Considering that the popularity of American cinema is mainly responsible for turning superman into a global hero, I would argue that Superman still fights under social and cultural American beliefs, but now on an international level. I wonder what social and cultural beliefs other counties strongly practice that Superman doesn't portray in today's most recent media?

This is definitely something worth talking about in the video documentary! the process Superman went through from an American hero to a global icon is very important and complex, especially considering that American cinema is what played a big role in this transformation.

Now that I think about it, Superman still mostly appeals to American values because of this whole idea of "individualism" and "personal freedom" which a very strong American cultural value. I wonder what examples i could ind of this in recent Superman films/comics?

I wonder if the inspriation for Superman's red and blue suit came from the colors of the American flag?

Nowadays when I think of Superman, I think of a hero who fights for the world and doesn't choose sides. because of Superman's strong origins as an American icon, I wonder if theres still any resemblance today of his origins as an American hero in the most recent Superman films and comics?

What does this idea of the "Melting Pot" describe exactly?

This idea of Superman being a vigilante is reinforced in this article just as it was stated in source one. Growing up, I never really thought of Superman someone acting unlawfully. I would be interested to hear some specific examples that.

Author response:

(This author response relates to the first round of peer review by Biophysics Colab. Reviews and responses to both rounds of review are available here: https://sciety.org/articles/activity/10.1101/2023.10.23.563601.)

General Assessment:

Pannexin (Panx) hemichannels are a family of heptameric membrane proteins that form pores in the plasma membrane through which ions and relatively large organic molecules can permeate. ATP release through Panx channels during the process of apoptosis is one established biological role of these proteins in the immune system, but they are widely expressed in many cells throughout the body, including the nervous system, and likely play many interesting and important roles that are yet to be defined. Although several structures have now been solved of different Panx subtypes from different species, their biophysical mechanisms remain poorly understood, including what physiological signals control their activation. Electrophysiological measurements of ionic currents flowing in response to Panx channel activation have shown that some subtypes can be activated by strong membrane depolarization or caspase cleavage of the C-terminus. Here, Henze and colleagues set out to identify endogenous activators of Panx channels, focusing on the Panx1 and Panx2 subtypes, by fractionating mouse liver extracts and screening for activation of Panx channels expressed in mammalian cells using whole-cell patch clamp recordings. The authors present a comprehensive examination with robust methodologies and supporting data that demonstrate that lysophospholipids (LPCs) directly Panx-1 and 2 channels. These methodologies include channel mutagenesis, electrophysiology, ATP release and fluorescence assays, molecular modelling, and cryogenic electron microscopy (cryo-EM). Mouse liver extracts were initially used to identify LPC activators, but the authors go on to individually evaluate many different types of LPCs to determine those that are more specific for Panx channel activation. Importantly, the enzymes that endogenously regulate the production of these LPCs were also assessed along with other by-products that were shown not to promote pannexin channel activation. In addition, the authors used synovial fluid from canine patients, which is enriched in LPCs, to highlight the importance of the findings in pathology. Overall, we think this is likely to be a landmark study because it provides strong evidence that LPCs can function as activators of Panx1 and Panx2 channels, linking two established mediators of inflammatory responses and opening an entirely new area for exploring the biological roles of Panx channels. Although the mechanism of LPC activation of Panx channels remains unresolved, this study provides an excellent foundation for future studies and importantly provides clinical relevance.

We thank the reviewers for their time and effort in reviewing our manuscript. Based on their valuable comments and suggestions, we have made substantial revisions. The updated manuscript now includes two new experiments supporting that lysophospholipid-triggered channel activation promotes the release of signaling molecules critical for immune response and demonstrates that this novel class of agonist activates the inflammasome in human macrophages through endogenously expressed Panx1. To better highlight the significance of our findings, we have excluded the cryo-EM panel from this manuscript. We believe these changes address the main concerns raised by the reviewers and enhance the overall clarity and impact of our findings. Below, we provide a point-by-point response to each of the reviewers’ comments.

Recommendations:

(1) The authors present a tremendous amount of data using different approaches, cells and assays along with a written presentation that is quite abbreviated, which may make comprehension challenging for some readers. We would encourage the authors to expand the written presentation to more fully describe the experiments that were done and how the data were analysed so that the 2 key conclusions can be more fully appreciated by readers. A lot of data is also presented in supplemental figures that could be brought into the main figures and more thoroughly presented and discussed.

We appreciate and agree with the reviewers’ observation. Our initial manuscript may have been challenging to follow due to our use of both wild-type and GS-tagged versions of Panx1 from human and frog origins, combined with different fluorescence techniques across cell types. In this revision, we used only human wild-type Panx1 expressed in HEK293S GnTI- cells, except for activity-guided fractionation experiments, where we used GS-tagged Panx1 expressed in HEK293 cells (Fig. 1). For functional reconstitution studies, we employed YO-PRO-1 uptake assays, as optimizing the Venus-based assay was challenging. We have clarified these exceptions in the main text. We think these adjustments simplify the narrative and ensure an appropriate balance between main and supplemental figures.

(2) It would also be useful to present data on the ion selectivity of Panx channels activated by LPC. How does this compare to data obtained when the channel is activated by depolarization? If the two stimuli activate related open states then the ion selectivity may be quite similar, but perhaps not if the two stimuli activate different open states. The authors earlier work in eLife shows interesting shifts in reversal potentials (Vrev) when substituting external chloride with gluconate but not when substituting external sodium with N-methyl-D-glucamine, and these changed with mutations within the external pore of Panx channels. Related measurements comparing channels activated by LPC with membrane depolarization would be valuable for assessing whether similar or distinct open states are activated by LPC and voltage. It would be ideal to make Vrev measurements using a fixed step depolarization to open the channel and then various steps to more negative voltages to measure tail currents in pinpointing Vrev (a so called instantaneous IV).

We fully agree with the reviewer on the importance of ion selectivity experiments. However, comparing the properties of LPC-activated channels with those activated by membrane depolarization presented technical challenges, as LPC appears to stimulate Panx1 in synergy with voltage. Prolonged LPC exposure destabilizes patches, complicating G-V curve acquisition and kinetic analyses. While such experiments could provide mechanistic insights, we think they are beyond the scope of current study.

(3) Data is presented for expression of Panx channels in different cell types (HEK vs HEKS GnTI-) and different constructs (Panx1 vs Panx1-GS vs other engineered constructs). The authors have tried to be clear about what was done in each experiment, but it can be challenging for the reader to keep everything straight. The labelling in Fig 1E helps a lot, and we encourage the authors to use that approach systematically throughout. It would also help to clearly identify the cell type and channel construct whenever showing traces, like those in Fig 1D. Doing this systematically throughout all the figures would also make it clear where a control is missing. For example, if labelling for the type of cell was included in Fig 1D it would be immediately clear that a GnTI- vector alone control for WT Panx1 is missing as the vector control shown is for HEK cells and formally that is only a control for Panx2 and 3. Can the authors explain why PLC activates Panx1 overexpressed in HEK293 GnTl- cells but not in HEK293 cells? Is this purely a function of expression levels? If so, it would be good to provide that supporting information.

As mentioned above, we believe our revised version is more straightforward to digest. We have improved labeling and provided explanations where necessary to clarify the manuscript. While Panx1 expression levels are indeed higher in GnTI- than in HEK293 cells, we are uncertain whether the absence of detectable currents in HEK293 cells is solely due to expression levels. Some post-translational modifications that inhibit Panx1, such as lysine acetylation, may also impact activity. Future studies are needed to explore these mechanisms further.

(4) The mVenus quenching experiments are somewhat confusing in the way data are presented. In Fig 2B the y axis is labelled fluorescence (%) but when the channel is closed at time = 0 the value of fluorescence is 0 rather than 100 %, and as the channel opens when LPC is added the values grow towards 100 instead of towards 0 as iodide permeates and quenches. It would be helpful if these types of data could be presented more intuitively. Also, how was the initial rate calculated that is plotted in Fig 2C? It would be helpful to show how this is done in a figure panel somewhere. Why was the initial rate expressed as a percent maximum, what is the maximum and why are the values so low? Why is the effect of CBX so weak in these quenching experiments with Panx1 compared to other assays? This assay is used in a lot of experiments so anything that could be done to bolster confidence is what it reports on would be valuable to readers. Bringing in as many control experiments that have been done, including any that are already published, would be helpful.

We modified the Y-axis in Figure 2 to “Quench (%)” for clarity. The data reflects fluorescence reduction over time, starting from LPC addition, normalized to the maximal decrease observed after Triton-X100 addition (3 minutes), enabling consistent quenching value comparisons. Although the quenching value appears small, normalization against complete cell solubilization provides reproducible comparisons. We do not fully understand why CBX effects vary in Venus quenching experiments, but we speculate that its steroid-like pentacyclic structure may influence the lysophospholipid agonistic effects. As noted in prior studies (DOI: 10.1085/jgp.201511505; DOI: 10.7554/eLife.54670), CBX likely acts as an allosteric modulator rather than a simple pore blocker, potentially contributing to these variations.

(5) Could provide more information to help rationalize how Yo-Pro-1, which has a charge of +2, can permeate what are thought to be anion favouring Panx channels? We appreciate that the biophysical properties of Panx channel remain mysterious, but it would help to hear how a bit more about the authors thinking. It might also help to cite other papers that have measured Yo-Pro-1 uptake through Panx channels. Was the Strep-tagged construct of Panx1 expressed in GnTI- cells and shown to be functional using electrophysiology?

Our recent study suggest that the electrostatic landscape along the permeation pathway may influence its ion selectivity (DOI: 10.1101/2024.06.13.598903). However, we have not yet fully elucidated how Panx1 permeates both anions and cations. Based on our findings, ion selectivity may vary with activation stimulus intensity and duration. Cation permeation through Panx1 is often demonstrated with YO-PRO-1, which measures uptake over minutes, unlike electrophysiological measurements conducted over milliseconds to seconds. We referenced two representative studies employing YO-PRO-1 to assess Panx1 activity. Whole-cell current measurements from a similar construct with an intracellular loop insertion indicate that our STREP-tagged construct likely retains functional capacity.

(6) In Fig 5 panel C, data is presented as the ratio of LPC induced current at -60 mV to that measured at +110 mV in the absence of LPC. What is the rationale for analysing the data this way? It would be helpful to also plot the two values separately for all of the constructs presented so the reader can see whether any of the mutants disproportionately alter LPC induced current relative to depolarization activated current. Also, for all currents shown in the figures, the authors should include a dashed coloured line at zero current, both for the LPC activated currents and the voltage steps.

We used the ratio of LPC-induced current to the current measured at +110 mV to determine whether any of the mutants disproportionately affect LPC-induced current relative to depolarization-activated current. Since the mutants that did not respond to LPC also exhibited smaller voltage-stimulated currents than those that did respond, we reasoned that using this ratio would better capture the information the reviewer is suggesting to gauge. Showing the zero current level may be helpful if the goal was to compare basal currents, which in our experience vary significantly from patch to patch. However, since we are comparing LPC- and voltage-induced currents within the same patch, we believe that including basal current measurements would not add useful information to our study.

Given that new experiments included to further highlight the significance of the discovery of Panx1 agonists, we opted to separate structure-based mechanistic studies from this manuscript and removed this experiment along with the docking and cryo-EM studies.

(7) The fragmented NTD density shown in Fig S8 panel A may resemble either lipid density or the average density of both NTD and lipid. For example, Class7 and Class8 in Fig.S8 panel D displayed split densities, which may resemble a phosphate head group and two tails of lipid. A protomer mask may not be the ideal approach to separate different classes of NTD because as shown in Fig S8 panel D, most high-resolution features are located on TM1-4, suggesting that the classification was focused on TM1-4. A more suitable approach would involve using a smaller mask including NTD, TM1, and the neighbouring TM2 region to separate different NTD classes.

We agree with the reviewer and attempted 3D classification using multiple smaller masks including the suggested region. However, the maps remained poorly defined, and we were unable to confidently assign the NTD.

(8) The authors don’t discuss whether the LPC-bound structures display changes in the external part of the pore, which is the anion-selective filter and the narrower part of the pore. If there are no conformational changes there, then the present structures cannot explain permeability to large molecules like ATP. In this context, a plot for the pore dimension will be helpful to see differences along the pore between their different structures. It would also be clearer if the authors overlaid maps of protomers to illustrate differences at the NTD and the "selectivity filter."

Both maps show that the narrowest constriction, formed by W74, has a diameter of approximately 9 Å. Previous steered molecular dynamics simulations suggest that ATP can permeate through such a constriction, implying an ion selection mechanism distinct from a simple steric barrier.

(9) The time between the addition of LPC to the nanodisc-reconstituted protein and grid preparation is not mentioned. Dynamic diffusion of LPC could result in equal probabilities for the bound and unbound forms. This raises the possibility of finding the Primed state in the LPC-bound state as well. Additionally, can the authors rationalize how LPC might reach the pore region when the channel is in the closed state before the application of LPC?

We appreciate the reviewer’s insight. We incubated LPC and nanodisc-reconstituted protein for 30 minutes, speculating that LPC approaches the pore similarly to other lipids in prior structures. In separate studies, we are optimizing conditions to capture more defined conformations.

(10) In the cryo-EM map of the “resting” state (EMDB-21150), a part of the density was interpreted as NTD flipped to the intracellular side. This density, however, is poorly defined, and not connected to the S1 helix, raising concerns about whether this density corresponds to the NTD as seen in the “resting” state structure (PDB-ID: 6VD7). In addition, some residues in the C-terminus (after K333 in frog PANX1) are missing from the atomic model. Some of these residues are predicted by AlphaFold2 to form a short alpha helix and are shown to form a short alpha helix in some published PANX1 structures. Interestingly, in both the AF2 model and 6WBF, this short alpha helix is located approximately in the weak density that the authors suggest represents the “flipped” NTD. We encourage the authors to be cautious in interpreting this part as the “flipped” NTD without further validation or justification.

We agree that the density corresponding the extended NTD into the cytoplasm is relatively weak. In our recent study, we compared two Panx1 structures with or without the mentioned C-terminal helix and found evidence suggesting the likelihood of NTD extension (DOI: 10.1101/2024.06.13.598903). Nevertheless, to prevent potential confusion, we have removed the cryo-EM panel from this manuscript.

(11) Since the authors did not observe densities of bound PLC in the cryo-EM map, it is important to acknowledge in the text the inherent limitations of using docking and mutagenesis methods to locate where PLC binds.

Thank you for the suggestion. We have removed this section to avoid potential confusion.

Optional suggestions:

(1) The authors used MeOH to extract mouse liver for reversed-phase chromatography. Was the study designed to focus on hydrophobic compounds that likely bind to the TMD? Panx1 has both ECD and ICD with substantial sizes that could interact with water soluble compounds? Also, the use of whole-cell recordings to screen fractions would not likely identify polar compounds that interact with the cytoplasmic part of the TMD? It would be useful for the authors to comment on these aspects of their screen and provide their rationale for fractionating liver rather than other tissues.

We have added a rationale in line 90, stating: “The soluble fractions were excluded from this study, as the most polar fraction induced strong channel activities in the absence of exogenously expressed pannexins.” Additionally, we have included a figure to support this rationale (Fig. S1A).

(2) The authors show that LPCs reversibly increase inward currents at a holding voltage of -60 mV (not always specified in legends) in cells expressing Panx1 and 2, and then show families of currents activated by depolarizing voltage steps in the absence of LPC without asking what happens when you depolarize the membrane after LPC activation? If LPCs can be applied for long enough without disrupting recordings, it would be valuable to obtain both I-V relations and G-V relations before and after LPC activation of Panx channels. Does LPC disproportionately increase current at some voltages compared to others? Is the outward rectification reduced by LPC? Does Vrev remain unchanged (see point above)? Its hard to predict what would be observed, but almost any outcome from these experiments would suggest additional experiments to explore the extent to which the open states activated by LPC and depolarization are similar or distinct.

Unfortunately, in our hands, the prolonged application of lysolipids at concentrations necessary to achieve significant currents tends to destabilize the patch. This makes it challenging to obtain G-V curves or perform the previously mentioned kinetic analyses. We believe this destabilization may be due to lysolipids’ surfactant-like qualities, which can disrupt the giga seal. Additionally, prolonged exposure seems to cause channel desensitization, which could be another confounding factor.

(3) From the results presented, the authors cannot rule out that mutagenesis-induced insensitivity of Panx channels to LPCs results from allosteric perturbations in the channels rather than direct binding/gating by LPCs. In Fig 5 panel A-C, the authors introduced double mutants on TM1 and TM2 to interfere with LPC binding, however, the double mutants may also disrupt the interaction network formed within NTD, TM1, and TM2. This disruption could potentially rearrange the conformation of NTD, favouring the resting closed state. Three double Asn mutants, which abolished LPC induced current, also exhibited lower currents through voltage activation in Fig 5S, raising the possibility the mutant channels fail to activate in response to LPC due to an increased energy barrier. One way to gain further insight would be to mutate residues in NTD that interact with those substituted by the three double Asn mutants and to measuring currents from both voltage activation and LPC activation. Such results might help to elucidate whether the three double Asn mutants interfere with LPC binding. It would also be important to show that the voltage-activated currents in Fig. S5 are sensitive to CBX?

Thank you for the comment, with which we agree. Our initial intention was to use the mutagenesis studies to experimentally support the docking study. Due to uncertainties associated with the presented cryo-EM maps, we have decided to remove this study from the current manuscript. We will consider the proposed experiments in a future study.

(4) Could the authors elaborate on how LPC opens Panx1 by altering the conformation of the NTDs in an uncoordinated manner, going from “primed” state to the “active” state. In the “primed” state, the NTDs seem to be ordered by forming interactions with the TMD, thus resulting in the largest (possible?) pore size around the NTDs. In contrast, in the “active” state, the authors suggest that the NTDs are fragmented as a result of uncoordinated rearrangement, which conceivably will lead to a reduction in pore size around NTDs (isn’t it?). It is therefore not intuitive to understand why a conformation with a smaller pore size represents an “active” state.

We believe the uncoordinated arrangement of NTDs is dynamic, allowing for potential variations in pore size during the activated conformation. Alternatively, NTD movement may be coupled with conformational changes in TM1 and the extracellular domain, which in turn could alter the electrostatic properties of the permeation pathway. We believe a functional study exploring this mechanism would be more appropriately presented as a separate study.

(5) Can the authors provide a positive control for these negative results presented in Fig S1B and C?

The positive results are presented in Fig. 1D and E.

(6) Raw images in Fig S6 and Fig S7 should contain units of measurement.

Thank you for pointing this out.

(7) It may be beneficial to show the superposition between primed state and activated state in both protomer and overall structure. In addition, superposition between primed state and PDB 7F8J.

We attempted to superimpose the cryo-EM maps; however, visually highlighting the differences in figure format proved challenging. Higher-resolution maps would allow for model building, which would more effectively convey these distinctions.

(8) Including particles number in each class in Fig S8 panel C and D would help in evaluating the quality of classification.

Noted.

(9) A table for cryo-EM statistics should be included.

Thanks, noted.

(10) n values are often provided as a range within legends but it would be better to provide individual values for each dataset. In many figures you can see most of the data points, which is great, but it would be easy to add n values to the plots themselves, perhaps in parentheses above the data points.

While we agree that transparency is essential, adding n-values to each graph would make some figures less clear and potentially harder to interpret in this case. We believe that the dot plots, n-value range, and statistical analysis provide adequate support for our claims.

(11) The way caspase activation of Panx channels is presented in the introduction could be viewed as dismissive or inflammatory for those who have studied that mechanism. We think the caspase activation literature is quite convincing and there is no need to be dismissive when pointing out that there are good reasons to believe that other mechanisms of activation likely exist. We encourage you to revise the introduction accordingly.

Thank you for this comment. Although we intended to support the caspase activation mechanism in our introduction, we understand that the reviewer’s interpretation indicates a need for clarification. We hope the revised introduction removes any perception of dismissiveness.

(12) Why is the patient data in Fig 4F normalized differently than everything else? Once the above issues with mVenus quenching data are clarified, it would be good to be systematic and use the same approach here.

For Fig. 4F, we used a distinct normalization method to account for substantial day-to-day variation in experiments involving body fluids. Notably, we did not apply this normalization to other experimental panels due to their considerably lower day-to-day variation.

(13) What was the rational for using the structure from ref 35 in the docking task?

The docking task utilized the human orthologue with a flipped-up NTD. We believe that this flipped-up conformation is likely the active form that responds to lysolipids. As our functional experiments primarily use the human orthologue for biological relevance, this structure choice is consistent. Our docking data shows that LPC does not dock at this site when using a construct with the downward-flipped NTD.

(14) Perhaps better to refer to double Asn ‘substitutions’ rather than as ‘mutations’ because that makes one think they are Asn in the wt protein.

Done.

(15) From Fig S1, we gather that Panx2 is much larger than Panx1 and 3. If that is the case, its worth noting that to readers somewhere.

We have added the molecular weight of each subtype in the figure legend.

(16) Please provide holding voltages and zero current levels in all figures presenting currents.

We provided holding voltages. However, the zero current levels vary among the examples presented, making direct comparisons difficult. Since we are comparing currents with and without LPC, we believe that indicating zero current levels is unnecessary for this study.

(17) While the authors successfully establish lysophospholipid-gating of Panx1 and Panx2, Panx3 appears unaffected. It may be advisable to be more specific in the title of the article.

We are uncertain whether Panx3 is unaffected by lysophospholipids, as we have not observed activation of this subtype under any tested conditions.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

This a comprehensive study that sheds light on how Wag31 functions and localises in mycobacterial cells. A clear link to interactions with CL is shown using a combination of microscopy in combination with fusion fluorescent constructs, and lipid specific dyes. Furthermore, studies using mutant versions of Wag31 shed light on the functionalities of each domain in the protein. My concerns/suggestions for the manuscript are minor:

(1) Ln 130. A better clarification/discussion is required here. It is clear that both depletion and overexpression have an effect on levels of various lipids, but subsequent descriptions show that they affect different classes of lipids.

We thank the reviewer for the comment. We have added a better clarification on this in the discussion of revised manuscript. The lipid classes that get impacted by the depletion of Wag31 vs overexpression are different. Wag31 is an adaptor protein that interacts with proteins of the ACCase complex (Meniche et al., 2014; Xu et al., 2014) that synthesize fatty acid precursors and regulate their activity (Habibi Arejan et al., 2022).

The varied response on lipid homeostasis could be attributed to a change in the stoichiometry of these interactions of Wag31. While Wag31 depletion would prevent such interactions from occurring and might affect lipid synthesis that directly depends on Wag31-protein partner interactions, its overexpression would lead to promiscuous interactions and a change in the stoichiometry of native interactions that would ultimately modulate lipid synthesis pathways.

(2) The pulldown assays results are interesting, but links are tentative.

We thank the reviewer for the comment. The interactome of Wag31 was identified through the immunoprecipitation of FLAG-Wag31 complemented at an integrative locus in Wag31 mutant background to avoid overexpression artifacts. We used Msm::gfp expressing an integrative copy (at L5 locus) of FLAG-GFP as a control to subtract non-specific interactions. The experiment was performed in biological triplicates, and interactors that appeared in all replicates but not in the control were selected for further analysis. Although we identified more than 100 interactors of Wag31, we analyzed only the top 25 hits, with a PSM cut-off 18 and unique peptides5. Additionally, two of Wag31's established interactors, AccD5 and Rne, were among the top five hits, thus validating our data.

As mentioned in line 139 of the previous version of the manuscript, we agree that the interactions can either be direct or through a third partner. The fact that we obtained known interactors of Wag31 makes us believe these interactions are genuine. Moreover, for validation, we performed pulldown experiments by mixing E. coli lysates expressing His-Wag31 full-length or truncated protein with M. smegmatis lysates expressing FLAG-tagged interacting proteins. The wash conditions used were quite stringent for these pull-down assays—the wash buffer contained 1% Triton X100 that eliminates all non-specific and indirect interactions. However, we agree that we cannot conclusively state that the interactions are direct without purifying the proteins and performing the experiment. As mentioned above, this caveat was stated in the previous version of the manuscript.

(3) The authors may perhaps like to rephrase claims of effects lipid homeostasis, as my understanding is that lipid localisation rather than catabolism/breakdown is affected.

We thank the reviewer for the comment. In this manuscript, we are trying to convey that Wag31 is a spatiotemporal regulator of lipid metabolism. It is a peripheral protein that is hooked to the membrane via Cardiolipin and forms a scaffold at the poles, which helps localize several enzymes involved in lipid metabolism.

Homeostasis is the process by which an organism maintains a steady-state of balance and stability in response to changes. Depletion of Wag31 not only results in delocalisation of lipids in intracellular lipid inclusions but also leads to changes in the levels of various lipid classes. Advancement in the field of spatial biology underscores the importance of native localization of various biological molecules crucial for maintaining a steady-cell of the cell. Hence, we have used the word “homeostasis” to describe both the changes observed in lipid metabolism.

Reviewer #2 (Public review):

Summary:

Kapoor et. al. investigated the role of the mycobacterial protein Wag31 in lipid and peptidoglycan synthesis and sought to delineate the role of the N- and C- terminal domains of Wag31. They demonstrated that modulating Wag31 levels influences lipid homeostasis in M. smegmatis and cardiolipin (CL) localisation in cells. Wag31 was found to preferentially bind CL-containing liposomes, and deleting the N-terminus of the protein significantly decreased this interaction. Novel interactions between Wag31 and proteins involved in lipid metabolism and cell wall synthesis were identified, suggesting that Wag31 recruits proteins to the intracellular membrane domain by direct interaction.

Strengths:

(1) The importance of Wag31 in maintaining lipid homeostasis is supported by several lines of evidence. (2) The interaction between Wag31 and cardiolipin, and the role of the N-terminus in this interaction was convincingly demonstrated.

Weaknesses:

(1) MS experiments provide some evidence for novel protein-protein interactions. However, the pulldown experiments lack a valid negative control.

We thank the reviewer for the comment. We have included two non-interactors of Wag31 i.e. MmpL4 and MmpS5 which were not identified in our interactome database as negative controls in the experiment. As shown in Figure S3, we performed His pull-down experiments with both of them independently twice, each time with a positive control (known interactor of Wag31 (Msm2092)). Fig. S3b revised shows E. coli lysate expressing His-Wag31 which was incubated with Msm lysates expressing either FLAG tagged-MmpL4 or -MmpS5 or Msm2092 (revised Fig. S3c). The mixed lysates were pulled down with Cobalt beads that bind to the His-tagged protein and analysed using Western blot analysis by probing with anti-FLAG antibody (revised Fig. S3d.). The data presented confirms that the interactions validated through the pull down assay were indeed specific.

(2) The role of the N-terminus in the protein-protein interaction has not been ruled out.

We thank the reviewer for the comment. Wag31_Msm is a 272 amino acids long protein. The Nterminal of Wag31, which houses the DivIVA-domain, comprises the first 60 amino acids. Previously, we attempted to express the N-terminal (60 aa long) and the C-terminal (212 aa long) truncated proteins in various mycobacterial shuttle vectors to perform MS/MS experiments. Despite numerous efforts, neither expressed with the N/C-terminal FLAG tag or no tag in episomal or integrative vectors due to instability of the protein. Eventually, we successfully expressed the C-terminal Wag31 with an N and Cterminal hexa-His tag. However, this expression was not sufficient or stable enough for us to perform Ni²⁺-affinity pull-down experiments for mass spectrometry. N-terminal of Wag31 could not be expressed in M. smegmatis even with N and C-terminal Hexa-His tags.

To rule out the role of the N-terminal in mediating protein-protein interactions, we cloned the N-terminal of Wag31 that comprises the DivIVA-domain in pET28b vector (Fig. 7a revised). Subsequently, the truncated protein, hereafter called  Wag31_∆C  flanked by 6X His tags at both the termini was expressed in E. coli and mixed with Msm lysates expressing interactors of Wag31 (Fig. 7b-c revised). Earlier experiments with Wag31<sub>∆1-60</sub or Wag31_∆N (in the revised manuscript) were performed with MurG, SepIVA, Msm2092 and AccA3 (Fig. 7e-g). Thus, we used the same set of interactors to test our hypothesis. Briefly, His-  Wag31_∆C  was mixed with Msm lysates expressing either FLAG-MurG, -SepIVA, -Msm2092 or -AccA3 and pull down experiments were performed as described previously. FLAGMmpS5, a non-interactor of Wag31 was used as a negative control. As shown in Fig. 7d revised, His-Wag31 could bind to all the four interactors whereas His- Wag31_∆C  couldn’t, strengthening the conclusion that interactions of Wag31 with other proteins are mediated by its Cterminal. However, we can’t ignore the possibility of other interactors binding to the N-terminal of Wag31. Unfortunately, due to poor expression/instability of  Wag31_∆C  in mycobacterial shuttle vectors, we are unable to perform a global interactome analysis of  Wag31_∆C

Reviewer #3 (Public review):

Summary:

This manuscript describes the characterization of mycobacterial cytoskeleton protein Wag31, examining its role in orchestrating protein-lipid and protein-protein interactions essential for mycobacterial survival. The most significant finding is that Wag31, which directs polar elongation and maintains the intracellular membrane domain, was revealed to have membrane tethering capabilities.

Strengths:

The authors provided a detailed analysis of Wag31 domain architecture, revealing distinct functional roles: the N-terminal domain facilitates lipid binding and membrane tethering, while the C-terminal domain mediates protein-protein interactions. Overall, this study offers a robust and new understanding of Wag31 function.

Weaknesses:

The following major concerns should be addressed.

• Authors use 10-N-Nonyl-acridine orange (NAO) as a marker for cardiolipin localization. However, given that NAO is known to bind to various anionic phospholipids, how do the authors know that what they are seeing is specifically visualizing cardiolipin and not a different anionic phospholipid? For example, phosphatidylinositol is another abundant anionic phospholipid in mycobacterial plasma membrane.

We thank the reviewer for the comment. Despite its promiscuous binding to other anionic phospholipids, 10-N-Nonyl-acridine orange is widely used to stain Cardiolipin and determine its localisation in bacterial cells and mitochondria of eukaryotes (Garcia Fernandez et al., 2004; Mileykovskaya & Dowhan, 2000; Renner & Weibel, 2011). This is because it has a stronger affinity for Cardiolipin than other anionic phospholipids with the affinity constant being 2 × 10⁶ M−¹ for Cardiolipin association and 7 × 10⁴ M−¹ for that of phosphatidylserine and phosphatidylinositol association (Petit et al., 1992). Additionally, there is not yet another stain available for detecting Cardiolipin. Our proteinlipid binding assays suggest that Wag31 preferentially binds to Cardiolipin over other anionic phospholipids (Fig. 4b), hence it is likely that the majority of redistribution of NAO fluorescence that we observe might be contributed by Cardiolipin mislocalization due to altered Wag31 levels, with smaller degree of NAO redistribution intensity coming indirectly from other anionic phospholipids displaced from the membrane due to the loss of membrane integrity and cell shape changes due to Wag31.

• Authors' data show that the N-terminal region of Wag31 is important for membrane tethering. The authors' data also show that the N-terminal region is important for sustaining mycobacterial morphology. However, the authors' statement in Line 256 "These results highlight the importance of tethering for sustaining mycobacterial morphology and survival" requires additional proof. It remains possible that the N-terminal region has another unknown activity, and this yet-unknown activity rather than the membrane tethering activity drives the morphological maintenance. Similarly, the N-terminal region is important for lipid homeostasis, but the statement in Line 270, "the maintenance of lipid homeostasis by Wag31 is a consequence of its tethering activity" requires additional proof. The authors should tone down these overstatements or provide additional data to support their claims.

We agree with the reviewer that there exists a possibility for another function of the N-terminal that may contribute to sustaining mycobacterial physiology and survival. We would revise our statements in the paper to reflect the data. Results shown suggest that the tethering activity of the Nterminal region may contribute to mycobacterial morphology and survival. However, additional functions of this region can’t be ruled out. Similarly, the maintenance of lipid homeostasis by Wag31 may be associated with its tethering activity, although other mechanisms could also contribute to this process.

• Authors suggest that Wag31 acts as a scaffold for the IMD (Fig. 8). However, Meniche et. al. has shown that MurG as well as GlfT2, two well-characterized IMD proteins, do not colocalize with Wag31 (DivIVA) (https://doi.org/10.1073/pnas.1402158111). IMD proteins are always slightly subpolar while Wag31 is located to the tip of the cell. Therefore, the authors' biochemical data cannot be easily reconciled with microscopic observations in the literature. This raises a question regarding the validity of protein-protein interaction shown in Figure 7. Since this pull-down assay was conducted by mixing E. coli lysate expressing Wag31 and Msm lysate expression Wag31 interactors like MurG, it is possible that the interactions are not direct. Authors should interpret their data more cautiously. If authors cannot provide additional data and sufficient justifications, they should avoid proposing a confusing model like Figure 8 that contradicts published observations.

In the literature, MurG and GlfT2 have been shown to have polar localisation (Freeman et al., 2023; Hayashi et al., 2016; Kado et al., 2023) and two groups have shown slightly sub-polar localisation of MurG (García-Heredia et al., 2021; Meniche et al., 2014). Additionally, (Freeman et al., 2023) showed SepIVA to be a spatio-temporal regulator of MurG. MS/MS analysis of Wag31 immunoprecipitation data yielded both MurG and SepIVA to be interactors of Wag31 (Fig. 3). Given Wag31 also displays polar localisation, it is likely that it associates with the polar MurG. However, since a sub-polar localisation of MurG has also been reported, it is possible that they do not interact directly and another protein mediates their interaction. Based on the above, we will modify the model proposed in Fig. 8.

We agree that for validation of interaction, we performed pulldown experiments by mixing E. coli lysates expressing His-Wag31 full-length or truncated protein with M. smegmatis lysates expressing FLAG-tagged interacting proteins. The wash conditions used were quite stringent for these pull-down assays—the wash buffer contained 1% Triton X100 that eliminates all non-specific and indirect interactions. However, we agree that we cannot conclusively state that the interactions are direct without purifying the proteins and performing the experiment. We will describe this caveat in the revised manuscript and propose a model that reflects the results we obtained.

References:

Freeman, A. H., Tembiwa, K., Brenner, J. R., Chase, M. R., Fortune, S. M., Morita, Y. S., & Boutte, C. C. (2023). Arginine methylation sites on SepIVA help balance elongation and septation in Mycobacterium smegmatis. Mol Microbiol, 119(2), 208-223. https://doi.org/10.1111/mmi.15006

Garcia Fernandez, M. I., Ceccarelli, D., & Muscatello, U. (2004). Use of the fluorescent dye 10-N-nonyl acridine orange in quantitative and location assays of cardiolipin: a study on different experimental models. Anal Biochem, 328(2), 174-180. https://doi.org/10.1016/j.ab.2004.01.020

García-Heredia, A., Kado, T., Sein, C. E., Puffal, J., Osman, S. H., Judd, J., Gray, T. A., Morita, Y. S., & Siegrist, M. S. (2021). Membrane-partitioned cell wall synthesis in mycobacteria. eLife, 10. https://doi.org/10.7554/eLife.60263

Habibi Arejan, N., Ensinck, D., Diacovich, L., Patel, P. B., Quintanilla, S. Y., Emami Saleh, A., Gramajo, H., & Boutte, C. C. (2022). Polar protein Wag31 both activates and inhibits cell wall metabolism at the poles and septum. Front Microbiol, 13, 1085918. https://doi.org/10.3389/fmicb.2022.1085918

Hayashi, J. M., Luo, C. Y., Mayfield, J. A., Hsu, T., Fukuda, T., Walfield, A. L., Giffen, S. R., Leszyk, J. D., Baer, C. E., Bennion, O. T., Madduri, A., Shaffer, S. A., Aldridge, B. B., Sassetti, C. M., Sandler, S. J., Kinoshita, T., Moody, D. B., & Morita, Y. S. (2016). Spatially distinct and metabolically active membrane domain in mycobacteria. Proc Natl Acad Sci U S A, 113(19), 5400-5405. https://doi.org/10.1073/pnas.1525165113

Kado, T., Akbary, Z., Motooka, D., Sparks, I. L., Melzer, E. S., Nakamura, S., Rojas, E. R., Morita, Y. S., & Siegrist, M. S. (2023). A cell wall synthase accelerates plasma membrane partitioning in mycobacteria. eLife, 12, e81924. https://doi.org/10.7554/eLife.81924

Meniche, X., Otten, R., Siegrist, M. S., Baer, C. E., Murphy, K. C., Bertozzi, C. R., & Sassetti, C. M. (2014). Subpolar addition of new cell wall is directed by DivIVA in mycobacteria. Proc Natl Acad Sci U S A, 111(31), E32433251. https://doi.org/10.1073/pnas.1402158111

Mileykovskaya, E., & Dowhan, W. (2000). Visualization of phospholipid domains in Escherichia coli by using the cardiolipin-specific fluorescent dye 10-N-nonyl acridine orange. J Bacteriol, 182(4), 1172-1175. https://doi.org/10.1128/JB.182.4.1172-1175.2000

Petit, J. M., Maftah, A., Ratinaud, M. H., & Julien, R. (1992). 10N-nonyl acridine orange interacts with cardiolipin and allows the quantification of this phospholipid in isolated mitochondria. Eur J Biochem, 209(1), 267273. https://doi.org/10.1111/j.1432-1033.1992.tb17285.x

Renner, L. D., & Weibel, D. B. (2011). Cardiolipin microdomains localize to negatively curved regions of Escherichia coli membranes. Proc Natl Acad Sci U S A, 108(15), 6264-6269. https://doi.org/10.1073/pnas.1015757108

Schägger, H. (2006). Tricine-SDS-PAGE. Nat Protoc, 1(1), 16-22. https://doi.org/10.1038/nprot.2006.4

Xu, W. X., Zhang, L., Mai, J. T., Peng, R. C., Yang, E. Z., Peng, C., & Wang, H. H. (2014). The Wag31 protein interacts with AccA3 and coordinates cell wall lipid permeability and lipophilic drug resistance in Mycobacterium smegmatis. Biochem Biophys Res Commun, 448(3), 255-260. https://doi.org/10.1016/j.bbrc.2014.04.116

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) Ln 130. A better clarification/discussion is required here. It is clear that both depletion and overexpression have an effect in levels of various lipids, but subsequent descriptions show that they affect different classes of lipids.

We thank the reviewer for the comment. We have included a clarification for this in the discussion section.

(2) The pulldown assays results are interesting, but the links are tentative.

We thank the reviewer for the comment. The interactome of Wag31 was identified through the immunoprecipitation of Flag-tagged Wag31 complemented at an integrative locus in Wag31 mutant background to avoid overexpression artifacts. We used Msm::gfp expressing an integrative copy (at L5 locus) of FLAG-GFP as a control to subtract non-specific interactions. The experiment was performed in biological triplicates, and interactors that appeared in all replicates were selected for further analysis. Although we identified more than 100 interactors of Wag31, we analyzed only the top 25 hits, with a PSM cut-off 18 and unique peptides5. Additionally, two of Wag31's established interactors, AccD5 and Rne, were among the top five hits, thus validating our data.

Though we agree that the interactions can either be direct or through a third partner, the fact that we obtained known interactors of Wag31 makes us believe these interactions are genuine. Moreover, for validation, we performed pulldown experiments by mixing E. coli lysates expressing HisWag31 full-length or truncated protein with M. smegmatis lysates expressing FLAG-tagged interacting proteins. The wash conditions used were quite stringent for these pull-down assays—the wash buffer contained 1% Triton X100 that eliminates all non-specific and indirect interactions. However, we agree that we cannot conclusively state that the interactions are direct without purifying the proteins and performing the experiment. We will describe this caveat in the revised manuscript.

(3) The authors may perhaps like to rephrase claims of effects lipid homeostasis, as my understanding is that lipid localisation rather than catabolism/breakdown is affected.

We thank the reviewer for the comment. In this manuscript, we are trying to convey that Wag31 is a spatiotemporal regulator of lipid metabolism. It is a peripheral protein that is hooked to the membrane via Cardiolipin and forms a scaffold at the poles, which helps localize several enzymes involved in lipid metabolism.

Homeostasis is the process by which an organism maintains a steady-state of balance and stability in response to changes. Depletion of Wag31 not only results in delocalisation of lipids in intracellular lipid inclusions but also leads to changes in the levels of various lipid classes. Advancement in the field of spatial biology underscores the importance of native localization of various biological molecules crucial for maintaining a steady-cell of the cell. Hence, we have used the word “homeostasis” to describe both the changes observed in lipid metabolism.

Reviewer #2 (Recommendations for the authors):

I recommend the following experiments to strengthen the data presented:

(1) Include a non-interacting FLAG-tagged protein as a negative control in the pull-down experiment to strengthen this data.

We thank the reviewer for the comment. As suggested, we have included non-interacting FLAGtagged proteins as negative controls in the pulldown experiment. We chose MmpL4 and MmpS5 which were not found in the Wag31 interactome data. We performed pull-down experiments with both of them and included an interactor of Wag31 i.e. Msm2092 as a positive control. Fig. S3b revised shows E. coli lysate expressing His-Wag31 which was incubated with Msm lysates expressing either FLAG taggedMmpL4 or -MmpS5 or -Msm2092 (Fig. S3c revised). The mixed lysates were pulled down with Cobalt beads that bind to the His-tagged protein and analysed using Western blot analysis by probing with anti-FLAG antibody. The pull down experiments were performed independently twice, every time with Msm2092 as the positive control (Fig. S3d. revised).

(2) Perform the pull-down experiments using only the Wag31 N-terminus to rule out any role that it may have in the protein-protein interactions.

We thank the reviewer for the comment. To rule out the possibility of N-terminal of Wag31 in mediating protein-protein interactions, we cloned the N-terminal of Wag31 that comprises the DivIVAdomain in pET28b vector (Fig. 7a revised). Subsequently, the truncated protein, hereafter called Wag31_∆C flanked by 6X His tags at both the termini was expressed in E. coli and subsequently mixed with Msm lysates expressing interactors of Wag31 (Fig. 7b-c revised). Earlier experiments with Wag31_∆1-60 or Wag31_∆N  were performed with MurG, SepIVA, Msm2092 and AccA3 (Fig. 7 previous) so we used the same set of interactors to test our hypothesis. Briefly, His-Wag31_∆Cwas mixed with Msm lysates expressing either FLAG-MurG, -SepIVA, -Msm2092 or -AccA3 and pull down experiments were performed as described previously. FLAG-MmpS5, a non-interactor of Wag31 was used as a negative control. As shown in Fig. 7d revised, His-Wag31 could bind to all the four interactors whereas His-Wag31_∆C couldn’t, strengthening the conclusion that interactions of Wag31 with other proteins are mediated by its C-terminal. However, we can’t ignore the possibility of other proteins binding to the Nterminal of Wag31. Unfortunately, due to poor expression/instability of Wag31_∆C in mycobacterial shuttle vectors, we couldn’t perform a global interactome analysis of Wag31_∆C.

Minor comments:

- Please check the legend of Fig. 1g, it appears to be labelled incorrectly.

We have checked it. It is correct. From Fig. 1g we are trying to reflect on the percentages of cells of the three strains i.e. Msm+ATc, Δwag31-ATc, and Δwag31+ATc displaying rod, round or bulged morphology.

- For MS/MS analysis, a GFP control is mentioned but it is not indicated how this was incorporated in the data analysis. This information should be added.

We have incorporated that in the revised methodology.

- The information presented in Fig. 3a, e and f could be combined in one table.

We appreciate the idea of the reviewer but we prefer a pictorial representation of the data. It allows readers to consume the information in parts, make quicker comparisons and understand trends easily.

- Fig. 4c Wag31K20A appears smaller in size than the wild-type protein - why is this the case? Is this not a single amino acid substitution?

Though K20A is a single amino acid substitution, it alters the mobility of Wag31 on SDS-PAGE gel. The sequence analysis of the plasmid expressing Wag31_K20A doesn’t show additional mutations other than the desired K20A. The change in mobility could be due to a change in the conformation of Wag31_K20A or its ability to bind to SDS or both that modify its mobility under the influence of electric field.

- Please clarify what is contained in the first panel of fig 4e. compared to what is in the second panel.

The first panel represents CL-Dil-Liposomes before incubation with Wag31-GFP and the second panel shows CL-Dil-Liposomes after incubation with Wag31-GFP. The third panel shows the mixture as observed in the green channel to investigate the localisation of Wag31-GFP in the liposome-protein mix. Fourth panel shows the merged of second and third.

- The data in Fig 6d suggests higher levels of CL in the ∆wag31 compared to wild-type - how do the authors reconcile this with the MS data in Fig. 2g showing lower CL levels?

Fig. 6d represents the distribution of CL localisation in the tested strains of mycobacteria whereas Fig. 2g shows the absolute levels of CL in various strains. We attribute greater confidence on the lipidomics data which suggests down regulation of CL species. The NAO staining and microscopy is merely for studying localization of the CL along the cell, and cannot be used to reliably quantify or equate it to CL levels. The staining using a probe such as NAO is dependent on factors such as hydrophobicity and permeability of the cell wall, which we expect to be severely altered in a Wag31 mutant. Therefore, the increased staining of NAO seen in Wag31 mutant could just be reflective of the increased uptake of the dye rather than absolute levels of CL. The specificity of staining and localization however can be expected to be unaltered.

Reviewer #3 (Recommendations for the authors):

Following are suggestions for improving the writing and presentation.

• Figure 1, the meaning of the yellow arrows present in f and h should be mentioned in the figure legend.

We have incorporated that in the revised legend. In Fig.1f, the yellow arrowhead represents the bulged pole morphology whereas in Fig. 1h, it indicates intracellular lipid inclusions.

• Figure 7 legend refers to panels g, h, and i. However, Figure 7 only has panels a-c. The legend lacks a description of panel c.

We have corrected the typos and the legend.

• Figure S1, F2-R2 and F3-R3 expected sizes should be stated in the legend of the figure.

We have updated the legends.

• Figure S5, is this the same figure as 5e? If so, there is no need for this figure.

We have removed Fig. S5.

• Methods need to be written more carefully with enough details. I listed some of the concerns below.

Detailed methodology was previously provided in the supplementary material and now we have moved it to the materials and methods in the revised manuscript.

• Line 392, provide more details on western blotting. What is the secondary antibody? What image documentation system was used?

We have updated the methodology.

• Line 400, while the methods may be the same as the reference 64, authors should still provide key details such as the way samples were fixed and processed for SEM and TEM.

We have provided a detailed description of the same in methodology in the revised version.

• Line 437, how do authors calculate the concentration of liposome to be 10 µM? Do they possibly mean the concentration of phospholipids used to make the liposomes?

Yes, this is the concentration of total lipids used to make liposomes. 1 μM of Wag31 or its mutants were mixed with 100 nm extruded liposomes containing 10 μm total lipid in separate Eppendorf tubes.

• Supplemental Line 9, "turns of" should read "turns off".

We have edited this.

• Supplemental Line 13, define LHS and RHS.

LHS or left hand sequence and RHS or right hand sequence refers to the upstream and downstream flanking regions of the gene of interest.

• Supplemental Line 20, indicate the manufacturer of the microscope and type of the objective lens.

We have added these details now.

• Supplemental Line 31, define MeOH, or use a chemical formula like chloroform.

MeOH is methanol. We have provided a chemical formula in the revised version.

• Supplemental Line 53, indicate the concentration of trypsin.

We have included that in the revised version.

• Supplemental Line 72, g is not a unit. "30,000 g" should be "30,000x g".

We have revised this in the manuscript.

• Supplemental Line 114, provide more details on western blotting. What is the manufacturer of antiFLAG antibody? What is the secondary antibody? How was the antibody binding visualized? What image documentation system was used?

We have provided these details in the revised version.

10/04 : ils ont fait une levé de fonds il y a moins de 1 ans

Mais je lui faisun email car il peut rechercher des sociétés à acheter

Limites du numérique

Peu d'outils numériques, oui, en effet. Les causes sont aussi dues au manque de moyens dans les écoles.

Les écoles en milieu rural ont moins de moyens : * Moins d'élèves, donc moins de moyens de financement, car le budget est souvent attribué par des élèves. * Certains établissements ont du mal à financer le matériel.

Aussi, peu de formations en ce qui concerne les enseignants au vu des restrictions budgétaires.

Il existe également des difficultés de recrutement, car le milieu rural attire moins de personnes que les côtes, les îles ou encore le milieu urbain. Comme il y a peu d'élèves, il y a aussi beaucoup moins d'enseignants.

10/04 AM : Pas intéressé , entreprise vendu il y a 3mois

Author response:

The following is the authors’ response to the previous reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

This paper contains what could be described as a "classic" approach towards evaluating a novel taste stimuli in an animal model, including standard behavioral tests (some with nerve transections), taste nerve physiology, and immunocytochemistry of taste cells of the tongue. The stimulus being tested is ornithine, from a class of stimuli called "kokumi" (in terms of human taste); these kokumi stimuli appear to enhance other canonical tastes, increasing what are essentially hedonic attributes of other stimuli. The mechanism for ornithine detection is thought to be GPRC6A receptors expressed in taste cells. The authors showed evidence for this in an earlier paper with mice; this paper evaluates ornithine taste in a rat model, and comes to a similar conclusion, albeit with some small differences between the two rodent species.

Strengths:

The data show effects of ornithine on taste/intake in laboratory rats: In two-bottle and briefer intake tests, adding ornithine results in higher intake of most, but all not all stimuli tested. Bilateral chorda tympani (CT) nerve cuts or the addition of GPRC6A antagonists decreased or eliminated these effects. Ornithine also evoked responses by itself in the CT nerve, but mainly at higher concentrations; at lower concentrations it potentiated the response to monosodium glutamate. Finally, immunocytochemistry of taste cell expression indicated that GPRC6A was expressed predominantly in the anterior tongue, and co-localized (to a small extent) with only IP3R3, indicative of expression in a subset of type II taste receptor cells.

Weaknesses:

As the authors are aware, it is difficult to assess a complex human taste with complex attributes, such as kokumi, in an animal model. In these experiments they attempt to uncover mechanistic insights about how ornithine potentiates other stimuli by using a variety of established experimental approaches in rats. They partially succeed by finding evidence that GPRC6A may mediate effects of ornithine when it is used at lower concentrations. In the revision they have scaled back their interpretations accordingly. A supplementary experiment measuring certain aspects of the effects of ornithine added to Miso soup in human subjects is included for the express purpose of establishing that the kokumi sensation of a complex solution is enhanced by ornithine; however, they do not use any such complex solutions in the rat studies. Moreover, the sample size of the human experiment is (still) small - it really doesn't belong in the same manuscript with the rat studies.

Despite the reviewer’s suggestion, we would like to include the human sensory experiment. Our rationale is that we must first demonstrate that the kokumi of miso soup is enhanced by the addition of ornithine, which is then followed by basic animal experiments to investigate the underlying mechanisms of kokumi in humans.

We did not present the additive effects of ornithine on miso soup in the present rat study because our previous companion paper (Fig. 1B in Mizuta et al., 2021, Ref. #26) already confirmed that miso soup supplemented with 3 mM L-ornithine (but not D-ornithine) was statistically significantly (P < 0.001) preferred to plain miso soup by mice.

Furthermore, we believe that our sample size (n = 22) is comparable to those employed in other studies. For example, the representative kokumi studies by Ohsu et al. (Ref. #9), Ueda et al. (Ref. #10), Shibata et al. (Ref. #20), Dunkel et al. (Ref. #37), and Yang et al. (Ref. #44) used sample sizes of 20, 19, 17, 9, and 15, respectively.

Reviewer #2 (Public review):

Summary:

The authors used rats to determine the receptor for a food-related perception (kokumi) that has been characterized in humans. They employ a combination of behavioral, electrophysiological, and immunohistochemical results to support their conclusion that ornithine-mediated kokumi effects are mediated by the GPRC6A receptor. They complemented the rat data with some human psychophysical data. I find the results intriguing, but believe that the authors overinterpret their data.

Strengths:

The authors provide compelling evidence that ornithine enhances the palatability of several chemical stimuli (i.e., IMP, MSG, MPG, Intralipos, sucrose, NaCl, quinine). Ornithine also increases CT nerve responses to MSG. Additionally, the authors provide evidence that the effects of ornithine are mediated by GPRC6A, a G-protein-coupled receptor family C group 6 subtype A, and that this receptor is expressed primarily in fungiform taste buds. Taken together, these results indicate that ornithine enhances the palatability of multiple taste stimuli in rats and that the enhancement is mediated, at least in part, within fungiform taste buds. This is an important finding that could stand on its own. The question of whether ornithine produces these effects by eliciting kokumi-like perceptions (see below) should be presented as speculation in the Discussion section.

Weaknesses:

I am still unconvinced that the measurements in rats reflect the "kokumi" taste percept described in humans. The authors conducted long-term preference tests, 10-min avidity tests and whole chorda tympani (CT) nerve recordings. None of these procedures specifically model features of "kokumi" perception in humans, which (according to the authors) include increasing "intensity of whole complex tastes (rich flavor with complex tastes), mouthfulness (spread of taste and flavor throughout the oral cavity), and persistence of taste (lingering flavor)." While it may be possible to develop behavioral assays in rats (or mice) that effectively model kokumi taste perception in humans, the authors have not made any effort to do so. As a result, I do not think that the rat data provide support for the main conclusion of the study--that "ornithine is a kokumi substance and GPRC6A is a novel kokumi receptor."

Kokumi can be assessed in humans, as demonstrated by the enhanced kokumi perception observed when miso soup is supplemented with ornithine (Fig. S1). Currently, we do not have a method to measure the same kokumi perception in animals. However, in the two-bottle preference test, our previous companion paper (Fig. 1B in Mizuta et al. 2021, Ref. #26) confirmed that miso soup supplemented with 3 mM L-ornithine (but not D-ornithine) was statistically significantly (P < 0.001) preferred over plain miso soup by mice.

Of the three attributes of kokumi perception in humans, the “intensity of whole complex tastes (rich flavor with complex tastes)” was partly demonstrated in the present rat study. In contrast, “mouthfulness (the spread of taste and flavor throughout the oral cavity)” could not be directly detected in animals and had to be inferred in the Discussion. “Persistence of taste (lingering flavor)” was evident at least in the chorda tympani responses; however, because the tongue was rinsed 30 seconds after the onset of stimulation, the duration of the response was not fully recorded.

It is well accepted in sensory physiology that the stronger the stimulus, the larger the tonic response—and consequently, the longer it takes for the response to return to baseline. For example, Kawasaki et al. (2016, Ref. #45) clearly showed that the duration of sensation increased proportionally with the concentration of MSG, lactic acid, and NaCl in human sensory tests. The essence of this explanation has been incorporated into the Discussion (p. 12).

Why are the authors hypothesizing that the primary impacts of ornithine are on the peripheral taste system? While the CT recordings provide support for peripheral taste enhancement, they do not rule out the possibility of additional central enhancement. Indeed, based on the definition of human kokumi described above, it is likely that the effects of kokumi stimuli in humans are mediated at least in part by the central flavor system.

We agree with the reviewer’s comment. Our CT recordings indicate that the effects of kokumi stimuli on taste enhancement occur primarily at the peripheral taste organs. The resulting sensory signals are then transmitted to the brain, where they are processed by the central gustatory and flavor systems, ultimately giving rise to kokumi attributes. This central involvement in kokumi perception is discussed on page 12. Although kokumi substances exert their effects at low concentrations—levels at which the substance itself (e.g., ornithine) does not become more favorable or (in the case of γ-Glu-Val-Gly) exhibits no distinct taste—we cannot rule out the possibility that even faint taste signals from these substances are transmitted to the brain and interact with other taste modalities.

The authors include (in the supplemental data section) a pilot study that examined the impact of ornithine on variety of subjective measures of flavor perception in humans. The presence of this pilot study within the larger rat study does not really mice sense. While I agree with the authors that there is value in conducting parallel tests in both humans and rodents, I think that this can only be done effectively when the measurements in both species are the same. For this reason, I recommend that the human data be published in a separate article.

Despite the reviewer’s suggestion, we intend to include the human sensory experiment. Our rationale is that we must first demonstrate that the kokumi of miso soup is enhanced by the addition of ornithine, and then follow up with basic animal experiments to investigate the potential underlying mechanisms of kokumi in humans.

In our previous companion paper (Fig. 1B in Mizuta et al., 2021, Ref. #26), we confirmed with statistical significance (P < 0.001) that mice preferred miso soup supplemented with 3 mM L-ornithine (but not D-ornithine) over plain miso soup. However, as explained in our response to Reviewer #2’s first concern (in the Public review), it is difficult to measure two of the three kokumi attributes—aside from the “intensity of whole complex tastes (rich flavor with complex tastes)”—in animal models.

The authors indicated on several occasions (e.g., see Abstract) that ornithine produced "synergistic" effects on the CT nerve response to chemical stimuli. "Synergy" is used to describe a situation where two stimuli produce an effect that is greater than the sum of the response to each stimulus alone (i.e., 2 + 2 = 5). As far as I can tell, the CT recordings in Fig. 3 do not reflect a synergism.

We appreciate your comments regarding the definition of synergy. In Fig. 5 (not Fig. 3), please note the difference in the scaling of the ordinate between Fig. 5D (ornithine responses) and Fig. 5E (MSG responses). When both responses are presented on the same scale, it becomes evident that the response to 1 mM ornithine is negligibly small compared to the MSG response, which clearly indicates that the response to the mixture of MSG and 1 mM ornithine exceeds the sum of the individual responses to MSG and 1 mM ornithine. Therefore, we have described the effect as “synergistic” rather than “additive.” The same observation applies to the mice experiments in our previous companion paper (Fig. 8 in Mizuta et al. 2021, Ref. #26), where synergistic effects are similarly demonstrated by graphical representation. We have also added the following sentence to the legend of Fig. 5:

“Note the different scaling of the ordinate in (D) and (E).”

Reviewer #3 (Public review):

Summary:

In this study the authors set out to investigate whether GPRC6A mediates kokumi taste initiated by the amino acid L-ornithine. They used Wistar rats, a standard laboratory strain, as the primary model and also performed an informative taste test in humans, in which miso soup was supplemented with various concentrations of L-ornithine. The findings are valuable and overall the evidence is solid. L-Ornithine should be considered to be a useful test substance in future studies of kokumi taste and the class C G protein coupled receptor known as GPRC6A (C6A) along with its homolog, the calcium-sensing receptor (CaSR) should be considered candidate mediators of kokumi taste. The researchers confirmed in rats their previous work on Ornithine and C6A in mice (Mizuta et al Nutrients 2021).

Strengths:

The overall experimental design is solid based on two bottle preference tests in rats. After determining the optimal concentration for L-Ornithine (1 mM) in the presence of MSG, it was added to various tastants including: inosine 5'-monophosphate; monosodium glutamate (MSG); mono-potassium glutamate (MPG); intralipos (a soybean oil emulsion); sucrose; sodium chloride (NaCl; salt); citric acid (sour) and quinine hydrochloride (bitter). Robust effects of ornithine were observed in the cases of IMP, MSG, MPG and sucrose; and little or no effects were observed in the cases of sodium chloride, citric acid; quinine HCl. The researchers then focused on the preference for Ornithine-containing MSG solutions. Inclusion of the C6A inhibitors Calindol (0.3 mM but not 0.06 mM) or the gallate derivative EGCG (0.1 mM but not 0.03 mM) eliminated the preference for solutions that contained Ornithine in addition to MSG. The researchers next performed transections of the chord tympani nerves (with sham operation controls) in anesthetized rats to identify a role of the chorda tympani branches of the facial nerves (cranial nerve VII) in the preference for Ornithine-containing MSG solutions. This finding implicates the anterior half-two thirds of the tongue in ornithine-induced kokumi taste. They then used electrical recordings from intact chorda tympani nerves in anesthetized rats to demonstrate that ornithine enhanced MSG-induced responses following the application of tastants to the anterior surface of the tongue. They went on to show that this enhanced response was insensitive to amiloride, selected to inhibit 'salt tastant' responses mediated by the epithelial Na+ channel, but eliminated by Calindol. Finally they performed immunohistochemistry on sections of rat tongue demonstrating C6A positive spindle-shaped cells in fungiform papillae that partially overlapped in its distribution with the IP3 type-3 receptor, used as a marker of Type-II cells, but not with (i) gustducin, the G protein partner of Tas1 receptors (T1Rs), used as a marker of a subset of type-II cells; or (ii) 5-HT (serotonin) and Synaptosome-associated protein 25 kDa (SNAP-25) used as markers of Type-III cells.

At least two other receptors in addition to C6A might mediate taste responses to ornithine: (i) the CaSR, which binds and responds to multiple L-amino acids (Conigrave et al, PNAS 2000), and which has been previously reported to mediate kokumi taste (Ohsu et al., JBC 2010) as well as responses to Ornithine (Shin et al., Cell Signaling 2020); and (ii) T1R1/T1R3 heterodimers which also respond to L-amino acids and exhibit enhanced responses to IMP (Nelson et al., Nature 2001). These alternatives are appropriately discussed and, taken together, the experimental results favor the authors' interpretation that C6A mediates the Ornithine responses. The authors provide preliminary data in Suppl. 3 for the possibility of co-expression of C6A with the CaSR.

Weaknesses:

The authors point out that animal models pose some difficulties of interpretation in studies of taste and raise the possibility in the Discussion that umami substances may enhance the taste response to ornithine (Line 271, Page 9).

Ornithine and umami substances interact to produce synergistic effects in both directions—ornithine enhances responses to umami substances, and vice versa. These effects may depend on the concentrations used, as described in the Discussion (pp. 9–10). Further studies are required to clarify the precise nature of this interaction.

One issue that is not addressed, and could be usefully addressed in the Discussion, relates to the potential effects of kokumi substances on the threshold concentrations of key tastants such as glutamate. Thus, an extension of taste distribution to additional areas of the mouth (previously referred to as 'mouthfulness') and persistence of taste/flavor responses (previously referred to as 'continuity') could arise from a reduction in the threshold concentrations of umami and other substances that evoke taste responses.

Thank you for this important suggestion. If ornithine reduces the threshold concentrations of tastants—including glutamate—and enhances their suprathreshold responses, then adding ornithine may activate additional taste cells. This effect could explain kokumi attributes such as an “extension of taste distribution” and possibly the “persistence of responses.” As shown in Fig. 2, the lowest concentrations used for each taste stimulus are near or below the thresholds, which indicates that threshold concentrations are reduced—especially for MSG and MPG. We have incorporated this possibility into the Discussion as follows (p.12):

“Kokumi substances may reduce the threshold concentrations as well as they increase the suprathreshold responses of tastants. Once the threshold concentrations are lowered, additional taste cells in the oral cavity become activated, and this information is transmitted to the brain. As a result, the brain perceives this input as coming from a wider area of the mouth.”

The status of one of the compounds used as an inhibitor of C6A, the gallate derivative EGCG, as a potential inhibitor of the CaSR or T1R1/T1R3 is unknown. It would have been helpful to show that a specific inhibitor of the CaSR failed to block the ornithine response.

Thank you for this important comment. We attempted to identify a specific inhibitor of CaSR. Although we considered using NPS-2143—a commonly used CaSR inhibitor—it is known to also inhibit GPRC6A. We agree that using a specific CaSR inhibitor would be beneficial and plan to pursue this in future studies.

It would have been helpful to include a positive control kokumi substance in the two bottle preference experiment (e.g., one of the known gamma glutamyl peptides such as gamma-glu-Val-Gly or glutathione), to compare the relative potencies of the control kokumi compound and Ornithine, and to compare the sensitivities of the two responses to C6A and CaSR inhibitors.

We agree with this comment. In retrospect, it may have been advantageous to directly compare the potencies of CaSR and GPRC6A agonists in enhancing taste preferences—and to evaluate the sensitivity of these preferences to CaSR and GPRC6A antagonists. However, we did not include γ-Glu-Val-Gly in the present study because we have already reported its supplementation effects on the ingestion of basic taste solutions in rats using the same methodology in a separate paper (Yamamoto and Mizuta, 2022, Ref. #25). The results from both studies are compared in the Discussion (p. 11).

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Major:

I am not convinced by the Author's arguments for including the human data. I appreciate their efforts in adding a few (5) subjects and improving the description, but it still feels like it is shoehorned into this paper, and would be better published as a different manuscript.

This human study is short, but it is complete rather than preliminary. The rationale for us to include the human data as supplementary information is shown in responses to the reviewer’s Public review.

Minor concerns:

Page 3 paragraph 1: Suggest "contributing to palatability".

Thank you for this suggestion. We have rewritten the text as follows:

“…, the brain further processes these sensations to evoke emotional responses, contributing to palatability or unpleasantness”.

Page 4 paragraph 2: The text still assumes that "kokumi" is a meaningful descriptor for what rodents experience. Re-wording the following sentence like this could help:

"Neuroscientific studies in mice and rats provide evidence that gluthione and y-Glu-Val-Gly activate CaSRs, and modify behavioral responses to other tastants in a way that may correspond to kokumi taste as experienced by humans. However, to our..."

Or something similar.

Thank you for this suggestion. We have rewritten the sentence according to your suggestion as follows:

"Neuroscientific studies (23,25,30) in mice and rats provide evidence that glutathione and y-Glu-Val-Gly activate CaSRs, and modify behavioral responses to other tastants in a way that may correspond to kokumi as experienced by humans”.

Page 7 paragraph 1 - put the concentrations of Calindol and EGCG used (in the physiology exps) in the text.

We have added the concentrations: “300 µM calindol and 100 µM EGCG”.

Reviewer #2 (Recommendations for the authors):

I have included all of my recommendations in the public review section.

Reviewer #3 (Recommendations for the authors):

Although the definitions of 'thickness', 'mouthfulness' and 'continuity' have been revised very helpfully in the Introduction, 'mouthfulness' reappears at other points in the MS e.g., Page 4, Results, Line 3; Page 9, Line 3. It is best replaced by the new definition in these other locations too.

We wish to clarify that our revised text stated, “…to clarify that kokumi attributes are inherently gustatory, in the present study we use the terms ‘intensity of whole complex tastes (rich flavor with complex tastes)’ instead of ‘thickness,’ ‘mouthfulness (spread of taste and flavor throughout the oral cavity)’ instead of ‘continuity,’ and ‘persistence of taste (lingering flavor)’ instead of ‘continuity.’” The term “mouthfulness” was retained in our text, though we provided a more specific explanation. In the re-revised version, we have added “(spread of taste in the oral cavity)” immediately after “mouthfulness.”

I doubt that many scientific readers will be familliar with the term 'intragemmal nerve fibres' (Page 8, Line 4). It is used appropriately but it would be helpful to briefly define/explain it.

We have added an explanation as follows:

“… intragemmal nerve fibers, which are nerve processes that extend directly into the structure of the taste bud to transmit taste signals from taste cells to the brain.”

I previously pointed out the overlap between the CaSR's amino acid (AA) and gamma-glutamyl-peptide binding site. I was surprised by the authors' response which appeared to miss the point being made. It was based on the impacts of selected mutations in the receptor's Venus FlyTrap domain (Broadhead JBC 2011) on the responses to AAs and glutathione analogs. The significantly more active analog, S-methylglutathione is of additional interest because, like glutathione itself, it is present in mammalian body fluids. My apologies to the authors for not more carefully explaining this point.

Thank you for this comment. Both CaSR and GPRC6A are recognized as broad-spectrum amino acid sensors; however, their agonist profiles differ. Aromatic amino acids preferentially activate CaSR, whereas basic amino acids tend to activate GPRC6A. For instance, among basic amino acids, ornithine is a potent and specific activator of GPRC6A, while γ-Glu-Val-Gly in addition to amino acids is a high-potency activator of CaSR. It remains unclear how effectively ornithine activates CaSR and whether γ-glutamyl peptides also activate GPRC6A. These questions should be addressed in future studies.

Author response:

The following is the authors’ response to the original reviews

eLife Assessment

This valuable study uses consensus-independent component analysis to highlight transcriptional components (TC) in high-grade serous ovarian cancers (HGSOC). The study presents a convincing preliminary finding by identifying a TC linked to synaptic signaling that is associated with shorter overall survival in HGSOC patients, highlighting the potential role of neuronal interactions in the tumour microenvironment. This finding is corroborated by comparing spatially resolved transcriptomics in a small-scale study; a weakness is in being descriptive, non-mechanistic, and requiring experimental validation.”

We sincerely thank the editors for their valuable and constructive feedback. We are grateful for the recognition of our findings and the importance of identifying transcriptional components in high-grade serous ovarian cancers.

We acknowledge the editors’ observation regarding the descriptive nature of our study and its limited mechanistic depth. We agree that additional experimental validation would further strengthen our conclusions. We are planning and executing the experiments for a future study to provide mechanistic insights into the associations found in this study. In addition, recent reviews focused on the emerging field of cancer neuroscience emphasize the early stages the field is in, specifically in terms of a mechanistic understanding of the contributions of tumor-infiltrating nerves in tumor initiation and progression (Amit et al., 2024; Hwang et al., 2024). Nonetheless, we wish to emphasize that emerging mechanistic preclinical studies have demonstrated the influence of tumour-infiltrating nerves on disease progression (Allen et al., 2018; Balood et al., 2022; Darragh et al., 2024; Globig et al., 2023; Jin et al., 2022; Restaino et al., 2023; Zahalka et al., 2017). Several of these studies include contributions from our co-authors and feature in vitro and in vivo research on head and neck squamous cell carcinoma as well as high-grade serous ovarian carcinoma samples. This study further strengthens the preclinical work by showing in patient data, the potential relevance of neuronal signaling on disease outcome.

For instance, Restiano et al. (2023) demonstrated that substance P, released from tumour-infiltrating nociceptors, potentiates MAP kinase signaling in cancer cells, thereby driving disease progression. Crucially, this effect was shown to be reversible in vivo by blocking the substance P receptor (Restaino et al., 2023). These findings offer compelling evidence of the role of tumour innervation in cancer biology.

Our current study in tumor samples of patients with high-grade serous ovarian cancer identifies a transcriptional component that is enriched for genes for which the protein is located in the synapse. We believe that the previously published mechanistic insights support our findings and suggest that this transcriptional component could serve as a valuable screening tool to identify innervated tumours based on bulk transcriptomes. Clinically, this information is highly relevant, as patients with innervated tumours may benefit from alternate therapeutic strategies targeting these innervations.

Reviewer #1 (Public review)

This manuscript explores the transcriptional landscape of high-grade serous ovarian cancer (HGSOC) using consensus-independent component analysis (c-ICA) to identify transcriptional components (TCs) associated with patient outcomes. The study analyzes 678 HGSOC transcriptomes, supplemented with 447 transcriptomes from other ovarian cancer types and noncancerous tissues. By identifying 374 TCs, the authors aim to uncover subtle transcriptional patterns that could serve as novel drug targets. Notably, a transcriptional component linked to synaptic signaling was associated with shorter overall survival (OS) in patients, suggesting a potential role for neuronal interactions in the tumour microenvironment. Given notable weaknesses like lack of validation cohort or validation using another platform (other than the 11 samples with ST), the data is considered highly descriptive and preliminary.

Strengths:

(1) Innovative Methodology:

The use of c-ICA to dissect bulk transcriptomes into independent components is a novel approach that allows for the identification of subtle transcriptional patterns that may be overshadowed in traditional analyses.

We thank the reviewer for recognizing the strengths and novelty of our study. We appreciate the positive feedback on using consensus-independent component analysis (c-ICA) to decompose bulk transcriptomes, which allowed us to detect subtle transcriptional signals often overlooked in traditional analyses.

(2) Comprehensive Data Integration:

The study integrates a large dataset from multiple public repositories, enhancing the robustness of the findings. The inclusion of spatially resolved transcriptomes adds a valuable dimension to the analysis.

We thank the reviewer for recognizing the robustness of our study through comprehensive data integration. We appreciate the acknowledgment of our efforts to leverage a large, multi-source dataset, as well as the additional insights gained from spatially resolved transcriptomes. We consider this integrative approach enhances the depth of our analysis and contributes to a more nuanced understanding of the tumour microenvironment.

(3) Clinical Relevance:

The identification of a synaptic signaling-related TC associated with poor prognosis highlights a potential new avenue for therapeutic intervention, emphasizing the role of the tumour microenvironment in cancer progression.

We appreciate the recognition of the clinical implications of our findings. The identification of a synaptic signaling-related transcriptional component associated with poor prognosis underscores the potential for novel therapeutic targets within the tumour microenvironment. We agree that this insight could open new avenues for intervention and further highlights the role of neuronal interactions in cancer progression.

Weaknesses:

(1) Mechanistic Insights:

While the study identifies TCs associated with survival, it provides limited mechanistic insights into how these components influence cancer progression. Further experimental validation is necessary to elucidate the underlying biological processes.

We acknowledge the point regarding the limited mechanistic insights provided in our study. We agree that further experimental validation would significantly enhance our understanding of how the biological processes captured by these transcriptional components influence cancer progression. We are planning and executing the experiments for  a future study to provide mechanistic insights into the associations found in this study.

Our analyses were performed on publicly available bulk and spatial resolved expression profiles. To investigate the mechanistic insights in future studies, we plan to integrate spatial transcriptomic data with immunohistochemical analysis of the same tumour samples to validate our findings. Additionally, we have initiated efforts to set up in vitro co-cultures of neurons and ovarian cancer cells. These co-cultures will enable us to investigate how synaptic signaling impacts ovarian cancer cell behavior.

(2) Generalizability:

The findings are primarily based on transcriptomic data from HGSOC. It remains unclear how these results apply to other subtypes of ovarian cancer or different cancer types.

To respond to this remark, we utilized survival data from Bolton et al. (2022) and TCGA to investigate associations between TC activity scores and overall survival of patients with ovarian clear cell carcinoma, the second most common subtype of epithelial ovarian cancer, and  other cancer types respectively. However, we acknowledge the limitations of TCGA survival data, as highlighted in the referenced article (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8726696/). Additionally, as shown in Figure 5, we provided evidence of TC121 activity across various cancer types, suggesting broader relevance. For the results of the analyses mentioned above, please refer to our response to remark 1.3 of the recommendation section (page 4).

(3) Innovative Methodology:

Requires more validation using different platforms (IHC) to validate the performance of this bulk-derived data. Also, the lack of control over data quality is a concern.

We acknowledge the value of validating our results with alternative platforms such as IHC. We are planning and executing the experiments for a future study to provide mechanistic insights into the associations found in this study.

We implemented regarding data quality control, the following measures to ensure the reliability of our analysis:

Bulk Transcriptional Profiles: To assess data quality, we conducted principal component analysis (PCA) on the sample Pearson product-moment correlation matrix. The first principal component (PCqc), which explains approximately 80-90% of the variance, was used to distinguish technical variability from biological signals (Bhattacharya et al., 2020). Samples with a correlation coefficient below 0.8 relative to PCqc were identified as outliers and excluded. Additionally, MD5 hash values were generated for each CEL file to identify and remove duplicate samples. Expression values were standardized to a mean of zero and a variance of one for each gene to minimize probeset- or gene-specific variability across datasets (GEO, CCLE, GDSC, and TCGA).

Spatial Transcriptional Profiles: PCA was also applied to spatial transcriptomic data for quality control. Only samples with consistent loading factor signs for the first principal component across all individual spot profiles were retained. Samples failing this criterion were excluded from further analyses.

(4) Clinical Application:

Although the study suggests potential drug targets, the translation of these findings into clinical practice is not addressed. Probably given the lack of some QA/QC procedures it'll be hard to translate these results. Future studies should focus on validating these targets in clinical settings.”

Regarding clinical applications, we acknowledge the importance of further exploring strategies targeting synaptic signaling and neurotransmitter release in the tumour microenvironment (TME). As partially discussed in the first version of the manuscript, drugs such as ifenprodil and lamotrigine—commonly used to treat neuronal disorders—can block glutamate release, thereby inhibiting subsequent synaptic signaling. Additionally, the vesicular monoamine transporter (VMAT) inhibitor reserpine blocks the formation of synaptic vesicles (Reid et al., 2013; Williams et al., 2001). Previous in vitro studies with HGSOC cell lines demonstrated that ifenprodil significantly reduced cancer cell proliferation, while reserpine triggered apoptosis in cancer cells (North et al., 2015; Ramamoorthy et al., 2019). The findings highlight the potential of such approaches to disrupt synaptic neurotransmission in the TME.

To address potential translation of our findings into clinical practice more comprehensively, we have included additional details in the manuscript:

Section discussion, page 16, lines 338-341:

“This interaction can be targeted with pan-TRK inhibitors such as entrectinib and larotrectinib. Both drugs are showing promising results in multiple phase II trials, including ovarian cancer and breast cancer patients. Furthermore, a TRKB-specific inhibitor was developed (ANA-12), but has not been subjected to any clinical trials in cancer so far (Ardini et al., 2016; Burris et al., 2015; Drilon et al., 2018, 2017).”

On page 17, lines 361-374:

“Strategies to disrupt neuronal signaling and neurotransmitter release in neurons target key elements of excitatory neurotransmission, such as calcium flux and vesicle formation. Drugs like ifenprodil and lamotrigine, commonly used to treat neuronal disorders, block glutamate release and subsequent neuronal signaling. Additionally, the vesicular monoamine transporter (VMAT) inhibitor reserpine prevents synaptic vesicle formation (Reid et al., 2013; Williams, 2001). In vitro studies with HGSOC cell lines have demonstrated that ifenprodil significantly inhibits tumour proliferation, while reserpine induces apoptosis in cancer cells (North et al., 2015; Ramamoorthy et al., 2019). These approaches hold promise for inhibiting neuronal signaling and interactions in the TME.”

Reviewer #2 (Public review):

Summary:

Consensus-independent component analysis and closely related methods have previously been used to reveal components of transcriptomic data that are not captured by principal component or gene-gene coexpression analyses.

Here, the authors asked whether applying consensus-independent component analysis (c-ICA) to published high-grade serous ovarian cancer (HGSOC) microarray-based transcriptomes would reveal subtle transcriptional patterns that are not captured by existing molecular omics classifications of HGSOC.

Statistical associations of these (hitherto masked) transcriptional components with prognostic outcomes in HGSOC could lead to additional insights into underlying mechanisms and, coupled with corroborating evidence from spatial transcriptomics, are proposed for further investigation.

This approach is complementary to existing transcriptomics classifications of HGSOC.

The authors have previously applied the same approach in colorectal carcinoma (Knapen et al. (2024) Commun. Med).

Strengths:

(1) Overall, this study describes a solid data-driven description of c-ICA-derived transcriptional components that the authors identified in HGSOC microarray transcriptomics data, supported by detailed methods and supplementary documentation.

We thank the reviewer for acknowledging the strength of our data-driven approach and the use of consensus-independent component analysis (c-ICA) to identify transcriptional components within HGSOC microarray data. We aimed to provide comprehensive methodological detail and supplementary documentation to support the reproducibility and robustness of our findings. We believe this approach allows for the identification of subtle transcriptional signals that might have been overlooked by traditional analysis methods.

(2) The biological interpretation of transcriptional components is convincing based on (data-driven) permutation analysis and a suite of analyses of association with copy-number, gene sets, and prognostic outcomes.

We appreciate the positive feedback on the biological interpretation of our transcriptional components. We are pleased that our approach, which includes data-driven permutation testing and analyses of associations with copy-number alterations, gene sets, and prognostic outcomes, was found to be convincing. These analyses were integral to enhancing our findings’ robustness and biological relevance.

(3) The resulting annotated transcriptional components have been made available in a searchable online format.

Thank you for this important positive remark.

(4) For the highlighted transcriptional component which has been annotated as related to synaptic signalling, the detection of the transcriptional component among 11 published spatial transcriptomics samples from ovarian cancers appears to support this preliminary finding and requires further mechanistic follow-up.

Thank you for acknowledging the accessibility of our annotated transcriptional components. We prioritized making these data available in a searchable online format to facilitate further research and enable the community to explore and validate our findings.

Weaknesses:

(1) This study has not explicitly compared the c-ICA transcriptional components to the existing reported transcriptional landscape and classifications for ovarian cancers (e.g. Smith et al Nat Comms 2023; TCGA Nature 2011; Engqvist et al Sci Rep 2020) which would enable a further assessment of the additional contribution of c-ICA - whether the cICA approach captured entirely complementary components, or whether some components are correlated with the existing reported ovarian transcriptomic classifications.

We acknowledge the reviewer’s insightful suggestion to compare our c-ICA-derived transcriptional components with previously reported ovarian cancer classifications, such as those from Smith et al. (2023), TCGA (2011), and Engqvist et al. (2020). To address this, we incorporated analyses comparing the activity scores of our transcriptional components with these published landscapes and classifications, particularly focusing on any associations with overall survival. Additionally, we evaluated correlations between gene signatures from a subset of these studies and our identified TCs, enhancing our understanding of the unique contributions of the c-ICA approach. Please refer to our response to remark 10 for the results of these analyses.

(2) Here, the authors primarily interpret the c-ICA transcriptional components as a deconvolution of bulk transcriptomics due to the presence of cells from tumour cells and the tumour microenvironment.

However, c-ICA is not explicitly a deconvolution method with respect to cell types: the transcriptional components do not necessarily correspond to distinct cell types, and may reflect differential dysregulation within a cell type. This application of c-ICA for the purpose of data-driven deconvolution of cell populations is distinct from other deconvolution methods that explicitly use a prior cell signature matrix.”

We acknowledge that c-ICA, unlike traditional deconvolution methods, is not specifically designed for cell-type deconvolution and does not rely on a predefined cell signature matrix. While we explored the transcriptional components in the context of tumour and microenvironmental interactions, we agree that these components may not correspond directly to distinct cell types but rather reflect complex patterns of dysregulation, potentially within individual cell populations.

Our goal with c-ICA was to uncover hidden transcriptional patterns possibly influenced by cellular heterogeneity. However, we recognize these patterns may also arise from regulatory processes within a single cell type. To investigate further, we used single-cell transcriptional data (~60,000 cell-types annotated profiles from GSE158722) and projected our transcriptional components onto these profiles to obtain activity scores, allowing us to assess each TC’s behavior across diverse cellular contexts after removing the first principal component to minimize background effects. Please refer to our response to remark 2.2 in the recommendations to the authors (page 14) for the results of this analysis.

References

Allen JK, Armaiz-Pena GN, Nagaraja AS, Sadaoui NC, Ortiz T, Dood R, Ozcan M, Herder DM, Haemerrle M, Gharpure KM, Rupaimoole R, Previs R, Wu SY, Pradeep S, Xu X, Han HD, Zand B, Dalton HJ, Taylor M, Hu W, Bottsford-Miller J, Moreno-Smith M, Kang Y, Mangala LS, Rodriguez-Aguayo C, Sehgal V, Spaeth EL, Ram PT, Wong ST, Marini FC, Lopez-Berestein G, Cole SW, Lutgendorf SK, diBiasi M, Sood AK. 2018. Sustained adrenergic signaling promotes intratumoral innervation through BDNF induction. Cancer Res 78 (12):3233-3242.

Ardini E, Menichincheri M, Banfi P, Bosotti R, Ponti CD, Pulci R, Ballinari D, Ciomei M, Texido G, Degrassi A, Avanzi N, Amboldi N, Saccardo MB, Casero D, Orsini P, Bandiera T, Mologni L, Anderson D, Wei G, Harris J, Vernier J-M, Li G, Felder E, Donati D, Isacchi A, Pesenti E, Magnaghi P, Galvani A. 2016. Entrectinib, a Pan–TRK, ROS1, and ALK Inhibitor with activity in multiple molecularly defined cancer Indications. Mol Cancer Ther 15:628–639.

Balood M, Ahmadi M, Eichwald T, Ahmadi A, Majdoubi A, Roversi Karine, Roversi Katiane, Lucido CT, Restaino AC, Huang S, Ji L, Huang K-C, Semerena E, Thomas SC, Trevino AE, Merrison H, Parrin A, Doyle B, Vermeer DW, Spanos WC, Williamson CS, Seehus CR, Foster SL, Dai H, Shu CJ, Rangachari M, Thibodeau J, Rincon SVD, Drapkin R, Rafei M, Ghasemlou N, Vermeer PD, Woolf CJ, Talbot S. 2022. Nociceptor neurons affect cancer immunosurveillance. Nature 611:405–412.

Bhattacharya A, Bense RD, Urzúa-Traslaviña CG, Vries EGE de, Vugt MATM van, Fehrmann RSN. 2020. Transcriptional effects of copy number alterations in a large set of human cancers. Nat Commun 11:715.

Burris HA, Shaw AT, Bauer TM, Farago AF, Doebele RC, Smith S, Nanda N, Cruickshank S, Low JA, Brose MS. 2015. Abstract 4529: Pharmacokinetics (PK) of LOXO-101 during the first-in-human Phase I study in patients with advanced solid tumors: Interim update. Cancer Res 75:4529–4529.

DOI: 10.1371/journal.ppat.1007216

Resource: Bloomington Drosophila Stock Center (RRID:SCR_006457)

Curator: @maulamb

SciCrunch record: RRID:SCR_006457

What is this?

DOI: 10.1186/s13765-025-00986-y

Resource: GraphPad Prism (RRID:SCR_002798)

Curator: @scibot

SciCrunch record: RRID:SCR_002798

What is this?

DOI: 10.1186/s13765-025-00986-y

Resource: SPSS (RRID:SCR_002865)

Curator: @scibot

SciCrunch record: RRID:SCR_002865

What is this?

Growing up in a christian household helped me out through school. I would tend to find myself more patient during tests or quizzes knowing everything is working out for Him.

why my research is gd

在时空数据库的背景下，时空依赖的函数（Spatio-Temporal Functional Dependency, STFD） 是一种描述数据中时空属性之间关系的数学工具。它扩展了传统数据库中的函数依赖（Functional Dependency, FD）概念，将时间和空间维度同时纳入考虑，用于刻画数据在时空变化中的规律性。以下是对其核心概念和意义的详细解释：

1. 时空依赖的函数的基本定义

在传统数据库中，函数依赖 \( X \rightarrow Y \) 表示属性集 \( X \) 的值唯一确定属性集 \( Y \) 的值。而在时空数据库中，时空函数依赖需要同时考虑时间（T）和空间（S）的维度。例如： - 时空函数依赖 \( X \rightarrow Y \) 可能表示：在某一时刻 \( t \) 和某一空间位置 \( s \)，属性 \( X \) 的值唯一决定属性 \( Y \) 的值。 - 更严格的定义可能涉及时空键（Spatio-Temporal Key）或时空码（Spatio-Temporal Code），即一组属性（包括时间、空间和其他属性）能够唯一标识一条时空记录。

2. 时空依赖的函数的特点

(1) 多维性

时空依赖同时考虑时间、空间和属性的关联，例如： - 示例：在物流系统中，一辆货车的实时位置（空间坐标）和时间戳（时间）共同决定了其行驶状态（如速度、货物状态）。 - 数学表达：可能表示为 \( (T, S) \rightarrow A \)，其中 \( T \) 是时间，\( S \) 是空间坐标，\( A \) 是其他属性（如温度、速度）。

(2) 动态性

时空数据是动态变化的，因此函数依赖可能随时间或空间的变化而变化： - 示例：某地区的气温（属性 \( A \)）在不同时间（\( T \)）和不同地理位置（\( S \)）下可能有不同的依赖关系。例如，\( (T, S) \rightarrow A \) 表示气温由时间和空间唯一决定。

(3) 约束冗余

时空函数依赖用于减少数据冗余和保持数据一致性。例如： - 如果 \( (T, S) \rightarrow A \) 成立，则在任何时刻 \( t \) 和位置 \( s \)，属性 \( A \) 的值必须唯一，避免重复或矛盾的数据记录。

3. 时空依赖的函数在数据库规范化中的作用

时空数据库的规范化（如时空一范式、二范式等）依赖于时空函数依赖的分析，以消除冗余并确保数据完整性。例如： - 时空一范式（ST-1NF）：要求所有时空数据以原子形式存储，消除嵌套或重复的时空记录。 - 时空二范式（ST-2NF）：在满足 ST-1NF 的基础上，确保不存在非主属性对时空键的部分依赖。 - 时空三范式（ST-3NF）：进一步消除非主属性之间的传递依赖，例如 \( T \rightarrow S \)，而 \( S \rightarrow A \)，则 \( T \rightarrow A \) 可能形成传递依赖，需通过范式化处理。

4. 时空依赖的函数的应用场景

(1) 物流与交通管理

• 示例：追踪货车的实时位置（空间）和时间，确保每辆车的行驶路径（属性）由时空键唯一确定。
• 依赖关系：\( (T, 车辆ID) \rightarrow 路径 \)，表示同一时间、同一车辆的路径唯一。

2) 环境监测

• 示例：大气污染传感器的监测数据（如PM2.5浓度）需记录时间、地理位置，且浓度值由时空键唯一决定： [ (T, 经度, 纬度) \rightarrow PM2.5 ]

(3) 社交媒体与位置服务

• 示例：用户的位置（空间）和时间戳（时间）共同决定其活动状态（如签到地点、动态内容）。

5. 时空依赖与传统函数依赖的区别

| 特性 | 传统函数依赖（FD） | 时空函数依赖（STFD） | |-------------------|---------------------------------------|----------------------------------------| | 维度 | 仅涉及属性间的依赖 | 同时涉及时间、空间和属性的依赖 | | 动态性 | 静态关系，不随时间变化 | 动态关系，可能随时间或空间变化 | | 冗余控制 | 通过消除属性冗余减少数据重复 | 通过时空键控制时空维度的冗余 | | 规范化目标 | 优化关系数据库的结构 | 优化时空数据的存储与查询效率 |

6. 时空依赖的函数的挑战

1. 复杂性：时空数据的高维性增加了依赖关系的分析难度。
2. 动态维护：时空数据随时间变化，依赖关系可能需要动态更新。
3. 存储与查询效率：时空函数依赖需平衡数据冗余与查询性能，例如索引设计需同时考虑时空维度。

总结

时空依赖的函数是时空数据库中用于描述数据在时空维度上规律性的重要工具。它通过时空键和函数依赖规则，帮助设计高效、一致的时空数据模型，减少冗余，并支持复杂时空查询（如轨迹分析、时空模式挖掘）。其核心在于将时间、空间与属性的关联纳入统一框架，为处理动态变化的地理信息、物流轨迹、环境监测等场景提供了理论基础。

Author response:

The following is the authors’ response to the original reviews

Reviewer #1 (Public review):

Summary:

The authors examine CD8 T cell selective pressure in early HCV infection using. They propose that after initial CD8-T mediated loss of virus fitness, in some participants around 3 months after infection, HCV acquires compensatory mutations and improved fitness leading to virus progression.

Strengths:

Throughout the paper, the authors apply well-established approaches in studies of acute to chronic HIV infection for studies of HCV infection. This lends rigor the to the authors' work.

Weaknesses:

(1) The Discussion could be strengthened by a direct discussion of the parallels/differences in results between HIV and HCV infections in terms of T cell selection, entropy, and fitness.

We have added a direct discussion of the parallels/differences between HIV and HCV throughout the discussion including at lines 308 – 310 and 315 -327.

Lines 308-310: “In fact, many parallels can be drawn between HIV infections and HCV infections in the context of emerging viral species that escape T cell immune responses.”

Lines: 315-327: “One major difference between HCV and HIV infection is the event where patients infected with HCV have an approximately 25% chance to naturally clear the infection as opposed to just achieving viral control in HIV infections. Here, we probed the underlying mechanism, and questioned how the host immune response and HCV mutational landscape can allow the virus to escape the immune system. To understand this process, taking inspiration from HIV studies (24), a quantitative analysis of viral fitness relative to viral haplotypes was conducted using longitudinal samples to investigate whether a similar phenomenon was identified in HCV infections for our cohort for patients who progress to chronic infection. We observed a decrease in population average relative fitness in the period of <90DPI with respect to the T/F virus in chronic subjects infected with HCV. The decrease in fitness correlated positively with IFN-γ ELISPOT responses and negatively with SE indicating that CD8+ T-cell responses drove the rapid emergence of immune escape variants, which initially reduced viral fitness. This is similarly reflected in HIV infected patients where strong CD8+ T-cell responses drove quicker emergence of immune escape variants, often accompanied by compensatory mutations (24).”

(2) In the Results, please describe the Barton model functionality and why the fitness landscape model was most applicable for studies of HCV viral diversity.

This has been added to the introduction section rather than Results as we feel that it is more appropriate to show why it is most applicable to HCV viral diversity in the background section of the manuscript. We write at lines 77-90:

“Barton et al.’s [23] approach to understand HIV mutational landscape resulting in immune escape had two fundamental points: 1) replicative fitness depends on the virus sequence and the requirement to consider the effect of co-occurring mutations, and 2) evolutionary dynamics (e.g. host immune pressure). Together they pave the way to predict the mutational space in which viral strains can change given the unique immune pressure exerted by individuals infected with HIV. This model fits well with the pathology of HCV infection. For instance, HIV and HCV are both RNA viruses with rapid rate of mutation. Additionally, like HIV, chronic infection is an outcome for HCV infected individuals, however, unlike HIV, there is a 25% probability that individuals infected with HCV will naturally clear the virus. Previously published studies [9] have shown that HIV also goes through a genetic bottleneck which results in the T/F virus losing dominance and replaced by a chronic subtype, identified by the immune escape mutations. The concepts in Barton’s model and its functionality to assess the fitness based on the complex interaction between viral sequence composition and host immune response is also applicable to early HCV infection.”

(3) Recognize the caveats of the HCV mapping data presented.

We have now recognized the caveats of the HCV mapping data at lines 354-256 “While our findings here are promising, it should be recognized that although the bioinformatics tool (iedb_tool.py) proved useful for identifying potential epitopes, there could be epitopes that are not predicted or false-positive from the output which could lead to missing real epitopes”

(4) The authors should provide more data or cite publications to support the authors' statement that HCV-specific CD8 T cell responses decline following infection.

We have now clarified at lines 352-353 that the decline was toward “selected epitopes that showed evidence of escape”.

Furthermore, we have cited two publications at line 352 that support our statement.

(5) Similarly, as the authors' measurements of HCV T and humoral responses were not exhaustive, the text describing the decline of T cells with the onset of humoral immunity needs caveats or more rigorous discussion with citations (Discussion lines 319-321).

We have now added a caveat in the discussion at lines 357-360 which reads

“In conclusion, this study provides initial insights into the evolutionary dynamics of HCV, showing that an early, robust CD8+ T-cell response without nAbs strongly selects against the T/F virus, enabling it to escape and establish chronic infection. However, these findings are preliminary and not exhaustive, warranting further investigation to fully understand these dynamics. “

(6) What role does antigen drive play in these data -for both T can and antibody induction?

It is possible that HLA-adapted mutations could limit CD8 T cell induction if the HLAs were matched between transmission pairs, as has been shown previously for HIV (https://doi.org/10.1371/journal.ppat.1008177) with some data for HCV (https://journals.asm.org/doi/10.1128/jvi.00912-06). However, we apologise as we are not entirely sure that this is what the reviewer is asking for in this instance.

(7) Figure 3 - are the X and Y axes wrongly labelled? The Divergent ranges of population fitness do not make sense.

Our apologies, there was an error with the plot in Figure 3 and the X and Y axis were wrongly labelled. This has now been resolved.

(8) Figure S3 - is the green line, average virus fitness?

This has now been clarified in Figure S3.

(9) Use the term antibody epitopes, not B cell epitopes.

We now use the term antibody epitopes throughout the manuscript.

Reviewer #1 (Recommendations for the authors):

Recommendations for improving the writing and presentation:

(1) Introduction:

Line 52: 'carry mutations B/T cell epitopes'. Two points

i) These are antibody epitopes (and antibody selection) not B cell epitopes

We have corrected this sentence at line 55 which now reads: “carry mutations within epitopes targeted by B cells and CD8+ T cells”.

ii) To avoid confusion, add text that mutations were generated following selection in the donor.

For HCV, it is unclear if mutations are generated following selection or have been occurring in low frequencies outside detection range. Only when selection by host immune pressure arises do the potentially low-frequency variants become dominant. However, we do acknowledge it is potentially misleading to only mention new variants replacing the transmitted/founder population. We have modified the sentence at line 52 to read:

“At this stage either an existing variant that was occurring in low-frequency outside detection range or an existing variant with novel mutations generated following immune selection is observed in those who progress to chronic infection”

- Lines 51-56: Human studies of escape and progression are associative, not causative as implied.

Correct, evidence suggesting that escape and progression are currently associative. We have now corrected these lines to no longer suggest causation.

- Line 65: Suggest you clarify your meaning of 'easier'?

This sentence, now at line 72, has been modified to: “subtype 1b viruses have a higher probability to evade immune responses”

(2) Results:

- Line 147: Barton model (ref'd in Intro) is directly referred to here but not referenced.

The reference has been added.

- The authors should cite previous HIV literature describing associations between the rate of escape and Shannon Entropy e.g. the interaction between immunodominance, entropy, and rate of escape in acute HIV infection was described in Liu et al JCI 2013 but is not cited.

We have now cited previous HIV research at line 147-151, adding Liu et al:

“Additionally, the interaction between immunodominance, entropy, and escape rate in acute HIV infection has been described, where immunodominance during acute infection was the most significant factor influencing CD8+ T cell pressure, with higher immunodominance linked to faster escape (27). In contrast, lower epitope entropy slowed escape, and together, immunodominance and entropy explained half of the variability in escape timing (27).”

- Line 319: The authors suggest that HCV-specific CD8 T cell response declines following early infection. On what are they basing this statement? The authors show their measured T cell responses decline but their approach uses selected epitopes and they are therefore unable to assess total HCV T cell response in participants (Where there is no escape, are T cell magnitudes maintained or do they still decline?). Can the authors cite other studies to support their statement?

We have now clarified that the decline was toward “selected epitopes that showed evidence of escape”. Furthermore, we also cite two studies to support our findings.

- Throughout the authors talk in terms of CD8 T cells but the ELISpot detects both CD4 and CD8 T cell responses. I suggest the authors be more explicit that their peptide design (9-10mers) is strongly biased to only the detection of CD8 T cells.

To make this clearer and more explicit we have now added to the methods section at line 433-435:

“While the ELISpot assay detects responses from both CD4 and CD8 T cells, our peptide design (9-10mers) is strongly biased toward CD8 T-cell detection. We have therefore interpreted ELISpot responses primarily in terms of CD8 T-cell activity.”

- The points made in lines 307-321 could be more succinct

We have now edited the discussion (lines 307 – 321) to make the points more succinct (now lines 307-323).

Minor corrections to text, figures:

- Figure 2: suggest making the Key bigger and more obvious.

We have now made the key bigger and more obvious

- Figure 3 A & D....is there an error on the X-axis...are you really reporting ELISpot data of < 1 spot/10^6? Perhaps the X and Y axes are wrongly labelled?

Our apologies, there was an error with the plot in Figure 3 and the X and Y axis were wrongly labelled. This has now been resolved.

- Figure 5: As this is PBMC, remove CD8 from the description of ELISpot. 

We have now removed CD8 from the description of ELISpot in both Figure 5 and Figure S3

Reviewer #2 (Public review):

Summary:

In this work, Walker and collaborators study the evolution of hepatitis C virus (HCV) in a cohort of 14 subjects with recent HCV infections. They focus in particular on the interplay between HCV and the immune system, including the accumulation of mutations in CD8+ T cell epitopes to evade immunity. Using a computational method to estimate the fitness effects of HCV mutations, they find that viral fitness declines as the virus mutates to escape T-cell responses. In long-term infections, they found that viral fitness can rebound later in infection as HCV accumulates additional mutations.

Strengths:

This work is especially interesting for several reasons. Individuals who developed chronic infections were followed over fairly long times and, in most cases, samples of the viral population were obtained frequently. At the same time, the authors also measured CD8+ T cell and antibody responses to infection. The analysis of HCV evolution focused not only on variation within particular CD8+ T cell epitopes but also on the surrounding proteins. Overall, this work is notable for integrating information about HCV sequence evolution, host immune responses, and computational metrics of fitness and sequence variation. The evidence presented by the authors supports the main conclusions of the paper described above.

Weaknesses:

One notable weakness of the present version of the manuscript is a lack of clarity in the description of the method of fitness estimation. In the previous studies of HIV and HCV cited by the authors, fitness models were derived by fitting the model (equation between lines 435 and 436) to viral sequence data collected from many different individuals. In the section "Estimating survival fitness of viral variants," it is not entirely clear if Walker and collaborators have used the same approach (i.e., fitting the model to viral sequences from many individuals), or whether they have used the sequence data from each individual to produce models that are specific to each subject. If it is the former, then the authors should describe where these sequences were obtained and the statistics of the data.

If the fitness models were inferred based on the data from each subject, then more explanation is needed. In prior work, the use of these models to estimate fitness was justified by arguing that sequence variants common to many individuals are likely to be well-tolerated by the virus, while ones that are rare are likely to have high fitness costs. This justification is less clear for sequence variation within a single individual, where the viral population has had much less time to "explore" the sequence landscape. Nonetheless, there is precedent for this kind of analysis (see, e.g., Asti et al., PLoS Comput Biol 2016). If the authors took this approach, then this point should be discussed clearly and contrasted with the prior HIV and HCV studies.

We thank the reviewer for pointing out the weakness in our explanation and description of the fitness model. The model has been generated using publicly released viral sequences and this has been described in a previous publication by Hart et al. 2015. T/F virus from each of the subjects chronically infected with HCV in our cohort were given to the model by Hart et al. to estimate the initial viral fitness of the T/F variant. Subsequent time points of each subject containing the subvariants of the viral population were also estimated using the same model (each subtype). For each subject, these subvariant viral fitness values were divided by the fitness value of the initial T/F virus (hence relative fitness of the earliest time points with no mutations in the epitope regions were a value of 1.000). All other fitness values are therefore relative fitness to the T/F variant.

We have further clarified this point in the methods section “Estimating survival fitness of viral variant” to better describe how the data of the model was sourced (Lines 465-499).

To add to the reviewer’s point, we agree that sequence variants common to many individuals are likely to be well-tolerated by the virus and this event was observed in our findings as our data suggested that immune escape variants tended to revert to variants that were closer the global consensus strain. Our previous publications have indicated that T/F viruses during transmission were variants that were “fit” for transmission between hosts, especially in cases where the donor was a chronic progressor, a single T/F is often observed. Progression to immune escape and adaptation to chronic infection in the new host has an in-between process of genetic expansion via replication followed by a bottleneck event under immune pressure where overall fitness (overall survivability including replication and exploring immune escape pathways) can change. Under this assumption we questioned whether the observation reported in HIV studies (i.e. mutation landscapes that allow HIV adaptation to host) also happens in HCV infections. Furthermore, cohort used in this study is a rare cohort where patients were tracked from uninfected, to HCV RNA+, to seroconversion and finally either clearing the virus or progression to chronic infection. Thus, it is of importance to understand the difference between clearance and chronic progression.

Another important point for clarification is the definition of fitness. In the abstract, the authors note that multiple studies have shown that viral escape variants can have reduced fitness, "diminishing the survival of the viral strain within the host, and the capacity of the variant to survive future transmission events." It would be helpful to distinguish between this notion of fitness, which has sometimes been referred to as "intrinsic fitness," and a definition of fitness that describes the success of different viral strains within a particular individual, including the potential benefits of immune escape. In many cases, escape variants displace variants without escape mutations, showing that their ability to survive and replicate within a specific host is actually improved relative to variants without escape mutations. However, escape mutations may harm the virus's ability to replicate in other contexts. Given the major role that fitness plays in this paper, it would be helpful for readers to clearly discuss how fitness is defined and to distinguish between fitness within and between hosts (potentially also mentioning relevant concepts such as "transmission fitness," i.e., the relative ability of a particular variant to establish new infections).

Thank you for pointing out the weakness of our definition of fitness. We have now clarified this at multiple sections of the paper: In the abstract at lines 18-21 and in the introduction at lines 64-69.

These read:

Lines 18-21: “However, this generic definition can be further divided into two categories where intrinsic fitness describes the viral fitness without the influence of any immune pressure and effective fitness considers both intrinsic fitness with the influence of host immune pressure.”

Lines 64-69: “This generic definition of fitness can be further divided into intrinsic fitness (also referred to as replicative fitness), where the fitness of sequence composition of the variant is estimated without the influence of host immune pressure. On the other hand, effective fitness (from here on referred to as viral fitness) considers fundamental intrinsic fitness with host immune pressure acting as a selective force to direct mutational landscape (19)[REF], which subsequently influences future transmission events as it dictates which subvariants remain in the quasispecies.”

One concern about the analysis is in the test of Shannon entropy as a way to quantify the rate of escape. The authors describe computing the entropy at multiple time points preceding the time when escape mutations were observed to fix in a particular epitope. Which entropy values were used to compare with the escape rate? If just the time point directly preceding the fixation of escape mutations, could escape mutations have already been present in the population at that time, increasing the entropy and thus drawing an association with the rate of escape? It would also be helpful for readers to include a definition of entropy in the methods, in addition to a reference to prior work. For example, it is not clear what is being averaged when "average SE" is described.

We thank the reviewer to point out the ambiguity in describing average SE. This has been rectified by adding more information in the methods section (Lines 397 to 400):

“Briefly, SE was calculated using the frequency of occurrence of SNPs based on per codon position, this was further normalized by the length of the number of codons in the sequence which made up respective protein. An average SE value was calculated for each time point in each protein region for all subjects until the fixation event.”

To answer the reviewer’s question, we computed entropy at multiple time points preceding the observation in the escape mutation. The escape rate was calculated for the epitopes targeted by immune response. We compared the average SE based on change of each codon position and then normalised by protein length, where the region contained the epitope and the time it took to reach fixation. We observed that if the protein region had a higher rate of variation (i.e. higher average SE) then we also see a quicker emergence of an immune escape epitope. Since we took SE from the very first time point and all subsequent time points until fixation, we do not think that escape mutations already been present at the population would alter the findings of the association with rate of escape. Especially, these escape mutations were rarely observed at early time points. It is likely that due to host immune pressure that the escape variant could be observed, the SE therefore suggest the liberty of exploration in the mutation landscape. If the region was highly restrictive where any mutations would result in a failed variant, then we should observe relatively lower values of average SE. In other words, the higher variability that is allowed in the region, the greater the probability that it will find a solution to achieve immune escape.

Reviewer #2 (Recommendations for the authors):

In addition to the main points above, there are a few minor comments and suggestions about the presentation of the data.

(1) It's not clear how, precisely, the model-based fitness has been calculated and normalized. It would be helpful for the authors to describe this explicitly. Especially in Figure 3, the plotted fitness values lie in dramatically different ranges, which should be explained (maybe this is just an error with the plot?).

We have now clarified how the model-based fitness has been calculated and normalized in the method section “Estimating survival fitness of viral variants” at line 465-472.

“The model used for estimating viral fitness has been previously described by Hart et al. (19). Briefly, the original approach used HCV subtype 1a sequences to generate the model for the NS5B protein region. To update the model for other regions (NS3 and NS2) as well as other HCV subtypes in this study, subtype 1b and subtype 3a sequences were extracted from the Los Almos National Laboratory HCV database. An intrinsic fitness model was first generated for each subtype for NS5B, NS3 and NS2 region of the HCV polyprotein. Then using, longitudinally sequenced data from patients chronically infected with HCV as well as clinically documented immune escape to describe high viral fitness variants, we generated estimates of the viral fitness for subjects chronically infected with HCV in our cohort.”

Our apologies, there was an error with the plot in Figure 3. This has now been resolved.

(2) In different plots, the authors show every pairwise comparison of ELISPOT values, population fitness, average SE, and rate of escape. It may be helpful to make one large matrix of plots that shows all of these pairwise comparisons at the same time. This could make it clear how all the variables are associated with one another. To be clear, this is a suggestion that the authors can consider at their discretion.

Thank you for the suggestion to create a matrix of plots for pairwise comparisons. While this approach could indeed clarify variable associations, implementing it is outside the scope of this project. We appreciate the idea and may consider it in future studies as we continue to expand on this work.

presumably because y torques are disallowed, for conservation of m

02/04 : FUSIONNER IL Y A 1 moiis

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Referee #3

Evidence, reproducibility and clarity

The study by Yasmin & colleagues tackles an important question, what is the molecular nature of specificity that arises from otherwise highly similar proteins. In this case, they focus on two proteins with epigenetic activity, DNMT3A and DNMT3B, using a functional readout of their ability to methylate DNA in a model that specifically requires DNMT3B function at a subset of the genome, i.e. DNMT3B-dependent regions. This includes characterizing the role of DNMT3B in these regions in stem cell-to-embryoid body differentiation experiments, using genomic assays to probe DNA methylation dynamics. By removing DNMT3B and ectopically expressing a variety of sophisticated mutants, the authors attempt to show the protein domains required for specificity. However, several questions remain about the strength of the data to support the claims, particularly with respect to the ectopically expressed mutant DNMT3B proteins.

Major comments:

1. The strength of this study is in the very nice addback strategy for probing DNMT3B specificity, where the designed mutants seem highly useful to ask critical questions. However, the stability of the mutant proteins (i.e. cellular expression levels) and question of protien levels in the nucleus are insufficient evidence for the conclusions stated in the paper. With the exception of the Dnmt3b1-KI clones (top panel fig 3B), it seems like most mutants are not expressed at wildtype levels. How much of the results are driven by differences in expression, relative to the wildtype protein? While this a technically challenging problem, there are various methods to establish roughly matched expression such as integration into a stronger locus for expression or tuning the promoter sequence for expression of a construct. Given the mutants are key for the main conclusions of the study, this seems critical to address, though would substantially increase effort required for the paper.
2. Characterisation of the datasets supporting effects seems lacking in several instances. For example, the text states that DMNT3B null cells behave similarly to wildtype cells but supporting data (FigS2A-C) or that Dnmt3b1-KI and Dnmt3b3-KI behave normally with respect to differentiation (FigS4C), seem insufficient evidence for this, with largely summary plots supporting the statements. Similarly, several of the MBDseq datasets seem discordant, such as FigS2G or FigS4D(right panel) where the x-y axis for scatterplots are clearly not equivalent suggesting global effects on the data. The authors should also clearly demonstrate the levels of DNMT3A throughout their EB timecourse for mutant lines, where this seems especially important given their readout is DNA methylation dynamics.
3. An optional analysis that could support the claims of the paper would be to contrast the effect sizes in their cellular model with existing datasets that profile DNA methylation dynamics in vivo, where these have been captured at early developmental levels. This would nicely show that their functional readout in relation to normal processes.

Minor comments:

1. Several figures require addressing, listed here:
   • Fig1B the points are not so legible when overplotted, consider reducing the size of the datapoint circles or turning into "*" representations.
   • Fig4I seems not to have a figure legend.
   • FigS2G should be represented as a square and not as a rectangle, as this visually condenses on axis relative to the other.
   • FigS3A is unclear, could more be added to the legend to describe what exactly is the schematic representing?
   • FigS3D the axis seems not aligned with the barplot positioning?
2. The Dnmt3b-PAS-KI clone 1 does not seem to well-cluster with the 2nd and 3rd clone, could this be a clonal effect at the global level?
3. The text states (page 7, third paragraph) that in the two differentiation models the identity of the CGIs that exhibit different dynamics largely match, though no direct comparison (i.e. delta-delta effect) is show, rather a summary plot of either is presented side-by-side. This seems insufficient evidence of the statement, and a direct comparison of the fold changes would help.
4. The clonal effect sizes would benefit from more quantitative comparisons throughout the manuscript, broken down to raw data. For example, the statement in page 8 paragraph two that the effects on independent clones were fully consistent is show from largely a PCA plot, which seems incomplete evidence that replicates behave consistently. More transparent analysis of clonality from the raw data would be helpful for the reader.
5. The statement in the discussion that the authors experimental system affords 'homogeneous and highly synchronised onset and progression of XCI", but it seems unclear from the data provided in the manuscript that cells exhibit differentiation in a synchronized manner. Softening this statement seems apt here.

Significance

The question of specificity is highly important, not just to the field of epigenetics and DNA methylation where this study is particularly relevant, but also to a broader audience. Many of our cells proteins are highly homologous but have nevertheless highly divergent activities. Molecular explanations of specificity are therefore critical to understand phenotype and how traits can be acquired through gene paralogue evolution. Here, by focusing on a particularly apt example, the similar DNMT3A/B proteins, this study offers a nice breakdown with the potential to tie back the results to locus specific activity in the genome. The strongest aspect is the comparison of sophisticated mutants in a matched experimental setting, however, the experiments do not seem sufficient to support the broad conclusions of the study. From a genomics standpoint, the experimental setup is impressive, but requires additional work to show that matched expression levels of wildtype/mutant proteins still maintains the phenotypes reported.

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Referee #1

Evidence, reproducibility and clarity

Summary:

Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate).

In this study the authors used a previously established mESCs iXist-ChrX cell line to investigate the mechanisms underpinning developmentally regulated CGI methylation through differentiation into embryoid bodies and MBD-seq profiling. They show that their system recapitulates developmentally regulated DNA methylation at CGIs on the X chromosome and autosomes before using a knockout and rescue system to determine that this is dependent on the DNMT3B1 isoform. Through domain swap experiments, they then go on to suggest that this requires the PWWP-ADD domain and that the N-terminal region of DNMT3B1.

Major comments:

• Are the key conclusions convincing?
• Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?
• Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.
• Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments.
• Are the data and the methods presented in such a way that they can be reproduced?
• Are the experiments adequately replicated and statistical analysis adequate?

Overall, this is an interesting study that provides insights into the role of different domains of DNMT3B in establishing DNA methylation patterns during development. However, it suffers from poor annotation and description throughout. More specific comments are:

1. The manuscript provides some details as to the replication of experiments but fails to show the replicate data in the vast majority of cases. Instead, representative data is presented alongside global correlations for some experiments. However, global correlations can mask differences between replicates. The data from all replicates should be shown in the manuscript and clear details provided regarding the replication strategy in the methods and figure legends. For example, were the different knock-in clones generated from independent DNMT3B knockout clones? For individual experiments this would be a minor point however, collectively this is a major point. It is particularly important given the variation highlighted in point 2 below.
2. Different DNMT3B knock-in clones show high variability in expression levels. Have the authors investigated whether the discrepancy in protein levels contributes to the differences in methylation patterns seen? A non-comprehensive list of examples is given in the minor comments section.
3. There is variation in the level of expression of different DNMT3B constructs detected by Western blot. Could this be caused by differences in protein stability? It would be helpful to see an assessment of protein stability to determine whether this contributes to the variable expression. For example, the DNMT3B3-KI has lower levels than the DNMT3B1-KI (Figure 3B) and this could potentially contribute to the differences in DNA methylation observed.
4. The study lacks statistical tests to support the conclusions drawn from the analysis of the sequencing data. For example, are the differences in CGI methylation between DNMT3B1-KI and DNMT3B3-KI statistically significant? For individual analyses this would be a minor comment but given the lack throughout the study, this classes as a major comment.
5. Chromatin marks play major roles in DNMT3A and DNMT3B recruitment (Tibben & Rothbart 2024) and the N-terminal region, PWWP and ADD domains have direct or indirect chromatin reading activity. However, the manuscript does not detail the chromatin environment of the CGIs studied. This could potentially be addressed through experiments, analysis of existing data or discussion.
6. As the authors state in their discussion, MBD-seq may only detect very dense methylation. This could potentially obscure lower levels of DNA in some conditions. Analysis of a few loci by alternative methods, such as targeted bsPCR or EM-PCR would help support key results and rule out the possibility that some of the rescue constructs are able to partially rescue DNA methylation patterns.
7. While some expression data is shown, there is currently no investigation as to whether the different DNMT3B domain swap constructs have impact on transcriptional silencing on Xi/autosomal sites.
8. The text relating to the section on the PWWP-ADD domain is very brief and currently unclear. Expanding this section and specifying which data are derived from ES cells vs differentiated cells would help to clarify. We also suggest that it would be clearer to move this data to the main figure and to move the results of the catalytic domain experiments, which are negative, to the supplementary.
9. The authors suggest that PWW-ADD domain region of DNMT3B is required for developmental methylation of Xi and autosomal CGIs. However, there is no further dissection as to whether this requirement is due to the PWWP, ADD or the intervening region.
10. Throughout the text autosomal and Xi CGIs are both analysed. The introduction highlights SMCHD1 as important in methylating CGIs on Xi but that PCGF6 complex for the autosomal targets. This suggests separate mechanisms target DNMT3B to these loci. However, based on the results presented here, these two different types of targets have similar DNMT3B1 domain requirements. It would be interesting to see discussion with regard to this point in the manuscript.

Minor comments:

• Specific experimental issues that are easily addressable.
• Are prior studies referenced appropriately?
• Are the text and figures clear and accurate?
• Do you have suggestions that would help the authors improve the presentation of their data and conclusions?
• Introduction: Methylation of tumour suppressor gene CGI promoters is mentioned alongside examples of developmental CGI methylation. It would be useful to place rare event in context. Methylation of CGIs is common in cancer but very few of these correspond to tumour suppressor genes. It would also be useful to discuss how DNMT3B might be involved in these events.
• Introduction: The authors mention the DNMT3A1's N-terminal region recruits it to H2AK119ub1. It would also be useful to discuss recent work on the N-terminal of DNMT3B which can bind HP1-alpha to mediate H3K9me3 recruitment (Taglini et al 2024). This paper is currently only cited in the discussion.
• Throughout the manuscript DNMT3B1 is referred to as the catalytic isoform, however it is not the only catalytic isoform of DNMT3B.
• Page 5: 'the presence of non-catalytic isoforms, notably DNMT3L and DNMT3B3'. This statement incorrectly suggests DNMT3L is a non-catalytic isoform of DNMT3B.
• The authors refer to the protein complex as heterodimers when referring to a DNMT3B -DNMT3L or DNMT3B3-DNMT3A complex. However, the consensus of structural studies is that they form tetramers.
• Marker sizes are not included on blots with the exception of Figure 3B.
• Western blots are cropped closely. It would be useful if full blots were shown in the supplementary particularly given the presence of extra bands in some blots and different DNMT3B isoforms.
• Details about the Western blot methods (eg visualisation and antibodies) are missing.
• It would be useful if the size of different groups was annotated in plots. This is given in Figure 1D for example but not in Figure 2E.
• The authors show data confirming DNMT3B knockout by western blot. However, they do not provide details of the strategy for generating the knockout (ie vector used, sgRNAs, screening process). Could the authors also provide additional details as to whether there is any sequencing to confirm the results on the knockout?
• Page 9: The authors state "Since Dnmt3b-/- cells have normal levels of DNMT3A", but show no data to support this statement. This is particularly relevant as they have generated new DNMT3B knockout clones for this study so they cannot be assumed to behave similarly to previous studies.
• Figure 1B: There is poor PCA clustering between replicates at some timepoints, particularly Day 8.
• Figure 1G: Different colours are used for the different timepoints in this figure. We are unclear if this is deliberate.
• Page 7: The authors state "... the two categories largely matched between the two differentiation systems (Figure S1C-G)". It is difficult to draw this interpretation from the data presented as no explicit overlap is shown.
• Figure 2E: MBD-seq peaks for DNMT3B-independent loci in WT sample have a dip in the middle of the peak (also seen in Figure 5F). Could the authors explain why this might be and why it only appears in some experiments?
• Clarification as to whether the DNMT3B -dependent and -independent loci are located on chromosome X or autosomes (e.g.: Figure 2D, E).
• Figure S2C: the chromatin RNA-seq is not explained in the text or figure.
• Figure S2E: Suggests WT is one of 5 replicates. The authors should show all replicates.
• Figure S2H: What genes are included in the metaplot?
• DNMT3B rescue knock-in clones. As shown in figure 2A, there are two different Dnmt3b-/- cell line clones. Could the authors clarify whether all the CRISPR KI clones are produced from the same parental Dnmt3b-/- cell line clone?
• Figure 3B: The two clones shown for Dnmt3b3-KI have variable expression. Do the individual replicates for the Dnmt3b3-KI clones show similar methylation patterns?
• Figure 3E: in the MBD-seq metaplots, there is a peak present at -/+ 4kb. What are these peaks and why do they appear at 4kb distance? Similar peaks are seen in other metaplots.
• In figure 3F, the signal for both Dnmt3b1-KI and Dnmt3b3-KI at DNMT3B-independent CGIs is higher than in the KO. This suggests that these may not be DNMT3B independent but this point is unaddressed in the text.
• Figure S3A: it is currently unclear what has been modelled in this figure, adding labels of what has been plotted along the x- and y- axis may aid in understanding.
• Figure 4A: Dnmt3b1-3a-Cat-KI appears very highly expressed. Is the WT shown the endogenous protein? Could this higher expression be because the chimeric protein is more stable than DNMT3B1? There are also multiple bands on this blot.
• Plots panels are inconsistently ordered, e.g.: Figure 3F is dependent then independent. 4F is independent then dependent.
• Figure 4G: the expression level of the Dnmt3b-PAS-KI varies significantly between the clones shown. There are also two bands on the blot, both for the wt and KI. Clarify if WT is endogenous.
• Figure 4H: The figure lacks a legend to indicate the scale of the colour density used.
• Figure 4F,H: Could the authors clarifying what data (clones and number of replicates) are presented in the representative plots. Does the different protein levels between the clones result in any differences in DNA methylation?
• Page 12: The authors cite Boyko et al when discussing potential differences between the ADD domains of DNMT3A and B. However, they do not cite the study of Lu et al., 2023 (https://doi.org/10.1093/nar/gkad972) which reaches a different conclusion.
• Figure S4A: The position of the 750 residue is inconsistent across the isoforms in this schematic.
• Figure 5A: Schematic suggests the chimeric protein is DNMT3A2(N)-3B. However, DNMT3A2 lacks the N terminal region so presumably this should be DNMT3A1(N)-3B. This applies other figure panels using this construct.
• Figure 5B: Many lanes on this blot are unlabelled and it would be useful to clarify what these extra lanes show.
• Figure 5C: For Dnmt3a2(N)3b-KI the levels of methylation appear to be lower than Dnmt3b-/- and it would be useful to understand why this might be the case.
• Figure 5D: would be helpful to indicate which CGIs are DNMT3B dependent and independent.
• Figure 5F: Dnmt3a2(N)3b-KI data not included for the autosomal peaks
• Figure 5G, H: It is difficult to see if there are any differences between the deletions in this heatmap. For example, it appears that levels of methylation on autosomal DNMT3B-dependent loci are very similar between the KO and rescue constructs. ∆D also appears to have a lesser effect than the other deletions on the Xi CGIs. A more quantitative representation of the data would help with interpretation.
• S5E has different colour scale to other heatmaps. Red is low and in other heatmaps red is high.
• Figure S6C: sequence conservation is shown for primates. However as mouse Dnmt3b is used throughout the paper, including the mouse NT would be a useful comparison. This is particularly relevant given that the NT is the region that varies the most between mouse and human proteins (Molaro et al 2020 https://doi.org/10.1093/molbev/msaa044 ).
• Figure S6D: There is variable expression levels between the clones of the different deletions. The deletion ∆C is also not shown in this figure meaning that no data is shown to support the statement that it is unstable.
• It would be useful to clarify in the text that "deletion of residues 98-146" corresponds to ∆C. It is also unclear why MBD-seq data for this deletion was included if it is unstable.
• Discussion, page 15: The authors propose that DNMT3B could directly bind to H3K9me3. However, a study they cite, Taglini et al., 2024 (Figure S8C, D), suggests this is not the case.
• Discussion: When discussing regulatory element methylation on Xi. An uncited statement is included: 'This observation may help to explain a prior observation that loss of DNMT3B1 alone does not result in significant de-repression of Xi during embryogenesis'. However this model appears contradictory to the observation that DNMT1 is not required for Xi silencing, given that DNMT1 KO embryos would be expected to have very low DNA methylation (eg Sado et al 2000, https://doi.org/10.1006/dbio.2000.9823 and Sado et al 2004, https://doi.org/10.1242/dev.00995).

Referee cross-commenting

Having reviewed the comments of the other reviewers, we agree that they are very similar and we have no issues with them. We note that Reviewer 3 notes that considering nuclear protein levels is important in the context of this study and we agree that this is an important additional consideration that we did not consider in our review.

Significance

The manuscript is an interesting study on the role of DNMT3B in X inactivation and development. It will be of interest to scientists who work on these fundamental processes. In addition, given the roles of DNA methylation in gene regulation, cancer, aging and disease more generally the findings are likely to be of interest to many others.

Our expertise is in epigenetics and its regulation in disease, with a specific focus on DNA methylation and DNMTs.

Il faut devellopper ces statistiques: genre, age... ainsi que le terme de union amoureuse. Cela est trop vague, on ne peut rien déduire de cela. Et comparer ces stats avec des stats de relations qui naissent hors des sites internet afin d'en degager des informations précises et justes

Il est important d'expliquer en quoi ces sites differents les uns des autres. Les cibles et attentes y sont tous differentes et ne pas prendre cela en compte n'est pas objectif

Otra de las referecias de Anjana en su charla y sobre como opera la OOP.

Cada icono en una interfaz es un objeto, al que se le dan atributos que se relacionan y se le da una funcionalidad o método

Si creo que lo importante en la programación orientada en objetos es el mensaje, pueden existir o crearsen diversos objetos con atributos propios pero debe de haber un mensaje que interconecte y de una funcionalidad

Miles de celulas componen un organo NO una sola, asi como el cuerpo consta de varias partes manos, pies etc, pero todo es un conjunto compuesto de varias partes.

Como en el cerebro de crean relaciones neuronales el llevan los estimulos en esta caso el mensaje, y de este modo cada celula es un mundo propio el cual tiene impreso dentro de si un fin, este junto con las otras celulas se comunica para realizar la función para la que fue creada.

pienso que algunos inventos estan basados en la estructura y el funcionamiento del cuerpo humano, por ejemplo uno de los componentes esenciales de un auto es el carburador o cuerpo de injección que en el cuerpo humano se asemeja al corazón.

Realmente también pensaba en que iba encaminada a los objetos, creía que con este paradigma se podian ahcer que los objetos se movieran de un lado a otro.

Ahora ya entendiendo un poco más del tema, pienso en lo que creia hace tan solo unas 2 clases y no dejo de reirme. Tal vez a muchos nos pasó lo mismo.

Lo interesante de Kay, es que no solamente lleva la computación a nivel del cuerpo humano, células, organos y tejidos para dar un ejemplo de la comunicación entre computadoras, sino que también lo víncula con las matemáticas y la teoría de los conjuntos, donde una clase A, tiene unas caracteristicas que pueden ser comparadas con la clase B, pero a su vez pueden formar la clase C. Al final puede resultar que son compartidarias de la clase Do E, hasta contener otros conjuntos que pueden comunicarse entre ellos para formar el conjunto general.

Kay tenia las mismas ideas de ver al computador y sus programas, de igual manera que Vakil en el video de paradigmas, ya que ambos hacen la compración entre cuerpo humano, y células. Sin embargo, aqui es importante revisar, cual paradigma podria clasificarse, ya que no todos son flexibles.

Calculo Lambda Comportamiento funcional de ver la computación, términos de comportamientos. (Javascript) operadores lógicos modelos parecido a Erlang (Lenguaje) enfocado al paso de mensajes informales donde se trata explicar mediante flujo de datos, donde se pueden comunicar entre si. Restrictiva para el uso de datos maneja encriptación de la información ya que es confidencial y de uso personal.

Son sistemas muy pequeños que pueden llegar a ser muy complejos dependiendo su creación, esta programación busca modelar servicios amigables para uso de las personas del comun.

Lenguaje de objetos desarrollado por Alan, el hace referencia a los operadores boléanos Se usan en bases de datos y motores de búsqueda. Son útiles para encontrar resultados más específicos y jerarquizar los más relevantes como los dice Anjana Vakil, nos ayuda crear una estructura mas robusta al momento de programar.

Segun Erlang (Lenguaje), Gestionar la comunicación mensajería (conexión entre personas). De forma de programación pequeños objetos que se pasan entre si y que pueden ser remplazados fácilmente si falla alguno EJ: Muere una célula y es remplazada por una nueva. es una forma de metáfora para explicar la forma de programación de objetos

La programación de objetos busca organizar los datos y modelarlos para que sea mas fácil su interacción para ellos necesita tener una sistema Se busca que los problemas complejos en la programación se han mas fáciles, en un lenguaje mas sencillo para ser interpretado por la maquina y también por el programador.

Para eso es necesario contar con valores específicos como una clasificación según su importancia y así lograr modelar los datos en una estructura de forma correcta, hay distintas alternativas y formas para llamar (Nombre del método). Uno puede escoger el método a ejecutar.

Una computadora no es como la estructura de un reloj (rígida), ya que es un (Modelo programación imperativa) y si llegara a presentar un error es difícil de corregir.

Mientras una computadora es flexible a los cambios y puede tener varios programas mas pequeños y de esos pequeños unos mas diminutos. Mas fácil de interactuar maquina y humano.

ALAN KAY, Fundador de la programación enfocada a objetos. Fue uno de los principales desarrolladores del lenguaje SMALLTALK que uso como un sistema de programación para modelar objetos. También para facilitar su trabajo creo un prototipo de libro para poder acceder y crear y desarrollar programación sin importar el sitio o el momento.

El estudio otras cosas (Biología) que no era nada relacionado con computadoras, el aprendió la programación inicialmente empíricamente y lo asocio sus estudios con metáforas de las células Donde relaciona la programación (DATOS) como si fueran células y órganos Modelo de programación orgánico y flexible a los cambios.

Con la metáfora de las células, @Alan Kay quería demostrar, más que la forma como se unían unas a otras para conformar grupos de células más grandes, tejidos y órganos; a el le interesaba la interacción que existe entre estas, tanto en el interior como en el exterior. Como lo plantea Anjana Vakil, en el POO en las células se crean protocolos de comunicación a nivel molecular entre células que permiten que todo el sistema funcione. Min. 15:54 Esto significa que, se tienen estas pequeñas unidades de células y gracias a este protocolo de mensajería de receptores moleculares, se construye un sistema mucho más grande e intercomunicado.

En la POO el "método" es importante, ya que, es con esté elemento con el cual se construye el mensaje y se le ordena cómo debe traer la información o lo que debe traer. Según los vídeos analizados de @Anjana-Vakil, se presentan algunos ejemplos de métodos en la POO, tales como: "Send" "do" "es amigo de" entre otros, los cuales podemos ver aquí una representación gráfica de como iría dentro del mensaje https://imgur.com/8VqmLIF

Para poder modelar objetos y en general, para escribir código y adentrarse al mundo de la Programación Orientada a Objetos es necesario tener ciertas nociones con la forma, la estructura y las distintas particularidades de los lenguajes de programación. Aquí tenemos in recurso de información que nos da algunas nociones relacionadas con la escritura de código orientado a objetos usando Python

Para Alan Kay el mensaje era clave en la forma cómo e comunicaban las células entre si, y quiso transferir esta analogía a la POO, buscando crear programas mucho más grandes, flexibles, moldeables y escalables. Capaces de funcionar bajo la premisa de si un pequeño objeto se daña, esté sea remplazable por otro, como pasaría en las células del cuerpo.

Cuando estaba revisando los videos relacionados en este documento para la construcción de los mapas mentales, también pensaba que la POO consistía como en una forma de programación para las cosas. Pensaba que la POO era similar al Internet de las Cosas, pensaba que era una forma de comunicación y conexión entre los objetos(las cosas). Pero cuando se reviso el recurso de información, comprendí que los objetos de la POO no son objetos propiamente dichos, sino son elementos tienen clases e instancias que conforman otros elementos más grandes a nivel computacional. También se puede echar un vistazo a este recurso: https://profile.es/blog/que-es-la-programacion-orientada-a-objetos/

Entiendo que el evaluar es colocar en la caja la sintaxis de lo que se quiere realizar y el le arroja una pre evaluación antes de lanzar el comando para identificar algún error. Después con el botón inspeccionar se lanza y además se puede visualizar el código fuente del total de la acciona

Este termino describe muy bien este ejercicio de realizar una lectura anotada ya que se interactúa en el momento con el texto, también cuando realizamos código y a la vez podemos ver le interface como en HEDGEDOC.

Profe Offray, en este ejercicio me queda una duda y es que en la explicación dice que si 4 esta entre 0 y 10, em mensaje me da verdadero, pero que si cambio el 4 por un 3, me da falso. Pero en teoría sería también verdadero, porque 3 también esta entre 0 y 10.

Al realizar este ejercicio, una vez le di inspeccionar, me sale el texto tsubrayado, pero tal cual como se escribe (comillas, corchetes cuadrado, etc), eso es un error o es la forma incorrecta de escribirlo?

Ya que al revisar la instrucción dice que se debe separa con una coma, pero se ve claramente que lo separa el punto.

Al realizar este ejercicio, me sale en la pestaña, "Preview" el número 13, pero, ¿Qùe significa el número 13, a que hace realación?

Intente realizar el ejercicio, pero me genera error.

Exactamente, ¿Qué es una jerigonza y como detectarla?

¿Que puede significar este instrucción? Al realizar el ejercicio, me queda en blanco la página.

¿Esto tiene que ver con las opciones de busqueda al momento de abrir un campo nuevo, donde podemos escoger si es texto, Pharo, Pictura, Element?

Docente @offray para este caso un mensaje se interpretaría 3 (anObjecto) + (aMessage) 2 (anotherobject)?

Docente @offray ¿Cómo aplica la lógica receiver, selector, arguments para este carácter?

Docente @offray ¿Cómo aplica la lógica receiver, selector, arguments para este carácter?

La herramienta es muy buena porque nos permite ver la información en diferentes formas de representación, dependiendo de la ventana a la cual se le haga clic, se puede ver la misma información, pero esta vez desde el código fuente, podemos interactuar con los cambios de presentación. En un texto estático esta acción sería imposible de ejecutar. Ahora la inquietud es: ¿Es posible hacer esta acción con los demás recursos que encuentro en la red si lo descargo como imagen en Glamorustolkin y Grafoscopio respectivamente?

Al ejecutar este mensaje en Pharo, se abre un mensaje incrustado mediante un triangulito (ver imagen adjunta), ¿pero que significa este mensaje en la construcción del código? ¿lo debo escribir? o ¿este se construye automáticamente a medida que escribo el mensaje?

Profesor @offray acorde al texto, esto son los atributos de un objeto?

El objetivo de una herramienta como Grafoscopio (inmersa en Glamorous) nos permitirá trabajar las representaciones simbólicas a través de código y las gráficas (a través de visualizaciones.

En el dasboard de GTK se puede apreciar que se cuenta con dos opciones para "inspenciionar"

1. A través del ícono [►i].
2. Por medio de una previsualización desde el ícono punto que se encuentra al final · del comando.

La lectura realizada pude observar que Grafoscopio y Pharo tiene múltiples funciones que ayudan a mejorar el mensaje que se quiere transmitir al usuario final. El desarrollador de esta programación tiene varias opciones para modelar representando los datos: Números, Caracteres, Símbolos, Arreglos dinámicos.

Todo con la finalidad de hacer un sitio amigable, y también facilita llevar una trazabilidad de lo que se esta creando, ya que guarda que cambio fueron realizados dejando resaltado las modificaciones.

Es necesario aplicar la lógica en las operaciones matemáticas para que las formulas tengan la debida interpretación en Pharo/GT., como ejemplo se aplica una operación que implica el debido uso de los paréntesis para que la operación sea exitosa (2+2/2) + (10) = 12

La importancia de tener claro los comandos que se van a usar para así evitar inconvenientes en la programación, tener un enfoque claro de lo que se quiere modelar para el usuario final.

Sin embargo se agradecería que dentro de la clase se pudieran hacer otros ejemplos ilustrativos para complementar el ejercicio de autoaprendizaje realizado a través de la lectura.

Al momento de interactuar con la nueva herramienta Grafoscopio, puedo observar algo positivo como es que maneje características de programación parecidas. Hedge Doc docutopia.sustrato

Ejemplo interfaz

Profesor @offray acorde al texto, esto no es lo mismo que los atributos de un objeto?

Profesor @offray acorde al texto, estos son los "comandos" que entendería el objeto al momento de darle una indicación o instrucción (método)?

Al inspeccionar el comendo Date Today se denota que el aplicativo indica le fecha del día en que se ejecuta la "inspección" en glamorous., por otro lado, se comprende que la vista meta permitió aplicar otro comando, que en este caso es Date Tomorrow donde se indica cual sería la fecha del día siguiente., lo que permite tener una mayor comprensión de los diferentes comandos que entiende un objeto de aprendizaje acorde a la forma en que se construye por parte del programador.

En la clases de unidades Semánticas e podido ver el detrás de cámaras como se modela o se estructura un sitio desde la programación y sus diferentes códigos, caracteres, símbolos, que forman parte del desarrollo para lograr mostrar un sitio amigable e interactivo por el usuario final.

Un error de digitación Resultados

Relacionando los videos analizados de Anjana Vakil sobre los paradigmas de programación y sobre la programación orientada a objetos, ahora es un poco más comprensible la importancia de los mensajes entre objetos en el campo de la computación. Ya que, es la forma como interactúan entre sí y es la forma como nosotros podemos dar órdenes a esos objetos, a través de comandos. El quick del asunto está en ¿Cómo se escriben los comandos?, ¡cómo los incorporamos al sistema? ¿Qué lenguaje usamos?, ¿Cuál es su estructura?, y ¿Qué queremos recibir como respuesta? 

Este comentario es a modo de inquietud: Por ejemplo, cuando ejecuto el código y navego por las diferentes pestañas a la derecha del GT ¿Cómo puedo identificar que la información allí consignada corresponde a la instrucción de ejecución del código y no a un error por incompatibilidades con el estema operativo o a fallos del programa o a corrupción de la información? Teniendo en cuenta que, en este caso, no somos expertos en escritura e interpretación de códigos.

Este apartado del texto en lo personal me parece muy interesante, más aún, cuando, por ejemplo, mi forma de aprender es a medida que voy leyendo o recibiendo una instrucción o viendo un video, entre otras; poder ir haciendo las cosas. Leo/observo la información, la proceso (la interiorizo) y debo hacer algo práctico para entenderlo y lograr que no se me olvide y poderlo hacer nuevamente en el futuro.

Por esta razón, lo que se plantea en este texto y en este apartado en particular, es fundamental dentro de los procesos de aprendizaje, por qué nos permite entender el por qué de las cosas, por qué suceden así y no de otra forma, y que pasa cuando hago lo que dice la instrucción, veo en tiempo real cuál es el resultado, si es que hay un resultado.

En ejemplo más claro de esta forma de aprender y de las limitaciones en algunos recursos, es cuando revise los videos de Anjana Vakil, para la construcción de los mapas mentales, me hubiera gustado poder practicar en algún lado como sería la forma de escribir los códigos, ya que, así se explique muy bien, para mí la forma de entender cómo funcionan las cosas es en la acción y en el hecho en particular.

Author response:

The following is the authors’ response to the original reviews

Reviewer #1 (Public Review):

The article emphasizes vocal social behavior but none of the experiments involve a social element. Marmosets are recorded in isolation which could be sufficient for examining the development of vocal behavior in that particular context. However, the early-life maturation of vocal behavior is strongly influenced by social interactions with conspecifics. For example, the transition of cries and subharmonic phees which are high-entropy calls to more low-entropy mature phees is affected by social reinforcement from the parents. And this effect extends cross context where differences in these interaction patterns extend to vocal behavior when the marmosets are alone. From the chord diagrams, cries still consist of a significant proportion of call types in lesioned animals. Additionally, though it is an intriguing finding that the infants' phee calls have acoustic differences being 'blunted of variation, less diverse and more regular,' the suggestion that the social message conveyed by these infants was 'deficient, limited, and/or indiscriminate' is not but can be tested with, for example, playback experiments.

We recognize that our definition of vocal social behavior is not within the normal realm of direct social interactions. We were particularly interested in marmoset vocalizations as a social signal, such as phees, cries and twitter, even when their family members or conspecifics are not visibly present. Generally speaking, in the laboratory, infant marmosets make few calls when in the presence of another conspecific, but when isolated they naturally make phee calls to reach out to their distantly located relatives. In this context, while we did not assess the animals interacting directly, we assessed what are normally referred to as ‘social contact calls,’ hence the term ‘social vocalizations.’ Playback recordings might provide potential evidence of antiphonal calling as a means of social interaction and might reveal the poor quality of the social message conveyed by the infant, but even here, the vocalizing marmoset would be calling to a non-visible conspecific. Thus, although our experiment lacked a direct social element, our data suggest that in the absence of a functioning ACC in early life, infant calls that convey social information, and which would elicit feedback from parents and other family members, may be compromised, and this could potentially influence how that infant develops its social interactive skills. We have now commented on the significance of social vocalizations in the introductory text (page 3) and discussion (page 15).

The manuscript would benefit from the addition of more details to be able to better determine if the conclusions are well supported by the data. Understanding that this is very difficult data to get, the number of marmosets and some variability in the collection of the data would allow for the plotting of each individual across figures. For example, in the behavioral figures, which is the marmoset that is in the behavioral data that has a sparing of the ACC lesion in one hemisphere? Certain figures, described below in the recommendations for the authors, could also do with additional description.

Thanks for these suggestions. We have plotted the individual animals in the relevant figures and addressed the comments and recommendations listed below.

Reviewer #1 (Recommendations For The Authors):

Given the number of marmosets, variability in the collected data, lesion extent, and different controls, I would like to see more plots with individuals indicated (perhaps with different symbols). More details could also be added for several plots.

Figure 2D (new) and 2E now have plots that represent the individual animals, each represented by a different symbol.

Figure 2A) Since lesions are bilateral, could you also show the extent of the lesions on the other side for completeness?

Our intention was to process one hemisphere of each brain for Golgi staining to examine changes in cell morphology in the ACC and associated brain regions following the lesion. Unfortunately, the Golgi stain was unsuccessful. Consequently, we were unable to use the tissue to reconstruct the bilateral extent of the lesion. We did, however, first establish the bilateral nature of the lesion through coronal slices of the animals MRI scan before processing the intact hemisphere to confirm the bilateral extent of the lesion. The MRI scans (every 5th section) for each control and lesioned animal is compiled in a figure in the supplementary materials (Fig. S1). These scans show that the ACC-lesioned animals have bilateral lesions with one animal (ACC1) showing some sparing in one hemisphere, as we noted in the text. We have now made reference to this supplemental figure in the text (page 5).

Figure 2B/C) In Figure 2B, control and ACC lesions are in the columns while right next to it in 2C, ACC lesion and control are in the rows. Could these figures be adjusted so that they are consistent?

We have now adjusted these figures and updated the figure legends accordingly.

Figure 2C) Is there quantification of the 'loss of neurons and respective increase in glial cells at the lesioned site especially at the interface between gray and white matter'? There are multiple slices for each animal.

Thanks for suggesting this. We have now quantified these data which are presented as a new graph as Fig. 2D. These data revealed a significant loss of neurons (NeuN) in the ACC group as well as an increase in glial cells (GFAP and Iba1) relative to the controls. The figure legend and results have also been updated.

Figure 2C) It is difficult for me to distinguish between white and purple - could you show color channels independently since images were split into separate channels for each fluorophore?

Fig. 2C has been revised to better visualize the neurons and glia at the gray and white matter interface. We found that grayscale images for each channel offered a better contrast than separating the channels for each fluorophore.

Figure 2C/D) I like how there are individual dots here for the individual marmosets. Since there are four in each group, could they be represented throughout with symbols (with a key indicating the pair and also the control condition)? For example, were there changes in the histology for control animals that got saline injections as opposed to those that didn't get any surgery?

We have highlighted the individual animals with different symbols in the figures. Although some animals were twin pairs, it was not possible to have twins in all cases. Only two sets were twins. We have indicated the symbols that represent the twin pair in Fig. 2 as well as the MRI scans of the twin pairs in Fig. S1. There were no observed changes in histology for the sham animals relative to the other non-sham controls. The MRI scan for one sham CON2 shows herniated tissue in the right hemisphere which is a normal consequence of brain exposure caused by a craniotomy.

Figure 3D-E) Here, individual data points could be informative especially given that some animals are missing data past the third week.

To prevent cluttering the figure with too many data points, we have added the sample size for each group in the figure legend (pages 33).

Figure 3D/F) What exactly is the period that goes into this analysis? In the text, 'Further analysis showed that the ACC lesion had minimal effects on the rate of most call types during this period'. Is this period from weeks 3 to 6 relative to the proportions in week 2? I think I also don't quite understand the chord diagram. The legend says 'the numbers around each chord diagram represents relative probability value for each call type transition' so how does that relate to the proportion of these call types? It looks like there is a wider slice for cries for ACC-lesioned animals each week. I also don't see in the week 4 chord diagram, the text description of 'elevation in the rate of 'other' calls, which comprised tsik, egg, eck, chatter and seep calls. These calls were significantly elevated in animals after the ACC lesion."

We apologize for the confusion. Fig 3D and Fig 3F are not directly related. Fig. 3D shows the different types of emitted calls. The figure shows the averaged data per group pooled from post-surgery weeks (week 3 – week 6). It represents the proportion of individual call types relative to the total number of calls during each recording period. The only major finding here was the increased rate of ‘other’ calls comprising tsik, egg, ock, chatter and seep calls. These calls were significantly elevated in animals after the ACC lesion.

While Fig. 3D represents the differences in the proportion of calls, the chord diagrams in Fig. 3F represents the probability of call-to-call transition obtained from a probability matrix. At postnatal week 6, marmosets with ACC lesions showed a higher likelihood of transitions between all call types, but less frequent transitions between social contact calls relative to sham controls. The chord diagrams visualize the weighted probabilities and directionality of these transitions between the different call types. Weighted probabilities were used to account for variations in call counts. The thickness of the arrows or links indicates the probability of a call transition, while the numbers surrounding each chord diagram represent the relative probability value for each specific transition. We have now reworded the text and clarified these details in the figure legend (pages 32-33).

Figure 3E) How is the ratio on the y-axis calculated here?

The y-axis represents the averaged value of the ratios of the number of social contact calls relative to non-social contact calls in each recording per subject per group (i.e., (x̄ (# social calls / # non-social calls). This is now included in the figure legend and the axis is updated (page 32).

Also, cries could be considered a 'social contact call' since they are produced by infants to elicit responses from the parents. There is also the hypothesis in the literature that cries transition into phees.

The reviewer is correct. Cries are often considered a social contact call because they elicit parental feedback. We decided to separate cry-calls from other social contact calls for two reasons. First, in our sample, we found cry behavior to be highly variable across the animals. For example, one control infant cried incessantly whereas another control infant cried less than normal. This extreme variability in animals of the same group masked the features between animals that reliably differentiated between them. Second, cry-calls elicit feedback from parents who are normally within the vicinity of the infant whereas phee calls elicit antiphonal phee calls from any distantly located conspecific. In other words, the context in which these calls are often elicited are very different.

The use of 'syntactical' is a bit jarring to me because outside of linguistics, its use in animal communication generally refers to meaning-bearing units that can be combined into well-formed complexes such as pod-specific whale songs or predator alarm calls with concatenated syllable types in some species of monkeys. To my knowledge, individual phee syllables have not been currently shown to carry information on their own and may be better described as 'sequential' rather than 'syntactical'.

We agree. We have made this change accordingly.

Figure 4B) How many phee calls with differing numbers of syllables are present each week? How equal is the distribution given that later analyses go up to 5 syllables?

The total number of phee calls with differing number of syllables ranged between 20-40 phees. This number varied between subjects, per week. The most common were 3- and 4-syllable phee calls which ranged from 7-15. Due to this variability, Fig. 4B presents the average syllable count. The axis is now updated.

Figure 4C-E) How is the data combined here? Is there a 2nd syllable, the combined data from the 2nd syllable from phee calls of all lengths (1 - 5?). If so, are there differences based on how long the total sequence is?

The combined data represents the specific syllable (e.g., the 1st syllable in a 2-syllable phee, in a 3-syllable phee and in a 4-syllable phee) irrespective of the length of the sequence in a sequence. No differences were observed between 2nd syllable in a 2 syllable phee and 2nd syllable in a 3 or a 4 syllable phee. We have included this detail in the figure legend (page 33-34).

So duration is a vocal parameter that is highly dependent on physical factors such as body size and lung volume, where there differences in physical growth between the pairs of ACC-lesioned marmosets and their twins? Entropy is less closely tied to these physical factors but has previously been shown to decrease as phee calls mature, which we can also see in the negative relationship of the control animals. Do you know of experiments that show that lower entropy calls are more 'blunted'?

Thank you for raising the important issue of physical growth factors. For twin pairs, it is not uncommon for one infant to be slightly bigger, heavier or stronger than the other presumably because one gets more access to food. With increasing age, we did not observe significant changes in bodyweight between the groups. We examined grip strength in all infants as a means of assessing how well the infant was able to access food during nursing. Poor grip strength would indicate a lower propensity to ‘hang on’ to the mother for nursing which could lead to lower weight gain and reduced physical growth. We found that both grip strength and body weight increased as the infants got older and both parameters were equivalent. We have included an additional figure to show the normal increase in both weight and grip strength to the supplemental materials (Fig. S3) and have made reference to this in the text (page 8).

As for entropy, it’s impact on the emotional quality of vocalizations has not been systematically explored. Generally speaking, high entropy relates to high randomness and distortion in the signal. Accordingly, one view posits low-entropy phee calls represent mature sounding calls relative to noisy and immature high-entropy calls (e.g., Takahasi et al 2017). In the current study, the reduction in syllable entropy observed for both groups of animals with increasing age is consistent with this view. At the same time entropy can relate to vocal complexity; high entropy refers to complex and variable sound patterns whereas low entropy sounds are predictable, less diverse and simple vocal sequences (Kershenbaum, A. 2013. Entropy rate as a measure of animal vocal complexity. Bioacoustics, 23(3), 195–208). One possibility is that call maturity does not equate directly to emotional quality. In other words, a low-entropy mature call can also be lacking in emotion as observed in humans with ACC damage; these patients show mature speech, but they lack the variations in rhythms, patterns and intonation (i.e., prosody) that would normally convey emotional salience and meaning. Our observation of a reduction in phee syllable entropy in the ACC group in the context of being short and loud with reduced peak frequency is consistent with this view. Our use of the word ‘blunt’ was to convey how the calls exhibited by the ACC group were potentially lacking emotional meaning. Beyond this speculation, we are not aware of any papers that have examined the relationship between entropy and blunted calls directly. We have now included this speculation in the discussion (pages 12-13).

Reviewer #2 (Public Review):

The authors state that the integrity of white matter tracts at the injection site was impacted but do not show data.

We have added representative micrographs of a control and ACC-lesioned animal in a new supplementary figure which shows the neurotoxin impacted the integrity of white matter tracts local to the site of the lesion (Fig. S2).

The study only provides data up to the 6th week after birth. Given the plasticity of the cortex, it would be interesting to see if these impairments in vocal behavior persist throughout adulthood or if the lesioned marmosets will recover their social-vocal behavior compared to the control animals.

We agree. Our original intention was to examine behavior into adulthood. Unfortunately, the COVID-19 pandemic compromised the continuation of the study. We were limited by the data that we were allowed to acquire due to imposed restrictions. Some non-vocalization data collected when the animals were young adults is currently being prepared for another paper.

Even though this study focuses entirely on the development of social vocalizations, providing data about altered social non-vocal behaviors that accompany ACC lesions is missing. This data can provide further insights and generate new hypotheses about the exact role of ACC in social vocal development. For example, do these marmosets behave differently towards their conspecifics or family members and vice versa, and is this an alternate cause for the observed changes in social-vocal development?

We agree. At the time however, apparatus for assessing behavior between the infant’s family and non-family members was not available. Assessing such behaviors in the animals holding room posed some difficulty since marmosets are easily distracted by other animals as well as the presence of an experimenter, amongst other things. This is an area of investigation we are currently pursuing.

Reviewer #3 (Public Review):

It is striking to find that the vocal repertoire of infant marmosets was not significantly affected by ACC lesions. During development, the neural circuits are still maturing and the role of different brain regions may evolve over time. While the ACC likely contributes to vocalizations across the lifespan, its relative importance may vary depending on the developmental stage. In neonates, vocalizations may be more reflexive or driven by physiological needs. At this stage, the ACC may play a role in basic socioemotional regulation but may not be as critical for vocal production. Since the animals lived for two years, further analysis might be helpful to elucidate the precise role of ACC in the vocal behavior of marmosets.

Figure 3D. According to the Introduction "...infant ACC lesions abolish the characteristic cries that infants normally issue when separated from its mother". Are the present results in marmosets showing the opposite effect? Please discuss.

To date, the work of Maclean (1985) is the only publication that describes the effect of early cingulate ablation on the spontaneous production of ‘separation calls’ largely construed as cries, coos and whimpers in response to maternal separation. All of this work was largely performed in rhesus macaques or squirrel monkeys. In addition to ablating the cingulate cortex, Maclean found that it was necessary to ablate the subcallosal (areas 25) and preseptal cingulate cortex (presumably referring to prelimbic area 32) to permanently eliminate the spontaneous production of separation cry calls. Our ablation of the ACC was more circumscribed to area 24 and is therefore consistent with MacLean’s earlier work that removal of ACC alone does not eliminate cry behavior. In adults, ACC ablation is insufficient at eliminating vocalization as well. We make reference to this on pages 13-14 of the discussion.

Figure 3E and Discussion. Phees are mature contact calls and cries immature contact calls (Zhang et al, 2019, Nat Commun). Therefore, I would rather say that the proportion of immature (cries) contact calls increases vs the mature (phee, trill, twitters) contact calls in the ACC group. Cries are also "isolated-induced contact calls" to attract the attention of the caregivers.

The reviewer is correct in that cries are directed towards caregivers but in our sample, cry behavior was highly variable between the infants. Consequently, in Fig. 3E social contact calls include phee, twitter and trill calls but does not include cries which were separated (see also response to reviewer #1). Many of the calls made during babbling were immature in their spectral pattern (compare phee calls between Fig. 3A and 3B). Cries typically transitioned into phees, twitters or trills before they fully matured. Fig 3E shows that the controls made more isolation-induced social contact calls at postnatal week 6 which were presumably maturing at this time point. Thus, if anything, there was an increase in the proportion of mature contact calls vs immature contact calls with increasing age.

Figure 4D. Animal location and head direction within the recording incubator can have significant effects on the perceived amplitude of a call. Were these factors taken into account?

The reviewer makes an excellent observation. Unfortunately, we did not account for location and head direction because the infants were quite mobile in the incubator. The directional microphone was hidden from view because the infants were distracted by it, and positioned ~12 cm from the marmoset, and placed in the exact same location for every recording. In addition, calls with phantom frequencies were eliminated during visual inspection of spectrograms. Beyond these details, location and head direction were not taken into account.

Figure 4E. When a phee call has a higher amplitude, as is the case for the ACC group (Figure 4D), the energy of the signal will be concentrated more strongly at the phee call frequency ~8KHz. This concentration of the energy reduces the variability in the frequency distribution, leading to lower entropy. The interpretation of the results should be reconsidered. A faint call (control group) can exhibit more variability in the frequency content since the energy is distributed across a wider range of frequencies contributing to higher entropy. It can still be "fixed, regular, and stereotyped" if the behavior is consistent or predictable with little variation. Also, to define ACC calls as "monotonic" I would rather search for the lack of frequency modulation, amplitude variation, or narrower bandwidth.

We very much appreciate this explanation. We were able to identify the maximum frequency that closely matched pitch of a sound for each syllable in a multisyllabic phee. New Fig. 4E shows that the peak frequency for each phee syllable was lower in the ACC-lesioned monkeys which may directly translate to the low entropy observed in this group. The term “monotonic” was used to relate our data to the classical and long-standing evidence of human ACC lesions causing monotonous intonation of speech. When all factors are taken into account, it is evident that the vocal phee signature of the ACC-lesioned animal was structurally different to the controls implicating a less complex and stereotyped ACC signal. Further studies are needed to systematically explore the relationship between entropy and emotional quality of vocalizations

Apart from the changes in the vocal behavior, did the AAC lesions manifest in any other observable cognitive, emotional, or social behavior? ACC plays a role in processing pain and modulating pain perception. Could that be the reason for the observed increase in the proportion of cries in the ACC group and the increase in the phee call amplitude? Did the cries in the ACC group also display a higher amplitude than the cries in the control group?

It was our intention to acquire as much data as possible from these infants as they matured from a cognitive, social and emotional perspective. Unfortunately, our study was hampered by variety of reasons including the COVID-19 pandemic which imposed major restrictions on our ability to continue with the experiment in a time sensitive manner. In addition, the development and construction of the custom apparatus to measure these behaviors was stalled during this period further preventing us from collecting behavioral data at regular time intervals. As for the cry behavior, the number of cries, in the ACC group were very low especially at postnatal week 5 and 6. Consequently, there were very few data points to work with.

Discussion. Louder calls have the potential to travel longer distances compared to fainter calls, possess higher energy levels, and can propagate through the environment more effectively. If the ACC group produced louder phee syllables, how could be the message conveyed over long distances "deficient, limited, and/or indiscriminate"?

Thanks for raising this interesting concept. Not all calls emitted by the animals were loud. We specifically examined the long-distance phee call in this regard. The phee syllables emitted by the ACC group were high amplitude with low frequencies, short duration and low entropy. Taking these factors into account, it is conceivable that the phee calls produced by the ACC group could not effectively convey their message over long distances despite their propagation through the environment. We have made reference to this in the discussion where we focus is specifically on the phee calls only (pages 12).

Abstract: Do marmosets have syntax? Consider replacing "syntactical" with a more appropriate term (maybe "syntax-like").

Thanks for this suggestion. We have replaced the term syntactical with ‘sequential’ as per the recommendation of reviewer #1.

Introduction: "...cries that infants normally issue when separated from its mother". Please replace "its" with "their".

This has been corrected.

Results: Is the reference to Fig 1B related to the text?

We have included and referred to Fig. 1B in the text (results and methods) to show other researchers how they can use this technique as a reliable and safe means of monitoring tidal volume under anesthesia in small infant marmoset without intubation.

I understand that both "spectrograph" and "spectrogram" are used to analyze the frequency content of a signal. Nevertheless, "spectrogram" refers to the visual representation of the frequency content of a signal over time, and this term is commonly used in audio signal processing and specifically in the vocal communication field. I would recommend replacing "spectrograph" with "spectrogram".

Thanks for this suggestion. We have corrected this throughout the manuscript.

(Concerning the previous comment in the public review). Cries are uttered to attract the attention of the caregivers. The increase in the proportion of cries in the ACC group does not match the sentence: "...these infants appeared to make little effort in using vocalizations to solicit social contact when socially isolated".

We apologize for the confusion. It is not the case that the ACC animals make more cries. Cry calls were highly variable amongst the animals. Consequently, although Fig 3D gives the impression that the proportion of cries in higher in ACC animals they did not differ significantly from the controls. Due to their high variability, cries were removed in the measurement of social contact. Accordingly, Fig. 3E does not include cry behavior; it shows that the ACC animals engage less in social contact calls.

Related to Figure 3. What is the difference between "egg" and "eck" calls? Do you mean "ock"?

We apologize. This was a typo. It should be ock calls.

Figure 4B. Is the sample size five animals per group and per week? Overlapping data points seem to be placed next to each other. Why in some groups (e.g. ACC 6 weeks) less than five dots are visible?

The sample size differed per week because of the lack of recording during the COVID restrictions. In Fig 4b, we have now separated the overlapping dots. We have also added the sample size of the groups in the figure legends.

Would the authors expect to see stronger differences between the lesioned and the control groups when comparing a later developmental stage? The animals were euthanized at the age of

These speculation is certainly feasible and yes, we were hoping to establish this level of detail with testing at later developmental stages. This is an aspect of development we are currently pursuing.

Could these experiments be conducted?

I’m afraid these animals are longer available, but we are currently conducting experiments in other animals with early life neurochemical manipulations who show behavioral changes into early adulthood.

ACC lesion: It is reported that the lesions extended past 24b into motor area 6M. Did the animal display any motor control disability?

Surprisingly, despite the lesion encroaching into 6M, these animals showed no observable motor impairment. We assessed the animals grip strength and body weight and discovered normal strength and growth in weight in both controls and the lesioned group. We have added this data as supplemental information (Fig. S3).

Author response:

The following is the authors’ response to the original reviews

Reviewer #1 (Public review):

Summary:

This study investigates what happens to the stimulus-driven responses of V4 neurons when an item is held in working memory. Monkeys are trained to perform memory-guided saccades: they must remember the location of a visual cue and then, after a delay, make an eye movement to the remembered location. In addition, a background stimulus (a grating) is presented that varies in contrast and orientation across trials. This stimulus serves to probe the V4 responses, is present throughout the trial, and is task-irrelevant. Using this design, the authors report memory-driven changes in the LFP power spectrum, changes in synchronization between the V4 spikes and the ongoing LFP, and no significant changes in firing rate.

Strengths:

(1) The logic of the experiment is nicely laid out.

(2) The presentation is clear and concise.

(3) The analyses are thorough, careful, and yield unambiguous results.

(4) Together, the recording and inactivation data demonstrate quite convincingly that the signal stored in FEF is communicated to V4 and that, under the current experimental conditions, the impact from FEF manifests as variations in the timing of the stimulus-evoked V4 spikes and not in the intensity of the evoked activity (i.e., firing rate).

Weaknesses:

I think there are two limitations of the study that are important for evaluating the potential functional implications of the data. If these were acknowledged and discussed, it would be easier to situate these results in the broader context of the topic, and their importance would be conveyed more fairly and transparently.

(1) While it may be true that no firing rate modulations were observed in this case, this may have been because the probe stimuli in the task were behaviorally irrelevant; if anything, they might have served as distracters to the monkey's actual task (the MGS). From this perspective, the lack of rate modulation could simply mean that the monkeys were successful in attending the relevant cue and shielding their performance from the potentially distracting effect of the background gratings. Had the visual probes been in some way behaviorally relevant and/or spatially localized (instead of full field), the data might have looked very different.

Any task design involves tradeoffs; if the visual stimulus was behaviorally relevant, then any observed neurophysiological changes would be more confounded by possible attentional effects. We cannot exclude the possibility that a different task or different stimuli would produce different results; we ourselves have reported firing rate enhancements for other types of visual probes during an MGS task (Merrikhi et al. 2017). We have added an acknowledgement of these limitations in the discussion section (lines 323-330 in untracked version). At minimum, our results show a dissociation between the top-down modulation of phase coding, which is enhanced during WM even for these task-irrelevant stimuli, and rate coding. Establishing whether and how this phase coding is related to perception and behavior will be an important direction for future work.

With this in mind, it would be prudent to dial down the tone of the conclusions, which stretch well beyond the current experimental conditions (see recommendations).

We have edited the title (removing the word ‘primarily’) and key sentences throughout to tone down the conclusions, generally to state that the importance of a phase code in WM modulations is *possible* given the observed results, rather than certain (see abstract lines 26-27, introduction lines 59-62, conclusion lines 310-311).

(2) Another point worth discussing is that although the FEF delay-period activity corresponds to a remembered location, it can also be interpreted as an attended location, or as a motor plan for the upcoming eye movement. These are overlapping constructs that are difficult to disentangle, but it would be important to mention them given prior studies of attentional or saccade-related modulation in V4. The firing rate modulations reported in some of those cases provide a stark contrast with the findings here, and I again suspect that the differences may be due at least in part to the differing experimental conditions, rather than a drastically different encoding mode or functional linkage between FEF and V4.

We have added a paragraph to the discussion section addressing links to attention and motor planning (lines 315-333), and specifically acknowledging the inherent difficulties of fully dissociating these effects when interpreting our results (lines 323-330).

Reviewer #2 (Public review):

Summary:

It is generally believed that higher-order areas in the prefrontal cortex guide selection during working memory and attention through signals that selectively recruit neuronal populations in sensory areas that encode the relevant feature. In this work, Parto-Dezfouli and colleagues tested how these prefrontal signals influence activity in visual area V4 using a spatial working memory task. They recorded neuronal activity from visual area V4 and found that information about visual features at the behaviorally relevant part of space during the memory period is carried in a spatially selective manner in the timing of spikes relative to a beta oscillation (phase coding) rather than in the average firing rate (rate code). The authors further tested whether there is a causal link between prefrontal input and the phase encoding of visual information during the memory period. They found that indeed inactivation of the frontal eye fields, a prefrontal area known to send spatial signals to V4, decreased beta oscillatory activity in V4 and information about the visual features. The authors went one step further to develop a neural model that replicated the experimental findings and suggested that changes in the average firing rate of individual neurons might be a result of small changes in the exact beta oscillation frequency within V4. These data provide important new insights into the possible mechanisms through which top-down signals can influence activity in hierarchically lower sensory areas and can therefore have a significant impact on the Systems, Cognitive, and Computational Neuroscience fields.

Strengths:

This is a well-written paper with a well-thought-out experimental design. The authors used a smart variation of the memory-guided saccade task to assess how information about the visual features of stimuli is encoded during the memory period. By using a grating of various contrasts and orientations as the background the authors ensured that bottom-up visual input would drive responses in visual area V4 in the delay period, something that is not commonly done in experimental settings in the same task. Moreover, one of the major strengths of the study is the use of different approaches including analysis of electrophysiological data using advanced computational methods of analysis, manipulation of activity through inactivation of the prefrontal cortex to establish causality of top-down signals on local activity signatures (beta oscillations, spike locking and information carried) as well as computational neuronal modeling. This has helped extend an observation into a possible mechanism well supported by the results.

Weaknesses:

Although the authors provide support for their conclusions from different approaches, I found that the selection of some of the analyses and statistical assessments made it harder for the reader to follow the comparison between a rate code and a phase code. Specifically, the authors wish to assess whether stimulus information is carried selectively for the relevant position through a firing rate or a phase code. Results for the rate code are shown in Figures 1B-G and for the phase code are shown in Figure 2. Whereas an F-statistic is shown over time in Figure 1F (and Figure S1) no such analysis is shown for LFP power. Similarly, following FEF inactivation there is no data on how that influences V4 firing rates and information carried by firing rates in the two conditions (for positions inside and outside the V4 RF). In the same vein, no data are shown on how the inactivation affects beta phase coding in the OUT condition.

Per the reviewer’s suggestion, we have added several new supplementary figures. We now show the F-statistic for discriminability over time for the LFP timecourse (Fig. S2), and as a function of power in various frequencies (Fig. S4). We have added before/after inactivation comparisons of the LFP and spiking activity, and their respective F-statistics for discrimination between contrasts and orientations in Fig. S9. Lastly, we added a supplementary figure evaluating the impact of FEF inactivation on beta phase coding in the OUT condition, showing no significant change (Fig. S11).

Moreover, some of the statistical assessments could be carried out differently including all conditions to provide more insight into mechanisms. For example, a two-way ANOVA followed by post hoc tests could be employed to include comparisons across both spatial (IN, OUT) and visual feature conditions (see results in Figures 2D, S4, etc.). Figure 2D suggests that the absence of selectivity in the OUT condition (no significant difference between high and low contrast stimuli) is mainly due to an increase in slope in the OUT condition for the low contrast stimulus compared to that for the same stimulus in the IN condition. If this turns out to be true it would provide important information that the authors should address.

We have updated the STA slope measurement, excluding the low contrast condition which lacks a clear peak in the STA. Additionally, we equalized the bin widths and aligned the x-axes for better visual comparability. Then, we performed a two-way ANOVA, analyzing the effects of spatial features (IN vs. OUT) and visual conditions (contrast and orientation). The results showed a significant effect of the visual feature on both orientation (F = 3.96, p=0.046) and contrast (F = 14.26, p<10^-3). However, neither the spatial feature nor the spatial-visual interaction exhibited significant effects for orientation (F = 0.52, p=0.473, F=1.56, p=0.212) or contrast (F = 2.19, p=0.139, F=1.15, p=0.283).

There are also a few conceptual gaps that leave the reader wondering whether the results and conclusion are general enough. Specifically,

(1) The authors used microstimulation in the FEF to determine RFs. It is thus possible that the FEF sites that were inactivated were largely more motor-related. Given that beta oscillations and motor preparatory activity have been found to be correlated and motor sites show increased beta oscillatory activity in the delay period, it is possible that the effect of FEF inactivation on V4 beta oscillations is due to inactivation of the main source of beta activity. Had the authors inactivated sites with a preponderance of visual neurons in the FEF would the results be different?

We do not believe this to be likely based on what is known anatomically and functionally about this circuitry. Anatomically, the projections from FEF to V4 arise primarily from the supragranular layers, not layers which contain the highest proportion of motor activity (Barone et al. 2000, Pouget et al. 2009, Markov et al. 2013). Functionally, based on electrical identification of V4-projecting FEF neurons, we know that FEF to V4 projections are predominantly characterized by delay rather than motor activity (Merrikhi et al. 2017). We have now tried to emphasize these points when we introduce the inactivation experiments (lines 185-186).

Experimentally, the spread of the pharmacological effect with our infusion system is quite large relative to any clustering of visual vs. motor neurons within the FEF, with behavioral consequences of inactivation spreading to cover a substantial portion of the visual hemifield (e.g., Noudoost et al. 2014, Clark et al. 2014), and so our manipulation lacks the spatial resolution to selectively target motor vs. other FEF neurons.

(2) Somewhat related to this point and given the prominence of low-frequency activity in deeper layers of the visual cortex according to some previous studies, it is not clear where the authors' V4 recordings were located. The authors report that they do have data from linear arrays, so it should be possible to address this.

Unfortunately, our chamber placement for V4 has produced linear array penetration angles which do not reliably allow identification of cortical layers. We are aware of previous results showing layer-specific effects of attention in V4 (e.g., Pettine et al. 2019, Buffalo et al. 2011), and it would indeed be interesting to determine whether our observed WM-driven changes follow similar patterns. We may be able to analyze a subset of the data with current source density analysis to look for layer-specific effects in the future, but are not able to provide any information at this time.

(3) The authors suggest that a change in the exact frequency of oscillation underlies the increase in firing rate for different stimulus features. However, the shift in frequency is prominent for contrast but not for orientation, something that raises questions about the general applicability of this observation for different visual features.

While the shift in peak frequency across contrasts is more prominent than that across orientations (Fig. S3A-B), the relationship between orientation and peak frequency is also significant (one-way ANOVA for peak frequency across contrasts, F_Contrast=10.72, p<10^-4; or across orientations, F_Orientation=3, p=0.030; stats have been added to Fig. S3 caption). This finding also aligns with previous studies, which reported slight peak frequency shifts (~1–2 Hz) in the context of attention (Fries, 2015). To address the question of whether the frequency-firing rate correlation generalizes to orientation-driven changes, we now examine the relationship between peak frequency and firing rate separately for each contrast level (Fig. S14). The average normalized response as a function of peak frequency, pooled across subsamples of trials from each of 145 V4 neurons (100 subsamples/neuron), IN vs. OUT conditions, shows a significant correlation during the delay period for each contrast (contrast low (F_Condition=0.03, p=0.867; F_Frequency=141.86, p<10^-18; F_Interaction=10.70, p=0.002, ANCOVA), contrast middle (F_Condition=7.18, p=0.009; F_Frequency=96.76, p<10^-14; F_Interaction=0.13, p=0.716, ANCOVA), contrast high (F_Condition=12.51, p=0.001; F_Frequency=333.74, p<10^-29; F_Interaction=7.91, p=0.006, ANCOVA).

(4) One of the major points of the study is the primacy of the phase code over the rate code during the delay period. Specifically, here it is shown that information about the visual features of a stimulus carried by the rate code is similar for relevant and irrelevant locations during the delay period. This contrasts with what several studies have shown for attention in which case information carried in firing rates about stimuli in the attended location is enhanced relative to that for stimuli in the unattended location. If we are to understand how top-down signals work in cognitive functions it is inevitable to compare working memory with attention. The possible source of this difference is not clear and is not discussed. The reader is left wondering whether perhaps a different measure or analysis (e.g. a percent explained variance analysis) might reveal differences during the delay period for different visual features across the two spatial conditions.

We have added discussion regarding the relationship of these results to previous findings during attention in the discussion section (lines 315-333).

The use of the memory-guided saccade task has certain disadvantages in the context of this study. Although delay activity is interpreted as memory activity by the authors, it is in principle possible that it reflects preparation for the upcoming saccade, spatial attention (particularly since there is a stimulus in the RF), etc. This could potentially change the conclusion and perspective.

We have added a new discussion paragraph addressing the relationship to attention and motor planning (lines 315-333). We have also moderated the language used to describe our conclusions throughout the manuscript in light of this ambiguity.

For the position outside the V4 RF, there is a decrease in both beta oscillations and the clustering of spikes at a specific phase. It is therefore possible that the decrease in information about the stimuli features is a byproduct of the decrease in beta power and phase locking. Decreased oscillatory activity and phase locking can result in less reliable estimates of phase, which could decrease the mutual information estimates.

Looking at the SNR as a ratio of power in the beta band to all other bands, there is no significant drop in SNR between conditions (SNRIN = 4.074+-984, SNROUT = 4.333+-0.834 OUT, p=0.341, Wilcoxon signed-rank). Therefore, we do not think that the change in phase coding is merely a result of less reliable phase estimates.

The authors propose that coherent oscillations could be the mechanism through which the prefrontal cortex influences beta activity in V4. I assume they mean coherent oscillations between the prefrontal cortex and V4. Given that they do have simultaneous recordings from the two areas they could test this hypothesis on their own data, however, they do not provide any results on that.

This paper only includes inactivation data. We are working on analyzing the simultaneous recording data for a future publication.

The authors make a strong point about the relevance of changes in the oscillation frequency and how this may result in an increase in firing rate although it could also be the reverse - an increase in firing rate leading to an increase in the frequency peak. It is not clear at all how these changes in frequency could come about. A more nuanced discussion based on both experimental and modeling data is necessary to appreciate the source and role (if any) of this observation.

As the reviewer notes, it is difficult to determine whether the frequency changes drive the rate changes, vice versa, or whether both are generated in parallel by a common source. We have adjusted our language to reflect this (lines 291-293). Future modeling work may be able to shed more light on the causal relationships between various neural signatures.

Reviewer #3 (Public review):

Summary:

In this report, the authors test the necessity of prefrontal cortex (specifically, FEF) activity in driving changes in oscillatory power, spike rate, and spike timing of extrastriate visual cortex neurons during a visual-spatial working memory (WM) task. The authors recorded LFP and spikes in V4 while macaques remembered a single spatial location over a delay period during which task-irrelevant background gratings were displayed on the screen with varying orientation and contrast. V4 oscillations (in the beta range) scaled with WM maintenance, and the information encoded by spike timing relative to beta band LFP about the task-irrelevant background orientation depended on remembered location. They also compared recorded signals in V4 with and without muscimol inactivation of FEF, demonstrating the importance of FEF input for WM-induced changes in oscillatory amplitude, phase coding, and information encoded about background orientations. Finally, they built a network model that can account for some of these results. Together, these results show that FEF provides meaningful input to the visual cortex that is used to alter neural activity and that these signals can impact information coding of task-irrelevant information during a WM delay.

Strengths:

(1) Elegant and robust experiment that allows for clear tests for the necessity of FEF activity in WM-induced changes in V4 activity.

(2) Comprehensive and broad analyses of interactions between LFP and spike timing provide compelling evidence for FEF-modulated phase coding of task-irrelevant stimuli at remembered location.

(3) Convincing modeling efforts.

Weaknesses:

(1) 0% contrast background data (standard memory-guided saccade task) are not reported in the manuscript. While these data cannot be used to consider information content of spike rate/time about task-irrelevant background stimuli, this condition is still informative as a 'baseline' (and a more typical example of a WM task).

We have added a new supplementary figure to show the effect of WM on V4 LFP power and SPL in 0% contrast trials (Fig. S6). These results (increases in beta LFP power and SPL when remembering the V4 RF location) match our previous report for the effect of spatial WM on LFP power and SPL within extrastriate area MT (Bahmani et al. 2018).

(2) Throughout the manuscript, the primary measurements of neural coding pertain to task-irrelevant stimuli (the orientation/contrast of the background, which is unrelated to the animal's task to remember a spatial location). The remembered location impacts the coding of these stimulus variables, but it's unclear how this relates to WM representations themselves.

Indeed, here we have focused on how maintaining spatial WM impacts visual processing of incoming sensory information, rather than on how the spatial WM signal itself is represented and maintained. Behaviorally, this impact on visual signals could be related to the effects of the content of WM on perception and reaction times (e.g., Soto et al. 2008, Awh et al. 1998, Teng et al. 2019), but no such link to behavior is shown in our data.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

As mentioned above, the two points I raised in the public review merit a bit of development in the Discussion. In addition, the authors should revise some of their conclusions.

For instance (L217):

"The finding that WM mainly modulates phase coded information within extrastriate areas fundamentally shifts our understanding of how the top-down influence of prefrontal cortex shapes the neural representation, suggesting that inducing oscillations is the main way WM recruits sensory areas."

In my opinion, this one is over-the-top on various counts.

Here is another exaggerated instance (L298):

"...leading us to conclude that representations based on the average firing rate of neurons are not the primary way that top-down signals enhance sensory processing."

Again, as noted above, the problem is that one could make the case that the top-down signals are, in fact, highly effective, since they are completely quashing any distracter-related modulation in firing rate across RFs. There is only so much that one can conclude from responses to stimuli that are task-irrelevant, uniform across space, and constant over the course of a trial.

I think even the title goes too far. What the work shows, by all accounts, is that the sustained activity in FEF has a definitive impact on V4 *even* with respect to a sustained, irrelevant background stimulus. The result is very robust in this sense. However, this is quite different from saying that the *primary* means of functional control for FEF is via phase coding. Establishing that would require ruling out other forms of control (i.e., rate coding) in all or a wide range of experimental conditions. That is far from the restricted set of conditions tested here and is also at variance with many other experiments demonstrating effects of attention or even FEF microstimulation on V4 firing activity.

To reiterate, in my opinion, the work is carefully executed and the data are interesting and largely unambiguous. I simply take issue with what can be reliably concluded, and how the results fit with the rest of the literature. Revisions along these lines would improve the readability of the paper considerably.

We have edited the title (removing the word ‘primarily’) and key sentences throughout to tone down the conclusions, generally to state that the importance of a phase code in WM modulations is *possible* given the observed results, rather than certain (see abstract lines 26-27, introduction lines 59-62, conclusion lines 310-311).

Reviewer #3 (Recommendations for the authors):

(1) My primary comment that came up multiple times as I read the manuscript (and which is summarized above) is that I wasn't ever sure why the authors are focused on analyzing neural coding of task-irrelevant sensory information during a WM task as a function of WM contents (remembered location). Most studies of neural codes supporting WM often focus on coding the remembered information - not other information. Conceptually, it seems that the brain would want to suppress - or at least not enhance - representations of task-irrelevant information when performing a demanding task, especially when there is no search requirement, and when there is no feature correspondence between the remembered and viewed stimuli. (i.e., the interaction between WM and visual input is more obvious for visual search for a remembered target). Why, in theory, would a visual region need to improve its coding of non-remembered information as a function of WM? This isn't meant to detract from the results, which are indeed very interesting and I think quite informative. The authors are correct that this is certainly relevant for sensory recruitment models of WM - there's clear evidence for a role of feedback from PFC to extrastriate cortex - but what role, specifically, each region plays in this task is critical to describe clearly, especially given the task-irrelevance of the input. Put another way: what if the animal was remembering an oriented grating? In that case, MI between spike-based measures and orientation would be directly relevant to questions of neural WM representations, as the remembered feature is itself being modeled. But here, the focus seems to be on incidental coding.

Indeed, here we have focused on how maintaining spatial WM impacts visual processing of incoming sensory information, rather than on how the spatial WM signal itself is represented and maintained. Behaviorally, this impact on visual signals could be related to the effects of the content of WM on perception and reaction times (e.g., Soto et al. 2008, Awh et al. 1998, Teng et al. 2019), but no such link to behavior is shown in our data.

Whether similar phase coding is also used to represent the content of object WM (for example, if the animal was remembering an oriented grating), or whether phase coding is only observed for WM’s modulation of the representation of incoming sensory signals, is an important question to be addressed in future work.

(2) Related to the above, the phrasing of the second sentence of the Discussion (lines 291-292) is ambiguous - do the authors mean that the FEF sends signals that carry WM content to V4, or that FEF sends projections to V4, and V4 has the WM content? As presently phrased, either of these are reasonable interpretations, yet they're directly opposing one another (the next sentence clarifies, but I imagine the authors want to minimize any confusion).

We have edited this sentence to read, “Within prefrontal areas, FEF sends direct projections to extrastriate visual areas, and activity in these projections reflects the content of WM.”

(3) I'm curious about how the authors consider the spatial WM task here different from a cued spatial attention task. Indeed, both require sustained use of a location for further task performance. The section of the Discussion addressing similar results with attention (lines 307-311) presently just summarizes the similarities of results but doesn't offer a theoretical perspective for how/why these different types of tasks would be expected to show similar neural mechanisms.

We have added discussion regarding the relationship of these results to previous findings during attention in the discussion section (lines 315-333).

(4) As far as I can tell, there is no consideration of behavioral performance on the memory-guided saccade task (RT, precision) across the different stimulus background conditions. This should be reported for completeness, and to determine whether there is an impact of the (likely) task-irrelevant background on task performance. This analysis should also be reported for Figure 3's results characterizing how FEF inactivation disrupts behavior (if background conditions were varied, see point 7 below).

We have added the effect of inactivation on behavioral RT and % correct across the different stimulus background conditions (Fig. S8). Background contrast and orientation did not impact either RT or % correct.

(5) Results from Figure 2 (especially Figures 2A-B) concerning phase-locked spiking in V4 should be shown for 0%-contrast trials as well, as these trials better align with 'typical' WM tasks.

We have added a new supplementary figure to show the effect of WM on V4 LFP power and SPL in 0% contrast trials (Fig. S6). These results (increases in beta LFP power and SPL) match our previous report for the effect of spatial WM on LFP power and SPL within extrastriate area MT (Bahmani et al. 2018).

(6) The magnitude of SPL difference in aggregate (Figure 2B) is much, much smaller than that of the example site shown (Figure 2A), such that Figure 2A's neuron doesn't appear to be visible on Figure 2B's scatterplot. Perhaps a more representative sample could be shown? Or, the full range of x/y axes in Figure 2B could be plotted to illustrate the full distribution.

We have updated Fig. 2A with a more representative sample neuron.

(7) I'm a bit confused about the FEF inactivation experiments. In the Methods (lines 512-513), the authors mention there was no background stimulus presented during the inactivation experiment, and instead, a typical 8-location MGS task was employed. However, in the results on pg 8 (Lines 201-214), and Figure 3G, the authors quantify a phase code MI. The previous phase code MI analysis was looking at MI between each spike's phase and the background stimulus - but if there's no background, what's used to compute phase code MI? Perhaps what they meant to write was that, in addition to the primary task with a manipulation of background properties, an 8-location MGS task was additionally employed.

The reviewer is correct that both tasks were used after inactivation (the 8-location task to assess the spread of the behavioral effect of inactivation, and the MGS-background task for measuring MI). We have edited the methods text to clarify.

(8) How is % Correct defined for the MGS task? (what is the error threshold? Especially for the results described in lines 192-193).

The % correct is defined as correct completed trials divided by the total number of trials; the target window was a circle with radius of 2 or 4 dva (depending on cue eccentricity). These details have been added to the Methods.

(9) The paragraph from lines 183-200 describes a number of behavioral results concerning "scatter" and "RT" - the RT shown seems extremely high, and perhaps is normalized. Details of this normalization should be included in the Methods. The "scatter" is listed as dva, but it's not clear how scatter is quantified (std dev of endpoint distribution? Mean absolute error), nor how target eccentricity is incorporated (as scatter is likely higher for greater target eccentricity).

We have renamed ‘scatter’ to ‘saccade error’ in the text to match the figure, and now provide details in the Methods section. Both RT and saccade error are normalized for each session, details are now provided in the Methods. Since error was normalized for each session before performing population statistics, no other adjustment for eccentricity was made.

Reviewer #3 (Public review):

Summary:

This paper points out an inconsistency of the roles of the striatal spiny neurons projecting to the indirect pathway (iSPN) and the synaptic plasticity rule of those neurons expressing dopamine D2 receptors, and proposes a novel, intriguing mechanisms that iSPNs are activated by the efference copy of the chosen action that they are supposed to inhibit.

The proposed model was supported by simulations and analysis of the neural recording data during spontaneous behaviors.

Strengths:

Previous models suggested that the striatal neurons learn action values functions, but how the information about the chosen action is fed back to the striatum for learning was not clear. The author pointed out that this is a fundamental problem for iSPNs that are supposed to inhibit specific actions and its synaptic inputs are potentiated with dopamine dips.

The authors proposes a novel hypothesis that iSPNs are activated by efference copy of the selected action which they are supposed to inhibit during action selection. Even though intriguing and seemingly unnatural, the authors demonstrated that the model based on the hypothesis can circumvent the problem of iSPNs learning to disinhibit the actions associated with negative reward errors. They further showed by analyzing the cell-type specific neural recording data by Markowitz et al. (2018) that iSPN activities tend to be anti-correlated before and after action selection.

Weaknesses:

(1) It is not correct to call the action value learning using the externally-selected action as "off-policy." Both off-policy algorithm Q-learning and on-policy algorithm SARSA update the action value of the chosen action, which can be different from the greedy action implicated by the present action values. In standard reinforce learning terminology, on-policy or off-policy is regarding the actions in the subsequent state, whether to use the next action value of (to be) chosen action or that of greedy choice as in equation (7).<br /> It is worth noting that this paper suggested that dopamine neurons encode on-policy TD errors: Morris G, Nevet A, Arkadir D, Vaadia E, Bergman H (2006). Midbrain dopamine neurons encode decisions for future action. Nat Neurosci, 9, 1057-63. https://doi.org/10.1038/nn1743

(2) It is also confusing to contract TD learning and Q-learning, as the latter is considered as on type of TD learning. In the TD error signal by state value function (6) is dependent on the chosen action a_{t-1} implicitly in r_t and s_t based on the reward and state transition function.

(3) It is not clear why interferences of the activities for action selection and learning can be avoided, especially when actions are taken with short intervals or even temporal overlaps. How can the efference copy activation for the previous action be dissociated with the sensory cued activation for the next action selection?

(4) Although it may be difficult to single out the neural pathway that carries the efference copy signal to the striatum, it is desired to consider their requirements and difference possibilities. A major issue is that the time delay from actions to reward feedback can be highly variable.

An interesting candidate is the long-latency neurons in the CM thalamus projecting to striatal cholinergic interneurons, which are activated following low-reward actions:<br /> Minamimoto T, Hori Y, Kimura M (2005). Complementary process to response bias in the centromedian nucleus of the thalamus. Science, 308, 1798-801. https://doi.org/10.1126/science.1109154

(5) In the paragraph before Eq. (3), Eq (1) should be Eq. (2) for the iSPN.

Here are comments back to the authors' replies with the revised version:

(1) I do not agree on the use of inaccurate technical terms. On-policy does not require that the policy is greedy with respect to the actions values, as authors seem to assume here.

In fact, the policy (10) is just a standard soft-max action selection based on the action values by the difference of dSPN and iSPN outputs.

Furthermore, in the immediate reward setting tested in this paper, action values are independent of the policy, so there is no distinction between on-policy vs. off-policy. This is also apparent from the "TD" errors in (19) and (21), where there is no TD.

(2) To really compare the different forms of TD, multi-step RL tasks should be used.

(3) This fundamental limitation should be explicitly documented in the manuscript. This is not just the same as any RL algorithms. Having two action representations within each action step make temporal credit assignment more difficult.

Author response:

The following is the authors’ response to the original reviews

Reviewer #1:

Summary:

The authors propose a new model of biologically realistic reinforcement learning in the direct and indirect pathway spiny projection neurons in the striatum. These pathways are widely considered to provide a neural substrate for reinforcement learning in the brain. However, we do not yet have a full understanding of mechanistic learning rules that would allow successful reinforcement learning like computations in these circuits. The authors outline some key limitations of current models and propose an interesting solution by leveraging learning with efferent inputs of selected actions. They show that the model simulations are able to recapitulate experimental findings about the activity profile in these populations of mice during spontaneous behavior. They also show how their model is able to implement off-policy reinforcement learning.

Strengths:

The manuscript has been very clearly written and the results have been presented in a readily digestible manner. The limitations of existing models, that motivate the presented work, have been clearly presented and the proposed solution seems very interesting. The novel contribution of the proposed model is the idea that different patterns of activity drive current action selection and learning. Not only does this allow the model is able to implement reinforcement learning computations well, but this suggestion may have interesting implications regarding why some processes selectively affect ongoing behavior and others affect learning. The model is able to recapitulate some interesting experimental findings about various activity characteristics of dSPN and iSPN pathway neuronal populations in spontaneously behaving mice. The authors also show that their proposed model can implement off-policy reinforcement learning algorithms with biologically realistic learning rules. This is interesting since off-policy learning provides some unique computational benefits and it is very likely that learning in neural circuits may, at least to some extent, implement such computations.

We thank the reviewer for the positive comments.

Weaknesses:

A weakness in this work is that it isn’t clear how a key component in the model - an efferent copy of selected actions - would be accessible to these striatal populations. The authors propose several plausible candidates, but future work may clarify the feasibility of this proposal.

We agree that the biological substrate of the efference copy remains a key open question. We discuss potential pathways in the Discussion section of our manuscript and hope that future experimental studies clarify the question.

Reviewer #2:

Summary:

The basal ganglia is often understood within a reinforcement learning (RL) framework, where dopamine neurons convey a reward prediction error that modulates cortico-striatal connections onto spiny projection neurons (SPNS) in the striatum. However, current models of plasticity rules are inconsistent with learning in a reinforcement learning framework.

This paper proposes a new model that describes how distinct learning rules in direct and indirect pathway striatal neurons allow them to implement reinforcement learning models. It proposes that two distinct components of striatal activity affect action selection and learning. They show that the proposed implementation allows learning in simple tasks and is consistent with experimental data from calcium imaging data in direct and indirect SPNs in freely moving mice.

Strengths:

Despite the success of reward prediction errors at characterizing the responses of dopamine neurons as the temporal difference error within an RL framework, the implementation of RL algorithms in the rest of the basal ganglia has been unclear. A key missing aspect has been the lack of a RL implementation that is consistent with the distinction of direct- and indirect SPNs. This paper proposes a new model that is able to learn successfully in simple RL tasks and explains recent experimental results.

The author shows that their proposed model, unlike previous implementations, this model can perform well in RL tasks. The new model allows them to make experimental predictions. They test some of these predictions and show that the dynamics of dSPNs and iSPNs correspond to model predictions.

More generally, this new model can be used to understand striatal dynamics across direct and indirect SPNs in future experiments.

We thank the reviewer for the positive comments.

Weaknesses:

The authors could characterize better the reliability of their experimental predictions and the description of the parameters of some of the simulations.

In addition to the descriptions in the Methods, we have provided code implementing the key features of our simulations, which should contribute to reproducibility of our results.

The authors propose some ideas about how the specificity of the striatal efferent inputs but should highlight better that this is a key feature of the model whose anatomical implementation has yet to be resolved.

We have clarified in the Discussion section “Biological substrates of striatal efferent inputs” that these represent assumptions or predictions that have not yet been demonstrated experimentally.

Reviewer #3:

Summary:

This paper points out an inconsistency of the roles of the striatal spiny neurons projecting to the indirect pathway (iSPN) and the synaptic plasticity rule of those neurons expressing dopamine D2 receptors and proposes a novel, intriguing mechanisms that iSPNs are activated by the efference copy of the chosen action that they are supposed to inhibit.

The proposed model was supported by simulations and analysis of the neural recording data during spontaneous behaviors.

Strengths:

Previous models suggested that the striatal neurons learn action-value functions, but how the information about the chosen action is fed back to the striatum for learning was not clear. The author pointed out that this is a fundamental problem for iSPNs that are supposed to inhibit specific actions and its synaptic inputs are potentiated with dopamine dips.

The authors propose a novel hypothesis that iSPNs are activated by efference copy of the selected action which they are supposed to inhibit during action selection. Even though intriguing and seemingly unnatural, the authors demonstrated that the model based on the hypothesis can circumvent the problem of iSPNs learning to disinhibit the actions associated with negative reward errors. They further showed by analyzing the cell-type specific neural recording data by Markowitz et al. (2018) that iSPN activities tend to be anti-correlated before and after action selection.

We thank the reviewer for the positive comments.

Weaknesses:

It is not correct to call the action value learning using the externally-selected action as “offpolicy.” Both off-policy algorithm Q-learning and on-policy algorithm SARSA update the action value of the chosen action, which can be different from the greedy action implicated by the present action values. In standard reinforcement learning terminology, on-policy or off-policy is regarding the actions in the subsequent state, whether to use the next action value of (to be) chosen action or that of greedy choice as in equation (7).

It is worth noting that this paper suggested that dopamine neurons encode on-policy TD errors: Morris G, Nevet A, Arkadir D, Vaadia E, Bergman H (2006). Midbrain dopamine neurons encode decisions for future action. Nat Neurosci, 9, 1057-63. https://doi.org/10.1038/nn1743.

We regret that we do not completely follow the reviewer’s comment. We use “off-policy” to refer to the fact that, considered in isolation, the basal ganglia reinforcement learning system that we model learns a target policy that may be distinct from the behavioral policy of the organism as a whole.

It is also confusing to contract TD learning and Q-learning, as the latter is considered as one type of TD learning. In the TD error signal by state value function (6) is dependent on the chosen action at−1 implicitly in rt and st based on the reward and state transition function.

We agree that this was confusing. We have therefore changed the places in our paper where we intended to refer to “TD learning of a value function V (s)” to specifically mention V (s), rather than just “TD learning.”

It is not clear why interferences of the activities for action selection and learning can be avoided, especially when actions are taken with short intervals or even temporal overlaps. How can the efference copy activation for the previous action be dissociated with the sensory cued activation for the next action selection?

The non-interference arises from the orthogonality of the difference (action selection) and sum (efference copy) modes, as described in Figure 3. However, we agree with the reviewer that the problem of temporal credit assignment, when many actions are taken before reward feedback is obtained, is present in our model, as in any standard RL model.

Although it may be difficult to single out the neural pathway that carries the efference copy signal to the striatum, it is desired to consider their requirements and difference possibilities. A major issue is that the time delay from actions to reward feedback can be highly variable.

An interesting candidate is the long-latency neurons in the CM thalamus projecting to striatal cholinergic interneurons, which are activated following low-reward actions: Minamimoto T, Hori Y, Kimura M (2005). Complementary process to response bias in the centromedian nucleus of the thalamus. Science, 308, 1798-801. https://doi.org/10.1126/science.1109154.

We are grateful for the interesting suggestion and reference, which we have added to the manuscript. However, we note that the issue of delayed reward feedback may also be partially addressed by using a sufficiently long eligibility trace.

In the paragraph before Eq. (3), Eq. (1) should be Eq. (2) for the iSPN.

Corrected.

DOI: 10.1186/s12967-025-06283-y

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What is this?

DOI: 10.1016/j.celrep.2025.115508

Resource: (Molecular Probes Cat# A-21271, RRID:AB_221448)

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What is this?

DOI: 10.1016/j.cell.2025.03.005

Resource: RRID:Addgene_44362

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What is this?

Briefing Document : Développer la coopération, la confiance et l’autonomie des élèves

Source : Excerpts from "Développer la coopération, la confiance et l’autonomie des élèves - Élise Huillery"

Thème Central : La nécessité de reconnaître et de développer les compétences sociales et comportementales (CSC) des élèves comme un levier essentiel de la réussite scolaire, de l'insertion professionnelle et du bien-être sociétal. Élise Huillery argumente que ces compétences sont actuellement un "angle mort" du système éducatif français, bien que leur importance soit de plus en plus reconnue.

Points Clés et Arguments Principaux :

1. Définition et Importance des Compétences Sociales et Comportementales (CSC) :

• Compétences Comportementales (Rapport à soi) : Estime de soi, optimisme, état d'esprit de développement (croire en la malléabilité de l'intelligence), locus de contrôle (sentiment de maîtrise sur ce qui nous arrive), contrôle de l'impulsivité, autodiscipline.
• Huillery précise : "les compétences comportementales ça va référer à tout ce qui est rapport à soi-même donc on va retrouver dans les compétences comportementales dans le rapport à soi des des l'estime de soi une forme d'optimisme par rapport à à ses chances de réussite un état d'esprit de développement [...] un locus de contrôle [...] et la capacité en fait tout tout ce que je viens de citer ça réfère à un sentiment qu'on est capable de progresser qu'on est capable en faisant des efforts d'y arriver et puis il y a également de des des compétences très importantes qui sont le contrôle de son impulsivité et l'autodiscipline."
• Compétences Sociales (Rapport aux autres) : Capacité à coopérer, empathie, respect, tolérance, contrôle de l'impulsivité et de l'agressivité envers les autres, sentiment d'appartenance.
• Elle explique : "dans les champs des compétences sociales donc là on va parler du rapport aux autres et de la qualité du rapport aux autres donc entre autres on va avoir la capacité à coopérer à être empathique à respecter à tolérer à contrôler également l'impulsivité l'agressivité par rapport aux autres mais également le sentiment d'appartenance le sentiment de faire partie d'une équipe que l'on soutient et par laquelle on est soutenu".
• Terminologie : Huillery clarifie l'utilisation de termes variés tels que "soft skills", "compétences non cognitives" ou "compétences socio-émotionnelles", soulignant qu'il s'agit bien de compétences cognitives qui se développent dans le cerveau. Elle préfère l'appellation "compétences sociales et comportementales" pour éviter les connotations réductrices de certains termes.

2. Le Déficit Français en Matière de CSC :

• État d'esprit de développement : Les élèves français ont un score d'état d'esprit de développement inférieur à la moyenne des pays de l'OCDE, indiquant une moindre croyance en la capacité de l'intelligence à évoluer.
• "ce qu'on voit c'est que la France est en dessous de la moyenne des pays de l'OCDE concernant le score d'état d'esprit de développement donc la croyance que l'intelligence c'est quelque chose qui peut évoluer et qui peut s'entraîner et se développer en fait."
• Anxiété, Sentiment de Compétence, Persévérance, Ouverture à la Résolution de Problèmes, Locus de Contrôle : Les données PISA montrent que les élèves français sont plus anxieux, ont un sentiment de compétence en mathématiques plus faible (même en étant dans la moyenne des performances), sont moins persévérants, moins ouverts à la résolution de problèmes et ont un locus de contrôle plus externe (sentiment que ce qui leur arrive dépend de facteurs extérieurs).
• Concernant l'anxiété : "ce qu'on voit c'est que la France a une les élèves français [...] ont un niveau d'anxiété qui est nettement au-dessus des pays de l'OCDE".
• Sur le locus de contrôle : "l'écart par rapport à la moyenne des autres pays est très fort on a des élèves pour qui le locus de contrôle est beaucoup plus externe et donc bah par contraposer moins interne donc en gros nos élèves pensent plus qu'ailleurs que ce qui leur arrive dépend de l'extérieur de contrainte extérieure et non pas de leur stratégie de leur travail de leurs efforts à eux."
• Coopération : La France se situe au dernier rang des pays de l'OCDE en termes de capacité et de perception de la pratique coopérative.
• "pour la coopération c'est la catastrophe nous sommes le dernier pays dernier pays de tous les pays de l'OCDE dans notre dans la capacité en fait l'indice de coopération il mesure la comment les élèves voient la pratique coopérative et comment ils l'utilisent et alors là on est les derniers."
• Sentiment d'Appartenance et Résolution Collaborative de Problèmes : Bien que moins marqué, la France est également en dessous de la moyenne de l'OCDE dans ces domaines.
• Similitudes chez les Adultes : Huillery souligne que ces déficits observés chez les élèves se retrouvent également chez les adultes, d'après les enquêtes comme le PIAC.

3. L'Impact des CSC sur la Réussite Scolaire, l'Insertion Professionnelle et la Société :

• État d'esprit de développement et réussite scolaire : Des études menées dans plusieurs pays, dont la France, montrent que le développement d'un état d'esprit de développement chez les élèves a des effets positifs et significatifs sur leurs résultats scolaires et leurs apprentissages, même à long terme. Une expérimentation menée dans des collèges REP+ en France a confirmé ces effets positifs sur l'état d'esprit, le comportement en classe et les résultats scolaires des élèves.
• "ces programmes ont tous montré des effets positifs et significatifs sur des tests scolaires sur les notes des élèves sur des apprentissages même 2 ans et demi après les interventions".
• Apprentissage Coopératif et Feedback : L'apprentissage coopératif est une pratique pédagogique à fort impact sur l'apprentissage des élèves, avec un coût relativement faible (principalement de la formation des enseignants). Le feedback individualisé et positif est identifié comme l'intervention éducative ayant l'impact le plus élevé sur la progression des élèves.
• Concernant l'apprentissage coopératif : "l'apprentissage coopératif ça fait partie des pratiques pédagogiques qui ont été le plus étudié avec le un niveau de de confiance dans les résultats élevés et qui montre des effets qui sont bien plus importants euh par rapport au au à la réduction des tailles de classe".
• Sur le feedback positif : "le feedback doit aussi être positif c'est-à-dire que le feedback doit dire à l'élève pas seulement ce sur quoi il faut qu'il retravaille [...] il faut il faut faire aussi beaucoup de feedback positif en disant c'est super tu as progressé là-dessus je suis très contente que tu aies réussi à faire ce cette chose-là même si là et on va essayer de retravailler et cetera."
• Programme de Montréal (Contrôle de Soi et Habilités Sociales) : Un programme ciblant des enfants défavorisés âgés de 7 à 9 ans, axé sur le contrôle de soi et les habiletés sociales, a eu des effets spectaculaires à long terme : amélioration des compétences sociales, augmentation du taux d'obtention du baccalauréat, hausse des revenus annuels, baisse de l'inactivité, diminution de la criminalité et de la dépendance aux prestations sociales. Ce programme s'est avéré être un investissement très rentable pour la société.
• "ce programme se finance lui-même fois 11 donc pour 1 € dépensé dans le programme on a 11 enfin c'est des dollars on a 11 dollars de gagner entre guillemets si on fait toute la somme de ces bénéfices à la fois individuels et sociaux."
• Les CSC comme facteurs instrumentaux : Huillery insiste sur le fait que le développement des CSC n'est pas seulement souhaitable en soi, mais qu'elles sont des compétences "instrumentales" qui favorisent la réussite scolaire et l'insertion professionnelle, générant des "bénéfices publics" importants.

4. Recommandations pour l'École :

• Développer l'apprentissage coopératif : Mettre en place des pratiques pédagogiques favorisant la coopération et la collaboration entre les élèves.
• Travailler sur l'état d'esprit de développement : Intégrer la promotion de cet état d'esprit au cœur du système éducatif, en formant les enseignants et les parents.
• Réformer l'évaluation des élèves : Adopter des méthodes d'évaluation qui favorisent un feedback positif et encourageant, aligné sur la progression et non uniquement sur le constat des erreurs.
• Former les enseignants (initiale et continue) : Renforcer la formation des enseignants aux pédagogies et aux attitudes qui développent les CSC.
• Impliquer et former les parents : Reconnaître le rôle central des parents et les former aux pratiques et attitudes favorisant le développement des CSC chez leurs enfants.

Conclusion Principale :

Le discours d'Élise Huillery met en lumière un enjeu crucial pour le système éducatif français : la prise en compte et le développement actif des compétences sociales et comportementales des élèves.

En s'appuyant sur des données de recherche solides, elle démontre que ces compétences ne sont pas des "soft skills" secondaires, mais des leviers fondamentaux pour améliorer la réussite scolaire, favoriser l'insertion professionnelle et générer des bénéfices significatifs pour la société dans son ensemble.

Elle appelle à une transformation des pratiques pédagogiques, de la formation des enseignants et de l'implication des parents pour combler le déficit français dans ce domaine et exploiter pleinement le potentiel des élèves.

以下是关于标准模型中规范群 \( \text{SU}(3) \times \text{SU}(2) \times \text{U}(1) \) 的表示及其通过特征标理论分析的详细解释：

1. 标准模型规范群的结构

标准模型的规范群是三个群的直积： [ \text{G} = \text{SU}(3)\text{C} \times \text{SU}(2)\text{L} \times \text{U}(1)\text{Y}, ] 其中： - \( \text{SU}(3)\text{C} \)：强相互作用，描述夸克之间的色荷对称性。 - \( \text{SU}(2)\text{L} \times \text{U}(1)\text{Y} \)：弱电相互作用，通过希格斯机制破缺为电磁相互作用 \( \text{U}(1)_\text{EM} \)。

2. 规范群的表示与特征标

(1) SU(3) 的表示

• 不可约表示：由最高权 \( (\lambda, \mu) \) 标记，维度为： [ \frac{1}{2} (\lambda + 1)(\mu + 1)(\lambda + \mu + 2). ]
• 基本表示：
• 3：夸克的色三重态（红、绿、蓝）。
• \(\overline{3}\)：反夸克的反三重态。
• 特征标：SU(3) 的特征标 \( \chi_{(\lambda, \mu)} \) 由表示的最高权决定，例如：
• \( \chi_3(g) = \text{tr}(\rho(g)) \)，其中 \( \rho \) 是SU(3)的3维表示。

(2) SU(2) 的表示

• 不可约表示：由自旋 \( j \) 标记，维度为 \( 2j + 1 \)。
• 基本表示：
• 2：左旋费米子（如上/下夸克、电子/中微子）的双旋量。
• 1：右旋费米子（无SU(2)作用）。
• 特征标：SU(2) 的特征标为： [ \chi_j(g) = \frac{\sin((j + 1/2)\theta)}{\sin(\theta/2)}, ] 其中 \( g \) 对应旋转角度 \( \theta \)。

(3) U(1) 的表示

• 不可约表示：由电荷 \( Y \)（超荷）标记，特征标为： [ \chi_Y(g) = e^{iY\theta}, ] 其中 \( g \) 对应相位因子 \( e^{i\theta} \)。

3. 直积群的表示与特征标

规范群 \( \text{G} \) 的表示是三个子群表示的直积： [ \rho = \rho_{\text{SU}(3)} \otimes \rho_{\text{SU}(2)} \otimes \rho_{\text{U}(1)}. ] 其特征标为各分量特征标的乘积： [ \chi_\rho(g_1, g_2, g_3) = \chi_{\text{SU}(3)}(g_1) \cdot \chi_{\text{SU}(2)}(g_2) \cdot \chi_{\text{U}(1)}(g_3). ]

4. 特征标理论的应用

(1) 粒子的量子数

标准模型中的粒子由规范群的表示决定其量子数： - 夸克：属于 \( 3 \otimes 2 \otimes Y \) 表示，例如： - 上夸克 \( u \)：\( Y = \frac{1}{3} \)，电荷 \( Q = T_3 + Y = \frac{2}{3} \)（通过希格斯机制）。 - 下夸克 \( d \)：\( Y = \frac{1}{3} \)，电荷 \( Q = -\frac{1}{3} \)。

(2) 场的相互作用

• 规范玻色子：
• \( \text{SU}(3) \)：8种胶子（\( \text{adj}(3) \) 表示）。
• \( \text{SU}(2) \)：3种 \( W^\pm, Z \) 玻色子（\( \text{adj}(2) \) 表示）。
• \( \text{U}(1) \)：光子（\( \text{adj}(1) \) 为平凡表示）。

(3) 希格斯机制

• 希格斯场：属于 \( 1 \otimes 2 \otimes Y=-1 \) 表示。
• 对称性破缺：希格斯场的真空期望值 \( \langle H \rangle \) 破缺 \( \text{SU}(2)L \times \text{U}(1)_Y \to \text{U}(1)\text{EM} \)，导致：
• \( W^\pm, Z \) 获得质量。
• 光子 \( A_\mu \) 的电荷 \( Q = T_3 + Y \sin^2\theta_W \)。

(4) 粒子的组合与衰变

• 夸克反夸克对：\( 3 \otimes \overline{3} = 8 \oplus 1 \)，对应八重态和单重态介子。
• 费米子与玻色子的耦合：通过特征标分析场的相互作用顶点，例如： [ \text{上夸克} \otimes \text{下夸克} \otimes W^+ \sim (2 \otimes 2) \ni 1 \text{（通过SU(2)分解）}. ]

5. 具体例子：费米子的表示

(1) 上夸克 \( u_L \)

• 表示：\( 3 \otimes 2 \otimes Y = \frac{1}{3} \)。
• 特征标： [ \chi_{u_L}(g) = \chi_3(g_3) \cdot \chi_{1/2}(g_2) \cdot e^{i(Y\theta)}. ]

(2) 电子 \( e_R \)

• 表示：\( 1 \otimes 1 \otimes Y = -1 \)（右旋电子无SU(2)作用）。
• 电荷：\( Q = T_3 + Y = 0 + (-1) = -1 \)。

(3) \( W^+ \) 玻色子

• 表示：\( 1 \otimes 3 \otimes Y = 0 \)（\( \text{SU}(2) \) 的伴随表示）。
• 质量：通过希格斯场 \( H \) 的真空期望值 \( \langle H \rangle \) 获得。

6. 特征标理论的数学工具

(1) 分解表示

通过特征标正交性分解直积表示： [ \text{SU}(3) \otimes \text{SU}(2) \otimes \text{U}(1) \text{ 的表示} \to \bigoplus_{i,j,k} N_{ijk} \cdot (i,j,k), ] 其中 \( N_{ijk} \) 是非负整数，由特征标内积计算。

(2) 电荷的计算

电磁荷 \( Q \) 由弱超荷 \( Y \) 和SU(2)第三分量 \( T_3 \) 组合而来： [ Q = T_3 + \frac{Y}{2}. ] 例如，上夸克 \( u_L \)： [ Q = \frac{1}{2} + \frac{1}{3} \cdot \frac{1}{2} = \frac{2}{3}. ]

7. 总结：特征标理论在标准模型中的核心作用

| 应用 | 特征标理论的作用 | |------------------------|------------------------------------------------------------------------------------| | 粒子分类 | 确定粒子属于哪个表示，计算其量子数（如电荷、色荷）。 | | 相互作用分析 | 通过表示的张量积分解，确定粒子间可能的相互作用（如弱相互作用的费米子-玻色子耦合）。 | | 对称性破缺 | 分析希格斯场如何破缺对称性，导致规范玻色子获得质量。 | | 实验预测 | 预测新粒子的存在（如顶夸克、希格斯玻色子）及其性质。 |

8. 扩展思考：更复杂的表示与超对称

特征标理论还可推广到： - 超对称模型：引入超对称群 \( \text{SU}(3) \times \text{SU}(2) \times \text{U}(1) \times \text{SU}(N)_\text{SUSY} \)。 - GUT理论：如 \( \text{SU}(5) \) 或 \( \text{SO}(10) \)，其表示通过分支规则分解为标准模型的子表示。

通过特征标理论，标准模型的规范群结构得以系统化分析，成为现代粒子物理中连接对称性、相互作用和粒子性质的核心工具。

让我们通过具体的例子和几何直观来详细解释这一概念：

1. 子空间 \( H \) 的定义

在二维实数空间 \( \mathbb{R}^2 \) 中，子群 \( H \) 定义为： [ H = {(x, 0) \mid x \in \mathbb{R}} ] 这是一个一维子空间，即 x 轴。它满足以下性质： - 对于任意 \( (x, 0), (y, 0) \in H \)，它们的和 \( (x + y, 0) \in H \)。 - 对于任意实数 \( c \)，\( c \cdot (x, 0) = (cx, 0) \in H \)。

2. 陪集 \( g + H \) 的构造

取 \( \mathbb{R}^2 \) 中的一个点 \( g = (a, b) \)，则陪集 \( g + H \) 定义为： [ g + H = {(a, b) + (x, 0) \mid (x, 0) \in H} ] 具体计算每个元素： [ (a, b) + (x, 0) = (a + x, b + 0) = (a + x, b) ] 因此，陪集 \( g + H \) 的所有元素形式为： [ {(a + x, b) \mid x \in \mathbb{R}} ] 这是一条 水平直线，其 \( y \) 坐标固定为 \( b \)，而 \( x \) 坐标可以取任意实数值。

3. 几何解释：平行于 \( H \) 的仿射子空间

• 子空间 \( H \) 是 x 轴：它过原点，方向为水平方向。
• 陪集 \( g + H \) 是平行于 \( H \) 的直线：因为：
• \( g + H \) 的方向与 \( H \) 完全相同（水平方向）。
• \( g + H \) 的位置由点 \( g = (a, b) \) 决定，即它沿着 \( y = b \) 的方向平移了 \( b \) 单位。

例如： - 若 \( g = (2, 3) \)，则陪集 \( g + H \) 是直线 \( y = 3 \)，与 x 轴平行。 - 若 \( g = (0, 0) \)，则陪集 \( g + H = H \) 本身（即 x 轴）。

4. 陪集的性质

（1）平行性

陪集 \( g + H \) 的方向与 \( H \) 完全相同，因此它们是平行的。这是因为： - \( H \) 的方向由其基向量 \( (1, 0) \) 决定。 - \( g + H \) 的方向同样由 \( (1, 0) \) 决定，只是位置平移了 \( (a, b) \)。

（2）划分整个空间

所有陪集 \( g + H \)（对于不同的 \( g \in \mathbb{R}^2 \)）将 \( \mathbb{R}^2 \) 分解为无数条平行的水平线，且这些直线： - 互不相交：如果两条水平线 \( y = b_1 \) 和 \( y = b_2 \) 满足 \( b_1 \neq b_2 \)，则它们没有公共点。 - 覆盖整个空间：每个点 \( (x, y) \in \mathbb{R}^2 \) 都属于唯一的陪集 \( (x, y) + H = y \text{-水平线} \)。

（3）仿射子空间

陪集 \( g + H \) 是一个仿射子空间，它与子空间 \( H \) 平行，但不经过原点（除非 \( g \) 是原点）。仿射子空间的几何意义是： - 它是子空间 \( H \) 经过平移后的结果。 - 它的结构与 \( H \) 相同（维度、方向），但位置不同。

5. 代数与几何的对应关系

（1）代数定义

陪集 \( g + H \) 的代数定义： [ g + H = {g + h \mid h \in H} ] 对应几何上的水平直线 \( y = b \)，其中 \( g = (a, b) \) 是平移向量。

（2）等价关系

陪集的划分源于等价关系 \( x \sim y \iff x - y \in H \)。在 \( \mathbb{R}^2 \) 中： - 两点 \( (x_1, y_1) \) 和 \( (x_2, y_2) \) 等价当且仅当它们的 \( y \) 坐标相同（即 \( y_1 = y_2 \)）。 - 因此，等价类正是水平直线 \( y = b \)。

6. 例子验证

例子 1：\( g = (0, 0) \)

• 陪集 \( g + H = (0, 0) + H = H \)，即 x 轴本身。

例子 2：\( g = (2, 3) \)

• 陪集 \( g + H = {(2 + x, 3) \mid x \in \mathbb{R}} \)，即水平直线 \( y = 3 \)。

例子 3：\( g = (5, -1) \)

• 陪集 \( g + H = {(5 + x, -1) \mid x \in \mathbb{R}} \)，即水平直线 \( y = -1 \)。

7. 总结

在 \( \mathbb{R}^2 \) 中： - 子空间 \( H \) 是 x 轴（一维子空间）。 - 陪集 \( g + H \) 是一条水平直线，与 x 轴平行，其位置由点 \( g = (a, b) \) 的 \( y \) 坐标 \( b \) 决定。 - 所有陪集构成 \( \mathbb{R}^2 \) 的水平直线族，彼此平行且覆盖整个平面。

这种几何解释不仅直观，还揭示了代数结构（子群、陪集）与几何对象（子空间、仿射空间）之间的深刻联系。希望这个解释能帮助您更好地理解这一概念！如果有其他问题，欢迎继续提问！

A long list of references for the article are listed here, with all of them being in MLA format. Many of these are links to other articles, graphics, studies and interviews that Davies used or referenced to strengthen her own article.

Cosas que no sabía y aún no entiendo https://platzi.com/clases/2292-terminal/37341-que-es-la-terminal/

No comprendo por qué estos formatos interactúan diferente a los documentos convencionales que se usan. Por ejemplo, hacer este ejercicio de lectura comentada con las herramientas convencionales que puede ofrecer Acrobat, por ejemplo.

Cómo abrir la consola de comandos

verifiqué y sí tengo instalado mdbook

Nótese que la lectura hipertextual nos ofrece enlaces para ampliar el contexto. Es importante cliquearlos y tener presente no sólo como los enlaces propuestos nos ofrecen mayor claridad, sino qué otros enlaces agregaríamos.

Esto es en el contexto de la documentación del LabCI, pero podría usarse también para procesos investigativos de pregrado y postgrado ofreciendo una versión web del documento en línea.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This study aimed to investigate the effects of optically stimulating the A13 region in healthy mice and a unilateral 6-OHDA mouse model of Parkinson's disease (PD). The primary objectives were to assess changes in locomotion, motor behaviors, and the neural connectome. For this, the authors examined the dopaminergic loss induced by 6-OHDA lesioning. They found a significant loss of tyrosine hydroxylase (TH+) neurons in the substantia nigra pars compacta (SNc) while the dopaminergic cells in the A13 region were largely preserved. Then, they optically stimulated the A13 region using a viral vector to deliver the channelrhodopsine (CamKII promoter). In both sham and PD model mice, optogenetic stimulation of the A13 region induced pro-locomotor effects, including increased locomotion, more locomotion bouts, longer durations of locomotion, and higher movement speeds. Additionally, PD model mice exhibited increased ipsi lesional turning during A13 region photoactivation. Lastly, the authors used whole-brain imaging to explore changes in the A13 region's connectome after 6-OHDA lesions. These alterations involved a complex rewiring of neural circuits, impacting both afferent and efferent projections. In summary, this study unveiled the pro-locomotor effects of A13 region photoactivation in both healthy and PD model mice. The study also indicates the preservation of A13 dopaminergic cells and the anatomical changes in neural circuitry following PD-like lesions that represent the anatomical substrate for a parallel motor pathway.

Strengths:

These findings hold significant relevance for the field of motor control, providing valuable insights into the organization of the motor system in mammals. Additionally, they offer potential avenues for addressing motor deficits in Parkinson's disease (PD). The study fills a crucial knowledge gap, underscoring its importance, and the results bolster its clinical relevance and overall strength.

The authors adeptly set the stage for their research by framing the central questions in the introduction, and they provide thoughtful interpretations of the data in the discussion section. The results section, while straightforward, effectively supports the study's primary conclusion - the pro-locomotor effects of A13 region stimulation, both in normal motor control and in the 6-OHDA model of brain damage.

We thank the reviewer for their positive comments.

Weaknesses:

(1) Anatomical investigation. I have a major concern regarding the anatomical investigation of plastic changes in the A13 connectome (Figures 4 and 5). While the methodology employed to assess the connectome is technically advanced and powerful, the results lack mechanistic insight at the cell or circuit level into the pro-locomotor effects of A13 region stimulation in both physiological and pathological conditions. This concern is exacerbated by a textual description of results that doesn't pinpoint precise brain areas or subareas but instead references large brain portions like the cortical plate, making it challenging to discern the implications for A13 stimulation. Lastly, the study is generally well-written with a smooth and straightforward style, but the connectome section presents challenges in readability and comprehension. The presentation of results, particularly the correlation matrices and correlation strength, doesn't facilitate biological understanding. It would be beneficial to explore specific pathways responsible for driving the locomotor effects of A13 stimulation, including examining the strength of connections to well-known locomotor-associated regions like the Pedunculopontine nucleus, Cuneiformis nucleus, LPGi, and others in the diencephalon, midbrain, pons, and medulla.

We initially considered two approaches. The first was to look at specific projections to the motor regions, focusing on the MLR. The second was to utilize a whole-brain analysis, which is presented here. Given what we know about the zona incerta, especially its integrative role, we felt that examining the full connectome was a reasonable starting point.

The value of the whole-brain approach is that it provides a high-level overview of the afferents and efferents to the region. The changes in the brain that occur following Parkinson-like lesions, such as those in the nigrostriatal pathway, are complex and can affect neighbouring regions such as the A13. Therefore, we wished to highlight the A13, which we considered a therapeutic target, and examine changes in connectivity that could occur following acute lesions affecting the SNc. We acknowledge that this study does not provide a causal link, but it presents the fundamental background information for subsequent hypothesis-driven, focused, region-specific analysis.

The terms provided were taken from the Allen Brain Atlas terminology and presented as abbreviations. We have added two new figures focusing on motor regions to make the information more comprehensible (new Figures 4 and 5) and rewrote the connectomics section to make it easier to understand.

Additionally, identifying the primary inputs to A13 associated with motor function would enhance the study's clarity and relevance.

This is a great point to help simplify the whole-brain results. We have presented the motor-related inputs and outputs as part of a new figure in the main paper (Figure 5) and added accompanying text in the results section. We have also updated the correlation matrices to concentrate on motor regions (Figure 4). This highlights possible therapeutic pathways. We have also enhanced our discussion of these motor-related pathways. We have retained the entire dataset and added it to our data repository for those interested.

The study raises intriguing questions about compensatory mechanisms in Parkinson's disease and a new perspective on the preservation of dopaminergic cells in A13, despite the SNc degeneration, and the plastic changes to input/output matrices. To gain inspiration for a more straightforward reanalysis and discussion of the results, I recommend the authors refer to the paper titled "Specific populations of basal ganglia output neurons target distinct brain stem areas while collateralizing throughout the diencephalon from the David Kleinfeld laboratory." This could guide the authors in investigating motor pathways across different brain regions.

Thank you for the advice. As pointed out, Kleinfeld’s group presented their data in a nice, focused way. For the connectomic piece, we have added Figure 5, which provides a better representation than our previous submission.

(2) Description of locomotor performance. Figure 3 provides valuable data on the locomotor effects of A13 region photoactivation in both control and 6-OHDA mice. However, a more detailed analysis of the changes in locomotion during stimulation would enhance our understanding of the pro-locomotor effects, especially in the context of 6-OHDA lesions. For example, it would be informative to explore whether the probability of locomotion changes during stimulation in the control and 6-OHDA groups. Investigating reaction time, speed, total distance, and could reveal how A13 is influencing locomotion, particularly after 6-OHDA lesions. The laboratory of Whelan has a deep knowledge of locomotion and the neural circuits driving it so these features may be instructive to infer insights on the neural circuits driving movement. On the same line, examining features like the frequency or power of stimulation related to walking patterns may help elucidate whether A13 is engaging with the Mesencephalic Locomotor Region (MLR) to drive the pro-locomotor effects. These insights would provide a more comprehensive understanding of the mechanisms underlying A13-mediated locomotor changes in both healthy and pathological conditions.

Thank you for these suggestions. We have reorganized Figure 3 to highlight the metrics by separating the 6-OHDA from the Sham experiments (3F-J, which highlights distance travelled, average speed and duration). We have also added additional text to highlight these metrics better in the text. We have relabelled Supplementary Figure S3, which presents reaction time as latency to initiate locomotion and updated the main text to address the reviewers' points.

Reviewer #2 (Public Review):

Summary:

The paper by Kim et al. investigates the potential of stimulating the dopaminergic A13 region to promote locomotor restoration in a Parkinson's mouse model. Using wild-type mice, 6-OHDA injection depletes dopaminergic neurons in the substantia nigra pars compacta, without impairing those of the A13 region and the ventral tegmentum area, as previously reported in humans. Moreover, photostimulation of presumably excitatory (CAMKIIa) neurons in the vicinity of the A13 region improves bradykinesia and akinetic symptoms after 6-OHDA injection. Whole-brain imaging with retrograde and anterograde tracers reveals that the A13 region undergoes substantial changes in the distribution of its afferents and projections after 6-OHDA injection. The study suggests that if the remodeling of the A13 region connectome does not promote recovery following chronic dopaminergic depletion, photostimulation of the A13 region restores locomotor functions.

Strengths:

Photostimulation of presumably excitatory (CAMKIIa) neurons in the vicinity of the A13 region promotes locomotion and locomotor recovery of wild-type mice 1 month after 6-OHDA injection in the medial forebrain bundle, thus identifying a new potential target for restoring motor functions in Parkinson's disease patients.

Weaknesses:

Electrical stimulation of the medial Zona Incerta, in which the A13 region is located, has been previously reported to promote locomotion (Grossman et al., 1958). Recent mouse studies have shown that if optogenetic or chemogenetic stimulation of GABAergic neurons of the Zona Incerta promotes and restores locomotor functions after 6-OHDA injection (Chen et al., 2023), stimulation of glutamatergic ZI neurons worsens motor symptoms after 6-OHDA (Lie et al., 2022).

Thank you - we have added this reference. It is helpful as Grossman did stimulate the zona incerta in the cat and elicit locomotion, suggesting that stimulation of the area in normal mice has external validity. Grossman’s results prompted a later clinical examination of the zona incerta, but it concentrated on the zona incerta regions close to the subthalamic regions (Ossowska 2019), further caudal to the area we focused on. Chen et al. (2023) targeted the area in the lateral aspect of central/medial zona incerta, formed by dorsal and ventral zona incerta, which may account for the differing results. Our data were robust for stimulation of the medial aspect of the rostromedial zona incerta. The thigmotactic behaviour that we observed in our work that focused on CamKII neurons has not been observed with chemogenetic, optogenetic activation or with photoinhibition of GABAergic central/medial ZI (Chen et al. 2023).

GABAergic activation of mZI to Cuneiform projections (Sharma et al. 2024) also did not produce thigmotactic behavior. We have added these points to the discussion.

Although CAMKIIa is a marker of presumably excitatory neurons and can be used as an alternative marker of dopaminergic neurons, behavioral results of this study raise questions about the neuronal population targeted in the vicinity of the A13 region. Moreover, if YFP and CHR2-YFP neurons express dopamine (TH) within the A13 region (Fig. 2), there is also a large population of transduced neurons within and outside of the A13 region that do not, thus suggesting the recruitment of other neuronal cell types that could be GABAergic or glutamatergic.

We found that CamKII transfection of the A13 region was extremely effective in promoting locomotor activity, which was critical for our work in exploring its possible therapeutic potential. We have since quantified the cell number, we found that the c-fos cell number was increased following ChR2 activation. There is evidence of TH activation - but the data suggest that other cell types contribute. C-fos alone is a blunt tool to assess specificity - rather, it is better at showing overall photostimulus efficacy - which we have demonstrated. Moreover, there is evidence that cell types are not purely dopaminergic, with GABA co-localized (Negishi et al. 2020). We acknowledge that specific viral approaches that target the GABAergic, glutamatergic, and dopaminergic circuits would be very useful. The range of tools to target A13 dopaminergic circuits is more limited than the SNc, for example, because the A13 region lacks DAT, and TH-IRES-Cre approaches, while helpful, are less specific than DAT-Cre mouse models. Intersectional approaches targeting multiple transmitters (glutamate & dopamine, for example) may be one solution as we do not expect that a single transmitter-specific pathway would work, as well as broad targeting of the A13 region. Our recent work suggests that GABAergic neuron activation may have more general effects on behaviour rather than control of ongoing locomotor parameters (Sharma et al. 2024). Recent work shows a positive valence effect of dopamine A13 activation on motivated food-seeking behavior, which differs from consummatory behavior observed with GABAergic modulation (Ye, Nunez, and Zhang 2023). Chemogenetic inactivation and ablation of dopaminergic A13 revealed that they contribute to grip strength and prehensile movements, uncoupling food-seeking grasping behavior from motivational factors (Garau et al. 2023). Overall, this suggests differing effects of GABA compared to DA and/or glutamatergic cell types, consistent with our effects of stimulating CamKII. The discussion has been updated.

Regarding the analysis of interregional connectivity of the A13 region, there is a lack of specificity (the viral approach did not specifically target the A13 region), the number of mice is low for such correlation analyses (2 sham and 3 6-OHDA mice), and there are no statistics comparing 6-OHDA versus sham (Fig. 4) or contra- versus ipsilesional sides (Fig. 5). Moreover, the data are too processed, and the color matrices (Fig. 4) are too packed in the current format to enable proper visualization of the data. The A13 afferents/efferents analysis is based on normalized relative values; absolute values should also be presented to support the claim about their upregulation or downregulation.

Generally, papers using tissue-clearing imaging approaches have low sample sizes due to technical complexity and challenges. The technical challenges of obtaining these data were substantial in both collection and analysis. There are multiple technical complexities arising from dual injections (A13 and MFB coordinates) and targeting the area correctly. The A13 region is difficult to target as it spans only around 300 µm in the anterior-posterior axis. While clearing the brain takes weeks, and light-sheet imaging also takes time, the time necessary to analyze the tissue using whole-brain quantification is labor intensive, especially with a lack of a standardized analysis pipeline from atlas registrations, signal segmentations, and quantifications. The field is still relatively new, requiring additional time to refine pipelines.

Correlation matrices are often used in analyzing connectivity patterns on a brain-wide scale, as they can identify any observable patterns within a large amount of data. We used correlation matrices to display estimated correlation coefficients between the afferent and efferent proportions from one brain subregion to another across 251 brain regions in total in a pairwise manner (not for hypothesis testing). We provided descriptive statistics (mean and error bars) in the original Figure 5C and G. As mentioned in comments for Reviewer 1, we have now presented the data in revised Figure 4 and 5 that focuses specifically on motor-related pathways to provide information on possible pathways. The has simplified the correlation matrices and highlighted the differences in 6-OHDA efferent data especially. As suggested, raw values are shared in a supplemental file on our data repository.

In the absence of changes in the number of dopaminergic A13 neurons after 6-OHDA injection, results from this correlation analysis are difficult to interpret as they might reflect changes from various impaired brain regions independently of the A13 region.

We acknowledge that models of Parkinson’s disease, particularly those using 6-OHDA, induce plasticity in various regions, which may subsequently affect A13 connectivity. We aim to emphasize the residual, intact A13 pathways that could serve as therapeutic targets in future investigations. This emphasis is pertinent in the context of potential clinical applications, as the overall input and output to the region fundamentally dictate the significance of the A13 region in lesioned nigrostriatal models. We agree with the reviewer that the changes certainly can be independent of A13; however, the fact that there was a significant change in the connectome post-6-OHDA injection and striatonigral degeneration is in and of itself important to document. We have added a sentence acknowledging this limitation to the discussion.

There is no causal link between anatomical and behavioral data, which raises questions about the relevance of the anatomical data.

This point was also addressed earlier in response to a comment from Reviewer 1. Focusing on specific motor pathways is one avenue to explore. However, given that the zona incerta acts as an integrative hub, we believed it is prudent to initially examine both afferent and efferent pathways using a brain-wide approach. For instance, without employing this methodology, the potential significance of cortical interconnectivity to the A13 region might not have been fully appreciated. As mentioned previously, we will place additional emphasis on motor-related regions in our revised paper, thereby enhancing the relevance of the anatomical data presented. With these modifications, we anticipate that our data will underscore specific motor-related targets for future exploration, employing optogenetic targeting to assess necessity and sufficiency.

Overall, the study does not take advantage of genetic tools accessible in the mouse to address the direct or indirect behavioral and anatomical contributions of the A13 region to motor control and recovery after 6-OHDA injection.

Our study has not specifically targeted neurons that express dopaminergic, glutamatergic, or GABAergic properties (refer to earlier comment for more detail). However, like others, we find that targeting one neuronal population often does not result in a pure transmitter phenotype. For instance, evidence suggests co-localization of dopamine neurons with a subpopulation of GABA neurons in the A13/medial zona incerta (Negishi et al. 2020). In the hypothalamus, research by Deisseroth and colleagues (Romanov et al. 2017) indicates the presence of multiple classes of dopamine cells, each containing different ratios of co-localized peptides and/or fast neurotransmitters. Consequently, we believe our work lays the foundation for the investigations suggested by the reviewer. Furthermore, if one considers this work in the context of a preclinical study to determine whether the A13 might be a target in human Parkinson's disease, the existing technology that could be utilized is deep brain stimulation (DBS) or electrical modulation, which would also affect different neuronal populations in a non-specific manner.

While optogenetic stimulation therapy is longer term, using CamKII combined with the DJ hybrid AAV could be a translatable strategy for targeting A13 neuronal populations in non-human primates (Watakabe et al. 2015; Watanabe et al. 2020). We have added to the discussion.

Reviewer #3 (Public Review):

Kim, Lognon et al. present an important finding on pro-locomotor effects of optogenetic activation of the A13 region, which they identify as a dopamine-containing area of the medial zona incerta that undergoes profound remodeling in terms of afferent and efferent connectivity after administration of 6-OHDA to the MFB. The authors claim to address a model of PD-related gait dysfunction, a contentious problem that can be difficult to treat with dopaminergic medication or DBS in conventional targets. They make use of an impressive array of technologies to gain insight into the role of A13 remodeling in the 6-OHDA model of PD. The evidence provided is solid and the paper is well written, but there are several general issues that reduce the value of the paper in its current form, and a number of specific, more minor ones. Also, some suggestions, that may improve the paper compared to its recent form, come to mind.

Thank you for the suggestions and careful consideration of our work - it is appreciated.

The most fundamental issue that needs to be addressed is the relation of the structural to the behavioral findings. It would be very interesting to see whether the structural heterogeneity in afferent/effects projections induced by 6-OHDA is related to the degree of symptom severity and motor improvement during A13 stimulation.

As mentioned in comments for Reviewer 1, we have performed additional analysis and present this in Figure 5. We have also revised Figure 4, focusing on motor regions. Our work will provide a roadmap for future studies to disentangle divergent or convergent A13 pathways that are involved in different or all PD-related motor symptoms. Because we could not measure behavioural change in the same animals studied with the anatomic study (essentially because the optrode would have significantly disrupted the connectome we are measuring), we cannot directly compare behaviour to structure.

The authors provide extensive interrogation of large-scale changes in the organization of the A13 region afferent and efferent distributions. It remains unclear how many animals were included to produce Fig 4 and 5. Fig S5 suggests that only 3 animals were used, is that correct? Please provide details about the heterogeneity between animals. Please provide a table detailing how many animals were used for which experiment. Were the same animals used for several experiments?

The behavioral set and the anatomical set were necessarily distinct. In the anatomical experiments, we employed both anterograde and retrograde viral approaches to target the afferent and efferent A13 populations with fluorescent proteins. For the behavioral approach, a single ChR2 opsin was utilized to photostimulate the A13 region; hence combining the two populations was not feasible. We were also concerned that the optrode itself would interfere with connectomics. A lower number of animals were used for the whole-brain work due to technical limitations described earlier. We have now provided additional information regarding numbers in all figures and the text. Using Spearman’s correlation analysis, we found afferent and efferent proportions across animals to be consistent, with an average correlation of 0.91, which is reported in Figure S6.

While the authors provide evidence that photoactivation of the A13 is sufficient in driving locomotion in the OFT, this pro-locomotor effect seems to be independent of 6-OHDA-induced pathophysiology. Only in the pole test do they find that there seems to be a difference between Sham vs 6-OHDA concerning the effects of photoactivation of the A13. Because of these behavioral findings, optogenic activation of A13 may represent a gain of function rather than disease-specific rescue. This needs to be highlighted more explicitly in the title, abstract, and conclusion.

Optogenetic activation of A13 may represent a gain of function in both healthy and 6-OHDA mice, highlighting a parallel descending motor pathway that remains intact. 6-OHDA lesions have multiple effects on motor and cognitive function. This makes a single pathway unlikely to rescue all deficits observed in 6-OHDA models. The lack of locomotion observed in 6-OHDA models can be reversed by A13 region photostimulation. Therefore, this is a reversal of a loss of function, in this case. However, the increase in turning represents a gain of function. We have highlighted this as suggested in the discussion.

The authors claim that A13 may be a possible target for DBS to treat gait dysfunction. However, the experimental evidence provided (in particular the lack of disease-specific changes in the OFT) seems insufficient to draw such conclusions. It needs to be highlighted that optogenetic activation does not necessarily have the same effects as DBS (see the recent review from Neumann et al. in Brain: https://pubmed.ncbi.nlm.nih.gov/37450573/). This is important because ZI-DBS so far had very mixed clinical effects. The authors should provide plausible reasons for these discrepancies. Is cell-specificity, which only optogenetic interventions can achieve, necessary? Can new forms of cyclic burst DBS achieve similar specificity (Spix et al, Science 2021)? Please comment.

Thank you for the valuable comments. They have been incorporated into the discussion.

Our study highlights a parallel motor pathway provided by the A13 region that remains intact in 6-OHDA mice and can be sufficiently driven to rescue the hypolocomotor pathology observed in the OFT and overcome bradykinesia and akinesia. The photoactivation of ipsilesional A13 also has an overall additive effect on ipsiversive circling, representing a gain of function on the intact side that contributes to the magnitude of overall motor asymmetry against the lesioned side. The effects of DBS are rather complex, ranging from micro-, meso-, to macro-scales, involving activation, inhibition, and informational lesioning, and network interactions. This could contribute to the mixed clinical effects observed with ZI-DBS, in addition to differences in targeting and DBS programming among the studies (see review (Ossowska 2019) ). Also the DBS studies targeting ZI have never targeted the rostromedial ZI which extends towards the hypothalamus and contains the A13. Furthermore, DBS and electrical stimulation of neural tissue, in general, are always limited by current spread and lower thresholds of activation of axons (e.g., axons of passage), both of which can reduce the specificity of the true therapeutic target. Optogenetic studies have provided mechanistic insights that could be leveraged in overcoming some of the limitations in targeting with conventional DBS approaches. Spix et al. (2021) provided an interesting approach highlighting these advancements. They devised burst stimulation to facilitate population-specific neuromodulation within the external globus pallidus. Moreover, they found a complementary role for optogenetics in exploring the pathway-specific activation of neurons activated by DBS. To ascertain whether A13 DBS may be a viable therapy for PD gait, it will be necessary to perform many more preclinical experiments, and tuning of DBS parameters could be facilitated by optogenetic stimulation in these murine models. We have added to the discussion.

In a recent study, Jeon et al (Topographic connectivity and cellular profiling reveal detailed input pathways and functionally distinct cell types in the subthalamic nucleus, 2022, Cell Reports) provided evidence on the topographically graded organization of STN afferents and McElvain et al. (Specific populations of basal ganglia output neurons target distinct brain stem areas while collateralizing throughout the diencephalon, 2021, Neuron) have shown similar topographical resolution for SNr efferents. Can a similar topographical organization of efferents and afferents be derived for the A13/ ZI in total?

The ZI can be subdivided into four subregions in the antero-posterior axis: rostral (ZIr), dorsal (ZId), ventral (ZIv), and caudal (ZIc) regions. The dorsal and ventral ZI is also referred together as central/medial/intermediate ZI. There are topographical gradients in different cell types and connectivity across these subregions (see reviews: (Mitrofanis 2005; Monosov et al. 2022; Ossowska 2019). Recent work by Yang and colleagues (2022) demonstrated a topographical organization among the inputs and outputs of GABAergic (VGAT) populations across four ZI subregions. Given that A13 region encompasses a smaller portion (the medial aspect) of both rostral and medial/central ZI (three of four ZI subregions) and coexpress VGAT, A13 region likely falls under rostral and intermediate medial ZI dataset found in Yang et al. (2022). With our data, we would not be able to capture the breadth of topographical organization shown in Yang et al (2022).

In conclusion, this is an interesting study that can be improved by taking into consideration the points mentioned above.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) Figure 2 indeed presents valuable information regarding the effects of A13 region photoactivation. To enhance the comprehensiveness of this figure and gain a deeper understanding of the neurons driving the pro-locomotor effect of stimulation, it would be beneficial to include quantifications of various cell types:

• cFos-Positive Cells/TH-Positive Cells: it can help determine the impact of A13 stimulation on dopaminergic neurons and the associated pro-locomotor effect in the healthy condition and especially in the context of Parkinson's disease (PD) modeling.

• cFos-Positive Cells /TH-Negative Cells: Investigating the number of TH-negative cells activated by stimulation is also important, as it may reveal non-dopaminergic neurons that play a role in locomotor responses. Identifying the location and characteristics of these TH-negative cells can provide insights into their functional significance.

We have completed this analysis. The data is presented in Figure 2F, where we show increased c-fos intensity with photoactivation. We observed an increase in the number of cells activated in the A13 region. However, we did not definitively see increases in TH+ cells, suggesting a heterogeneous set of neurons responsible for the effects—possibly glutamatergic neurons.

Incorporating these quantifications into Figure 2 would enhance the figure's informativeness and provide a more comprehensive view of the neuronal populations involved in the locomotor effects of A13 stimulation.

We have added text and a new graph.

(2) Refer to Figure 3. In the main text (page 5) when describing the animal with 6-OHDA the wrong panels are indicated. It is indicated in Figure 2A-E but it should be replaced with 3A-E.

Please do that.

Done, and we have updated the figure to improve readability, by separating the 6-OHDA findings from sham in all graphs.

Reviewer #2 (Recommendations For The Authors):

Abstract

Page 1: Inhibitory or lesion studies will be necessary to support the claim that the global remodeling of afferent and efferent projections of the A13 region highlights the Zona Incerta's role as a crucial hub for the rapid selection of motor function.

Overall, there is quite a bit of evidence that the zona incerta is a hub for afferent/efferents.

Mitrofanis (2005) and, more recently, Wang et al. (2020) summarize some of the evidence. Yang (2022) illustrates that the zona incerta shows multiple inputs to GABAergic neurons and outputs to diverse regions. Recent work suggests that the zona incerta contributes to various motor functions such as hunting, exploratory locomotion, and integrating multiple modalities (Zhao et al. 2019; Wang et al. 2019; Monosov et al. 2022; Chometton et al. 2017). The introduction has been updated.

Introduction

Page 2, paragraph 2: "However, little attention has been placed on the medial zona incerta (mZI), particularly the A13, the only dopamine-containing region of the rostral ZI" Is the A13 region located in the rostral or medial ZI or both?

It should have been written “rostromedial” ZI. The A13 is located in the medial aspect of rostromedial ZI. Introduction has been updated.

Page 2, para 3: Li et al (2021) used a mini-endoscope to record the GCaMP6 signal. Masini and Kiehn, 2022 transiently blocked the dopaminergic transmission; they never used 6-OHDA.

Please correct through the text.

Corrected.

Page 2, para 4: the A13 connectome encompasses the cerebral cortex,... MLR. The MLR is a functional region, correct this for the CNF and PPN.

Corrected.

Page 3, the last paragraph of the introduction could be clarified by presenting the behavioral data first, followed by the anatomy.

This has been corrected

Figure 1 is nice and clear, and well summarizes the experimental design.

Thank you.

Figure 2 shows an example of the extent of the ChR2-YFP expression and the position of an optical fiber tip above the dopaminergic A13 region from a mouse. Without any quantification, these images could be included in Figure 1. Despite a very small volume (36.8nL) of AAV, the extent of ChR2-YFP expression is quite large and includes dopaminergic and unidentified neurons within the A13 region but also a large population of unidentified neurons outside of it, thus raising questions about the volume and the types of neurons recruited.

This is an important consideration. The issue of viral spread is complex and depends on factors including tissue type, serotype, and promotor of the virus. Li et al. (2021), for example, used different virus serotypes and promotors, injecting 150nL, whereas we used AAV DJ, injecting 36.8nL. AAV-DJ is a hybrid viral type consisting of multiple serotypes. It has a high transduction efficiency, which leads to greater gene delivery than single-serotype AAV viral constructs (Mao et al. 2016). A secondary consideration regarding translation was that AAV-DJ could effectively transduce non-primate neurons (Watanabe et al. 2020). We have addressed the issue of neurons recruited earlier, provided c-Fos quantification, and provided a new supplementary figure showing viral spread (Figure S1).

Anatomical reconstruction of the extent of the ChR2-YFP expression and the location of the tip of the optical fiber will be necessary to confirm that ChR2-YFP expression was restricted to the A13 region.

We will provide additional information regarding viral spread, ferrule tip placement, and c-fos cell counts. This has been done in Figure 2 and we also present a new Figure S1 where we have quantified the viral spread.

Page 5, 1st para: Double-check the references, as not all of them are 6-OHDA injections in the MLF.

Corrected. Removed Kiehn reference.

Page 5, 1st para, 4th line: Replace ferrule with optical canula or fiber.

Done

Page 5, 1st para, 9th line: Replace Figure 2 with Figure 3.

Done

Page 5, 2nd para: About the refractory decrease in traveled distance by sham-ChR2 mice: is this significant?

It was not significant (Figure S1C, 1-way RM ANOVA: F5,25 = 0.486, P \= 0.783). This has been updated in the text.

Figure 3 showing behavioral assessments is nice, but the stats are not always clear. In Fig 3A, are each of the off and on boxes 1 minute long? The figure legend states the test lasts 1 min, but isn't it 4 minutes? In Figure 3B-E and 3J-M, what are the differences? Do the stats identify a significant difference only during the stimulation phase? Fig. 3F-I are nice and could have been presented as primary examples prior to data analysis in Fig. 3B-E. Group labels above the graph would help.

Yes, the off-on boxes are 1 minute long. The error is corrected in the legend. Great suggestion for F-I - they have been moved ahead of the summary figures. We have also updated new Fig 3F-,I, J, L, M) to make the differences between 6-OHDA and sham graphs easier to visualize. The stats do indicate a significant difference during the stimulation phase. We have added group labels, and reorganized the figure, and it is much easier to read now.

Fig. 3L-M, what do PreSur, Post, and Ferrule mean? I assume that Ferrule refers to mice tested with the optical fiber without stimulation, whereas Stim. refers to the stimulation. It would be helpful to standardize the format of stats in Fig. 3B-E and 3-J-M. What are time points a, b, and c referring to?

We have renamed the figure names to be more intuitive. We have standardized the presentation of statistics in the figure, and eliminated the a,b,c nomenclature. We have also updated the caption to provide descriptions of the tests in Fig 3 L-M.

Figure S2A: the higher variability in 6-OHDA-YFP mice in comparison to 6-OHDA-ChR2 mice prior to stimulation suggests that 6-OHDA-YFP mice were less impaired. Why use boxplots only for these data? Would a pairwise comparison be more appropriate?

We have removed these plots from Figure S2. We now present the Baseline to Pre values across the experimental timespan to illustrate the fact that distance travelled returned to baseline values for all trials conducted.

Fig. S2B: add the statistical marker.

We have removed this from Figure S2.

Page 7, para 1, line 8: to add "in comparison to 6-OHDA-YFP and YFP mice" to during photostimulation... (Figure 3E).

Done

Page 7, para 3, line 5: about larger improvement, replace "sham ChR2" with "6-OHDA."

Done

Page 8, para 1, line 4: Perier et al., 2000 reported that 6-OHDA injection increased the firing frequency of the ZI over a month.

Added the timeframe to this sentence.

Page 8, para 2, line 1: Since the results were expected, add some references.

Done.

Page 8, para 3, line 4. Double-check the reference.

Corrected.

Page 8: About large-scale changes in the A13 region, the relevance of correlation matrices is difficult to grasp. Analysis of local connectivity would have been more informative in the context of GABAergic and glutamatergic neurons of the ZI in the vicinity of the A13 region.

We have updated the figures for connectivity throughout the manuscript. Overall, there are new Figures 4 and 5 in the main text. We also provide a revised Supplementary Figure 8. Unfortunately, we could not do that experiment regarding local connectivity. In light of our new work (Sharma et al. 2024), it is clear that this will be critical going forward.

Page 8, para 3, line: given Fig. 2, there is concern about the claim that only the A13 region was targeted. The time of the analysis after 6-OHDA should be mentioned. Some sections of the paragraph could be moved to methods.

We have provided more information about the viral spread in the text and Supplementary Figure 1. The functional and anatomical experiments are separate, which we realize caused confusion. We have mentioned analysis time after 6-OHDA and inserted this into the text.

Fig. 4: The color code helps the reader visualize distribution differences. However, statistical analyses comparing 6-OHDA versus sham should be included. Quantification per region would greatly help readers visualize the data and support the conclusion. The relationship between the type of correlation (positive or negative) and absolute change (increase or decrease) is unknown in the current format, which limits the interpretation of the data. Moreover, examples of raw images of axons and cells should be presented for several brain regions. The experimental design with a timeline, as in Fig. 1, would be helpful. The legend for Fig. 4 is a bit long. Some sections are very descriptive, whereas others are more interpretive.

We have provided a new Figure 5 where we present quantification per region, and the correlation matrices have been updated in Figure 4. We have also focused on motor regions as mentioned earlier. We also provide examples of raw regions in Supplementary Figure 8. Raw values are shared on our data repository.

Page 10, para 1, line 1: add "afferent" to "changes in -afferent and- projection patterns."

Done

Page 10, para 1, line 9: remove the 2nd "compared to sham" in the sentence.

Done

Page 10, para 1, line 10: remove "coordinated" in "several regions showed a coordinated reduction in afferent density." We cannot say anything about the timing of events, as there is only info at 1 month.

Done

Page 10, para 2: the section should be written in the past tense.

Done

Page 13, para 2, the last sentence is overstated. Please remove "cells" and refer to the A13 region instead.

Done

About differential remodelling of the A13 region connectome: Figure 5C and 5G: The proportion of total afferents ipsi- and contralateral to 6-OHDA injection argues that the A13 region primarily receives inputs from the cortical plate and the striatum. Unfortunately, there are no statistics.

Due to the small sample size, we provided descriptive statistics (mean and error bars) in Figure 5A. As mentioned in comments for Reviewers 1 and 2, we have revised Figure 5 to present data focusing on motor-related pathways to provide clarity. In addition, absolute values are shared on our data repository.

Figure 5 D and 5H: Changes in the proportion of total afferents/projections are relatively modest (less than 10% of the whole population for the highest changes). There is no standard deviation for these data and no statistics. Do they reflect real changes or variability from the injection site?

The changes are relatively modest (less than 10%) since a small brain region usually provides a small proportion of total input (McElvain et al. 2021; Yang et al. 2022). The changes in the proportions reflect real differences between average proportions observed in sham and 6-OHDA mice. The variability in the total labelling of neurons and fibers was minimized by normalizing individual regional counts against total counts found in each animal. This figure has been updated as reviewers requested.

Fig 5F and H: The example in F shows a huge decrease in the striatum, but H indicates only a 2% change, which makes the example not very representative. Absolute values would be helpful.

While a 2% change may seem small, it represents a relatively large change in the A13 efferent connectome. To provide further clarity, we have provided absolute values as suggested in our new supplemental table.

Figure 6 is inaccurate and unnecessary.

Figure 6 has been removed.

Discussion

Although interesting, the discussion is too long.

The discussion has been reduced by about three quarters of a page.

Methods

Page 17, para 1: include the stereotaxic coordinates of the optical cannula above the A13 region.

Added.

References

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Author response:

eLife Assessment

This study provides a valuable contribution to understanding how negative affect influences food-choice decision making in bulimia nervosa, using a mechanistic approach with a drift diffusion model (DDM) to examine the weighting of tastiness and healthiness attributes. The solid evidence is supported by a robust crossover design and rigorous statistical methods, although concerns about low trial counts, possible overfitting, and the absence of temporally aligned binge-eating measures limit the strength of causal claims. Addressing modeling transparency, sample size limitations, and the specificity of mood induction effects, would enhance the study's impact and generalizability to broader populations.

We thank the Editor and Reviewers for their summary of the strengths of our study, and for their thoughtful review and feedback on our manuscript. We apologize for the confusion in how we described the multiple steps performed and hierarchical methods used to ensure that the model we report in the main text was the best fit to the data while not overfitting. We are not certain about what is meant by “[a]ddressing model transparency,” but as described in our response to Reviewer 1 below, we have now more clearly explained (with references) that the use of hierarchical estimation procedures allows for information sharing across participants, which improves the reliability and stability of parameter estimates—even when the number of trials per individual is small. We have clarified for the less familiar reader how our Bayesian model selection criterion penalizes models with more parameters (more complex models). Although details about model diagnostics, recoverability, and posterior predictive checks are all provided in the Supplementary Materials, we have clarified for the less familiar reader how each of these steps ensures that the parameters we estimate are not only identifiable and interpretable, but also ensure that the model can reproduce key patterns in the data, supporting the validity of the model. Additionally, we have provided all scripts for estimating the models by linking to our public Github repository. Furthermore, we have edited language throughout to eliminate any implication of causal claims and acknowledged the limitation of the small sample size.

Public Reviews:

Reviewer #1 (Public review):

Summary:

Using a computational modeling approach based on the drift diffusion model (DDM) introduced by Ratcliff and McKoon in 2008, the article by Shevlin and colleagues investigates whether there are differences between neutral and negative emotional states in:

(1) The timings of the integration in food choices of the perceived healthiness and tastiness of food options between individuals with bulimia nervosa (BN) and healthy participants.

(2) The weighting of the perceived healthiness and tastiness of these options.

Strengths:

By looking at the mechanistic part of the decision process, the approach has the potential to improve the understanding of pathological food choices. The article is based on secondary research data.

Weaknesses:

I have two major concerns and a major improvement point.

The major concerns deal with the reliability of the results of the DDM (first two sections of the Results, pages 6 and 7), which are central to the manuscript, and the consistency of the results with regards to the identification of mechanisms related to binge eating in BN patients (i.e. last section of the results, page 7).

(1) Ratcliff and McKoon in 2008 used tasks involving around 1000 trials per participant. The Chen et al. experiment the authors refer to involves around 400 trials per participant. On the other hand, Shevlin and colleagues ask each participant to make two sets of 42 choices with two times fewer participants than in the Chen et al. experiment. Shevlin and colleagues also fit a DDM with additional parameters (e.g. a drift rate that varies according to subjective rating of the options) as compared to the initial version of Ratcliff and McKoon. With regards to the number of parameters estimated in the DDM within each group of participants and each emotional condition, the 5- to 10-fold ratio in the number of trials between the Shevlin and colleagues' experiment and the experiments they refer to (Ratcliff and McKoon, 2008; Chen et al. 2022) raises serious concerns about a potential overfitting of the data by the DDM. This point is not highlighted in the Discussion. Robustness and sensitivity analyses are critical in this case.

We thank the Reviewer for their thoughtful critique. We agree that a limited number of trials can forestall reliable estimation, which we acknowledge in the Discussion section. However, we used a hierarchical estimation approach which leverages group information to constrain individual-level estimates. This use of group-level parameters to inform individual-level estimates reduces overfitting and noise that can arise when trial counts are low, and the regularization inherent in hierarchical fitting prevents extreme parameter estimates that could arise from noisy or limited data (Rouder & Lu, 2005). As a result, hierarchical estimation has been repeatedly shown to work well in settings with low trial counts, including as few as 40 trials per condition (Ratcliff & Childers, 2015; Wiecki et al., 2013), and previous applications of the time-varying DDM to food choice task data has included experiments with as few as 60 trials per condition (Maier et al., 2020). We have added references to these more recent approaches and specifically note their advantages for the modeling of tasks with fewer trials. Additionally, our successful parameter recovery described in the Supplementary Materials supports the robustness of the estimation procedure and the reliability of our results.

The authors compare different DDMs to show that the DDM they used to report statistical results in the main text is the best according to the WAIC criterion. This may be viewed as a robustness analysis. However, the other DDM models (i.e. M0, M1, M2 in the supplementary materials) they used to make the comparison have fewer parameters to estimate than the one they used in the main text. Fits are usually expected to follow the rule that the more there are parameters to estimate in a model, the better it fits the data. Additionally, a quick plot of the data in supplementary table S12 (i.e. WAIC as a function of the number of parameters varying by food type in the model - i.e. 0 for M0, 2 for M1, 1 for M2 and 3 for M3) suggests that models M1 and potentially M2 may be also suitable: there is a break in the improvement of WAIC between model M0 and the three other models. I would thus suggest checking how the results reported in the main text differ when using models M1 and M2 instead of M3 (for the taste and health weights when comparing M3 with M1, for τS when comparing M3 with M2). If the differences are important, the results currently reported in the main text are not very reliable.

We thank the Reviewer for highlighting that it would be helpful for the paper to explicitly note that we specifically selected WAIC as one of two methods to assess model fit because it penalizes for model complexity. We now explicitly state that, in addition to being more robust than other metrics like AIC or BIC when comparing hierarchical Bayesian models like those in the current study, model fit metrics like WAIC penalize for model complexity based on the number of parameters (Watanabe, 2010). Therefore, it is not the case that more complex models (i.e., having additional parameters) would automatically have lower WAICs. Additionally, we note that our second method to assess model fit, posterior predictive checks demonstrate that only model M3 can reproduce key behavioral patterns present in the empirical data. As described in the Supplementary Materials, M1 and M2 miss those patterns in the data. In summary, we used best practices to assess model fit and reliability (Wilson & Collins, 2019): results from the WAIC comparison (which in fact penalizes models with more parameters) and results from posterior predictive checks align in showing that M3 best fit to our data. We have added a sentence to the manuscript to state this explicitly.

(2) The second main concern deals with the association reported between the DDM parameters and binge eating episodes (i.e. last paragraph of the results section, page 7). The authors claim that the DDM parameters "predict" binge eating episodes (in the Abstract among other places) while the binge eating frequency does not seem to have been collected prospectively. Besides this methodological issue, the interpretation of this association is exaggerated: during the task, BN patients did not make binge-related food choices in the negative emotional state. Therefore, it is impossible to draw clear conclusions about binge eating, as other explanations seem equally plausible. For example, the results the authors report with the DDM may be a marker of a strategy of the patients to cope with food tastiness in order to make restrictive-like food choices. A comparison of the authors' results with restrictive AN patients would be of interest. Moreover, correlating results of a nearly instantaneous behavior (i.e. a couple of minutes to perform the task with the 42 food choices) with an observation made over several months (i.e. binge eating frequency collected over three months) is questionable: the negative emotional state of patients varies across the day without systematically leading patients to engage in a binge eating episode in such states.

I would suggest in such an experiment to collect the binge craving elicited by each food and the overall binge craving of patients immediately before and after the task. Correlating the DDM results with these ratings would provide more compelling results. Without these data, I would suggest removing the last paragraph of the Results.

We thank the Reviewer for these interesting suggestions and appreciate the opportunity to clarify that we agree that claims about causal connections between our decision parameters and symptom severity metrics would be inappropriate. Per the Reviewer’s suggestions, we have eliminated the use of the word “predict” to describe the tested association with symptom metrics.  We also agree that more time-locked associations with craving ratings and near-instantaneous behavior would be useful, and we have added this as an important direction for future research in the discussion. However, associating task-based behavior with validated self-report measures that assess symptom severity over long periods of time that precede the task visit (e.g., over the past 2 weeks in depression, over the past month in eating disorders) is common practice in computational psychiatry, psychiatric neuroimaging, and clinical cognitive neuroscience (Hauser et al., 2022; Huys et al., 2021; Wise et al., 2023), and this approach has been used several times specifically with food choice tasks (Dalton et al., 2020; Steinglass et al., 2015). We have revised the language throughout the manuscript to clarify: the results suggest that individuals whose task behavior is more reactive to negative affect tend to be the most symptomatic, but the results do not allow us to determine whether this reactivity causes the symptoms.

In response to this Reviewer’s important point about negative affect not always producing loss-of-control eating in individuals with BN, we also now explicitly note that while several studies employing ecological momentary assessments (EMA) have repeatedly shown that increases in negative affect significantly increase the likelihood of subsequent loss-of-control eating (Alpers & Tuschen-Caffier, 2001; Berg et al., 2013; Haedt-Matt & Keel, 2011; Hilbert & Tuschen-Caffier, 2007; Smyth et al., 2007), not all loss-of-control eating occurs in the context of negative affect, and that future studies should integrate food choice task data pre and post-affect inductions with measures that capture the specific frequency of loss of control eating episodes that occur during states of high negative affect.

(3) My major improvement point is to tone down as much as possible any claim of a link with binge eating across the entire manuscript and to focus more on the restrictive behavior of BN patients in between binge eating episodes (see my second major concern about the methods). Additionally, since this article is a secondary research paper and since some of the authors have already used the task with AN patients, if possible I would run the same analyses with AN patients to test whether there are differences between AN (provided they were of the restrictive subtype) and BN.

We appreciate the Reviewer’s perspective and suggestions. We have adjusted our language linking loss-of-control eating frequency with decision parameters, and we have added additional sentences focusing on the implications for the restrictive behavior of patients with BN between binge eating episodes. In the Supplementary Materials. We have added an analysis of the restraint subscale of the EDE-Q and confirmed no relationship with parameters of interest. While we agree additional analyses with AN patients would be of interest, this is outside the scope of the paper. Our team have collected data from individuals with AN using this task, but not with any affect induction or measure of affect. Therefore, we have added this important direction for future research to the discussion.

Reviewer #2 (Public review):

Summary:

Binge eating is often preceded by heightened negative affect, but the specific processes underlying this link are not well understood. The purpose of this manuscript was to examine whether affect state (neutral or negative mood) impacts food choice decision-making processes that may increase the likelihood of binge eating in individuals with bulimia nervosa (BN). The researchers used a randomized crossover design in women with BN (n=25) and controls (n=21), in which participants underwent a negative or neutral mood induction prior to completing a food-choice task. The researchers found that despite no differences in food choices in the negative and neutral conditions, women with BN demonstrated a stronger bias toward considering the 'tastiness' before the 'healthiness' of the food after the negative mood induction.

Strengths:

The topic is important and clinically relevant and methods are sound. The use of computational modeling to understand nuances in decision-making processes and how that might relate to eating disorder symptom severity is a strength of the study.

Weaknesses:

The sample size was relatively small and may have been underpowered to find differences in outcomes (i.e., food choice behaviors). Participants were all women with BN, which limits the generalizability of findings to the larger population of individuals who engage in binge eating. It is likely that the negative affect manipulation was weak and may not have been potent enough to change behavior. Moreover, it is unclear how long the negative affect persisted during the actual task. It is possible that any increases in negative affect would have dissipated by the time participants were engaged in the decision-making task.

We thank the Reviewer for their comments on the strengths of the paper, and for highlighting these important considerations regarding the sample demographics and the negative affect induction. As in the original paper that focused only on ultimate food choice behaviors, we now specifically acknowledge that the study was only powered to detect small to medium group differences in the effect of negative emotion on these final choice behaviors. Regarding the sample demographics, we agree that the study’s inclusion of only female participants is a limitation.  Although the original decision for this sampling strategy was informed by data suggesting that bulimia nervosa is roughly six times more prevalent among females than males (Udo & Grilo, 2018), we now note in the discussion that our female-only sample limits the generalizability of the findings.

We also agree with the Reviewer’s noted limitations of the negative mood induction, and based on the reviewer’s suggestions, we have added to our original description of these limitations in the Discussion. Specifically, we now note that although the task was completed immediately after the affect induction, the study did not include intermittent mood assessments throughout the choice task, so it is unclear how long the negative affect persisted during the actual task.

Reviewer #3 (Public review):

Summary:

The study uses the food choice task, a well-established method in eating disorder research, particularly in anorexia nervosa. However, it introduces a novel analytical approach - the diffusion decision model - to deconstruct food choices and assess the influence of negative affect on how and when tastiness and healthiness are considered in decision-making among individuals with bulimia nervosa and healthy controls.

Strengths:

The introduction provides a comprehensive review of the literature, and the study design appears robust. It incorporates separate sessions for neutral and negative affect conditions and counterbalances tastiness and healthiness ratings. The statistical methods are rigorous, employing multiple testing corrections.

A key finding - that negative affect induction biases individuals with bulimia nervosa toward prioritizing tastiness over healthiness - offers an intriguing perspective on how negative affect may drive binge eating behaviors.

Weaknesses:

A notable limitation is the absence of a sample size calculation, which, combined with the relatively small sample, may have contributed to null findings. Additionally, while the affect induction method is validated, it is less effective than alternatives such as image or film-based stimuli (Dana et al., 2020), potentially influencing the results.

We agree that the small sample size and specific affect induction method may have contributed to the null model-agnostic behavioral findings. Based on this Reviewer’s and Reviewer 2’s comments, we have added these factors to our original acknowledgements of limitations in the Discussion.

Another concern is the lack of clarity regarding which specific negative emotions were elicited. This is crucial, as research suggests that certain emotions, such as guilt, are more strongly linked to binge eating than others. Furthermore, recent studies indicate that negative affect can lead to both restriction and binge eating, depending on factors like negative urgency and craving (Leenaerts et al., 2023; Wonderlich et al., 2024). The study does not address this, though it could explain why, despite the observed bias toward tastiness, negative affect did not significantly impact food choices.

We thank the Reviewer for raising these important points and possibilities. In the supplementary materials, we have added an additional analysis of the specific POMS subscales that comprise the total negative affect calculation that was reported in the original paper (Gianini et al., 2019), and which we now report in the main text. Ultimately, we found that, across both groups, the negative affect induction increased responses related to anger, confusion, depression, and tension while reducing vigor.

We agree with the Reviewer that factors like negative urgency and cravings are relevant here. The study did not collect any measures of craving, and in response to Reviewer 1 and this Reviewer, we now note in the discussion that replication studies including momentary craving assessments will be important. While we don’t have any measurements of cravings, we did measure negative urgency. Despite these prior findings, the original paper (Gianini et al., 2019) did not find that negative urgency was related to restrictive food choices. We have now repeated those analyses, and we also were unable to find any meaningful patterns. Nonetheless, we have added an analysis of negative urgency scores and decision parameters to the supplementary materials.      

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Reviewer #2 (Public review):

The authors tested tactile acuity on the breast of females using several tasks and reported overall low acuity compared to the back, which is typically considered to have the worst acuity of all body parts. Moreover, there was evidence that acuity is worse the larger the breast; this finding mirrors similar findings for the hand and therefore suggests that the number of tactile sensors is fixed and must be distributed across a larger extent of skin when a body part is larger, thus resulting in comparably lower tactile acuity.

Strengths:

I find this an interesting paper with results that are relevant to the tactile community. The authors apply several tasks allowing them to link the paper with previous results. The methodology and psychophysical analysis are sound.

Weaknesses:

The analysis of localization error direction, with the result that the nipple area may be a landmark for tactile localization, is interesting and aligns the paper with some other recent papers that have suggested that such landmarks should exist. However, there are major issues with methodology and statistics, so that currently the conclusions are not supported.

In the following, line numbers refer to the re-formatted manuscript provided by the authors upon request and are mentioned for them to find the relevant passages faster.

(1) Comments on analysis of tactile acuity:

- I had a hard time understanding some parts of the report. What is meant by "broadly no relationship" in line 137?

- It is suggested that spatial expansion (which is correlated with body part size) is related between medial breast and hand - is this to say that women with large hands have large medial breast size? Nipple size was measured, but hand size was not measured, is this correct?

- It is furthermore unclear how the authors differentiate medial breast and NAC. The sentence in lines 140-141 seems to imply the two terms are considered the same, as a conclusion about NAC is drawn from a result about the medial breast. This requires clarification.

- Finally, given that the authors suspect that overall localization ability (or attention) may be overshadowed by a size effect, would not an analysis be adequate that integrates both, e.g. a regression with multiple predictors?

(2) Comments on analysis of "The nipple is a unit":

- Statistics in this section are not adequately described and may be partly false.

- In the paragraph about testing quadrants of the nipple, it is stated that only 3 of 10 participants barely outperformed chance with a p < 0.01. It is unclear how a significant t-test is an indication of "barely above chance".

- The final part of the paragraph on nipple quadrants (starting line 176) explains that there was a trend (4 of 10 participants) for lower tactile acuity being related to the inability to differentiate quadrants. It seems to me that such a result would not be expected: The stated hypothesis is that all participants have the same number of tactile sensors in their nipple and areola, independent of NAC size. In this section, participants determine the quadrant of a single touch. Theoretically, all participants should be equally able to perform this task, because they all have the same number of receptors in each quadrant of nipple and areola. Thus, the result in Figure 2C is curious.

(3) Comments on analysis of "Absolute localization on the breast is anchored to the nipple"

- Again, there are things that are unclear with the statistics and description of the analysis.

- This section reports an Anova (line 193/194) with a factor "participant". This doesn't appear sensible. Please clarify. The factor distance is also unclear; is this a categorical or a continuous variable? Line 400 implies a 6-level factor, but Anovas and their factors, respectively, are not described in methods (nor are any of the other statistical approaches).

- The analysis on imprecision using mean pairwise error (line 199) is unclear: does pairwise refer to x/y or to touch vs. center of the nipple?

- p8, upper text, what is meant by "relative over-representation of the depth axis"? Does this refer to the breast having depth but the equivalent area on the back not having depth? What are the horizontal planes (probably meant to be singular?) - do you simply mean that depth was ignored for the calculation of errors? This seems to be implied in Figure 3AB.

- Lines 232-241, I cannot follow the conclusions drawn here. First, it is not clear to a reader what the aim of the presented analyses is: what are you looking for when you analyze the vectors? Second, "vector strength" should be briefly explained in the main text. Third, it is not clear how the final conclusion is drawn. If there is a bias of all locations towards the nipple, then a point closer to the nipple cannot exhibit a large bias, because the nipple is close-by. Therefore, one would expect that points close to the nipple exhibit smaller errors, but this would not imply higher acuity - just less space for localizing anything. The higher acuity conclusion is at odds with the remaining results, isn't it: acuity is low on the outer breast, but even lower at the NAC, so why would it be high in between the two?

(4) Comments on the Discussion:

The discussion makes some concrete suggestions for sensors in implants (line 283). It is not clear how the stated numbers were computed. Also, why should 4 sensors nipple quadrants receive individual sensors if the result here was that participants cannot distinguish these quadrants?

Additional comments:

I would find it interesting to know whether participants with small breast measurement delta had breast acuity comparable to the back. Alternatively, it would be interesting to know whether breast and back acuity are comparable in men. Such a result would imply that the torso has uniform acuity overall, but any spatial extension of the breast is unaccounted for. The lowest single participant data points in Figure 1B appear similar, which might support this idea.

除了层级关系和并、交操作，格理论还有一些其他的核心思想：

1. 格的代数定义：

你已经从有序集合的角度理解了格。但实际上，格也可以完全通过代数的方式来定义。一个非空集合 L 连同两个二元运算 ∨ (并) 和 ∧ (交) 被称为一个格，如果这两个运算满足以下公理：

• 交换律 (Commutativity):
  • a ∨ b = b ∨ a
  • a ∧ b = b ∧ a
• 结合律 (Associativity):
  • (a ∨ b) ∨ c = a ∨ (b ∨ c)
  • (a ∧ b) ∧ c = a ∧ (b ∧ c)
• 幂等律 (Idempotency):
  • a ∨ a = a
  • a ∧ a = a
• 吸收律 (Absorption Laws):
  • a ∨ (a ∧ b) = a
  • a ∧ (a ∨ b) = a

这个代数定义与基于偏序的定义是等价的。也就是说，如果一个集合上的关系构成一个格（根据偏序定义），那么它的并和交操作也必然满足上述代数公理；反之亦然。这个代数视角使得我们可以用代数工具来研究格的性质。

2. 对偶原理 (Duality Principle):

格理论中一个非常强大且优雅的思想是对偶原理。它指出，任何在格中成立的命题，如果我们将其中的偏序关系 ≤ 替换为 ≥，并将并运算 ∨ 替换为交运算 ∧，反之亦然，那么得到的新命题仍然在格中成立。

这个原理极大地简化了格理论的研究，因为一旦我们证明了一个关于并的性质，我们就可以通过对偶原理直接得到一个关于交的对应性质，而无需重新证明。

3. 特殊类型的格及其性质：

格理论研究各种具有特殊性质的格，这些性质使得它们在不同的数学和应用领域中非常重要：

• 有界格 (Bounded Lattices): 存在一个最小元素（底，通常记为 0 或 ⊥）和一个最大元素（顶，通常记为 1 或 ⊤）。例如，幂集格中，空集是底，全集是顶。
• 完备格 (Complete Lattices): 每个子集都存在最小上界（并）和最大下界（交），而不仅仅是每对元素。所有有限格都是完备格。
• 分配格 (Distributive Lattices): 满足分配律：
  • x ∧ (y ∨ z) = (x ∧ y) ∨ (x ∧ z)
  • x ∨ (y ∧ z) = (x ∨ y) ∧ (x ∨ z) 并非所有的格都是分配格，但分配格具有很多良好的性质，例如在逻辑和电路设计中非常重要。
• 模格 (Modular Lattices): 满足模律：如果 x ≤ z，那么 x ∨ (y ∧ z) = (x ∨ y) ∧ z。所有分配格都是模格，但反之不成立。模格在代数结构的研究中很重要。
• 有补格 (Complemented Lattices): 在有界格中，对于每个元素 a，都存在一个补元 b，使得 a ∨ b = 1 且 a ∧ b = 0。
• 布尔格 (Boolean Lattices): 是既是分配格又是补格的格。布尔代数是数理逻辑和计算机科学的基础。

4. 子格与同态：

• 子格 (Sublattices): 一个格的子集，如果在相同的偏序关系下，并且对于子集中的任意两个元素，它们的并和交仍然在这个子集中，那么这个子集就是一个子格。
• 格同态 (Lattice Homomorphisms): 是指在两个格之间保持并和交运算的映射。研究子格和同态有助于我们理解格的结构以及不同格之间的关系。

5. 格理论与其他数学领域的联系：

格理论并非孤立存在，它与许多其他数学领域有着深刻的联系，包括：

• 抽象代数： 格可以看作是一种特殊的代数结构，反过来，许多代数结构（如群、环）的子结构（如子群、理想）也常常形成格。
• 序理论： 格是序理论的核心研究对象，用于形式化和研究各种类型的有序关系。
• 逻辑学： 布尔代数是经典命题逻辑的代数模型，而更一般的格结构则与非经典逻辑相关。
• 计算机科学： 格理论在形式概念分析、程序语言语义、类型系统、并发理论等领域都有重要应用。

总而言之，除了你已经理解的层级关系和并、交操作之外，格理论还包含代数定义、对偶原理以及各种具有特殊性质的格的分类和研究。这些核心思想共同构成了格理论这个强大而富有魅力的数学分支。

《九章算术》作为中国古代数学的巅峰之作，其代数思想不仅体现了古代中国数学的高度抽象与系统性，还为后世代数学的发展奠定了基础。以下是《九章算术》中代数思想的详细解析：

一、代数思想的核心体现

1. 方程与线性方程组的解法

• 线性方程组的“方程”章（第八章）是《九章算术》代数思想的集中体现：
  • 分离系数法：将方程组的系数和常数项单独列出，形成类似现代矩阵的排列方式。例如： [ \begin{cases} 3x + 2y + z = 39 \ 2x + 3y + z = 34 \ x + 2y + 3z = 26 \ \end{cases} ] 对应的“方程”章解法是将系数和常数项按行排列，通过“直除法”消元求解。
  • 直除法：通过消元法逐步消去变量，其步骤与现代线性代数中的高斯消元法（Gaussian Elimination）原理一致。例如：
  • 用第一个方程的系数消去第二个、第三个方程中的某个变量；
  • 重复此过程，最终得到单变量方程，求解后回代得到所有变量的值。
  • 负数的引入：在解方程组时，首次系统使用负数（称为“正负术”）。例如：
  • 当方程中出现减法运算时，用红筹（负数）和黑筹（正数）区分，且给出了正负数的加减法则： [ (+a) + (-b) = \begin{cases}
    • (a - b) & \text{若 } a > b \
    • (b - a) & \text{若 } a < b \ \end{cases} ]
  • 这比西方对负数的系统认识（如斐波那契在13世纪的贡献）早了约1500年。

2. 比例与代数运算

• 比例问题的“粟米”“衰分”章：
  • 今有术：通过比例关系解决谷物折算问题，例如： [ \text{今有粟米} \ 100 \ \text{石，可换多少钱？} ] 通过比例公式 \( \frac{a}{b} = \frac{c}{d} \) 求解，其本质是线性代数中的比例分配问题。
  • 衰分术：按比例分配资源，例如按等级分配税收，涉及复比例和连锁比例的计算，体现了方程思想的雏形。

3. 开方术与高次方程

• 开平方与开立方（“少广”章）：
  • 通过“开方术”求解面积或体积对应的边长，例如： [ \text{已知正方形面积为} \ 52225 \ \text{步}^2，\text{求边长}。 ] 采用类似现代笔算开平方的方法，分步试商，最终得到 \( \sqrt{52225} = 229 \)。
  • 开带从平方：解形如 \( x^2 + bx = c \) 的二次方程，通过几何分割法转化为面积问题，体现了代数与几何的结合。

4. 盈不足术（双设法）

• “盈不足”章提出一种近似解法，通过两次假设（盈与不足）逼近方程的精确解：
  • 例如： [ \text{今有人共买物，人出八，盈三；人出七，不足四。问人数、物价各几何？} ] 设人数为 \( x \)，物价为 \( y \)，则： [ \begin{cases} 8x = y + 3 \ 7x = y - 4 \ \end{cases} ] 通过两次假设的差值计算解，其方法与现代线性插值法一致。

二、代数思想的系统性与创新性

1. 代数符号的萌芽：

2. 虽然未使用符号（如 \( x, y \)），但通过文字描述与筹算结合的方式，实现了方程的表达与运算。例如：

   • 用“方程”章的“分离系数”法，以筹算的排列位置表示变量系数，类似现代矩阵的布局。
   • 用“正负术”明确区分正负数，为代数运算提供了符号化基础。

3. 方程理论的完整性：

4. 《九章算术》是世界上最早系统解决线性方程组问题的著作：

   • 在西方，直到17世纪莱布尼茨提出类似的消元法，而《九章算术》的解法早了约1500年。
   • 其方法论影响深远，如日本数学家关孝和在17世纪发展出“傍谱法”（类似行列式）即受此启发。

5. 数系的扩展：

6. 负数的合法化是代数学的重大突破：

   • 《九章算术》不仅承认负数的存在，还给出了正负数的运算规则，为代数方程的解提供了完整的数系基础。
   • 西方直到16世纪才由意大利数学家卡当系统讨论负数，而印度数学家婆罗摩笈多在7世纪才提出类似规则。

7. 应用驱动的代数思维：

8. 全书246个问题均源于实际需求（如田亩测量、赋税分配、工程计算），体现了实用主义与抽象思维的结合：
   • 例如“均输”章通过比例分配解决赋税问题，其数学方法被后世称为“中国剩余定理”的雏形。

三、代数思想的历史地位与影响

1. 对中国数学的影响：
2. 《九章算术》确立了中国古代数学以“算法为中心”的传统，其代数方法被后世数学家（如刘徽、祖冲之）继承和发展。

3. 刘徽在《九章算术注》中进一步完善了“方程”章的理论，提出“齐同原理”（即通分原则），并发展出“互乘相消法”解方程。

4. 对世界数学的贡献：

5. 线性方程组解法：比西方早1500年，成为代数方程理论的重要里程碑。
6. 负数的使用：领先印度约500年，比欧洲早1000余年，为代数符号化提供了关键思想。

7. 比例理论：其比例分配方法被阿拉伯数学家翻译并传播至欧洲，成为文艺复兴时期数学教育的基础。

8. 与西方代数的对比：

9. 古希腊：以几何为主导，代数问题常转化为几何图形（如用面积解方程）。
10. 《九章算术》：以算法和算术操作为核心，直接处理代数方程，更接近现代代数的抽象性。

四、具体示例解析

例1：线性方程组的解法（方程章）

问题：<br /> [ \begin{cases} 3x + 2y + z = 39 \ 2x + 3y + z = 34 \ x + 2y + 3z = 26 \ \end{cases} ] 解法步骤（直除法）： 1. 分离系数：将系数和常数项排列为： [ \begin{array}{ccc|c} 3 & 2 & 1 & 39 \ 2 & 3 & 1 & 34 \ 1 & 2 & 3 & 26 \ \end{array} ] 2. 消元：用第一行消去第二、第三行的 \( x \)： - 第二行：\( 2x = 34 - 3y - z \)，用第一行的 \( 3x \) 乘以 \( 2/3 \) 得 \( 2x = 26 - (4/3)y - (2/3)z \)，相减消去 \( x \)。 - 类似操作最终得到单变量方程，解得 \( x = 9 \)，\( y = 4 \)，\( z = 5 \)。

例2：负数的应用（方程章）

问题：<br /> [ \begin{cases} x + y = 5 \ x - y = 1 \ \end{cases} ] 解法： 1. 将方程系数分离： [ \begin{array}{cc|c} 1 & 1 & 5 \ 1 & -1 & 1 \ \end{array} ] 2. 通过加减消去 \( y \)： - 第一行加第二行：\( 2x = 6 \Rightarrow x = 3 \)； - 代入得 \( y = 2 \)。

五、总结

《九章算术》的代数思想以实用问题驱动，通过系统化的算法和符号化萌芽（如负数、分离系数法），构建了方程理论、比例分配、高次方程求解等核心内容。其成就不仅体现在对当时实际问题的解决，更在于为代数学的抽象化、符号化提供了重要基础，影响了后世数学的发展。这些思想在今天看来，仍展现出古代中国数学家超前的逻辑思维与创新精神。

如果需要进一步探讨具体章节或方法的细节，请随时提问！

farmacologicas diarrea cronica

哈恩-巴拿赫定理：凸性与分离的桥梁

哈恩-巴拿赫定理不仅是泛函分析的核心工具，更是凸分析与几何分离理论的桥梁。它通过将线性泛函的存在性与凸集的几何性质联系起来，揭示了代数结构与几何直观之间的深刻联系。以下是其作为“桥梁”的详细解释：

1. 凸性与分离的基本概念

1.1 凸集的定义

一个集合 \( C \subseteq X \)（\( X \) 是线性空间）是凸集，若对任意 \( x, y \in C \) 和 \( \lambda \in [0,1] \)，有<br /> [ \lambda x + (1-\lambda) y \in C. ]<br /> 几何意义：连接任意两点的线段完全包含在集合内。

1.2 分离（Separation）

两个不相交的凸集 \( A \) 和 \( B \) 被称为可分离的，若存在一个超平面（即线性泛函的零点集）将它们分开。具体形式包括：<br /> - 严格分离：存在 \( F \in X^ \) 和 \( s \in \mathbb{R} \)，使得<br /> [ F(a) < s < F(b) \quad \forall a \in A, \, b \in B. ]<br /> - 弱分离：存在 \( F \in X^ \) 和 \( s \in \mathbb{R} \)，使得<br /> [ F(a) \leq s \leq F(b) \quad \forall a \in A, \, b \in B. ]

2. 哈恩-巴拿赫定理的几何形式

哈恩-巴拿赫定理的几何版本（凸集分离定理）直接体现了其作为“桥梁”的作用：<br /> 定理：<br /> 设 \( X \) 是实线性空间，\( A, B \subseteq X \) 是两个不相交的凸集。<br /> 1. 严格分离条件：若 \( A \) 是开集，则存在严格分离超平面。<br /> 2. 弱分离条件：若 \( A \) 是紧集且 \( B \) 是闭凸集，则存在弱分离超平面。

3. 如何成为“桥梁”？

3.1 代数到几何的转化

• 泛函的构造：哈恩-巴拿赫定理通过次线性泛函的条件，将局部定义的线性泛函扩展到全局，从而构造出分离超平面所需的泛函。
• 几何解释：分离超平面的存在性直接对应于某个线性泛函的符号变化，例如，超平面 \( F(x) = s \) 将空间分为 \( F(x) < s \) 和 \( F(x) > s \) 两部分。

3.2 凸性与线性泛函的关联

• 凸集的支撑超平面：对于凸集 \( C \)，若点 \( x_0 \) 在 \( C \) 的边界上，则存在支撑超平面 \( F(x) = F(x_0) \) 使得 \( F(x) \leq F(x_0) \) 对所有 \( x \in C \) 成立。
• 应用：在优化中，支撑超平面与极值点、次梯度等概念密切相关。

3.3 分离定理的推导

以严格分离为例：<br /> 1. 构造次线性泛函：设 \( A \) 是开凸集，\( B \) 是凸集，且 \( A \cap B = \emptyset \)。<br /> 2. 定义辅助泛函：取 \( x_0 \in A \)，构造 \( p(x) = \inf { t \geq 0 \mid x_0 + t^{-1}(x - x_0) \in A } \)（衡量 \( x \) 与 \( A \) 的“距离”）。<br /> 3. 应用哈恩-巴拿赫定理：扩展局部定义的泛函 \( f \)，得到全局泛函 \( F \)，从而分离 \( A \) 和 \( B \)。

4. 典型应用场景

4.1 优化中的极值问题

• 支持超平面与极值：<br /> 若 \( C \) 是凸集且 \( x^ \) 是 \( C \) 中的极值点，则存在非零泛函 \( F \) 使得 \( F(x^) \geq F(x) \) 对所有 \( x \in C \) 成立。
• KKT条件：在凸优化中，最优解的必要条件可通过分离定理导出。

4.2 经济学中的均衡分析

• 福利经济学第二定理：<br /> 在满足某些条件的经济模型中，帕累托最优分配可通过价格机制实现。分离定理提供了数学基础，证明存在价格向量（对应线性泛函）使得市场供需平衡。

4.3 几何中的凸集分离

• 凸多面体的分离：<br /> 在有限维空间中，两个不相交的凸多面体可通过超平面严格分离。例如，分离两个不相交的多边形。

5. 深层数学意义

5.1 凸性与线性泛函的对偶性

• 对偶空间的作用：<br /> 凸集的分离依赖于对偶空间 \( X^* \) 中的泛函，这体现了原空间 \( X \) 与对偶空间的对称性。
• 凸锥的分离：<br /> 若 \( C \) 是凸锥且 \( 0 \notin C \)，则存在 \( F \in X^ \) 使得 \( F(x) > 0 \) 对所有 \( x \in C \) 成立（冯·诺依曼分离定理*）。

5.2 分离定理的局限性

• 条件的重要性：<br /> 严格分离需要 \( A \) 是开集，否则可能仅存在弱分离。例如，两个闭凸集可能无法严格分离（如 \( A = {0} \)，\( B = (0,1] \) 在 \( \mathbb{R} \) 中）。

6. 示例：支撑超平面的存在性

问题：证明凸集 \( C \subseteq \mathbb{R}^n \) 的边界点 \( x_0 \) 存在支撑超平面。<br /> 步骤：<br /> 1. 定义子空间与泛函：<br /> 取 \( M = \text{span}{x_0} \)，在 \( M \) 上定义 \( f(\alpha x_0) = \alpha \|x_0\| \)。<br /> 2. 应用哈恩-巴拿赫定理：<br /> 扩展 \( f \) 到全局泛函 \( F \)，使得 \( F(x_0) = \|x_0\| \)，且 \( F(x) \leq \|x\| \) 对所有 \( x \in \mathbb{R}^n \) 成立。<br /> 3. 支撑超平面：<br /> 超平面 \( F(x) = F(x_0) \) 满足 \( F(x) \leq F(x_0) \) 对所有 \( x \in C \) 成立（因 \( C \) 是凸集且 \( x_0 \) 在边界上）。

7. 总结：哈恩-巴拿赫定理的桥梁作用

| 维度 | 凸性 | 分离理论 | 哈恩-巴拿赫的作用 | |-------------------|-----------------------------------|-----------------------------------|---------------------------------------| | 核心对象 | 凸集、凸锥、极值点 | 超平面、支撑超平面、分离定理 | 线性泛函的存在性与扩展性 | | 关键工具 | 凸包、凸组合、次梯度 | 泛函的符号分离、距离泛函 | 通过次线性条件构造泛函并保证其存在性 | | 应用领域 | 优化、经济学、几何 | 均衡分析、凸集分类、反例构造 | 统一不同领域的分离问题为泛函扩展问题 |

8. 常见疑问解答

Q1：为什么凸性是分离定理的前提？

• 凸性确保了超平面的“一致性”：若集合非凸，可能无法找到单一超平面将两个区域完全分开。

Q2：分离定理是否适用于非凸集？

• 部分情况适用，但需要额外条件。例如，两个闭集可能无法分离，除非满足某种凸性或分离距离的条件。

Q3：支撑超平面与梯度的关系？

• 在有限维空间中，支撑超平面的法向量对应于凸集在该点的次梯度，这与微分学中的梯度概念一致。

9. 结语

哈恩-巴拿赫定理通过将线性泛函的存在性与凸集的几何分离性联系起来，成为连接代数与几何的核心桥梁。它不仅为凸分析提供了理论基石，还在优化、经济学、控制论等领域中扮演着“翻译者”的角色——将几何问题转化为泛函问题，或反之。正如数学家约翰·冯·诺依曼（John von Neumann）所言：“线性泛函的分离能力，是数学中凸性概念的终极体现。”

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