MAO-A/AEP upregulation) is demonstrated separately, but no experiment links them in a single preparation. Consider in vivo Ca²⁺ or GRAB NE photometry during restraint stress while probing α2A levels with i.p. clonidine injection or optogenetic over excitation coupled to biochemical readouts. Such integrated evidence would help to overcome the correlational nature of the manuscript to a more mechanistic study.

It is not possible to measure free cytosolic NA levels with GRAB NE photometry when α2A AR is internalized as described above (see the response to the comment made by reviewer #1 as the recommendation for the authors).

(2) Pharmacology and NE concentration

The use of 100 µM noradrenaline saturates α and β adrenergic receptors alike. Please provide ramp measurements of GIRK current in dose-response at 1-10 µM NE (blocked by atipamezole) to confirm that the rundown really reflects α2A activity rather than mixed receptor effects.

It is true that 100 µM noradrenaline activates both α and β adrenergic receptors alike. However, it was clearly showed that enhancement of GIRK-I by 100 µM noradrenaline was completely antagonized by 10 µM atipamezole and the Ca²⁺ dependent rundown of NA-induced GIRK-I was prevented by 10 µM atipamezole. Considering the Ki values of atipamezole for α2A AR (=1~3 nM) (Vacher et al., 2010, J Med Chem) and β AR (>10 µM) (Virtanen et al., 1989, Arch Int Pharmacodyn Ther), these results really reflect α2A AR activity but not β AR activity (Figure S5). Furthermore, because it is already well established that NA-induced GIRK-I was mediated by α2A AR activity in LC neurons (Arima et al., 1998, J Physiol; Williams et al., 1985, Neuroscience), it is not necessarily need to re-examine 1-10 µM NA on GIRK-I.

(3) Calcium dependence is not yet definitive

The rundown is induced with a TEA-enhanced pulse protocol. Blocking L-type channels with nifedipine (or using Cd²⁺) during this protocol should show whether Ca²⁺ entry is necessary. Without such a control, the Ca²⁺ link remains inferential.

The Ca²⁺ link was precisely demonstrated by a series of voltage clamp experiment, in which Ca²⁺ influx was orderly potentiated by increasing the number of positive voltage pulses (Figures S1 and S2). As the number of positive voltage pulses was increased, the rundown of GIRK-I was accelerated or enhanced more. The relationship between the number of spikes and the Ca²⁺ influx detected as Ca²⁺ transients was well documented in Ca2+ imaging experiments using fura-2 (Figure S3).

The presence or absence of Ca²⁺ currents and the amount of Ca²⁺ currents determined the trend of the rundown of GIRK-I (Figs. 2, S1 and S2). The same voltage protocol hardly caused the rundown when it did not induce Ca²⁺ currents in the absence of TEA (Fig. S1F; compare with Fig. 2B), and the series of voltage-clamp protocols convincingly demonstrated the orderly involvement of [Ca²⁺]_i in accelerating the rundown of GIRK-I. Therefore, blockade of Ca²⁺ currents by nifedipine may not be so beneficial.

(4) Age mismatch and disease claims

All electrophysiology and biochemical data come from juvenile (< P30) mice, yet the conclusions stress Alzheimer-related degeneration. Key endpoints need to be replicated in adult or aged mice, or the manuscript should soften its neurodegenerative scope.

As described in the section of Conclusion, we never stress Alzheimer-related degeneration, but might give such an impression. To avoid such a misunderstanding, we have added a description “However, the present mechanism must be proven to be valid in adult or old mice, to validate its involvement in the pathogenesis of AD.” (page 14, lines 448-450).

(5) Direct evidence for extracellular/cytosolic NE

The proposed rise in reuptake NA is inferred from electrophysiology. Modern fluorescent sensors (GRAB NE, nLight) or fast scan voltammetry could quantify NE overflow and clearance during stress, directly testing the model.

Please see the response to the comment made by Reviewer #1 as the Recommendations for the authors (2) as described above.

(6) Quantitative histology

Figure 5 presents attractive images but no numerical analysis. Please provide ROI-based fluorescence quantification (with n values) or move the images to the supplement and rely on the Western blots.

We have moved the immunohistochemical results in Fig. 5 to the supplement as we believe the quantification of immunohistochemical staining is not necessarily correct.

Joint Public Review:

Summary:

This study investigates plasticity effects in brain function and structure from training in navigation and verbal memory.

The authors used a longitudinal design with a total of 75 participants across two sites. Participants were randomised to one of three conditions: verbal memory training, navigation training, or a video control condition. The results show behavioural effects in relevant tasks following the training interventions. The central claim of the paper is that network-based measures of task-based activation are affected by the training interventions, but structural brain metrics (T2w-derived volume and diffusion-weighted imaging microstructure) are not impacted by any of the training protocols tested.

Strengths:

(1) This is a well-designed study which uses two training conditions, an active control, and randomisation, as appropriate. It is also notable that the authors combined data acquisition across two sites to reach the needed sample size and accounted for it in their statistical analyses quite thoroughly. In addition, I commend the authors on using pre-registration of the analysis to enhance the reproducibility of their work.

(2) Some analyses in the paper are exhaustive and compelling in showcasing the presence of longitudinal behavioural effects, functional activation changes, and lack of hippocampal volume changes. The breadth of analysis on hippocampal volume (including hippocampal subfields) is convincing in supporting the claim regarding a lack of volumetric effect in the hippocampus.

Weaknesses:

(1) The rationale for the study and its relationship with previous literature is not fully clear from the paper. In particular, there is a very large literature that has already explored the longitudinal effects of different types of training on functional and structural neuroimaging. However, this literature is barely acknowledged in the Introduction, which focuses on cross-sectional studies. Studies like the one by Draganski et al. 2004 are cited but not discussed, and are clumped together with cross-sectional studies, which is confusing. As a reader, it is difficult to understand whether the study was meant to be confirmatory based on previous literature, or whether it fills a specific gap in the literature on longitudinal neuroimaging effects of training interventions.

(2) The main claim regarding the lack of changes in brain structure seems only partially supported by the analyses provided. The limited whole-brain evidence from structural neuroimaging makes it difficult to confirm whether there is indeed no effect of training. Beyond hippocampal analyses, many whole-brain analyses of both volumetric and diffusion-weighted imaging metrics are only based on coarse ROIs (for example, 34 cortical parcellations for grey matter analyses). Although vertex-wise analyses in FreeSurfer are reported, it is unclear what metrics were examined (cortical thickness? area? volume?). Diffusion-weighted imaging seems to focus on whole-tract atlas ROIs, which can be less accurate/sensitive than tractography-defined ROIs or voxel-wise approaches.

(3) Quality control of images is only mentioned for FA images in subject space. Given that most analyses are based on atlas ROIs, visual checks following registration are fundamental and should be described in further detail.

Reviewer #2 (Public review):

Summary:

This article aims to demonstrate that local production of CO₂ at the axonal node opens Cx32 hemichannels in the Schwann cell paranode, and that CO₂ diffuses through the AQP1 channel to reach Cx32 and trigger its opening. The authors also present evidence supporting a physiological role for this regulatory mechanism. They propose that CO₂-dependent Cx32 activation mediates activity-dependent Ca²⁺ influx into the paranode, and by increasing the leak current across the myelin sheath, it contributes to a slowing of action potential conduction velocity.

The study presents a very interesting and novel mechanism for the physiological regulation of Cx32 hemichannels. The findings are relevant to the field, and the methods and results are of good quality, with some improvements in interpretation and explanation required, and some minor experimental suggestions.

Strengths:

The article is solid in terms of the novelty of the findings and relevance for the physiology of myelinated axons. In addition, it is of major interest for the Connexin field because it explores a physiological way to open Cx32 hemichannels. The experiments are well elaborated, and most of them are sufficient for the main points described by the authors. The finding that nervous activity will trigger the mechanism of hemichannel opening by CO2 is probably the most relevant biological mechanism derived from this article.

Weaknesses:

Throughout the manuscript, the authors interpret their findings as if the described mechanism specifically occurs in the node and paranode regions. However, there is no direct evidence identifying the precise site of CO₂ production or the activation site of Cx32 hemichannels. Therefore, statements such as the one in the title ("activity-dependent CO₂ production in the axonal node opens Cx32 in the Schwann cell paranode") should be reconsidered or removed, as they may be misleading and are not essential to the interpretation of the data. In addition, the participation of aquaporin AQP1 as the main conduit for CO2 diffusion through the plasma membrane could have another interpretation.

Author response:

Reviewer #1 (Public review): 

The manuscript by Butler et al. explores a novel physiological role for connexin 32 (Cx32) hemichannels in Schwann cells at peripheral nerves. Building on the authors' prior work on CO₂-sensitive gating of connexins, this study proposes that mitochondrial CO₂ production dependent on neuronal activity promotes the opening of Cx32 hemichannels in the paranode, which in turn modulates neuronal activity by reducing conduction velocity. This hypothesis is addressed using a multifaceted approach that includes immunofluorescence microscopy, dye uptake assays, calcium imaging, computational modeling, and extracellular recordings in isolated sciatic nerves. 

Among the strengths of the study are the interdisciplinary integration of imaging, in silico approaches, and functional data. Also, this study proposes a new mechanism with profound physiological relevance. Specifically, Butler et al. provide new insights into glial modulation of electrical conduction in sensory/motor myelinated nerves. 

In the current state, the study has some limitations. The evidence linking Cx32 to the observed dye uptake and conduction velocity changes relies primarily on pharmacological inhibition with carbenoxolone, which lacks specificity. The imaging data show overlapping marker signals that preclude the anatomical distinction between nodes and paranodes. FITC uptake, while convincing to test Cx32 hemichannel gating, lacks spatial-temporal information and validation of distribution and localization to viable intracellular compartments. Moreover, while the findings are intriguing, functional proof that Cx32 regulates conduction velocity through ATP release or other downstream effects remains incomplete. Further work using targeted genetic tools, live-tissue imaging, and additional controls would strengthen the mechanistic conclusions. 

Overall, the manuscript offers compelling preliminary evidence that supports a new role for Cx32 in peripheral nerve physiology and raises important questions for future investigation. 

We thank the reviewer for their comments and agree that the evidence for involvement of Cx32 is indirect. We are planning to perform genetic manipulations to strengthen this link. We shall review our presentation of the morphology in terms of the node/paranode/juxtaparanode distribution and adjust accordingly. We have in the interim generated new data using GCaMP transduced into Schwann cells that provides the live-tissue imaging that the reviewer requests. This strengthens our conclusions, and we will add these data into the paper.

Reviewer #2 (Public review): 

Summary: 

This article aims to demonstrate that local production of CO₂ at the axonal node opens Cx32 hemichannels in the Schwann cell paranode, and that CO₂ diffuses through the AQP1 channel to reach Cx32 and trigger its opening. The authors also present evidence supporting a physiological role for this regulatory mechanism. They propose that CO₂-dependent Cx32 activation mediates activity-dependent Ca²⁺ influx into the paranode, and by increasing the leak current across the myelin sheath, it contributes to a slowing of action potential conduction velocity. 

The study presents a very interesting and novel mechanism for the physiological regulation of Cx32 hemichannels. The findings are relevant to the field, and the methods and results are of good quality, with some improvements in interpretation and explanation required, and some minor experimental suggestions. 

Strengths: 

The article is solid in terms of the novelty of the findings and relevance for the physiology of myelinated axons. In addition, it is of major interest for the Connexin field because it explores a physiological way to open Cx32 hemichannels. The experiments are well elaborated, and most of them are sufficient for the main points described by the authors. The finding that nervous activity will trigger the mechanism of hemichannel opening by CO2 is probably the most relevant biological mechanism derived from this article. 

Weaknesses: 

Throughout the manuscript, the authors interpret their findings as if the described mechanism specifically occurs in the node and paranode regions. However, there is no direct evidence identifying the precise site of CO₂ production or the activation site of Cx32 hemichannels. Therefore, statements such as the one in the title ("activity-dependent CO₂ production in the axonal node opens Cx32 in the Schwann cell paranode") should be reconsidered or removed, as they may be misleading and are not essential to the interpretation of the data. In addition, the participation of aquaporin AQP1 as the main conduit for CO2 diffusion through the plasma membrane could have another interpretation. 

We thank the reviewer for their comments and agree that we do not have direct evidence for the site of CO2 production or the site of activation of Cx32 hemichannels. This direct evidence is extremely difficult to obtain, and we therefore depend on indirect arguments. Mitochondria represent the major source of CO2, and their distribution will therefore indicate where CO2 is likely to be produced. We agree that this is not essential to the interpretation of the data and will adjust the text as recommended. We will add a section to the Discussion to consider this point in more detail.

DOI: 10.1158/2767-9764.CRC-25-0064

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What is this?

DOI: 10.1158/2159-8290.CD-24-1425

Resource: (BioLegend Cat# 104428, RRID:AB_830799)

Curator: @scibot

SciCrunch record: RRID:AB_830799

What is this?

DOI: 10.1158/2159-8290.CD-24-1390

Resource: Velocyto (RRID:SCR_018167)

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SciCrunch record: RRID:SCR_018167

What is this?

DOI: 10.1016/j.molcel.2025.07.007

Resource: (JCRB Cat# JCRB0225, RRID:CVCL_0219)

Curator: @scibot

SciCrunch record: RRID:CVCL_0219

What is this?

Pour avoir la version française de Visual Code : - Cliquer sur "Extensions" - Taper "French" dans la barre de recherche - Choisir le premier résultat (ou sinon, regarder si c'est qqch "d'officiel") - Télécharger … et c'est prêt !

On a ici inversé l'ordre des attributs par rapport à l'ordre qui était dans la démonstration vidéo. On peut donc noter nos attributs dans l'ordre qu'on veut, même quand certains sont obligatoires et d'autres optionnels ?

ne gond ha a "megerősítés" marad csak a magyar szövegekben a "Confirm" szóra inkább a jóváhagyást használnám.

Válasz küldése ( ha nyelvesítetni szeretnénk)

Feladat zárása( Ha nyelvesítetni szeretnénk)

I've tried using a hiking metaphor to describe a similar phenomenon (specifically, and perversely, as a preface when trying to explain second panel syndrome.

Peut-être cette brève définition aurait-elle davantage sa place ci-haut, lors de la première mention des algorithmes « agnostiques », dans la section « Une remédiation des collection par-delà les logiques documentaires ? »

Ici encore, la source précise est manquante.

Il faudrait fournir le numéro de page de la citation ici.

Le numéro de page manque également ici

Ici aussi, la citation est présentée sans numéro de page.

Il est ici question d'un algorithme d'humanités numériques « agnostique ». Toutefois, vous n'offrez une définition que plus tard, dans le §4 de la section « Les données liquides ». Pourquoi ne pas le définir lors de la première mention, même si ce n'est qu'en note de bas de page?

Il faudrait offrir un numéro de page pour cette citation.

Je formule la même remarque que plus haut concernant le mot « usagers ».

Ici aussi, le numéro de la page est manquant.

Il faudrait préciser le numéro de page auquel on retrouve cette citation.

Il faudrait ici aussi préciser la source. Si les citations courtes qui ferment le paragraphe ci-haut sont tirées du même passage, il n'est pas nécessaire de l'indiquer deux fois, et vous pouvez vous limiter à spécifier le numéro de page après la citation longue.

Il faudrait préciser le numéro de page.

Le numéro de page est manquant du renvoi de la citation.

Je suggère de remplacer le premier « pour » par « en vue de » afin d'éviter la répétition.

Cette locution est-elle tirée d'une source en particulier? Le cas échéant, il faudrait l'indiquer.

Il faudrait préciser le numéro de page d'où vous tirez cette citation.

L'intégration de la citation provoque une rupture syntaxique, en raison du participe présent « en anticipant ». Il faudrait replacer davantage la phrase dans son contexte original, ou tronquer davantage la citation.

L'erreur d'orthographe (on devrait lire « aristotélicienne ») est-elle également présente dans la citation d'origine? Si c'est le cas, il faudrait ajouter une mention [sic].

L'emphase est-elle originale? Si oui, il convient de le préciser.

Il faudrait préciser la source de cette locution précise et peu commune. Provient-elle de Technique et temps ou du rapport de Stiegler?

Le verbe « prévu » apparaît deux fois dans cet intervalle. Je suggère « On envisage pour l'année 1995 ... » afin d'éviter la répétition.

Il faudrait indiquer le numéro de page d'où est tirée la citation.

Cette locution est-elle tirée d'un ouvrage en particulier? Si oui, il faudrait en indiquer la source.

l'affirmation est mal construite (dépendre/indispensable). donc pas fausse en tout cas.

Super bien pensé ! Suggestion : ça serait bien de faire ne sorte que, une fois que l'on clique sur un lien, un nouvel onglet s'ouvre. C'est très pénible de faire "clic droit" --> "ouvrir dans un nouvel onglet" à chaque lien ! Et au passage, je conseille de suivre la formation sur Chrome parce que les exemples du prof sont réalisés sur Chrome, et donc ça diffère de Safari par exemple.

Reviewer #1 (Public review):

M. tuberculosis exhibits metabolic flexibility, enabling it to adapt to various environmental stresses, including antibiotic treatment. In this manuscript, Serafini et al. investigate the metabolic remodeling of M. tuberculosis used to survive iron-limited conditions by employing LC-MS metabolomics and 13C isotope tracing experiments. The results demonstrate that metabolic activity in the oxidative branch of the TCA cycle slows down, while the reductive branch is reverted to facilitate the biosynthesis of malate, which is subsequently secreted.

Overall, this study is experimentally well-designed, particularly the use of 13C isotope tracing to monitor TCA cycle remodeling under iron-limited conditions. The findings are valuable as they offer potential new targets for antibiotics aimed at non-replicating M. tuberculosis occurring in the hosts. However, despite these strengths, the reviewer has concerns regarding the mechanistic basis underlying the observed metabolic remodeling and its role in M. tuberculosis pathogenesis.

Major Comments:

The authors argue that iron starvation is a physiologically relevant stressor encountered by M. tuberculosis post-infection. Using Erdman and H37Rv strains under DFO conditions, Erdman loses viability, whereas H37Rv maintains it. Nonetheless, both strains exhibit similar metabolic remodeling in the TCA cycle based upon metabolomics and isotope tracing data. The authors should clarify the specific metabolic adaptations in H37Rv that enable it to sustain viability under DFO conditions.

The authors report no significant changes in NAD/NADH and ATP levels in H37Rv and Erdman exposed to DFO conditions. They observe TCA cycle remodeling, particularly the reversal of the reaction between OAA and MAL, catalyzed by malate dehydrogenase, an enzyme that uses NAD+ and NADH as cofactors. The directionality of this reaction likely depends on the relative levels of NAD+ and NADH. Additionally, other dehydrogenases, such as pyruvate DH and aKG DH, also require NAD+/NADH cofactors. In Figure 1I, NAD+ and NADH levels are monitored only at day 3 post-exposure to DFO conditions. Since Erdman loses viability after 2-3 weeks, the authors should include measurements of NAD+, NADH, and ATP levels at weekly intervals up to 3 weeks. Furthermore, glycine levels - which are linked to NAD+ recycling via the conversion of glyoxylate - should be measured under both HI and DFO conditions as an indirect indicator of the NAD+/NADH ratio.

In Figure 2A, it is unclear why a 100-fold accumulation of aKG does not correspond proportionally to the accumulation of (iso)citrate.

The authors state that fumarate, aKG, (iso)citrate, malate, and pyruvate are secreted under DFO conditions. While the secretion of aKG and pyruvate makes sense, given their marked intracellular accumulation, it is puzzling why (iso)citrate, malate, and fumarate are secreted even though there are no changes in their intracellular abundance. To rule out the possibility that these metabolites are released due to bacterial lysis rather than active secretion, the authors should analyze the 13C-labeled fractions of these metabolites in the culture filtrate using the M. tuberculosis culture in media containing 13C glycerol.

To validate the role of the PCK-mediated reductive TCA cycle in malate biosynthesis and secretion under DFO conditions, the authors should generate a malate dehydrogenase (MDH) knockdown strain, considering that MDH is essential, and examine the 13C labeling patterns and NAD/NADH under DFO conditions.

The authors also observe decreased GABA abundance and overall 13C labeling in DFO conditions, suggesting that the GABA shunt is the primary route for Succinate biosynthesis under DFO conditions. Thus, it is strongly recommended that the authors perform a 13C glutamate tracing experiment to directly track labeling in aKG and GABA shunt metabolites, providing more definitive evidence for the involvement of the GABA shunt.

Author response:

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

Reviewer #1 (Public review): 

To elucidate the mechanisms and evolution of animal biomineralization, Voigt et al. focused on the sponge phylum - the earliest branching extant metazoan lineages exhibiting biomineralized structures - with a particular emphasis on deciphering the molecular underpinnings of spicule formation. This study centered on calcareous sponges, specifically Sycon ciliatum, as characterized in previous work by Voigt et al. In S. ciliatum, two morphologically distinct spicule types are produced by a set of two different types of cells that secrete extracellular matrix proteins, onto which calcium carbonate is subsequently deposited. Comparative transcriptomic analysis between a region with active spicule formation and other body regions identified 829 candidate genes involved in this process. Among these, the authors focused on the calcarine gene family, which is analogous to the Galaxins, the matrix proteins known to participate in coral calcification. The authors performed three-dimensional structure prediction using AlphaFold, examined mRNA expression of Calcarin genes in spiculeforming cell types via in situ hybridization, conducted proteomic analysis of matrix proteins isolated from purified spicules, and carried out chromosome arrangement analysis of the Calcarin genes.

Based on these analyses, it was revealed that the combination of Calcarin genes expressed during spicule formation differs between the founder cells-responsible for producing diactines and triactinesand the thickener cells that differentiate from them, underscoring the necessity for precise regulation of Calcarin gene expression in proper biomineralization. Furthermore, the observation that 4 Calcarin genes are arranged in tandem arrays on the chromosome suggests that two rounds of gene duplication followed by neofunctionalization have contributed to the intricate formation of S. ciliatum spicules. Additionally, similar subtle spatiotemporal expression patterns and tandem chromosomal arrangements of Galaxins during coral calcification indicate parallel evolution of biomineralization genes between S. ciliatum and aragonitic corals. 

Strengths: 

(1) An integrative research approach, encompassing transcriptomic, genomic, and proteomic analyses as well as detailed FISH. 

(2) High-quality FISH images of Calcarin genes, along with a concise summary clearly illustrating their expression patterns, is appreciated. 

(3) It was suggested that thickener cells originate from founder cells. To the best of my knowledge, this is the first study to demonstrate trans-differentiation of sponge cells based on the cell-typespecific gene expression, as determined by in situ hybridization. 

(4) The comparison between Calcarins of Calcite sponge and Galaxins of aragonitic corals from various perspective-including protein tertiary structure predictions, gene expression profiling during calcification, and chromosomal sequence analysis to reveal significant similarities between them. 

We thank the reviewer for this assessment. 

(1) The conclusions of this paper are generally well supported by the data; however, some FISH images require clearer indication or explanation.

We have modified Fig. 3 by including some insets indicating the depicted part of the sponge body and to change the color-scheme as suggested by reviewer3 for the FISH images. In accordance to the following comment, we decided to remove single-channel views in Fig. 3 A. 

(2) Figure S2 (B, C, D): The fluorescent signals in these images are difficult to discern. If the authors choose to present signals at such low magnification, enhancing the fluorescence signals would improve clarity. Additionally, incorporating Figure S2A as an inset within Figure S2E may be sufficient to convey the necessary information about signal localization. 

We changed the figure according to the suggestions.

(3) Figure S3A: The claim that Cal2-expressing spherical cells are closely associated with the choanoderm at the distal end of the radial tube is difficult to follow. Are these Cal2-expressing spherical cells interspersed among choanoderm cells, or are they positioned along the basal surface of the choanoderm? Clarifying their precise localization and indicating it in the image would strengthen the interpretation. 

In the figure, the view is on the choanoderm that lines the inner surface of the radial tube. Our interpretation is that the spherical cells are positioned at the basal surface of the choanoderm. We updated Fig. S3, which now includes another view to support our interpretation and also indicate some choanocytes.

(4) To further highlight the similarities between S.ciliatum and aragonitic corals in the molecular mechanisms of calcification, consider including a supplementary figure providing a concise depiction of the coral calcification process. This would offer valuable context for readers.

We considered this suggestion, and have included such a supplementary figure (Fig. S9).

Reviewer #2 (Public review): 

Summary: 

This paper reports on the discovery of calcarins, a protein family that seems involved in calcification in the sponge Sycon ciliatum, based on specific expression in sclerocytes and detection by mass spectrometry within spicules. Two aspects stand out: (1) the unexpected similarity between Sycon calcarins and the galaxins of stony corals, which are also involved in mineralization, suggesting a surprising, parallel co-option of similar genes for mineralization in these two groups; (2) the impressively cell-type-specific expression of specific calcarins, many of which are restricted to either founder or thickener cells, and to either diactines, triactines, or tetractines. The finding that calcarins likely diversified at least partly by tandem duplications (giving rise to gene clusters) is a nice bonus. 

Strengths: 

I enjoyed the thoroughness of the paper, with multiple lines of evidence supporting the hypothesized role of calcarins: spatially and temporally resolved RNAseq, mass spectrometry, and whole-mount in situ hybridization using CISH and HCR-FISH (the images are really beautiful and very convincing). The structural predictions and the similarity to galaxins are very surprising and extremely interesting, as they suggest parallel evolution of biomineralization in sponges and cnidarians during the Cambrian explosion by co-option of the same "molecular bricks". 

Weaknesses: 

I did not detect any major weakness, beyond those inherent to working with sponges (lack of direct functional inhibition of these genes) or with fast-evolving gene families with complex evolutionary histories (lack of a phylogenetic tree that would clarify the history of galaxins/calcarins and related proteins). 

We thank the reviewer for this assessment and the detailed comments be addressed below.

Reviewer #3 (Public review):

Summary: 

The study explores the extent to which the biomineralization process in the calcitic sponge Sycon ciliatum resembles aragonitic skeleton formation in stony corals. To investigate this, the authors performed transcriptomic, genomic, and proteomic analyses on S. ciliatum and examined the expression patterns of biomineralization-related genes using in situ hybridization. Among the 829 differentially expressed genes identified in sponge regions associated with spicule formation, the authors focused on calcarin genes, which encode matrix proteins analogous to coral galaxins. The expression patterns of calcarins were found to be diverse but specific to particular spicule types. Notably, these patterns resemble those of galaxins in stony corals. Moreover, the genomic organization of calcarine genes in S. ciliatum closely mirrors that of galaxin genes in corals, suggesting a case of parallel evolution in carbonate biomineralization between calcitic sponges and aragonitic corals. 

Strengths: 

The manuscript is well written, and the figures are of high quality. The study design and methodologies are clearly described and well-suited to addressing the central research question. Particularly noteworthy is the authors´ integration of various omics approaches with molecular and cell biology techniques. Their results support the intriguing conclusion that there is a case of parallel evolution in skeleton-building gene sets between calcitic sponges and aragonitic corals. The conclusions are well supported by the data and analyses presented. 

Weaknesses: 

The manuscript is strong, and I have not identified any significant weaknesses in its current form. 

We thank the reviewer for the insight and addressed the detailed comments below.

Reviewer #1 (Recommendations for the authors): 

The description of the region "radial tube" is unclear. Please define and explain it at its first mention in the manuscript, and, if possible, refer to the appropriate figure(s) (e.g., Figure 1A). 

We now explain radial tubes at the beginning of the results and added a label in figure 1A. “Sycon ciliatum is a tube-shaped sponge with a single apical osculum and a sponge wall of radial tubes around the central atrium (Fig. 1A). The radial tubes are internally lined with choanoderm, which forms elongated chambers in an angle of approximately 90° to the tube axis”. 

Reviewer #2 (Recommendations for the authors): 

Scientific suggestions: 

(1) Page 13: "Despite their presence in the same orthogroups, the octocoral and stony coral proteins were only distantly related to the calcareous sponge calcarins (e.g., 12-24% identity between octocoral and calcareous sequences in orthogroup Cal 2-4-6), resulting in poor alignment. Their homology to calcarins, therefore, remains to be determined." Could 3D structures of these coral proteins be predicted with AlphaFold to substantiate (or nuance) the comparison with calcarins? 

We run additional alphafold predictions for two octocoral and two scleractinian galaxins. A galaxin-like sequence from Pinnigorgia flava was only a short fragment and therefore we did not attempt any structure predictions. The result shows that the octocoral galaxin-like proteins show some structural similarity (12 beta-harpins), while the scleractinian galaxin-like proteins differ from the sponge counterparts of the same orthogroup. We added this information to the results and in the new Fig. S7.

Minor improvements to the text: 

(1)  Page 7 : "The expression of Cal1 to Cal8 was investigated using chromogenic in situ hybridization (CISH) and hairpin-chain reaction fluorescence in situ hybridization (HCR-FISH), confirming their presence in sclerocytes." - Figure 3 should be cited here. 

We refer to the figure now.

(2) Page 8-9: "Cal6 expression mirrors that of Cal2, occurring in rounded cells at the distal tip of radial tubes and in a ring of cells around the oscular ring." - Please cite a figure here. 

We refer now to Fig. 3K

(3) Page 11-12: Please define eigengene, this term is not necessarily common knowledge. 

We provide now a short definition in this sentence: “ The analysis provided eight meta-modules, of which four showed significant changes in expression module eigengenes —summary profiles that capture the overall expression pattern of each module— between samples with high spicule formation context (osculum region and regeneration stages older than four days) and samples with low spicule formation (sponge-wall and early regeneration stages until day 3-4) (Fig. S5).” 

(4) Page 13: "Species without skeletons, such as the cnidarians Hydra, Actinia, Exaiptasia, and Nematostella, also possess galaxin-like proteins." This is too concise - can you explain what evidence was used? PANTHER, AlphaFold, OrthoFinder, Blastp...? 

The evidence used is from PANTHER, and we enhanced clarification of this by modifying the last sentence of the section.

(5) Page 20: "We have identified calcarins, galaxin-like proteins, as crucial components of the biomineralization toolkit in calcareous sponges." I'm not sure you showed they are crucial (this would require functional evidence). Perhaps "novel" components or some other adjective would fit better. 

We changed the adjective to “novel”.

Suggestions for the figures: 

(1) Figure 1A: radial tubes should be labelled. 

A label was added.

(2) Figure 3 is beautiful but hard to parse. The name of all markers should be written on each panel (notably B, C, and D) and ideally placed in a consistent position (top right corner?) so that the reader's eye doesn't have to look for them anew in each panel. Consider depicting the same gene with the same color in all panels if possible (confocal imaging gives virtual colors anyway, there's no reason to be bound to the real-life color of the fluorophores used - if that was the original intent). Finally, the red/green color scheme is not colorblind-readable, so please consider switching to another scheme (white/cyan/magenta, for example).

We have updated the figure according to the suggestions. The names of all markers are now included on each panel. Placing them in the upper right corner was not feasible for all panels, so we adjusted their placement as needed. Reoccurring genes are shown in the same color where possible. To improve accessibility for individuals with red/green color vision deficiency, we adopted a cyan/magenta/yellow color scheme. Each HCR-FISH image was processed in ImageJ by splitting the image into channels, applying cyan, magenta, or yellow lookup tables, converting each channel to RGB, and then stacking and blending them using the Z-Project function with maximum intensity projection. Since the original channel information is not preserved after this processing, we provide the original red/green/blue version of the figure in the supplementary material in Fig S11. Additionally, we added small sketches of Figure 1A to indicate the sponge body regions depicted, where relevant.

(3) Figure S3: the blue staining is not explained. It is also unclear where choanocytes are - could individual choanocytes be indicated with arrows or lines? 

We added the information to the figure legend. The blue channel shows “Autofluorescence detected with the Leica TXR filter (approx. 590–650 nm), included to help distinguish true signal from background autofluorescence observed in the FITC channel (used for Spiculin detection).”

Reviewer #3 (Recommendations for the authors): 

I have no major concerns about the manuscript - only minor edits and comments, which are listed below: 

(1) On page 13, the authors refer to Figure S8; however, I believe this should be Figure S7. 

We now refer to the correct Figure. Because of introducing a new Fig. S7, now the correct reference is Fig. S8.

(2) On page 16, please correct "Spciulin" to "Spiculin". 

Now corrected.

(3) On page 17, there are two commas following "(Sycon)"; please remove one. 

Corrected.

(4) In the Data Accessibility section, none of the provided links appear to work. Please ensure all links are functional. 

We apologize for this oversight and now provide working links. 

(5) In Figure 3, the description of panel L is missing from the figure legend. 

We added the description of this panel.

(6) On page 39, change "Fig. 4" to "Figure 4" to maintain consistency throughout the manuscript. 

Changed.

(7) Figure S7 is not cited in the main text. Please, address this. 

Corrected (see above at point 1)

(8) In the legend for Table S2, the reference to Soubigou et al. (3) is incorrect, as it is not listed in the SI reference section. Please correct this. 

Soubigou et al. (2020) is now included in the SI reference list.

Inmunidad innata - 1ra linea de defensa (carece de memoria inmunológica) va al SITIO INICIAL de infección I.. Adaptativa - 2 ste de defensa/ especifico para el patógeno invasor (TIENE memoria inmunológica)

Anticuerpo: proteina que se produce en rta a un patógeno Antígeno: induce la producción de anticuerpos

Author response:

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

Reviewer #1 (Public review):

Summary:

Migration of the primordial germ cells (PGCs) in mice is asynchronous, such that leading and lagging populations of migrating PGCs emerge. Prior studies found that interactions between the cells the PGCs encounter along their migration routes regulates their proliferation. In this study, the authors used single cell RNAseq to investigate PGC heterogeneity and to characterize their niches during their migration along the AP axis. Unlike prior scRNAseq studies of mammalian PGCs, the authors conducted a time course covering 3 distinct stages of PGC migration (pre, mid, and post migration) and isolated PGCs from defined somite positions along the AP axis. In doing so, this allowed the authors to uncover differences in gene expression between leading and lagging PGCs and their niches and to investigate how their transcript profiles change over time. Among the pathways with the biggest differences were regulators of actin polymerization and epigenetic programming factors and Nodal response genes. In addition, the authors report changes in somatic niches, specifically greater non-canonical WNT in posterior PGCs compared to anterior PGCs. This relationship between the hindgut epithelium and migrating PGCs was also detected in reanalysis of a previously published dataset of human PGCs. Using whole mount immunofluorescence, the authors confirmed elevated Nodal signaling based on detection of the LEFTY antagonists and targets of Nodal during late stage PGC migration. Taken together, the authors have assembled a temporal and spatial atlas of mouse PGCs and their niches. This resource and the data herein provide support for the model that interactions of migrating mouse PGCs with their niches influences their proliferation, cytoskeletal regulation, epigenetic state and pluripotent state.

Overall, the findings provide new insights into heterogeneity among leading and lagging PGC populations and their niches along the AP axis, as well as comparisons between mouse and human migrating PGCs. The data are clearly presented, and the text is clear and well-written. This atlas resource will be valuable to reproductive and developmental biologists as a tool for generating hypotheses and for comparisons of PGCs across species.

Strengths:

(1) High quality atlas of individual PGCs prior to, during and post migration and their niches at defined positions along the AP axis.

(2) Comparisons to available datasets, including human embryos, provide insight into potentially conserved relationships among PGCs and the identified pathways and gene expression changes.

(3) Detailed picture of PGC heterogeneity.

(4) Valuable resource for the field.

(5) Some validation of Nodal results and further support for models in the literature based on less comprehensive expression analysis.

Weaknesses:

(1) No indication of which sex(es) were used for the mouse data and whether or not sex-related differences exist or can excluded at the stages examined. This should be clarified.

We have added: “Embryos of both sexes were pooled without genotyping, as the timepoints analyzed were prior to sex specification” to both the Animals section of the Materials and Methods and the Figure 1 legend. In addition, bioinformatic evaluation of potential sex biases in Nodal-Lefty signaling using Y-chromosome gene expression is reported in supplementary figure 4 and discussed in Discussion paragraph 2.

Reviewer #2 (Public review):

Summary:

This work addresses the question of how 'leading' and 'lagging' PGCs differ, molecularly, during their migration to the mouse genital ridges/gonads during fetal life (E9.5, E10.5, E11.5), and how this is regulated by different somatic environments encountered during the process of migration. E9.5 and E10.5 cells differed in expression of genes involved in canonical WNT signaling and focal adhesions. Differences in cell adhesion, actin cytoskeletal dynamics were identified between leading and lagging cells, at E9.5, before migration into the gonads. At E10.5, when some PGCs have reached the genital ridges, differences in Nodal signaling response genes and reprogramming factors were identified. This last point was verified by whole mount IF for proteins downstream of Nodal signaling, Lefty1/2. At E11.5, there was upregulation of genes associated with chromatin remodeling and oxidative phosphorylation. Some aspects of the findings were also found to be likely true in human development, established via analysis of a dataset previously published by others.

Strengths:

The work is strong in that a large number of PGCs were isolated and sequenced, along with associated somatic cells. The authors dealt with problem of very small number of migrating mouse PGCs by pooling cells from embryos (after ascertaining age matching using somite counting). 'Leading' and 'lagging' populations were separated by anterior and posterior embryo halves and the well-established Oct4-deltaPE-eGFP reporter mouse line was used.

Weaknesses:

The work seems to have been carefully done, but I do not feel the manuscript is very accessible, and I do not consider it well written. The novel findings are not easy to find. The addition of at least one figure to show the locations of putative signaling etc. would be welcome.

Thank you for the excellent suggestion. Fig. 6 has been added to highlight the main novel findings of this work and integrate them among contributions of earlier studies to provide a more complete view of signaling pathways and cell behaviors governing PGC migration.

(1) The initial discussion of CellRank analysis (under 'Transcriptomic shifts over developmental time...' heading) is somewhat confusing - e.g. If CellRank's 'pseudotime analysis' produces a result that seems surprising (some E9.5 cells remain in a terminal state with other E9.5 cells) and 'realtime analysis' produces something that makes more sense, is there any point including the pseudotime analysis (since you have cells from known timepoints)? Perhaps the 'batch effects' possible explanation (in Discussion) should be introduced here. Do we learn anything novel from this CellRank analysis? The 'genetic drivers' identified seem to be genes already known to be key to cell transitions during this period of development.

Thank you for this important observation. We have clarified the text in this section and added “This discrepancy may reflect differences in differentiation potential of some E9.5 PGCs that end in a terminal state among anterior E9.5 PGCs, but could also result from technical batch effects generated during library preparation. These possible interpretations are further discussed in the Discussion section.” to the pertinent results section and added additional relevant thoughts on the implications of this finding in Discussion paragraphs 4 and 7. We feel that it is important to include both results to the reader, as it is challenging to differentiate between heterogeneous developmental and migratory potential among E9.5 anterior PGCs and differential influence of batch effects across sequencing libraries with the data available.

(2) In Discussion - with respect to Y-chromosome correlation, it is not clear why this analysis would be done at E10.5, when E11.5 data is available (because some testis-specific effect might be more apparent at the later stage).

Since we had identified autocrine Nodal signaling primarily in anterior late migratory PGCs at E10.5 and knew that Nodal signaling was involved in sex specification of testicular germ cells into prospermatogonia by E12.5, we wanted to determine whether the Nodal signaling in late migratory PGCs at E10.5 was likely to be a sex-specific effect or was common to PGCs in both sexes. This was assessed in supplementary figure 4 and determined unlikely to be related to sex specification of PGCs as Nodal signaling was not strongly correlated with Y-chromosome transcripts in migratory PGCs. Assessing the relationship between Nodal signaling and Y-chromsome transcription at E11.5, when migration is complete, would be unlikely to help us further understand the dynamics of Nodal signaling during late PGC migration.

(3) Figure 2A - it seems surprising that there are two clusters of E9.5 anterior cells

Thank you for the interesting observation! One possibility is that the two states represent differential developmental competence as is suggested by the presence of one E9.5 anterior cluster along the differentiation trajectory in Fig 2A and one not within this differentiation trajectory. Another is that technical aspects of generating these sequencing libraries affected some cells more than others, resulting in clustering of highly affected and less affected cells, which would also be consistent with some E9.5 anterior cells lying within the differentiation trajectory and some not. Since it is challenging to differentiate between these possibilities with the data available, we have intentionally avoided overstating interpretations of this result in the manuscript text. We have included discussion of the potential implications of the transcriptional divergence you identify in Discussion paragraphs 4 and 7.

(4) Figure 5F - there does seem to be more LEFTY1/2 staining in the anterior region, but also more germ cells as highlighted by GFP

This is true; based on our selected anatomic landmarks for “anterior” and “posterior” as indicated in Methods, the “anterior” compartment typically contains more PGCs. Thus, we have included violin plots with all data points shown of signal intensities of both LEFTY1/2 and pSMAD2/3 in Fig. 5G and 5I so that the reader can evaluate the entire distribution of PGC signal intensities for each embryo.

Reviewer #3 (Public review):

Summary:

The migration of primordial germ cells (PGCs) to the developing gonad is a poorly understood, yet essential step in reproductive development. Here, the authors examine whether there are differences in leading and lagging migratory PGCs using single-cell RNA sequencing of mouse embryos. Cleverly, the authors dissected embryonic trunks along the anterior-to-posterior axis prior to scRNAseq in order to distinguish leading and lagging migratory PGCs. After batch corrections, their analyses revealed several known and novel differences in gene expression within and around leading and lagging PGCs, intercellular signaling networks, as well as number of genes upregulated upon gonad colonization. The authors then compared their datasets with publicly available human datasets to identify common biological themes. Altogether, this rigorous study reveals several differences between leading and lagging migratory PGCs, hints at signatures for different fates among the population of migratory PGCs, and provides new potential markers for post-migratory PGCs in both humans and mice. While many of the interesting hypotheses that arise from this work are not extensively tested, these data provide a rich platform for future investigations.

Strengths:

The authors have successfully navigated significant technical challenges to obtain a substantial number of mouse migratory primordial germ cells for robust transcriptomic analysis. Here the authors were able to collect quality data on ~13,000 PGCs and ~7,800 surrounding somatic cells, which is ten times more PGCs than previous studies.

The decision to physically separate leading and lagging primordial germ cells was clever and well-validated based on expected anterior-to-posterior transcriptional signatures.

Within the PGCs and surrounding tissues, the authors found many gene expression dynamics they would expect to see both along the PGC migratory path as well as across developmental time, increasing confidence in the new differentially expressed genes they found.

The comparison of their mouse-based migratory PGC datasets with existing human migratory PGC datasets is appreciated.

The quality control, ambient RNA contamination elimination, batch correction, cell identification and analysis of scRNAseq data were thorough and well-done such that the new hypotheses and markers found through this study are dependable.

The subsetting of cells in their trajectory analysis is appreciated, further strengthening their cell terminal state predictions.

Weaknesses:

Although it is useful to compare their mouse-based dataset with human datasets, the authors used two different analysis pipelines for each dataset. While this may have been due to the small number of cells in the human dataset as mentioned, it does make it difficult to compare them.

Direct comparisons between findings in human and mouse focused on CellChat cell-cell communication prediction results, which were conducted in an identical fashion using the same analysis methods for both datasets.

There were few validation experiments within this study. For one such experiment, whether there is a difference in pSMAD2/3 along the AP axis is unclear and not quantified as was nicely done for Lefty1/2.

Additional validation of the pSMAD2/3 signal intensity along the AP axis was performed and is now included in Fig. 5.

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Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity):

In Arabidopsis, DNA demethylation is catalyzed by a family of DNA glycosylases including DME, ROS1, DML2, and DML3. DME activity in the central cell leads to the hypomethylation of maternal alleles in endosperm. While ROS1, DML2, and DML3 function in vegetative tissues to prevent spreading DNA methylation from TE boundaries, their function in the endosperm was unclear.<br /> Using whole genome methylome analysis, the authors showed that ROS1 prevents hypermethylation of paternal alleles in the endosperm thus promotes epigenetic symmetry between maternal and paternal genomes.<br /> The approach and experimental desighs are appropriate, and the key conclusions are adequately supported by the results.<br /> However, there is not sufficient evidence to support the claim that DME demethylates the maternal allele at ROS1-dependent biallelically-demethylated regions. To clarify the issue, the authors could analyze if there is an overlap between DMRs identified in ros1 endosperm and those identified in dme endosperm using published data. If there is any, the authors could show a genome browser example of DMR including dme data.

Response: Thank you for your insight on our work. To address your concern and further test our model that DME prevents methylation of the maternal allele at regions where ROS1 is prevents methylation of the paternal allele, we turned to the allele-specific bisulfite-sequencing data published in Ibarra et al 2012. These data were from endosperm isolated at 7-8 DAP from aborting seeds of dme-2 +/- (Col-gl) plants pollinated by L_er_. Our analysis of these data is now included in Figures 6 and 7 and Supplemental Figures 13-17. We show that when the loss-of-function allele dme-2 is inherited maternally, average methylation of the maternal allele increases at ROS1-dependent regions (in the revised version of the paper now referred to as ROS1 paternal, DME maternal regions) from less than 10% CG methylation to approximately 40% CG methylation (Fig. 6D), consistent with our previous analysis using the non-allelic Hsieh et al 2009 data (now moved to Supplemental Figure 15). These results thus provide additional evidence that DME removes maternal allele methylation at regions where ROS1 removes paternal allele methylation (compare Fig. 6B and 6D). We included relevant genome browser examples in Figure 7E and Supplemental Figure 14. In the revised version, the relationship between ROS1 and DME is further expanded upon in the text.

Reviewer #1 (Significance):

Endosperm is a tissue unique to flowering plants. Though it is an ephemeral tissue, the endosperm plays essential roles for seed development and germination. The endosperm is also the site genomic imprinting occurs, and it has a distinct epigenomic landscape. This work provides a new insight that ROS1 may antagonize imprinted gene expression in the endosperm. However, it was not shown whether imprinted gene expression is indeed affected in ros1, or whether the ros1 mutation has phenotypic consequences. These results would be useful to discuss the evolution and significance of genomic imprinting.

Response: We agree that the biological significance of ROS1-mediated paternal allele demethylation is presently unknown. We performed RNA-seq on wild-type and ros1 3C and 6C endosperm nuclei, but these data were unfortunately not of high enough quality to include in the manuscript. In the Discussion we suggest that disrupting ROS1-mediated paternal allele demethylation might lead to a gain of imprinting over evolutionary time. In future work we are planning to address potential relationships to gene imprinting using a molecular, RNA-sequencing approach as well as an evolutionary comparative approach. As expected, given the expectation that imprinted genes are associated with a parent-of-origin specific epigenetic mark, we did not find any relationship between known imprinted genes and ROS1-dependent regions that are biallelically-demethylated regions in wild-type endosperm (see lines 362-372).

Reviewer #2 (Evidence, reproducibility and clarity):

SUMMARY

Hemenway and Gehring present evidence that the paternal genome in Arabidopsis endosperm is demethylated at several hundred loci by the DNA glycosylase/lyase ROS1. The evidence is primarily based on analysis of DNA methylation of ros1 mutants and of hybrid crosses where each parental genome can be differentiated by SNPs. I have some comments/questions/concerns, two of them potentially serious, but I think Hemenway and Gehring can address them through additional analyses of data that they already have available and a bit of clarification in writing.

Response: Thank you for your thoughtful review of this study. Your insight and suggestions have helped add clarity to the paper.

MAJOR COMMENTS:

1. Could the excess methylation in ros1-3 relative to ros1-7 shown in Figures 1A and 1C be explained by a second mutation in the ros1-3 background that elevates methylation at some loci? Any mutation that increased RdDM at these loci, for example could have this effect. This could confound the identification and interpretation of biallelicly demethylated loci.

Response: We propose a simpler explanation for the additional hypermethylation observed in ros1-3: ros1-3 is a loss-of-function (null) allele whereas ros1-7 is likely a hypomorphic allele. For clarity, we have added a diagram of all of the alleles used in this study as Supplemental Figure 1B. The ros1-3 allele was first described in Penterman et al, PNAS, 2007. It is a T-DNA insertion allele that was isolated in the Ws accession and then backcrossed 6 times to Col-0, greatly minimizing the risk of unlinked secondary mutations being present. There is no genetic evidence that there is another T-DNA insertion in this line. The ros1-7 allele was described in Williams et al, Plos Genet, 2015. It was isolated from the Arabidopsis Col-0 TILLING population and is missense mutation (E956K) in a residue in the glycosylase domain that is conserved among the four DNA glycosylases. It is known that ROS1 transcripts are produced from the ros1-7 allele (Williams et al 2015). We observe less hypermethylation in the ros1-7 background compared to the ros1-3 background, and thus propose that the ros1-7 allele is a hypomorphic allele of ROS1. The use of two independent ros1 mutant alleles for initial endosperm methylation profiling strengthens the findings of our study. Importantly, regions that are hypermethylated in ros1-3 are also hypermethylated in ros1-7, but to a lesser extent, and vice versa (Fig 1D, Supplemental Figs. 3 and 4).

We also use a third allele in this study, ros1-1, which is a nonsense allele in the C24 accession. Notably, we find that the regions are demethylated on both maternal and paternal alleles in wild-type C24 gain DNA methylation primarily on the paternal allele in ros1-1 endosperm (Figure 4C,D and Supplemental Figure 10). This is discussed further in response to your second point.

Given these lines of evidence, a gain-of-function mutation in a methylation pathway, like RdDM, in the ros1-3 background is an unlikely explanation for increased hypermethylation compared to ros1-7. The use of three independent ros1 alleles for methylation profiling, all of which lead to the same conclusions, is a major strength of our study.

1. It appears that the main focus of the manuscript, the existence of loci that are paternally demethylated by ROS1, is supported by a set of 274 DMRs. This is a small number relative to the size of the genome and raises suspicions of rare false positives. Even the most stringent p-values that DMR-finding tools report do not guarantee that the DMRs are actually reproducible in an independent experiment. Demonstrating overlap between these 274 DMRs and an independently defined set using a different WT control and different ros1 allele would suffice to remove this concern. It appears that authors already have the needed raw data with ros1-1 and ros1-7 alleles.

Response: First, we should clarify that paternal demethylation by ROS1 is supported by more than the 274 DMRs. All ros1 CG hyperDMRs show an increase in paternal allele methylation in ros1 (Fig. 4B,D). The 274 DMRs are a distinct subset defined as having less methylation on the maternal allele than the paternal allele in ros1 endosperm and where there is no maternal allele hypomethylation in wild-type endosperm (refer to Fig. 5B).

We agree with your sentiments about DMR-finders and we are cautious of relying exclusively on DMR calls when making conclusions. We verify the nature of identified DMRs using metaplots and weighted average comparisons throughout the paper, which we think increases confidence in the conclusions and goes beyond a simple DMR-calling approach.

We argue that we have replicated the major conclusion of the paper, that ROS1 prevents paternal allele hypermethylation at target regions in the endosperm, in the following ways:

1. In the dataset without allelic-specific methylation information (Figures 1-3), we found that both ros1-3 and ros1-7 CG hyperDMRs have a limited capacity for hypermethylation in the endosperm relative to leaf or sperm (Table 1, Fig 3, Supplemental Fig. 4). In the allele-specific dataset, ros1-3 CG hyperDMRs were revealed to have particularly low maternal mCG relative to paternal mCG in ros1 mutant endosperm (Fig 4A-B, Supplemental Fig. 10).
2. We found that ros1-3 and ros1-1 hyperDMRs, which we identified using non-allelic data, are biased for paternal allele hypermethylation in the endosperm of F1 hybrids (Fig 4B,D). The replicability of the paternal bias in hypermethylation in both ros1-3 in the Col-0 ecotype and ros1-1 in the C24 ecotype is a critical result, and we have moved the ros1-1 hyperDMR plots from the supplement to main figure 4C-D in the revised version of the manuscript as a result of your comment.
3. The 274 DMRs identified as “biallelically-demethylated, ROS1-dependent” are by definition replicated between reciprocal cross directions. (Note that we now refer to these regions as ROS1 paternal, DME maternal regions in the revision.) Regions in this category had to be called as maternally-hypomethylated in both ros1-1 x ros1-3 and ros1-3 x ros1-1 endosperm. These regions also had to not be identified as maternally-hypomethylated in both C24 x Col-0 and Col-0 x C24. We hope this is clarified for readers by Table 1, which we have included based on your suggestion in comment #3, as well as other clarifying edits we made in this section of the paper.comparisons between maternal and paternal methylation in endosperm, DMRs defined by comparison between mutants and wildtype, and more. These need clearer descriptions of which sets are being referred to throughout the main text and in figure legends. A table summarizing them might help (not in the supplement). Use of consistent and precisely defined terms would help. Stating the number of DMRs along with the name for each set would help a lot, even though this would make for some redundancy. (The number of DMRs in each set not only helps with interpretation but also act as a sort of ID). The reason I put this as a major concern is because the text and figures are difficult to understand, and it is currently hard to evaluate both the results and the authors' conclusions from those results.

Response: Thank you for your feedback and suggestions. We have edited the main text so that only one descriptive name is used for each DMR type throughout the paper. We have also renamed regions for greater clarity. The previous “ROS1-independent, maternally demethylated regions” are now referred to as “DME maternal regions”. The previous “ROS1-_independent, biallelically-demethylated regions” are now referred to as “_ROS1 paternal, DME maternal regions”. These changes provide greater clarity and also emphasize the role of DME at regions that are paternally hypermethylated in ros1. We have added Table 1 to summarize the DMR classes of interest.

MINOR COMMENTS

1. The sRNA results in Figure 2B are difficult to interpret because they do not reveal anything about the number of TEs that have siRNAs overlapping them or their flanks. While the magnitude of some of the highest endosperm sRNA peaks is higher than the embryo peaks, that could be explained by a small number of TEs with large numbers of sRNAs. To make this result more interpretable, we also need some information about how many TEs have a significant number of sRNAs associated with them in endosperm and embryo in each region (e.g., middle, 5', 3', and flanks of TEs). What a "significant number of sRNAs" is would be up to the authors to decide based on the distribution of sRNA counts they observe for TEs. Perhaps the top quartile of TEs? Combined with the same analysis done in parallel with non-ROS1 target TEs, this would reveal whether there is any evidence for ROS1 counteracting sRNA-driven methylation spread from TEs.

Response: Thank you for the suggestion. We now present these data and the data for individual TEs underlying the metaplots in Supplemental Figure 7. As suggested by the reviewer, ROS1 TEs do not have uniformly higher levels of sRNA in their flanks in the endosperm compared to the embryo. We have modified our interpretations accordingly.

1. The statement "we are likely underestimating the true degree of differential methylation among genotypes" should be validated and partially quantified using a methylation metaplot like Figure 2A, but substitute DMRs for TEs. Related to that, Figure 1B needs an indicator of scale in bp.

Response: We have now included a methylation metaplot over ros1-3 hyperDMRs and ros1-7 hyperDMRs as Supplemental Figure 3 These plots show that indeed there is additional hypermethylation in DMR-proximal regions. We have added a scale bar to Figure 1B and other browser examples in the paper.

1. The statement "Over half of ROS1 target regions identified in the ros1-3 mutant endosperm were within 1 kb or intersecting a TE (Fig. 1D)" is hard to interpret without some kind of ROS1 non-target regions or whole-genome control comparison. How different are the numbers in Fig. 1D from a random expectation?

Response: We have now included a control for random regions in Figure 1E. We define these as regions where there was sufficient methylation data coverage and a low enough methylation level in wild-type to detect hypermethylation if it existed.

1. The sentence at line 262 is confusing. Is the comparison between dme mutant and ros1 mutant or between different types of regions? And it appears that the comparison value is missing in the "3-5% CG methylation gain..." e.g., "3-5% CG methylation vs 10-20%" or something like that.

Response: This section has been re-written as we now focus on allele-specific dme endosperm methylation data for our comparisons.

1. The dme mutant data in Figure 5C appear to be key to the model in Figure 7. The relative impact of the dme mutant in the two types of regions should be quantified.

Response: Thank you for this comment. To further probe our model that DME prevents hypermethylation of the maternal allele at regions where ROS1 is preventing hypermethylation of the paternal allele, we turned to the allele-specific bisulfite-sequencing data published in Ibarra et al 2012 (see also response to reviewer #1). Using these data, we show that when the loss-of-function allele dme-2 is inherited maternally, ROS1 paternal, DME maternal regions (previous referred to as ROS1-_dependent, biallelically-demethylated regions) are CG hypermethylated on the maternal allele (Figure 6D). Thus, these results both replicate the observations made with the Hsieh et al 2009 data, and provide additional evidence that _DME prevents maternal allele hypermethylation at regions were ROS1 is preventing paternal allele hypermethylation. These results have replaced the Hsieh et al 2009 results in Figure 6, and we have moved the analysis of Hsieh et al 2009 data to Supplemental Figure 15.

1. Looks like sRNA methods are missing.

Response: Thank you for identifying this. We previously included the reference for the analyzed dataset we used and the method for plotting under an unclear section header. These methods are now in the section “Analysis of average methylation and 24-nt sRNA patterns for features of interest”, and we have added additional reference to the specific dataset we used.

1. Supplemental Figure 1 is hard to interpret since it only list gene IDs, not gene names.

Response: As suggested, we have added gene names to this figure.

The last comments are suggestions for increasing the impact of this study:

1. Figure 2A and 3B suggest that ROS1 target TEs show demethylation in their flanks but not in the TE themselves. This is an interesting result. If it is true, more DMRs would be expected in the ROS1 target flanks than in the ROS1 target TEs. Reporting how many ROS1 target TEs have DMRs in them and what proportion have DMRs in their flanking 1-Kb regions would answer this question. Given the significance of this result, it also deserves a bit more context: Is the magnitude of increased methylation flanking TEs in ros1 mutant endosperm different than in ros1 mutant leaves or other tissue? Does methylation in TE flanks behave the way in dme mutant endosperm?

Response: We define “ROS1 target TEs” (now referred to more simply as ROS1 TEs) as TEs within 1kb or intersecting a ros1-3 hyperDMR. Consistent with your interpretation, 80% of the TEs in this category do not have a DMR overlapping them, instead they have a TE within 1kb. We now mention this in the text on line 150.

The total level of DNA methylation at ROS1 TEs is lower in the endosperm than in leaf, as DNA methylation levels are overall lower in endosperm than in leaf. The magnitude of increased methylation flanking TEs in ros1 mutant endosperm is not different between the two tissues. This is observable in Supplemental Fig. 5 in the revised version of the paper, and we report this result in the revised text. In the revision we also present methylation profiles of DME TEs in WT and ros1 endosperm (Fig. 7B-D). DME TEs are hypomethylated in both the body and flanks in WT and ros1.

1. The idea of biallelic demethylation has been theoretically suggested in maize to explain weak overlap between endosperm DMRs and imprinting (Gent et al 2022). If that were true in Arabidopsis, then ROS1 target, biallelicly demethylated loci would be less likely to have imprinted expression than maternally demethylated loci. This prediction could be tested using available data in Arabidopsis.

Response: Indeed, as you hypothesize, there are no known imprinted genes (Pignatta et al 2014) associated with biallelically-demethylated, ROS1-dependent regions (now referred to as ROS1 paternal, DME maternal regions). Expectedly, there are imprinted genes associated with maternally-demethylated regions (now referred to as DME regions). 23 imprinted genes identified in the Pignatta et al 2014 study are within 1 kb or intersecting a DME region. This is discussed on lines 364-374.

1. There is currently no evidence for biological significance of biallelicly demethylated loci. Knowing where they are in the genome might give some hints. A figure like Fig. 1D but specifically showing the biallelicly demethylated DMRs would be valuable.

Response: This is now included in Figure 7A.

1. It is hard to make the comparisons between genotypes and parental genomes in Figure 6 and know what they mean. Maybe a different way of displaying the data would help. Or maybe even a different labeling system could make it a little more accessible.

Response: We have revised this figure (now Fig. 8) in the following ways, which we believe address your comments and clarify the main conclusions:

Figure 8C is now a boxplot comparing methylation of the paternal allele of ROS1 paternal, DME maternal regions (previously referred to as biallelically-demethylated, ROS1-dependent regions) across endosperm ROS1 genotypes. This plot shows increased methylation of paternal alleles when the paternal parent is a ros1 mutant, regardless of whether the resultant F1 endosperm is homozygous or heterozygous for ros1 (columns 3, 4, 6).

Figure 8B remains as a scatterplot, where we can observe significant correlation between individual ROS1 paternal, DME maternal regions in homozygous ros1 endosperm and heterozygous ros1/+ endosperm. Note that paternal allele methylation is higher in homozygous ros1 endosperm for most regions.

Reviewer #2 (Significance):

Demethylation of the maternal genome in endosperm has been the subject of much research because it can result in genomic imprinting of gene expression. The enzymes responsible, DNA glycosylases/lyases, also demethylate DNA in other cell types as well, where DNA methylation is not confined to one parental genome (biallelic or biparental as opposed to uniparental demethylation). To the best of my knowledge, the extent or even existence of biallelelic demethylation in endosperm has not been studied until now (except for a superficial look in a bioRxiv preprint, https://www.biorxiv.org/content/10.1101/2024.07.31.606038v1). Hemenway and Gehring have carried out a thoughtful and detailed analysis of the topic in Arabidopsis at least as far as it depends on the DNA glycosylase ROS1.

A limitation is that the study design would miss biallelic demethylation by any of the other three DNA glycosylases in Arabidopsis. A second limitation is that there is no clear biological significance, just some conjecture about evolution. Nonetheless, given the novelty of the topic, biological significance may follow.

The audience for biallelic DNA demethylation in Arabidopsis endosperm is certainly in the "specialized" category, but its relevance to the larger topic of gene regulation in endosperm will attract a larger audience.

Response: With regard to the other demethylases, note that we also profiled methylation in ros1 dml2 dml3 triple mutant endosperm. We did not find evidence for many DMRs that were present in the triple mutant that were not present in the ros1 single mutant. We do not rule out a function for DML2 or DML3 in the endosperm, but this is not observed at the level of bulk endosperm.

The reviewer is correct that we have shown a molecular phenotype (paternal allele hypermethylation) and not a developmental or morphological phenotype. A function that occurs in one parent but not the other is, to us, exciting. Our thoughts about how this finding might relate to imprinting are indeed speculative, but not wildly so.

Reviewer #3 (Evidence, reproducibility and clarity):

DNA demethylases play a key role in DNA methylation patterning during flowering plant reproduction. The demethylase DME, in particular, is critical for proper endosperm development. While the function of DME in endosperm development has been explored, the contributions of the other demethylases in the same family, ROS1, DML2 and DML3 in Arabidopsis, have not yet been investigated. In vegetative tissues, ROS1 prevents hypermethylation of some loci. In this work, Hemenway and Gehring explore whether ROS1, DML2 and DML3 also affect DNA methylation patterns in endosperm. Using EM-seq of sorted endosperm nuclei, they show that loss of ROS1 indeed causes hypermethylation of a number of loci, particularly the flanks of methylated transposons, while loss of DML2 and DML3 has minimal additional effect. By obtaining allele-specific EM-seq data through crosses of Col and C24, the authors show that ros1 endosperm hypermethylation is mostly restricted to the paternal allele. The authors propose that at some sites, ROS1 helps bring down paternal methylation levels to match maternal methylation levels, which are typically reduced in endosperm due to DME activity in the female gametophyte prior to fertilization. In a ros1 mutant with paternal hypermethylation, these sites become differentially methylated on the maternal and paternal alleles, resembling imprinted loci. This work convincingly establishes a function for ROS1 in DNA methylation patterning in endosperm. However, I struggled with the clarity of the writing and reasoning in a few places, and would suggest clarification of a few points and additional analyses below.

Response: Thank you for your thoughtful review of our paper. Your questions and suggestions have been invaluable in revising the work.

I think making a few simple changes to streamline nomenclature would improve readability. For example, in the section starting on line 129, the same set of genomic features are called ROS1 target-proximal TEs, TEs that are near a ROS1 target region, and ROS1 target-associated TE regions. Also for example in line 254 "regions that are maternally-demethylated in wild-type endosperm, and are not dependent on ROS1 for proper demethylation" - are these the same as the "ROS1-independent, maternally-demethylated" regions in Fig. 5a? Given how complex these terms are, being consistent throughout the manuscript really helps the reader.

Response: We edited the text and figures so that only one descriptive name is used for each DMR class or region throughout the paper. Thank you for this feedback; these edits have made the paper much clearer.

Is there any notable effect of ros1 on gene expression in endosperm? Endosperm is a terminal tissue, so maintaining DNA methylation boundaries as ROS1 does in vegetative tissues seems less important. It begs the question of why ROS1 is doing this in endosperm, is it just because it's there, or is there an endosperm-specific function? Exploring effects on imprinting would be particularly interesting (does loss of ROS1 'create' imprinted loci at these newly asymmetrically methylated sites?) but probably beyond the scope of the present work.

Response: We agree, the question of the functional consequence of ROS1 activity in the endosperm is something we are keen to address in future work. We performed RNA-seq on wild-type and ros1 3C and 6C endosperm nuclei, but these data were unfortunately not of high enough quality to include in the manuscript. We are in particular interested in this question you have proposed – if loss of ROS1 can ‘create’ imprinted loci. We are planning to address this both using a molecular, RNA-sequencing approach as well as an evolutionary comparative approach. This is an important and exciting future direction.

Is DME expressed in sperm, or is expression of DME affected in ros1 sperm or endosperm? One other explanation for ros1 hypermethylation occurring primarily on the paternal allele is that, potentially, DME can substitute for ROS1 in the central cell where DME is already very active, but not in sperm cells. Related, how well expressed is ROS1 vs. DME in sperm cells?

Response: This is an important series of questions, and something we are very interested in as well. Studies of Arabidopsis pollen have shown that both ROS1 and DME, while they prevent some hypermethylation in sperm, are more active in the vegetative nucleus of pollen than in sperm. ROS1 is expressed at a low level in the microspore and bicellular pollen and DME is expressed at a low level throughout pollen development. We have included Supplemental Fig. 17 with available expression data to make this point in the paper. Likely, any effects of loss of ROS1 or DME on sperm DNA methylation are inherited from precursor cells (Ibarra et al 2012, Calarco et al 2012, Khouider et al 2021). Your proposal that perhaps DME can sub in for ROS1 in the central cell but not in sperm is intriguing. Unfortunately there’s not enough data in the central cell to convincingly address this at this time.

To investigate the relationship between DME and ROS1 in the male germline, we used the bisulfite-sequencing data generated in sperm cells in Khouider et al 2021. We calculated average DNA methylation levels in dme/+, ros1, dme/+;ros1, and wild-type Col-0 sperm cells at ROS1 paternal, DME maternal regions, shown in Supplemental Fig. 18A. We observed little increase in mCG methylation in dme/+ sperm relative to wild-type Col-0 sperm. This is consistent with your proposed model that DME is unable to demethylate these regions outside of the female germline. As expected, there is increased mCG in ROS1 paternal, DME maternal regions in ros1-3 mutant sperm relative to wild-type Col-0 sperm. DME maternal regions are highly methylated in wild-type Col-0 sperm.

Fig 2b shows that ROS1 target-associated TEs are enriched for sRNAs in endosperm relative to embryo, whereas the reverse is true for non-ROS1-assoc TEs. Since TEs are not always well annotated and some may be missing from this analysis, what about trying the reverse analysis - are regions enriched for 24nt sRNAs in endosperm significantly hypermethylated in ros1 endosperm? All regions or only some?

Response: We performed an analysis to address your inquiry and observed a low magnitude increase in DNA methylation in ros1 mutant endosperm at regions defined by Erdmann et al as more sRNA producing in the endosperm relative to the embryo (endosperm DSRs). Endosperm DSRs are generally lowly methylated in wild-type endosperm, as was observed originally in Erdmann et al 2017. Small increases in DNA methylation are observed at endosperm DSRs in all sequence contexts in ros1 endosperm. Overall, this is consistent with ROS1 targets being a subset of sRNA-producing regions in the endosperm. This analysis is now included in Supplemental Fig. 7C.

What is the relationship between previously-defined DME targets and ROS1 targets identified in this paper? DME tends to target small euchromatic TE bodies, whereas Fig. 3 suggests that ROS1 helps prevent methylation spreading on the outer edges of the TEs, rather than in the TE body. Do all DME targets tend to be adjacent to or flanked by ROS1 target sites? Or are the TEs affected by DME (in body) and by ROS1 (at edges) largely nonoverlapping? Fig. 5a suggests that the ROS1-dependent, biallelically-demethylated sites are both DME and ROS1 targets, but how often do these really appear to overlap? More than by chance?

Response: We have sought to address your comments through a series of analyses that we have included in Fig. 7 and Supplemental Fig. 16. We found that ROS1 paternal, DME maternal regions (formerly referred to as ROS1-dependent, biallelically-demethylated regions) and DME maternal regions (formerly referred to as ROS1-independent, maternally-demethylated regions) do not occupy the same genomic regions. However, we do observe some evidence for ROS1 activity in flanking regions of DME targets (Fig. 6A, Fig. 7B-D). To look at TEs specifically, as you suggest, we first identified TEs that were within 1kb or intersecting a DME maternal region. Based on our characterization of these regions, we assume these to be DME-targeted TEs. We then performed ends analysis to see if there was evidence of ROS1 activity at the ends of these TEs. Indeed, at a global level there is a slight hypermethylation of the paternal allele in a ros1 mutant at the end of these DME TEs (Fig. 7B). To better visualize how many DME TEs are showing ROS1 activity at their ends, we then plotted the difference between the median ros1-3 methylation and median Col-0 values in the non-allelic endosperm for each TE in a clustered heatmap (Fig. 7C). The parent-of-origin data does not have enough coverage for clustering in this way, so we used the non-allelic data. A small fraction of “DME TEs” gain methylation in the ros1 mutant endosperm relative to wild-type (Fig. 7C-D).

Are the TEs whose boundaries are demethylated by ROS1 more likely to be expressed in vegetative or endosperm tissues than TEs not affected by loss of ROS1? Expressed TEs likely produce more sRNAs, which would increase RdDM in a way that might need to be more actively countered by ROS1 than transcriptionally silent or evolutionarily older TEs.

Response: This is an interesting line of inquiry, although perhaps out of the scope of our present study. It has been shown that TEs demethylated by ROS1 are targeted by the RdDM pathway in Arabidopsis vegetative tissue (Tang et al 2016). Using data from Erdmann et al 2017, we looked at 24 nt sRNAs at ROS1-TEs in the endosperm and embryo (Supplemental Fig. 7). sRNA production at ROS1 TE-flanking regions is observed in both embryo and endosperm, but clearly not all ROS1 TEs produce 24 nt sRNA production in the seed. Future work comparing sRNA profiles in a ros1 mutant to those of wild-type could inform our understanding of TE spreading in a ros1 mutant, as would a comprehensive analysis of TE expression, again in both a ros1 mutant and in wild-type. It’s unclear to us if the endosperm would be the most informative or useful tissue to perform such analyses in.

Fig6 - as noted in the text, one way to test whether demethylation by ROS1 occurs before or after fertilization is to provide functional ROS1 through only one parent via reciprocal WT x ros-1 crosses, so that the endosperm always has ROS1 but either sperm or central cell does not, and see if this can rescue the paternal hypermethylation. If ROS1 acts prior to fertilization, then paternal ROS1 will rescue ros1 hypermethylation, but maternal ROS1 won't. If after fertilization, then either maternally or paternally supplied ROS1 will rescue the hypermethylation phenotype (assuming both are well expressed). Thus, to distinguish the two, it is sufficient to test whether maternally supplied ROS1 in an otherwise mutant background can rescue the hypermethylation phenotype, which is what is shown in Fig. 6. However, I think it's also important to show that paternally supplied ROS1 can also rescue the hypermethylation phenotype, which is not currently shown. The plots showing no effect on maternal mCG aren't as informative, since maternal methylation levels are mostly unaffected by ros1 anyway. Instead of comparing pairs of samples in a scatterplot, it might be clearer to show paternal mCG across all four comparisons (WT x WT, WT x ros1, ros1 x WT, and ros1 x ros1) side by side in a heatmap, using clustering to group similar behavior.

Response: We have revised this figure, now Fig. 8, in the following ways, which we believe addresses your comments and clarify the main conclusions (see same response to reviewer 2 for point 14):

Figure 8B remains as a scatterplot, where we observe significant correlation between individual ROS1 paternal, DME maternal regions in homozygous ros1 endosperm and heterozygous ros1/+ endosperm. Note that paternal allele methylation is higher in homozygous ros1 endosperm for most regions.

Figure 8C is now a boxplot comparing methylation of the paternal allele of ROS1 paternal, DME maternal regions (previously referred to as biallelically-demethylated, ROS1-dependent regions) across endosperm ROS1 genotypes. This plot shows increased methylation of paternal alleles when the paternal parent is a ros1 mutant, regardless of whether the resultant F1 endosperm is homozygous or heterozygous for ros1 (columns 3, 4, 6).

I would also suggest including a little more information in the main plots rather than only in the figure legends. For example, in Fig 2 including a label of 'ROS1-associated TE' for the two plots on the left, and 'TEs not associated with ROS1' on the right. Or for example in Fig. 3a indicating 'ros1-3 CG hyperDMRs' somewhere on the plot. This would just help make the figures easier to read at a glance. Please add common gene names to figures, instead just the ATG gene ID (Fig. S1a).

Response: Thank you for this feedback, we have made the suggested edits and additional edits of a similar nature.

Minor:<br /> - Fig. 1E is referenced in the text before Fig. 1D<br /> - Fig. S4 and S5 - there are more lines in the plot than the 6 genotypes listed in the legend, do these represent different replicates? If so that should be noted in the legend<br /> - Fig. 1B has no color legend for the different methylation sequence contexts (looks like same as 1A,C but should indicate either in plot or legend)<br /> - Line 42 should be "correspond to TE ends"<br /> - Line 93 "Based on previous studies..." should have references to those studies<br /> - When referring to the protein (rather than the genetic locus or mutant), ROS1 should not be italicized - for example line 130<br /> - Line 150 "we conclude that the loss"<br /> - Should add a y=x line to scatterplots, like those in Fig. 6<br /> - In fig. 1d, it's hard to evaluate the significance of the overlap of ROS1 targets with genes and TEs. Comparing these numbers to a control where the ROS1 targets have been randomly shuffled would help.

Response: We have made edits and additions where requested.

Reviewer #3 (Significance):

In this work, Hemenway and Gehring explore whether ROS1, DML2 and DML3 also affect DNA methylation patterns in endosperm. Using EM-seq of sorted endosperm nuclei, they show that loss of ROS1 indeed causes hypermethylation of a number of loci, particularly the flanks of methylated transposons, while loss of DML2 and DML3 has minimal additional effect. By obtaining allele-specific EM-seq data through crosses of Col and C24, the authors show that ros1 endosperm hypermethylation is mostly restricted to the paternal allele. The authors propose that at some sites, ROS1 helps bring down paternal methylation levels to match maternal methylation levels, which are typically reduced in endosperm due to DME activity in the female gametophyte prior to fertilization. In a ros1 mutant with paternal hypermethylation, these sites become differentially methylated on the maternal and paternal alleles, resembling imprinted loci. This work convincingly establishes a function for ROS1 in DNA methylation patterning in endosperm. However, I struggled with the clarity of the writing and reasoning in a few places, and would suggest clarification of a few points and additional analyses.

Response: Thank you for your comments. We have worked on streamlining the text and analysis.

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Referee #3

Evidence, reproducibility and clarity

DNA demethylases play a key role in DNA methylation patterning during flowering plant reproduction. The demethylase DME, in particular, is critical for proper endosperm development. While the function of DME in endosperm development has been explored, the contributions of the other demethylases in the same family, ROS1, DML2 and DML3 in Arabidopsis, have not yet been investigated. In vegetative tissues, ROS1 prevents hypermethylation of some loci. In this work, Hemenway and Gehring explore whether ROS1, DML2 and DML3 also affect DNA methylation patterns in endosperm. Using EM-seq of sorted endosperm nuclei, they show that loss of ROS1 indeed causes hypermethylation of a number of loci, particularly the flanks of methylated transposons, while loss of DML2 and DML3 has minimal additional effect. By obtaining allele-specific EM-seq data through crosses of Col and C24, the authors show that ros1 endosperm hypermethylation is mostly restricted to the paternal allele. The authors propose that at some sites, ROS1 helps bring down paternal methylation levels to match maternal methylation levels, which are typically reduced in endosperm due to DME activity in the female gametophyte prior to fertilization. In a ros1 mutant with paternal hypermethylation, these sites become differentially methylated on the maternal and paternal alleles, resembling imprinted loci. This work convincingly establishes a function for ROS1 in DNA methylation patterning in endosperm. However, I struggled with the clarity of the writing and reasoning in a few places, and would suggest clarification of a few points and additional analyses below.

I think making a few simple changes to streamline nomenclature would improve readability. For example, in the section starting on line 129, the same set of genomic features are called ROS1 target-proximal TEs, TEs that are near a ROS1 target region, and ROS1 target-associated TE regions. Also for example in line 254 "regions that are maternally-demethylated in wild-type endosperm, and are not dependent on ROS1 for proper demethylation" - are these the same as the "ROS1-independent, maternally-demethylated" regions in Fig. 5a? Given how complex these terms are, being consistent throughout the manuscript really helps the reader.

Is there any notable effect of ros1 on gene expression in endosperm? Endosperm is a terminal tissue, so maintaining DNA methylation boundaries as ROS1 does in vegetative tissues seems less important. It begs the question of why ROS1 is doing this in endosperm, is it just because it's there, or is there an endosperm-specific function? Exploring effects on imprinting would be particularly interesting (does loss of ROS1 'create' imprinted loci at these newly asymmetrically methylated sites?) but probably beyond the scope of the present work.

Is DME expressed in sperm, or is expression of DME affected in ros1 sperm or endosperm? One other explanation for ros1 hypermethylation occurring primarily on the paternal allele is that, potentially, DME can substitute for ROS1 in the central cell where DME is already very active, but not in sperm cells. Related, how well expressed is ROS1 vs. DME in sperm cells?

Fig 2b shows that ROS1 target-associated TEs are enriched for sRNAs in endosperm relative to embryo, whereas the reverse is true for non-ROS1-assoc TEs. Since TEs are not always well annotated and some may be missing from this analysis, what about trying the reverse analysis - are regions enriched for 24nt sRNAs in endosperm significantly hypermethylated in ros1 endosperm? All regions or only some?

What is the relationship between previously-defined DME targets and ROS1 targets identified in this paper? DME tends to target small euchromatic TE bodies, whereas Fig. 3 suggests that ROS1 helps prevent methylation spreading on the outer edges of the TEs, rather than in the TE body. Do all DME targets tend to be adjacent to or flanked by ROS1 target sites? Or are the TEs affected by DME (in body) and by ROS1 (at edges) largely nonoverlapping? Fig. 5a suggests that the ROS1-dependent, biallelically-demethylated sites are both DME and ROS1 targets, but how often do these really appear to overlap? More than by chance?

Are the TEs whose boundaries are demethylated by ROS1 more likely to be expressed in vegetative or endosperm tissues than TEs not affected by loss of ROS1? Expressed TEs likely produce more sRNAs, which would increase RdDM in a way that might need to be more actively countered by ROS1 than transcriptionally silent or evolutionarily older TEs.

Fig6 - as noted in the text, one way to test whether demethylation by ROS1 occurs before or after fertilization is to provide functional ROS1 through only one parent via reciprocal WT x ros-1 crosses, so that the endosperm always has ROS1 but either sperm or central cell does not, and see if this can rescue the paternal hypermethylation. If ROS1 acts prior to fertilization, then paternal ROS1 will rescue ros1 hypermethylation, but maternal ROS1 won't. If after fertilization, then either maternally or paternally supplied ROS1 will rescue the hypermethylation phenotype (assuming both are well expressed). Thus, to distinguish the two, it is sufficient to test whether maternally supplied ROS1 in an otherwise mutant background can rescue the hypermethylation phenotype, which is what is shown in Fig. 6. However, I think it's also important to show that paternally supplied ROS1 can also rescue the hypermethylation phenotype, which is not currently shown. The plots showing no effect on maternal mCG aren't as informative, since maternal methylation levels are mostly unaffected by ros1 anyway. Instead of comparing pairs of samples in a scatterplot, it might be clearer to show paternal mCG across all four comparisons (WT x WT, WT x ros1, ros1 x WT, and ros1 x ros1) side by side in a heatmap, using clustering to group similar behavior.

I would also suggest including a little more information in the main plots rather than only in the figure legends. For example, in Fig 2 including a label of 'ROS1-associated TE' for the two plots on the left, and 'TEs not associated with ROS1' on the right. Or for example in Fig. 3a indicating 'ros1-3 CG hyperDMRs' somewhere on the plot. This would just help make the figures easier to read at a glance. Please add common gene names to figures, instead just the ATG gene ID (Fig. S1a).

Minor:

• Fig. 1E is referenced in the text before Fig. 1D
• Fig. S4 and S5 - there are more lines in the plot than the 6 genotypes listed in the legend, do these represent different replicates? If so that should be noted in the legend
• Fig. 1B has no color legend for the different methylation sequence contexts (looks like same as 1A,C but should indicate either in plot or legend)
• Line 42 should be "correspond to TE ends"
• Line 93 "Based on previous studies..." should have references to those studies
• When referring to the protein (rather than the genetic locus or mutant), ROS1 should not be italicized - for example line 130
• Line 150 "we conclude that the loss"
• Should add a y=x line to scatterplots, like those in Fig. 6
• In fig. 1d, it's hard to evaluate the significance of the overlap of ROS1 targets with genes and TEs. Comparing these numbers to a control where the ROS1 targets have been randomly shuffled would help.

Significance

In this work, Hemenway and Gehring explore whether ROS1, DML2 and DML3 also affect DNA methylation patterns in endosperm. Using EM-seq of sorted endosperm nuclei, they show that loss of ROS1 indeed causes hypermethylation of a number of loci, particularly the flanks of methylated transposons, while loss of DML2 and DML3 has minimal additional effect. By obtaining allele-specific EM-seq data through crosses of Col and C24, the authors show that ros1 endosperm hypermethylation is mostly restricted to the paternal allele. The authors propose that at some sites, ROS1 helps bring down paternal methylation levels to match maternal methylation levels, which are typically reduced in endosperm due to DME activity in the female gametophyte prior to fertilization. In a ros1 mutant with paternal hypermethylation, these sites become differentially methylated on the maternal and paternal alleles, resembling imprinted loci. This work convincingly establishes a function for ROS1 in DNA methylation patterning in endosperm. However, I struggled with the clarity of the writing and reasoning in a few places, and would suggest clarification of a few points and additional analyses

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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Referee #2

Evidence, reproducibility and clarity

Summary

Hemenway and Gehring present evidence that the paternal genome in Arabidopsis endosperm is demethylated at several hundred loci by the DNA glycosylase/lyase ROS1. The evidence is primarily based on analysis of DNA methylation of ros1 mutants and of hybrid crosses where each parental genome can be differentiated by SNPs. I have some comments/questions/concerns, two of them potentially serious, but I think Hemenway and Gehring can address them through additional analyses of data that they already have available and a bit of clarification in writing.

Major comments:

1. Could the excess methylation in ros1-3 relative to ros1-7 shown in Figures 1A and 1C be explained by a second mutation in the ros1-3 background that elevates methylation at some loci? Any mutation that increased RdDM at these loci, for example could have this effect. This could confound the identification and interpretation of biallelicly demethylated loci.
2. It appears that the main focus of the manuscript, the existence of loci that are paternally demethylated by ROS1, is supported by a set of 274 DMRs. This is a small number relative to the size of the genome and raises suspicions of rare false positives. Even the most stringent p-values that DMR-finding tools report do not guarantee that the DMRs are actually reproducible in an independent experiment. Demonstrating overlap between these 274 DMRs and an independently defined set using a different WT control and different ros1 allele would suffice to remove this concern. It appears that authors already have the needed raw data with ros1-1 and ros1-7 alleles.
3. Because of the multiple sets of DMRs identified and used throughout the paper, it is hard to follow which one is which. There are DMRs defined solely by one sequence context, DMRs defined by all three contexts merged, DMRs defined by comparisons between maternal and paternal methylation in endosperm, DMRs defined by comparison between mutants and wildtype, and more. These need clearer descriptions of which sets are being referred to throughout the main text and in figure legends. A table summarizing them might help (not in the supplement). Use of consistent and precisely defined terms would help. Stating the number of DMRs along with the name for each set would help a lot, even though this would make for some redundancy. (The number of DMRs in each set not only helps with interpretation but also act as a sort of ID). The reason I put this as a major concern is because the text and figures are difficult to understand, and it is currently hard to evaluate both the results and the authors' conclusions from those results.

Minor comments

1. The sRNA results in Figure 2B are difficult to interpret because they do not reveal anything about the number of TEs that have siRNAs overlapping them or their flanks. While the magnitude of some of the highest endosperm sRNA peaks is higher than the embryo peaks, that could be explained by a small number of TEs with large numbers of sRNAs. To make this result more interpretable, we also need some information about how many TEs have a significant number of sRNAs associated with them in endosperm and embryo in each region (e.g., middle, 5', 3', and flanks of TEs). What a "significant number of sRNAs" is would be up to the authors to decide based on the distribution of sRNA counts they observe for TEs. Perhaps the top quartile of TEs? Combined with the same analysis done in parallel with non-ROS1 target TEs, this would reveal whether there is any evidence for ROS1 counteracting sRNA-driven methylation spread from TEs.
2. The statement "we are likely underestimating the true degree of differential methylation among genotypes" should be validated and partially quantified using a methylation metaplot like Figure 2A, but substitute DMRs for TEs. Related to that, Figure 1B needs an indicator of scale in bp.
3. The statement "Over half of ROS1 target regions identified in the ros1-3 mutant endosperm were within 1 kb or intersecting a TE (Fig. 1D)" is hard to interpret without some kind of ROS1 non-target regions or whole-genome control comparison. How different are the numbers in Fig. 1D from a random expectation?
4. The sentence at line 262 is confusing. Is the comparison between dme mutant and ros1 mutant or between different types of regions? And it appears that the comparison value is missing in the "3-5% CG methylation gain..." e.g., "3-5% CG methylation vs 10-20%" or something like that.
5. The dme mutant data in Figure 5C appear to be key to the model in Figure 7. The relative impact of the dme mutant in the two types of regions should be quantified.
6. Looks like sRNA methods are missing.
7. Supplemental Figure 1 is hard to interpret since it only list gene IDs, not gene names.

The last comments are suggestions for increasing the impact of this study:<br /> 11. Figure 2A and 3B suggest that ROS1 target TEs show demethylation in their flanks but not in the TE themselves. This is an interesting result. If it is true, more DMRs would be expected in the ROS1 target flanks than in the ROS1 target TEs. Reporting how many ROS1 target TEs have DMRs in them and what proportion have DMRs in their flanking 1-Kb regions would answer this question. Given the significance of this result, it also deserves a bit more context: Is the magnitude of increased methylation flanking TEs in ros1 mutant endosperm different than in ros1 mutant leaves or other tissue? Does methylation in TE flanks behave the way in dme mutant endosperm?<br /> 12. The idea of biallelic demethylation has been theoretically suggested in maize to explain weak overlap between endosperm DMRs and imprinting (Gent et al 2022). If that were true in Arabidopsis, then ROS1 target, biallelicly demethylated loci would be less likely to have imprinted expression than maternally demethylated loci. This prediction could be tested using available data in Arabidopsis.<br /> 13. There is currently no evidence for biological significance of biallelicly demethylated loci. Knowing where they are in the genome might give some hints. A figure like Fig. 1D but specifically showing the biallelicly demethylated DMRs would be valuable.<br /> 14. It is hard to make the comparisons between genotypes and parental genomes in Figure 6 and know what they mean. Maybe a different way of displaying the data would help. Or maybe even a different labeling system could make it a little more accessible.

Significance

Demethylation of the maternal genome in endosperm has been the subject of much research because it can result in genomic imprinting of gene expression. The enzymes responsible, DNA glycosylases/lyases, also demethylate DNA in other cell types as well, where DNA methylation is not confined to one parental genome (biallelic or biparental as opposed to uniparental demethylation). To the best of my knowledge, the extent or even existence of biallelelic demethylation in endosperm has not been studied until now (except for a superficial look in a bioRxiv preprint, https://www.biorxiv.org/content/10.1101/2024.07.31.606038v1). Hemenway and Gehring have carried out a thoughtful and detailed analysis of the topic in Arabidopsis at least as far as it depends on the DNA glycosylase ROS1.

A limitation is that the study design would miss biallelic demethylation by any of the other three DNA glycosylases in Arabidopsis. A second limitation is that there is no clear biological significance, just some conjecture about evolution. Nonetheless, given the novelty of the topic, biological significance may follow.

The audience for biallelic DNA demethylation in Arabidopsis endosperm is certainly in the "specialized" category, but its relevance to the larger topic of gene regulation in endosperm will attract a larger audience.

Add IPFS! Desktop

Can do it manually

DOI: 10.1038/s41598-019-41734-9

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What is this?

DOI: 10.3389/fimmu.2025.1622865

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DOI: 10.1152/ajpgi.00274.2023

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What is this?

DOI: 10.1111/jnc.70163

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DOI: 10.1101/2025.07.28.666118

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What is this?

DOI: 10.1038/s41467-025-62351-3

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What is this?

DOI: 10.1038/s41419-025-07902-8

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DOI: 10.1016/j.isci.2025.113044

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DOI: 10.1016/j.devcel.2025.07.006

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What is this?

DOI: 10.1002/cne.25665

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What is this?

Added mass effect (more important in water than in air) - when you move in water you accelerate the surrounding fluid making it harder to move

Pourquoi on met un deux à la ligne 31 s'il vous plaît?

Reviewer #1 (Public review):

Summary:

The manuscript by Ozcan et al., presents compelling evidence demonstrating the latent potential of glial precursors of the adult cerebral cortex for neuronal reprogramming. The findings substantially advance our understanding of the potential of endogenous cells in the adult brain to be reprogrammed. Moreover, they describe a molecular cocktail that directs reprogramming toward corticospinal neurons (CSN).

Strengths:

Experimentally, the work is compelling and beautifully designed. The work provides a characterization of endogenous progenitors, genetic strategies to isolate them, and proof of concept of exploiting these progenitors' potential to produce a specific desired neuronal type with "a la carte" combination of transcription factors.

Weaknesses:

This study demonstrates reprogramming in vitro. Future research will need to assess how these reprogrammed corticospinal neurons integrate and function under physiological conditions and in models of trauma or neurodegeneration.

Although still in its early stages, neural reprogramming holds significant promise. This study reinforces the hope that, in the future, it may be possible to restore lost or damaged neurons through targeted cellular reprogramming.

Author response:

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

Reviewer #1 (Public Review):

Summary:

The manuscript by Ozcan et al., presents compelling evidence demonstrating the latent potential of glial precursors of the adult cerebral cortex for neuronal reprogramming. The findings substantially advance our understanding of the potential of endogenous cells in the adult brain to be reprogrammed. Moreover, they describe a molecular cocktail that directs reprogramming toward corticospinal neurons (CSN).

Strengths:

Experimentally, the work is compelling and beautifully designed, with no major caveats. The main conclusions are fully supported by the experiments. The work provides a characterization of endogenous progenitors, genetic strategies to isolate them, and proof of concept of exploiting these progenitors' potential to produce a specific desired neuronal type with "a la carte" combination of transcription factors.

Weaknesses:

Some issues need to be addressed or clarified before publication. The manuscript requires editing. It is dense and rich in details while in other parts there are a few mistakes.

We thank the reviewer for their excellent summary and for their extremely positive review of our paper. We are pleased that the experimental design and conclusions were judged to be wellsupported.

We have revised the paper to enhance clarity, include additional relevant citations, and refine terminology in some sections of the original version.

We appreciate the reviewer’s thoughtful review and agree that these revisions enhance the paper.

Reviewer #2 (Public Review):

Summary:

Here the authors show a novel direct neuronal reprogramming model using a very pure culture system of oligodendrocyte progenitor cells and demonstrate hallmarks of corticospinal neurons to be induced when using Neurogenin2, a dominant-negative form of Olig2 in combination with the CSN master regulator Fezf2.

Strengths:

This is a major achievement as the specification of reprogrammed neurons towards adequate neuronal subtypes is crucial for repair and still largely missing. The work is carefully done and the comparison of the neurons induced only by Neurogenin 2 versus the NVOF cocktail is very interesting and convincingly demonstrates a further subtype specification by the cocktail.

Weaknesses:

As carefully as it is done in vitro, the identity of projection neurons can best be assessed in vivo. If this is not possible, it could be interesting to co-culture different brain regions and see if these neurons reprogrammed with the cocktail, indeed preferentially send out axons to innervate a co-cultured spinal cord versus other brain region tissue.

We appreciate the reviewer’s positive evaluation of our work and their recognition of its significance in advancing neuronal subtype specification through directed differentiation of endogenous progenitors. 

We agree with the reviewer’s suggestion that a very interesting future stage of this work would be to investigate the projection neuron identity in vivo. We aim to pursue follow-up studies to investigate in vivo integration and connectivity of such neurons generated by directed differentiation from endogenous SOX6+/NG2+ cortical progenitors. As the reviewer insightfully suggests, co-culturing different brain regions with these neurons could offer an alternative strategy to partially assess potential preferential connectivity into cultured spinal cord vs. alternate tissue.

We agree with the reviewer that future investigation in vivo will further strengthen the implications of this work.

Reviewer #3 (Public Review):

Summary:

Ozkan, Padmanabhan, and colleagues aim to develop a lineage reprogramming strategy towards generating subcerebral projection neurons from endogenous glia with the specificity needed for disease modelling and brain repair. They set out by targeting specifically Sox6-positive NG2 glia. This choice is motivated by the authors' observation that the early postnatal forebrain of Sox6 knockout mice displays marked ectopic expression of the proneural transcription factor (TF) Neurog2, suggesting a latent neurogenic program may be derepressed in NG2 cells, which normally express Sox6. Cultured NG2 glia transfected with a construct ("NVOF") encoding Neurog2, the corticofugal neuron-specifying TF Fezf2, and a constitutive repressor form of Olig2 are efficiently reprogrammed to neurons. These acquire complex morphologies resembling those of mature endogenous neurons and are characterized by fewer abnormalities when compared to neurons induced by Neurog2 alone. NVOF-induced neurons, as a population, also express a narrower range of cortical neuron subtype-specific markers, suggesting narrowed subtype specification, a potential step forward for Neurog2-driven neuronal reprogramming. Comparison of NVOF- and Neurog2-induced neurons to endogenous subcerebral projection neurons (SCPN) also indicates Fezf2 may aid Neurog2 in directing the generation of SCPN-like neurons at the expense of other cortical neuronal subtypes.

Strengths:

The report describes a novel, highly homogeneous in vitro system amenable to efficient reprogramming. The authors provide evidence that Fezf2 shapes the outcome of Neurog2-driven reprogramming towards a subcerebral projection neuron identity, consistent with its known developmental roles. Also, the use of the modified RNA for transient expression of Neurog2 is very elegant.

Weaknesses:

The molecular characterization of NVOF-induced neurons is carried out at the bulk level, therefore not allowing to fully assess heterogeneity among NVOF-induced neurons. The suggestion of a latent neurogenic potential in postnatal cortical glia is only partially supported by the data from the Sox6 knockout. Finally, some of the many exciting implications of the study remain untested.

Discussion:

The study has many exciting implications that could be further tested. For example, an ultimate proof of the subcerebral projection neuron identity would be to graft NVOF cells into neonatal mice and study their projections. Another important implication is that Sox6-deficient NG2 glia may not only express Neurog2 but activate a more complete neurogenic programme, a possibility that remains untested here.

Also, is the subcerebral projection neuron dependent on the starting cell population? Could other NG2 glia, not expressing Sox6, also be co-axed by the NVOF cocktail into subcerebral projection neurons? And if not, do they express other (Sox) transcription factors that render them more amenable to reprogramming into other cortical neuron subtypes? The authors state that SOX6-positive NG2 glia are a quiescent progenitor population. Given that NG2 glia is believed to undergo proliferation as a whole, are Sox6-positive NG2 glia an exception from this rule? Finally, the authors seem to imply that subcerebral projection neurons and Sox6-positive NG2 glia are lineage-related. However, direct evidence for this conjecture seems missing.

We appreciate the reviewer’s thoughtful and detailed review of this work. We especially appreciate the positive evaluation of the work and the highlighting of multiple strengths of our approach, including the role of Fezf2 in refining neuronal subtype identity and the use of modified RNA to enable transient expression of Neurog2.

We acknowledge the reviewer’s comment that single-cell transcriptomic analysis would indeed provide a more granular view of likely heterogeneity. This current study focuses on investigating the feasibility of directed differentiation of corticospinal-like neurons from endogenous progenitors. Future work employing single-cell sequencing could indeed help delineate the heterogeneity of neurons generated by directed differentiation, and potentially contribute toward identification of potential molecular roadblocks in different subsets.

Regarding the suggestion that SOX6-deficient NG2+ progenitors might activate a broader neurogenic program, we agree that this is an intriguing possibility. We are currently conducting indepth investigation of the loss of SOX6 function in NG2+ progenitors, and we aim to submit this quite distinct work for separate publication.

The reviewer raises an important point about whether SOX6+/NG2+ progenitors and subcerebral projection neurons are indeed normally lineage-related. In the current work, we utilized postnatal cortical SOX6+/NG2+ progenitors that are thought to be largely derived from EMX1+ and GSH2+ ventricular zone neural progenitors. Our unpublished data from the separate study noted above indicate that SOX6 is expressed by both these lineages in vivo. Since subcerebral projection neurons are derived from EMX1+ ventricular zone progenitors (SOX6-expressing), at least some of the SOX6+/NG2+ progenitors are expected to share a lineage relationship with subcerebral projection neurons. While our data strongly suggest such a link, we agree that direct lineagetracing could be pursued in future work. 

Finally, we agree with the reviewer’s suggestion that in vivo transplantation to assess the identity and connectivity of neurons generated by directed differentiation would be very interesting, and is a natural next phase of this work. We aim to pursue such work in future investigations.

We again thank the reviewer for their insightful comments.

Reviewer #1 (Recommendations For The Authors): 

The most important clarification for me concerns the initial description of the progenitors. I think there is a mistake with the transgenic line NG2. The dsRed mouse used in Figure 1 C is not described until later in the results describing Figure 2. This was confusing. Moreover, perhaps this is a reason why I get confused and do not understand how the authors conclude that SOX6+ cells are a subset of NG2positive cells. Panel C shows the opposite. Please correct the description and show the quantification of data in panel 1C.

We thank the reviewer for their thoughtful review and for highlighting this important point. We appreciate the reviewer pointing out the benefit of further clarity regarding the NG2.DsRed transgenic mouse description in Figure 1C. We have revised the text to clarify the use of the transgenic line and ensure that the DsRed mouse is properly introduced. Additionally, we have further clarified the description explaining the basis for concluding that SOX6+ cells are a subset of NG2+ cells and further integrate this conclusion with the data presented.

During cell sorting from the cortices of NG2.DsRed mice, we observe two distinct populations of NG2-DsRed+ cells based on fluorescence intensity in FACS: NG2-DsRed “bright” and NG2-DsRed “dim” populations. The NG2-DsRed “dim” population consists of a heterogenous mix of NESTIN+ progenitors, GFAP+ astrocytes/progenitors, a subset of NG2+ cells, and other unidentified cells. In contrast, the DsRed “bright” population includes a broader group of progenitors that also give rise to oligodendrocytes (please see Zhu, Bergles, and Nishiyama 2008), along with pericytes. 

Previous studies have shown that, while dorsal/pallial VZ progenitors express SOX6 during embryonic development, SOX6 expression becomes restricted to interneurons postnatally (these do not express NG2 proteoglycan; Azim et al., 2009) and to the broader group of NG2+ progenitors that also give rise to oligodendrocytes. The ICC image in Fig. 1C shows bright NG2+ cells in the cortex, many of which express SOX6. Thus, we conclude that SOX6+ cells constitute a subset of NG2-DsRed+ cells. 

In a similar line, the work is beautiful, but the manuscript can gain a lot from shortening and some more editing. for example:

(1) In the abstract, the word inappropriate should be removed. It seems to me that is an unnecessary subjective qualification - it is hardly possible that in biology we found repression of something inappropriate.

We have removed the word “inappropriate”.

(2) FACS-purify these genetically accessible....establish a pure culture. Genetically accessible is nice, and I understand that it conveys that they can be traced in the mouse, but everything is genetically accessible with the right tool, and perhaps it is more informative to explain which gene or report is used for the isolation. These cells are not accessible in humans. Also, I consider it best to remove pure- the culture is pure (purified by FACS) cells.

We have revised the text to specify the gene/reporter used for isolation instead of using "genetically accessible", and we removed "pure", since FACS purification is already explicitly mentioned.

(3) In the initial paragraph in the results: "They are exposed to the same morphogen gradients throughout embryonic development, and thus, compared to distant cell types, have similar epigenomic and transcription landscapes." This is proven in the cited publication, but the way is stated here seems a bit of an unnecessary overstatement. The hypothesis stated after this paragraph is as good as it is with or without this argument.

We have revised the text and simplified the statement. We agree that the hypothesis remains clear and well-supported without this emphasis.

(4) In the result sections, "two distinct populations of DsREd-positive cells were identified based on fluorescence intensity"- I know it is correct, but when reading the percentages, I was confused because those percentages divided the population into three fractions. What the authors do not explain is that they discard the intermediate-expressing population.

We appreciate the reviewer highlighting this inadvertent point of confusion. We erred by discussing only the two populations of central interest to us (DsRed-bright and DsRed-dim), and did not explicitly mention the DsRed-negative population. We have now clarified the text to include all three cell populations and their percentages of the total cells in all three populations (in the original manuscript and still now, ~75-78% were DsRed-negative). We have also further clarified that only DsRed-Bright cells (identified as progenitors) were used for all subsequent experiments.

These examples illustrate the type of editing that would be appreciated but which is entirely up to the authors.

We thank the reviewer for their thoughtful suggestions toward improving clarity and precision. We have incorporated these recommendations, along with suggestions from the other two reviewers, in the revised paper.

Reviewer #2 (Recommendations For The Authors):

(1)  The authors start their results section by showing in situ Hybridization for Ngn2 in control and Sox6KO mice. These control sections do not look convincing, as there is not even some signal in the adult VZSVZ region and virtually no background. Please show sections where some positive signal can also be detected in the control sections.

We agree with the reviewer that making direct comparisons in ISH experiments is an important point. In our ISH experiments, to ensure consistency and appropriate comparisons, we process WT and KO sections together and stop the signal development simultaneously. We could have extended the development time to enhance WT signal to a detectable level, but that would have led to excessive background and over-saturated signal in the KO sections.

To address the reviewer’s point, we have added a new supplementary figure with an additional pair of WT and KO sections, along with reference data from the Allen Brain Atlas. The WT section shows faint Neurog2 expression in the dentate gyrus region of the hippocampus, while the KO section confirms very substantial upregulation of Neurog2 in the absence of SOX6 function. These additional data enhance the clarity and depth of our results.

Please see the following link for the Allen Brain Atlas ISH data demonstrating that Neurog2 expression in the postnatal (P4) SVZ/SGZ is inherently low. (https://developingmouse.brainmap.org/experiment/show/100093831). 

(2) As a hallmark of projection neurons is where they send their axons, it would be important to include a biological assay for this. Of course, in vivo experiments would be great, but if this is not possible, the authors could co-culture sections from the late embryonic cortex, striatum, and spinal cord to see if the reprogrammed neurons preferentially extend their axons towards one of these targets (as normally developing neurons would, see e.g. Bolz et al., 1990).

We agree with the reviewer’s suggestion that a very interesting future stage of this work would be to investigate the projection neuron identity including connectivity in vivo. We aim to pursue follow-up studies to investigate in vivo integration and connectivity of such neurons generated by directed differentiation from endogenous SOX6+/NG2+ cortical progenitors. As the reviewer insightfully suggests, co-culturing different brain regions with these neurons could offer an alternative strategy to partially assess potential preferential connectivity into cultured spinal cord vs. alternate tissue. This area of investigation is of substantial interest to our lab, and we aim to pursue it in the coming years– it is a very large undertaking by either approach.

(3) However, if the loss of Sox6 is sufficient for Ngn2 to be upregulated, why did the authors not pursue this approach in their reprogramming experiments? Are these endogenous levels sufficient for reprogramming? Please add some OPC cultures from WT and KO mice to explore their conversion to neurons and possibly combine them with Olig2VP16 and Fezf2.

We thank the reviewer for this insightful comment and for raising this broader area of inquiry regarding whether SOX6 might be down-regulated to enhance induction of neurogenesis. We are writing a separate manuscript regarding function of SOX6 in these progenitors during normal or molecularly manipulated development. We investigate function of SOX6 using both whole body null mice and a series of conditional null mice. We aim to post that work as a preprint and submit it for review and publication in the coming months. Beyond that work, the potential strategy of downregulating SOX6 function while simultaneously upregulating other molecular controls to refine directed neuronal differentiation is also of substantial interest to us, and we aim to pursue this in follow-up work. Though these are both interesting questions/topics, we respectfully submit that these broad areas of parallel, complex, and future investigation would substantially expand the scope of work in this paper, so we aim to address them in separate studies.

(4) Please indicate independent biological replicates as individual data points in all histograms, i.e. also in Figure 2K, Figure 4I, S2H.

We have updated the figure legends indicating the biological replicates, and explained the broad media optimization that was used successfully in all further experiments.

(5) GFP labelling in Figures S2K-N is not convincing - too high background. Please optimize.

We have redesigned this figure and now present it as a new supplementary figure, with GFP pseudocolored in gray and enlarged subpanels for improved visualization of cell morphology.

Reviewer #3 (Recommendations For The Authors):

This is an extremely well-written manuscript with very exciting implications. Obviously, not all can be tested here. Some of the suggestions are relatively easy and may be worth testing right away, others may require more extensive study in the future. In my view, completing some of the points below could make this paper a landmark study.

I start with the key questions:

(1) Do grafted NVOF cells give rise to subcerebral projection neurons in vivo?

We agree with the reviewer’s suggestion that a very interesting future stage of this work would be to investigate the projection neuron identity including connectivity in vivo. As noted above in response to Reviewer 2, we aim to pursue follow-up studies to investigate in vivo integration and connectivity of such neurons generated by directed differentiation from endogenous SOX6+/NG2+ cortical progenitors. This question is of substantial interest to us, and we aim to pursue it in the coming years– as the reviewer notes, this is a very large undertaking, and beyond the scope of this paper.

(2) What is the fate of the Sox6 deficient NG2 glia that express Neurog2? One could isolate these cells and subject them to scRNA sequencing to see how far neurogenesis proceeds without addition of exogenous factors.

We thank the reviewer for this insightful question. As noted in our response to Reviewer 2, we are writing a separate manuscript regarding function of SOX6 in these progenitors during normal or molecularly manipulated development. We investigate function of SOX6 using both whole body null mice and a series of conditional null mice. We aim to post that work as a preprint and submit it for review and publication in the coming months, likely in early summer. We respectfully submit that this broad area of parallel, complex investigation would substantially expand the scope of work in this paper and make this paper too complex and multi-directional, so we aim to publish them as separate papers for the benefit of clarity for readers.

(3) Obviously, what happens to Sox6-deficient (or non-deficient cells) when forced to express NVOF? In this context, it might be fair to cite Felske et al (PLoS Biol, 2023) who report Neurog2 and Fezf2-induced reprogramming in the postnatal brain. In their model, these authors did not distinguish between converted astrocytes and NG2 glia. Thus, some of the reprogrammed cells may comprise the SOX6positive cells described here.

We thank the reviewer for highlighting for us that we inadvertently omitted referencing the important paper by Felske et al., 2023. We have now included this citation. 

We thank the reviewer for raising this broader area of inquiry regarding whether SOX6 might be down-regulated to enhance induction of neurogenesis. Beyond the work noted above regarding function of SOX6 in these progenitors during normal or molecularly manipulated development, the potential strategy of downregulating SOX6 function while simultaneously upregulating other molecular controls to refine directed neuronal differentiation is of substantial interest to us, and we aim to pursue this in follow-up work. We again respectfully submit that this area of complex, future investigation should be addressed in future studies.

Very interesting unaddressed questions include:

(1) Are Sox6+ NG glia of dorsal origin? This is implied but not shown. One could use Emx1Cre lines to assess this. Are Sox6+ glia and subcerebral projection neurons clonally related? This may be more challenging. In this context, it might be again fair to refer to Herrero-Navarro et al (Science Advances 2021) who show that glia lineage related to nearby neurons gives rise to induced neurons with regional specificity.

The reviewer raises an important question regarding the competence of SOX6+/NG2+ progenitors from distinct origins to generate corticospinal-like neurons by directed differentiation. In ongoing unpublished work, we have identified SOX6 expression by NG2+ progenitors of the three lineages derived from ventricular zone progenitors that express either Emx1, Gsh2, or Nkx2.1 transcription factors. The EMX1+ lineage-derived SOX6+/NG2+ progenitors are directly lineage related to cortical projection neurons. As the reviewer suggests, future experiments could explore potential differences in competence between these three populations.

We again thank the reviewer for highlighting for us that we also inadvertently omitted referencing the exciting study by Herrero-Navarro that addresses the question of regional heterogeneity within astrocytes and the differential reprogramming potential related to their origins. We have now cited this paper in the manuscript.

(2) Do other NG2 glia not give rise to subcerebral projection neurons when challenged with NVOF? Thus, how important is Sox6 expression really?

The question of the specific competence of dorsal/cortical SOX6+/NG2+ progenitors to differentiate into corticospinal-like neurons, and the strategy of downregulating SOX6 function while simultaneously upregulating other molecular controls to direct neuronal differentiation, are both of great interest to us. In pilot experiments, we observed reduced competence of ventrallyderived SOX6+/NG2+ progenitors to generate similar neurons. We plan to pursue the SOX6 manipulation in follow up work.

(3) Do Sox6+ NG2 glia proliferate like other NG2 glia and thereby represent a replenishable pool of progenitors?

Yes; as noted in the text shortly after Figure 1, and as presented in Figure S3l-L, these progenitors proliferate robustly in response to the mitogens PDGF-A and FGF2.

(4) How heterogenous are the NVOF-induced neurons? The bulk highlights the overall specificity, but does not tell whether all cells make it equally well.

We agree with the reviewer that this is an interesting question. ICC analysis (Fig. 4G-4H) presents the variation in the levels of a few functionally important proteins in the population of NVOFinduced neurons. This could be due to any or all of at least three potential possibilities: 1) potential diversity in the population of purified SOX6+/NG2+ progenitors; 2) technical variability in the amount of NVOF plasmid delivered to individual progenitors during transfection; and/or 3) natural stochastic TF-level variations generating closely-related neuron types, that also occurs during normal development. Future experiments could explore these questions.

Author response:

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

Reviewer#1 (Public review):

This work regards the role of Aurora Kinase A (AurA) in trained immunity. The authors claim that AurA is essential to the induction of trained immunity. The paper starts with a series of experiments showing the effects of suppressing AurA on beta-glucan-trained immunity. This is followed by an account of how AurA inhibition changes the epigenetic and metabolic reprogramming that are characteristic of trained immunity. The authors then zoom in on specific metabolic and epigenetic processes (regulation of S-adenosylmethionine metabolism & histone methylation). Finally, an inhibitor of AurA is used to reduce beta-glucan's anti-tumour effects in a subcutaneous MC-38 model.

Strengths:<br /> With the exception of my confusion around the methods used for relative gene expression measurements, the experimental methods are generally well-described. I appreciate the authors' broad approach to studying different key aspects of trained immunity (from comprehensive transcriptome/chromatin accessibility measurements to detailed mechanistic experiments). Approaching the hypothesis from many different angles inspires confidence in the results (although not completely - see weaknesses section). Furthermore, the large drug-screening panel is a valuable tool as these drugs are readily available for translational drug-repurposing research.

We thank the reviewer for the positive and encouraging comments.

Weaknesses:

(1) The manuscript contains factual inaccuracies such as:

(a) Intro: the claim that trained cells display a shift from OXPHOS to glycolysis based on the paper by Cheng et al. in 2014; this was later shown to be dependent on the dose of stimulation and actually both glycolysis and OXPHOS are generally upregulated in trained cells (pmid 32320649).

We appreciate the reviewer for pointing out this inaccuracy, and we have revised our statement to ensure accurate and updated description in manuscript. We are aware that trained immunity involves different metabolic pathways, including both glycolysis and oxidative phosphorylation [1, 2]. We also detected Oxygen Consumption Rate (please see response to comment 8 of reviewer#1) but observed no obvious increase of oxygen consumption in trained BMDMs in our experiment setting. As the reviewer pointed out, it might be dependent on the dose of stimulation.

(b) Discussion: Trained immunity was first described as such in 2011, not decades ago.

We are sorry for the inaccurate description, and we have corrected the statement in our revised manuscript as “Although the concept of ‘trained immunity’ has been proposed since 2011, the detailed mechanisms that regulate trained immunity are still not completely understood.”

(2) The authors approach their hypothesis from different angles, which inspires a degree of confidence in the results. However, the statistical methods and reporting are underwhelming.

(a) Graphs depict mean +/- SEM, whereas mean +/- SD is almost always more informative. (b) The use of 1-tailed tests is dubious in this scenario. Furthermore, in many experiments/figures the case could be made that the comparisons should be considered paired (the responses of cells from the same animal are inherently not independent due to their shared genetic background and, up until cell isolation, the same host factors like serum composition/microbiome/systemic inflammation etc). (c) It could be explained a little more clearly how multiple testing correction was done and why specific tests were chosen in each instance.

We sincerely thank the reviewer for this thoughtful comment. (a) The data from animal experiments in which trained immunity was induced in vivo are presented as mean ± SD, while the statistical results from cell-based experiments are presented as mean ± SEM in the revised manuscript. (b) We have replaced one-tailed test with two-tailed test (see Figure 3J in revised manuscript, with updated P value label). We agree that cells derived from the same animal and subjected to different treatment conditions may be deemed paired data. We reanalyzed our data using paired statistical tests. While this led to a slight reduction in statistical significance for some comparisons, the overall trends remained consistent, and our biological interpretation remains unchanged. For in vitro experiments unpaired statistical tests are commonly used in literature [3, 4]. Thus, we still used unpaired test results here. (c) We have provided a detailed description of how multiple comparisons were performed in revised figure legends.

(d) Most experiments are done with n = 3, some experiments are done with n = 5. This is not a lot. While I don't think power analyses should be required for simple in vitro experiments, I would be wary of drawing conclusions based on n = 3. It is also not indicated if the data points were acquired in independent experiments. ATAC-seq/RNA-seq was, judging by the figures, done on only 2 mice per group. No power calculations were done for the in vivo tumor model.

We are sorry for the confusion in our description in figure legends. For the in vivo experiment, we determined the sample size (n=5, n refers to number of mice used as biological replicates) by referring to the animal numbers used for similar experiments in literatures. And according to a reported resource equation approach for calculating sample size in animal studies [5], n=5-7 is suitable for most of our mouse experiments. The in vitro cell assay was performed at least three independent experiments (BMs isolated from different mice), and each experiment was independently replicated at least three times and points represents biological replicates in our revised manuscript. In Figure 1A, 5 biological replicates of these experiments are presented to carefully determine a working concentration of alisertib that would not significantly affect the viability of trained macrophages, and that was subsequently used in all related cell-based experiments. As for seq data, we acknowledge the reviewer's concern regarding the small sample size (n=2) in our RNA-seq/ATAC-seq experiment. We consider the sequencing experiment mainly as an exploratory/screening approach, and performed rigorous quality control and normalization of the sequencing data to ensure the reliability of our findings. For RNA-seq data analysis, we referred to the DESeq2 manual, which specifies that its statistical framework is based on the Negative Binomial Distribution and is capable of robustly inferring differential gene expression with a minimum of two replicates per group. Therefore, the inclusion of two replicates per group was deemed sufficient for our analysis. Nevertheless, the genomic and transcriptome sequencing data were used primarily for preliminary screening, where the candidates have been extensively validated through additional experiments. For example, we conducted ChIP followed by qPCR for detecting active histone modification enrichment in Il6 and Tnf region to further verify the increased accessibility of trained immunity-induced inflammatory genes.

(e) Furthermore, the data spread in many experiments (particularly BMDM experiments) is extremely small. I wonder if these are true biological replicates, meaning each point represents BMDMs from a different animal? (disclaimer: I work with human materials where the spread is of course always much larger than in animal experiments, so I might be misjudging this.).

Thanks for your comments. In our initially submitted manuscript, some of the statistical results were presented as the representative data (technical replicates) from one of three independent biological replicates (including BMDMs experiments showing the suppression and rescue experiments of trained immunity under different inhibitors or activators, see original Figure 1B-C, Figure 5D, and Figure 5H, also related to Figure 1B-C, Figure 5D, and Figure 5H respectively in our revised manuscript) while other experimental data are biological replicates including CCK8 experiment, metabolic assay and ChIP-qPCR. In response to your valuable suggestion, we have revised the manuscript to present all statistical results as biological replicates from three independent experiments (presented as mean ± SEM), and we have provided all the original data for the statistical analysis results (please see Appendix 2 in resubmit system).

(3) Maybe the authors are reserving this for a separate paper, but it would be fantastic if the authors would report the outcomes of the entire drug screening instead of only a selected few. The field would benefit from this as it would save needless repeat experiments. The list of drugs contains several known inhibitors of training (e.g. mTOR inhibitors) so there must have been more 'hits' than the reported 8 Aurora inhibitors.

Thank you for your suggestion and we have briefly reported the outcomes of the entire drug screening in the revised manuscript. The targets of our epigenetic drug library are primarily categorized into several major classes, including Aurora kinase family, histone methyltransferase and demethylase (HMTs and KDMs), acetyltransferase and deacetylase (HDACs and SIRTs), JAK-STAT kinase family, AKT/mTOR/HIF, PARP family, and BRD family (see New Figure 1, related to Figure 1-figure supplement 1B in revised manuscript). Notably, previous studies have reported that inhibition of mTOR-HIF1α signaling axis suppressed trained immunity[6]. Our screening results also indicated that most inhibitors targeting mTOR-HIF1α signaling exhibit an inhibitory effect on trained immunity. Additionally, cyproheptadine, a specific inhibitor for SETD7, which was required for trained immunity as previously reported [7], was also identified in our screening.

JAK-STAT signaling is closely linked to the interferon signaling pathway, and certain JAK kinase inhibitors also target SYK and TYK kinases. A previous drug library screening study has reported that SYK inhibitors suppressed trained immunity [8]. Consistently, our screening results reveal that most JAK kinase inhibitors exhibit suppressive effects on trained immunity.

BRD (Bromodomain) and Aurora are well-established kinase families in the field of oncology. Compared to BRD, the clinical applications of the Aurora kinase inhibitor are still at early stage. In previous studies using inflammatory arthritis models where trained immunity was established, both adaptive and innate immune cells exhibited upregulated expression of AurA [9, 10]. Our study provides further evidence supporting an essential role of AurA in trained immunity, showing that AurA inhibition leads to the suppression of trained immunity.

(4) Relating to the drug screen and subsequent experiments: it is unclear to me in supplementary figure 1B which concentrations belong to secondary screens #1/#2 - the methods mention 5 µM for the primary screen and "0.2 and 1 µM" for secondary screens, is it in this order or in order of descending concentration?

Thank you for your comments and we are sorry for unclear labelled results in original manuscript (related to Figure 1-supplement 1C). We performed secondary drug screen at two concentrations, and drug concentrations corresponding to secondary screen#1 and #2 are 0.2 and 1 μM respectively. It was just in this order, but not in an order of descending concentration.

(a) It is unclear if the drug screen was performed with technical replicates or not - the supplementary figure 1B suggests no replicates and quite a large spread (in some cases lower concentration works better?)

Thank you for your question. The drug screen was performed without technical replicates for initial screening purpose, and we need to verify any hit in the following experiment individually. Yes, we observed that lower concentration works better in some cases. We speculate that it might be due to the fact that the drug's effect correlates positively with its concentration only within a specific range. But in our primary screening, we simply choose one concentration for all the drugs. This is a limitation for our screening, and we acknowledge this limitation in our discussion part.

(5) The methods for (presumably) qPCR for measuring gene expression in Figure 1C are missing. Which reference gene was used and is this a suitably stable gene?

We are sorry for this omission. The mRNA expression of Il6 and Tnf in trained BMDMs was analyzed by a quantitative real-time PCR via a DDCt method, and the result was normalized to untrained BMDMs with Actb (β-actin) as a reference gene, a well-documented gene with stable expression in macrophages. We have supplemented the description for measuring gene expression in Material and Methods in our revised manuscript.

(6) From the complete unedited blot image of Figure 1D it appears that the p-Aurora and total Aurora are not from the same gel (discordant number of lanes and positioning). This could be alright if there are no/only slight technical errors, but I find it misleading as it is presented as if the actin (loading control to account for aforementioned technical errors!) counts for the entire figure.

We are very sorry for this omission. In the original data, p-Aurora and total Aurora were from different gels. In this experiment the membrane stripping/reprobing after p-Aurora antibody did not work well, so we couldn’t get all results from one gel, and we had to run another gel using the same samples to blot with anti-aurora antibody and used β-tubulin as loading control for total AurA (please see New Figure 2A, also related to original Figure 1D). We have provided the source data for β-tubulin from the same membrane of total AurA (please see Figure 1-source data). To avoid any potential misleading, we have repeated this experiment and updated this Figure (please see New Figure 2B, also related to Figure 1D in revised manuscript) with phospho-AurA, total AurA and β-actin from the same gel. The bands for phospho AurA (T288) were obtained using a new antibody (Invitrogen, 44-1210G) and we have revised this information in Material and Methods. We have provided data of three biological replicates to confirm the experiment result also see New Figure 2B, related to Figure 1D in revised manuscript, and the raw data have been added in source data for Figure 1)

(7) Figure 2: This figure highlights results that are by far not the strongest ones - I think the 'top hits' deserve some more glory. A small explanation on why the highlighted results were selected would have been fitting.

We appreciate the valuable suggestion. Figure 2 (see also Figure 2 in revised manuscript) presented information on the chromatin landscape affected by AurA inhibition to confirm that AurA inhibition impaired key gene activation involved in pro-inflammatory macrophage activation by β-glucan. In Figure 2B we highlighted a few classical GO terms downregulated including “regulation of growth”, “myeloid leukocyte activation” and “MAPK cascade” (see also Figure 2B in revised manuscript), among which “regulation of growth” is known function of Aurora A, just to show that alisertib indeed inhibited Aurora A function in vivo as expected. “Myeloid leukocyte activation” and “MAPK cascade” were to show the impaired pro-inflammatory gene accessibility. We highlighted KEGG terms downregulated like “JAK-STAT signaling pathway”, “TNF signaling pathway” and “NF-kappa B signaling pathway” in Figure 2F (see also Figure 2F in revised manuscript), as these pathways are highly relevant to trained immunity. Meanwhile, KEGG terms “FOXO signaling pathway” (see also Figure 2G in revised manuscript) was highlighted to confirm the anti-inflammation effect of alisertib in trained BMDMs, which was further illustrated in Figure 5 (see also Figure 5 in revised manuscript, illustrating FOXO3 acts downstream of AurA). Some top hits in Figure 2B like “positive regulation of cell adhesion”, and “pathway of neurodegeneration” and "ubiquitin mediated proteolysis" in Figure 2F and 2G, is not directly related to trained immunity, thus we did not highlight them, but may provide some potential information for future investigation on other functions of Aurora A.

(8) Figure 3 incl supplement: the carbon tracing experiments show more glucose-carbon going into TCA cycle (suggesting upregulated oxidative metabolism), but no mito stress test was performed on the seahorse.

We appreciate this question raised by the reviewer. We previously performed seahorse XF analyze to measure oxygen consumption rate (OCR) in β-glucan-trained BMDMs. The results showed no obvious increase in oxidative phosphorylation (OXPHOS) indicated by OCR under β-glucan stimulation (related to Figure 3-figure supplement 1 A) although the carbon tracing experiments showed more glucose-carbon going into TCA cycle. We speculate that the observed discrepancy between increased glucose incorporation into TCA cycle and unchanged OXPHOS may reflect a characteristic metabolic reprogramming induced by trained immunity. The increased incorporation of glucose-derived carbon into the TCA cycle likely serves a biosynthetic purpose—supplying intermediates for anabolic processes—rather than augmenting mitochondrial respiration[6]. Moreover, the unchanged OXPHOS may be attributed to a reduced reliance on fatty acid oxidation- “catabolism”, with glucose-derived acetyl-CoA becoming the predominant substrate. Thus, while overall OXPHOS remains stable, the glucose contribution to the TCA cycle increases. This is in line with reports showing that trained immunity promotes fatty acid synthesis- “anabolism”[11]. Alternatively, the partial decoupling of the TCA cycle from OXPHOS could result from the diversion of intermediates such as fumarate out of the cycle. Oxygen consumption rate (OCR) after a mito stress test upon sequential addition of oligomycin (Oligo, 1 μM), FCCP (1 mM), and Rotenone/antimycin (R/A, 0.5 μM), in BMDMs with different treatment for 24 h. β-glucan, 50 μg/mL; alisertib, 1 μM.

(9) Inconsistent use of an 'alisertib-alone' control in addition to 'medium', 'b-glucan', 'b-glucan + alisertib'. This control would be of great added value in many cases, in my opinion.

Thank you for your comment. We appreciate that including “alisertib-alone” group throughout all the experiments may further solidify the results. We set the aim of the current study to investigate the role of Aurora kinase A in trained immunity. Therefore, in most settings, we did not include the group of alisertib only without β-glucan stimulation.

(10) Figure 4A: looking at the unedited blot images, the blot for H3K36me3 appears in its original orientation, whereas other images appear horizontally mirrored. Please note, I don't think there is any malicious intent but this is quite sloppy and the authors should explain why/how this happened (are they different gels and the loading sequence was reversed?)

Thank you for pointing out this error. After checking the original data, we found that we indeed misassembled the orientation of several blots in original data submitted. We went through the assembling process and figured out that the orientation of blots in original data was assembled according to the loading sequences, but not saved correctly, so that the orientations in Figure 4A were not consistent with the unedited blot image. We are sorry for this careless mistake, and we have double checked to make sure all the blots are correctly assembled in the revised manuscript. We also provided three replicates of for the Western blot results showing the level of H3K36me3 in trained BMDMs was inhibited by alisertib (as seen in New Figure 7 at recommendation 2 of reviewer#2).

(11) For many figures, for example prominently figure 5, the text describes 'beta-glucan training' whereas the figures actually depict acute stimulation with beta-glucan. While this is partially a semantic issue (technically, the stimulation is 'the training-phase' of the experiment), this could confuse the reader.

Thanks for the reviewer’s suggestion and we have reorganized our language to ensure clarity and avoid any inconsistencies that might lead to misunderstanding.

(12) Figure 6: Cytokines, especially IL-6 and IL-1β, can be excreted by tumour cells and have pro-tumoral functions. This is not likely in the context of the other results in this case, but since there is flow cytometry data from the tumour material it would have been nice to see also intracellular cytokine staining to pinpoint the source of these cytokines.

Thanks for the reviewer’s suggestion. In Figure 6, we performed assay in mouse tumor model and found that trained immunity upregulated cytokines level like IL-6 in tumor tissue, which was downregulated by alisertib administration. In order to rule out the possibility that the detected cytokines such as IL-6 was from tumor cells, we performed intracellular cytokine staining of single cells isolated from tumor tissues (please see New Figure 4). The result showed that only a small fraction of non-immune cells (CD45^- population) expressed IL-6 (0.37% ± 0.11%), whereas a significantly higher proportion of IL-6-positive cells was observed among CD45⁺ population (deemed as immune cells, 13.66% ± 1.82%), myeloid cells (CD45⁺CD11b⁺, 15.60% ± 2.19%), and in particular, macrophages (CD45⁺CD11b⁺F4/80⁺37.24% ± 3.04%). These findings strongly suggest that immune cells, especially macrophages, are the predominant source of IL-6 cytokine within the tumor microenvironment. Moreover, we also detected higher IL-6 positive population in myeloid cells and macrophages (please see Figure 6I in revised manuscript).

Reviewer#2 (Public review):

Summary:

This manuscript investigates the inhibition of Aurora A and its impact on β-glucan-induced trained immunity via the FOXO3/GNMT pathway. The study demonstrates that inhibition of Aurora A leads to overconsumption of SAM, which subsequently impairs the epigenetic reprogramming of H3K4me3 and H3K36me3, effectively abolishing the training effect.

Strengths:

The authors identify the role of Aurora A through small molecule screening and validation using a variety of molecular and biochemical approaches. Overall, the findings are interesting and shed light on the previously underexplored role of Aurora A in the induction of β-glucan-driven epigenetic change.

We thank the reviewer for the positive and encouraging comments.

Weaknesses:

Given the established role of histone methylations, such as H3K4me3, in trained immunity, it is not surprising that depletion of the methyl donor SAM impairs the training response. Nonetheless, this study provides solid evidence supporting the role of Aurora A in β-glucan-induced trained immunity in murine macrophages. The part of in vivo trained immunity antitumor effect is insufficient to support the final claim as using Alisertib could inhibits Aurora A other cell types other than myeloid cells.

We appreciate the question raised by the reviewer. Though SAM generally acts as a methyl donor, whether the epigenetic reprogram in trained immunity is directly linked to SAM metabolism was not formally tested previously. In our study, we provided evidence suggesting the necessity of SAM maintenance in supporting trained immunity. As for in vivo tumor model, we agree that alisertib may inhibits Aurora A in many cell types besides myeloid cells. To further address the reviewer’s concern, we have performed the suggested bone marrow transplantation experiment (trained mice as donor and naïve mice as recipient) to verify the contribution of myeloid cell-mediated trained immunity for antitumor effect (please see New Figure 8, also related to Figure 6C, 6D and Figure 6-figure supplement 1B and 1C in revised manuscript).

Reviewer #1 (Recommendations for the authors):

Some examples of spelling errors and other mistakes (by far not a complete list):

(a) Introduction, second sentence: reads as if Candida albicans (which should be italicised and capitalised properly) and BCG are microbial polysaccharide components.

(b) Methods: ECAR is ExtraCellular Acidification Rate, not 'Extracellular Acid Ratio'

(c) Figure 2C: β-glucan is misspelled in the graph title.

(d) TNFα has been renamed to 'TNF' for a long time now.

(e) Inconsistent use of Tnf and Tfnα (the correct gene symbol is Tnf) (NB: this field does not allow me to italicise gene symbols)

(f) Figure supplement 1B: 'secdonary'

(g) Caption of figure 4: "Turkey's multiple-comparison test"

(h) etc

I would ask the authors that they please go over the entire manuscript very carefully to correct such errors.

We apologize for these errors and careless mistakes. We greatly appreciate your suggestions, and have carefully proofread the revised manuscript to make sure no further mistakes.

Please also address the points I raised in the public review about statistical approaches. Even more important than the relatively low 'n' is my question about biological replicates. Please clarify what you mean by 'biological replicate'.If you are able to repeat at least the in vitro experiments (if this is too much work pick the most important ones) a few more times this would really strengthen the results.

Thank you for your comment. Our biological replicates refer to independently repeated experiments using bone marrow cells isolated from different mice, and n represents the number of mice used. We repeated each experiment at least three times using BMDMs isolated from different mice (n =3, biological replicates). Specifically, we repeated several in vitro experiments showing inhibition of AurA upregulated GNMT in trained BMDMs and showing transcription factor FOXO3 acted as a key protein in AurA-mediated GNMT expression to control trained immunity as well as showing mTOR agonist rescued trained immunity inhibited by alisertib (see New Figure 5, related to Figure 5B-C, Figure 5H in revised manuscript). Additionally, we have provided data with three biological replicates to show the β-glucan induced phosphorylation of AurA (see comment 6 of reviewer#1) and changes of histone modification marker under AurA inhibition and GNMT deficiency (see recommendation 2 of reviewer#2). We also repeated in vivo tumor model to analysis intratumor cytokines (see recommendation 12 of reviewer#1).

Finally: the authors report 'no funders' during submission, but the manuscript contains funding details. Please modify this in the eLife submission system if possible.

Thank you for your kind reminder and we have modified funding information in the submission system.

Reviewer #2 (Recommendations for the authors):

(1) I have the following methodological and interpretative comments for consideration:

Aurora A has been previously implicated in M1 macrophage differentiation and NF-κB signaling. What is the effect of Aurora A inhibition on basal LPS stimulation? Considering that β-glucan + Ali also skews macrophage priming towards an M2 phenotype, as shown in Fig. 2E, further clarification on this point would strengthen the study.

Thanks for your suggestion. Previous study showed AurA was upregulated in LPS-stimulated macrophages and the inhibition of AurA downregulated M1 markers of LPS-stimulated macrophages through NF-κB pathway but did not affect IL-4-induced M2 macrophage polarization [12]. Consistently, we also found that AurA inhibition downregulated inflammatory response upon basal LPS stimulation as shown by decreased IL-6 level (see New Figure 6). In original Figure 2E (also related to Figure 2E in revised manuscript), we showed an increased accessibility of Mrc1 and Chil3 under “β-glucan +Ali” before re-challenge, both of which are typical M2 macrophage markers. Motif analysis showed that AurA inhibition would upregulate genes controlled by PPARγ (STAT6 was not predicted). Different from STAT6, a classical transcriptional factor in controlling M2 polarization (M2a) dependent on IL-4 or IL-13, PPARγ mediates M2 polarization toward M2c and mainly controls cellular metabolism on anti-inflammation independent on IL-4 or IL-13. Thus, we speculate that inhibition of AurA might promote non-classical M2 polarization, and the details warrant future investigation.

(2) In Figure 4A, it looks like that H3K27me3 is also significantly upregulated by β-glucan and inhibited by Ali. How many biological replicates were performed for these experiments? It would be beneficial to include densitometric analyses to visualize differences across multiple Western blot experiments for better reproducibility and quantitative assessment. In addition, what is the effect of treatment of Ali alone on the epigenetic profiling of macrophages?

We are sorry for this confusion. Each experiment was performed with at least three independent biological replicates. In original Figure 4-figure supplement 1 (also related to Figure 4-figure supplementary 1 in the revised manuscript), we presented the densitometric analysis results from three independent Western blot experiments, which showed that β-glucan did not affect H3K27me3 levels under our experimental conditions. Three biological replicates data for histone modification were shown as follows (New Figure 7, as related to Figure 4-figure supplement 1 in revised manuscript). We appreciate that assay for “Ali alone” in macrophages may add more value to the findings. We set the aim of the current study to investigate the role of Aurora kinase A in trained immunity, and we know that alisertib itself would not induce or suppress trained immunity. Therefore, in most settings, we did not test the effect of Alisertib alone without β-glucan stimulation.

(3) The IL-6 and TNF concentrations exhibit considerable variability (Fig. 3K and Fig. 5H), ranging from below 10 pg/mL to 500-1000 pg/mL. Please specify the number of replicates for these experiments and provide more detail on how variability was managed. Including this information would enhance the robustness of the conclusions.

Thank you for your comment. These experiments were replicated as least three times using BMDMs isolated from different mice. The observed variations in cytokines concentration may be attributed to factors such as differences in cell density, variability among individual mice, and the passage number of the MC38 cells used for supernatant collection. We have prepared new batch of BMDMs and repeated the experiment and provided consistent results in the revised manuscript (please see Figure 5H in revised manuscript). Data for biological replicates have been provided (please see Appendix 2 in resubmit system).

(4) The impact of Aurora A inhibition on β-glucan-induced anti-tumor responses appears complex. Specifically, GNMT expression is significantly upregulated in F4/80- cells, with stronger effects compared to F4/80+ cells as seen in Fig. 6D. To discern whether this is due to the abolishment of trained immunity in myeloid cells or an effect of Ali on tumor cells which inhibit tumor growth, I suggest performing bone marrow transplantation. Transplant naïve or trained donor BM into naïve recipients, followed by MC38 tumor transplantation, to clarify the mechanistic contribution of trained immunity versus off-target effects.

Thanks for your valuable suggestion. Following your suggestion, we have performed bone marrow transplantation to clarify that alisertib acts on the BM cells to inhibit anti-tumor effect induced by trained immunity (see New Figure 8, related to Figure 6C-D in revised manuscript). As the results shown below, transplantation of trained BM cells conferred antitumor activity in recipient mice, while transplantation of trained BM cells with alisertib treatment lost such activity, further demonstrating that alisertib inhibited AurA in trained BM cells to impair their antitumor activity.

References

(1) Ferreira, A.V., et al., Metabolic Regulation in the Induction of Trained Immunity. Semin Immunopathol, 2024. 46(3-4): p. 7.

(2) Keating, S.T., et al., Rewiring of glucose metabolism defines trained immunity induced by oxidized low-density lipoprotein. J Mol Med (Berl), 2020. 98(6): p. 819-831.

(3) Cui, L., et al., N(6)-methyladenosine modification-tuned lipid metabolism controls skin immune homeostasis via regulating neutrophil chemotaxis. Sci Adv, 2024. 10(40): p. eadp5332.

(4) Yu, W., et al., One-Carbon Metabolism Supports S-Adenosylmethionine and Histone Methylation to Drive Inflammatory Macrophages. Mol Cell, 2019. 75(6): p. 1147-1160 e5.

(5) Arifin, W.N. and W.M. Zahiruddin, Sample Size Calculation in Animal Studies Using Resource Equation Approach. Malays J Med Sci, 2017. 24(5): p. 101-105.

(6) Cheng, S.C., et al., mTOR- and HIF-1α-mediated aerobic glycolysis as metabolic basis for trained immunity. Science, 2014. 345(6204): p. 1250684.

(7) Keating, S.T., et al., The Set7 Lysine Methyltransferase Regulates Plasticity in Oxidative Phosphorylation Necessary for Trained Immunity Induced by β-Glucan. Cell Rep, 2020. 31(3): p. 107548.

(8) John, S.P., et al., Small-molecule screening identifies Syk kinase inhibition and rutaecarpine as modulators of macrophage training and SARS-CoV-2 infection. Cell Rep, 2022. 41(1): p. 111441.

(9) Glant, T.T., et al., Differentially expressed epigenome modifiers, including aurora kinases A and B, in immune cells in rheumatoid arthritis in humans and mouse models. Arthritis Rheum, 2013. 65(7): p. 1725-35.

(10) Jeljeli, M.M. and I.E. Adamopoulos, Innate immune memory in inflammatory arthritis. Nat Rev Rheumatol, 2023. 19(10): p. 627-639

(11) Ferreira, A.V., et al., Fatty acid desaturation and lipoxygenase pathways support trained immunity. Nat Commun, 2023. 14(1): p. 7385.

(12) Ding, L., et al., Aurora kinase a regulates m1 macrophage polarization and plays a role in experimental autoimmune encephalomyelitis. Inflammation, 2015. 38(2): p. 800-11.

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