La idea o concepto de legibilidad fuerte ¿a qué hace referencia?. Pereciese que puede haber un anclaje teórico y metódico frente a lo aquí enunciado que eventualmente puede ayudar e expandir el capítulo de metodología del proyecto doctoral.

Hace unas semanas descargué Obsidian en mi computador y con tutoriales de youtube, intenté aprender a usarlo, pero me causó mucha frustración. No sé si es por mi sistema operativo, pero había una casilla que salía en mi pantalla, pero en ninguna de todos los videotuoriales que revisé, así que no puede crear ni la cuenta. Y bueno, tuve la sensación de que si así era el login el manejo sería más tedioso. Mi única habilidad es con Zotero y Mendeley para usarlo como gestores bibliográficos, gracias a una capacitación en la biblioteca de la universidad.

Esta información es vital para mí, para mí mi gestor bibliográfico es Zotero, y más por un asunto de desconocimiento. Sería muy beneficioso que pudieras realizar una clase magistral sobre estas estrategias que pudiera ser multimodal, es decir; usar en el pc, la tablet y poder tener acceso a la información en cualquier lugar.

Creo que este es un proceso por el que pasamos todos. Sí bien siempre pensamos que nuestro método de toma de notas es útil. Cuando debemos buscarlas como lo mencionas, es muy complicado y tedioso, trtar de encontrar exactamente la información que necesitas.

This was seen in the CDU's socail market economy plan thats in ghdi SLAYY

Reviewer #2 (Public review):

Summary

The authors introduce DNA O-MAP, a method that combines oligo-based in situ hybridization with peroxidase-mediated proximity biotinylation to profile proteins and DNA-DNA interactions linked to targeted genomic regions. In the revised manuscript, they expand the method beyond repetitive elements by profiling non-repetitive gene clusters (HOXA and HOXB), studying inhibitor-induced chromatin remodeling, and differentiating homolog-specific proteomes on both the active and inactive X chromosome. These additions considerably broaden the scope of the work and indicate that DNA O-MAP is currently most effective for analyzing gene-cluster size or domain-level chromatin environments, rather than focusing on individual promoters or cis-regulatory elements.

Strengths

The study demonstrates that DNA O-MAP can be applied to both repetitive domains and non-repetitive genomic regions, including gene clusters spanning 80 kilobases and larger single-copy chromosomal intervals, rather than isolated cis-regulatory elements.

Orthogonal validation using ENCODE ChIP-seq data supports several differentially enriched proteins observed between the HOXA and HOXB gene clusters proteomes.

The ability to detect quantitative changes in local protein environments after chemical perturbation demonstrates the method's sensitivity at the level of extended genomic domains.

Homolog-resolved analysis of the active and inactive X chromosome provides an additional demonstration of biological specificity and technical flexibility at the megabase scale.

The revised manuscript appropriately frames DNA O-MAP as a method for interrogating local domain-level genomic environments, rather than exhaustively defining the protein composition of individual regulatory elements.

Weaknesses

As with all proximity labeling approaches, the effective resolution of DNA O-MAP is constrained by the spatial distance of peroxidase-mediated labeling rather than by genomic distance. Consequently, for gene-cluster-scale targets, enrichment extends beyond the targeted interval into surrounding chromosomal regions, potentially limiting the method's specificity at the level of individual promoters, enhancers, or gene bodies.

Specificity is demonstrated through comparative and internally controlled analyses rather than through a quantitative estimate of false discovery rate for locus specificity. Readers should therefore interpret individual protein enrichments as indicative of local chromatin environments rather than definitive evidence of direct binding to a specific regulatory element.

Orthogonal validation is necessarily selective and hypothesis-driven. A broader validation would be required before newly enriched proteins can be interpreted as bona fide region-resident factors.

Comparisons to prior locus-proteomics methods remain indirect and should be interpreted primarily in terms of demonstrated feasibility, scalability, and reduced cell-number requirements rather than absolute performance or resolution.

Reviewer #3 (Public review):

Significance of the Findings:

The study by Liu et al. presents a novel method, DNA-O-MAP, which combines locus-specific hybridisation with proximity biotinylation to isolate specific genomic regions and their associated proteins. The potential significance of this approach lies in its purported ability to target genomic loci with heightened specificity by enabling extensive washing prior to the biotinylation reaction, theoretically improving the signal-to-noise ratio when compared with other methods such as dCas9-based techniques. Should the method prove successful, it could represent a notable advancement in the field of chromatin biology, particularly in establishing the proteomes of individual chromatin regions-an extremely challenging objective that has not yet been comprehensively addressed by existing methodologies.

Strength of the Evidence:

The evidence presented by the authors is somewhat mixed, and the robustness of the findings appears to be preliminary at this stage. While certain data indicate that DNA-O-MAP may function effectively for repetitive DNA regions, a number of the claims made in the manuscript are either unsupported or require further substantiation. There are significant concerns about the resolution of the method, with substantial biotinylation signals extending well beyond the intended target regions (megabases around the target), suggesting a lack of specificity and poor resolution, particularly for smaller loci. Furthermore, comparisons with previous techniques are unfounded since the authors have not provided direct comparisons with the same mass spectrometry (MS) equipment and protocols. Additionally, although the authors assert an advantage in multiplexing, this claim appears overstated, as previous methods could achieve similar outcomes through TMT multiplexing. Therefore, while the method has potential, the evidence requires more rigorous support, comprehensive benchmarking, and further experimental validation to demonstrate the claimed improvements in specificity and practical applicability.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

The authors describe a method to probe both the proteins associated with genomic elements in cells, as well as 3D contacts between sites in chromatin. The approach is interesting and promising, and it is great to see a proximity labeling method like this that can make both proteins and 3D contacts. It utilizes DNA oligomers, which will likely make it a widely adopted method. However, the manuscript over-interprets its successes, which are likely due to the limited appropriate controls, and of any validation experiments. I think the study requires better proteomic controls, and some validation experiments of the "new" proteins and 3D contacts described. In addition, toning down the claims made in the paper would assist those looking to implement one of the various available proximity labeling methods and would make this manuscript more reliable to non-experts.

Strengths:

(1) The mapping of 3D contacts for 20 kb regions using proximity labeling is beautiful.

(2) The use of in situ hybridization will probably improve background and specificity.

(3) The use of fixed cells should prove enabling and is a strong alternative to similar, living cell methods.

Weaknesses:

(1) A major drawback to the experimental approach of this study is the "multiplexed comparisons". Using the mtDNA as a comparator is not a great comparison - there is no reason to think the telomeres/centrosomes would look like mtDNA as a whole. The mito proteome is much less complex. It is going to provide a large number of false positives. The centromere/telomere comparison is ok, if one is interested in what's different between those two repetitive elements.

We appreciate the reviewers' point here. In fact we selected the mitochondrial DNA as a target for just the reason that the reviewer notes. mtDNA should be spatially distinct from the nuclear targets and allow us to determine if we were in fact seeing spatially distinct proteins at the interorganelle (mtDNA vs. telomeres/centrosomes) and intraorganelle (telomeres vs centromeres) levels.

But the more realistic use case of this method would be "what is at a specific genomic element"? A purely nuclear-localized control would be needed for that. Or a genomic element that has nothing interesting at it (I do not know of one).

We have now added two studies in Figure 4 and Figure 5 detailing the use of OMAP to investigate specific genomic elements. In this case the Hox clusters (HOXA and HOXB) and haplotype-specific analysis of X-chromosome inactivation centers in female murine (EY.T4) cells. The controls in these cases are more specific, in line with those suggested by the reviewer as we (1) compare HOXA and HOXB with or without EZH2 inhibition using the same sets of probes and (2) specifically compare the region surrounding the XIC in female cells for the inactive and active X chromosomes.

You can see this in the label-free work: non-specific, nuclear GO terms are enriched likely due to the random plus non-random labeling in the nucleus. What would a Telo vs general nucleus GSEA look like? (GSEA should be used for quantitative data, no GO). That would provide some specificity. Figures 2G and S4A are encouraging, but a) these proteins are largely sequestered in their respective locations, and b) no validation by an orthogonal method like ChIP or Cut and Run/Tag is used.

We performed GSEA on the enrichment scores for the label-free proteomics data from the SAINT output in Figure 1D and that several of these proteins (e.g., those highlighted in Figure 2A: TERF1, CENPN, TOM70) have already been extensively validated to co-localize to these locations.

To the reviewers request for additional validation, we analyzed ChIP-seq data for several proteins to determine if they were enriched surrounding specific loci. In the case of the HoxA/B analysis, we found that HDAC3 and TCF12 were enriched at HOXB compared to HOXA, and SMARCB1 and ZC3H13 were enriched at HOXA compared to HOXB (Figure 4C). HDAC3 and TCF12 ChIP data confirmed increased peak calls at HOXB and SMARCB1 and ZC3H13 ChIP data confirmed increased peak calls at HOXA for these four selected proteins (Figure 4D).

You can also see this in the enormous number of "enriched" proteins in the supplemental volcano plots. The hypothesis-supporting ones are labeled, but do the authors really believe all of those proteins are specific to the loci being looked at? Maybe compared to mitochondria, but it's hard to believe there are not a lot of false positives in those blue clouds. I believe the authors are more seeing mito vs nucleus + Telo than the stated comparison. For example, if you have no labeling in the nucleus in the control (Figures 1C and 2C) you cannot separate background labeling from specific labeling. Same with mito vs. nuc+Telo. It is not the proper control to say what is specifically at the Telo.

We agree with the reviewer that compared to mitochondrial targeting, there could be non-specific nuclear comparisons. We note again though that we purposefully stayed away from using the word “specifically” when describing the proteomics work developed here. The reason being that we are not atlasing a large number of targets to define specificity. Instead, we highlight in Figure 2 that we did observe differences in proteins associating with telomeres and mitochondrial DNA. That may be non-specific, and in fact, this is also why we decided to include two nuclear targets to determine what might be specifically enriched. Thus, we compared centromeric and telomeric protein enrichment as determined by OMAP and observed consistent differential enrichment of shelterin proteins at telomeres (Figure 2I) and CENP-A complex members at centromeres (Figure 2J). We could have done the relative comparisons to no-oligo controls, analogous to how CASPEX compared targeted analyses to no-sgRNA controls (PMID: 29735997). However, we found that the mitochondrial targeted samples were generally better as a comparator because (1) we have clear means to validate differences and (2) the local environment around DNA is being labeled.

I would like to see a Telo vs nuclear control and a Centromere vs nuc control. One could then subtract the background from both experiments, then contrast Telo vs Cent for a proper, rigorous comparison. However, I realize that is a lot of work, so rewriting the manuscript to better and more accurately reflect what was accomplished here, and its limitations, would suffice.

Assuming the nuclear control was the same, It is unclear how this ratio-of-ratios ([Telo/Ctrl]/[Cent/ctrl]) experiment would be inherently different from the direct comparison between Telo and Centromere. Again, assuming the backgrounds are derived from the same cellular samples. More than likely adding the extra ratios could increase the artifactual variance in the estimates, reducing the power of the comparisons as has been seen in proteomics data using ratio-of-ratio comparisons in the past (Super-SILAC).

(2) A second major drawback is the lack of validation experiments. References to literature are helpful but do not make up for the lack of validation of a new method claiming new protein-DNA or DNA-DNA interactions. At least a handful of newly described proximal proteins need to be validated by an orthogonal method, like ChIP qPCR, other genomic methods, or gel shifts if they are likely to directly bind DNA. It is ok to have false positives in a challenging assay like this. But it needs to be well and clearly estimated and communicated.

We appreciate the reviewers' point here. To be clear, we have not made any claims about new proteins at specific loci. Instead we validated that known telomeric and centromeric associating proteins were consistently enriched by DNA OMAP (Figure 2). We also want to emphasize that while valuable, the current paper is not an atlasing paper to define the full and specific proteomes of two genomic loci. We instead show how this method can be used to observe quantitative differences in proteins enriched at certain loci (HOXA/B work, Figure 4) and even between haplotypes (Xi/Xa work, Figure 5).

(3) The mapping of 3D contacts for 20 kb regions is beautiful. Some added discussion on this method's benefits over HiC-variants would be welcomed.

We appreciate the reviewers' point here and have added the following text to the discussion: “Additionally, we show that this method is also able to detect DNA-DNA contacts through biotinylation of loop anchors. Our approach functions similarly to 4C[86]. However, our approach of biotin labeling of contacts does not rely on pairwise ligation events. Thus, detection of contacts through DNA O-MAP will vary in the sampling of DNA-DNA contacts in comparison.”

(4) The study claims this method circumvents the need for transfectable cells. However, the authors go on to describe how they needed tons of cells, now in solution, to get it to work. The intro should be more in line with what was actually accomplished.

We took the reviewers point and have worked to scale down the DNA OMAP experiments while revising this manuscript. As noted in Figure 5, we have been able to scale this work down to work on plates with ~10x fewer cells than with our initial experiments. This is on top of the initial DNA OMAP work in Figure 1 and 2, as well as our additional work in Figure 4, where we are using 30-60 million cells in solutions which is still 10x less material than previous work (PMID: 29735997). Thus, the newest DNA OMAP platform uses ~100x fewer cells than previous work.

(5) Comments like "Compared to other repetitive elements in the human genome...." appear to circumvent the fact that this method is still (apparently) largely limited to repetitive elements. Other than Glopro, which did analyze non-repetitive promoter elements, most comparable methods looked at telomeres. So, this isn't quite the advancement you are implying. Plus, the overlap with telomeric proteins and other studies should be addressed. However, that will be challenging due to the controls used here, discussed above.

As noted above, we have added Figures 4 and 5 to address the reviewer concerns by targeting multiple non-repetitive loci (HOXA and HOXB clusters and a 4.5Mb region straddling X-inactivation center on both the active and inactive X homolog). Targeting the regions around the X-inactivation center shows the potential to perform haplotype-resolved proteome analysis of chromatin interactors.

For the telomeric protein overlap, we tried to do this specifically in Figure 1F, we agree with the reviewer that the controls used dramatically change the proteins considered enriched. The goal of the network analysis was to show (1) that we identify proteins previously observed in telomere proteomic datasets and (2) that we gain a more complete view of proteins based on capturing more known interacting proteins than many previous methods as was noted for the RNA OMAP platform (PMID: 39468212). For example, we observed enrichment of PRPF40A in the telomeric DNA OMAP data. From the Bioplex interactome, PRPF40A was observed to interact with TERF2IP and TERF2, suggesting that through these interactions PRPF40A may colocalize at telomeres. Similarly, we observed enrichment of SF3A1, SF3B1, and SF3B2. The SF3 proteins are known regulators of telomere maintenance (PMID: 27818134), but have not previously been observed in telomeric proteomics datasets, except now in DNA OMAP.

We have added the following text to the Results to clarify these points:

“To benchmark DNA O-MAP, we compared the full set of telomeric proteins to proteins observed in five established telomeric datasets (PICh, C-BERST, CAPLOCUS, CAPTURE, BioID)12,14,16,35,36 (Figure 1F). DNA O-MAP captured both previously observed telomeric interacting proteins (shelterins) as well as telomere associated proteins (ribonucleoproteins). We identified multiple heterogeneous nuclear ribonucleoproteins (hnRNPs) previously annotated as telomere-associated, including HNRNPA1 and HNRNPU. HNRNPA1 has been demonstrated to displace replication protein A (RPA) and directly interact with single-stranded telomeric DNA to regulate telomerase activity37–39. HNRNPU belongs to the telomerase-associated proteome40 where it binds the telomeric G-quadruplex to prevent RPA from recognizing chromosome ends41. We mapped DNA O-MAP enriched telomeric proteins to the BioPlex protein interactome and observed that in addition to capturing proteins from previously observed telomeric datasets (Figure 1F), DNA O-MAP enriched for interactors of previously observed telomeric proteins. Previous data found RBM17 and SNRPA1 at telomeres, and in BioPlex these proteins interact with three SF3 proteins (SF3A1, SF3B1, SF3B2). Though they were not identified in previous telomeric proteome datasets, all three of these SF3 proteins were enriched in the DNA O-MAP telomeric data. Furthermore, through interactions with G-quadruplex binding factors, these SF3 proteins are regulators of telomere maintenance (PMID: 27818134). Taken together, this data supports the effectiveness of DNA O-MAP for sensitively and selectively isolating loci-specific proteomes.”

Reviewer #2 (Public review):

Summary

Liu and MacGann et al. introduce the method DNA O-MAP that uses oligo-based ISH probes to recruit horseradish peroxidase for targeted proximity biotinylation at specific DNA loci. The method's specificity was tested by profiling the proteomic composition at repetitive DNA loci such as telomeres and pericentromeric alpha satellite repeats. In addition, the authors provide proof-of-principle for the capture and mapping of contact frequencies between individual DNA loop anchors.

Strengths

Identifying locus-specific proteomes still represents a major technical challenge and remains an outstanding issue (1). Theoretically, this method could benefit from the specificity of ISH probes and be applied to identify proteomes at non-repetitive DNA loci. This method also requires significantly fewer cells than other ISH- or dCas9-based locus-enrichment methods. Another potential advantage to be tested is the lack of cell line engineering that allows its application to primary cell lines or tissue.

We thank the reviewers for their comments and note that we have followed up on the idea of targeting non-repetitive DNA loci (HOXA and HOXB clusters and a 4.5Mb section of the X chromosome on each homolog) in the revised manuscript (Figures 4 and 5).

Weaknesses

The authors indicate that DNA O-MAP is superior to other methods for identifying locus-specific proteomes. Still, no proof exists that this method could uncover proteomes at non-repetitive DNA loci. Also, there is very little validation of novel factors to confirm the superiority of the technique regarding specificity.

Our primary claim for DNA OMAP is that it requires orders of magnitude fewer cells than previous studies. Based on comments along these lines from both reviewers, we performed DNA OMAP targeting non-repetitive DNA loci (HOXA and HOXB clusters and a 4.5Mb section of the X chromosome on each homolog) in the revised manuscript (Figure 4 and 5). For the X chromosome targeting, we used ~3 million cells per condition with methods that we optimized during revision. When targeting HOXA and HOXA, we were able to identify HDAC3 and TCF12 enrichment at HOXB compared to HOXA as well as ZC3H13 and SMARB1 enrichment at HOXA compared to HOXB, which is consistent with ChIP-seq reads from ENCODE for these proteins (Figure 4C, D). Both the HOXand X chromosome work help to address limitations noted in the Gauchier et al. paper the reviewer notes as both show progress towards overcoming “the major signal-to-noise ratio problem will need to be addressed before they can fully describe the specific composition of single-copy loci”.

The authors first tested their method's specificity at repetitive telomeric regions, and like other approaches, expected low-abundant telomere-specific proteins were absent (for example, all subunits of the telomerase holoenzyme complex). Detecting known proteins while identifying noncanonical and unexpected protein factors with high confidence could indicate that DNA O-MAP does not fully capture biologically crucial proteins due to insufficient enrichment of locus-specific factors. The newly identified proteins in Figure 1E might still be relevant, but independent validation is missing entirely. In my opinion, the current data cannot be interpreted as successfully describing local protein composition.

We analyzed ChIP-seq reads for our HOXA and HOXB (Figure 4C,D) which recapitulate our findings for four of our differentially enriched proteins. We also note that with the addition of the nonrepetitive loci (Figures 4 and 5), we have performed DNA OMAP on seven different targets (telomeres, pericentromeres, mitoDNA, HOXA, HOXB, Xi, and Xa) and identified expected targets at each of these. The consistency of these data, which mirrors the consistency of the RNA implementation of OMAP (PMID: 39468212), reinforces that we can successfully enrich local proteomes at genomic loci.

Finally, the authors could have discussed the limitations of DNA O-MAP and made a fair comparison to other existing methods (2-5). Unlike targeted proximity biotinylation methods, DNA O-MAP requires paraformaldehyde crosslinking, which has several disadvantages. For instance, transient protein-protein interactions may not be efficiently retained on crosslinked chromatin. Similarly, some proteins may not be crosslinked by formaldehyde and thus will be lost during preparation (6).

Based on this critique we have gone back through the manuscript to improve the fairness of our comparisons and expanded the limitations in our discussion section.

To the point about fixation, Schmiedeberg et al., which the reviewer references, does describe crosslinking requiring longer interactions (~5 s). Yet, as featured in reviews, many additional studies have found that “it has been possible to perform ChIP on transcription factors whose interactions with chromatin are known from imaging studies to be highly transient” (Review PMID: 26354429). We note similar results in proteomics analysis in Subbotin and Chait that state that the linkage of lysine-based fixatives like formaldehyde and “glutaraldehyde to reactive amines within the cellular milieu were sufficient to preserve even labile and transient interactions (PMID: 25172955).

(1) Gauchier M, van Mierlo G, Vermeulen M, Dejardin J. Purification and enrichment of specific chromatin loci. Nat Methods. 2020;17(4):380-9.

(2) Dejardin J, Kingston RE. Purification of proteins associated with specific genomic Loci. Cell. 2009;136(1):175-86.

(3) Liu X, Zhang Y, Chen Y, Li M, Zhou F, Li K, et al. In Situ Capture of Chromatin Interactions by Biotinylated dCas9. Cell. 2017;170(5):1028-43 e19.

(4) Villasenor R, Pfaendler R, Ambrosi C, Butz S, Giuliani S, Bryan E, et al. ChromID identifies the protein interactome at chromatin marks. Nat Biotechnol. 2020;38(6):728-36.

(5) Santos-Barriopedro I, van Mierlo G, Vermeulen M. Off-the-shelf proximity biotinylation for interaction proteomics. Nat Commun. 2021;12(1):5015.

(6) Schmiedeberg L, Skene P, Deaton A, Bird A. A temporal threshold for formaldehyde crosslinking and fixation. PLoS One. 2009;4(2):e4636.

Reviewer #3 (Public review):

Significance of the Findings:

The study by Liu et al. presents a novel method, DNA-O-MAP, which combines locus-specific hybridisation with proximity biotinylation to isolate specific genomic regions and their associated proteins. The potential significance of this approach lies in its purported ability to target genomic loci with heightened specificity by enabling extensive washing prior to the biotinylation reaction, theoretically improving the signal-to-noise ratio when compared with other methods such as dCas9-based techniques. Should the method prove successful, it could represent a notable advancement in the field of chromatin biology, particularly in establishing the proteomes of individual chromatin regions - an extremely challenging objective that has not yet been comprehensively addressed by existing methodologies.

Strength of the Evidence:

The evidence presented by the authors is somewhat mixed, and the robustness of the findings appears to be preliminary at this stage. While certain data indicate that DNA-O-MAP may function effectively for repetitive DNA regions, a number of the claims made in the manuscript are either unsupported or require further substantiation. There are significant concerns about the resolution of the method, with substantial biotinylation signals extending well beyond the intended target regions (megabases around the target), suggesting a lack of specificity and poor resolution, particularly for smaller loci.

We thank the reviewers for their comments and note that we have followed up on the idea of targeting non-repetitive DNA loci (HOX clusters and part of the X chromosome) in the revised manuscript (Figures 4 and 5).

Furthermore, comparisons with previous techniques are unfounded since the authors have not provided direct comparisons with the same mass spectrometry (MS) equipment and protocols. Additionally, although the authors assert an advantage in multiplexing, this claim appears overstated, as previous methods could achieve similar outcomes through TMT multiplexing. Therefore, while the method has potential, the evidence requires more rigorous support, comprehensive benchmarking, and further experimental validation to demonstrate the claimed improvements in specificity and practical applicability.

We have made the comparisons as best as possible. In fact, we found it difficult to find examples of recent implementations of many of these methods. Purchasing the exact mass spectrometers or performing every version of chromatin proteomics would be well beyond the scope of this work. On the other hand, OMAP has already generated data for three manuscripts. We are making the claim that using the instrumentation and methods available to us, we were able to reduce the number of cells required to analyze a given genomic loci. We then applied TMT multiplexing to further improve the throughput and perform replicate analyses. To fully validate that one protein exists at one loci and no other would require exhaustive atlasing of protein-genomic interactions which would be well beyond the scope of this single paper. Similarly, ChIP for every target identified to assess an empirical FDR would be well beyond the scope of this work.

Recommendations for the authors:

Reviewing Editor Comments:

In summary, all three reviewers raised major concerns about the limitations of the method, many of which could be resolved by more precise and transparent language about these limitations. If you choose to resubmit a revised version, you should address questions like: What scale does "individual locus" refer to? At what scale can the method map protein-DNA interactions at individual targeted loci, rather than large repetitive domains? What is the estimated false discovery rate for a set of enriched proteins? The eLife assessment for this version of the manuscript is based on reviewer concerns. Note that this assessment can be updated after receiving a response to reviewer comments.

Reviewer #1 (Recommendations for the authors):

(1)The first couple of paragraphs make it sound like your method would exclusively benefit from sample multiplexing with MS-based proteomics. That is a bit misleading. The other stated methods use TMT. They don't use it to compare very different genomic (or compartmental) regions, but there is no reason cberst, glopro or CasID could not.

A good point and we have updated the manuscript to reflect this. While previous methods generally did not use TMT, they could be adapted to do so and, similar to OMAP, improved by the use of more replicates in their analyses.

(2) Please make the colors in 1F for the dataset overlap easier to read. 2 and 4+ are too similar.

We appreciate the comment on making the colors easier to discern. Along these lines we’ve changed the color of “2” to make it easier to distinguish from “4+”.

(3) Label as many dots as legible in your volcano plots.

We’ve labeled a number of proteins that are relevant to the discussion in this paper as well as some additional proteins. We feel that additional labeling would detract from the points that we are trying to make in individual figure panels about groups of proteins, rather than general remodeling of all proteins.

(4) Figure 2E needs a divergent color scheme since it crosses 0. And is it scaled, log-transformed, or both? And compared to what then?

Figure 2E (heatmap) is z-scaled relative protein abundance measurements based on TMTpro reporter ion signal to noise (“s/n”). We have added additional information to the legend to highlight the information that the reviewer points out here. For the color, we are unsure of what is being asked for, as above 0 is red and below 0 is blue.

(5) Unclear what you are implying with "...only 1-2 biological replicates." I would omit or clarify.

Fair point, we have updated the manuscript to omit this section to simplify the introduction.

(6) H2O2 and biotin phenols might be toxic to living organisms. But so is 4% PFA and ISH. I realize you are trying to justify your new approach but you don't need to do it with exaggerated contrasts. This O-MAP is a great approach and probably more likely for people to adopt it because it's DNA ISH based. Plus, with the clinking, you are likely not displacing proteins via Cas9 landing.

We appreciate the reviewer’s comments about adoption and lack of protein displacement. We’ve scaled back on the claims and added more about limitations owing to crosslinking and ISH.

(7) How much genome does the Cent regions take up? You state 500 kb for Telos.

In the text we delineate how large of a region the PanAlpha probes target “The genome-wide binding profile of the pan-alpha probe closely overlaps with centromeres (Figure S1) and covers approximately 35 Mb of the genome according to in silico predictions.” Additionally, we’ve added Table S4 to summarize target locus sizes for all of the included targets.

(8) You seem to be underestimating the lysine labeling. Is that after TMT labeling and analysis? If so, you're already ignoring what couldn't be seen. I don't think it's that important but you included it, so please describe clearly why it's an issue and how much of an issue it is. How does that relate to lit values? And it's not just TMTpro, it's any lysine labeler.

We appreciate the reviewers point about specifying the reasoning and the lack of clarity around overall lysine labeling. That 1.38% is the number of peptides with remainder modifications due to formaldehyde crosslinking. For overall acylation of lysines with TMT labels, we generally expect (and achieve) >97% labeling of lysines with TMT reagents as the Kuster and Carr labs nicely demonstrated across a range of labeling conditions (PMID: 30967486).

Decrosslinking is a critical step generally for proteomics workflows on fixed or FFPE tissues and thus we sought to explore whether we could achieve sufficiently low residual lysine alkylation to enable protein quantitation by TMTpro reagents (or any lysine labeler, as the reviewer notes). For TMTpro-based methods on peptides, this is less of a concern generally as protease cleavage frees new primary amines at the N-termini of peptides which can be labeled for quantitation. But in part since we are describing a proteomics method on fixed tissues we wanted to share these data and the potential inclusion of residual fixation modifications for readers to potentially take into consideration when performing this method.

Reviewer #3 (Recommendations for the authors):

Liu et al. describe an original locus labelling approach that enables the isolation of specific genomic regions and their associated proteins. I have mixed views on this work, which, in my opinion, remains preliminary at this stage. Establishing the proteome of a single chromatin region is one of the most complex challenges in chromatin biology, as extensively discussed in Gauchier et al. (2020). Any breakthrough towards this goal is of significant interest to the community, making this manuscript potentially compelling. Indeed, some data suggest that the method works for repetitive DNA to some extent. However, much of the data is not very convincing, and in the case of small DNA targets, it argues against the use of DNA-O-MAP.

In contrast to existing methods, DNA-O-MAP combines locus-specific hybridisation in situ (using affordable oligonucleotides) with proximity biotinylation. A major advantage of this strategy over other locus-specific biotinylation methods is the possibility of extensively washing excess or non-specifically hybridised probes before the biotinylation reaction, theoretically limiting biotinylation to the target region and thus significantly enhancing the signal-to-noise ratio. Other methods involving proximity biotinylation, such as targeted dCas9, do not have this capacity, meaning biotinylation occurs not only at the locus where a small fraction of dCas9 molecules is targeted but also around non-bound dCas9 molecules (representing the vast majority of dCas9 expressed in a given cell). This aspect potentially represents an interesting advance.

We thank the reviewer for their thoughts and critiques, which we hope have in part relieved concerns pertaining to limitation on repetitive elements. To the latter points, we confirmed this with new specificity analysis that showed labeling to be highly specific to a given probe locus (Figure S3).

Below, I outline the significant issues:

The manuscript implies that DNA-O-MAP has better sensitivity than earlier techniques like CAPTURE, GLOPRO, or PICh. The authors state that PICh uses one trillion cells (which I doubt is accurate), and other methods require 300 million cells, whereas DNA-O-MAP uses only 60 million cells, suggesting the latter is more feasible. However, these earlier experiments were conducted almost 15 and 6 years ago, when mass spectrometry (MS) sensitivity was considerably lower than that of current instruments. The authors cannot know whether the proteome obtained by previous methods using 60 million cells, but analysed with current MS technology, would yield results inferior to those of DNA-O-MAP. Unless the authors directly compare these methods using the same number of cells and identical MS setups, I find their argument unjustified and misleading.

Based on the instrumentation listed, we actually do have a good idea of how sensitivity changes may have affected identifications and overall sensitivity. For example, the CASPEX data was collected on an Orbitrap Fusion Lumos, while our data was collected on an Orbitrap Fusion Eclipse. From our work characterizing these two instruments during the Eclipse development (PMID: 32250601), we do actually know that the ion optics improvements boosted sensitivity of the Eclipse used in our work compared to the Lumos by ~50%, meaning if GLOPRO was run on an Eclipse it would still require >200 million cells per replicate for input.

It is suggested that DNA-O-MAP is capable of 'multiplexing', whereas previous methods are not. This statement is also misleading. As I understand it, the targeted regions do not originate from a common pool of cells. Instead, TMT multiplexing only occurs after each group of cells has been independently labelled (Telo, Centro, Mito, control). Therefore, previous methods could also perform multiplexing with TMT. Moreover, it is unclear how each proteome was compared: one would expect many more proteins from centromeres than from telomeres (I am unsure about the number of mitochondria in these cells) since these regions are significantly larger than telomeres (possibly 10 to 100 times larger?). Have the authors attempted to normalise their proteomics data to the size (concatenated) of each target? This is particularly relevant when comparing histone enrichment at chromatin regions of differing sizes.

We agree with the reviewers that this was overstated. In fact the GLOPRO paper notes that they performed a MYC analysis with a previous generation of TMT that could multiplex 10 samples. We have amended the manuscript to be more specific in those contexts. As stated in the methods section, “Samples were column normalized for total protein concentration”, to account for the amount of protein and size of the different targets.

Figure 1C shows streptavidin dots resembling telomeres. To substantiate this claim, simultaneous immunofluorescence with a telomere-specific protein (e.g., TRF1 or TRF2) is required. It is currently unknown whether all or only a subset of telomeres are targeted by DNA-O-MAP, and it is also unclear if some streptavidin foci are non-telomeric. Quantification is needed to indicate the reproducibility of the labelling (the same comment applies to the centromere probes later in the manuscript; an immunofluorescence assay with CENPB would be informative, alongside quantifications).

We understand the reviewer’s concern about specificity and reproducibility of DNA-O-MAP. To address this we have added analysis showing the efficiency and specificity of our FISH and biotin labeling for Telomere, PanAlpha, and Mitochondria targeting oligos (Figure S3). We found that biotin deposition was highly specific to the intended targets with an average across the three probes of 98% specificity.

Perhaps more importantly, the authors suggest that it may be possible to enrich proteins that are not necessarily present at the target locus but are instead in spatial proximity (e.g., RNA polymerase I subunits enriched upon centromere targeting). Does this not undermine the purpose of retrieving locus-specific proteomes?

The goal of DNA OMAP is to identify a local neighborhood of proteins around a specific genomic loci, similar to GLOPRO. As we note in the work presented in Figure 4 and 5 now, these neighborhoods are inherently interesting for comparison of quantitative changes that occur around a genomic locus.

Possibly related to the previous issue, when DNA-O-MAP is used to assess DNA-DNA interactions, probes covering regions of 20-25 kb are employed. Therefore, one would expect these regions to be significantly biotinylated compared to flanking regions. However, Genome Browser screenshots indicate extensive biotinylation signals spanning several megabases around the 20-25 kb targets. If the method were highly resolutive, the target region would be primarily enriched, with possibly discrete lower enrichment at distant interacting regions. The lack of discrete enrichment suggests poor resolution, likely due to the likely large scale of proximity biotinylation. This compromises the effectiveness of DNA-O-MAP, especially if it is intended to target small loci with complex sequences. Could the authors quantify the absolute number of reads from the target region compared to those from elsewhere in the genome (both megabases around the locus and other chromosomes, where many co-enriched regions seem to exist)? This would provide insights into both enrichment and specificity.

Thanks for this suggestion, we have included a new Figure S8 to look at normalized read depth as a function of distance from the genomic target. The resolution of DNA OMAP, like all peroxidase mediated proximity labeling methods, is not dependent on the sequence length of the DNA region, but the 30-40nm of physical space around the HRP molecule that is targeted to the genomic loci. 

Minor Issues:

(1) Page 3, second paragraph: It is unclear why probes producing a visible signal in situ necessarily translates to their ability to retrieve a specific proteome.

We have revised the manuscript to de-emphasize the visible signal aspect of probe targeting and re-emphasize our initial point that the number of probes needed to properly target unique regions makes the use of locked nucleic acid probes cost-prohibitive. The basic point though, we and others previously showed with RNA OMAP (PMID: 39468212) and Apex/proximity labeling strategies, the ability to deposit biotin and visualize generally directly translates to recovery of proximally labeled proteins (PMID: 26866790).

(2) Page 3, last paragraph: "to reach a higher degree of enrichment...": Has it been demonstrated that direct protein biotinylation provides higher enrichment of relevant proteins? Certainly, there is higher enrichment of proteins, but whether they are relevant is another matter.

Our point here was that the methods using direct protein biotinylation have higher levels of enrichment and thus require less cells than the previously mentioned PICh method, which is why we wrote the following: “In the case of GLoPro, APEX-based proximity labeling enhanced protein detection sensitivity, reducing the input required for each replicate analysis to ~300 million cells—a 10-fold reduction in cell input compared to PICh which used 3 billion cells.”

Regarding if these proteins are relevant or not, we show enrichment of known proteins that are critical to the function of their occupied genomic region at telomeres and centromeres. Additionally, we’ve made added quantitative comparisons to assess relevance in our analysis of Hox and our targeted region of the X chromosome through comparisons to ChIP data at these regions. The improved enrichment that we’ve established in our initial submission as well as in the updated version also means that we can further scale down the number of cells required.

(3) Figure 2B is misleading; it appears as though all three regions are targeted in the same cell, suggesting true multiplexing, which, I believe, is not the case.

To avoid any potential confusion about how the samples were derived we’ve updated this figure panel to show three separate cells, each with a different region being targeted.

(3) If I understand correctly, the 'no probe' control should primarily retrieve endogenously biotinylated proteins (carboxylases), which are mainly found in mitochondria. Why does the Pearson clustering in Supplementary Figure 2 not place this control proteome closer to the mitochondrial proteome?

Under the assumption that the ~10 carboxylases are biotinylated at the same levels in all cells, yet the proportion of these carboxylases compared to all enriched proteins for a given target is markedly reduced. Thus, as a proportion of the enriched proteome we note in Figure S4 that mitochondrial DNA OMAP enriches proteins besides the carboxylases. We believe this explains why the ‘no probe’ sample can be clearly separated along PC2 in Figure 2D.

(4) Was CENPA enriched in the centromere DNA-O-MAP? If not, have the authors scaled up (e.g., with ten times more cells) to see if the local proteome becomes deeper and detects relevant low-abundance proteins like CENPA or HJURP? This would be very informative.

We did not observe CENPA, and we had originally contemplated the experiment the reviewer suggested, but noted that CENPA has only two tryptic peptides (>7 AA, <35AA), and they are both in the commonly phosphorylated region of the protein. Rather than scale up these experiments, we decided to attempt DNA OMAP on the non-repetitive locus experiments.

(5) Using a few million cells, I do not see how the starting chromatin amount could range from 0.5 to 7 mg, as shown in Figures 2 and 3. How were these figures calculated? One diploid cell contains approximately 6 pg of DNA/chromatin, which means one billion cells represent about 6 mg of DNA/chromatin (a typical measurement for these methods).

Thanks to the reviewer for catching this, that should have been the total lysate amount, not chromatin mass. We have corrected Figures 2 and 3.

(6) Figure S1: There is no indication of the metrics used for the shades of red.

We have added a gradient legend to depict this.

(7) What is the purpose of HCl in the experiment?

HCl treatment was done to reduce autofluorescence for imaging (PMID: 39548245).

(8) I could not find the MS dataset on the server using the provided accession number (PDX054080).

Thank you for pointing this out, we have confirmed the dataset is public now and added the new datasets for the Xi/Xa and Hox studies. We also note that the accession should be “PXD054080”

(9) Why desthiobiotin instead of biotin?

We have tested both; desthiobiotin was helpful to reduce adsorption to surfaces. Either biotin or desthiobiotin can be used, though, for OMAP.

Ver onde posso falar sobre isso,...Porque ja havia falado de como as pessoas podem falar oque quiser nas redes sociais...Esse conta o outro lado da moeda, como as pessoas se expoem facil na internet em troca de likes

esta sigla debe explicarse o aparecer antes del teorema

Via de regra, é proibido o desconto de qualquer valor do salário do empregado. Apenas se admite nas hipóteses de: - Adiantamento; - Dispositivo de lei ou de contrato coletivo; - Dano culposo, se previsto em contrato; - Dano doloso.

Nessa linha:

b) Dano doloso

• Se o empregado provoca um dano qualquer ao empregador, e o faz dolosamente, ou seja, com a intenção de fazê-lo, deve ressarcir o empregador dos prejuízos experimentados. E este ressarcimento pode ser feito inclusive através do desconto nos salários, a teor do disposto no art. 462, § 1º, da CLT.

• Caso o valor a ser ressarcido seja superior a 70% do valor do salário, entende-se que somente pode ser descontado, por mês, até este limite, ante o disposto na OJ 18 da SDC do TST:

OJ-SDC-18. Descontos autorizados no salário pelo trabalhador. Limitação máxima de 70% do salário-base (inserida em 25.05.1998).

• Os descontos efetuados com base em cláusula de acordo firmado entre as partes não podem ser superiores a 70% do salário-base percebido pelo empregado, pois deve-se assegurar um mínimo de salário em espécie ao trabalhador.

c) Dano culposo, se autorizado em contrato o desconto

• No caso de dano causado ao empregador pelo empregado, tendo agido este com culpa (seja por imperícia, imprudência ou negligência), pode o empregador descontar do salário o prejuízo experimentado, desde que o empregado tenha autorizado expressamente o desconto em tais hipóteses.

• Na prática, quase todos os empregados autorizam o desconto por dano culposo no momento da admissão, ao assinar o famoso contrato de adesão imposto pelo empregador.

• Nesta hipótese de desconto por dano culposo, surge a polêmica questão da OJ 251 do TST:

OJ-SDI1-251. Descontos. Frentista. Cheques sem fundos (inserida em 13.03.2002).

É lícito o desconto salarial referente à devolução de cheques sem fundos, quando o frentista não observar as recomendações previstas em instrumento coletivo.

• A interpretação que se dá a tal verbete é no sentido de que o TST flexibilizou o rigor do dispositivo celetista, passando a prever, ao menos neste caso, a autorização genérica para desconto na própria norma coletiva, pelo que o desconto prescindiria de autorização contratual do empregado.

• No mundo dos fatos, a hipótese é mais ou menos a seguinte: a norma coletiva prevê que o frentista deve anotar, no verso do cheque recebido, os dados básicos do emitente, como endereço e telefone, bem como a placa do veículo. Caso deixe de fazê-lo, se sujeita ao desconto salarial caso o cheque não tenha provisão de fundos.

(RESENDE, Ricardo. Direito do Trabalho - 9ª Edição 2023. 9. ed. Rio de Janeiro: Método, 2023. E-book. p.568. . Acesso em: 19 mai. 2025.)

• Obs.: Não confundir a possibilidade de desconto salarial decorrente de dano doloso/culposo com desconto de valores da rescisão contratual.

• Enquanto jurisprudência entende pela possibilidade de desconto de até 70% do salário na hipótese dano doloso, na hipótese de rescisão contratual, o limite é o salário mensal para fins de desconto. O valor remanescente será tido como dívida civil.

is this working?

Not quite -- these will come out of the PQ project -- see the 'request to evaluators' here: https://coda.io/d/Unjournal-Public-Pages_ddIEzDONWdb/Cultured-meat-PQ-Request-to-TEA-evaluators_suyucPLB#_luoC2X-O

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

The authors test the hypotheses, using an effort-exertion and an effort-based decision-making task, while recording brain dynamics with EEG, that the brain processes reward outcomes for effort differentially when they earned for themselves versus others.

The strengths of this experiment include what appears to be a novel finding of opposite signed effects of effort on the processing of reward outcomes when the recipient is self versus others. Also, the experiment is well-designed, the study seems sufficiently powered, and the data and code are publicly available.

We thank Reviewer #1 for the affirmative appraisal of our manuscript as well as the thoughtful and insightful comments, which have enabled us to significantly improve the manuscript.

(1) Inferences rely heavily on the results of mixed effects models which may or may not be properly specified and are not supported by complementary analyses.

We thank Reviewer #1 for raising this critical issue of model specification. We have re-fitted our mixed-effects models and performed complementary analyses to validate the robustness of our findings. Specifically, we adopted the maximal converging random-effects structure (including random slopes for Recipient, Effort, and Magnitude where feasible) while ensuring model stability (see Responses to Reviewer #1’s Recommendations point 2). Crucially, our primary findings, including the Recipient × Effort and Recipient × Effort × Magnitude interactions, remained robust. Furthermore, additional analyses confirmed that these results were not confounded by factors such as response speed and subjective effort rating (see Responses to Reviewer #1’s Recommendations point 5).

(2) Also, not all results hang together in a sensible way. For example, participants report feeling less subjective effort, but also more disliking of tasks when they were earning rewards for others versus self. Given that participants took longer to complete tasks when earning effort for others, it is conceivable that participants might have been working less hard for others versus themselves, and this may complicate the interpretation of results.

We thank Reviewer #1 for this insightful point (which also relates to Reviewer #3’s point 5). In our study, participants were asked to rate three specific dimensions: Effort (“How much effort did you exert to complete each effort condition when earning rewards for yourself [or the other person]?”), Difficulty (“How much difficulty did you perceive in each effort condition when earning rewards for yourself [or the other person]?”), and liking (“How much did you like each effort condition when earning rewards for yourself [or the other person]?”).

We acknowledge the Reviewer #1’s concern that the lower subjective effort ratings for others seems contradictory to the higher disliking and longer completion times. We propose that in this paradigm, subjective effort ratings are susceptible to demand characteristics and likely captured motivational engagement (e.g., “how hard I tried” or “how willing I was”) rather than perceived task demands. To disentangle these factors, we included a measure of perceived task difficulty, which is anchored in task properties and is less prone to social desirability biases (Harmon-Jones et al., 2020; Wright et al., 1990). We found no differences in perceived difficulty between self- and other-benefiting trials (Figure 2D), suggesting that the task demands were perceived as equivalent across conditions. To examine this interpretation more directly, we analyzed correlations among participants’ ratings of difficulty, effort, and liking. As illustrated in Figure S1, we found no correlation between difficulty and effort ratings. Crucially, liking ratings were negatively correlated with difficulty ratings.

More importantly, our performance data contradict the interpretation that participants “worked less hard” for others in terms of task completion. While participants took longer to complete tasks for others, they maintained comparable, near-ceiling success rates for self (97%) and other (96%) recipients (b = -0.46, p = 0.632; Supplementary Table S1). This dissociation suggests that although participants were less motivated (e.g., lower subjective ratings, longer completion times, and greater disliking) to work for others, they ultimately exerted the necessary physical effort to achieve successful outcomes. Thus, the results consistently point to a decrease in prosocial motivation (consistent with prosocial apathy) rather than a failure of effort exertion.

Wright, R. A., Shaw, L. L., & Jones, C. R. (1990). Task demand and cardiovascular response magnitude: Further evidence of the mediating role of success importance. Journal of Personality and Social Psychology, 59(6), 1250-1260. https://doi.org/10.1037/0022-3514.59.6.1250

Harmon-Jones, E., Willoughby, C., Paul, K., & Harmon-Jones, C. (2020). The effect of perceived effort and perceived control on reward valuation: Using the reward positivity to test a dissonance theory prediction. Biological Psychology, 107910. https://doi.org/10.1016/j.biopsycho.2020.107910

Reviewer #2 (Public review):

Measurements of the reward positivity, an electrophysiological component elicited during reward evaluation, have previously been used to understand how self-benefitting effort expenditure influences the processing of rewards. The present study is the first to complement those measurements with electrophysiological reward after-effects of effort expenditure during prosocial acts. The results provide solid evidence that effort adds reward value when the recipient of the reward is the self but discounts reward value when the beneficiary is another individual.

An important strength of the study is that the amount of effort, the prospective reward, the recipient of the reward, and whether the reward was actually gained or not were parametrically and orthogonally varied. In addition, the researchers examined whether the pattern of results generalized to decisions about future efforts. The sample size (N=40) and mixed-effects regression models are also appropriate for addressing the key research questions. Those conclusions are plausible and adequately supported by statistical analyses.

We appreciate Reviewer #2’s positive appraisal of our manuscript. We are fortunate to receive your thoughtful and insightful suggestions and have revised the manuscript accordingly.

(1) Although the obtained results are highly plausible, I am concerned whether the reward positivity (RewP) and P3 were adequately measured. The RewP and P3 were defined as the average voltage values in the time intervals 300-400 ms and 300-440 ms after feedback onset, respectively. So they largely overlapped in time. Although the RewP measure was based on frontocentral electrodes (FC3, FCz, and FC4) and the P3 on posterior electrodes (P3, Pz, and P4), the scalp topographies in Figure 3 show that the RewP effects were larger at the posterior electrodes used for the P3 than at frontocentral electrodes. So there is a concern that the RewP and P3 were not independently measured. This type of problem can often be resolved using a spatiotemporal principal component analysis. My faith in the conclusions drawn would be further strengthened if the researchers extracted separate principal components for the RewP and P3 and performed their statistical analyses on the corresponding factor scores.

We thank Reviewer #2 for raising this issue. We would like to clarify that these two components were time-locked to different types of feedback and therefore reflect neural responses to distinct stages of the prosocial effort task. Specifically, the P3 was time-locked to performance feedback (the effort-completion cue; e.g., the tick shown in Figure 1B), whereas the RewP was time-locked to reward feedback (e.g., the display of “+0.6”). Thus, despite the numerical similarity in the post-stimulus windows, the components capture neural activity evoked by independent events separated in time, corresponding to the performance monitoring versus reward evaluation stages of the task. To avoid misunderstanding, we have made this distinction more explicit in the revised manuscript, which now reads, “Single-trial RewP amplitude was measured as mean voltage from 300 to 400 ms relative to reward feedback onset (i.e., reward delivery) over frontocentral channels (FC3, FCz, FC4). We also measured the parietal P3 (300–440 ms; averaged across P3, Pz, and P4) in response to performance feedback (i.e., effort completion), given its relationship with motivational salience (Bowyer et al., 2021; Ma et al., 2014)” (page 27, para. 1, lines 2–6).

Reviewer #3 (Public review):

This study investigates how effort influences reward evaluation during prosocial behaviour using EEG and experimental tasks manipulating effort and rewards for self and others. Results reveal a dissociable effect: for self-benefitting effort, rewards are evaluated more positively as effort increases, while for other-benefitting effort, rewards are evaluated less positively with higher effort. This dissociation, driven by reward system activation and independent of performance, provides new insights into the neural mechanisms of effort and reward in prosocial contexts.

This work makes a valuable contribution to the prosocial behaviour literature by addressing areas that previous research has largely overlooked. It highlights the paradoxical effect of effort on reward evaluation and opens new avenues for investigating the mechanisms underlying this phenomenon. The study employs well-established tasks with robust replication in the literature and innovatively incorporates ERPs to examine effort-based prosocial decision-making - an area insufficiently explored in prior work. Moreover, the analyses are rigorous and grounded in established methodologies, further enhancing the study's credibility. These elements collectively underscore the study's significance in advancing our understanding of effort-based decision-making.

We thank Reviewer #3 for the positive assessment. We are particularly encouraged by the reviewer’s recognition of our novel integration of ERPs to uncover the distinct effects of effort on reward evaluation for self versus others. We have carefully addressed the specific recommendations raised in the subsequent comments to further strengthen the rigor and clarity of the manuscript.

(1) Incomplete EEG Reporting: The methods indicate that EEG activity was recorded for both tasks; however, the manuscript reports EEG results only for the first task, omitting the decision-making task. If the authors claim a paradoxical effect of effort on self versus other rewards, as revealed by the RewP component, this should also be confirmed with results from the decision-making task. Omitting these findings weakens the overall argument.

We thank Reviewer #3 for giving us the opportunity to verify the specific roles of our two tasks. The primary aim of our study is to elucidate the neural after-effects of effort exertion on subsequent reward evaluation during prosocial acts. The prosocial effort task was specifically designed for this purpose, as it involves actual effort expenditure followed by reward outcomes. Furthermore, this task uses preset effort-reward combinations, ensuring balanced trial counts and adequate signal-to-noise ratios across conditions, a critical requirement for robust ERP analysis. In contrast, the prosocial decision-making task was included specifically to quantify behavioral preference (i.e., prosocial effort discounting) rather than neural reward processing. Specifically, this task involves choices without immediate effort execution and reward feedback, making it impossible to examine the neural after-effects of effort exertion. However, the decision-making task remains indispensable for our study structure: it provides an independent behavioral phenomenon of prosocial apathy, which allowed us to link individual differences in behavioral motivation to the neural dissociations observed in the prosocial effort tasks (as detailed in our Responses to Reviewer #3’s 2). Thus, the two tasks provide complementary, rather than redundant, insights into the behavioral and neural mechanism of prosocial effort.

(2) Neural and Behavioural Integration: The neural results should be contrasted with behavioural data both within and between tasks. Specifically, the manuscript could examine whether neural responses predict performance within each task and whether neural and behavioural signals correlate across tasks. This integration would provide a more comprehensive understanding of the mechanisms at play.

We thank Reviewer #3 for this insightful and helpful suggestion. We agree that linking neural signatures with behavioral patterns is crucial for establishing the functional significance for our ERP findings. Regarding within-task association, it is important to note that the prosocial effort task was designed to require participants to exert fixed, preset levels of physical effort to earn uncertain rewards. This experimental control was necessary to standardize effort exertion across self-benefiting and other benefiting trials, thereby minimizing confounds such as differences in physical or perceived effort prior to the feedback phase. Indeed, the neural after-effects remained after controlling for these behavioral measures (i.e., response speed and self-reported effort; as detailed in responses to Reviewer #1’Recommendations point 5). Furthermore, unlike the prosocial effort task, the decision-making task inherently precludes the examination of the neural after-effects of effort; therefore, within-task association in this task was not possible.

Given these considerations, we focused on the cross-task association. We examined whether the neural after-effects of effort (indexed by the RewP) in the prosocial effort task were modulated by individual differences in effort discounting. We used the K value estimated from the prosocial decision-making task as the index of effort discounting. We entered the K value (log-transformed and z-scored) as a continuous predictor into the mixed-effects models of RewP amplitudes. The full regression estimates for the model are presented in Table S1 (left).

We observed a significant four-way interaction among recipient, effort, magnitude, and K value (b = 0.58, p = 0.013). To decompose this complex interaction, we performed simple slopes analyses separately for self- and other-benefiting trials at high and low levels of reward magnitude and discounting rate (±1 SD). As shown in Figure S2, for self-benefiting trials, the effort-enhancement effect on the RewP was significant only for participants with high discounting rates at low reward magnitude (b = 1.02, 95% CI = [0.22, 1.82], p = 0.012). In contrast, participants with low discounting rates exhibited no significant effort effect (b = -0.37, 95% CI = [-0.89, 0.15], p = 0.159). At high reward magnitude, simple slopes analyses detected no significant effort effects for either high (b = 0.35, 95% CI = [-0.44, 1.14], p = 0.383) or low (b = 0.45, 95% CI = [-0.07, 0.97], p = 0.093) discounting individuals. These findings strongly support the cognitive dissonance account (Aronson & Mills, 1959): those who find effort most aversive are most compelled to inflate the value of small rewards to justify their exertion. For these individuals, the completion of a costly action for a small reward may trigger a stronger internal justification effect, resulting in an amplified neural reward response.

For other-benefiting trials, participants with low discounting rates exhibited a significant effort-discounting effect at high reward magnitude (b = -0.97, 95% CI = [-1.74, -0.20], p = 0.014). In contrast, no significant effort effects were observed for participants with high discounting rates at either high (b = -0.45, 95% CI = [-0.97, 0.08], p = 0.098) or low (b = -0.16, 95% CI = [-0.69, 0.38], p = 0.564) reward magnitudes, nor for participants with low discounting rates at low reward magnitude (b = 0.14, 95% CI = [-0.64, 0.92], p = 0.729). These results suggest that the justification mechanism observed for self-benefiting effort appears absent for other-benefiting effort. Instead, we observed a persistent effort discounting before, during, and after effort expenditure, which was most pronounced in individuals with low effort sensitivity (low K) when reward magnitude was high. This seemingly paradoxical pattern might be interpreted through the lens of disadvantageous inequity aversion (Fehr & Schmidt, 1999). Specifically, the combination of high personal effort and high monetary reward for another person creates a salient disparity between the participant’s incurred cost and the recipient’s gain. Although low-K individuals are behaviorally willing to tolerate this cost, their neural valuation system may nonetheless track the “unfairness” of this asymmetry, thereby attenuating the neural reward signal (Tricomi et al., 2010). These insights suggest that facilitating prosocial behavior may require not just lowering costs, but potentially framing outcomes to trigger the effort justification mechanisms that drive the effort paradox observed in self-benefiting acts (Inzlicht & Campbell, 2022).

To confirm this four-way interaction, we also replaced the high-effort choice proportions in the decision-making task and observed a similar four-way interaction among recipient, effort, magnitude, and high-effort choice proportions (b = -0.58, p = 0.014; see Table S1 for detailed regression estimates). Together, this cross-task analysis not only provides a more comprehensive understanding of the mechanisms at play but also justifies the inclusion of the prosocial decision-making task. We sincerely thank Reviewer #3’ for this valuable suggestion, which has significantly strengthened our manuscript. We have included this analysis (page 16, para. 2; page 17, paras. 1–2) and discussed the results (page 20, para. 2, lines 10–15; page 20, para. 3; page 21, para. 1, lines 1–8) in the revised manuscript.

Aronson, E., & Mills, J. (1959). The effect of severity of initiation on liking for a group. The Journal of Abnormal and Social Psychology, 59(2), 177-181. https://doi.org/10.1037/h0047195

Fehr, E., & Schmidt, K. M. (1999). A theory of fairness, competition, and cooperation. The Quarterly Journal of Economics, 114(3), 817-868. http://www.jstor.org/stable/2586885

Tricomi, E., Rangel, A., Camerer, C. F., & O'Doherty, J. P. (2010). Neural evidence for inequality-averse social preferences. Nature, 463(7284), 1089-1091. https://doi.org/10.1038/nature08785

(3) Success Rate and Model Structure: The manuscript does not clearly report the success rate in the prosocial effort task. If success rates are low, risk aversion could confound the results. Additionally, it is unclear whether the models accounted for successful versus unsuccessful trials or whether success was included as a covariate. If this information is present, it needs to be explicitly clarified. The exclusion criteria for unsuccessful trials in both tasks should also be detailed. Moreover, the decision to exclude electrodes as independent variables in the models warrants an explanation.

We appreciate the opportunity to clarify these points. In the revised manuscript, we have now explicitly reported the descriptive statistics and the results of a mixed-effects logistic model on response success in the revised manuscript (page 8, para. 1, lines 2–4; Supplementary Table S1). Participants achieved similarly high success rates in both self (M = 97%) and other trials (M = 96%; Figure S3). As shown in Table S2, success rates decreased as effort increased (b = -4.77, p < 0.001). However, no other effects reached significance (ps > 0.245). These near-ceiling success rates indicate strong task engagement and effectively rule out risk aversion as a potential confound.

Regarding model structure, we excluded unsuccessful trials from statistical analyses because they were rare and distributed equally across conditions. Given the near-ceiling performance, we did not include success rate as a covariate, as it offers limited variance.

Finally, we did not include electrodes as an independent variable because our hypotheses focused on condition effects rather than topographic differences. Following established research (e.g., Krigolson, 2018; Proudfit, 2015), we averaged RewP amplitudes across a frontocentral cluster (FC3, FCz, and FC4) and P3 amplitudes across a parietal cluster (P3, Pz, and P4), where activity is typically maximal. Averaging across these theoretically grounded clusters improves the signal-to-noise ratio and provides more reliable estimates of the underlying components. We have explicitly included this rationale in the revised manuscript, which reads, “Data were averaged across the selected electrode clusters to improve signal-to-noise ratio and reliability” (page 27, para. 1, lines 9–10).

Proudfit, G. H. (2015). The reward positivity: From basic research on reward to a biomarker for depression. Psychophysiology, 52(4), 449-459. https://doi.org/10.1111/psyp.12370

Krigolson, O. E. (2018). Event-related brain potentials and the study of reward processing: Methodological considerations. Int J Psychophysiol, 132(Pt B), 175-183. https://doi.org/10.1016/j.ijpsycho.2017.11.007

(4) Prosocial Decision Computational Modelling: The prosocial decision task largely replicates prior behavioural findings but misses the opportunity to directly test the hypotheses derived from neural data in the prosocial effort task. If the authors propose a paradoxical effect of effort on self-rewards and an inverse effect for prosocial effort, this could be formalised in a computational model. A model comparison could evaluate the proposed mechanism against alternative theories, incorporating the complex interplay of effort and reward for self and others. Furthermore, these parameters should be correlated with neural signals, adding a critical layer of evidence to the claims. As it is, the inclusion of the prosocial decision task seems irrelevant.

We thank Reviewer #3 for this thoughtful suggestion regarding the value of computational modelling. We fully agree that formalizing mechanisms is crucial, but we would like to clarify why a computational model of decision-making cannot directly capture the paradoxical after-effects observed in our neural data. The paradoxical after-effect of effort exertion we report refers to experienced utility (i.e., how prior costs modulate the hedonic consumption of a reward), whereas the decision task measures decision utility (i.e., how prospective costs and benefits are integrated to guide choice). We included the prosocial decision task to establish a behavioral baseline and replicate the well-documented phenomenon of prosocial apathy. Consistent with prior work (e.g., Lockwood et al., 2017; Lockwood et al., 2022), our data show that at the decision stage (ex-ante), effort functions as a universal cost: participants discounted rewards for both self and others, differing only quantitatively (steeper discounting for others). It is only after effort is exerted (ex-post) that the pattern reverses: effort is valued for self but remains costly for others, representing a qualitative shift. Crucially, incorporating a "paradoxical valuation" parameter (i.e., effort as a reward) into our decision model would mathematically contradict the behavioral reality. Since participants actively avoided high-effort options, a model assuming effort adds value might fail to fit the choice data. The theoretical novelty of our study lies precisely in this temporal dissociation: whereas self-benefiting effort paradoxically enhances reward valuation, other-benefiting effort induces a persistent reward devaluation.

To address the reviewer’s interest in bridging these two domains, we examined whether these distinct stages are linked at the level of individual differences. We hypothesized that an individual’s sensitivity to prospective effort cost (discounting rate K) might modulate their susceptibility to the retrospective neural after-effect. As detailed in our Responses to Reviewer #3’s point 2, we found that for self-benefiting trials, high-discounting individuals showed an effort-enhancement effect on the RewP at low reward magnitude, while for other-benefiting trials, low-discounting individuals exhibited effort-discounting effects at high reward magnitude. We sincerely thank Reviewer #3’ for this valuable suggestion, which has successfully correlated the two tasks and facilitated our understanding of the mechanisms at play.

Lockwood, P. L., Hamonet, M., Zhang, S. H., Ratnavel, A., Salmony, F. U., Husain, M., & Apps, M. A. J. (2017). Prosocial apathy for helping others when effort is required. Nat Hum Behav, 1(7), 0131. https://doi.org/10.1038/s41562-017-0131.

Lockwood, P. L., Wittmann, M. K., Nili, H., Matsumoto-Ryan, M., Abdurahman, A., Cutler, J., Husain, M., & Apps, M. A. J. (2022). Distinct neural representations for prosocial and self-benefiting effort. Curr Biol, 32(19), 4172-4185 e4177. https://doi.org/10.1016/j.cub.2022.08.010.

(5) Contradiction Between Effort Perception and Neural Results: Participants reported effort as less effortful in the prosocial condition compared to the self condition, which seems contradictory to the neural findings and the authors' interpretation. If effort has a discounting effect on rewards for others, one might expect it to feel more effortful. How do the authors reconcile these results? Additionally, the relationship between behavioural data and neural responses should be examined to clarify these inconsistencies.

This point aligns with the issues raised in Reviewer #1’s point 2. We acknowledge the apparent discrepancy between lower reported effort in the prosocial condition and the neural discounting effect. As detailed in our Responses to Reviewer #1’s point 2, we reconcile this by proposing that subjective effort ratings in this paradigm likely reflect motivational engagement (e.g., “how hard I tried” or “how willing I was”) rather than perceived task demands. Under this interpretation, the lower effort ratings for others reflect a withdrawal of engagement (consistent with prosocial apathy), which conceptually aligns with, rather than contradicts, the neural discounting effect. To validate this, we contrasted effort ratings with difficulty ratings (a more reliable index of objective demand). Our correlational analysis revealed no significant relationship between difficulty and effort ratings (r = -0.21, p = 0.196), suggesting that they capture distinct constructs. Furthermore, liking ratings were negatively correlated with difficulty ratings (r = -0.43, p = 0.011) but not with effort ratings (r = 0.32, p = 0.061), further dissociating the two measures. Crucially, as detailed in our Responses to Reviewer #1’s Recommendations point 5, our RewP effects remained significant even after controlling for individual effort ratings. This demonstrates that the neural effort-discounting effect for others is a physiological signature that operates independently of the subjective report bias.

(6) Necessary Revisions to Manuscript: If the authors address the issues above, corresponding updates to the introduction and discussion sections could strengthen the narrative and align the manuscript with the additional analyses.

We thank Reviewer #3 for the above insightful and helpful comments. We have carefully addressed these issues raised above and have updated the manuscript accordingly, including abstract, introduction, result, and discussion sections.

Recommendations for the Authors:

Reviewer #1 (Recommendations for the authors):

Major comments:

(1) The two biggest concerns I have are

- Whether the mixed-effect models are properly specified, and

- Whether the main interaction between the Recipient and effort on the reward positivity (RewP) reflects different levels of effort exertion when working for self versus others.

We thank Reviewer #1 for identifying these two critical issues. We have carefully considered these points and conducted additional analyses to address them. Below, we provide a detailed response to each concern, explaining how we have improved the model specification and ruled out alternative interpretations regarding effort exertion.

(2) On the first point, I noticed that the authors selectively excluded random effects for Effort and Magnitude when regressing RewP on Effort, Magnitude, Recipient, and Valence. This is important because the key result in the paper is a fixed effect two-way interaction between Recipient and Effort and a three-way interaction between Recipient, Effort, and Magnitude. It is not clear that these results will remain significant when Effort and Magnitude are included as random effects in the model. Thus the authors should justify their exclusion as random effects, and/or show that the results don't depend on including those random effects in the model. The same logic applies to the specification of other mixed effects models (e.g. the effect of Magnitude in the model predicting RTs).

We thank Reviewer #1 for raising this important methodological point. We fully agree that including random slopes wherever possible reduces Type 1 error rates and yields more conservative tests of fixed effects. In our analyses, we determined the random effects structure for each model using singular value decomposition (SVD). Specifically, we began with a maximal model that included by-participant random slopes for all main effects and interactions as well as a participant-level random intercept. When the model failed to converge or yielded a singular fit, we applied SVD to identify redundant dimensions (i.e., components explaining zero variance) and iteratively removed these terms until convergence was achieved. This procedure allowed us to retain the maximal converging random-effects structure while ensuring model stability. We have clarified this procedure in the revised manuscript as follows, “For each model, we fitted the maximal random-effects structure and, when the model was overparameterized, used singular value decomposition to simplify the random-effects structure until the model converged” (page 28, para. 1, lines 5–8).

Regarding the RewP model, including all variables (i.e., Recipient, Effort, Magnitude, and Valence) in the random-effects structure resulted in a boundary (singular) fit. Examination of the variance-covariance structure of the random effects revealed that the random slopes for Valence and Magnitude were perfectly negatively correlated (r = -1.00), indicating severe overparameterization. In our original submission, we removed the random slopes for Effort and Magnitude because the SVD analysis indicated redundant dimensions in the model structure.

However, we agree with the Reviewer that retaining slopes for variables involved in key interactions is crucial. Therefore, we re-evaluated the model strategy: instead of removing Effort and Magnitude, we removed the random slope for Valence (which was the primary source of the perfect correlation). This modification successfully resolved the singularity while allowing us to retain the random slopes for the critical variables (i.e., Effort and Magnitude).

Critically, this updated model yielded the same pattern of results as our original submission: the two-way interaction between Recipient and Effort and the three-way interaction between Recipient, Effort, and Magnitude remained significant (see Table S3). As expected, including the random slopes for Effort and Magnitude yielded a more conservative test of the fixed effects. While the critical three-way interaction remained significant (p = 0.019), the simple slope for the Self condition at high reward magnitude shifted slightly from significant (p = 0.041) to marginally significant (p = 0.056). However, the effect size remained largely unchanged (b = 0.42 vs. original b = 0.43), and the dissociation pattern, where self-benefiting trials show a positive trend while other-benefiting trials show a significant negative slope, remains robust and is statistically supported by the significant interaction. We have adopted this updated model in the revised manuscript and updated the relevant sections accordingly. Finally, note that we have removed the RewP table from the Supplementary Materials because the RewP model results are now presented as a figure in the main text (as suggested by Reviewer #1’s Recommendations point 3).

We have also carefully verified the random effects structures for other mixed-effects models, including the RT and Performance-P3 models in the prosocial effort task, as well as the decision time and decision choice models in the prosocial decision-making task. The updated information is detailed as follows:

Regarding the RT model, we replaced it with a more reasonable model of response speed (button presses per second), as suggested by Reviewer #1 (see our responses to Reviewer #1’s Recommendations point 4 for details).

Regarding the performance-P3 model, the random-effects structure could only support Effort, as in our original submission; thus, the results remain unchanged.

Regarding the decision time model, we have updated our results to include the quadratic effort term, as suggested by Reviewer #1 (see our responses to Reviewer #1’s Recommendations point 6 for details).

Regarding the decision choice model, we included Recipient, Effort, and Magnitude in the random-effects structure. As shown in Table S4, the results remain largely consistent with the original model, except for a newly significant interaction between effort and magnitude. Follow-up simple slopes analyses revealed that the discounted effect of effort was more pronounced at low reward magnitude (M − 1SD: b = -2.69, 95% CI = [-3.09, -2.29], p < 0.001) than at high reward magnitude (M + 1SD: b = -2.38, 95% CI = [-2.82, -1.94],p < 0.001).

In summary, we have improved the model specification following Reviewer #1’s suggestion. Crucially, the results remain qualitatively consistent with our original findings. We have updated the Results section, figures (Figures 2, 4, and 5), and OSF documents (including a new R Markdown file and an HTML output file detailing the final results) to reflect these analyses. Additionally, we have explicitly stated the method used for calculating p-values in the mixed-effects models (page 28, para. 1, lines 8–10), which was omitted in the original submission.

(3) Regarding the mixed models, it would also be good to show a graphical depiction summarizing key effects (e.g. the Recipient by Effort interaction on RewP) rather than just showing the predictions of the fitted mixed effects models.

This point is well-taken. Please see Figure S4, which visualizes the key effects and has now been included in the revised manuscript as Figure 4A.

(4) Finally, regarding the mixed effect models of RTs - given the common finding that RTs are not normally distributed, the Authors might be better off regressing 1/RT (interpreted as speed rather than latency) since 1/RT will often make distributions less asymmetric and heavy-tailed.

We thank Reviewer #1 for this helpful suggestion regarding data distribution. In our original analysis, the dependent variable was “completion time” (i.e., the latency to complete the required button presses with the 6-s window). We agree that these raw latency data exhibited characteristic non-normality (see Figure S5, Left). Based on Reviewer #1’s suggestion, we adopted “response speed” (calculated as button presses per second) as the dependent variable. As expected, this transformation substantially improved the normality of the distribution (see Figure S5, Right). We have refitted the mixed-effects model using this speed metric. Critically, the results largely replicated the patterns observed in our original model, with the exception that the main effect of reward magnitude did not reach significance in the speed model (see Table 5). Given the superior distributional properties of the speed metric, we have replaced the original latency analysis with the response speed model in the revised manuscript. We have updated the Results section (page 8, para. 1, lines 4–9) and Figures 2B–C accordingly.

(5) Regarding the level of effort exerted, there are two reasons to suspect that participants exerted less for others versus themselves. The first is that they were slower to complete the button pressing for others versus themselves. The second is that they reported paradoxically less subjective effort for others versus self (paradoxical because they also reported liking the task less for others versus self). The explanation for both may be that they exerted less effort for others versus self and this has important implications for interpreting the main effects. If they exerted less effort for others, this may partly account for the key Recipient:Effort and Recipient:Effort:Magnitude interactions in the mixed effects regression of RewP. Do either median effort durations or self-reported effort predict the magnitude of the Recipient:Effort and Recipient:Effort:Magnitude interactions (if these were included as random effects)? If so, that would provide evidence supporting this story. Alternatively, if median durations or self-reported effort were included as covariates, do these interactions still obtain? In any case, the Authors should include caveats regarding this potential explanation of the self-versus-other interactions with effort and magnitude on the RewP" (or explain why this can not explain the interactions).

We thank Reviewer #1 for raising this important interpretational issue. We acknowledge the concern that differences in physical exertion or perceived effort could potentially confound the neural findings. However, we argue that the observed RewP effects are not driven by these factors for several reasons.

First, the prosocial effort task enforced fixed effort thresholds (10%–90% of their maximum effort level) across self-benefiting and other-benefiting trials. Importantly, participants achieved ceiling-level success rates that were highly comparable between self-benefiting (97%) and other-benefiting (96%) trials, indicating that they successfully exerted the required effort across conditions.

Second, regarding the slower response speed for others (we used response speed instead of completion time, as the former is more suitable for statistical analysis; see details in Responses to Reviewer #1’s Recommendations point 4), we interpret this as a reduction in motivation rather than a reduction in the amount of effort exerted. Similarly, as detailed in our Responses to Reviewer#1’s point 2, subjective effort ratings in this paradigm appear to be influenced by demand characteristics and do not reliably track physical exertion. For instance, liking ratings were associated with difficulty (r = -0.43, p = 0.011) instead of effort (r = 0.32, p = 0.061) ratings.

To empirically rule out the possibility that these behavioral differences account for the neural effect, we followed the reviewer’s suggestion and re-ran the mixed-effects model predicting RewP amplitudes with trial-by-trial response speed and subjective effort rating included as covariates. These control analyses revealed that neither response speed (b = -0.07, p = 0.614) nor self-reported effort (b = 0.10, p = 0.186) significantly predicted RewP amplitudes (see Table S6). Most importantly, the key interactions of interest (Recipient × Effort and Recipient × Effort × Magnitude) remained significant and virtually unchanged. These findings suggest that the observed neural after-effects of prosocial effort are not driven by variations in motor execution or perceived effort.

Minor comments:

(6) In Figure 5A a quadratic effect (not a linear effect) seems fairly obvious in decision times as a function of effort level. This makes sense given that participants are close to indifference, on average, around the 50-70% effort level. I recommend fitting a model that has a quadratic predictor and not just a linear predictor when regression decision times on effort levels.

We thank Reviewer #1 for this insightful suggestion. We agree that decision times likely track decision conflict, which typically peaks near indifference points (e.g., moderate effort levels). Accordingly, we reanalyzed the decision time data using a mixed-effects model that included both linear and quadratic terms for effort. As detailed in Table S7, this analysis revealed a significant quadratic main effect of effort, which was further qualified by a significant interaction between the quadratic effort term and reward magnitude. Decomposition of this interaction (Figure S6) revealed that the quadratic effort effect was more pronounced at low reward magnitude (M − 1SD: b = -160.10, 95% CI = [-218.30, -101.90], p < 0.001) than at high reward magnitude (M + 1SD: b = -99.50, 95% CI = [-157.60, -41.40], p = 0.001). However, we found no significant interactions involving the quadratic effort term and recipient. We have updated the Results section (page 13, para. 2; page 14, para. 1) and Figures 5A–B (right panel) to reflect these findings.

(7) The distinction between the effort and decision-making tasks wasn't super clear from the main text. A sentence early on in the results section could be useful for readers' understanding.

This point is well taken. In the revised manuscript, we have clarified this distinction at the beginning of the Results section (page 6, para. 2, lines 1–10). In addition, we have explicitly indicated the corresponding task within each subsection heading in the Results:

“2.1 Investing effort for others is less motivating than for self in the prosocial effort task” (page 7)

“2.2 Effort adds reward value for self but discounts reward value for others in the prosocial effort task” (page 9)

“2.3 Reward is devalued by effort to a higher degree for others than for self in the prosocial decision-making task” (page 13)

(8) To what does "three trials" refer to on lines 143-144?

Thank you for raising this point. Participants completed three trials in which they were asked to press a button as rapidly as possible with their non-dominant pinky finger for 6000 ms. The maximum effort level was operationalized as the average button-press count across the three trials. To improve clarity, we have also provided more detailed description in the Results section, which reads: “The mean maximum effort level (i.e., the average button-press count across three 6000-ms trials; see Procedure for details) ….” (page 7, para. 1, lines 1–2).

(9) It is unclear how the authors select their time windows for ERP analyses.

We thank Reviewer #1 for this comment. Measurement parameters (i.e., time windows and channel sites) were determined based on the grand-averaged ERP waveforms and topographic maps collapsed across all conditions. This procedure is orthogonal to the conditions of interest and prevents bias in the selection of measurement windows and channels, consistent with the “orthogonal selection approach” (Luck & Gaspelin, 2017). We have clarified this point in the revised manuscript, which now reads, “Measurement parameters (time windows and channel sites) were determined from the grand-averaged ERP waveforms and topographic maps collapsed across all conditions, which was thus orthogonal to the conditions of interest (Luck & Gaspelin, 2017)” (page 27, para. 1, lines 6–9).

Luck, S., & Gaspelin, N. (2017). How to get statistically significant effects in any ERP experiment (and why you shouldn't). Psychophysiology, 54(1), 146-157.

(10) There are a few typos throughout. For example, Line 124 should read "other half benefitted...", Line 127 should read "interest at each effort level...", "following" on Line 369, and Supplemental table titles incorrectly spell the word "Results".

We thank Reviewer #1 for catching these errors. We have corrected all the specific typos noted (page 6, para. 2, lines 11 and 15; page 22, para. 3, line 2; Supplementary Table S2). Furthermore, we have conducted a thorough proofreading of the entire text and supplementary materials to ensure linguistic accuracy and consistency throughout the manuscript.

Reviewer #2 (Recommendations for the authors):

Minor comments:

(1) Lines 84-86. "The RewP ... has its neural sources in the anterior cingulate cortex (Gehring & Willoughby, 2002) and ventral striatum (Foti et al., 2011)." This is a better reference for the ACC source: https://pubmed.ncbi.nlm.nih.gov/23973408/. And perhaps remove the reference to the ventral striatum; most people would agree that activity in the ventral striatum cannot be measured with scalp EEG.

We thank Reviewer #2 for providing the updated reference, which has been cited in the revised manuscript. We agree that activity in the VS cannot be reliably measured with scalp EEG and thus have removed the reference to the VS. The revised sentence now reads, “… has its neural sources in the anterior cingulate cortex (Gehring & Willoughby, 2002; Hauser et al., 2014)” (page 4, para. 2, lines 12–13).

(2) Lines 152-153. What exactly is shown in Figure 2A? How did the authors average across subjects?

We thank Reviewer #2 for raising this issue. Figure 2A depicts the distribution of the maximum effort level, defined as the average button-press count across three 6000-ms trials completed before the prosocial effort task. In these trials, participants were instructed to press the button as rapidly as possible with their non-dominant pinky fingers. To improve clarity, we have revised the figure caption as: “(A) Distribution of the maximum effort level (i.e., the average button-press count across three 6000-ms trials) across participants” (Figure 2).

(3) Lines 160-164. "As expected (Figure 2D), participants perceived increased effort as more difficult ... and more disliking (b = -0.62, p < 0.001) when the beneficiary was others than themselves." Does this sentence describe the main effect of the beneficiary or the interaction between beneficiary and effort level, as the start of the sentence ("increased effort") suggests?

We thank Reviewer #2 for pointing out this ambiguity. The sentence describes the main effect of beneficiary rather than the interaction between beneficiary and effort level. In the revised manuscript, we have rephrased the sentence as: “They felt less effort (b = -0.32, p = 0.019) and more disliking (b = -0.62, p = 0.001) for other-benefiting trials compared to self-benefiting trials” (page 9, para. 1, lines 4–6).

(4) Lines 195-196. "..., we conducted post-hoc simple slopes analyses at -1 SD ("Low") and + SD ("High") reward magnitude." I did not understand what the authors meant with these reward magnitudes, given that the actual potential rewards were ¥0.2, ¥0.4, ¥0.6, ¥0.8, and ¥1.0.

In our analyses, the actual reward magnitudes (¥0.2, ¥0.4, ¥0.6, ¥0.8, and ¥1.0) were z-scored and entered as a continuous regressor in the mixed-effects models. Post-hoc simple slopes analyses were then conducted at ±1 SD from the mean of the z-scored reward magnitude. To clarify, we have revised the sentence as “… we conducted post-hoc simple slopes analyses at 1 standard deviation (SD) below (“Low”) and above (“High”) the mean reward magnitude” (page 11, para. 2, lines 8–9). This standard method for testing simple effects for continuous predictors is recommended by Aiken and West (1991). Aiken, L. S., West, S. G., & Reno, R. R. (1991). Multiple regression: Testing and interpreting interactions. Sage.

(5) Lines 253 and 275. I would not call this a computational model. The authors fit a curve to data, there is no model of the computations involved.

This point is well taken. We have replaced “computational model” with “discounting” (Figure 5) and “parabolic discounting model” (page 15, para. 1, line 15).

(6) Line 710. Figure S1 does not show topographic maps of the P3, as the figure caption suggests.

We thank Reviewer #2 for identifying this oversight. We have now included topographic maps of the P3 in Figure S1.

(7) Please check language in lines 33 (effect between), 38 (shape), 49 (highest cost form?), 74 (tunning), 90 (omit following), 127 (interest on at each effort level), 135 (press buttons >> rapidly press a button?), 142 (motivated), 219 (should low be high?), 265-266 (missing word), 275 (confirmed by following), 292 (an action can be effortful, a feeling cannot), 315 (when it comes into), 330-331 (data is plural; the aftereffect of prosocial effect), 387 (interest on at each effort level), 405 (should quickly be often?).

We thank Reviewer #2 for the careful review and feedback about these language issues. We have revised all the phrasing you identified. The corrections are as follows:

Line 33: “effect between” has been changed to “effects for” (page 2, para. 1, line 6).

Line 38: “shape” has been updated to “shapes” (page 2, para. 1, line 13).

Line 49: “highest cost form?” has been revised to “the most common cost type” (page 3, para. 1, lines 7–8).

Line 74: “tunning” has been corrected to “tuning” (page 4, para. 2, line 1).

Line 90: omit following. Done (page 5, para. 1, line 2).

Line 127: “interest on at each effort level” has been corrected to “liking for each effort level” (page 6, para. 2, line 15).

Line 135: “press buttons” has been updated to “rapidly press a button” (the caption of Figure 1).

Line 142: “motivated” has been revised to “motivating” (page 7).

Line 219: should low be high? Yes, we have corrected this (the caption of Figure 4).

Lines 265–266: The missing word “with” has been inserted (page 15, para. 1, line 2).

Line 275: “confirmed by following” has been revised as “corroborated by a parabolic …” (page 15, para. 1, line 15).

Line 292: an action can be effortful, a feeling cannot. We have changed the word “effortful” to “effort” (page 18, para. 2, line 3).

Line 315: “when it comes into” has been revised to “when it came to” (page 19, para. 1, line 10).

Lines 330–331: These two expressions have been revised to “our data establish …” and “the after-effect of prosocial effort” (page 20, para. 1, lines 2–3).

Line 387: “interest on at each effort level” has been corrected to “interest at each effort level” (page 23, para. 2, line 5).

Line 405: should quickly be often? We agree that “quickly” might imply latency or speed of a single press, whereas the task required maximizing the frequency of presses within the time window. To capture this meaning accurately, we have revised the phrase to “pressed a button as rapidly as possible” (implying repetition rate) in the revised manuscript (page 24, para. 2, lines 3–4).

DOI: 10.1016/j.stem.2026.02.008

Resource: (Thermo Fisher Scientific Cat# A-31573, RRID:AB_2536183)

Curator: @scibot

SciCrunch record: RRID:AB_2536183

What is this?

acá punto seguido. Y bajar el tono, tipo: Una revisión preliminar de estos archivos hizo evidente la necesidad de armonizar y consolidar una base de datos que permitiera los análisis solicitados

la letra tiene que ser más grande y/o una fuente más amigable a la lectura en html

Para hablar de estos códigos se deberían anticipar o dejar esta parte de la explicación para el texto extendido y no aquí en el resumen ejecutivo. Propongo sacar esta parte del resumen. Este párrafo inciaría en: En materia de completitud...

Esta explicación no es necesaria. Dejaría sólo:

El resultado final fue una base maestra de 7.410 filas, que corresponde al número de citas disponibles en los once modulos temáticos. La base consolidada tiene una unidad de análisis explícita: cada fila corresponde a una evidencia o cita curricular específica ubicada en un documento, módulo y página determinados, organizando la evidencia curricular bajo una estructura común. Sobre esa unidad de análisis se articulan variables núcleo, variables de trazabilidad e indicadores recodificados.

Personalmente es muy acertado lo que se comenta ya que en ciertos ecosistemas digitales se impone Python o simplemente no se utiliza , teniendo en cuenta estas herramientas nos podemos desenvolver mucho mejor en entornos de programación

Así mismo como pasa en la ciencia, la comunidad de los diferentes lenguajes de programación (en este caso Julia) busca hacer una integración a partir de proyectos de código abierto en colaboración de sus mismos usuarios o comunidad, lo cual genera proyectos de un gran potencial y desarrollo desde el mismo. Tal cual como cuando hay un proyecto de cualquier tipo (ya sea videojuego, herramienta, u otro) en línea y eso apoyado desde los mismos interesados o comunidad.

Me llama la atención la idea de que estén enfocados a un público principiante, puesto a que si considero muy cierto que hay suficientes herramientas avanzadas para usuarios avanzados, sin embargo es importante enfocarse en aquellos que son principiantes en el campo, o ¿que herramientas de programación accesible conocen?

La idea de facilitar y ser amigable con la tematica de programación y a la vez exploración es interesante, pues muchas de las cosas a las que les tenemos miedo es por la forma en que nos acercamos por primera vez a ello, si ruvimos un evento traumante en la programación nos cerramos al aprendizaje del tema, si se facilita o se convierte algo que esta en chino a un lenguaje amigable podemos aprender y aportar sobe este campo.

Esto es bastante real, ya que la mayoría de lenguajes de programación nacen de una necesidad.

Cuando algo interrumpe lo que es conocido es como si en medio de un engranaje le pusieran un gran obstáculo eso hace que todo cambie de camino o ritmo. Puede resultar en un primer momento molesto o aturdidor peor es fundamental para tener un cambio, una nueva visión.

Ejemplo, ¿en cuantos casos ocurre?

Explicar brevemente que es 0 o 1. 1 = cita corresponde a cierto tema. Por ejemplo....

Propongo: El corpus orginal presenta una alta heterogeneidad entre archivos. En la práctica,...

Desde el punto de vista analítico, mantener los insumos originales implicaba una serie de consecuencias. Primero, sobreconteo o subconteo: si existían duplicados no resueltos o filas separadoras tratadas como observaciones reales, los descriptivos y frecuencias podían distorsionarse. Segundo, pérdida de trazabilidad: si no existía una llave operativa estable para seguir cada registro a través de las distintas transformaciones, se debilitaba la posibilidad de auditar el proceso completo. Tercero, la posibilidad de comparación inválida: si los indicadores no compartían un dominio común o si columnas análogas estaban definidas de forma distinta entre módulos, cualquier intento de producir síntesis comparables podía volverse metodológicamente frágil. Finalmente, opacidad: incluso si el procesamiento lograba “funcionar”, sin documentación explícita de las decisiones sería difícil defender por qué determinadas reglas fueron aplicadas y otras descartadas.

La posibilidad de que internet algún día pueda transformar los gobierno lleva a la pregunta de cómo democratizar la información o el acceso a ella. Y esta cuestión de la posibilidad del codigo abierto tambien lleva a la pregunta por la desigual en el acceso a las herramientas que podrian hacer el mundo mas democratico y vivble para todos. Entretanto, el desafio de la educación y la pedagogica sería justamente ese: entregar a los educando, de la manera mas completa y perfecta la mayor cantidad de herramientas posibles para transformar sus vidas y sus entornos, sobre todo alli donde mas falta hace.

represents Zeenas and Ethan's marriage breaking.

Def. de parasito

Trobo a faltar l'anomenar el domini públic català. El domini públic no son "llicències obertes", sinó, més aviat contingut amb drets morals exhaurits i, per tant moltes vegades més obert que les llicències amb restriccions d'embargo o de restriccions comercials.

Que vol dir "nou agents"? Suposso que és gent de llocs com a Free Software Barcelona, però sense una referència directa, no sé si son només les participants d'aquest grup o si hi ha d'altres.

Em sembla molt interessant aquesta proposta, per exemple de cara a la poca, o fins i tot nula participació de catalanoparlans a espais com a Wikimania.

És interessant perquè entenc que aquesta iniciativa, si es mesura amb indicadors, va caure en el pla anterior. Com que no tenim indicadors, no es pot veure què tan bé o malament implica el "Rellançar". Pot ser s'ha fet una primera acció, que va anar molt bé i aquí busquem continuitat, o potser que teniem una bona quantitat de viquiprojectes LGBTQIA+ i van desapareixer, que seria catastròfic. Sense indicadors, no sabem.

Hi ha un protocol, procés o cap pas a pas per les aliances? Penso per exemple en si hem de fer un conceni amb el Centre Cívic Palmira Domènech, per institucionalitzar el Punt d'edició El Prat, perquè fer-ho pot ajudar a les participants, encara que no sigui amb pagaments de diners.

M'agrada que tenim les dinàmiques, però m'agradaria tenir accés a indicadors. Per exemple: saber si abans hem assolit un indicador i creixem en aquest nou cicle o si és crític i hem de pujar un indicador que abans estava malament.

DOI: 10.7554/eLife.108453

Resource: None

Curator: @scibot

SciCrunch record: RRID:BDSC_606023

What is this?

DOI: 10.1111/jnc.70396

Resource: (IMSR Cat# JAX_006678,RRID:IMSR_JAX:006678)

Curator: @scibot

SciCrunch record: RRID:IMSR_JAX:006678

What is this?

Processo de Responsabilização

• Comissão de <u>2 ou +</u> servidores estáveis conduzem o processo. Se empregados, devem pertencer ao quadro permanente com pelo menos 3 anos de tempo de serviço;

  • Obs.: Não confundir com quantidade de membros da comissão de contratação, que contará com 3 servidores.

• Prazo de 15 dias úteis para contestação;

• Prazo de 15 dias úteis para alegações finais;
• Se houver atos ilícitos previstos na Lei Anticorrupção, deverá haver apuração conjunta no mesmo processo.
• Aplicação de sanção de declaração de inidoneidade para contratar e licitar é de competência exclusiva de Ministro de Estado/Secretário do Estado;
• Contra aplicação de declaração de inidoneidade para contratar e licitar somente cabe pedido de reconsideração no prazo de 15 dias úteis;
• Prazo recursal é de 3 dias úteis;
• Tanto recurso, quanto pedido de reconsideração terão efeito suspensivo.

• O uso do contrato é a regra para as contratações públicas. No entanto, admite-se exceções.

• As exceções são: contratação por dispensa de licitação e compra de entrega imediata e integral.

JURISPRUDÊNCIA EM TESES - EDIÇÃO Nº 160 - DIREITO DO CONSUMIDOR - IV

• 8) O Código de Defesa do Consumidor - CDC, em regra, é inaplicável aos contratos administrativos, tendo em vista as prerrogativas já asseguradas pela lei à administração pública.

• 9) Em situações <u>excepcionais</u>, a administração pública pode ser considerada consumidora de serviços (art. 2º do CDC) por ser possível reconhecer sua vulnerabilidade, <u>mesmo em relações contratuais regidas, preponderantemente, por normas de direito público</u>, e por se aplicarem aos contratos administrativos, de forma supletiva, as normas de direito privado (art. 54 da Lei n. 8.666/1993).

• 10) O Código de Defesa do Consumidor é inaplicável a contrato acessório de contrato administrativo, pois não se origina de uma relação de consumo.

• Por essa previsão, também é pertinente destacar que será possível o emprego da compensação pela Administração Pública em contratos administrativos, a despeito da inexistência de prévio ajuste ou de autorização do particular. Nesse sentido:

• Informativo nº 789

• 3 de outubro de 2023.
• SEGUNDA TURMA
• Compartilhe:
• Processo: REsp 1.913.122-DF, Rel. Ministro Francisco Falcão, Segunda Turma, por unanimidade, julgado em 12/9/2023, DJe 15/9/2023.

Ramo do Direito DIREITO ADMINISTRATIVO, DIREITO CIVIL

TemaPaz, Justiça e Instituições Eficazes <br /> Contrato administrativo. Aplicação supletiva das normas de direito privado. Art. 54 da Lei n. 8.666/1993. Compensação. Possibilidade. Autorização do particular. Prescindibilidade.

Destaque - É possível a compensação de créditos decorrentes da aquisição de imóveis em contrato administrativo firmado entre empresa pública e particular, mesmo sem autorização deste.

Informações do Inteiro Teor - No caso, o particular ajuizou ação ordinária com pedido de tutela de urgência, pretendendo reaver valores pagos no contrato de compra e venda do imóvel, considerando que, após a rescisão unilateral do contrato, a empresa pública compensou valores devidos por ele. Sustenta que não requereu nem deu anuência com essa compensação, razão pela qual ela não poderia ocorrer.

• Quanto à possibilidade de compensação, o art. 54 da Lei n. 8.666/1993 estabelece que as regras do Direito Privado podem ser utilizadas supletivamente no âmbito dos contratos admirativos.

• À luz dessa previsão legal, é possível que o instituto da compensação, modalidade de extinção das obrigações, seja aplicado ao caso concreto, permitindo-se que a recorrente compense seus débitos com os créditos do particular, na forma prevista no art. 368 do Código Civil.

• A compensação ocorre quando duas pessoas forem, ao mesmo tempo, credoras e devedoras uma da outra, de modo que as respectivas obrigações se extinguem até onde se compensarem.

• Nesse contexto, a norma civilista exclui a possibilidade da compensação, somente no caso de mútuo acordo ou quando ocorrer renúncia prévia de uma das partes, na forma prevista no art. 375 do CC.

• A ata de registro de preço tem validade por 1 ano. Observe que poderá ser prorrogado pelo mesmo período, uma única vez, se comprovado que o preço é vantajoso para a administração

Esse artigo visa distinguir as acessões das benfeitorias. Vide art. 1.219. Isto é, não serão benfeitorias as melhorias ou intervenções não autorizadas pelo proprietário, detentor ou possuidor.

No mais, de suma importância distinguir o que é legalmente entendido como acessão e benfeitoria. Nesse sentido, é o REsp 1.109.406 - SE:

REINTEGRAÇÃO DE POSSE. DIREITO CIVIL. RECURSO ESPECIAL. POSSUIDORA DE MÁ-FÉ. DIREITO À INDENIZAÇÃO. DISTINÇÃO ENTRE BENFEITORIA NECESSÁRIA E ACESSÕES. ALEGADA ACESSÃO ARTIFICIAL. MATÉRIA FÁTICO-PROBATÓRIA. SÚMULA 7/STJ. - 1. As benfeitorias são obras ou despesas realizadas no bem, com o propósito de conservação, melhoramento ou embelezamento, tendo intrinsecamente caráter de acessoriedade, incorporando-se ao patrimônio do proprietário. - 2. O Código Civil (art. 1.220), baseado no princípio da vedação do enriquecimento sem causa, conferiu ao possuidor de má-fé o direito de se ressarcir das benfeitorias necessárias, não fazendo jus, contudo, ao direito de retenção. - 3. Diferentemente, as acessões artificiais são modos de aquisição originária da propriedade imóvel, consistentes em obras com a formação de coisas novas que se aderem à propriedade preexistente (superficies solo cedit), aumentando-a qualitativa ou quantitativamente. - 4. Conforme estabelece o art. 1.255 do CC, na acessões, o possuidor que tiver semeado, plantado ou edificado em terreno alheio só terá direito à indenização se tiver agido de boa-fé. - 5. Sobreleva notar a distinção das benfeitorias para com as acessões, sendo que "aquelas têm cunho complementar. Estas são coisas novas, como as plantações e as construções" (GOMES, Orlando. Direitos reais. 20. ed. Atualizada por Luiz Edson Fachin. Rio de Janeiro: Forense, 2010, p. 81). - 6. Na trilha dos fatos articulados, afastar a natureza de benfeitoria necessária para configurá-la como acessão artificial, isentando a autora do dever de indenizar a possuidora de má-fé, demandaria o reexame do contexto fático-probatório dos autos, o que encontra óbice na Súmula n. 07 do STJ. - 7. Recurso especial a que se nega provimento.

[...]

Processo na íntegra:

• 2.2. Diferentemente, as acessões artificiais são modos de aquisição originária da propriedade imóvel, consistentes em obras com a formação de coisas novas que se aderem à propriedade preexistente (superficies solo cedit), aumentando-a qualitativa ou quantitativamente.

• É obra nova sobre propriedade imóvel alheia que cria coisa distinta.

• Deveras, são "construções e plantações que têm caráter de novidade, pois não procedem de algo já existente, uma vez que objetivam dar destinação econômica a um bem que até então não tinha repercussão social. Por seu caráter inovador, são tratados com regras próprias, entre os modos originários de aquisição da propriedade" (FARIAS, Cristiano Chaves; ROSENVALD, Nelson. Direitos Reais. 5ª ed. Rio de Janeiro: Lumen Juris, 2008, p. 98).

[...]

• Dessarte, para a solução da controvérsia, sobreleva notar a distinção das benfeitorias para com as acessões, sendo que "aquelas têm cunho complementar. Estas são coisas novas, como as plantações e as construções".

• Nessa ordem de idéias, Maria Helena Diniz acentua que "não consitui uma acessão a conservação de plantações já existentes, pela substituição de algumas plantas mortas. Esse caso é uma benfeitoria por não haver nenhuma alteração na substância e destinação da coisa. Se fizermos um pomar em um terreno alheio, onde nada havia anteriormente, teremos uma acessão por plantação, que se caracteriza pela circunstância de produzir uma mudança, ainda que vantajosa, no destino econômico do imóvel" (Curso de Direito Civil Brasileiro - Direito das coisas. 17ª ed. São Paulo: Saraiva, 2002, p.137/138).

• Certo é que o critério de distinção é sutil porque ambas decorrem da intervenção humana, tornando-se muitas vezes delicado o enquadramento de uma obra como acessão ou benfeitoria, exatamente por se encontrarem em uma região fronteiriça.

RECURSO ESPECIAL. DIREITO CIVIL. OFENSA AO DEVIDO PROCESSO LEGAL. AUSÊNCIA DE PREQUESTIONAMENTO. CONTRATO DE LOCAÇÃO DE IMÓVEL URBANO NÃO RESIDENCIAL. CLÁUSULA DE RENÚNCIA À INDENIZAÇÃO POR BENFEITORIAS. VALIDADE. EXTENSÃO À ACESSÃO. IMPOSSIBILIDADE. RECURSO ESPECIAL PARCIALMENTE CONHECIDO E, NESSA EXTENSÃO, PROVIDO.

• 1. O propósito recursal consiste em definir se houve ofensa ao princípio do devido processo legal e se a cláusula de renúncia às benfeitorias constante em contrato de locação pode ser estendida às acessões.
• 2. A questão referente à ofensa ao princípio do devido processo legal não foi debatida pelas instâncias ordinárias, não havendo, portanto, o devido prequestionamento, tampouco arguiu-se ofensa ao art. 1.022 do CPC/2015, o que atrai o óbice das Súmulas 282/STF e 211/STJ.
• 3. Consoante o teor da Súmula n. 335/STJ, "nos contratos de locação, é válida a cláusula de renúncia à indenização das benfeitorias e ao direito de retenção".
• 4. Os negócios jurídicos benéficos e a renúncia interpretam-se <u>estritamente</u> (art. 114 do CC). Assim, a renúncia expressa à indenização por benfeitoria e adaptações realizadas no imóvel não pode ser interpretada extensivamente para a <u>acessão</u>.
• 5. Aquele que edifica em terreno alheio perde, em proveito do proprietário, a construção, mas se procedeu de boa-fé, terá direito à indenização (art. 1.255 do CC). Na espécie, a boa-fé do locatário foi devidamente demonstrada.
• 6. Recurso especial parcialmente conhecido e, nessa extensão, provido. (REsp n. 1.931.087/SP, relator Ministro Marco Aurélio Bellizze, Terceira Turma, julgado em 24/10/2023, DJe de 26/10/2023.)

REsp 1361182 / RS - Tema Repetitivo nº 610

1. É da invalidade, no todo ou em parte, do negócio jurídico, que nasce para o contratante lesado o direito de obter a restituição dos valores pagos a maior, porquanto o reconhecimento do caráter ilegal ou abusivo do contrato tem como consequência lógica a perda da causa que legitimava o pagamento efetuado. A partir daí fica caracterizado o enriquecimento sem causa, derivado de pagamento indevido a gerar o direito à repetição do indébito (arts. 182, 876 e 884 do Código Civil de 2002).
2. A doutrina moderna aponta pelo menos três teorias para explicar o enriquecimento sem causa: a) a teoria unitária da deslocação patrimonial; b) a teoria da ilicitude; e c) a teoria da divisão do instituto. Nesta última, basicamente, reconhecidas as origens distintas das anteriores, a estruturação do instituto é apresentada de maneira mais bem elaborada, abarcando o termo causa de forma ampla, subdividido, porém, em categorias mais comuns (não exaustivas), a partir dos variados significados que o vocábulo poderia fornecer, tais como o enriquecimento por prestação, por intervenção, resultante de despesas efetuadas por outrem, por desconsideração de patrimônio ou por outras causas.
3. No Brasil, antes mesmo do advento do Código Civil de 2002, em que há expressa previsão do instituto (arts. 884 a 886), doutrina e jurisprudência já admitiam o enriquecimento sem causa como <u>fonte de obrigação</u>, diante da vedação do locupletamento ilícito.
4. O art. 884 do Código Civil de 2002 adota a doutrina da divisão do instituto, admitindo, com isso, interpretação mais ampla a albergar o termo causa tanto no sentido de atribuição patrimonial (simples deslocamento patrimonial), como no sentido negocial (de origem contratual, por exemplo), cuja ausência, na modalidade de enriquecimento por prestação, demandaria um exame subjetivo, a partir da não obtenção da finalidade almejada com a prestação, hipótese que mais se adequada à prestação decorrente de cláusula indigitada nula (ausência de causa jurídica lícita).
5. Tanto os atos unilaterais de vontade (promessa de recompensa, arts. 854 e ss.; gestão de negócios, arts. 861 e ss.; pagamento indevido, arts. 876 e ss.; e o próprio enriquecimento sem causa, art. 884 e ss.) como os negociais, conforme o caso, comportam o ajuizamento de ação fundada no enriquecimento sem causa, cuja pretensão está abarcada pelo prazo prescricional trienal previsto no art. 206, § 3º, IV, do Código Civil de 2002.

Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

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Reply to the reviewers

Point-by-point response to Reviewer comments:

We copied the Reviewer comments below in italics. Revisions we propose in response to Reviewer comments are underlined.

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

The manuscript by Yin et al investigates how epidermal cells shape somatosensory neuron (SSN) morphology and function through selective ensheathment in Drosophila. This study builds on earlier work by another group showing that the phagocytic receptor Draper (Drpr) as a crucial epidermal factor that is important for dendrite pruning and clearance. In the present study, the authors how that Drpr also functions in the epidermis to establish the characteristic stretches of epidermal ensheathment of dendrite arborization neurons in the fruit fly Drosophila melanogaster. This is particularly true for highly branched types of dendrites but ont dendrites that show simpler branching patterns. Overexpression of Drpr increases ensheathment and nociceptor sensitivity, linking molecular recognition to sensory modulation. Further, Drpr is known to recognize phosphatidylserine (PS) on neurites to promote ensheathment and the authors show localization of a reporter for PS with epidermal membranes. Genetic manipulations that reduce PS results in a reduction in epidermal sheaths and the chemokine-like protein Orion promoting Drpr/PS interactions is required for these processes. Overall, the manuscript is well written, although at times maybe primarily for a fly audience. Reach could be improved by making it more accessible to a non-fly audience. The observation that Drpr is not only required for removing damaged or degenerating dendrites but also for their correct ensheathment of highly branched dendrites presents an important finding that could be of interest for a wider audience provided the following points are adequately addressed:

1. • The Introduction could be further elaborated to help readers understand the significance of epidermal dendrite ensheathment. Addressing the following points may achieve this: (i) The Introduction would benefit from including details on developmental disorders and neurological diseases associated with defects or abnormalities in dendrite ensheathment.*

We appreciate this suggestion. We allude to possible connections between ensheathment defects and human disease in the discussion but agree that it would be appropriate for the introduction; we will underscore this possible connection more clearly in our revised manuscript. We note studies of epidermal ensheathment are limited in mammalian systems, so links between dysregulation of epidermal ensheathment and disease have not been firmly established.

(ii) In lines 74-79, it is unclear whether the described findings are conserved across evolution or were demonstrated in a specific model organism.

The Reviewer refers to our statement about similarities in the cellular mechanism of epidermal ensheathment and phagocytosis. Indeed, these features are evolutionarily conserved in vertebrates, and we agree that it is worthwhile to emphasize this point. We added a statement underscoring the evolutionary conservation of the morphogenetic mechanism along with the relevant citation.

(iii) Including a description of the known literature on phagocytosis in this process would help readers better understand the novelty and significance of this study.

We agree with the Reviewer. In our revised introduction we will include a more detailed description of key features of phagocytic engulfment and highlight the salient differences between ensheathment and phagocytosis including the failure to complete the endocytic event in ensheathment and the persistence of PIP2 at the membrane.

(iv) Details of published Draper function in Han et al 2014 should be elaborated along with unanswered question that is addressed in this study.

The Han et al 2014 study established that epidermal cells, not Drosophila hemocytes (professional phagocytes), are primarily responsible for phagocytic clearance of damaged dendrites in the periphery. Similarly, the Rasmussen et al 2015 study we cite established that skin cells in vertebrates (zebrafish) act as primary phagocytes in removal of damaged peripheral neurites. These studies demonstrate the phagocytic capacity of epidermal cells, particularly in recognition of somatosensory neurites, and the Han study demonstrates that Draper is required for this epidermal phagocytosis. Neither of these studies addresses mechanisms of epidermal ensheathment; we will clarify this point in our revised introduction.

• It is unclear why the authors focus exclusively on Drpr and Crq, without addressing emp and CG4006, both of which show higher expression levels than the former. Moreover, the conclusion that 14 out of 16 engulfment receptor genes have no role based solely on RNAi knockdown experiments is a very strong statement that may requires additional validation. The authors should provide evidence that the RNAi knockdowns achieved complete loss of gene function to support their claim about 16 engulfment receptors. In addition, at most the authors can conclude that the tested genes are individually not required.*

The Reviewer makes several points that warrant discussion. First, the Reviewer asks “why the authors focus exclusively on Drpr and Crq, without addressing emp and CG40066.” The rationale for focusing on Drpr and Crq in our discussion of the expression data is that both Drpr and Crq function in phagocytic engulfment of damaged neurites. Our focus on Drpr for the remainder of the study is guided by the knockdown phenotypes; if either emp, CG40066, or any other receptor showed robust and reproducible effects on ensheathment we would have discussed them at length. Indeed, we identified a potentially novel ensheathment phenotype for NimB4 and devote a small portion of our discussion to its possible function. However, our primary focus in this study was to identify phagocytic receptors required for epidermal ensheathment of somatosensory neurites and drpr was the top hit from our RNAi screen.

Second, we acknowledge that RNAi knockdown is often incomplete and without additional validation a negative result using RNAi is difficult to interpret. In our original text we state: “epidermal RNAi of 14/16 engulfment receptor genes had no significant effect on the extent of dendrite ensheathment in third instar larvae (Figure 1, F and G), consistent with the notion that most epidermal engulfment receptors are dispensable for dendrite ensheathment.” We do not claim that other receptors have “no role”, simply that our results are consistent with the interpretation that most receptors are dispensable. Furthermore, we acknowledge that multiple other receptors likely contribute to other aspects of ensheathment (lines 131-145; NimB4 knockdown causes an “empty sheath” phenotype). However, the Reviewer’s comments convince us that we should more clearly word our interpretation of the negative RNAi results more to reflect the limitations of the approach; we will incorporate this into our revision.

Third, the Reviewer brings up the very important point that receptor redundancy could mask phenotypes. Indeed, our studies suggest that additional pathways likely function in parallel with Drpr. We agree that potential redundancy is an important consideration and absolutely warrants discussion in this section of the results; we will add this to our revised text and we have already updated the statement in the results to read “most epidermal phagocytic receptors are individually dispensable for dendrite ensheathment.”

The final point the Review makes is that analysis of the knockdown efficacy is warranted if we want to make strong claims about gene function for other receptors. We agree that this would be an important first step, but in many cases protein perdurance masks RNAi phenotypes as well. So, efficient knockdown alone is not enough to make concrete conclusions about gene function in this developmental context.

• What kind of genes are crq and ea?*

Crq is a Scavenger receptor and Eater is a Nimrod-family receptor (indicated in Figure 1A).

• Comparing Figures 1C and 1E, it appears that drpr knockdown has a differential effect on epidermal dendrite ensheathment between main and secondary branches. If this observation is correct, separate quantification for each branch type would be more appropriate, along with an explanation for the observed differences.*

We agree with the Reviewer’s assessment that ensheathment appears to be largely absent on terminal dendrites following drpr knockdown but some ensheathment persists on major dendrites. In prior published studies we demonstrated that terminal branches are less extensively ensheathed than primary dendrites in wild-type larvae (Jiang et al 2019 eLife). We will provide this important context in our revised submission. We hypothesize that Drpr uniformly affects ensheathment across the arbor but agree with the Reviewer that quantification is warranted to evaluate this hypothesis. We will add this analysis to our revised submission.

• For Figure 1K, it would be informative to examine how drpr knockdown affects dendrite length in these neurons.*

We agree with the Reviewer. We demonstrate that drpr null mutants have exuberant terminal branching, but we have not yet analyzed effects of epidermal drpr RNAi. We will add this analysis to our revised manuscript.

• For Drpr expression (Figure 3), it would be valuable to highlight any differences in expression between primary and secondary dendritic branches.*

The Reviewer’s question about Drpr distribution at sites of ensheathment will be particularly relevant if we observe differential impacts of Drpr knockdown on ensheathment at primary and higher order dendrites. In our initial submission we showed that >70% of PIP2+ (Fig. 3B) and cora+ (Fig. 3D) epidermal sheaths also exhibited Drpr accumulation; we likewise showed that Drpr accumulation adjacent to dendrites only occurred at sites labeled by the sheath marker cora (Fig. 3G). In our revised submission, we will examine whether Drpr accumulation is more prevalent at sites of PIP2 accumulation on main branches compared to terminal branches.

• Removing drpr leads to excessive branching of SSN dendrites. Does overexpression of drpr affect dendrite morphology in the opposite manner?*

The Reviewer asks an intriguing question about effects of drpr overexpression. We have not examined effects of epidermal drpr overexpression on dendrite morphogenesis, but we will add these experiments to our revised manuscript.

• Although drpr role in dendrite ensheathment is well explored, the interactions between drpr and PS seem underexplored. For example, do the changes in ensheathment as a result of manipulating PS levels require drpr? Does changing PS levels affect Drpr localization or levels?*

The Reviewer raises two questions about the relationship between PS exposure and Drpr.

First, they inquire whether changes in ensheathment resulting from manipulating PS levels require Drpr. We show that overexpressing the ATP8a flippase in C4da neurons, which limits PS exposure, limits the extent of ensheathment. Similarly, we show that sheath formation requires Drpr. In principle, we could assay effects of simultaneously overexpressing ATP8a in neurons and inactivating Drpr (using the Drpr null mutation), but such an experiment will likely be difficult to interpret because the individual treatments cause an almost complete loss of sheaths. We did not investigate whether increasing PS exposure increases ensheathment because prior studies demonstrated that ectopic PS exposure induces membrane shedding in C4da dendrites.

Second, they inquire whether PS levels affect Drpr localization or levels. We demonstrate that inactivation of the PS bridging molecule Orion prevents Drpr localization at sheaths, hence we predict that neuronal overexpression of the ATP8a flippase should have a similar effect. In the revised manuscript, we will examine this possibility (monitoring Drpr distribution at epidermal contact sites with neurons overexpressing ATP8a).

Minor Points:

1. • Why there is no gene in bold category for hemocytes in Figure 1A*

The bold type was used to indicate the receptors that were selected for screening, using a relaxed criteria for identifying receptors that were “expressed”: any receptor detected at a level of 0.1 TPM. To this point, the figure legend states: “Epidermal candidate genes in bold exhibited a TPM value > 0.1 in at least one biological sample and were selected for inclusion in RNAi screen for epidermal phagocytic receptors required for ensheathment.”

We acknowledge that this is a relaxed criteria for “expression” and likely includes receptors that are not appreciably expressed in epidermal cells. Within the text we compare the repertoire of hemocyte and epidermal phagocytic receptors using a more standard (albeit still relatively relaxed) threshold of 0.5 TPM. We added shading to the histograms in Fig. 1A to facilitate comparison of phagocytic receptor gene expression in hemocytes and epidermal cells.

• Line 67: "neurons BEING the most extensively..."*

• Line 126: should read "epidermal engulfment receptors are INDIVIDUALLY dispensable"*

• Line 216: "THE DrprD 5 mutation had no significant..."*

• Line 230: "overexpression" instead of "overexpressed"*

• Line 385: similar "TO"*

These grammatical errors have been corrected. We thank the Reviewer for their careful reading of the manuscript.

Reviewer #1 (Significance (Required)):

This is an interesting study that adds to our understanding of the role of phagocytic receptors in shaping dendrites. Specifically, the role of Drpr (Draper) is studied, a gene previously known as an important for removal degenerating dendrites. The limitations of the manuscript as is is that it seems to be written primarily for a fly audience. Contextualizing the results and in the significance of this like conserved pathway could increase the significance.

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

Summary:

Innervation of the skin by somatosensory neurons is a conserved process that enables perception and discrimination of mechanical stimuli. How do molecules exposed by neurons and skin cells collaborate to promote neurite-induced epidermal sheath formation? Here, the authors combine fruit fly molecular and genetic tools with high resolution imaging to address this fundamental question. Based on morphological similarity between phagocytosis and SSN ensheathment, the authors hypothesized that one or more phagocyte receptors might promote ensheathment through ligand-driven interactions with neurites. To test this hypothesis, the authors systematically screened phagocytic receptors expressed in the epidermis for functional roles in ensheathment. Through this screening approach, the authors found that the Draper (Drpr) receptor functions in epidermal cells as a significant factor required to promote ensheathment. They support this conclusion using a suite of cell- and tissue-specific RNAi tools and mutant fly lines in conjunction with elegant mechanistic work that establishes a role for the conserved "eat-me" signal phosphatidylserine (PS) in driving ensheathment.

Major comments:

Are the claims and the conclusions supported by the data or do they require additional experiments or analyses to support them?

The seven key claims presented in the abstract are strongly supported by experimental data and analyses presented in the manuscript. At least one experimental result displayed in a main figure in support of the indicated key claim is summarized below. This summary does not present a comprehensive list of all data in support of a particular claim. Rather, it is an effort to confirm that each key result presented to the readership in the abstract is supported by at least one rigorously analyzed experimental result.

We concur with the Reviewer’s interpretations of our work and appreciate the clarity of their summaries below.

1. • Drpr functions in epidermal cells to promote ensheathment: Expressing a Draper RNAi under control of a larval epidermal driver (A58) led to significant reduction in total sheath length (Fig 1H), average sheath length (Fig 1I), and fraction ensheathed (Fig 1J). Similar results were obtained using two different Draper RNAi constructs.*

The argument presented through RNAi results in Fig 1 is bolstered by data using an existing validated Draper mutant line in Fig 2A-E. A question of interest to this reviewer upon receiving the paper was whether Draper functions at initial stages of sheath formation, maintenance of existing sheaths, or both. The timelapse data in Fig 2F suggests that Draper activity is dispensable for maintaining existing sheaths.

• ...that Draper accumulates at sites of epidermal ensheathment but not contact sites of unsheathed neurons:*

Immunostaining experiments demonstrate that Drpr immunoreactivity is enriched at PIP2-positive membrane domains in epidermal cells (Fig 3A-B). Is this accumulation selective for epidermal sheaths? Yes. In Fig. 3E-G, the authors show that Drpr enrichment overlaps with the sheath marker cora but not with dendrites of C1da neurons or from unsheathed portions of C4da dendrite arbors. The authors confirm specificity of Drpr immunoreactivity through control experiments using a Drpr mutant (Supplementary Fig 2).

• ...that Drpr overexpression increased ensheathment:*

Enforced overexpression of Draper in epidermal cells via Epidermal GAL4 driving UAS-Drpr (Fig 5A) shows significantly higher levels of ensheathment of C4da neurons as compared to controls. The authors demonstrate specificity by showing that epidermal Drpr overexpression did not induce ectopic sheath formation in C1da neurons (Fig 5E-G).

• ...that extracellular PS accumulates at sites of ensheathment:*

Using a previously developed secreted AnnV-mScarlet reporter (Ji et al. 2023 https://doi.org/10.1073/pnas.2303392120), the authors demonstrate that PLC-PH-GFP labeled stretches were also labeled by AnnV-mScarlet (Fig 6A-B), consistent with their model that ensheathment by Drpr is mediated by PS exposure on dendrites.

• ...that overexpression of the PS Flippase ATP8a blocks ensheathment:*

This claim is supported by demonstrating that overexpression of ATP8A, a protein that drives drives unidirectional PS translocation from the outer to the inner leaflet of the plasma membrane, impacts C4da neurite ensheathment. Selective overexpression of ATP8A in C4da neurons using a ppk-GAL4 induced a significant reduction in epidermal sheaths (Fig 6C).

• ...that Orion is required for sheath formation:*

Inactivation of the chemokine-like PS bridging molecule Orion significantly reduces fraction of ensheathment (Fig 6I-L).

• Overexpression of Draper enhanced nociceptor sensitivity to mechanical stimulus*

Consistent with a functional role for epidermal ensheathment in responses to mechanical stimuli, the authors report a significant reduction in nocifensive responses in a behavioral assay presented in Fig 6H.

In conclusion, the authors' claims are supported by the data as presented in this version of the manuscript.

• Please request additional experiments only if they are essential for the conclusions. Alternatively, ask the authors to qualify their claims as preliminary or speculative, or to remove them altogether.

n/a

• If you have constructive further reaching suggestions that could significantly improve the study but would open new lines of investigations, please label them as "OPTIONAL".

n/a

• Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated time investment for substantial experiments.

n/a

• Are the data and the methods presented in such a way that they can be reproduced?

Yes. The quality of the cell imaging data presented in the figures is high. The figure legends are sufficient to follow the investigators' conceptual approach and technical progress as they build their model. Transparent presentation of the screening data in Fig. 1 F-G was particularly appreciated by these reviewers.

Are the experiments adequately replicated and statistical analysis adequate?

Yes. We specifically commend the table outlining all statistical tests presented in the supplementary methods and linked to each figure.

Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

Minor comments:

1. Could the authors further clarify Drpr's anticipated window of activity during sheath formation and/or speculate further on this point in the discussion? Live imaging in Fig. 2 suggest that Drpr is dispensable for maintenance of existing sheaths. Given that Drpr is proposed to be activated through transient phosphorylation that recruits the binding partner Shark (PMCID PMC2493287), it might be useful to clarify Drpr's window of activation (ie transient or constitutive) for an audience more familiar with Drpr's canonical functions in engulfment. The section prior to speculation about a possible role for negative regulators of phagocytosis (Line 360) might be a possible location for this addition.

We appreciate the insightful suggestion. As the Reviewer notes, our results are consistent with a model in which Drpr is required for formation but not maintenance of sheaths. Our original hypothesis was that Drpr would transiently localize to sheaths and be largely absent from mature sheaths. However, our antibody staining suggests that Drpr persists at mature sheaths (signal from endogenously labeled Drpr protein was too dim for live imaging in our hands). We therefore favor a model in which Drpr is transiently activated to promote sheath assembly.

In the context of engulfment, Src42A-dependent tyrosine phosphorylation of Draper promotes association of Shark and Draper pathway activation. Src42A activation is regulated by integrins and RTKs, providing a potential point of crosstalk with other pathway(s) likely involved in ensheathment. Intriguingly, membrane recruitment and activation of Talin depends in part on PIP2, and Talin promotes both Integrin activation and recruitment of PIP2-prodicing PIP5K Kinases, providing a potential feed-forward mechanism for increasing PIP2 accumulation, Talin recruitment, and Integrin activation, which can promote Src42A activation. In our revised discussion we will provide a more thorough treatment of mechanism(s) of Drpr activation.

• The authors might consider developing their conclusion a bit further for a broad audience. For example, the gesture to Piezo dependence in the current final sentence might provide an opening to discuss an exciting future avenue focused on integrating molecular mechansensors into a comprehensive model of selective SSN ensheathment important for the perception and discrimination of touch and pain.*

We appreciate the suggestion and agree that it is worthwhile to expand on the potential links between ensheathment and sensory neuron function in our revised discussion. Our studies thus far have largely explored mechanosensation, but it’s worth noting that the nociceptive neurons under study here are polymodal, and other functional classes of somatosensory neurons are ensheathed to differing degrees, so an intriguing open question is whether ensheathment selectively potentiates the function of mechanosensors or more generally enhances functional coupling of somatosensory neurons to the epidermis. Our finding that ensheathment levels can be bidirectionally regulated by drpr levels provides an entry point to more broadly characterizing functions for ensheathment.

• Word missing or extra "in" in Line 69 after ECM?*

Corrected.

• In Fig 1 and Fig 3, the PLC(delta)-PH-GFP reporter contains the delta symbol, in other throughout the paper it does not. In addition, Fig 5 is denoted "PIP2 (PLC-PH-GFP)". For consistency the authors might consider using PLC(delta)-PH-GFP across all figures.*

As suggested, we updated the figures and text to include the delta symbol in the reporter PLC(delta)-PH-GFP.

• Fig 6P - do the authors suggest Orion is distributed at high concentration throughout the entire upper portion of the figure? Perhaps the coloration could be changed if Orion binding is suggested to occur between Drpr and PS.*

We have not examined Orion distribution in the periphery, though prior studies demonstrate that it is secreted into the hemolymph from multiple sources. Our schematic focuses on sites of contact between epidermal cells and dendrites but omits the hemolymph, muscle, and other cell types in the periphery. In our initial schematic epidermal cells and Orion were shaded similarly; in our revision we chose a different color for epidermal cells to prevent confusion.

Optional suggestions for consideration to provide further context for a broad audience:

Optional 6. The authors might consider placing their work in the context of an emerging literature focused developmental roles for immune cell signaling molecules/other phagocyte receptors at steady state. While the present study focused on epidermal ensheathment of SSNs stands on its own as a notable contribution and does not require these citations to support its conclusions, context from an emerging literature bridging immunity and development might be of interest to a broad readership. Should the authors wish to strengthen the link between their work and findings from other systems indicating a shared role in immunity and development for key immunoreceptors and their binding partners, they might consider adding citations/phrasing indicating that Draper's molecular collaborator Shark kinase (PMCID PMC2493287) was initially discovered as a developmental gene required for dorsal closure (PMCID PMC316420). They might also consider highlighting the role of Draper's mammalian orthologs Megf10/Megf11 in regulating mosaic spacing of retinal neurons (PMCID PMC3310952).

We appreciate the Reviewer’s suggestions, in particular the value of further highlighting relevant links between immunity and development. Not including Megf10/Megf11 (Drpr vertebrate orthologue) in our discussion was an oversight as we predict that Megf10/Megf11 serves a similar role in ensheathment of vertebrate somatosensory neurons. In our revised manuscript we will incorporate a more thorough discussion of the emerging literature bridging immunity and development.

Optional 7. The authors might consider tying their extended discussion of integrins (~Line 320-Line 335) into their overall argument in a more cohesive manner. For example, how (if at all) do the authors see Drpr collaborating with other receptors to regulate initiation versus maintenance of sheaths? Is a model in which Drpr initiates ensheathment maintained by other molecules possible? Speculation on this point in the discussion might integrate other molecules into the authors' model in a cohesive manner and/or bolster the authors' discussion of Drpr's window of activation/deactivation during ensheathment.

Indeed, we envision a model in which Drpr cooperates with other receptors; we discussed one possible connection to integrins above and will incorporate a fuller treatment of the possible crosstalk between these pathways in our discussion. Regarding a model in which Drpr initiates ensheathment maintained by other molecules: yes, we agree that this is possible, but our results suggest that additional receptors likely participate in sheath initiation as well. Drpr inactivation substantially reduces but does not totally eliminate ensheathment, however the sheaths that form in drpr mutants are structurally distinct from mature sheaths (shorter, narrower, appear to recruit less Cora). Hence, we favor a model in which drpr signaling cooperates with a parallel, partially redundant pathway for initiating sheath formation in response to sheath-promoting signals. Integrin signaling is a plausible candidate for this parallel pathway for reasons we discuss in our original submission (and above); in our revised discussion we will more extensively discuss the potential cross-talk between Drpr signaling and Integrin signaling in initiation and maintenance of epidermal sheaths.

Reviewer #2 (Significance (Required)):

This study provides a new link between a conserved phagocyte receptor (Drpr) and epidermal ensheathment of somatosensory neurons, an important process at the heart of the regulated development and function of the nervous system. As such, the Yin et al. submission is a significant contribution to a rapidly moving research area of broad interest to an intellectually diverse readership interested in the molecular and cellular basis of neurodevelopment and interactions between the nervous system and the immune system.* *

An important strength of this study is the striking degree of the epidermal ensheathment phenotypes observed when normal Drpr expression is disrupted either through depletion, mutation, or targeted overexpression. For example, depletion of Drpr via RNAi induces a ~three fold reduction in total sheath length (Fig 1F - ~1.45 mm in controls as compared to ~0.5 mm with Drpr RNAi). Notably, epidermal enforced overexpression of Drpr induces a notable increase in the fraction of ensheathed neurons (Fig 5A-D). This strength of phenotype enables the investigators to deploy an elegant sequence of molecular and genetic tools to further probe mechanism and implicate extracellular PS in this process.* *

Reviewer area keywords as requested: phagocytes, immune cell signaling, signal transduction

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

The study by Yin and colleagues investigates how epidermal cells recognize and ensheath somatosensory neuron (SSN) dendrites in Drosophila larvae. The authors identify the phagocytic receptor Draper (Drpr) as a key mediator of selective epidermal ensheathment and demonstrate that this process relies on phosphatidylserine (PS) exposure on dendrites and the bridging molecule Orion. The work significantly advances our understanding of neuron/epidermis interactions and reveals a novel role for phagocytic recognition pathways in non-glial ensheathment.

The manuscript is clearly written, methodologically solid and supported by compelling data. The authors combine genetic, imaging and functional approaches to uncover a mechanism of structural and functional modulation of nociceptive neurons. The results will interest researchers studying neuronal morphogenesis, epithelial biology and non-glial phagocytic pathways.

Specific Critiques:

While the study is strong and timely, several issues should be addressed prior to acceptance:

Figure 1: The authors refer to the receptors as "engulfment receptors." I recommend calling them "phagocytic receptors" since not all are required for the engulfment step (e.g., Crq).

The Reviewer makes an important distinction. We have updated our manuscript to reflect this point, replacing “engulfment receptor” with “phagocytic receptor” in the text and in our title.

Figure 2: The title states "Drpr is required in epidermal cells..." yet the authors analyze a drpr null mutant, which lacks Drpr in all expressing cells (glia, macrophages and epidermal cells). The rationale for using the null mutant instead of epidermal-specific RNAi should be explained.

The increased dendrite number in drpr RNAi larvae should also be noted here.

We agree – the title is not appropriate for this version of the figure; we changed the title to better reflect the experiments being portrayed.

Our RNAi experiments in Figure 1 and 2 demonstrate that drpr is cell autonomously required in epidermal cells for dendrite ensheathment. Here, we include analysis of an amorphic drpr allele to (1) provide further genetic support underscoring the requirement for drpr in dendrite ensheathment and (2) to underscore the observation that a small number of immature sheaths form in the complete absence of drpr, arguing for the presence of an additional pathway that contributes to sheath formation.

Effects of epidermal drpr RNAi on dendrite number is not something we evaluated with our time-lapse studies in Figure 2. Instead, we monitored the effects of drpr knockdown on growth behavior of epidermal sheaths and found that epidermal drpr RNAi triggered an increase in the frequence of sheath retraction events and a decrease in sheath growth events.

Figure 3: Explain the numbers on the X-axis in panels B and D. Add a panel without blue dashed outlines to better visualize Drpr expression. Adjust the red boxes to precisely match the enlarged regions.

Each bar represents a single neuron; the numbers denote the number of sheaths sampled from each neuron. We added this to the figure and figure legend in our manuscript. We thank the Reviewer for identifying this oversight.

We appreciate the Reviewer’s perspective on the blue hatched lines; we removed the hatched lines from the ROI and adjusted the position of the red hatched box.

Figure 4: Why is the drpr mutant used here rather than RNAi? Please clarify the reasoning for choosing mutants in some experiments and knockdown in others.

In Figure 2, we show analysis of the amorphic allele to further corroborate our RNAi studies, as described above. We chose to use the drpr amorphic mutant for these studies because we have no GAL4-independent reporter to label C1da neurons for analysis of dendrite arborization patterns. Although we could use HRP staining in combination with epidermal drpr RNAi, live imaging of dendrite arbors labeled by a C1da neuron GAL4 driver provides a more sensitive and reliable readout for morphogenesis studies.

In our revised manuscript we will add analysis of C4da dendrite patterns in larvae expressing drpr RNAi in epidermal cells to evaluate whether the dendrite defects reflect epidermal requirements for drpr function.

Figure 5: Correct the placement of white boxes in panels E-F′.

We thank the Reviewer for identifying the mismatch. We corrected the placement to match the size of the ROIs.

*Figure 6: AnnV staining in B is difficult to detect. Please add a version of the panel showing AnnV alone. *

In our initial submission we include the overlay of PLC-PH-GFP and AnnV-mScarlet (B), an image showing the PLC-PH-GFP alone (B’) and an image showing the AnnV-mScarlet alone (B”).

AnnV labeling appears weak on sheaths. Since epidermal membranes are strongly labeled, confirm PS exposure on dendrites with a commercial fluorescent Annexin V reagent.

We appreciate the suggestion to use a commercial fluorescent Annexin V reagent and agree that it would strengthen our findings if such a reagent labeled sheaths. However, we intentionally prioritized analysis using the in vivo reporter because numerous studies indicate that epidermal sheaths are inaccessible to large molecules in solution (in the absence of detergent). One of the first assays used to monitor the in vivo distribution of sheaths was based on the inaccessibility of antibodies to ensheathed neurites (Kim et al, Neuron, 2012; also Tenenbaum et al, Current Biology, 2017; Jiang et al, eLife, 2019). More recently, we demonstrated that 10kDa dextran dyes are excluded from epidermal sheaths (Luedke et al, PLoS Genetics, 2024). Nevertheless, as part of our revision we will examine whether commercially available Annexin V reagents label sheaths.

In F and F" sheaths are labeled in areas without visible dendrites. Please clarify.

We note that although C4da dendrites are the most extensively ensheathed among da neurons, other neurons (most prominently C3da neurons) also exhibit significant ensheathment (Jiang et al, eLife, 2019). We use established markers of epidermal sheaths (Cora immunoreactivity in this panel; PIP2 reporters and/or Cora-GFP localization in other panels), hence Drpr accumulates at Cora+ sheaths on C4da neurons and Cora+ sheaths that form on other da neurons. We will clarify this point in the text of our revised manuscript.

In O and P, show Drpr staining without blue dashed sheath outlines.

We have removed the blue dashed outlines from the figure panels.

The legend contains numerous labeling errors: there is no B′ or B"; C-G should be E-G; G-I should be H-J; I-L should be K-N; M-O should be O-R. Please revise carefully.

The labeling errors have been corrected.

Sup Fig 1: Add a panel with only c4da labeling to visualize dendrites.

We have added a panel displaying only C4da dendrites to this figure.

Sup Fig 2: The anti-Drpr signal is unexpected in the null mutant. Validate with an additional antibody (e.g., mouse monoclonal anti-Drpr from the DSHB).

We appreciate the suggestion and have already tested the mouse monoclonal anti-Drpr antibody from DSHB and found that it is unsuitable for use in our preparations (ie, no Drpr-dependent immunoreactivity, even in specimens overexpressing Drpr).

With respect to the comment about the unexpected signal in the null mutant, we note that the antibody is a rabbit polyclonal and is not purified. In our experience it is not uncommon for rabbit serum (even pre-immune serum) to recognize multiple antigens in the larval skin. Nevertheless, our control studies demonstrate that Drpr immunoreactivity is eliminated at epidermal sheaths in Drpr null mutants.

Sup Fig 3: No panels A or B are shown; no PIP2 marker is present despite the legend. Please revise. Drpr overexpression appears to increase Cora levels in some cell. Could Drpr affect Cora expression or distribution? This should be addressed. Also dendrite number appears higher in Drpr-overexpressing larvae. Please state whether this is significant.

The labeling errors in the legend have been corrected; the corresponding studies with the PIP2 marker are presented in Figure 5.

All epidermal drivers we have characterized exhibit a low level of variegation in expression within a hemisegment that we have previously documented (Jiang et al 2014 Development; Jiang et al 2019 eLife), and we suspect that it may be related to epidermal endoreplication (epidermal cells do not synchronously endoreplicate). However, we have not observed any systematic difference in epidermal GAL4 driver or Cora-GFP expression in larvae overexpressing Drpr. We note that a single cell in the field of view in Supplemental Figure 3 exhibits a higher level of GFP fluorescence. We occasionally observe this, independent of background genotype.

All gene names must be italicized and lowercase (e.g., drpr), including in figure labels and legends.

All protein names must be capitalized and non-italic (e.g., Drpr, Cora).

We appreciate the Reviewer’s feedback. We used Drpr in keeping with many recent reports, but the Reviewer is correct in outlining the standard naming conventions. We have changed the gene names to reflect convention (lowercase, italics for genes that were initially identified according to phenotypic characterization; uppercase, italics for genes named according to homology to orthologues in other species such as NimB4 and ATP8A)

Define ROI on first use.

Done. We defined ROI in the methods section.

Ensure consistent phrasing: use "anti-Cora or anti-Drpr immunoreactivity" uniformly.

We have done so.

There a few typos which must be corrected:

• • Line 196: "containing" → "contain"*

• • Line 205: "antibodies Drpr" → "antibodies to Drpr" or "anti-Drpr antibodies"*

• • Line 331: "predominan" → "predominant"*

• • Line 353: "phagocyting" → "phagocytic"*

• • Line 385: "similar the effect" → "similar to the effect"*

• • Line 432: Title should be underlined*

• • Line 544: "drpr∆5" is missing the 5*

• • Line 569: "immunoreactivity a" → "immunoreactivity of"*

The typographical errors have been corrected. We thank the Reviewer for the close reading of the manuscript.

Reviewer #3 (Significance (Required)):

The manuscript makes a meaningful contribution to the field of neuron/epidermal cells interactions by demonstrating that recognized phagocytic machinery components can be co-opted for ensheathment of sensory neurites. This not only expands our understanding of skin innervation and mechanosensation but also raises intriguing implications for how similar mechanisms might operate in vertebrates (e.g., epidermal/nerve interactions, peripheral neuropathy). Given the functional link to nociceptive sensitivity, the work may have broader relevance for pain biology and sensory disorders.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

Referee #3

Evidence, reproducibility and clarity

The study by Yin and colleagues investigates how epidermal cells recognize and ensheath somatosensory neuron (SSN) dendrites in Drosophila larvae. The authors identify the phagocytic receptor Draper (Drpr) as a key mediator of selective epidermal ensheathment and demonstrate that this process relies on phosphatidylserine (PS) exposure on dendrites and the bridging molecule Orion. The work significantly advances our understanding of neuron/epidermis interactions and reveals a novel role for phagocytic recognition pathways in non-glial ensheathment. The manuscript is clearly written, methodologically solid and supported by compelling data. The authors combine genetic, imaging and functional approaches to uncover a mechanism of structural and functional modulation of nociceptive neurons. The results will interest researchers studying neuronal morphogenesis, epithelial biology and non-glial phagocytic pathways.

While the study is strong and timely, several issues should be addressed prior to acceptance: Figure 1: The authors refer to the receptors as "engulfment receptors." I recommend calling them "phagocytic receptors" since not all are required for the engulfment step (e.g., Crq).

Figure 2: The title states "Drpr is required in epidermal cells..." yet the authors analyze a drpr null mutant, which lacks Drpr in all expressing cells (glia, macrophages and epidermal cells). The rationale for using the null mutant instead of epidermal-specific RNAi should be explained. The increased dendrite number in drpr RNAi larvae should also be noted here.

Figure 3: Explain the numbers on the X-axis in panels B and D. Add a panel without blue dashed outlines to better visualize Drpr expression. Adjust the red boxes to precisely match the enlarged regions.

Figure 4: Why is the drpr mutant used here rather than RNAi? Please clarify the reasoning for choosing mutants in some experiments and knockdown in others.

Figure 5: Correct the placement of white boxes in panels E-F′.

Figure 6: AnnV staining in B is difficult to detect. Please add a version of the panel showing AnnV alone. AnnV labeling appears weak on sheaths. Since epidermal membranes are strongly labeled, confirm PS exposure on dendrites with a commercial fluorescent Annexin V reagent. In F and F" sheaths are labeled in areas without visible dendrites. Please clarify. In O and P, show Drpr staining without blue dashed sheath outlines. The legend contains numerous labeling errors: there is no B′ or B"; C-G should be E-G; G-I should be H-J; I-L should be K-N; M-O should be O-R. Please revise carefully.

Sup Fig 1: Add a panel with only c4da labeling to visualize dendrites. Sup Fig 2: The anti-Drpr signal is unexpected in the null mutant. Validate with an additional antibody (e.g., mouse monoclonal anti-Drpr from the DSHB). Sup Fig 3: No panels A or B are shown; no PIP2 marker is present despite the legend. Please revise. Drpr overexpression appears to increase Cora levels in some cell. Could Drpr affect Cora expression or distribution? This should be addressed. Also dendrite number appears higher in Drpr-overexpressing larvae. Please state whether this is significant.

All gene names must be italicized and lowercase (e.g., drpr), including in figure labels and legends. All protein names must be capitalized and non-italic (e.g., Drpr, Cora). Define ROI on first use. Ensure consistent phrasing: use "anti-Cora or anti-Drpr immunoreactivity" uniformly. There a few typos which must be corrected:

• Line 196: "containing" → "contain"
• Line 205: "antibodies Drpr" → "antibodies to Drpr" or "anti-Drpr antibodies"
• Line 331: "predominan" → "predominant"
• Line 353: "phagocyting" → "phagocytic"
• Line 385: "similar the effect" → "similar to the effect"
• Line 432: Title should be underlined
• Line 544: "drpr∆5" is missing the 5
• Line 569: "immunoreactivity a" → "immunoreactivity of"

Significance

The manuscript makes a meaningful contribution to the field of neuron/epidermal cells interactions by demonstrating that recognized phagocytic machinery components can be co-opted for ensheathment of sensory neurites. This not only expands our understanding of skin innervation and mechanosensation but also raises intriguing implications for how similar mechanisms might operate in vertebrates (e.g., epidermal/nerve interactions, peripheral neuropathy). Given the functional link to nociceptive sensitivity, the work may have broader relevance for pain biology and sensory disorders.

• Informativo nº 858
• 19 de agosto de 2025.
• Processo: REsp 2.191.479-SP, Rel. Ministra Maria Thereza de Assis Moura, Primeira Seção, por unanimidade, julgado em 13/8/2025. (Tema 1342). REsp 2.191.694-SP, Rel. Ministra Maria Thereza de Assis Moura, Primeira Seção, por unanimidade, julgado em 13/8/2025 (Tema 1342).

Ramo do Direito DIREITO TRIBUTÁRIO

Contribuição previdenciária patronal. Contribuição do Grau de Incidência de Incapacidade Laborativa decorrente dos Riscos Ambientais do Trabalho (GIIL-RAT). Contribuições a terceiro. Incidência. Contrato de aprendizagem. Tema 1342.

Destaque - A remuneração decorrente do contrato de aprendizagem (art. 428 da CLT) integra a base de cálculo da contribuição previdenciária patronal, da Contribuição do Grau de Incidência de Incapacidade Laborativa decorrente dos Riscos Ambientais do Trabalho (GIIL-RAT) e das contribuições a terceiros.

Informações do Inteiro Teor - Cinge-se a controvérsia a definir se a remuneração decorrente do contrato de aprendizagem (art. 428 da CLT) integra a base de cálculo da contribuição previdenciária patronal, inclusive as adicionais Contribuição do Grau de Incidência de Incapacidade Laborativa decorrente dos Riscos Ambientais do Trabalho (GIIL-RAT) e as contribuições a terceiros.

• De acordo com o art. 428 da CLT, o contrato de aprendizagem é um "contrato de trabalho especial**". Assim, o texto legal acentua o caráter empregatício da relação de aprendizagem.

• A doutrina também assevera que a aprendizagem é um contrato de trabalho, segundo as regras da CLT. Defende que a legislação "não deixa qualquer dúvida que o contrato de aprendizagem é uma forma de contrato de emprego"; que estabelece "uma relação empresa-empregado, quando o adolescente é submetido, no próprio emprego, à aprendizagem metódica".

• A jurisprudência do Tribunal Superior do Trabalho vai em idêntica direção. Afirma que o contrato de aprendizagem "é espécie de contrato de trabalho, e, nesse contexto, o aprendiz é destinatário de normas específicas da CLT, reunindo os pressupostos do art. 3º da norma celetista", e acrescenta que "lhe são assegurados todos os direitos de cunho trabalhista conferidos à modalidade especial de seu contrato a termo" (RR-24001-73.2014.5.24.0096, 7ª Turma, Rel. Ministro Evandro Pereira Valadao Lopes, julgado em 23/4/2025).

• Além disso, o reconhecimento de direitos previdenciários ao adolescente é princípio da legislação protetiva (art. 65 do ECA).

• Não se sustenta o argumento de que o contrato de aprendizagem não gera uma relação de emprego, sendo o aprendiz segurado facultativo, na forma do art. 14 da Lei n. 8.212 /1991 e de seu correspondente art. 13 da Lei n. 8.213/1991.

• Esses dispositivos apenas trazem uma idade mínima para a filiação como facultativo. Não é possível ver neles a indicação de que a pessoa com menos de 18 anos necessariamente é segurada facultativa. A forma de filiação de tal pessoa que tenha um contrato de trabalho será a de empregado. Portanto, esses dispositivos não impedem que a forma de filiação do aprendiz seja empregado - segurado obrigatório, portanto, não facultativo.

• Apesar de os aprendizes serem segurados obrigatórios, seria possível desonerar a contribuição do empregador sobre as suas remunerações. Para tanto, seria necessária uma isenção, a ser prevista em lei, na forma do art. 176 do Código Tributário Nacional.

• Embora os contribuintes recorrentes tenham sustentado que o art. 4º, § 4º, do Decreto-Lei n. 2.318/1986, cria tal isenção, ao excluir a remuneração dos "menores assistidos" da base de cálculo de encargos previdenciários, o "menor assistido" e o aprendiz não são a mesma figura.

• Nesse sentido, a jurisprudência do Superior Tribunal de Justiça afirma que o art. 4º, § 4º, do Decreto-Lei n. 2.318/1986 não está regulamentado e não se confunde com o contrato de aprendizagem, previsto no art. 428 da CLT. Logo, não há aplicação atual para esse ato normativo (AgInt no REsp 2.146.118, Rel. Ministro Teodoro Silva Santos, Segunda Turma, julgado em 7/10/2024; e AgInt nos EDcl no REsp n. 2.078.398, Rel. Ministro Francisco Falcão, Segunda Turma, julgado em 26/2/2024).

• Sendo assim, o aprendiz é empregado e recebe remunerações (salário e outras verbas), "destinadas a retribuir o trabalho, qualquer que seja a sua forma", as quais integram a base de cálculo da contribuição em questão e de seus adicionais, na forma do art. 22, I e II, da Lei n. 8.212/1991. Portanto, não há isenção prevista para as contribuições a cargo do empregador sobre a remuneração do aprendiz.

• Dessa forma, a remuneração decorrente do contrato de aprendizagem (art. 428 da CLT) integra a base de cálculo da contribuição previdenciária patronal, da Contribuição do Grau de Incidência de Incapacidade Laborativa decorrente dos Riscos Ambientais do Trabalho (GIIL-RAT) e das contribuições a terceiros.

Author response:

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

We thank the reviewers for their constructive and precise comments, which have helped us improve the consistency and clarity of our manuscript. Below, we provide a point-by-point response to each comment. In summary, the main changes introduced in the revised version are as follows:

(1) We replaced all the statistical analyses to their non-parametric equivalents to ensure compliance with test assumptions and consistency of the results;

(2) We compare the participants’ reaction times before and during connected practice, revealing a significant reduction in reaction times of both partners when connected;

(3) We added, in the supplementary materials, a table reporting the vigor scores of each participant in each experimental condition, facilitating the assessment of individual and dyadic behaviors;

(4) We have reviewed and refined the terminology throughout the manuscript and reduced the number of abbreviations to improve clarity.

Public Reviews:

Reviewer #1 (Public review):

Summary:

The authors present a novel investigation of the movement vigor of individuals completing a synchronous extension-flexion task. Participants were placed into groups of two (so-called "dyads") and asked to complete shared movements (connected via a virtual loaded spring) to targets placed at varying amplitudes. The authors attempted to quantify what, if any, adjustments in movement vigor individual participants made during the dyadic movements, given the combined or co-dependent nature of the task. This is a novel, timely question of interest within the broader field of human sensorimotor control.

Participants from each dyad were labeled as "slow" (low vigor) or "fast" (high vigor), and their respective contributions to the combined movement metrics were assessed. The authors presented four candidate models for dyad interactions: (a) independent motor plans (i.e., co-activity hypothesis), (b) individual-led motor plans (i.e., leader-follower hypothesis), (c) generalization to a weighted average motor plan (i.e., weighted adaptation hypothesis), and (d) an uncertainty-based model of dynamic partner-partner interaction (i.e., interactive adaptation hypothesis). The final model allowed for dynamic changes in individual motor plans (and therefore, movement vigor) based on partner-partner interactions and observations. After detailed observations of interaction torque and movement duration (or vigor), the authors concluded that the interactive adaptation model provided the best explanation of human-human interaction during self-paced dyadic movements.

Strengths:

The experimental setup (simultaneous wrist extension-flexion movements) has been thoroughly vetted. The task was designed particularly well, with adequate block pseudo-randomization to ensure general validity of the results. The analyses of torque interaction, movement kinematics, and vigor are sound, as are the statistical measures used to assess significance. The authors structured the work via a helpful comparison of several candidate models of human-human interaction dynamics, and how well said models explained variance in the vigor of solo and combined movements. The research question is timely and extends current neuroscientific understanding of sensorimotor control, particularly in social contexts.

We thank the reviewer for their in-depth analysis and constructive assessment of our manuscript.

Weaknesses:

(1) My chief concern about the study as it currently stands is the relatively low number of data points (n=10). The authors recruited 20 participants, but the primary conclusions are based on dyad-specific interactions (i.e., analyses of "fast" vs "slow" participants in each pair). Some of these analyses would benefit greatly, in terms of power, from the addition of more data points.

We understand and appreciate the reviewer’s concern regarding the effective sample size at the dyad level (n=10). While our primary analyses focus on dyad-specific interactions, we note that the reported effects are consistent across multiple dynamic conditions and are associated with large effect sizes. To provide a conservative assessment the Cohen’s D values reported correspond to the smallest effect size observed across the relevant statistical tests, thereby limiting the risk of false positives or overinterpretation. In addition, to ensure robustness given the sample size and distribution properties of the data, we have replaced all parametric tests with their non-parametric counterparts, as some analyses violated ANOVA assumptions. Friedman and Kruskal-Wallis tests are now used for paired and unpaired main effects respectively, and Wilcoxon and Mann-Whitney tests for paired and unpaired post-hoc comparisons respectively. Note that these changes did not alter the conclusions of the study.

(a) The distribution of delta-vigor (Fast group vs Slow group) is highly skewed (see Figures 3D, S6D), with over half of the dyads exhibiting delta-vigor less than 0.2 (i.e., less than 20% of unit vigor). Given the relatively low number of dyads, it would be helpful for the authors to provide explicit listings of VigorFast, VigorSlow, and VigorCombined for each of the 10 separate dyads or pairings.

We agree with this comment. However, we note that the distribution of vigor scores within a population is typically centered around 1, with large deviations observed only for the fastest and slowest participants [1]. As a result, the distri bution of ∆-vigor is inherently skewed. Correcting for this skewness would (i) require pairing participants based on their vigor, which is logistically difficult, and (ii) lead to an atypical sampling of dyads, with an over representation of pairs exhibiting very large vigor differences. The distributions of vigor scores for the fast and slow groups before and after the interaction are reported in Supplementary Fig. S21. In addition, as suggested by the reviewer, we have now included Table S.1 in the supplementary materials, listing the values VigorFast, VigorSlow, and VigorCombined for each of the 10 dyads. This table provides a complete view of the evolution of participant’s vigor throughout the experiment.

(b) The authors concluded that the interactive adaptation hypothesis provided the best summary of the combined movement dynamics in the study. If this is indeed the case, then the relative degree of difference in vigor between the fast and slow participants in a dyad should matter. How well did the interactive adaptation model explain variance in the dyads with relatively low delta-vigor (e.g., less than 0.2) vs relatively high delta-vigor?

We initially expected the magnitude of difference in individual vigor within a dyad to play a significant role. However, our analysis did not reveal any systematic effect of ∆-vigor on either the interaction force or the resulting dyadic vigor, as shown by the LMM analysis. Importantly, the interactive adaptation hypothesis does per se imply that the magnitude of vigor differences between the two partners should matter, only that their respective roles in selecting the adapted behavior is different. Although the model includes several free parameters, we did not attempt to fit it to individual dyads as would in principle be possible. Instead, we performed a sensitivity analysis to assess how variations in the difference in vigor between the partners influence model predictions. For this purpose, we simulated increasing values of µ and variations in the fast partner’s cost of time. In addition, we demonstrated that uncertainty in the estimated behavior of the slow partner, which is a priori specific to each individual, has a substantial impact on the optimal movement duration of the dyad. Overall, this analysis shows that the model captures the full range of qualitative trends observed in the experimental data. When applied to predict the behavior of the average dyad, the resulting movement time prediction error remain small, as detailed in the Results section.

(2) The authors shared the results of one analysis of reaction time, showing that the reaction times of the slow partners and the fast partners did not differ during the initial passive block. Did the authors observe any changes in RT of either the slow or fast partner during the combined (primary task) blocks (KL, KH, etc.)? If the pairs of participants did indeed employ a form of interactive adaptation, then it is certainly plausible that this interaction would manifest in the initial movement planning phase (i.e., RT) in addition to the vigor and smoothness of the movements themselves.

We thank the reviewer for this interesting question, that prompted us to extend our analysis of reaction times to the connected conditions. This additional analysis revealed a significant main effect of the condition on the reaction time for both the fast and slow groups (in both cases: W₂ > 0.39, p < 0.02). Post-hoc comparisons showed a significant reduction in reaction time between the initial null-field block (NF1) and the KH condition for the slow group (p = 0.03, D = 1.46), and a similar trend for the fast group (p = 0.06, D = 1.03). However, the reaction times remained comparable between the two groups, with no significant difference between them. We have incorporated these observations in the Results section (p.4, l.100–109) and expanded the Discussion (p.11, l.341–348) to address their implications for interactive adaptation in human-human and human-robot physical interactions.

Reviewer #2 (Public review):

Summary:

This study examines how individual movement vigor is integrated into a shared, dyadic vigor when two individuals are physically coupled. Participants performed wrist-reaching movements toward targets at different distances while mechanically linked via a virtual elastic band, and dyads were formed by pairing participants with different baseline vigor profiles. Under interaction conditions, movements converged to coordinated patterns that could not be explained by simple averaging, indicating that each dyad behaved as a single functional unit. Notably, under coupling, movement durations for both partners were shorter than in the solo condition, arguing against the view that each individual simply executed an independent movement plan. Furthermore, dyadic vigor was primarily predicted by the slower partner’s vigor rather than by the faster partner’s, suggesting that neither a leader-follower strategy nor a weighted averaging account fully explains the observed behavior. The authors propose a computational model in which both partners adapt to the emerging interaction dynamics ("interactive adaptation strategy"), providing a coherent explanation of the behavioral observations.

Strengths:

The study is carefully designed and addresses an important question about how individual movement vigor is integrated during joint action. The experimental paradigm allows systematic manipulation of interaction strength and partner asymmetry. The behavioral results show clear and robust patterns, particularly the shortening of movement durations under elastic coupling (KL and KH conditions) and the asymmetrical contribution of the slower partner’s vigor to dyadic vigor. The computational model captures the main behavioral patterns well and provides a principled framework for interpreting dyadic vigor not as a simple combination of two independent motor plans, but as an emergent property arising from mutual adaptation. Conceptually, the study is notable in extending the notion of vigor from an individual attribute to a dyad-level construct, opening a new perspective on coordinated movement and motor decision-making.

We thank the reviewer for their thorough analysis of our manuscript and their constructive feedback.

Weaknesses:

(1) A key conceptual issue concerns the apparent asymmetry between partners in the computational framework. While dyadic vigor is empirically better predicted by the slower partner’s vigor, the model formulation appears to emphasize the faster partner’s time-related cost and interaction forces. Although the cost function includes an uncertaintyrelated component associated with the slower partner, it remains unclear from the current formulation and description how dyadic vigor is formally derived from the slower partner’s control policy within the same modeling framework. This raises an important question regarding whether the model offers a symmetric account of dyadic vigor formation for both partners or whether it is effectively anchored to the faster partner’s control architecture.

We have modified our phrasing to clarify the principles according to which the computational framework was designed (p.7, l.226–231 and p.9, l.260–264). As stated in the Results section, the model is indeed asymmetric by design, which corresponds to the different roles of the fast and slow partner exhibited in the data. In that context, the uncertain term associated with the slow partners should be understood as an overarching constraint that conditions the strategy of the dyad, while the fast partner cost of time acts as a contributor to the expected dyad strategy. Conceptually and numerically as reported in the sensitivity analysis, this asymmetry corresponds to the role of the slow partners in setting the vigor ranking among the dyads and the role of the fast partner in setting the average dyadic behavior.

(2) A second conceptual issue concerns the interpretation of the term "motor plan." It remains unclear whether this term refers primarily to movement-related characteristics such as speed or duration, or more broadly to the underlying optimization structure that governs these variables. This distinction is theoretically important, as it determines whether the reported interaction effects should be understood as adjustments in movement characteristics or as changes in the structure of the control policy itself.

We agree with the reviewer that this terminology required clarification. In this paper, the term “motor plan” refers to the time series of control inputs planned by the CNS, rather than solely to kinematic descriptors such as speed or duration. These planned control signals are a direct consequence of the underlying optimization structure and cost functions that govern trajectory generation. We have clarified this definition in the Introduction (p.1, l.23–24).

Reviewer #3 (Public review):

Strengths:

This study provides novel insights into how individuals regulate the speed of their movements both alone and in pairs, highlighting consistent differences in movement vigor across people and showing that these differences can adapt in dyadic contexts. The findings are significant because they reveal stable individual patterns of action that are flexible when interacting with others, and they suggest that multiple factors, beyond reward sensitivity, may contribute to these idiosyncrasies. The evidence is generally strong, supported by careful behavioral measurements and appropriate modeling, though clarifying some statistical choices and including additional measures of accuracy and smoothness would further strengthen the support for the conclusions.

Thank you for this analysis and the insightful feedback.

Major Comments:

(1) Given the idiosyncrasies in individual vigor, would linear mixed models (LMMs) be more appropriate than ANOVAs in some analyses (e.g., in the section "Solo session"), as they can account for random intercepts and slopes on vigor measures? Some figures (e.g., Figure 2.B and 3.E) indeed seem to show that some aspects of behaviour may present variability in slopes and intercepts across participants. In fact, I now realize that LMMs are used in the "Emergence of dyadic vigor from the partners’ individual vigor" section, so could the authors clarify why different statistical approaches were applied depending on the sections?

We thank the reviewer for this thoughtful comment. We deliberately used different statistical approaches throughout the paper in order to address different types of questions. Note that the statistical tests were converted to their nonparametric equivalent for consistency (see answer to Reviewer 1).

- Friedman tests were used in a limited number of cases to assess population- or group-level effects, such as differences in movement time, smoothness, or accuracy across the solo, connected, and after-effects conditions. Such tests provide a straightforward framework for these descriptive, condition-level comparisons.

- The stability of individual and dyadic vigor scores across conditions was assessed using Pearson correlations across all condition pairs, which we consider the most direct and interpretable approach for evaluating consistency across sessions.

- LMMs were employed to examine how dyadic vigor relates to the partners’ individual vigor measured in the solo conditions, which revealed the critical contribution of the slow partner.

Rather than applying a single statistical framework throughout, we selected the method best suited to each question. While LMMs are well suited for modeling participant-specific variability when linking individual and dyadic measures, their systematic use in all analyses would be less intuitive and would not directly address several of the population-level comparisons central to this study.

(2) If I understand correctly, the introduction suggests that idiosyncrasies in movement vigor may be driven by interindividual differences in reward sensitivity. However, the current task does not involve any explicit rewards, yet the authors still observe idiosyncrasies in vigor, which is interesting. Could this indicate that other factors contribute to these consistent individual differences? For example, could sensitivity to temporal costs or physical effort explain the slow versus fast subgrouping? Specifically, might individuals more sensitive to temporal costs move faster to minimize opportunity costs, and might those less sensitive to effort costs also move faster? Along the same lines, could the two subgroups (slow vs. fast) be characterized in terms of underlying computational "phenotypes," such as their sensitivities to time and effort? If this is not feasible with the current dataset, it would still be valuable to discuss whether these factors could plausibly account for the observed patterns, based on existing literature.

We thank the reviewer for this interesting question. We first note that the notion of reward in motor control is quite broad. Although our task did not include explicit external (e.g. monetary) rewards, we assumed that participants attribute an implicit value to completing the task in accordance with the experimenter’s instructions. This assumption has been shown to be appropriate for characterising baseline behavior in previous studies [2–5].

As discussed in the Introduction, vigor is generally understood to emerge from a tradeoff between effort, accuracy, and time. The reviewer is correct in noting that inter-individual differences in vigor may reflect differences in reward sensitivity or in its discounting [3,6], given that time and reward are intrinsically coupled. Differences in vigor may also arise from inter-individual variability in sensitivity to effort or perceived task difficulty. Because these factors are intertwined—for example, increasing accuracy through co-contraction typically incurs greater effort [7])—it is challenging to disentangle their respective contributions based solely on behavioral data.

In the present study, our inverse optimal control procedure to identify the cost of time (and thus predict individuals’ vigor) relies on a predefined effort-accuracy tradeoff under fixed final time across multiple movement amplitudes [8]. As a result, the model does not allow us to independently estimate individual sensitivities to effort, accuracy, and time. Such characterization of computational "phenotypes" would likely require experimental paradigms in which each of these factors is systematically manipulated while the others are held constant, which is beyond the scope of the current dataset. In practice, the main value of behavioral modeling lies in revealing the relative weighting of these criteria by the CNS during motor planning [5]. We have expanded the Discussion to clarify these limitations and considerations (see Discussion p.12, l.396–401 & l.407–412).

Finally, we chose not to emphasize these broader issues in the present manuscript because (i) they are peripheral to our primary research question on how individual vigor influences human-human interaction, and (ii) although we do not yet have definitive and consensual answers, they have been addressed in multiple studies reviewed elsewhere [9,10].

(3) The observation that dyads did not lose accuracy or smoothness despite changes in vigor is interesting and suggests a shift in the speed-accuracy tradeoff. Could the authors include accuracy and smoothness measures in the main figures rather than only in supplementary materials? I think it would make the manuscript more complete.

We also find that the preservation of accuracy and smoothness despite changes in vigor is an interesting result, and we therefore chose to report these measures in the Supplementary Materials. However, we believe it is preferable not to include them in the main figures for the following reasons:

- We avoid framing our results in terms of a speed-accuracy trade-off, as Fitts’ work was initially designed to study fast movements [11], whereas our work focuses on self-paced movements. As outlined in the Introduction, vigor is more appropriately interpreted as reflecting a tradeoff between effort (related to movement speed), accuracy, and time. From this perspective, the reported changes of vigor already capture a shift in the underlying trade-off selected by the CNS, using a framework better suited to our experimental paradigm.

- The manuscript is technically dense and reports multiple analyses that are essential to establish (i) the existence and definition of dyadic vigor, and (ii) how it emerges from interaction between partners. Although the observed preservation of accuracy and improvements in smoothness are informative, they are not central to these two primary questions and would risk diverting attention from the core contributions of the paper. In addition, accuracy is not a feature predicted by our deterministic modeling and extensions would be needed to capture these aspect. Here we only attempted to replicate average behaviors.

(4) It is a bit unclear to me whether the variance assumptions for ANOVAs were checked, for instance, in Figure 3H.

We thank the reviewer for this comment, which prompted us to verify the assumptions underlying our ANOVAs. We found that a few distributions in the original analysis, as well as in some of the new tests, did not meet these assumptions. To ensure consistency, all statistical analyses have now been replaced with non-parametric tests: Friedman and Kruskal-Wallis tests for paired and unpaired main effects, Wilcoxon and Mann-Whitney tests for paired and unpaired post-hocs. The updated results do not change any of the conclusions. the only minor change is accuracy, that appeared slightly improved in a restricted number of connected conditions, and now appears mostly non-impacted.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Minor points:

(1) Lines 146-147. The authors state, "Whereas the fast partners maintained a similar duration". Figures S6H,I suggest that fast partners made slower movements during the paired task relative to the solo task, not movements with a similar duration.

We agree that Fig. S.6H,I suggest slightly slower movements for the fast partners, though not significant. We have modified the sentence to be less assertive than in the previous version (see p.6, l.155).

(2) In the Discussion (Lines 318-319), the authors state that their findings confirm and extend the "benefits of dyadic control in collaborative actions". What benefits are they referring to here, relative to individual control? It would be helpful if the authors would elaborate on this claim.

We have modified this sentence to clarify that the benefits of dyadic control refer to previously reported advantages over individual control, namely reduced movement time Reed and Peshkin (2008) [12] and improved tracking accuracy [13,14] (see p.11, l.336–337).

(3) On Lines 87-89, the authors reference a decomposition of variance of vigor scores across the NF1, VL, and VH conditions; however, I did not see an explanation of how this decomposition was performed. The method used to estimate variance explained by inter-individual vs intra-individual differences in vigor should be outlined for the reader.

Thank you for pointing out this missing information. We now explain in the statistical analysis section (see p.14, l.504–507), that the percentage of inter-individual variability in vigor is estimated using sum-square values as an estimation of inter- and intra-individual variability.

(4) How was the absolute interaction torque for a paired movement calculated? Was it an integral of the temporal profile of torque for some portion of the combined movement? The method for calculating the absolute interaction torque needs to be specified.

We have now clarified in the Methods (see p.14, l.490–491) that the reported average interaction effort was computed as the absolute value of the interaction torque as a function of time averaged over the entire movement.

(5) Lines 123-124: "... interaction torque showed no significant correlation with differences in individual vigor within dyads." This statement should be supported by appropriate statistical measures.

This result is now supported by reporting the corresponding Pearson correlation analyses. No significant correlations were found between interaction torque and differences in individual vigor within dyads (KL conditions: |r| < 0.43, p> 0.22; KH conditions: |r| < 0.18, p > 0.61, see p.5, l.132–133).

(6) For the analysis, presented in Figure 3C, and specified on lines 116-123, the text mentions the main effects of both condition and target. There doesn’t appear to be much of an effect of the target for the KH data. Should these results not be reported as an interaction effect between the two factors instead?

We agree with the reviewer and have corrected our presentation of these results (see p.4, l.126–128). Consistent with the reviewer’s observation, no significant effect of the target is found in the KH condition.

(7) Figures 3E and S6B. What is the purpose of including the averaged data for each pair in addition to both individuals’ data from each pair? It would be useful to distinguish the individual data from the average data for each pair. Frankly, the number of data points shown on this sub-figure is excessive.

There may have been a misunderstanding. Because the partners of a dyad are connected by a virtual elastic band (rather than a rigid bar), they do not execute identical movements. Therefore Figs. 3E,S6B display the movement time of all individual participants, together with the corresponding 20 individual regression lines, like in Fig. 2B. The solid black line represents the average across all individuals, and the averaged behaviors of dyads are not included. We have clarified this point by revising the caption of Fig. 3E (see p.5).

Noted mis-spellings:

Figure S.3A caption: "trials towards this target."

Page 10 Line 313: "Importantly, these findings show ...".

These mis-spellings have been corrected at supplementary p.2 and main text p.11, l.331. Thank you!

Reviewer #2 (Recommendations for the authors):

(1) To illustrate the contribution of the three components used to calibrate the overall cost function, it would be informative to include simulation analyses in which each component is selectively removed (i.e., ablation analyses).

We did not perform ablation analyses, as selectively removing components of the model can lead to instability or ill-suited control inputs, making the resulting simulations difficult to interpret. Instead, we conducted a sensitivity analysis of the key parameters shaping the overall cost function, including the estimated mean and deviation of the slow partner’s movement duration, the weight associated with uncertain torque minimization (Figs. S.18,S.19), and the fast partner’s cost of time (Fig. S20). This analysis reveals the predominant roles of the estimated slow partner movement patterns in determining the model predictions, in agreement with our experimental observations.

(2) Although the authors refer to the motor-off condition as "passive," participants actively generated the movements in the absence of external forces. Thus, this condition corresponds to active, unassisted movement. A different term may therefore reduce potential confusion for readers.

We agree that term “passive” was not well-chosen given the context of the paper, thus we have instead replaced this denomination as “null-field” condition. Consequently, the P1 and P2 blocks are now referred to as NF1 and NF2.

(3) Please clarify the instructions given to participants. Were they informed in advance that their movements would physically interact with those of their partner?

Thank you for pointing out this missing clarification. We have now specified in the Methods (p.14, l.465–469) that participants were not informed prior to any condition that they would interact with a human partner; they were only told that the robot would provide assistance. When debriefed at the end of the experiment, only one out of the 20 participants reported having realized that they were connected to another human. Most participants believed they were interacting either with a version of themselves or with a robot with some randomness.

(4) Line 475. Should "Fig. 2D" be "Fig. 2B"?

Thank you for catching this error. The reference has been corrected to Fig. 2B (see p.15, l.522).

Reviewer #3 (Recommendations for the authors):

(1) The analysis of reaction times shows no difference between groups in the passive block, which challenges the assumption that movement vigor covaries with decision speed or action initiation speed. It may be worth discussing this in the context of recent literature.

We agree that the initial analysis and discussion of reaction times were too superficial. In the revised manuscript, we now report that dyadic interaction leads to significantly shorter reaction times (p.4, l.100–109), concomitantly with improved movement velocity. We have also expanded the Discussion, on the relationship between decision and action speeds/durations (p.11, l.340–348).

(2) Many abbreviations are unusual for a non-expert. I would recommend using the full terms instead. At least initially, I found it difficult to follow the results because the abbreviations were not immediately clear (at least to me).

We agree that the paper had to many abbreviations. Therefore, we have removed the abbreviated names of the models and, when possible without impacting the readability, used the full names of the conditions.

(3) Relatedly, the notation in Figure 1 may be confusing. The labels "S" and "F" (slow and fast) correspond to different concepts than "F" and "L" (follower and leader), so the same participant could be labeled "F" as fast but not "F" as a leader.

Thank you for pointing out this potential source of confusion. We have therefore modified Fig. 1A (p.2) to avoid any potential confusion by using the full model names rather than abbreviations. In the remainder of the manuscript, "S" and "F" exclusively denote the slower and faster partners within a dyad, and we do not use abbreviations for "leader" or "follower" in the text.

(4) In figures like 2.C and 3.I, keeping the same scales on the x and y axes and adding a diagonal reference line would make it easier to see shifts across conditions.

As explained in the Methods, vigor scores in the low- and high-viscosity conditions were computed using the average movement durations from the NF1 condition as a reference. Consequently, because movements are slower in these conditions, the corresponding vigor values are lower than those in NF1. For this reason, using identical scales on the x- and y-axes and adding a 45◦ reference line could mislead the reader in thinking that the vigor scores are expected to be identical and reduce the readability of the figure.

(5) Multiple hypotheses about dyadic regulation of vigor are nicely explained; it could help to indicate if any of these were a priori favored based on prior literature.

Previous literature provides mixed evidence regarding how vigor might be regulated in dyadic interaction. For instance, Takagi et al. (2016) [15] reported that mechanically connected partners may rely on independent motor plans, which corresponds to the co-activity hypothesis considered here. However, in that study, movement duration was prescribed. We therefore expected that removing this constraint on movement duration could allow coordination strategies to emerge, particularly in view of findings on haptic communication during tracking of random targets while connected via an elastic band [13,14].

At the same time, a large body of work on human–human and human–robot interaction has interpreted coordination through a leader–follower framework. In our context, vigor is understood as the outcome of a tradeoff between effort and elapsed time, with time being associated with a decaying reward. Based on this framework, we hypothesized a priori that a leader–follower scheme would emerge, in which the fast partner—being more sensitive to time costs and/or less sensitive to effort—would tend to drive the interaction, even at the expense of increased effort. For these reasons, the leader–follower hypothesis was formulated as the expected outcome throughout the manuscript.

(6) In the introduction, statements such as "relative vigor of an individual is remarkably stable" appear true only in the solo condition. The same is true in the discussion where it is said that vigor is a stable trait. The whole study show that an individual can shift his/her vigor to the same vigor of another individual, so it doesn’t appear stable to me in such conditions but adaptable.

Let us first clarify that when we describe vigor as “remarkably stable”, we do not imply that individuals do not adjust their movement timing in response to changes in external dynamics. For example, movement durations increase in visco-resistive conditions even during solo performance; nevertheless, individuals who move faster in the absence of resistance will remain faster relative to others when resistance is introduced. In this sense, stability refers to the preservation of relative rankings across conditions, rather than invariance of absolute movement timing. Because interaction with another individual constitutes a substantial change in task dynamics, an effect on individual pace is therefore expected.

Told that (and as pointed to by the reviewer) (i) dyadic interactions lead to the emergence of a dyadic vigor characterized by average movement durations close to those of the fast partners, while the ranking across dyads is largely imposed by the slow partners; and (ii) these adaptations persist after the interaction phase. Importantly, the observed vigor adaptations appear to last longer in our physical interaction task than in previous attempts to manipulate vigor using visual feedback [16]. To account for this adaptability of vigor, we have (i) clarified claims in the Introduction regarding the stability of vigor (see p.1, l.18–20), and (ii) expanded the Discussion to more explicitly address vigor adaptability and the possible resulting consequences for the concept of vigor (see p.12, l.407–412).

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(14) A. Takagi, G. Ganesh, T. Yoshioka, M. Kawato, and E. Burdet, “Physically interacting individuals estimate the partner’s goal to enhance their movements,” Nature Human Behaviour, vol. 1, pp. 1–6, Mar. 2017.

(15) A. Takagi, N. Beckers, and E. Burdet, “Motion plan changes predictably in dyadic reaching,” PLOS ONE, vol. 11, p. e0167314, Dec. 2016.

(16) P. Mazzoni, B. Shabbott, and J. C. Cortes, “Motor control abnormalities in Parkinson’s disease,” Cold Spring Harbor Perspectives in Medicine, vol. 2, pp. a009282–a009282, Mar. 2012.

1. Transversal (Delineamento)
2. É como tirar uma fotografia de um momento específico.
3. Analisa a causa (exposição) e o efeito (doença) simultaneamente.

4. Você não consegue dizer o que veio primeiro (o ovo ou a galinha), ou seja, não estabelece causalidade direta, apenas associação.

5. Quantitativo (Abordagem)

6. Foca em números, estatísticas e métricas.
7. Traduz opiniões, prevalências ou dados biológicos em números para serem tabulados.
8. Em vez de descrever o "sentimento" de um paciente, você usa uma escala de 1 a 10 ou calcula a média de pressão arterial de um grupo.

9. Objetividade e possibilidade de generalizar os resultados para uma população maior.

10. Descritivo (Objetivo) Serve para relatar o que está acontecendo, sem intervir.

11. Descreve as características de uma população (quem, onde, quando). Informa a frequência, a distribuição e a prevalência de um fenômeno.
12. "60% dos alunos da faculdade X apresentam sintomas de burnout". O estudo não tenta explicar por que (isso seria analítico), apenas descreve a realidade encontrada.

• Modular Interface Structure: Planning Center is organized into separate applications (such as People, Check-Ins, and Giving), each with its own interface and configuration.
• Onboarding Process Across Modules: Churches using multiple modules may need to configure and learn each application separately, depending on the combination of tools in use.
• Documentation and Support Resources: Planning Center provides extensive documentation, guides, and community resources to support setup, troubleshooting, and ongoing use.

DOI: 10.1016/j.molcel.2026.02.016

Resource: (Thermo Fisher Scientific Cat# A-11029, RRID:AB_2534088)

Curator: @scibot

SciCrunch record: RRID:AB_2534088

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Delimitados semántico o categorización por temáticas para organizar mejor el pensamiento y la capacidad escritural

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Referee #2

Evidence, reproducibility and clarity

In this study, Wang et al. use Difteria Toxin (DT) to cause hair cell (HC) death in transgenic mice expressing the DT receptor in the HC of the inner ear. This model is assumed to cause HC loss in a selective way. The lesioned mice are assessed for translational vestibulo-ocular reflex (tVOR), vestibular sensory evoked potential (VsEP), rotational vestibulo-ocular reflex (rVOR), and single-unit recordings of vestibular afferents from cristae and maculae. Numbers of surviving HC, including total HC, type I HC (HCI) and type II HC (HCII) were also obtained at short and long times after DT exposure. By comparing the functional and histological results, the authors conclude that DT cause dose-dependent HC loss and vestibular function loss, that limited but significant HC regeneration occurs and that vestibular organs display variable but ample redundancy because robust physiological responses were obtained despite loss of high percentages of HCs.

However, there are several limitations in the experimental design, methodological choices and analysis of the results that weaken the conclusions stated by the authors. Also, some important aspects of the work are not clear enough for an in deep scrutiny.

The following list of weaknesses is not arranged in order of importance.

1. Choice and use of the Pou4f3DTR/+ transgenic on FVB and C57/Bl6 backgrounds.

1.a. Literature descriptions of the Pou4f3-DTR model used C57/Bl6 and CBA/J backgrounds and low mortality rates were found after DT administration. The present study generated Pou4f3-DTR mice on a FVB background and found that DT cause high mortality rates in this background. Comparison of the C57/Bl6 and FVB backgrounds are included in Figures 1 and 2 and the conclusion was that the C57/Bl6 background is more suitable for studying vestibular HC degeneration/regeneration. However, data are presented in Figures 3 to 7 without informing the reader whether these are from C57/Bl6 or FVB animals. Because of the information given in table 1, at least part of the data in these Figures is from the less suitable FVB mice. It is also possible that some data sets contain unbalanced numbers of animals from each strain in the different experimental conditions, with a potential impact on the robustness of the results. The strain identity of animals should be clarified across all data sets.

1.b. Why the Pou4f3-DTR transgene was introduced in the FVB strain? The FVB strain is frequently used in transgenesis because the prominent pronuclei in their fertilized eggs and large litter size. While generation of transgenic lines in FVB mice is common, why would you want to bring an already established transgenic modification to a FVB background? It is known that FVB mice become blind by wean age due to a mutation in the Pde6b gene. Were the authors trying to have the Pou4f3-DTR model in a strain of blind animals? It is anomalous that the rationale for the FVB derivation is not provided and that the blindness of this strain is not even mentioned in an article containing VOR data. 2. Toxicity of the DT.

2.1. The non-HC toxicity of DT is not evaluated. One of the stated reasons of the choice of the Pou4f3-DTR model to ablate HC is that other alternative models (aminoglycosides, cisplatin, IDPN) cause other toxic effects besides HC toxicity. However, the lack of evidence of other toxicities in Pou4f3-DTR mice after DT administration may simply be due to lack of assessment. Besides the inner ear, Pou4f3 is expressed in several structures including the genitourinary system, the retina, Merkel cells and subsets of somatosensory and brain neurons (https://www.ncbi.nlm.nih.gov/datasets/gene/18998/; PMID: 20826176; PMID: 22262898; PMID: 34266958; PMID: 33135183), so one would expect DT toxicity in these Pou4f3 expressing cells. Also, DT may cause other toxicities not explored in the model. The fact that the DT treatment is toxic beyond the intended HC toxicity is proven by the high (strain-dependent) mortality rate recorded in this study. A more detailed analysis of the effects of DT in the Pou4f3-DTR mice is needed before stating that the treatment is selective for the inner ear HCs. By the way, hyperactivity is not an additional toxic effect of ototoxic chemicals, it is a consequence of the vestibular function loss.

2.2. The dose-response relationship of the DT treatment is unclear. The authors state that DT caused a dose-dependent loss of HC. However, the effects across different DT doses were not compared directly. Instead, each DT dose was compared with a different set of controls, and then the percentage of HC loss was qualitatively compared without statistical comparison. Looking at the numbers, the percent loss after the 35x2 dose is greater than that recorded after the 50x2 dose, contradicting the conclusion of a dose-dependent effect. One possible explanation is that the DT treatment has an inverted-U dose-response relationship, and the 25x2, 35x2 and 50x2 doses draw the bottom of the U. Alternatively, you have a dose-dependent effect with a dose causing a moderate effect (15) and 3 doses (25x2, 35x2, 50x2) causing near-maximal effects with differences among these groups more related to experimental variability than to dose-dependency. <br /> 3. Experimental design, use of animals, role of batch-to-batch variability in apparent results.

3.a. The number of animals used in each experimental condition, their assignment to each assessment and participation in each dataset must be clarified. The reader is not informed on whether the animals used for physiological and histological analyses were the same or separate sets of animals were used. Also, the distribution of animals in different batches is not clarified and this may have originated apparent results through experimenter-generated bias. For instance, the HC count data are presented as two different, independent experiments, one evaluating different doses in the two strains at 14 days after exposure (Figures 1 and 2) and a second one comparing the HC counts at 2 weeks and 6 months after exposure (Figures 3 and 4). However, these were not separate experiments because at least some animals were shared in the two "experiments". This is demonstrated by the duplicate images between figures 1 and 2 and figures 3 and 4 (for instance, images D to D' in Figure 1 are the same than images C to C' in Figure 3). Therefore, at least part of the data for 2-week animals in Figure 3 have already been used as data of day-14 animals in Figure 1. This makes this reviewer suspect that 6-month animals in Figure 3 were treated with DT at different dates than 2-month animals in the same figure. Therefore, the small but significant "regeneration" could be simply due to differences in experimental outcome due to batch-to-batch experimental variability. In this kind of models, batch-to-batch experimental variability may be large and generate apparent group differences. For instance, in Figure 1, HC loss seems to be deeper after 35x2 than after 50x2. Although no statistical comparison is made between these groups, there seems to be an inversion of the dose-effect relationship that may simply depend on experimental (batch-to-batch) variability.

3.b. The aim of revealing the relationship between HC loss and function retention should ideally be addressed using an experimental design providing subject-based data for comparison. That is, you cause the lesion, next you evaluate the function, and then you obtain the tissues for histological assessment, so the individual functional values can be matched to the individual HC numbers for a robust assessment of the relationship. In this work, group data from functional analyses are compared to group data from histological analyses, but no information is given on whether the same or different animals were used. If the same animals were used, the lack of direct comparison of the individual data is surprising and suggest that perhaps the comparison was made and conflicting results were observed. Alternatively, if different sets of animals were used, the conclusions on the "redundancy" of the vestibular organs are severely weakened because batch-to-batch variability in the extent of the lesion may be large and the lesions in the animals used for physiological assessment were in fact not assessed. As noted above, the possibility of a large batch-to-batch variability in the extent of the lesion is supported by the observation that lesions in 35x2 mice were deeper than lesions in 50x2 mice.<br /> 4. The conclusions on HC regeneration needs a deeper scrutiny and the conclusion on its dose-dependency is not supported by the data.

4.1. The animals used for the experiments are too young to sustain claims on adult HC regeneration. DT was administered in "4-6 weeks old" animals. In rats and mice, many HC are generated at the early postnatal days and they mature over the first month. At 4 weeks after birth (postnatal day 28), the number of immature HCs in the rat utricle is small but significantly higher than at day 60 (PMID: 38895157). Therefore, 4-week-old animals may contain a higher reserve of immature cells to show up as "new HC" after damage than 6-week or 8-week-old animals. One possible origin of the differences between 2-week and 6-month DT animals would be that the 6-month group included more animals treated at 4 weeks while the 2-week group included more animals treated at 6 weeks.

4.2. The conclusions on regeneration are based on percentages of HC densities. In the first 2-week experiment the area of the epithelium is assessed, but areas are not taken into consideration when comparing HC densities at 2 weeks and 6 months after DT. Is it possible that the increase in HC density is caused by epithelial shrinkage rather than by emergence of new HC?

4.3. The spontaneous HC regeneration is stated to be "dose-dependent", meaning that more extensive lesions caused more vigorous regeneration. However, this is only an apparent effect caused by the use of percent data. Thus, the increase in HC counts in the utricle is said to represent a 52% after 25X2 and 118% after 50X2. However, if you look at the numbers instead of percentages, the mean number of HCs is 130 vs 86 (an increase of 44) after 25X2 and 78 vs 36 (an increase of 42) after 50X2. So, the cell counts indicate tat a similar number of "new" HCs appear after either dose. 5. The use of antibodies and the exact methodology for HC counts is unclear and perhaps defective.

5.1. The immunohistochemical protocol did not include a specific marker for HCI, so HCI were defined as MYO7A+/Sox2- cells, HCII were MYO7A+/Sox2+ cells and supporting cells were MYO7A-/ Sox2+cells. The use of additional markers for the HCI (Spp1) or the calyx (Caspr1, tenascin-C) would have provided a more robust dataset. Also, striola/central versus peripheral regions were simply defined by approximate anatomical comparison, when positive markers of the central region are available (oncomodulin, calretinin+ calyces).

5.2. The primary and secondary antibodies listed do not match. Two Myosin7a antibodies were used (mouse monoclonal from DSHB and rabbit polyclonal from Proteus) and a goat anti-Sox2. However, the secondaries listed are one anti-goat and two anti-rabbits. No anti-mouse is listed.

5.3. In the figures, the reader is not informed whether the data are from the mouse anti-MYO7A or the rabbit anti-MYO7A, or whether the figure includes mixed data from both antibodies. This is highly relevant because MYO7A was used as the only positive marker for HCI, MYO7A expression may be reduced in stressed HCs (PMID: 37195449), and the two anti-MYO7A antibodies have different affinity for the target. Thus, if the 2-week samples were labelled with the mouse anti-MYO7A and the 6-month samples were labelled with the rabbit antibody, added to the possibility of reduced MYO7A expression at 2 weeks, then the apparent regeneration may be simply apparent, not real regeneration.

5.4. The images were similarly obtained with the 63X objective in both the utricle and the crista. Why two different measures (per 10,000 square micrometres in utricle and per 2500 in crista) were computed if the original area used for counts was the same? The counts are said to be derived from these 63X square images or from merged images spanning the whole utricle. However, the results section does not include the information on the particular kind of image used for any of the counts, and all are presented similarly. The method used to obtain each count should be indicated and valid comparisons should only include counts obtained with the same method. 6. The presentation of the results and its interpretation is biased. Unbiased interpretation of the results do not support conclusions such as "we found that utricle function is largely preserved until hair cell loss exceeded 90%".

6.1. "...a trend of increase....1.2+/-0.4 to 2.7+/-0.6...". These are similar very low numbers, close to zero, not a trend of increase.

6.2. The reader is informed that VsEP "is particularly dependent...striolar type I hair cells". However, the next sentence stresses that measures "remained unchanged at low dose (15 ng/g), with 54% HC survival in striola" when the percentage survival of HCI was 62.7 %. The 54% survival was for total HCs.

6.3. Lack of statistical significance is interpreted as lack of significant biological effect, when this may simply result from lack of power of the experimental design. For instance, it is concluded that the 15 ng/g dose has no effect on VsEP amplitudes, because control and DT animals did not sow statistically significant differences in this parameter. However, the comparison was made using only 4 control animals, with one of them showing a value much lower than the other 3. Also, 7 of the 8 DT animals had amplitude values below these 3 control values, and the mean value in the DT group was about 30-40% lower than the control mean. Clearly, larger groups were necessary to conclude that the 15 DT dose had no effect. Or, as suggested above, use individual animal-based comparisons to compare HC loss to loss of function. Lack of statistical significance in experiments with an insufficient number of controls can't be used to conclude that responses "were intact".

6.4. "At 25 ng/g x2.....Notably, only 3 out of 13 exhibited elevated VsEP thresholds at this dose". However, looking at Fig 5C it seems more accurate to say that 8 out of 13 exhibited elevated thresholds. "At the highest dose (35 ng/g x2), 53.8% (7 out of 13) of the animals showed elevated VsEP thresholds", but in fact all 13 DT animals showed thresholds above the mean threshold value in the control group. 7. A total of 198 vestibular afferents were measured in 5 DT mice and 195 afferents in 4 control mice. An explanation is lacking about the representativeness of these populations, whether they represent a biased or unbiased representation of the total population of afferents. 8. Information of vehicle and volume of injection of DT is lacking. 9. Vestibular organs were "harvested". How? In PBS, fixative?<br /> 10. Why was the anterior crista used for HC counts? The VOR test used examines the reflexes generated in the lateral crista, and the lateral crista is easier to image. 11. There are several reference errors, including formal errors (duplicate o missing references) and content errors (references that do not include the information that you would expect from the text where they are cited).

Referees cross-commenting

While Referee #1 states that the experiments were carefully executed, in my opinion there are many details of the experimental design and execution that need to be better explained before this statement can be made.

Significance

The question addressed is of great interest for several reasons. To explain one, the degree of redundancy in the system greatly influences the possibilities of significant functional recovery that can be achieved by therapeutic interventions aimed at triggering HC regeneration after HC loss from any cause. The DT/transgenic mouse model is certainly an interesting model to address the question.

Ahora, traslademos esto al contexto de la circulación sanguínea y la hipertensión portal. En este caso, los términos se adaptan de la siguiente manera:

Presión (P): En lugar de Voltaje (V), consideramos la presión sanguínea. Es la fuerza que impulsa la sangre a través de los vasos. Flujo Sanguíneo (Q): En lugar de Corriente (I), hablamos del flujo sanguíneo, que es la cantidad de sangre que pasa por un vaso en un determinado tiempo. Resistencia Vascular (R): En lugar de Resistencia (R), nos referimos a la resistencia vascular. Esto representa qué tan estrechos o abiertos están los vasos sanguíneos, y qué tan difícil es para la sangre fluir a través de ellos.

Podemos tirar essa sessão

Vamos tirar o texto "Padrão" das integrações

Antes dessa sessão, trazer a sessão de "Empresas que já utilizam o Securityone" em um carrossel com o logo de parceiros.

Trazer imagem para baixa da h2 para quebrar um pouco o texto

Reviewer #1 (Public review):

In this manuscript, the authors report that GPR55 activation in presynaptic terminals of Purkinje cells decrease GABA release at the PC-DCN synapse. The authors use an impressive array of techniques (including highly challenging presynaptic recordings) to show that GPR55 activation reduces the readily releasable pool of vesicle without affecting presynaptic AP waveform and presynaptic Ca2+ influx. This is an interesting study, which is seemingly well-executed and proposes a novel mechanism for the control of neurotransmitter release. However, the authors' main conclusions are heavily, if not solely, based on pharmacological agents that most often than not demonstrate affinity at multiple targets. Below are points that the authors should consider in a revised version.

Major points:

(1) There is no clear evidence that GPR55 is specifically expressed in presynaptic terminals at the PC-DCN synapse. The authors cited Ryberg 2007 and Wu 2013 in the introduction, mentioning that GPR55 is potentially expressed in PCs. Ryberg (2007) offers no such evidence, and the expression in PC suggested by Wu (2013) does not necessarily correlate with presynaptic expression. The authors should perform additional experiments to demonstrate presynaptic expression of GPR55 at PC-DCN synapse.

(2) The authors' conclusions rest heavily on pharmacological experiments, with compounds that are sometimes not selective for single targets. Genetic deletion of GPR55 would be a more appropriate control. The authors should also expand their experiments with occlusion experiments, showing if the effects of LPI are absent after AM251 or O-1602 treatment. In addition, the authors may want to consider AM281 as a CB1R antagonist without reported effects at GPR55.

(3) It is not clear how long the different drugs were applied, and at what time the recording were performed during or following drug application. It appears that GPR55 agonists can have transient effects (Sylantyev, 2013; Rosenberg, 2023), possibly due to receptor internalization. The timeline of drug application should be reported, where IPSC amplitude is shown as a function of time and drug application windows are illustrated.

(4) A previous investigation on the role of GPR55 in the control of neurotransmitter release is not cited nor discussed Sylantyev et al., (2013, PNAS, Cannabinoid- and lysophosphatidylinositol-sensitive receptor GPR55 boosts neurotransmitter release at central synapses). Similarities and differences should be discussed.

Minor point:

(1) What is the source of LPI? What isoform was used? The multiple isoforms of LPI have different affinities for GPR55.

Comments on revisions:

In this revised version, the authors have addressed my major concerns. Notably, they used CRISPR/Cas9 genetic knockdown of GPR55 to independently validate their original findings. The main conclusions are now well supported and represent an important contribution to the field.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

In this manuscript, the authors report that GPR55 activation in presynaptic terminals of Purkinje cells decrease GABA release at the PC-DCN synapse. The authors use an impressive array of techniques (including highly challenging presynaptic recordings) to show that GPR55 activation reduces the readily releasable pool of vesicle without affecting presynaptic AP waveform and presynaptic Ca²⁺ influx. This is an interesting study, which is seemingly well-executed and proposes a novel mechanism for the control of neurotransmitter release. However, the authors' main conclusions are heavily, if not solely, based on pharmacological agents that most often than not demonstrate affinity at multiple targets. Below are points that the authors should consider in a revised version.

We are happy to hear the encouraging comments from this reviewer, and thank for pointing out the important issues including the previous study design depending only on pharmacological agents. To address these, we have performed additional experiments, as detailed below.

Major points:

(1) There is no clear evidence that GPR55 is specifically expressed in presynaptic terminals at the PC-DCN synapse. The authors cited Ryberg 2007 and Wu 2013 in the introduction, mentioning that GPR55 is potentially expressed in PCs. Ryberg (2007) offers no such evidence, and the expression in PC suggested by Wu (2013) does not necessarily correlate with presynaptic expression. The authors should perform additional experiments to demonstrate the presynaptic expression of GPR55 at PC-DCN synapse.

We completely agree with the reviewer in that our previous manuscript lacked the reliable information regarding presynaptic expression of GPR55 at PC boutons.

To clarify the localization, we first tried immunostaining of GPR55 using commercially available antibodies, but unfortunately they did not provide clear labeling of neurons and also even in GPR55-transfected HEK cells (used as positive control). Thus, we gave up the direct immunostaining. Alternatively, we attempted to label PC axonal boutons by GPR55-targeting dye together with a complementary strategy based on gene knock-down. Specifically, we used T1117, a fluorescent derivative of AM251 which is a GPR55 ligand used in the manuscript, and clear fluorescent signals were evident at GFP-labeled PC terminals. Still, by itself it was not clear whether the labeling was mediated by association with GPR55. Therefore, we also attempted to specifically suppress gene expression of GPR55 using CRISPR/Cas9-mediated genome editing in PCs, based on acute DNA micro-injection of plasmids into nuclei of PCs to express gRNAs targeting GPR55 together with Cas9. As a result, 5 days after the knock-down, T1117 labeling at axon terminals was reduced by ~50% compared to Cas9-alone controls. All these data are now shown in new Figure 2, and explained in the text p5-6, lines 141-159. Further, the reduction of GPR55 expression abolished the AM251-mediated reduction of vesicular exocytosis, as shown in new Figure 3D, E.

Taken together, these results essentially convince our main conclusions by strongly suggesting that GPR55 is present at PC axon terminals, where it negatively regulates the exocytosis upon activation by AM251.  

(2) The authors' conclusions rest heavily on pharmacological experiments, with compounds that are sometimes not selective for single targets. Genetic deletion of GPR55 would be a more appropriate control. The authors should also expand their experiments with occlusion experiments, showing if the effects of LPI are absent after AM251 or O-1602 treatment. In addition, the authors may want to consider AM281 as a CB1R antagonist without reported effects at GPR55.

We thank the reviewer for pointing out these important issues. First, as noted above to confirm the presence of GPR55 at axon terminals of PCs, we performed genetic deletion of GPR55 using CRISPR/Cas9 system. In PCs co-expressing Cas9 and two gRNAs targeting the ligand-binding domain of GPR55, AM251 failed to suppress the exocytosis at PC boutons, together with decreased T1117 labeling. Therefore, the idea that GPR55 negatively regulates transmitter release at PC boutons has now been strengthened. The new data is shown in Figure 3D and E, and explained in the text p6, lines 173-178.  

As suggested, we also carried out the occlusion experiments with LPI and AM251. First, LPI similarly reduced the readily releasable pool (RRP) size as AM251 did. Then, applied together, LPI and AM251 did not further reduce the RRP size compared with the effect by either compound alone. Thus, LPI and AM251 seem to act through the same pathway, consistent with the idea for role of GPR55 activation. The data is shown in new Figure 5—figure supplement 1 and explained in the text, p7-8, lines 215-221.

Regarding another point suggested by the reviewer, we applied AM281 and observed no effect on transmission at the PC–target neuron synapses (shown in new Figure 1F and I; explained in the text p5, lines 117-123), indicating that the effect of AM251 is likely to be mediated by GPR55, but not by CB1R.

Taken together, our additional experiments based on genetic and pharmacological experiments have consolidated our conclusion that GPR55 suppresses the presynaptic neurotransmitter release in PC boutons.

(3) It is not clear how long the different drugs were applied, and at what time the recordings were performed during or following drug application. It appears that GPR55 agonists can have transient effects (Sylantyev, 2013; Rosenberg, 2023), possibly due to receptor internalization. The timeline of drug application should be reported, where IPSC amplitude is shown as a function of time and drug application windows are illustrated.

Thank you for suggesting the better presentation of data. Accordingly, we have re-organized figures showing time course of changes in IPSCs before and after the drug application (new Figure 1 and 4; p4, lines 94-97; p5, lines 110-115; p7, lines 193-197). The current data presentation clearly shows that the effect of AM251 becomes evident in a few minutes after application, and somehow reaches a saturated level.

(4) A previous investigation on the role of GPR55 in the control of neurotransmitter release is not cited nor discussed (Sylantyev et al., (2013, PNAS, Cannabinoid- and lysophosphatidylinositolsensitive receptor GPR55 boosts neurotransmitter release at central synapses). Similarities and differences should be discussed.

We are really sorry for failing to adequately discuss this important work in our previous manuscript, and deeply appreciate the reviewer for pointing this out. We have now cited and discussed the work by Sylantyev et al. (2013), in the text (p12, lines 380-389), as following:

‘Pioneering studies clarified an important role of GPR55 in synaptic transmission at hippocampal excitatory synapses, demonstrating presynaptic enhancement of glutamate release presumably by elevating the cytoplasmic residual Ca²⁺ via release from intracellular stores (Sylantyev et al., 2013; Rosenberg et al., 2023), in contrast to the suppression of release in our observation. The lack of positive modulation of AP-triggered release through residual Ca²⁺ in PC terminals might be due to abundant amount of potent Ca²⁺ buffer calbindin (Fierro and Llano, 1996). Indeed, increased vesicular fusion only for the AP-insensitive spontaneous vesicular release (as mIPSCs) was observed upon the IP₃-mediated Ca²⁺ release from internal store (Gomez et al., 2020). Thus, minimal sensitivity of AP-triggered release to residual Ca²⁺ in PC boutons would underlie the distinct effects of GPR55 activation at the presynaptic side.’  

Minor point:

(1) What is the source of LPI? What isoform was used? The multiple isoforms of LPI have different affinities for GPR55.

Thank you for letting us know about the lack of important information in the previous manuscript. In our experiments, we used a soybean-derived LPI mixture containing approximately 58% C16:0 and 42% C18:0 or C18:2 species. According to Brenneman et al. (2025), these isoforms show moderate or strong effects in cultured DRG neurons, whereas the C20:4 isoform, reported to promote neuroinflammatory signaling, was contained only at very low levels. We have added this information to the revised manuscript and briefly discussed the influence of different LPI isoforms on the physiological outcomes of GPR55 activation (p5, lines 127-131; p15, lines 493-496).

Reviewer #2 (Public review):

Summary:

This paper investigates the mode of action of GPR55, a relatively understudied type of cannabinoid receptor, in presynaptic terminals of Purkinje cells. The authors use demanding techniques of patch clamp recording of the terminals, sometimes coupled with another recording of the postsynaptic cell. They find a lower release probability of synaptic vesicles after activation of GPR55 receptors, while presynaptic voltage-dependent calcium currents are unaffected. They propose that the size of a specific pool of synaptic vesicles supplying release sites is decreased upon activation of GPR55 receptors.

Strengths:

The paper uses cutting-edge techniques to shed light on a little-studied, potentially important type of cannabinoid receptor. The results are clearly presented, and the conclusions are for the most part sound.

We feel very happy to see the positive comments from the reviewer.  

Weaknesses:

The nature of the vesicular pool that is modified following activation of GPR55 is not definitively characterized.

We agree with the reviewer in that our data cannot fully address the changes of vesicle pools caused by GPR55. As detailed in responses to comments in ‘Recommendations for the authors’ from the reviewer, we have added explanation and discussion in the main text of the revised manuscript.

Reviewer #3 (Public review):

Summary:

Inoshita and Kawaguchi investigated the effects of GPR55 activation on synaptic transmission in vitro. To address this question, they performed direct patch-clamp recordings from axon terminals of cerebellar Purkinje cells and fluorescent imaging of vesicular exocytosis utilizing synaptopHluorin. They found that exogenous activation of GPR55 suppresses GABA release at Purkinje cell to deep cerebellar nuclei (PC-DCN) synapses by reducing the readily releasable pool (RRP) of vesicles. This mechanism may also operate at other synapses.

Strengths:

The main strength of this study lies in combining patch-clamp recordings from axon terminals with imaging of presynaptic vesicular exocytosis to reveal a novel mechanism by which activation of GPR55 suppresses inhibitory synaptic strength. The results strongly suggest that GPR55 activation reduces the RRP size without altering presynaptic calcium influx.

We thank the reviewer for giving the encouraging comments on our study.

Weaknesses:

The study relies on the exogenous application of GPR55 agonists. It remains unclear whether endogenous ligands released due to physiological or pathological activities would have similar effects. There is no information regarding the time course of the agonist-induced suppression. There is also little evidence that GPR55 is expressed in Purkinje cells. This study would benefit from using GPR55 knockout (KO) mice. The downstream mechanism by which GPR55 mediates the suppression of GABA release remains unknown.

We thank the reviewer for pointing out all of these important issues to be ideally addressed. As detailed in the responses to comments in the ‘Recommendations for the authors’ from the reviewers, we have addressed most of these weak points, and also added careful discussion in the text about the open questions to be solved in the future study.

Recommendations for the authors:

Reviewer #2 (Recommendations for the authors):

This is a high-quality paper that reports novel and interesting results. The authors should consider one main critique, related to Figure 6, as well as a number of minor points.

We thank the reviewer for making very positive assessment of our study. We have carefully considered the main critique regarding presynaptic vesicle pools (related to previous Figure 6), as well as other points, and accordingly revised manuscript.

Main critique:

In Figure 6, it is said that GPR55 locks SVs in a state that is insensitive to VGCCs, based on a series of experiments with synapto-pHluorin. This conclusion is open to several critiques:

The authors' model is shown in the diagram of Figure 6A. In this scheme, it appears as if recycled SVs eventually re-acidify in spite of the presence of bafilomycin, and that they are directed to a location close to the plasma membrane, but away from VGCCs. In fact, there is no evidence that the effects of bafilomycin could be limited in time. And there is a lot of evidence indicating that recycled SVs move back to release sites, close to VGCCs.

We are so sorry for presenting misleading figure panel in the previous Figure 6A. As the reviewer says, the effect of bafilomycin should be expected to last for long, and then the endocytosed vesicles cannot be re-acidified. Now, in new Figure 8A, we have changed the panel for explanation about the experimental situation of vesicles in the presence of bafilomycin. Another insightful point, kindly suggested by the reviewer, regarding the quick recruitment of newly endocytosed vesicles to release sites, is highly related to the interpretation of our data, but is a different issue from the situation explained in new Figure 8A. To avoid confusion, the arrow drawn in the previous version indicating the endocytosed vesicle movement back to the docked situation has been omitted in the new panel, and this critical issue is now carefully discussed in terms of the mechanism of GPR55 action on the release machinery (p15, lines 480-482).

The saturation of the train-induced signals is interpreted as reflecting an exhaustion of SVs initially close to VGCCs or more generally, susceptible to being released following VGCC activation.

In an alternative scenario, saturation occurs because AP trains, or KCl applications, become unable to activate VGCCs. This could occur either because long illumination causes photodamage of VGCCs, or because repeated activation of VGCCs leads to their inactivation. The latter explanation is possible in spite of a publication from the authors' laboratory describing the facilitation of presynaptic VGCCs following paired stimulations in this synapse (Diaz-Rojas et al., 2015).

We agree that it is an important control experiment to demonstrate that Ca²⁺ increase upon repetitive AP trains is intact even during or after the long photo-illumination for imaging. To test this possibility, we have performed additional fluorescent Ca²⁺ imaging at PC varicosities during individual 400-AP trains and also in response to 50 mM KCl following the series of AP trains. Now new data demonstrated that Ca²⁺ influx remains constant across all AP trains (shown in Figure 8— figure supplement 1), arguing against VGCC inactivation or photodamage as a major factor underlying the saturated signal increase in the synapto-pHluorin. We have added explanation regarding this issue in the text p11, lines 327-329.

The authors explain the larger effect of ionomycin compared with AP trains and KCl applications as reflecting a better capacity to increase the bulk calcium concentration. The above proposal for the inactivation of VGCCs offers an alternative explanation, in my view more likely.

As noted above, our newly added Ca²⁺ imaging data clearly showed that individual AP trains induced similar Ca²⁺ influxes during repetitive trials, in line with our original interpretation. In addition, the Ca²⁺ increase by KCl was shown to be more potent and broader in axon terminals and trunks. Nevertheless, the exocytic signal caused by ionomycin was clearly large, implying a critical effect of the source of Ca²⁺ influx in PC boutons. Therefore, we suppose that the marked effect of ionomycin on release reflects higher elevation of bulk Ca²⁺ in the cytoplasm arising from non-site selective Ca²⁺-ionophore (Figure 8—figure supplement 1, p11, lines 327-334; lines 342-349).

In yet another scenario, recycled SVs in bafilomycin retain their fluorescence since they do not reacidify, but they come back to release sites to undergo new rounds of exocytosis. The new exocytosis events do not increase the fluorescence since the pH in the vicinity of synapto-pHluorin does not change. NH4Cl would then increase the fluorescence by revealing SVs that had not undergone exocytosis-endocytosis cycles during AP trains or KCl exposure. In this last scenario, the GPR55-sensitive SV pool would be a specific sub-pool of SVs that can be recycled by repetitive 400 AP trains.

We deeply appreciate the reviewer for pointing out this important possibility. We completely agree that this scenario can also explain the pool which is sensitive to GPR55. Therefore, we have added explanation of this possibility in the text (p15, lines 474–482).

Figure 6F shows calcium imaging measurements of PC varicosities. Unfortunately, crucial measurements are missing. It would have been revealing to compare calcium rises for the first and the last of the 8 400-AP trains. And to compare calcium rises elicited by 60 mM KCl before and after the series of 8 400-AP trains.

This is an important control experiment. Therefore, we have performed additional Ca²⁺ imaging during the eight 400-AP trains and KCl application. The new results shown in the present Figure 8—figure supplement 1 clearly suggest that Ca²⁺ rises are comparable between the first and eighth trains, and that additional Ca²⁺ influx (which was large in amplitude and wide in area) could still be evoked by KCl after the eight trains. The experiments are explained in the text p11, lines 327336.

Minor points:

(1) Introduction: The Introduction would benefit from a more substantial description of what is known about GPR55 and downstream signaling pathways. Right now, it is stated that GPR55 is 'potentially expressed in PCs': What are the arguments behind this statement? Also, the signaling pathway is discussed on p.12, much too late in the ms. Why not move this section to the Introduction?

We thank the reviewer for the helpful suggestion. As recommended, in the revised manuscript, we have changed the Introduction by moving the sentences from other sections, including speculation about the expression of GPR55 in Purkinje cells (Ryberg et al., 2007; Wu et al., 2013) (p3-4, lines 71-75) and downstream signaling pathways (Gα_q/PLC/IP₃/Ca²⁺ and Gα₁₃/RhoA/ROCK) (p3, 63-68).  

(2) Legend to Figures 1, 2, and 4: What is the EGTA concentration in these experiments?

As suggested, the EGTA concentrations (0.5 or 5 mM) used in the individual experiments have now been clearly indicated both in the figure legends and in the Methods section (p18, lines 585586).

(3) Fig. 3C: These experiments show that some SV pool is depleted by AM251. The authors state that this is the RRP, but other options are possible. In the calyx of Held, similar experiments are supposed to deplete not only the FRP (=RRP, presumably) but also the SRP.

We thank the reviewer for pointing out the important aspect related to category for vesicle pools. In PC boutons, the membrane capacitance increases in response to different duration of depolarization pulses in a manner fitted by a single exponential curve (see Figure 5C for example). Our previous study (Kawaguchi and Sakaba, 2015) noted that the vesicle pools corresponding to FRP and SRP may not be easy to distinguish in PCs, suggesting apparently single component. That’s the reason why we simply describe the component as RRP in the present manuscript. Still, as suggested, careful discussion about typical fast- and slow components would be helpful to interpret our present findings. Therefore in the revised manuscript, we have added a sentence to explain this issue (p7, lines 211-214).

(4) p. 8: When the 400 APs protocol is introduced, the corresponding frequency (20 Hz?) should be mentioned. This information comes only much later in the ms.

We are sorry for our insufficient explanation in the previous manuscript. As suggested, we have clearly written the stimulation frequency ‘20 Hz’ in the main text where the 400 APs protocol first appears (p9, lines 277-278).

(5) Figure 5, panels B and F: synapto-pHluorin is labelled twice 'synapto-pHluolin'.

Sorry for careless typos. Now, those are corrected (new Figure 7).

(6) Legend to Figure 5, last line: 'x' is missing in the last equation.

Thank you for the careful and kind check. Now, ‘x’ has been added to the last equation in the legend for new Figure 7.

(7) p. 7, Interpretation of EGTA effects: The authors frame their interpretation of EGTA effects around the distance between release sites and VGCCs. However since AM251 appears to alter the recruitment of SVs, a more parsimonious interpretation would be that EGTA modifies the calciumdependent movement of SVs towards release sites.

Thank you for suggesting an insightful scenario. We agree that the capacitance jump upon long depolarization pulse would include exocytosis of substantial amount of vesicles which are newly recruited during the Ca²⁺ increase. Then, as the reviewer states, EGTA possibly lowers the Ca²⁺dependent replenishment of synaptic vesicles, and this replenishment system might be the target of GPR55 activation. Therefore, we have now clearly added an explanation about this possibility in the text (p15, lines 474-482).

(8) p. 13, Interpretation of GPR55 sensitive SV pool: The authors suggest a larger distance to VGCCs for this pool compared to naïve SVs. An alternative could be that in the presence of GPR55, the recruitment to release sites would be less efficient.

This is also an insightful suggestion to speculate the causal relationship between the GPR55mediated reduction of vesicular release and the vesicle pools. Accordingly, we have revised the Discussion (see “Dynamics of synaptic vesicles among distinct functional pools”) by clearly telling about the possibility of decreased recruitment of vesicles to release sites after the GPR55 activation (p15, lines 474-482). By totally considering all the suggested scenario, we believe that the possible mechanisms for GPR55-mediated reduction of release are much more clearly explained in the revised manuscript.

Reviewer #3 (Recommendations for the authors):

(1) The time course of the agonist-induced suppression should be reported (Figure 1).

This is an important point to show data clearly, as suggested also by the reviewer 1. Accordingly, we have changed the figure panels to show time courses of agonist-induced suppression (shown in new Figures 1 and 4).  

(2) Show that the suppression of GABAergic transmission mediated by AM251 and LPI is eliminated in GPR55 KO mice.

We appreciate the reviewer for putting us to try this important experiment. Owing to the suggestion, we attempted to knock-down the GPR55 expression using CRISPR/Cas9 in cultured Purkinje cells. To avoid potential developmental compensations, here we adopted the CRISPR/Cas9-based genome editing approach, rather than using global knock out mice. Those GPR55-KO cells, as noted above in response to the comment #2 of reviewer #1, showed decreased fluorescent labeling of PC axon terminals to fluorescent-variant of AM251 (shown in new Figure 2) and abolishment of AM251-mediated suppression of vesicle exocytosis (Figure 3D and E). These results are explained in the text p5-6, lines 141-159; p6, lines 173-178.  

(3) Include references supporting AM251 and LPI as GPR55 agonists and specify the E50 concentrations for each agonist. Furthermore, provide details about the GPR55 antagonist CID16600046.

As suggested, we have added references regarding GPR55 agonists, AM251 and LPI. In the text, the following information was added: AM251, originally characterized as an inverse agonist for CB1, has also been reported to act as a GPR55 agonist (Ryberg et al., 2007; Henstridge et al., 2009) (p5, lines 115-116). LPI is an established endogenous GPR55 agonist (Oka et al., 2007; Henstridge et al., 2009) (p5, lines 127-129). The reported EC₅₀ values are ~ 30 nM for LPI (Oka et al., 2007, HEK cell assay) and 39 nM for AM251 (Ryberg et al., 2007, HEK cell assay) (p4, lines 94-95; p5, lines 127-129). Regarding the GPR55 antagonist CID16020046, detailed information (IC₅₀ = 0.21 µM for GPR55 without significant effect on CB1 receptor) was added in the text with an appropriate citation (Kargl et al., 2013) (p5, lines 123-127). These points have also been added to the Methods section (p17, lines 587-589).

(4) Regarding the onset delay (Figure 4C; page 8, lines 3-4), consider the following: "AM251 induced a modest yet significant synaptic delay, estimated by the time to the onset of release" (or something similar).

We thank the reviewer for suggesting helpful explanation. Accordingly, we have changed the sentence to explain the delayed onset (p9, lines 264-265).

These three points should be properly acknowledged in the Discussion:

(1) Are action potentials (APs)/depolarizations and ionomycin applications comparable? Ionomycin mediates a large calcium rise significantly slower than the calcium rise mediated by fast depolarization. Such presynaptic calcium dynamics could account, in part, for the different results.

The qualitative difference of Ca²⁺ increase between APs/depolarization-mediated ones and ionomycin-mediated one is an important point. Thank you for pointing out this issue. In the revised manuscript, we have added an explanation about the possible difference arising from the distinct dynamics of Ca²⁺ increases caused by direct depolarization of axon terminals or by ionomycin (p14, lines 452-453).

(2) Previous studies on hippocampal CA3-CA1 pyramidal cell synapses indicate that GPR55 activation enhances glutamate release through presynaptic calcium modulation while diminishing inhibitory postsynaptic strength by reducing GABAA receptors (Sylantyev et al., PNAS 2013; Rosenberg et al., Neuron 2023). In contrast, Inoshita and Kawaguchi discovered that GPR35 suppresses PC-DCN inhibitory transmission by decreasing GABA release without affecting inhibitory postsynaptic strength. Some potential explanation for this discrepancy is warranted.

We appreciate the reviewer for pointing out this important issue, and feel sorry for not providing an appropriate discussion about the possible interpretation in the previous manuscript. In the revised manuscript, we have added explanations for this discrepancy. First, PC terminals show only limited influence by elevated cytoplasmic Ca²⁺ through ER store on GABA release (Gomez et al., 2020) probably due to abundant calbindin. Second, our present data clearly show the GPR55 signals at PC terminals (although indirect, see Figure 2), while hippocampal inhibitory neuronal boutons somehow showed lower GPR55 levels compared with excitatory neuronal boutons (Rosenberg et al., Neuron, 2023). Third, the subtypes and/or anchoring mechanism for postsynaptic GABA_A receptors might be different between two distinct postsynaptic neurons in the hippocampus and the cerebellum. These factors are now clearly discussed in the text (p12, lines 380-396).

(3) Earlier work has suggested that CB1 receptor activation can alter the release machinery. Therefore, the observation that GPR55 activation induces changes in the RRP is not entirely surprising.

As pointed out, previous studies showed that CB1R influences the synaptic release machinery, rather than Ca²⁺ influx (Ramirez-Franco et al., 2014). In that context, as the reviewer says, the GPR55-mediated RRP change can be regarded as a similar synaptic modulation mechanism as the CB1-mediated one. However, considering the different downstream signaling pathways, G_12/13- or G_q-mediated one and G_i/o-mediated one, our findings would provide an important scope about the regulation mechanisms of release machinery, which should be further analyzed in the future study. Now we have added these points in discussion (p13-14, lines 435-439).

(4) Add a section about the limitations of this study (see Weaknesses above).

As suggested, we have added a section about the limitations of this study at present, which we could not address in the revision and should be addressed in the future (p15, lines 488-508). Particularly, the actual endogenous agonist to activate GPR55, and the physiological situation in which the agonist is produced, much more direct evidence for GPR55 presence at PC boutons, and the downstream mechanisms of GPR55-mediated suppression of GABA release are now clearly notified in that section.

(5) Double-check grammar and typos ("anandamid").

We are really sorry for the poor writings in the previous manuscript. Now, we have carefully checked the text.

Empirismo.

• Frise-se que o recurso interposto não segue diretamente à autoridade superior.

• Primeiro, é destinado à autoridade prolatora da decisão, que, se não reconsiderar em 5 (cinco) dias, deverá encaminhar à autoridade superior

الجملة هاي معناها ببساطة إنك بتقدري تكتبي الأرقام الصحيحة (اللي بدون أعشار أو فواصل) جوا الكود بأكثر من "نظام عد"، مش بس بالنظام العادي اللي بنعرفه.

لغات البرمجة بشكل عام (سواء PHP أو بايثون وغيرها) بتفهم الأرقام بـ 4 طرق رئيسية عشان تناسب العمليات المختلفة في معالجة البيانات:

النظام العشري (Decimal - Base 10): هاد نظامنا الطبيعي اللي بنستخدمه بحياتنا اليومية. بيتكون من الأرقام (0 إلى 9).

مثال: $num = 15;

النظام الثنائي (Binary - Base 2): لغة الآلة الأساسية، وبتتكون بس من (0 و 1). عشان تفهم لغة البرمجة إنك بتكتبي بنظام ثنائي، لازم تبلشي الرقم بـ 0b.

مثال: $num = 0b1111; (هاد بيعادل رقم 15 بالعشري).

النظام السادس عشر (Hexadecimal - Base 16): بيستخدم الأرقام (0 إلى 9) والحروف (A إلى F). عشان تكتبي فيه لازم تبلشي الرقم بـ 0x. هاد النظام رح يمر عليكِ كثير بالبرمجة مثلاً لتمثيل أكواد الألوان أو في خوارزميات التشفير.

مثال: $num = 0xF; (هاد كمان بيعادل رقم 15).

النظام الثماني (Octal - Base 8): بيستخدم الأرقام (0 إلى 7) فقط. وبتبلشي الرقم بحرف 0o أو أحياناً بس صفر 0.

مثال: $num = 017; (وبرضه هاد بيعادل 15).

Nice work! This seems like a useful tool for making image analysis more accessible.

I was curious whether there was a specific reason for the limitation you mention here: "The pipeline does not currently support batch parallelization, processing images sequentially."

My first thought was that segmentation might be the main constraint (especially when using Cellpose). But it seems like one possible architecture would be to run segmentation first and save masks, then parallelize the downstream steps (puncta detection, colocalization, morphology measurements, etc.) across images or cells.

That part of the pipeline seems like it could be relatively straightforward for Claude to parallelize with something like multiprocessing or joblib and might significantly improve throughput for larger datasets, since downstream analysis would no longer be tied to per-image segmentation.

Was something like this considered, or are there constraints (e.g., Cellpose behavior, I/O, memory usage) that make parallelization less practical?

The dialogue flow here gives insight on the accent– seemingly country-like.

Parents were seen as a guiding force. even if someone else did look after the boy, he most likely would have pity and be seen as unfortunate.

the only

Despite strong individual interparticle forces, the macroscopic elastic properties of colloidal crystals are vastly weaker than those of conventional solids because of the much lower particle density and larger interparticle spacing.

Bit too much info for one sentence. Explain it across 2 or 3 sentences

? It would be helpful to know specifically which countries view the "OK" sign as an obscenity

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Zeng et al. have investigated the impact of inhibiting lactate dehydrogenase (LDH) on glycolysis and the tricarboxylic acid cycle. LDH is the terminal enzyme of aerobic glycolysis or fermentation that converts pyruvate and NADH to lactate and NAD+ and is essential for the fermentation pathway as it recycles NAD+ needed by upstream glyceraldehyde-3-phosphate dehydrogenase. As the authors point out in the introduction, multiple published reports have shown that inhibition of LDH in cancer cells typically leads to a switch from fermentative ATP production to respiratory ATP production (i.e., glucose uptake and lactate secretion are decreased, and oxygen consumption is increased). The presumed logic of this metabolic rearrangement is that when glycolytic ATP production is inhibited due to LDH inhibition, the cell switches to producing more ATP using respiration. This observation is similar to the well-established Crabtree and Pasteur effects, where cells switch between fermentation and respiration due to the availability of glucose and oxygen. Unexpectedly, the authors observed that inhibition of LDH led to inhibition of respiration and not activation as previously observed. The authors perform rigorous measurements of glycolysis and TCA cycle activity, demonstrating that under their experimental conditions, respiration is indeed inhibited. Given the large body of work reporting the opposite result, it is difficult to reconcile the reasons for the discrepancy. In this reviewer's opinion, a reason for the discrepancy may be that the authors performed their measurements 6 hours after inhibiting LDH. Six hours is a very long time for assessing the direct impact of a perturbation on metabolic pathway activity, which is regulated on a timescale of seconds to minutes. The observed effects are likely the result of a combination of many downstream responses that happen within 6 hours of inhibiting LDH that causes a large decrease in ATP production, inhibition of cell proliferation, and likely a range of stress responses, including gene expression changes.

Strengths:

The regulation of metabolic pathways is incompletely understood, and more research is needed, such as the one conducted here. The authors performed an impressive set of measurements of metabolite levels in response to inhibition of LDH using a combination of rigorous approaches.

Weaknesses:

Glycolysis, TCA cycle, and respiration are regulated on a timescale of seconds to minutes. The main weakness of this study is the long drug treatment time of 6 hours, which was chosen for all the experiments. In this reviewer's opinion, if the goal was to investigate the direct impact of LDH inhibition on glycolysis and the TCA cycle, most of the experiments should have been performed immediately after or within minutes of LDH inhibition. After 6 hours of inhibiting LDH and ATP production, cells undergo a whole range of responses, and most of the observed effects are likely indirect due to the many downstream effects of LDH and ATP production inhibition, such as decreased cell proliferation, decreased energy demand, activation of stress response pathways, etc.

We thank reviewer for the careful reading of our manuscript, the accurate summary of the prevailing model, and the positive assessment of the rigor of our measurements. We agree that much prior literature reports increased oxygen consumption following LDH inhibition, and we recognize that our finding—coordinated suppression of glycolysis, the TCA cycle, and OXPHOS—differs from this prevailing interpretation. We address below the reviewer’s main concern regarding the 6-hour time point and clarify the conceptual scope of our study.

(1) Scope: steady-state metabolic regulation versus immediate transient effects

The reviewer raises an important point that many metabolic perturbations can trigger rapid, transient responses within seconds to minutes, whereas our measurements were performed after sustained LDH inhibition. We agree that very early time points would be required if the primary goal were to isolate the most immediate, proximal consequence of LDH inhibition before downstream propagation. However, the objective of our study is different: we aim to characterize the metabolic steady state re-established after sustained inhibition of LDH activity, because this adapted steady state is more relevant for understanding long-term metabolic consequences and therapeutic outcomes of LDH inhibition in cancer cells.

(2) Genetic LDHA/LDHB knockout: comparison of two steady states

A related point applies to the LDHA/LDHB knockout models. We fully agree that the knockout process necessarily involves a temporal perturbation during cell line generation and adaptation. Nevertheless, the experimental comparison in our study is explicitly between two steady states: the baseline steady state of control cells and the steady state achieved after stable genetic disruption of LDHA or LDHB. The observation that LDHA or LDHB knockout alone had minimal effects on glycolysis and respiration indicates that partial reduction of LDH activity can be compensated in a steady-state manner, consistent with the exceptionally high catalytic capacity of LDH in cancer cells relative to upstream rate-limiting enzymes.

(3) LDH-activity-dependent quantitative relationships support stable metabolic states

Importantly, our conclusions do not rely on a single inhibitor condition at a single time point. Rather, we established quantitative steady-state relationships between residual LDH activity and pathway behavior across a wide range of LDH inhibition. These LDH-activity-dependent data strongly support that the system resides in stable metabolic states at different degrees of LDH activity, rather than reflecting non-specific collapse due to prolonged stress.

Specifically, we observed that when LDH activity was reduced from 100% to approximately ~9% (e.g., by genetic perturbation and partial pharmacologic inhibition), glucose consumption and lactate production remained essentially unchanged, indicating maintenance of a steady-state glycolytic flux despite substantial LDH inhibition. Only when LDH activity was further reduced below this threshold did glycolytic flux decrease in a graded manner, consistent with a nonlinear control structure (Figure 8 A & B)).

Likewise, the isotope tracing results showed distinct LDH-activity-dependent transitions in TCA cycle labeling patterns. Over the range in which LDH activity decreased from 100% to ~9%, the [¹³C₆]glucose-derived labeling pattern of citrate remained largely unchanged, whereas deeper inhibition led to a decrease in m2 citrate with a compensatory rise in higher-order citrate isotopologues, consistent with altered flux entry versus cycling/retention in the TCA cycle (Figure 8C). Similarly, [¹³C₅]glutamine tracing revealed that deeper LDH inhibition reduced the direct m5 contribution, accompanied by corresponding shifts in other isotopologues (Figure 8D). These graded, quantitative transitions—rather than an abrupt global failure—support the interpretation of distinct metabolic steady states across LDH activity levels, linking LDH inhibition to changes in both glycolysis and mitochondrial metabolism.

(4) Reconciling discrepancies with prior studies

We agree that multiple prior studies have reported increased oxygen consumption or enhanced oxidative metabolism following LDH inhibition in cancer cells. However, we note that this prevailing notion often persists because LDH inhibition is frequently discussed by analogy to the classical Pasteur and Crabtree effects, in which cells toggle between fermentation and respiration depending on oxygen and glucose availability. We believe this analogy can be misleading.

In the Pasteur effect, the metabolic shift is primarily driven by oxygen limitation, i.e., restriction of the terminal electron acceptor for the mitochondrial electron transport chain, which enforces reliance on fermentation. In the Crabtree effect, high glucose availability suppresses respiration through regulatory mechanisms while glycolysis is strongly activated. Both phenomena are fundamentally controlled by oxygen availability and respiratory capacity, rather than by inhibition of a specific cytosolic enzyme.

By contrast, LDH inhibition is mechanistically distinct: it directly perturbs cytosolic redox recycling by limiting NADH-to-NAD⁺ regeneration and can therefore constrain upstream glycolytic flux (particularly at GAPDH) and reshape pathway thermodynamics. Under conditions where LDH inhibition sufficiently limits effective NAD⁺ availability and reduces glycolytic flux into pyruvate, the downstream consequence is reduced carbon input into the TCA cycle and suppressed OXPHOS—consistent with our experimental measurements. We therefore suggest that divergent outcomes reported across studies likely reflect differences in residual LDH activity, cell-type–specific metabolic wiring, and the extent to which glycolytic flux remains sustained versus becoming redox-limited upstream, rather than a universal Pasteur/Crabtree-like “switch” from fermentation to respiration. Accordingly, interpreting LDH inhibition as a Pasteur/Crabtree-like toggle may oversimplify the biochemical consequences of disrupting cytosolic NAD⁺ regeneration.

We have revised the Discussion to clarify this conceptual distinction and to avoid relying on comparisons that are not mechanistically equivalent to LDH inhibition.

Reviewer #2 (Public Review):

Summary:

Zeng et al. investigated the role of LDH in determining the metabolic fate of pyruvate in HeLa and 4T1 cells. To do this, three broad perturbations were applied: knockout of two LDH isoforms (LDH-A and LDH-B), titration with a non-competitive LDH inhibitor (GNE-140), and exposure to either normoxic (21% O2) or hypoxic (1% O2) conditions. They show that knockout of either LDH isoform alone, though reducing both protein level and enzyme activity, has virtually no effect on either the incorporation of a stable 13C-label from a 13C6-glucose into any glycolytic or TCA cycle intermediate, nor on the measured intracellular concentrations of any glycolytic intermediate (Figure 2). The only apparent exception to this was the NADH/NAD+ ratio, measured as the ratio of F420/F480 emitted from a fluorescent tag (SoNar).

The addition of a chemical inhibitor, on the other hand, did lead to changes in glycolytic flux, the concentrations of glycolytic intermediates, and in the NADH/NAD+ ratio (Figure 3). Notably, this was most evident in the LDH-B-knockout, in agreement with the increased sensitivity of LDH-A to GNE-140 (Figure 2). In the LDH-B-knockout, increasing concentrations of GNE-140 increased the NADH/NAD+ ratio, reduced glucose uptake, and lactate production, and led to an accumulation of glycolytic intermediates immediately upstream of GAPDH (GA3P, DHAP, and FBP) and a decrease in the product of GAPDH (3PG). They continue to show that this effect is even stronger in cells exposed to hypoxic conditions (Figure 4). They propose that a shift to thermodynamic unfavourability, initiated by an increased NADH/NAD+ ratio inhibiting GAPDH explains the cascade, calculating ΔG values that become progressively more endergonic at increasing inhibitor concentrations.

Then - in two separate experiments - the authors track the incorporation of 13C into the intermediates of the TCA cycle from a 13C6-glucose and a 13C5-glutamine. They use the proportion of labelled intermediates as a proxy for how much pyruvate enters the TCA cycle (Figure 5). They conclude that the inhibition of LDH decreases fermentation, but also the TCA cycle and OXPHOS flux - and hence the flux of pyruvate to all of those pathways. Finally, they characterise the production of ATP from respiratory or fermentative routes, the concentration of a number of cofactors (ATP, ADP, AMP, NAD(P)H, NAD(P)+, and GSH/GSSG), the cell count, and cell viability under four conditions: with and without the highest inhibitor concentration, and at norm- and hypoxia. From this, they conclude that the inhibition of LDH inhibits the glycolysis, the TCA cycle, and OXPHOS simultaneously (Figure 7).

Strengths:

The authors present an impressively detailed set of measurements under a variety of conditions. It is clear that a huge effort was made to characterise the steady-state properties (metabolite concentrations, fluxes) as well as the partitioning of pyruvate between fermentation as opposed to the TCA cycle and OXPHOS.

A couple of intermediary conclusions are well supported, with the hypothesis underlying the next measurement clearly following. For instance, the authors refer to literature reports that LDH activity is highly redundant in cancer cells (lines 108 - 144). They prove this point convincingly in Figure 1, showing that both the A- and B-isoforms of LDH can be knocked out without any noticeable changes in specific glucose consumption or lactate production flux, or, for that matter, in the rate at which any of the pathway intermediates are produced. Pyruvate incorporation into the TCA cycle and the oxygen consumption rate are also shown to be unaffected.

They checked the specificity of the inhibitor and found good agreement between the inhibitory capacity of GNE-140 on the two isoforms of LDH and the glycolytic flux (lines 229 - 243). The authors also provide a logical interpretation of the first couple of consequences following LDH inhibition: an increased NADH/NAD+ ratio leading to the inhibition of GAPDH, causing upstream accumulations and downstream metabolite decreases (lines 348 - 355).

Weaknesses:

Despite the inarguable comprehensiveness of the data set, a number of conceptual shortcomings afflict the manuscript. First and foremost, reasoning is often not pursued to a logical conclusion. For instance, the accumulation of intermediates upstream of GAPDH is proffered as an explanation for the decreased flux through glycolysis. However, in Figure 3C it is clear that there is no accumulation of the intermediates upstream of PFK. It is unclear, therefore, how this traffic jam is propagated back to a decrease in glucose uptake. A possible explanation might lie with hexokinase and the decrease in ATP (and constant ADP) demonstrated in Figure 6B, but this link is not made.

We appreciate the reviewer's critical comment. In Figure 3C, there is no accumulation of F6P or G6P, which are upstream of PFK1. This is because the PFK1-catalyzed reaction sets a significant thermodynamic barrier. Even with treatment using 30 μM GNE-140, the ∆G_PFK1 (Gibbs free energy of the PFK1-catalyzed reaction) remains -9.455 kJ/mol (Figure 3D), indicating that the reaction is still far from thermodynamic equilibrium, thereby preventing the accumulation of F6P and G6P.

We agree with the reviewer that hexokinase inhibition may play a role, this requires further investigation.

The obvious link between the NADH/NAD+ ratio and pyruvate dehydrogenase (PDH) is also never addressed, a mechanism that might explain how the pyruvate incorporation into the TCA cycle is impaired by the inhibition of LDH (the observation with which they start their discussion, lines 511 - 514).

We agree with the reviewer’s comment. In this study, we did not explore how the inhibition of LDH affects pyruvate incorporation into the TCA cycle. As this mechanism was not investigated, we have titled the study:

"Elucidating the Kinetic and Thermodynamic Insights into the Regulation of Glycolysis by Lactate Dehydrogenase and Its Impact on the Tricarboxylic Acid Cycle and Oxidative Phosphorylation in Cancer Cells."

It was furthermore puzzling how the ΔG, calculated with intracellular metabolite concentrations (Figures 3 and 4) could be endergonic (positive) for PGAM at all conditions (also normoxic and without inhibitor). This would mean that under the conditions assayed, glycolysis would never flow completely forward. How any lactate or pyruvate is produced from glucose, is then unexplained.

This issue also concerned me during the study. However, given the high reproducibility of the data, we consider it is true, but requires explanation. The PGAM-catalyzed reaction is tightly linked to both upstream and downstream reactions in the glycolytic pathway. In glycolysis, three key reactions catalyzed by HK2, PFK1, and PK are highly exergonic, providing the driving force for the conversion of glucose to pyruvate. The other reactions, including the one catalyzed by PGAM, operate near thermodynamic equilibrium and primarily serve to equilibrate glycolytic intermediates rather than control the overall direction of glycolysis, as previously described by us (J Biol Chem. 2024 Aug8;300(9):107648).

The endergonic nature of the PGAM-catalyzed reaction does not prevent it from proceeding in the forward direction. Instead, the directionality of the pathway is dictated by the exergonic reaction of PFK1 upstream, which pushes the flux forward, and by PK downstream, which pulls the flux through the pathway. The combined effects of PFK1 and PK may account for the observed endergonic state of the PGAM reaction.

However, if the PGAM-catalyzed reaction were isolated from the glycolytic pathway, it would tend toward equilibrium and never surpass it, as there would be no driving force to move the reaction forward.

Finally, the interpretation of the label incorporation data is rather unconvincing. The authors observe an increasing labelled fraction of TCA cycle intermediates as a function of increasing inhibitor concentration. Strangely, they conclude that less labelled pyruvate enters the TCA cycle while simultaneously less labelled intermediates exit the TCA cycle pool, leading to increased labelling of this pool. The reasoning that they present for this (decreased m2 fraction as a function of DHE-140 concentration) is by no means a consistent or striking feature of their titration data and comes across as rather unconvincing. Yet they treat this anomaly as resolved in the discussion that follows.

GNE-140 treatment increased the labeling of TCA cycle intermediates by [¹³C₆]glucose but decreased the OXPHOS rate, we consider the conflicting results as an 'anomaly' that warrants further explanation. To address this, we analyzed the labeling pattern of TCA cycle intermediates using both [¹³C₆]glucose and [¹³C₅]glutamine. Tracing the incorporation of glucose- and glutamine-derived carbons into the TCA cycle suggests that LDH inhibition leads to a reduced flux of glucose-derived acetyl-CoA into the TCA cycle, coupled with a decreased flux of glutamine-derived α-KG, and a reduction in the efflux of intermediates from the cycle. These results align with theoretical predictions. Under any condition, the reactions that distribute TCA cycle intermediates to other pathways must be balanced by those that replenish them. In the GNE-140 treatment group, the entry of glutamine-derived carbon into the TCA cycle was reduced, implying that glucose-derived carbon (as acetyl-CoA) entering the TCA cycle must also be reduced, or vice versa.

This step-by-step investigation is detailed under the subheading "The Effect of LDHB KO and GNE-140 on the Contribution of Glucose Carbon to the TCA Cycle and OXPHOS" in the Results section in the manuscript.

In the Discussion, we emphasize that caution should be exercised when interpreting isotope tracing data. In this study, treatment of cells with GNE-140 led to an increase labeling percentage of TCA cycle intermediates by [¹³C₆]glucose (Figure 5A-E). However, this does not necessarily imply an increase in glucose carbon flux into TCA cycle; rather, it indicates a reduction in both the flux of glucose carbon into TCA cycle and the flux of intermediates leaving TCA cycle. When interpreting the data, multiple factors must be considered, including the carbon-13 labeling pattern of the intermediates (m1, m2, m3, ---) (Figure 5G-K), replenishment of intermediates by glutamine (Figure 5M-V), and mitochondrial oxygen consumption rate (Figure 5W). All these factors should be taken into account to derive a proper interpretation of the data.

Reviewer #3 (Public Review):

Hu et al in their manuscript attempt to interrogate the interplay between glycolysis, TCA activity, and OXPHOS using LDHA/B knockouts as well as LDH-specific inhibitors. Before I discuss the specifics, I have a few issues with the overall manuscript. First of all, based on numerous previous studies it is well established that glycolysis inhibition or forcing pyruvate into the TCA cycle (studies with PDKs inhibitors) leads to upregulation of TCA cycle activity, and OXPHOS, activation of glutaminolysis, etc (in this work authors claim that lowered glycolysis leads to lower levels of TCA activity/OXPHOS). The authors in the current work completely ignore recent studies that suggest that lactate itself is an important signaling metabolite that can modulate metabolism (actual mechanistic insights were recently presented by at least two groups (Thompson, Chouchani labs). In addition, extensive effort was dedicated to understanding the crosstalk between glycolysis/TCA cycle/OXPHOS using metabolic models (Titov, Rabinowitz labs). I have several comments on how experiments were performed. In the Methods section, it is stated that both HeLa and 4T1 cells were grown in RPMI-1640 medium with regular serum - but under these conditions, pyruvate is certainly present in the medium - this can easily complicate/invalidate some findings presented in this manuscript. In LDH enzymatic assays as described with cell homogenates controls were not explained or presented (a lot of enzymes in the homogenate can react with NADH!). One of the major issues I have is that glycolytic intermediates were measured in multiple enzyme-coupled assays. Although one might think it is a good approach to have quantitative numbers for each metabolite, the way it was done is that cell homogenates (potentially with still traces of activity of multiple glycolytic enzymes) were incubated with various combinations of the SAME enzymes and substrates they were supposed to measure as a part of the enzyme-based cycling reaction. I would prefer to see a comparison between numbers obtained in enzyme-based assays with GC-MS/LC-MS experiments (using calibration curves for respective metabolites, of course). Correct measurements of these metabolites are crucial especially when thermodynamic parameters for respective reactions are calculated. Concentrations of multiple graphs (Figure 1g etc.) are in "mM", I do not think that this is correct.

We thank the reviewer’s comment and the following are clarification of the conceptual framework, the quantitative methodology, and the experimental basis supporting our conclusions.

(1) “It is well established that glycolysis inhibition or forcing pyruvate into the TCA cycle… leads to upregulation of TCA/OXPHOS… (authors claim lowered glycolysis leads to lower TCA/OXPHOS)”

This framing is not accurate in the context of our study. PDK inhibition and LDH inhibition are fundamentally different perturbations. PDK inhibition directly promotes mitochondrial pyruvate oxidation by enabling PDH flux, whereas LDH inhibition primarily perturbs cytosolic redox balance (free NADH/NAD⁺) and thereby constrains upstream glycolytic reactions, particularly the GAPDH step. Therefore, the metabolic outcomes of these interventions are not expected to be identical and should not be treated as interchangeable.

Importantly, we do not “ignore” prior studies proposing increased OXPHOS after LDH inhibition; we explicitly cite and summarize this prevailing interpretation in the Introduction. Our study was motivated precisely because this interpretation does not resolve key quantitative inconsistencies, including (i) the large mismatch between glycolytic flux and mitochondrial oxidative capacity, and (ii) the exceptionally high catalytic capacity of LDH relative to upstream rate-limiting glycolytic enzymes. These constraints raise a mechanistic question: how does LDH inhibition actually suppress glycolytic flux in intact cancer cells, and what are the consequences for TCA cycle and OXPHOS?

Our central contribution is the identification of a biochemical mechanism supported by integrated measurements of fluxes, metabolite concentrations, redox state, and reaction thermodynamics: LDH inhibition increases free NADH/NAD⁺, decreases free NAD⁺ availability, inhibits GAPDH, drives accumulation/depletion patterns in glycolytic intermediates, shifts Gibbs free energies of near-equilibrium reactions (PFK1–PGAM segment), suppresses pyruvate production, and consequently reduces carbon input into TCA cycle and OXPHOS. These analyses are not provided by most prior work and directly address the mechanistic gap.

(2) Lactate signaling (Thompson/Chouchani) and metabolic modeling (Titov/Rabinowitz)

These research directions are valuable, but they address questions that are different from the one investigated here. Our manuscript focuses on steady-state biochemical control of metabolic flux by LDH inhibition through redox-linked kinetics and pathway thermodynamics.

(3) Pyruvate in RPMI

Pyruvate in standard medium does not invalidate our conclusions. All experimental comparisons were performed under identical conditions across groups, and the major conclusions rely on orthogonal measurements including glycolytic flux (glucose consumption/lactate production), OCR profiling, and isotope tracing with [¹³C₆]glucose and [¹³C₅] glutamine, which directly quantify carbon entry into lactate and TCA cycle intermediates. These tracer-based results are not confounded by unlabeled extracellular pyruvate in a way that would reverse the mechanistic conclusions.

(4) LDH activity assay in homogenates and “many enzymes can react with NADH”

This concern is overstated. In the LDH assay, substrates are pyruvate + NADH, and the measured signal reflects NADH oxidation coupled to pyruvate reduction. In cell lysates, LDH is uniquely abundant and catalytically efficient for this reaction pair, and the inhibitor-response behavior matches the known LDHA/LDHB selectivity of GNE-140 and the cellular phenotypes. Thus, the assay is mechanistically specific in this context.

(5) Enzyme-coupled metabolite assays and request for LC–MS validation

The reviewer’s implication that enzyme-coupled assays are intrinsically unreliable is incorrect. Enzymatic cycling assays are a widely used quantitative approach when performed with proper specificity and calibration, and they are particularly useful for labile glycolytic intermediates that are challenging to quantify reproducibly by MS without specialized quenching, derivatization, and isotope dilution standards.

We agree that MS-based quantification is valuable, and we have developed LC–MS methods for selected metabolites. However, absolute quantification of these intermediates remains technically difficult due to the inherent limitation of this method and, in our hands, did not provide uniformly robust performance for all intermediates required for thermodynamic analysis.

(6) Units (“mM”)

The metabolite concentration units are correct.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

If the goal is to investigate the direct impact of LDH inhibition, then in my opinion, most of these experiments need to be repeated at a very early time point immediately after or a few minutes after LDH inhibition. I understand that this is a tremendous amount of work that the authors might not want to pursue. I do want to highlight that the quality of the experiments performed in this work is impressive. I hope the authors continue investigating this subject and look forward to reading their future manuscripts on this topic.

We thank the reviewer for this thoughtful and constructive comment and for the positive assessment of the experimental quality of our work.

We fully agree that measurements at very early time points after LDH inhibition would be required if the goal were to isolate an immediate, proximal molecular event occurring before downstream propagation. However, the primary objective of our study is not to dissect a single instantaneous biochemical consequence of LDH inhibition, but rather to characterize the metabolic steady state that is re-established after sustained suppression of LDH activity, which we believe is more relevant for understanding the long-term metabolic and therapeutic consequences of LDH inhibition in cancer cells.

(1) Scope: steady-state metabolic regulation versus immediate transient effects

The reviewer raises an important point that many metabolic perturbations can trigger rapid, transient responses within seconds to minutes, whereas our measurements were performed after sustained LDH inhibition. We agree that very early time points would be required if the primary goal were to isolate the most immediate, proximal consequence of LDH inhibition before downstream propagation. However, the objective of our study is different: we aim to characterize the metabolic steady state re-established after sustained inhibition of LDH activity, because this adapted steady state is more relevant for understanding long-term metabolic consequences and therapeutic outcomes of LDH inhibition in cancer cells.

(2) Genetic LDHA/LDHB knockout: comparison of two steady states

A related point applies to the LDHA/LDHB knockout models. We fully agree that the knockout process necessarily involves a temporal perturbation during cell line generation and adaptation. Nevertheless, the experimental comparison in our study is explicitly between two steady states: the baseline steady state of control cells and the steady state achieved after stable genetic disruption of LDHA or LDHB. The observation that LDHA or LDHB knockout alone had minimal effects on glycolysis and respiration indicates that partial reduction of LDH activity can be compensated in a steady-state manner, consistent with the exceptionally high catalytic capacity of LDH in cancer cells relative to upstream rate-limiting enzymes.

(3) LDH-activity-dependent quantitative relationships support stable metabolic states

Importantly, our conclusions do not rely on a single inhibitor condition at a single time point. Rather, we established quantitative steady-state relationships between residual LDH activity and pathway behavior across a wide range of LDH inhibition. These LDH-activity-dependent data strongly support that the system resides in stable metabolic states at different degrees of LDH activity, rather than reflecting non-specific collapse due to prolonged stress.

Specifically, we observed that when LDH activity was reduced from 100% to approximately ~9% (e.g., by genetic perturbation and partial pharmacologic inhibition), glucose consumption and lactate production remained essentially unchanged, indicating maintenance of a steady-state glycolytic flux despite substantial LDH inhibition. Only when LDH activity was further reduced below this threshold did glycolytic flux decrease in a graded manner, consistent with a nonlinear control structure.

Likewise, the isotope tracing results showed distinct LDH-activity-dependent transitions in TCA cycle labeling patterns. Over the range in which LDH activity decreased from 100% to ~9%, the [¹³C₆]glucose-derived labeling pattern of citrate remained largely unchanged, whereas deeper inhibition led to a decrease in m2 citrate with a compensatory rise in higher-order citrate isotopologues, consistent with altered flux entry versus cycling/retention in the TCA cycle. Similarly, [¹³C₅]glutamine tracing revealed that deeper LDH inhibition reduced the direct m5 contribution, accompanied by corresponding shifts in other isotopologues. These graded, quantitative transitions—rather than an abrupt global failure—support the interpretation of distinct metabolic steady states across LDH activity levels, linking LDH inhibition to changes in both glycolysis and mitochondrial metabolism.

Reviewer #2 (Recommendations For The Authors):

All in all, the authors would benefit from collaboration with a group more well-versed in quantitative aspects of metabolism (such as Metabolic Control Analysis) and modelling methods (such as flux analysis) to boost the interpretation and impact of their really nice data set.

We sincerely thank the reviewer for this insightful and constructive suggestion. We fully agree that collaboration with groups specializing in quantitative metabolic analysis, such as Metabolic Control Analysis and flux modeling, would further expand the interpretative depth and broader impact of this work.

The primary objective of the present work, however, was not to construct a global mathematical model, but to experimentally dissect the biochemical mechanism by which LDH inhibition coordinately suppresses glycolysis, the TCA cycle, and OXPHOS, integrating enzyme kinetics with thermodynamic constraints at steady state. Within this scope, we focused on experimentally demonstrable relationships between LDH activity, redox balance, GAPDH perturbation, thermodynamic shifts in near-equilibrium reactions, and emergent flux suppression.

We fully recognize the power of MCA and related modeling approaches in formalizing control coefficients and system-level sensitivities, and we view our dataset as particularly well suited to support such future analyses. We therefore see this work as providing a robust experimental platform upon which more comprehensive quantitative modeling can be built, either in future studies or through collaboration with specialists in metabolic modeling.

Reviewer #3 (Recommendations For The Authors):

We sincerely thank the reviewer for the important suggestions.

(1) I strongly disagree that "regulation of glycolytic flux".. "remained largely unexplored.”

Our original wording was meant to emphasize not the absence of prior work on glycolytic flux regulation, but rather that the specific biochemical mechanism by which LDH regulates glycolytic flux—particularly through the integrated effects of enzyme kinetics, redox balance, and thermodynamic constraints within the pathway—has not been fully elucidated.

To avoid any ambiguity or overstatement, we have revised the relevant text to more precisely reflect this intent. The revised wording now reads:

“This study elucidates a biochemical mechanism by which lactate dehydrogenase influences glycolytic flux in cancer cells, revealing a kinetic–thermodynamic interplay that contributes to metabolic regulation.”

We believe this revised phrasing more accurately acknowledges prior work while clearly defining the specific mechanistic contribution of the present study.

(2) Very confusing in the Introduction section: "If LDH is inhibited at the LDH step..”

We sincerely thank the reviewer for pointing out the potential confusion caused by the phrase “If LDH is inhibited at the LDH step” in the Introduction.

Our intention was to contrast two conceptual models of LDH inhibition. The first is the conventional view, in which the effect of LDH inhibition is assumed to be confined to the LDH-catalyzed reaction itself, leading primarily to local accumulation of pyruvate and its redirection toward mitochondrial metabolism. The second, which is supported by our data, is that LDH inhibition initiates a system-wide biochemical response, perturbing redox balance, upstream enzyme kinetics, and the thermodynamic state of the glycolytic pathway, ultimately resulting in coordinated suppression of glycolysis, the TCA cycle, and OXPHOS.

We agree that the original phrasing was ambiguous and potentially misleading. To improve clarity, we have revised the text as follows:

“If the effect of LDH inhibition were confined solely to its catalytic step…”

(3) The entire introduction part when the authors attempt to explain how decreased glycolysis will lead to decreased mitochondrial respiration is confusing.

We would like to clarify that the Introduction does not attempt to explain how decreased glycolysis leads to decreased mitochondrial respiration. Rather, the final paragraph of the Introduction is intended to highlight an unresolved conceptual inconsistency in the existing literature and to motivate the central question addressed in this study.

Specifically, we summarize the prevailing view that LDH inhibition redirects pyruvate toward mitochondrial metabolism and enhances oxidative phosphorylation, and then point out that this interpretation is difficult to reconcile with quantitative considerations, such as the large disparity between glycolytic and mitochondrial flux capacities and the excess catalytic activity of LDH relative to upstream glycolytic enzymes. These observations are presented to emphasize that the biochemical mechanism linking LDH inhibition to changes in glycolysis and mitochondrial respiration has not been fully resolved.

Importantly, the Introduction does not propose a mechanistic explanation for the observed suppression of mitochondrial respiration; rather, it poses this as an open question, which is then systematically addressed through experimental analysis in the Results section.

(4) Line 144: "which is 81(HeLa-LDHAKO) -297(HeLa-Ctrl) times"- here and in many other places wording is confusing to the reader.

Our intention was to emphasize the significant redundancy of LDH activity relative to hexokinase (HK), the first rate-limiting enzyme in the glycolysis pathway, in cancer cells.

Specifically, we wanted to express that in HeLa-Ctrl cells, the total LDH activity is 297 times that of HK activity; while in HeLa-LDHAKO cells, although the total LDH activity decreased, it was still 81 times that of HK activity. This data comes from supplement Table 1 in the paper and aims to provide quantitative evidence for "why knocking out LDHA or LDHB alone is insufficient to significantly affect glycolysis flux," because the remaining LDH activity is still far higher than the HK activity at the pathway entrance, sufficient to maintain flux.

Based on your suggestion, we rewrite it in the revised draft with a more specific statement: "...the total activity of LDH in HeLa cells is very high, which is 297-fold higher than the first rate-limiting enzyme HK activity in HeLa-Ctrl cells and 81-fold higher in HeLa-LDHAKO cells.”

(5) Line 153: "in the following four aspects:"- but what are these aspects, the text below has no corresponding subtitles, etc.

Our intention was to indicate that after LDHA or LDHB knockout alone failed to affect the glycolysis rate, we further explored its potential impact on the glycolytic pathway from four deeper perspectives: the glucose carbon to pyruvate and lactate, the glucose carbon to subsidiary branches of glycolysis, the concentration of glycolytic intermediates and the thermodynamic state of the pathway, and the redox state of cytosolic free NADH/NAD⁺.

Following your valuable suggestion, we have now added the aforementioned clear subtitles to these four aspects in the revised manuscript.

(6) Lines 193, another example of the very confusing statement: "The results suggested that the loss of total LDH concentration was compensated.."

The actual catalytic activity (reaction rate) of LDH is determined by both its enzyme concentration and substrate concentration (pyruvate and NADH). When the total LDH protein concentration (enzyme amount) in the cell is reduced through gene knockout, the reaction equilibrium is disrupted. To maintain sufficient lactate production flux to support a high glycolysis rate, the cell compensates by increasing the concentration of one of the substrates—free NADH (as shown in Figure 1I). This results in an increased substrate concentration, despite a reduction in the amount of enzyme, thus partially maintaining the overall reaction rate.

We have revised the original statement to more accurately describe this kinetic equilibrium process: "The decrease in total LDH concentration was counterbalanced by a concomitant increase in the concentration of its substrate, free NADH, thereby maintaining the reaction velocity.”

(7) Line 222-223: "did not or marginally significantly affect....”

Our intention is to reflect the complexity of the data in Figure 1. Specifically: Regarding "did not affect": This means that there were no statistically significant differences in most key parameters, such as glycolytic flux (glucose consumption rate, lactate production rate). Regarding "or marginally significantly affected": This means that in a few indicators, although statistical calculations showed p-values less than 0.05, the absolute value of the difference was very small, with limited biological significance.

To clarify this, we rewrite it as: "...did not significantly affect glucose-derived pyruvate entering into TCA cycle, neither significantly affect mitochondrial respiration, although statistically significant but minimal changes were observed in a few specific parameters (e.g., m3-pyruvate% in medium).”

(8) It is very confusing to use the same colors for three GNE-140 drug concentrations (Figure 2a-b) and for 3 different cell lines right next to each other (Figure 2c-d).

The figures have been revised accordingly.

(9) Lines 263-273: nothing is new here as oxidized NAD+ is required for run glycolysis and LDH inhibition/KO leads to a high NADH/NAD+ ratio; Also below it is well known that reductive stress blocks serine biosynthesis;

It is well established that oxidized NAD⁺ is required for glycolysis, that LDH inhibition or knockout increases the NADH/NAD⁺ ratio, and that reductive stress can suppress serine biosynthesis. We did not intend to present these observations as novel.

The key point of this section is not the qualitative requirement of NAD⁺ for GAPDH, but rather the mechanistic alignment between LDH inhibition, changes in free NAD⁺ availability, and the emergence of GAPDH as a flux-controlling step within the glycolytic pathway under steady-state conditions. Previous studies have largely treated the increase in NADH/NAD⁺ following LDH inhibition as a correlative or downstream effect, without directly demonstrating how this redox shift quantitatively propagates upstream to reorganize glycolytic flux distribution and thermodynamic driving forces.

In our study, we explicitly link LDH inhibition to (i) an increase in free NADH/NAD⁺ ratio, (ii) inhibition of GAPDH activity in intact cells, (iii) accumulation of upstream glycolytic intermediates, (iv) suppression of serine biosynthesis from 3-phosphoglycerate, and critically, (v) coordinated shifts in the Gibbs free energies of reactions between PFK1 and PGAM. This integrated kinetic–thermodynamic framework goes beyond the established qualitative understanding of NAD⁺ dependence and provides a pathway-level mechanism by which LDH activity controls glycolytic flux.

(10) Lines 368-370: "... we reached an alternative interpretation of the data.."- does not provide much confidence.

Our intention was to prudently emphasize that we proposed a new interpretation based on detailed data, differing from conventional views. Our interpretation is grounded in key and consistent evidence from dual isotope tracing experiments using [¹³C₆]glucose and [¹³C₅]glutamine: The [¹³C₆]glucose tracing data: the labeling pattern of citrate, the starting product of TCA cycle, showed a significant decrease in m+2 %. This directly reflects a reduction in the flux of newly generated acetyl-CoA from glucose entering the TCA cycle. Simultaneously, the sum of other isotopologues % (m+1/ m+3/ m+4/m+5/m+6) increased, indicating a longer retention time of the labeled carbon in the cycle, implying a simultaneous decrease in the flux of cycle intermediates effluxed for biosynthesis. [¹³C₅]Glutamine tracing data: the labeling pattern of α-ketoglutarate showed a decrease in m+5 %, indicating a reduction in glutamine replenishment flux. The pattern of change in the total percentage of other isotopologues % (m+1/ m+2/ m+3/m+4) also supports the conclusion of reduced intermediate product efflux.

These two sets of data corroborate each other, pointing to a unified conclusion: LDH inhibition not only reduces carbon source inflow into the TCA cycle but also decreases intermediate product efflux, leading to a decrease in overall cycle activity. Therefore, our "alternative interpretation" is a well-supported and more consistent explanation of our overall experimental results. We revise the original wording to: "Integrated analysis of dual isotope tracing data demonstrates that LDH inhibition reduces both influx and efflux of the TCA cycle..."

(11) Lines 418-421: This entire discussion on how TCA cycle activity is decreased upon LDH inhibition is very confusing. I also would like to see these tracer studies when ETC is inhibited with different inhibitors.

We would like to clarify that the mitochondrial respiration rate data presented in Figure 5W are based on studies using different ETC inhibitors, and the cell treatment conditions (including culture time, etc.) for these oxygen consumption measurements are consistent with the conditions for the [¹³C₆]glucose and [¹³C₅]glutamine isotope tracing experiments (Figure 5A-V). Therefore, the changes in TCA cycle flux revealed by the tracing data and the inhibition of OXPHOS rate shown by the respiration measurements are mutually corroborating evidence from the same experimental conditions.

(12) Figure 6F, G - very limited representation of growth curves, why not perform these experiments with all corresponding cell lines and over multiple days. Especially since proliferation arrest vs cell death was implicated.

We have provided the growth curves of the HeLa-Ctrl and HeLa-LDHAKO cell lines under the corresponding treatments in Figure 6—figure supplement 1, as a supplement to Figure 6F, G (HeLa-LDHBKO cells). The choice of 48 hours as the cutoff observation point is based on clear biological evidence: under the stress of hypoxia (1% O₂) combined with GNE-140 treatment, HeLa-LDHBKO cells experienced substantial death within 24 to 48 hours, at which point the differences in the growth curves were already very significant.

(13) Move most of the Supplementary tables into an Excel file - so values can be easily accessed.

We have compiled the tables into an Excel file and submitted it along with the revised manuscript as supplementary material.

(14) Consider changing colors to more appealing- especially jarring is a bright blue, red, black combination on many bar graphs.

We have adjusted the color scheme of the figures (especially the bar graphs) in the paper, and have submitted them with the revised manuscript.

(15) Double check y-axis on multiple graphs it says "mM".

We have checked y-axis, the unit (mM) is correct.

(16) Instead TCA cycle use the TCA cycle.

In the revised manuscript, TCA cycle is used.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

This paper by Karimian et al proposes an oscillator model tuned to implement binding by synchrony (BBS*) principles in a visual task. The authors set out to show how well these BBS principles explain human behavior in figure-ground segregation tasks. The model is inspired by electrophysiological findings in non-human primates, suggesting that gamma oscillations in early visual cortex implement feature-binding through a synchronization of feature-selective neurons. The psychophysics experiment involves the identification of a figure consisting of gabor annuli, presented on a background of gabor annuli. The participants' task is to identify the orientation of the figure. The task difficulty is varied based on the contrast and density of the gabor annuli that make up the figure. The same figures (without the background) are used as inputs to the oscillator model. The authors report that both the discrimination accuracy in the psychophysics experiment and the synchrony of the oscillators in the proposed model follow a similar "Arnold Tongue" relationship when depicted as a function of the texture-defining features of the figure. This finding is interpreted as evidence for BBS/gamma synchrony being the underlying mechanism of the figure-ground segregation.

Note that I chose to use "BBS" over gamma synchrony (used by the authors) in this review, as I am not convinced that the authors show evidence for synchronization in the gamma-band.

We thank the reviewer for their careful assessment of our manuscript and useful comments that we believe have served to strengthen our work.

Strengths:

The design of the proposed model is well-informed by electrophysiological findings, and the idea of using computational modeling to bridge between intracranial recordings in non-human primates and behavioral results in human participants is interesting. Previous work has criticized the BBS synchrony theory based on the observation that synchronization in the gamma-band is highly localized and the frequency of the oscillation depends on the visual features of the stimulus. I appreciate how the authors demonstrate that frequency-dependence and local synchronization can be features of BBS, and not contradictory to the theory. As such, I feel that this work has the potential to contribute meaningfully to the debate on whether BBS is a biophysically realistic model of feature-binding in visual cortex.

Weaknesses:

I have several concerns regarding the presented claims, assessment of meaning and size of the presented effects, particularly with regard to the absence of a priori defined effect sizes.

Firstly, the paper makes strong claims about the frequency-specificity (i.e., gamma synchrony) and anatomical correlates (early visual cortex) of the observed effects. These claims are informed by previous electrophysiological work in non-human primates but are not directly supported by the paper itself. For instance, the title contains the word "gamma synchrony", but the authors do not demonstrate any EEG/MEG or intracranial data in from their human subjects supporting such claims, nor do they demonstrate that the frequencies in the oscillator model are within the gamma band. I think that the paper should more clearly distinguish between statements that are directly supported by the paper (such as: "an oscillator model based on BBS principles accounts for variance in human behavior") and abstract inferences based on the literature (such as "these effects could be attributed to gamma oscillations in early visual cortex, as the model was designed based on those principles").

We thank the reviewer for this helpful comment and agree that the scope of our claims should be clearly delineated between what is directly supported by our data and what is theoretically inferred from prior literature.

We revised the Abstract, Introduction, and early Discussion to moderate the strength of our statements and make the distinction explicit. The revised title now emphasizes that our study tests principles derived from prior work on gamma synchrony rather than directly demonstrating gamma activity in humans. Throughout the text, we use more cautious phrasing that highlights potential mechanisms and theoretical predictions. The intention of our study was not to position synchrony as the only viable mechanism of figure–ground perception. Rather, our goal was to reinvigorate it as a potential contender by showing that features often cited as limitations of synchrony-based binding may in fact be essential properties of the mechanism. We updated phrasing throughout the manuscript to make this clearer and avoid overstating the study’s contribution.

Importantly, our model is not agnostic with respect to frequency band. Oscillator frequencies exhibited by model units are within the gamma range by design. Frequency emerges directly from the contrast within each oscillator’s receptive field, following an empirically established relationship between stimulus contrast and gamma frequency. To our knowledge, such a robust, quantitative relationship between stimulus features to exact oscillation frequency has not been consistently demonstrated for other frequency bands. This relationship yields gamma-band frequencies for all contrasts used in our simulations. The model is thus indeed a gamma oscillator model of V1, not a generic instantiation of Binding by Synchrony (BBS) principles.

That said, we fully agree with the reviewer that our study cannot demonstrate a direct link between gamma synchrony in visual cortex and human behavior. Our behavioral and modeling results instead show that synchronization principles derived from gamma-band physiology in V1 can predict perceptual performance patterns. We now make this distinction explicit throughout the revised manuscript.

Secondly, unlike the human participants, the model strictly does not perform figure-ground segregation, as it only receives the figure as an input.

We thank the reviewer for the opportunity to clarify our modeling approach. We chose not to model the background to reduce computational cost, since including it requires a substantially larger number of oscillators without changing the model’s predictions. The model thus indeed only receives the figure region as input. We aimed to test the local grouping mechanism predicted by TWCO, rather than to simulate a full figure–ground segregation process including a read-out stage. Our model therefore isolates the conditions under which local synchrony emerges within the figure region, assuming that a downstream read-out mechanism (not explicitly modeled here) would detect regions of coherent activity. The exact nature of such a read-out mechanism was beyond the scope of our work.

To confirm that our simplified model is a valid proxy, we ran additional simulations including the background and found that a coherent figure assembly reliably emerges, as can be seen in the phase-locking patterns relative to a reference oscillator at the center of the figure. This validates that the principles of local grouping we studied in isolation hold even when the figure is embedded in a noisy surround. We have added an explicit note in the Results (paragraph 2) that we only simulate the figure and added Supplementary Figure S1 showing the additional simulations.

Finally, it is unclear what effect sizes the authors would have expected a priori, making it difficult to assess whether their oscillator model represents the data well or poorly. I consider this a major concern, as the relationship between the synchrony of the oscillatory model and the performance of the human participants is confounded by the visual features of the figure. Specifically, the authors use the BBS literature to motivate the hypothesis that perception of the texture-defined figure is related to the density and contrast heterogeneity of the texture elements (gabor annuli) of the figure. This hypothesis has to be true regardless of synchrony, as the figure will be easier to spot if it consists of a higher number of high-contrast gabors than the background. As the frequency and phase of the oscillators and coupling strength between oscillators in the grid change as a function of these visual features, I wonder how much of the correlation between model synchrony and human performance is mediated by the features of the figure. To interpret to what extent the similarity between model and human behavior relies on the oscillatory nature of the model, the authors should find a way to estimate an empirical threshold that accounts for these confounding effects. Alternatively, it would be interesting to understand whether a model based on competing theories (e.g., Binding by Enhanced Firing, Roelfsema, 2023) would perform better or worse at explaining the data.

We thank the reviewer for these insightful and constructive comments, which have prompted additional analyses that we believe substantially strengthen our work. The reviewer raises two main points: (1) the need for a benchmark to assess our model’s performance, and (2) the concern that the relationship between model synchrony and behavior might be a non-causal “confound” of the visual features. We address each point below.

(1) Benchmarking model performance

We agree that it is important to assess how well our model performs relative to the data and included this in the original manuscript. We did not predefine an absolute good fit threshold because absolute agreement depends on irreducible noise and inter-subject variability, making a universal cutoff arbitrary. Instead, we had benchmarked model performance in two complementary ways. First, the noise ceiling shown in Figure 5 provides an empirical benchmark for the maximum fit any model could achieve on our data. Simulated Arnold tongues (based on synchrony) approach this ceiling achieving 89% of possible similarity for correlation and 79% of possible similarity for weighted Jaccard similarity, respectively. Second, the parameter sweep (Figure 3) situates our model’s performance within the broader parameter space. It shows that the model, whose key parameters were fixed a priori from independent macaque neurophysiological data, lies close to the optimal regime for explaining the human data. It also provides an estimate of the lower bound (worst-performing point) on the fit that a misspecified model implementing the identical mechanism would achieve. Our model with fixed a priori parameters does 1.41 times better than a misspecified model for the correlation fit metric and 3 times better for weighted Jaccard similarity.

(2) Synchrony as mechanism vs. potential confound

We appreciate the reviewer’s suggestion to test whether synchrony explains behavior beyond stimulus features. In our framework, synchrony is a near-deterministic function of the manipulated stimulus features given fixed model parameters. As a result, synchrony and the stimulus features are collinear (R²≈0.8) leaving no independent variance for synchrony to explain once stimulus features are included. Adding both into one statistical model yields unstable coefficients and no out-of-sample improvement.

Mechanistically, we believe the relevant question is not whether synchrony explains behavior beyond stimulus features but whether synchrony is the correct transformation of the stimulus features to reproduce the behavioral pattern. Please note that in our design we ensured that mean contrast and luminance are identical in the figure and the background such that there are not more high-contrast Gabors in the figure than in the background. We did this with the aim to render mean contrast not a relevant feature. However, there are more high-contrast Gabors in the background, and it is conceivable that the absence of such high contrasts in the figure drives the detection/discrimination of the figure. We therefore agree that testing alternative models would further clarify the unique explanatory value of the synchrony mechanism. To that end, we derived two alternative rate-based readouts from the same V1 simulations of our model from which we derived synchrony. First, average firing rates inside the figure and second, the difference between average firing rates inside the figure and average firing rates in the background (rate difference). We analyzed each individually as predictors of behavior and performed a model comparison based on out-of-sample predictions. While rate difference (but not average firing) showed meaningful associations with performance when considered alone, the synchrony readout had a larger effect size and was favored by the model comparison. We added a new subsection comparing synchrony to rate-based alternatives in the Results (paragraphs 7-9), including additional Bayesian analyses and LOO-CV model comparison. Please note that the model comparison we added to the manuscript provides an additional benchmark beyond the map-level ceiling analysis. It indicates that the mapping from stimulus features to behavior via synchrony generalizes best without requiring an a priori good-fit threshold.

We agree that formally comparing our model to a sophisticated rate-based alternative, such as an instantiation of the Binding by Enhanced Firing model, is an important direction for future work. However, it remains an open and non-trivial question whether such a model could quantitatively reproduce the precise shape of the behavioral Arnold tongue that emerges from the systematic manipulation of our stimulus parameters. Implementing and parameterizing such a model in a comparable, biologically grounded framework is a substantial undertaking that lies beyond the scope of the current study. Therefore, our goal here was not to claim exclusivity for synchrony-based mechanisms, but rather to re-evaluate their plausibility by showing that features often seen as limitations (stimulus dependence and frequency heterogeneity) are, in fact, essential characteristics of the TWCO framework that can predict complex behavioral outcomes.

We would also like to clarify that our stimulus features were derived from theory rather than psychophysical literature. Starting from the principles of TWCO, we mapped frequency detuning and coupling strength onto known anatomical and physiological properties of early visual cortex, and only then derived the corresponding stimulus manipulations (contrast heterogeneity and grid coarseness). Demonstrating that these features predict behavior is therefore not trivial but constitutes a first empirical confirmation that the core TWCO variables match perception.

Apart from adding analyses of additional rate-based readouts of our model, we also refined our discussion of the relationship between these and a synchrony-based mechanism.

Reviewer #2 (Public review):

The authors aimed to investigate whether gamma synchrony serves a functional role in figure-ground perception. They specifically sought to test whether the stimulus-dependence of gamma synchrony, often considered a limitation, actually facilitates perceptual grouping. Using the theory of weakly coupled oscillators (TWCO), they developed a framework wherein synchronization depends on both frequency detuning (related to contrast heterogeneity) and coupling strength (related to proximity between visual elements). Through psychophysical experiments with texture discrimination tasks and computational modeling, they tested whether human performance follows patterns predicted by TWCO and whether perceptual learning enhances synchrony-based grouping.

We thank the reviewer for their thoughtful and constructive review. We believe the comments have served to improve our work.

Strengths:

(1) The theoretical framework connecting TWCO to visual perception is innovative and well-articulated, providing a potential mechanistic explanation for how gamma synchrony might contribute to both feature binding and separation.

(2) The methodology combines psychophysical measurements with computational modeling, with a solid quantitative agreement between model predictions and human performance.

(3) In particular, the demonstration that coupling strengths can be modified through experience is remarkable and suggests gamma synchrony could be an adaptable mechanism that improves with visual learning.

(4) The cross-validation approach, wherein model parameters derived from macaque neurophysiology successfully predict human performance, strengthens the biological plausibility of the framework.

Weaknesses:

(1) The highly controlled stimuli are far removed from natural scenes, raising questions about generalisability. But, of course, control (almost) excludes ecological validity. The study does not address the challenges of natural vision or leverage the rich statistical structure afforded by natural scenes.

We agree with the reviewer that the insights of the present study are limited to texture stimuli and have made adjustments in the Discussion (final two paragraphs) to avoid claiming generalizability to natural stimuli. We have also adjusted the title to specifically limit our results to texture stimuli. To establish the principles of TWCO, we needed tight control over the stimulus, but are intrigued by the idea to investigate natural scenes. We have added to our Discussion (paragraph 9) that future should evaluate to what extent the principles we investigate here apply to natural scenes. Synchrony-based mechanisms have been successfully used for image segmentation tasks in machine vision, showing that the proposed mechanism can in principle work for natural scenes.

(2) The experimental design appears primarily confirmatory rather than attempting to challenge the TWCO framework or test boundary conditions where it might fail.

We thank the reviewer for this important point. Our primary motivation was to address the neurophysiological properties of gamma synchrony that have been suggested to severely challenge the binding by synchrony mechanism. Particularly the strong dependence of gamma oscillations and synchrony on stimulus features. Our goal was to show that from the perspective of TWCO, these challenges become expected components of the mechanism. In essence, we wanted to promote a conceptual shift that converts what pushes a theory to its limit into something that is actually its central tenet. To facilitate this shift, we designed the experiment to directly test this core tenet.

While our approach was designed to test a central prediction of TWCO rather than explicitly challenge its boundaries, we respectfully argue that it was far from a simple confirmatory experiment. The design incorporated high-risk elements that provided considerable room for both the theory and our model to fail. First, the core prediction itself was non-obvious and highly specific. We did not simply test whether contrast heterogeneity and grid coarseness affect perception. We tested the stronger hypothesis that they would reflect a specific, interactive trade-off (the behavioral Arnold tongue) as specified by TWCO. Second, our modeling approach was deliberately constrained to provide a further stringent test. We did not post-hoc optimize the model's key parameters to fit our behavioral data. Instead, we fixed them a priori based on independent neurophysiological data from macaques. This was a high-risk choice, as a mismatch between a priori model predictions and the human data would have seriously challenged the framework's generalizability.

We agree that future research should further challenge TWCO. For instance, by using stimuli that require segregating several objects simultaneously or objects that cover more extensive regions of the visual field.

(3) Alternative explanations for the observed behavioral effects are not thoroughly explored. While the model provides a good fit to the data, this does not conclusively prove that gamma synchrony is the actual mechanism underlying the observed effects.

We agree that our results do not conclusively show that gamma synchrony is the actual mechanism underlying figure-ground segregation. We admit that the original phrasing used throughout the manuscript was too strong and gave the impression that we wanted to establish exactly that. However, the goal of our work was only to reinvigorate gamma synchrony as a potential contender by showing that features often cited as limitations of synchrony-based binding may in fact be essential properties of the mechanism. We have revised the title and made adjustments throughout the manuscript to better reflect this more moderate goal.

Additionally, we added tests of alternatives (Results, paragraphs 7–9) to clarify the unique explanatory value of the synchrony mechanism. To that end, we derived two alternative rate-based readouts from the same V1 simulations of our model. First, we extracted average firing rates inside the figure. Second, we computed the difference between average firing rates inside the figure and average firing rates in the background (rate difference). We analyzed each individually as predictors of behavior and performed a model comparison between these two and synchrony based on out-of-sample predictions. While the rate difference (but not average firing) showed meaningful associations with performance when considered alone, the synchrony readout had a larger effect size and was favored by the model comparison.

(4) Direct neurophysiological evidence linking the observed behavioral effects to gamma synchrony in humans is absent, creating a gap between the model and the neural mechanism.

We agree that the model only provides a how-possibly account linking stimulus features to performance. Showing that the brain actually relies on this mechanism would require showing that cortical synchrony mediates the effect of stimulus features on behavior beyond firing rates. Collecting such data would constitute a major effort that would go beyond the scope of this study. We acknowledge the need for electrophysiological data and the mediation analysis in the updated Discussion.

Achievement of Aims and Support for Conclusions:

The authors largely achieved their primary aim of demonstrating that human figure-ground perception follows patterns predicted by TWCO principles. Their psychophysical results reveal a behavioral "Arnold tongue" that matches the synchronization patterns predicted by their model, and their learning experiment shows that perceptual improvements correlate with predicted increases in synchrony.

The evidence supports their conclusion that gamma synchrony could serve as a viable neural grouping mechanism for figure-ground segregation. However, the conclusion that "stimulus-dependence of gamma synchrony is adaptable to the statistics of visual experiences" is only partially supported, as the study uses highly controlled artificial stimuli rather than naturalistic visual statistics, or shows a sensitivity to the structure of experience.

Likely Impact and Utility:

This work offers a fresh perspective on the functional role of gamma oscillations in visual perception. The integration of TWCO with perceptual learning provides a novel theoretical framework that could influence future research on neural synchrony.

The computational model, with parameters derived from neurophysiological data, offers a useful tool for predicting perceptual performance based on synchronization principles. This approach might be extended to study other perceptual phenomena and could inspire designs for artificial vision systems.

The learning component of the study may have a particular impact, as it suggests a mechanism by which perceptual expertise develops through modified coupling between neural assemblies. This could influence thinking about perceptual learning more broadly, but also raises questions about the underlying mechanism that the paper does not address.

Additional Context:

Historically, the functional significance of gamma oscillations has been debated, with early theories of temporal binding giving way to skepticism based on gamma's stimulus-dependence. This study reframes this debate by suggesting that stimulus-dependence is exactly what makes gamma useful for perceptual grouping.

The successful combination of computational neuroscience and psychophysics is a significant strength of this study.

The field would benefit from future work extending (if possible) these findings to more naturalistic stimuli and directly measuring neural activity during perceptual tasks. Additionally, studies comparing predictions from synchrony-based models against alternative mechanisms would help establish the specificity of the proposed framework.

Recommendations for the authors:

Reviewing Editor Comments:

In a joint discussion to integrate the peer reviews and agree on the eLife recommendations, both reviewers agreed that the work is valuable, but they were on the fence about whether the strength of evidence was incomplete or solid, eventually settling on incomplete. The reviewers make several recommendations for improving these ratings, which I (Reviewing Editor) have organised into 3 points below, with point 1 of particular importance. Underneath the summary, please see the individual recommendations of the reviewers.

(1) Strengthen evidence for the unique role of gamma synchrony in explaining the data, and ensuring claims are directly supported by relevant data:

Reviewers 2 and 3 both note the lack of direct evidence for gamma involvement, and reviewer 2 observes that the fit with behaviour may trivially be explained by a relationship between contrast heterogeneity and grid coarseness without need for oscillation. The reviewers felt that the approach of fitting the model to human data could be strengthened to help address this issue - and they offer various solutions, e.g., more principled a-priori criteria around good vs bad fit of the model to both main task and training data, and comparison to alternative binding models (Reviewer 2), identifying and testing boundary conditions of the model (Reviewer 3). There is also the possibility of collecting direct human neurophysiological evidence linking the behavioural data to neural mechanisms. Our discussion also highlighted the need to weaken claims (including in the title) where links are not directly demonstrated by methods from the present study, e.g., resting on indirect comparisons to primate literature.

We agree with the editor and reviewers that this was a critical point. To address it, we have made several major revisions.

As suggested, we have weakened claims where the links are not directly demonstrated by our data. The title has been revised to be more specific, and we have carefully edited the abstract, introduction, and discussion to distinguish between our model's predictions and direct neurophysiological evidence.

To address the concern that our model's fit might be trivially explained by visual features, we have performed a new analysis comparing the synchrony-based readout to two alternative rate-based readouts from the same V1 simulations. This new comparison shows that the synchrony readout provides a superior out-of-sample prediction of human behavior.

While a full implementation of a competing theory like "Binding by Enhanced Firing" would be a valuable next step, we note that parameterizing such a model in a comparably grounded framework is a substantial undertaking beyond the scope of the present study. Our new analysis provides an important first step in this direction.

(2) Make explicit and address the limitations of the stimuli:

Include that the model is not extracting the figure from the background, and the controlled stimuli may limit generalizability.

To address the concern that our model was not performing true figure-ground extraction, we performed a new set of simulations that included both the figure and the immediate background. The results confirm that synchrony dynamics within the figure region are not affected by the presence of the background. We added these validation results as supplementary materials. We have additionally made the modeling choice and its justification more explicit in the Results and Methods sections.

We have revised the Discussion to be more explicit about the limitations of using highly controlled texture stimuli. We now clearly state that our findings are specific to this context and that further research is required to determine if these principles generalize to the segregation of objects in natural scenes.

(3) Some clarifications to make more accessible:

Include the figure explaining the framework (Reviewers 1&2), and also the model details (Reviewer 2).

We have revised Figure 1 and its caption to more clearly illustrate the links from TWCO principles to their neural implementation in V1 and the resulting behavioral predictions.

We have expanded the Methods section to provide a more detailed and accessible description of the model's construction. We now clarify precisely how the oscillator grid was defined in visual space, how eccentricity-dependent receptive field sizes were implemented, and how these were mapped onto a retinotopic cortical surface to determine coupling strengths.

Reviewer #1 (Recommendations for the authors):

(A) Major concerns:

(1) My main concern:

My main concern is the repeated claims that the observed findings can be attributed to gamma synchrony in the early visual cortex. I find this claim misleading as the authors do not report any electrophysiological data that directly supports such claims. As stated in my public review, I feel that the authors should be clear about direct evidence versus more abstract inferences based on the literature.

In particular, I recommend changing claims about "gamma synchrony" to "Binding by Synchrony" That being said, the authors can outline that the model was built under the assumption that this synchrony is mediated by gamma in early visual cortex, but I don't think it should be part of their main conclusions.

We appreciate that TWCO’s general principles are frequency-agnostic and can be viewed as binding by synchrony in a broad sense. Our work, however, specifically instantiates these principles in V1 gamma: the model reflects TWCO dynamics together with V1 anatomy/physiology and the well-established contrast–frequency relationship in the gamma range (which, to our knowledge, has not been demonstrated with comparable specificity for other bands). In that sense, it is a gamma oscillator model of V1, rather than a generic BBS instantiation. Moreover, stimulus dependencies often cited as challenges to BBS have been used in particular to argue against gamma; showing that these very dependencies are integral to the TWCO mechanism is central to our contribution, and we therefore keep our conclusions focused on the gamma-specific instantiation tested here.

(2) Mediation of the observed effects by the visual features of the figure:

The authors motivate the hypothesis that BBS predicts that the perception of texture-defined objects depends on the density of texture elements and their contrast heterogeneity. This hypothesis seems trivial as those are the features that distinguish figure from ground. I think it would be important to clarify how this hypothesis is unique to BBS and not explained by competing theories, such as Binding by Enhanced Firing (Roelfsema, 2023). The authors should be clear about what part of the hypothesis is not trivial based on the task and clearly attributable to oscillators and synchrony.

Our stimulus features were derived from theory rather than psychophysical literature. Starting from the principles of TWCO, we mapped frequency detuning and coupling strength onto known anatomical and physiological properties of early visual cortex, and only then derived the corresponding stimulus manipulations (contrast heterogeneity and grid coarseness). We agree that grid coarseness (element distance) is an established facilitator of figure–ground perception. By contrast, contrast heterogeneity (feature variance) is less commonly emphasized as a figure–ground cue, compared to mean-based cues, but follows directly from TWCO’s frequency detuning. Importantly, mean contrast and luminance were matched exactly between figure and background in our stimuli. Demonstrating that contrast heterogeneity and grid coarseness not only independently affect figure-ground perception, but reflect a trade-off where higher heterogeneity needs to counteracted by reduced grid coarseness in the way TWCO specifies is therefore non-obvious and provides an initial empirical indication that the core TWCO variables might shape perception. We also agree that alternative models would further clarify the unique explanatory value of synchrony. In the revised manuscript, we compare rate-based readouts (mean figure rate; figure–background rate difference) with the synchrony readout from the same simulations. Rate difference indeed constitutes a predictor of performance, but the synchrony readout showed a larger effect and was preferred by out-of-sample model comparison.

Using a linear model, the authors assess the relationship between discrimination accuracy and synchrony. Did the authors also include the factors grid coarseness and contrast heterogeneity in this model? Again, as both the task performance (as shown by the GEE analysis) and oscillatory synchrony depend on these features, the relationship between model and behavioral performance will be mediated by the visual features.

Thank you for raising this. In our framework, detuning (via contrast heterogeneity) and coupling (via grid coarseness) are the inputs, synchrony is the proposed mechanistic mediator, and behavior is the output. Because synchrony in our model is a (near-)deterministic function of the manipulated features under fixed parameters, a joint features+synchrony regression is statistically ill-posed (perfect multicollinearity up to numerical error) and cannot add information. A proper mediation test would require trial-wise neural measurements of synchrony in the same task, which we do not have and acknowledge as a limitation in the Discussion. Accordingly, we show that both the features themselves (reflecting TWCO principles) and model-derived synchrony (realizing the proposed pathway) account for behavior.

We agree this does not establish a unique contribution of synchrony. To probe alternatives, we added rate-based readouts and a model comparison to the revised manuscript. These additional analyses indicate that synchrony outperforms simple rate-based mappings. We do not claim this rules out more sophisticated rate-based mechanisms. Our aim is to demonstrate that synchrony is a viable, behaviorally informative readout for downstream processing. We do not assert it is the only mechanism the brain uses. Synchrony had been discounted due to its stimulus dependence; our results are intended to rule it back in. We have made changes throughout the manuscript to better reflect this more modest aim.

(3) Goodness of fit measures are not established a prior:

I have described this concern in my public review. It is hard to assess what the authors would have interpreted as a good or a bad fit, especially without accounting for the confound in the relationship between oscillator synchrony and behavior. Similarly, when assessing the similarity between the behavioral and dynamic Arnold Tongues across different coupling parameters, the authors found that the chosen parameters (based on macaque data) were not optimal. They offer the explanation that the human cortex has a lower coupling decay than the macaque cortex, and the similarity is higher for lower values of coupling decay. While this explanation is not entirely implausible, it is unclear where an oscillator model with human values would be in the presented plot, as the authors didn't estimate those values from the human studies. Moreover, the task used in the Lowet et al., 2017 paper is very different from the task presented here, which could also account for differences. Overall, the explanation appears hand-wavy considering the lack of empirically defined goodness of fit measures.

Thank you for these concerns.

We did indeed not provide a priori thresholds for what would be considered good fit. Instead, we used two complementary benchmarks; namely noise ceilings and parameter exploration. The former provides an upper bound on what any model (not just ours but based on completely different mechanisms) could achieve given our data. The parameter sweep provides an indication how well our concrete model can maximally fit the data and how bad it can be based on possible parameters. These benchmarks are more informative than a fixed a-priori cutoff, which would depend on unknown noise and inter-subject variability. Both the noise ceiling and the parameter exploration indicate that our model, using a priori fixed parameters, performs well. Additionally, we redid all our statistical analyses after z-normalizing every predictor to provide easier interpretation of effect sizes.

Regarding the reason that key model parameters were not optimal, we believe our interpretation to be plausible. We agree that we currently do not have data to estimate the exact human decay factor and hence cannot establish how much model fit would be affected. However, the parameter exploration in Figure 3 shows that small to modest reductions in decay would improve model fit. We discuss this now in the revised manuscript.

The reviewer’s suggestion is intriguing. While Lowet et al. (2017) used a different task, the parameters we took from their work (decay rate and maximum coupling) are intended to reflect anatomical properties and thus should not be task-dependent. That said, Lowet et al. ‘s data carry uncertainty, so our estimates may not be exact; we note this explicitly in the revised Discussion. Whether a different task would have yielded better parameter estimates is difficult to determine, but we considered Lowet’s paradigm appropriate because it was designed to target the same V1 anatomical and physiological properties that map onto TWCO.

I have concerns about a similar confound in the training effects. If I'm not mistaken, the Hebbian Learning rule encourages synchronization between the oscillators in the grid. As such, it causes synchronization to increase over several simulations. Clearly, the task performance of the participants also improves over the sessions. Again, an empirical threshold would be required to assess whether the similarity in learning between model and performance goes beyond what is expected based on learning alone. How much of these effects can be attributed to the model being oscillatory?

The reviewer is correct that, in our framework, learning operates via changes in coupling that increase synchrony. Enhanced synchrony is the proposed (and in our model also the actual) pathway by which learning impacts behavior. We agree that learning could, in principle, act through pathways other than synchrony. Demonstrating this would not be achieved by a mediation analysis here, because that requires independent, trial-level neural measurements of the candidate pathways (synchrony and alternatives). In the absence of such data, the appropriate approach would be model comparison between competing mechanistic readouts. We have added such a model comparison for a synchrony readout versus two rate-based readouts derived from the same simulations for the first session; i.e., focusing on the pathway from stimulus features to behavior. However, a similar model comparison is not possible for learning. As we show in the supplementary materials, rate-based readouts of our V1 model are not at all affected by coupling strength. As such, they are insensitive to changes in coupling and are thus not viable as alternative mechanisms to explain performance changes due to learning. A fair test of rate-based alternatives would require building a detailed rate-based figure–ground segregation model that predicts session-wise changes. We agree that this is an important next step but it is also substantial undertaking beyond the scope of the present study.

(4) Similarly, for the comparison of the Arnold Tongue in the transfer session and the early session:

In the first part of the Results section, it says: "Our model rests on the assumption that learning-induced structural changes in early visual cortex are specific to the retinotopic locations of the trained stimuli. We evaluated whether this assumption holds for our human participants using the transfer session following the main training period. [...] If learning is indeed local, participants' performance in the transfer session should resemble that of early training sessions, indicating a reset in performance for the new retinal location."

The authors find that a model fit to session 3 explains the data in the transfer session best and consider this as evidence for the above-stated expectation. Again, it is unclear where the cutoff would have been for a session to be declared as early or late. For instance, had the participants only performed 4 sessions, would the performance be best explained by session 3 or session 1?

A high number of statistical tests are used, which, firstly, need to be corrected for multiple comparisons (did the authors do this?). Secondly, I feel that the regression models could be improved. For instance, the authors fit one model per session and then assess how well each model explains the variance in the transfer session. I think the authors might want to opt for one model with the regressors contrast heterogeneity, grid coarseness, and session (and their interaction). Using this approach, the authors would still be able to assess which session predicts the data best. Similarly, interindividual variability could be accounted for by adding participant-specific random effects to the model (and using a mixed model), instead of fitting individual models per participant.

We agree the “early vs late” cutoff was underspecified. In the revision, we predefine Session 2 as the early-learning reference, excluding Session 1 to avoid familiarization/response–mapping effects. We then fit a single Bayesian hierarchical model with contrast heterogeneity, grid coarseness, and session, plus a transfer indicator, and participant-level random effects. This allows us to place the transfer session on the same scale as training and to test a) whether the transfer session precedes the state in session 2 via the posterior contrast P(βtransfer<βSess2) and b) whether it is indistinguishable from the state in session two using an equivalence test derived from the fitted model. We find that the transfer session is equivalent to session 2. We added this updated analysis of the transfer session in the Results (paragraph 15).

In response to the suggestion to use a hierarchical regression model for analyzing the transfer session, we have decided to use such a model for all our analyses in a Bayesian framework. In this Bayesian framework, inference is based on the joint posterior (credible intervals/equivalence) of all predictors in a model and additional post-hoc multiplicity corrections are not required.

(5) Questions regarding the model:

What does it mean that the grid was "defined in visual space"? How biologically plausible with regard to the retinotopy and organization of the oscillators do the authors claim the model to be?

We are happy to clarify this point. We have a total of 400 oscillators reflecting neural assemblies in V1. We start by defining a regular, 20x20, grid of the receptive field (RF) centers of these oscillators inside the figure region. Each oscillator is then also assigned a RF size based on the eccentricity of its RF center. We use the threshold-linear relationship between RF eccentricity and RF size reported in [1] to assign RF sizes. Each oscillator thus has an individual, eccentricity-dependent, RF size.

For the coupling between oscillators, we need to know their cortical distances. We obtain these by first determining the cortical location of each oscillator through a complex-logarithmic topographic mapping of neuronal receptive field coordinates onto the cortical surface [2,3]. For this mapping, we use human parameter values estimated by [4]. From these cortical locations, we then compute pairwise Euclidean distances.

The model thus captures realistic retinotopy, eccentricity-dependent RF sizes, and distance-dependent coupling on the cortical surface. We have adjusted our Methods to make these steps clearer.

(1) Freeman, J., & Simoncelli, E. P. (2011). Metamers of the ventral stream. Nature neuroscience, 14(9), 1195-1201.

(2) Balasubramanian, M., & Schwartz, E. L. (2002). The isomap algorithm and topological stability. Science, 295(5552), 7. https://doi.org/10.1126/science.1066234

(3) Schwartz, E. L. (1980). Computational anatomy and functional architecture of striate cortex: a spatial mapping approach to perceptual coding. Vision Research, 20(8), 645–669. http://www.sciencedirect.com/science/article/pii/0042698980900905

(4) Polimeni, J. R., Hinds, O. P., Balasubramanian, M., van der Kouwe, A. J. W., Wald, L. L., Dale, A. M., & Schwartz, E. L. (2005). Two-dimensional mathematical structure of the human visuotopic map complex in V1, V2, and V3 measured via fMRI at 3 and 7 Tesla. Journal of Vision, 5(8), 898. https://doi.org/10.1167/5.8.898

Similarly, do the authors claim that each gabor annuli stimulates a single receptive field in V1?

We hope that with the additional explanation above, it is clearer that there is not a one-to-one mapping. Each oscillator samples the local image by pooling over all Gabor annuli that overlap its receptive field (partially or fully) and computes the average contrast within its RF. Conversely, a single annulus typically overlaps multiple RFs and contributes to each in proportion to the overlap.

I am unsure how the oscillators were organized, if not retinotopically. How is the retinotopic input fed into the non-retinotopically arranged oscillators?

We hope that with the additional explanation above, it is clearer that the network is strictly retinotopic.

The frequency of each oscillator changes according to ω=2πv with ν=25+0.25C. How were the values for the linear regression in v chosen? Reference?

The slope and intercept parameters for this equation were first reported in [5]. We added the reference to the Methods.

(5) Lowet, E., Roberts, M., Hadjipapas, A., Peter, A., van der Eerden, J., & De Weerd, P. (2015). Input-dependent frequency modulation of cortical gamma oscillations shapes spatial synchronization and enables phase coding. PLoS computational biology, 11(2), e1004072.

(6) Hebbian Learning Rule:

I am confused about how the effective learning rate E= ∈t is calculated. It is said that it is estimated based on the similarity between the second experimental session and the distribution of synchrony after letting the model learn. How can the model learn without knowing epsilon and t?

We agree with the reviewer that our procedure to estimate the effective learning rate requires further clarification. We performed a nested grid search. Essentially, we let the model learn between session 1 and 2 with each of 25 candidate effective learning rates and evaluate how well each of them allow the model to fit performance in session 2. We then select the best effective learning rate and create a new, smaller, grid around this value and repeat that procedure. In total we perform 5 nested grids to arrive at the final effective learning rate. We expanded the explanation in the Methods.

(B) Minor concerns:

(1) Small N: 2/3 of the studies that were cited to justify the small sample were notably different from the current experiment, i.e., Intoy 2020 is an eye movement task, Lange 2020 is a memory task (Tesileanu 2020 is more similar). I think a power analysis would be great to support, as the sample size seems quite low

Our study uses a within-subject design with ~750 trials per session (≈6,000 total) per participant, analyzed with a hierarchical model that pools information across trials and participants. To assess adequacy, we ran a simulation-based design analysis using the fitted hierarchical model (i.e., post hoc, based on the observed variance components). This analysis indicated a detection probability >90% for all key effects. We now report the results of this design analysis in the (Supplementary Table 1) and note this in the Results (paragraph 1).

Regarding the literature context, we agree the cited studies are not identical to ours; we referenced them to illustrate a common practice (small N with many trials) when targeting low-level, early-visual mechanisms. Intoy (pattern/contrast sensitivity) and Lange (perceptual learning in early vision) share that focus, while Tesileanu is methodologically closest.

(2) Figure 1 could be more informative and better described in the text. The authors often don't refer to the panels in Figure 1. Maybe it would help to swap a and b to describe the Arnold tongue first? It might also be a good idea to add the coupling strength and frequency detuning axes

We have swapped panels a and b and now refer to each panel in the main text to enhance clarity.

(3) Values of rho (distance - is this degrees visual angle)? Do the authors assume that the size of the stimuli corresponds to receptive fields in V1? If so, how is this justified?

The center-to-center distance between any pair of neighboring annuli is indeed expressed in degrees of visual angle. Rho is a scaling factor for this distance. With rho=1, the center-to-center distance corresponds to the diameter of the annuli; i.e., they touch but do not overlap each other. We do not assume any relation between the size of receptive fields and the size of the annuli. Receptive field sizes in our model are purely determined by their eccentricity and each oscillator can have several annuli within its receptive field while each annulus can fall within several overlapping receptive fields of different oscillators. We believe that the schematic illustration in Figure 1 might have given the impression that each oscillator sees exactly one annulus and added a note that this is not the case and merely an oversimplification to illustrate the relationship between contrast and intrinsic frequency.

(4) Some equations are embedded in the text, and some are not. It might be easier to find the respective equation if they all have an index. For instance, the authors mention the psychometric function that relates model synchrony and performance in the results section. It would be easier to find if it had an index that the authors could refer to.

We moved this equation as well as the contrast intrinsic frequency mapping from inline to displayed and numbered them.

(5) Is there a reference for "Our model rests on the assumption that learning-induced structural changes in early visual cortex are specific to the retinotopic locations of the trained stimuli"? (If so, it should be cited.)

We added references supporting this assumption.

(6) Figure 2b: colorbar missing label.

We added the label.

Reviewer #2 (Recommendations for the authors):

Cool work!

(1) The reader would benefit from (a single) comprehensive figure that visually explains the entire conceptual framework-from TWCO principles to neural implementation to behavioural predictions-accessible to readers without specialised knowledge of oscillatory dynamics. This will give the paper a greater impact.

We have adjusted Figure 1 in accordance with suggestions made by reviewer 1 and added further explanations to the caption and the Introduction to enhance clarity on how the principles of TWCO relate to neural implementation.

(2) I think this paper would benefit from the audience eLife provides, but the paper could move closer to the audience.

(3) Pride comes before the fall, but I am not the most uninformed reader, and it took me some effort to process everything.

Thank you, we took this to heart. In the Introduction, we now state more explicitly how each variable is operationalized and how these map onto TWCO with improved reference to relevant panels in the schematic figure. We agree the framework is conceptually dense. TWCO principles reach the stimuli through specific V1 anatomy and physiology, so there are several links to keep in mind. Our goal with the revised introduction and figure is to make those links better visible.

(4) You could consider discussing potential implications for understanding perceptual disorders characterized by altered neural synchrony (e.g., schizophrenia, autism) and how your learning paradigm might inform perceptual training interventions.

Thank you for this suggestion. We have added that TWCO might provide a new lens to study perceptual disorders to the Discussion. We provide a concrete example of the relation between grouping, gamma synchrony (in light of TWCO) and lateral connectivity in schizophrenia

(5) I think this paper has real strength, but rather than dispersing limitations throughout the discussion, create a dedicated section that systematically addresses ecological validity, alternative explanations, and generalisability concerns. This will also preempt criticism.

We appreciate the suggestion. Our preference is to discuss limitations in context, next to the specific results they qualify, so readers see why each limitation matters and how it affects interpretation. Nevertheless, paragraph 7 on page 20 summarizes most limitations in a single paragraph.

DOI: 10.1085/jgp.202413677

Resource: National Xenopus Resource (RRID:SCR_013731)

Curator: @scibot

SciCrunch record: RRID:SCR_013731

What is this?

This line repeats throughout the poem as a refrain. It sounds like the fairies are trying to persuade the child to leave the human world.

It has an inviting tone, but more so to me gives me an immediate sense of the faeries trying to deceive. To persuade the child to abandon the human world. Who's best interest is this really?

Author response:

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

We now performed new experiments that were included in the manuscript. Our new results show that that monocyte-derived dendritic cells primed in vivo during P. chabaudi infection, or in vitro with TNF express high levels or GLUT-1 (Figures 4M, 5D, 6L). Furthermore, our new data show that mice treated with 2-DG (na inhibitor of glycolysis) are more susceptible to infection (Figures 6N, O). In addition, new results of glucose uptake by muscle and adipose tissues were added to the manuscript. Finally, figure legends were revised, densitometric analysis performed, and other issues addressed in the text.

Please see below a point-by-point reply to the Reviewers’ comments.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The manuscript by Kely C. Matteucci et al. titled "Reprogramming of host energy metabolism mediated by the TNF-iNOS-HIF-1α axis plays a key role in host resistance to Plasmodium infection" describes that TNF induces HIF-1α stabilization that increases GLUT1 expression as well as glycolytic metabolism in monocytic and splenic CD11b+ cells in P. chabaudi infected mice. Also, TNF signaling plays a crucial role in host energy metabolism, controlling parasitemia, and regulating the clinical symptoms in experimental malaria.

This paper involves an incredible amount of work, and the authors have done an exciting study addressing the TNF-iNOS-HIF-1α axis as a critical role in host immune defense during Plasmodium infection.

Reviewer #2 (Public Review):

Summary:

The premise of the manuscript by Matteucci et al. is interesting and elaborates on a mechanism via which TNFa regulates monocyte activation and metabolism to promote murine survival during Plasmodium infection. The authors show that TNF signaling (via an unknown mechanism) induces nitrite synthesis, which (via yet an unknown mechanism), and stabilizes the transcription factor HIF1a. Furthermore, HIF1a (via an unknown mechanism) increases GLUT1 expression and increases glycolysis in monocytes. The authors demonstrate that this metabolic rewiring towards increased glycolysis in a subset of monocytes is necessary for monocyte activation including cytokine secretion, and parasite control.

Strengths:

The authors provide elegant in vivo experiments to characterize metabolic consequences of Plasmodium infection, and isolate cell populations whose metabolic state is regulated downstream of TNFa. Furthermore, the authors tie together several interesting observations to propose an interesting model.

Weaknesses:

The main conclusion of this work - that "Reprogramming of host energy metabolism mediated by the TNF-iNOS-HIF1a axis plays a key role in host resistance to Plasmodium infection" is unsubstantiated. The authors show that TNFa induces GLUT1 in monocytes, but never show a direct role for GLUT1 or glucose uptake in monocytes in host resistance to infection (nor the hypoglycemia phenotype they describe).

We kindly disagree with the Reviewer. There is a series of experiments showing that TNFR KO (Figures 1, 2, 4), HIF1a KO (Figure 5) and iNOS KO (Figure 6) mice have partially impaired inflammatory response and control of parasitemia (Figures Figures 1E, 5G and 6B).

To further address the issue raised by the reviewer, we performed two sets of experiments. First, we show, in vitro, the impact of TNF stimulation on GLUT1 expression and glucose uptake (Figure 4M, 5D, 6L). Our results show that GLUT1 is increased after 18 hours with TNF (100 ng/mL) stimulation in MODCs from WT mice but not from iNOS KO, HIF1a KO e TNFR KO mice. Similar results were obtained with monocytic cells derived from infected mice (Figure 4L, 5C, 6K). The results support the discussion by demonstrating that TNF stimulation influences GLUT1 expression in monocytic cells. This aligns with the proposed mechanism that TNF signaling regulates HIF-1α stabilization and glycolytic metabolism via RNI. The absence of GLUT1 upregulation and glucose uptake in TNFR KO, iNOS KO and HIF-1α KO mice further reinforces the role of RNI in promoting HIF-1α stabilization, as suggested in the discussion.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Major points

All Figure legends are not precise about the data express means {plus minus} standard errors of the means (SEM) or SD. Figure 1D shows no SD in the data from the uninfected group. It strongly suggests precise and improving all figure legends, giving more details in terms of including an explanation of all symbols, non-standard abbreviations, error bars (standard deviation or standard error), experimental and biological replicates, and the number of animals, and representative of the independent experiments.

We apologize for the lack of details in the Figure legends. As requested, we are now indicating whether we used SEM or STDV, number of mice per group, number of replicate experiments. We also clarified the groups that are being compared, and the statistical significance indicated by the symbols. We also standardized symbols as asterisk only, and number of asterisk indicating the significance.

Figure 1. The figure legend has no information about the organ for which TNF mRNA was measured (Figure 1D). Also, regarding the TNF data, Figure 1 C e 1D shows that the circulating levels of TNF and the expression of TNF mRNA in the liver peaked at the same time point, and after 6h, there is no difference between infected and uninfected mice. It would be expected that the TNF mRNA expression would be detected earlier than the protein, assuming that the primary source of TNF is from the liver. Is there another organ that could mainly source blood TNF levels? Did the authors have a chance to measure the blood TNF levels during infection (0-8dpi), besides the measurement at different times only on day 8?

We included in the legend of Figure 1D that mRNA was extracted from liver.

Liver and spleen are the main reservoir of infected erythrocytes and the main source of cytokines during the infection with the erythrocytic stage of malaria. The results presented in Figures 1C and 1D are from in vivo experiments, not a controlled cellular experiment in vitro. So, we can not conclude about exact time and synchronous production of TNF mRNA and protein. We have published earlier that during P. chabaudi infection, the peaks of TNF mRNA expression and the levels of circulating TNF protein occur between midnight and 6 am (Hirako at al., 2018). Hence the results are consistent in the results described here. In addition, this earlier study also shows that the same pattern of TNF at days 6 and 8 post-infection are similar. Furthermore, in another studies, we reported that the peak of TNF production occurs between days 6 and 10 post P. chabaudi infection (Franklin et al, PNAS, 2009; Franklin et al, Microbes and Infection, 2007). This is now clarified in the text (page 05, line 132):

“As previously demonstrated, the circulating levels of TNF and expression of TNF mRNA in the liver peaked at 6 am (end of dark cycle) at 8 dpi (Figure 1C and 1D), and has been reported to peak between days 6 and 10 post-infection, with a consistent pattern observed on days 6 and 8.”

Figure 2. "We observed that in naïve animals, all of these parameters were similar in TNFR^-/- and C57BL/6 mice (Figures 2A-D, top panels, and Figures 2E-H)." Interestingly, the respiratory exchange rate of TNFR^-/- uninfected mice seems higher in TNFR^-/- uninfected mice than in naïve uninfected mice, and this pattern seems to be more pronounced in TNFR^-/- uninfected mice. Is there any suggestion that could explain the change in respiratory exchange rate behavior without infection in those animals?

At the moment, we have not investigated the basis of this difference between uninfected WT and TNFR KO mice, which goes beyond the scope of this research. This is indeed an interesting observation that should be pursued in the future by our group and elsewhere. We mentioned this difference, when describing the results (page 06, lines 155):

“We observed that in naïve animals, all of these parameters were similar in TNFR^-/- and C57BL/6 mice (Figures 2A-D, top panels and Figures 2E-H), with a slightly higher respiratory exchange rate in uninfected TNFR^-/- mice. In contrast, all the evaluated parameters were decreased in infected C57BL/6 mice compared to their naïve counterparts during the light and dark cycles. When we analyzed only infected mice, the alterations in all parameters were milder in TNFR^-/- compared to C57BL/6 mice (Figures 2A-D bottom panels and 2E-H).”

Figure 3. To give an idea of the main population of non-parenchymal cells, it will be helpful to clarify briefly how non-parenchymal cells from the liver of infected or uninfected mice were isolated.

We described in detail at Material and Methods (Page 19, Lines 566.)

Figure 3, B, C, D, G and Figure 4K and Figure 5 A and B - Semi-quantitative data through the densitometric analysis of western blots should be included in all figures.

Thank you for the suggestion. We now included the densitometric analysis for all Western blot results in Supplementary figure.

Figure 4. The author describes, "We observed that except for Hexokinase-3, the expression of mRNAs of glycolytic enzymes (Hexokinase-1, PFKP, and PKM) was increased in C57BL/6 but not TNFR-/- 8dpi." Sometimes, it is hard to understand which groups have been compared to some data. Be precise in describing the statistical analysis between the groups. It seems that those genes were increased in "infected C57BL/6 in comparison to uninfected mice, but not TNFR-/- 8-dpi. Moreover, even though the authors include statistic symbols "ι, ιι, ιιι" in other legends, there is no explanation about statistic symbols in the legend of Figure 4.

As mentioned above, we improved the descriptions of all figures in the legend, and when necessary in the main text describing the results.

Figure 5. The authors describe, "We found that GLUT1 protein and glycolysis (ECAR) was impaired, respectively, in monocytic cells and splenic CD11b+ cells from infected, as compared to uninfected HIF-1aΔLyz2 mice (Figures 5C-5E)." The GLUT-1 expression was inhibited in both cells compared to HIF-1afl/fl mice but not even close to impaired GLUT-1 expression. There is still a robust amount of GLUT-1 expression, and significantly higher when compared to cells from uninfected mice.

We tuned our statement to partially impaired, indicating that other host or parasite components maybe be also influencing GLUT-1 expression. In fact, we have recently published that IFNγ has also an important role in regulating GLUT1 expression in MO-DCs and this reference is mentioned in the text (page 10, line 291):

“We found that glycolysis (ECAR) and GLUT1 expression were impaired, though partially, in monocytic and splenic CD11b+ cells from infected HIF-1aΔLyz2 mice (Figures 5C-5E) compared to infected WT mice. The level of GLUT1 expression that is still maintained is likely due to other host or parasite factors, such as IFN-γ (Ramalho 2024).”

Figure 6. It is essential to have more information about the number of replicates in Figure 6A. However, there are just two dots replicates in the condition CD11b+ splenic cells from C57BL/6 stimulated with or without LPS (purple bars). It is essential to be precise regarding the number of experimental and biological replicates in each experiment and the statistical analysis that has been applied, including this group. Furthermore, the author concludes, "...these data demonstrated that RNI induces HIF-1α expression...." This conclusion needs a more careful description since no data supports that monocytic cells or splenic CD11b+ cells from iNOS-/- infected mice decrease stabilization of HIF-1αm using blotting, as shown in Figure 5 A.

As mentioned above the number of replicates for each experiment was included in the figure legends.

Minor Points.

Figure 3. "Hepatocytes have an important role in glucose uptake from the circulation, and they do this primarily through GLUT2 (38), whose mRNA expression was downregulated (Figure 3A) and protein expression unchanged in response to Pc infection (Figure 4K)." I suggest moving the Figure 4K to Figure 3 to make it easy to follow the data description.

We thank the reviewer for the suggestion. However, we chose to keep Figure 4K in Figure 4, as this panel includes data from TNF receptor deficient mice, and the analysis of TNF knockout models is first introduced and discussed in Figure 4. For clarity and consistency, we therefore maintained this panel within Figure 4.

Line 433. Replace iNOS for iNOS-/- mice.

iNOS is now replaced for iNOS-/- mice.

Reviewer #2 (Recommendations For The Authors):

The premise of the manuscript by Matteucci et al. is interesting and elaborates on a mechanism via which TNFa regulates monocyte activation and metabolism to promote murine survival during Plasmodium infection. The authors show that TNF signaling (via an unknown mechanism) induces nitrite synthesis, which (via yet an unknown mechanism), and stabilizes the transcription factor HIF1a. Furthermore, HIF1a (via an unknown mechanism) increases GLUT1 expression and increases glycolysis in monocytes. The authors demonstrate that this metabolic rewiring towards increased glycolysis in a subset of monocytes is necessary for monocyte activation including cytokine secretion, and parasite control.

The main goal of this work is to study the interplay of TNF/HIF1a/iNOs in the pathogenesis in an experimental model of malaria. To dissect the molecular mechanism by which TNF induces reactive nitrogen species and regulates HIFa expression is beyond the scope of our research. Nevertheless, there is a vast literature addressing these issues. We now include in the discussion a paragraph describing the main conclusion of these studies published previously (page 12, line 363):

"Previous studies have shown that TNF induces the production of RNI through the upregulation of iNOS via the NF-κB pathway (63, 64). TNF-mediated iNOS expression is critical for NO production, which in turn stabilizes HIF-1α by inhibiting prolyl hydroxylases (PHDs) even under normoxic conditions (58, 59). HIF-1α then upregulates the expression of glycolytic genes, including GLUT1 (22, 62).”

Major comments

Issues concerning novelty

Some of the reported observations are not novel. TNFa and TNFa signaling has been demonstrated to contribute to the release of certain cytokines, and to contribute to the control parasitemia (PMID: 10225939). TNFa has been shown to increase glucose uptake in tissues (PMID: 2589544). There is a textbook about the role of INOS during the pathogenesis of malaria, including its association with parasite control (https://link.springer.com/chapter/10.1007/0-306-46816-6_15). Furthermore, other mechanisms controlling glycemia during Plasmodium infection have been shown (PMID: 35841892). The authors should adequately discuss other papers which have reported some of their findings.

Thanks for the comments on previously existing literature. We are well aware of some of this earlier literature. Some of these earlier findings are mentioned in our manuscript. We emphasized these fundamental findings in the discussion, as requested (page 12, line 368):

“TNF has been described as a critical mediator in malaria, driving cytokine release and parasitemia control (PMID: 10225939). It also enhances glucose uptake in tissues, aligning with our findings of increased glycolysis in monocytes (PMID: 2589544). The role of iNOS in malaria is well documented. IFN-γ and TNF induced the production of NO, which inhibits parasite growth but can cause tissue damage and organ dysfunction, especially in severe malaria (Mordmüller et al., 2002). Recent studies also highlight the complexity of glycemia regulation during Plasmodium infection describing its role in modulating parasite virulence and transmission (PMID:35841892). These studies demonstrate the critical function of TNF and iNOS in immune responses against Plasmodium, aligning with our findings of this axis and metabolic rewiring that are essential for monocyte activation and outcome of Pc infection.”

The authors claim that "Reprogramming of host energy metabolism mediated by the TNF-iNOS-HIF1a axis plays a key role in host resistance to Plasmodium infection," and contributes significantly to their effector functions (particularly parasite clearing), and the systemic drop in glycemia observed during Pc infection. Although the authors show that TNFa does result in altered metabolism and increased GLUT1 levels in a subpopulation of monocytes, the evidence that TNFa-induced glylcolysis plays a key role in host resistance is correlative at best.

This is an important question. We did show that TNFR KO have higher parasitemia. But TNF is pleiotropic cytokine and has multiple roles on innate and acquired immunity. The experiment we have performed and helps to address this issue is the in vivo treatment with 2DG. We found that treatment with this inhibitor of glycolysis results in a increase of parasitemia. These results are now included in Figure 6.

When considering that the majority of monocytic populations are reduced in frequency and only a small subset (i.e., Monocyte-derived DCs) increase in frequency (Fig 3K) during Pc infection, this makes it very difficult to demonstrate that a cell population whose overall frequency reduces contributes significantly to the drop in glycemia during Pc infection. The authors should therefore include experiments that demonstrate that the inhibition of glycolysis induced by TNFa in monocytes is protective and/or contributes to a decrease in extracellular glucose. The authors could assess the impact of the loss of function of GLUT1 on activated monocytes and monocyte-derived DCs on glycemia upon TNFa stimulation.

We agree. We focused on monocytes and the derived inflammatory monocytes and MO-DCs. In fact, the frequency of monocytes, considering the inflammatory monocytes and MO-DCs, is increased both in spleen and liver. One interesting result is that the HIF1a Lysm KO mice has impaired metabolism, attenuated hypoglycemia and increased parasitemia (Figure 5). Nevertheless, we agree that our current data thus not proof that the glycemia is due to the consumption of glucose by the activated monocytes, and that these are the only cells with increased glucose consumption. This is now added to the discussion (page 13, line 395):

"Although the frequency of MO-DCs increases during infection, other cell populations may also contribute to glucose consumption. Further experiments, including the assessment of GLUT1 function in these populations, are needed to clarify their contribution to glucose consumption during infection."

Furthermore, in the current state of the manuscript, it is unclear how activated monocyte populations uptake glucose. The authors claim that glucose uptake by activated monocytes is GLUT1-dependent, however, glucose transport via GLUT1 is insulin-dependent. Since Plasmodium infection is associated with insulin resistance, and almost unquantifiable levels of insulin (PMID: 35841892), and TNFa itself induces insulin resistance (PMCID: PMC43887), it is unclear how the activated monocyte population uptakes glucose. If the authors consider TNFa to be sufficient for GLUT1 induction, in vitro experiments (TNFa+monocytes) could bolster this claim (and support that GLUT1 is induced in an insulin-independent mechanism.

There is significant evidences indicating that in contrast to GLUT4, induction of GLUT1 in mice is independent of insulin (PMID: 9801136). In our case, seems to be induced by the cytokines TNF and IFN𝛾(this study and Ramalho et al., 2024). We now performed experiments exposing monocytes to TNF and evaluating GLUT1 expression. The results indicate that monocytes exposed to TNF (100 ng/mL) for 18 hours from WT mice exhibited a significant increase in GLUT1 expression. This increase was comparable to the increased-GLUT1 phenotype observed in infected animals. The results of this experiment were included in the manuscript.

A text was included to the discussion to clarify the issue of insulin dependence of GLUT1 expression (page 13, line 388):

“GLUT1 expression is recognized as independent of insulin, in contrast to GLUT4 (PMID: 9801136). In our model, this regulation appears to be driven by pro-inflammatory cytokines, particularly TNF. Supporting this, our results show that in vitro stimulation with TNF, significantly increases GLUT1 expression in monocytes, accordingly to the ex vivo phenotype observed in infected animals.”

Alternative hypothesis which might explain their phenotypes

Figure 2 A-H: The metabolic effects of the genetic manipulations including INOS KO, TNFR KO, and HIF-1α∆Lyz2 could be explained by lesser disease morbidity owed to a reduction of inflammatory response during infection. Under this condition, the development of anorexia will not be as profound in the knock-outs compared with wild-type littermate controls, since anorexia of infection is tightly linked to the magnitude of inflammatory response. Accordingly, infected knock-out animals can keep eating, which presumably impacts glycemia, maintenance of core body temperature, and overall energetics of infected mice. The authors should exclude this possibility.

We consider this possibility and the discussion now elaborates about this alternative hypothesis. We believe, that these two mechanisms are not mutually exclusive (page 16, line 474):

“Although restored physical activity, food consumption and energy expenditure in knockout mice may contribute to the observed systemic metabolic parameters by altering energy balance, these effects are not mutually exclusive with the TNF-driven, cell-intrinsic metabolic mechanisms described here.”

Minor comments

The authors showed increased parasitemia upon TNFR and HIF1a depletion in the LyZ2 compartment. The same was observed upon organismal INOS depletion. This raises the question of whether the TNFHIF-INOS signaling axis is adaptive or maladaptive during Pcc infection. The authors should show host survival in mice lacking TNFR and HIF1a in the LyZ2 compartment, and in mice lacking INOS (presumably, they have these data).

Despite the fact the various knockout mice have increased parasitemia and signs of disease, they all survive the infection. This is now included in the Figure legends.

Are the higher tissue glucose levels specific to the liver and the spleen or this is a more general event? Have the authors looked at other organs?

We now added the results of glucose uptake in the muscle and adipose tissues in figure 2. The fact that the glucose uptake is not increased in muscle and adipose tissue, further suggest that the increased glucose uptake in this model is insulin independent.

Figure 1F: All core body temperatures are within the physiological range, i.e., >36 degrees C. This makes it unclear why the authors regarded this as hypothermia. The authors should present experiments demonstrating the development of hypothermia in Figure 1F, as they claim this.

Temperature changes in mouse kept in animal house have been an issue discussed in the field. It is clear, however, that early in the morning (end of active period) mice have torpor. Lower temperature and physical activity.

In Figure 4, since the authors already suggested that extra-hepatic cells, and not the liver parenchyma, contribute to glucose uptake, the authors should clarify why they analyzed the whole liver in Figure 4, and not extra-hepatic cells. Furthermore, the authors should quantify the hepatic monocytic population in non-infected versus infected wild-type animals.

The reason we used whole liver, is that the number of non-parenchymal cells obtained from liver is limited for Western blot analysis. We thought that was important to show that expression of GLUT1 was decreased in the liver of TNFR KO mice. Nevertheless, the level of TNFR expression in different cell types in the liver was shown by flow cytometry. In addition, we performed the WB with cells extracted from the spleen, where lymphoid and myeloid cells are more abundant.

Line 87: Phagocytizing parasitized what?

This has been corrected in the manuscript.

Line 111 Define RNI before being used.

Is there a gender disparity in the TNFR KO phenotype? If yes, the authors should comment about this in their discussion.

This has been defined and addressed in the manuscript

Line 192: Did the authors mean 3B??

In 3M, please plot monocytes from uninfected animals.

The plot of uninfected animals are now included in Figure 3M

Line 390 Remove the extra dash in HIF1a.

Extra dash has been removed.

Line 397 Define RA

RA is now defined.

Is it possible to use a hanging indent here?

DOI: 10.1177/00368504261426216

Resource: (Proteintech Cat# SA00001-1, RRID:AB_2722565)

Curator: @scibot

SciCrunch record: RRID:AB_2722565

What is this?

The H.O.W. Ledger Mechanics. This is a "Low-Friction, High-Impact" protocol.

Heal (H): Neutralises the "Glitch" of identity lies.

One Thing (O): Tracks the "Obedience" metric (did you do what the Spirit prompted?).

Walk (W): Documents the "Presence" data. By limiting this to three sentences and five minutes, you bypass the "resistance" of the ego that says you are too busy to lead. If you cannot spare five minutes to plot your position, you aren't leading; you are just drifting.

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quote

DOI: 10.1016/j.cell.2026.02.003

Resource: (BioLegend Cat# 304002, RRID:AB_314390)

Curator: @scibot

SciCrunch record: RRID:AB_314390

What is this?

Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

Learn more at Review Commons

Reply to the reviewers

We sincerely thank all the reviewers for their thoughtful and constructive comments.

In our revision, we have addressed the reviewer's specific criticisms with additional experiments and text edits as described below. We believe the constructive feedback from peer reviews helped us to significantly extend our mechanistic findings and strengthen the manuscript through revision.

Point-by-point description of the revisions

Reviewer #1:

Summary:

The study by Zatulovskiy et al. examined how cell size influences cell susceptibility to ferroptosis. The authors found a size dependence specifically for ferroptosis-inducing drug Era2, but not for other drugs. Using various human cell lines (HMEC, HT 1080, RPE 1), the authors generated populations of small and large G1 cells by FACS, CDK4/6 inhibition (palbociclib), or inducible cyclin D1 knockdown, and measured cell susceptibility to ferroptosis. Larger cells were more resistant than smaller cells. Mechanistically, larger cells showed reduced plasma membrane lipid peroxidation, higher glutathione concentrations, and changes in relevant cellular proteins levels, as analyzed using previously published data. Deleting ACSL4, which is involved in ferroptosis, partly eliminated the size dependence of ferroptosis. The work concludes that cell size is a key determinant of ferroptosis susceptibility.

My major concerns about this work focus on whether many of the results reflect cell size or cell cycle effects, and whether the FACS-based size-scaling analyses have some misleading features to their design & presentation. If these concerns can be addressed with new experiments, then the conclusions of this paper are justified. If these concerns cannot be addressed, then the authors should more directly acknowledge the alternative hypothesis that cell cycle effects may explain many of their results.

The experiments seem to be replicated sufficiently, and most conclusions rely on data from multiple cell lines. My minor comments focus on needs to provide statistics and method details, and on suggestions on how to improve text clarity, but these edits are easily done and don't require new experiments. Overall, this is an interesting study, and it should be published once the concerns below are addressed.

Major comments:

• In experiments reported in Fig 1 and 2A, the authors sort small and large cells in G1, plate them, and later start the drug treatments & cell monitoring. Are these cells actively cycling (progressing in the cell cycle), and how fast? The large cells are likely to enter S phase earlier than the small cells, so by the time that the authors start their drug treatments, they may be comparing cells in different cell cycle stages, which could influence drug sensitivity more than cell size (as the authors also suggest later in Fig 2). This needs to be controlled for. Furthermore, even if the cells remain in G1 after sorting until the drug treatments are started, the authors should address the fact that the drugs are present for a long time, thus targeting the cells in various cell cycle stages.

We agree with the reviewer that the cell cycle stage could affect ferroptosis susceptibility and could be a confounding effect in asynchronous cells. One of us (Dixon) reported the cell cycle effects on ferroptosis previously, and we observe them in this manuscript too (Fig. 2B,C,E). We now state this more clearly both in the Results and in the Discussion sections, where we write:

Line 159: "We note that non-arrested cells had a lower susceptibility to Era2-induced ferroptosis compared to cells that were arrested in G1 for 2-3 days, despite being smaller in size. This is likely due to the difference in the fraction of cells in different cell cycle phases between arrested and non-arrested conditions since cells in S/G2/M phases are known to be more resistant to ferroptosis than cells in G0/G1 phases (Rodencal et al, 2024; Kuganesan et al, 2023)"

Line 533: "Cells in G1 phase of the cell cycle were reported to be more susceptible to ferroptosis (Rodencal et al, 2024; Kuganesan et al, 2023), which suggested that ferroptosis inducers could be used in combination with cancer drugs, like the CDK4/6 inhibitor palbociclib, that arrest cells in G1 phase of the cell cycle (Herrera-Abreu et al, 2024). However, while CDK4/6 inhibitors arrest cells in G1, they do not inhibit cell growth, such that the longer they are arrested, the larger the cells grow (Lanz et al, 2022; Crozier et al, 2023; Manohar et al, 2023). This results in a complex, non-monotonic ferroptotic response dynamics in cells treated with CDK4/6 inhibitors (Fig. 2B,E). Just following CDK4/6 inhibitor treatment, as more and more cells are arrested in G1 phase, cells become more sensitive to both RSL3- and erastin-induced ferroptosis (Kuganesan et al, 2023; Rodencal et al, 2024). However, the longer the cells are arrested, the larger they become, which further promotes their susceptibility to RSL3 (Fig. S1B) but reduces their susceptibility to Era2-induced ferroptosis (Fig. 2B). The fact that the cell cycle arrest and cell size increase have opposing effects on Era2-induced ferroptosis susceptibility could explain why different studies reported seemingly contradictory results, where sometimes an increased and sometimes a decreased or unchanged sensitivity to system xc- inhibitors was observed depending on the cell type, duration and type of cell cycle arrest (Lee et al, 2024; Kuganesan et al, 2023; Rodencal et al, 2024). Such complex interplay between the cell cycle and cell size effects on ferroptosis suggests that combination therapies utilizing CDK4/6 inhibitors and ferroptosis inducers would have to carefully choose a dosage schedule.""

Given the potentially confounding effects of the cell cycle in cycling cells sorted by size, we performed an additional experiment, in which RPE-1 cells were pre-treated with the CDK4/6 inhibitor palbociclib to synchronize them in G1 phase prior to treatment. These cells were then continuously exposed to palbociclib during the Era2 treatment (Fig. 2C-E). RPE-1 cells pre-treated with palbociclib for 2 and 4 days had the same cell cycle distribution with 94% of cells being arrested in G1, but with different sizes. Cells treated with palbociclib for 4 days were significantly larger and more resistant to Era2 as can be seen in the Figure 2C-E.

Additionally, in the experiment shown in Fig. 5E,F, where we FACS-sorted WT and ACSL4 KO HMEC cells by cell size, and then measured Era2 susceptibility, we pre-treated the cells with palbociclib for 24 h to synchronize them in G1 prior to the sorting. We then cultured the cells in the presence of palbociclib during the Era2 treatment to avoid the cell cycle effects observed in Fig. 2. In this case, we still observe that larger cells are more resistant to Era2, consistent with our conclusion that cell size protects against Era2-induced ferroptosis.

Reviewer #1: "Can the G1 arrest-driven changes in drug susceptibility (Fig 2 C-D) be attributed to cell size? Can the authors rescue the palbociclib treatment with rapamycin or other growth inhibitors that allow size to remain small during G1 arrest?"

We have attempted to perform these experiments, but when we co-treated the cells with palbociclib and mTORC inhibitors, but observed variable results, which are likely due to the fact that prolonged mTORC inhibition itself rewires cellular metabolism and reduces cell susceptibility to ferroptosis, as one of us (Dixon) found previously (Armenta et al. (2022), Ferroptosis inhibition by lysosome-dependent catabolism of extracellular protein. Cell Chemical Biology 29: 1588-1600.e7). Our results were consistent with this previous report and is now included in a new supporting figure panel (Fig. S3C).

Thus, upon palbociclib+rapamycin co-treatment there seems to be a competition between cell-size-mediated and metabolism-mediated effects of mTORC inhibition on ferroptosis, which leads to variable outcomes.

Reviewer #1: "In Fig 2E-F, is the cell cycle distribution of the samples influenced by CCND1 shRNA induction? Are the drug sensitivity effects due to cell size or cell cycle changes?"

The CCND1 manipulation model is extensively characterized in our recent work cited in this manuscript (You et al. (2025), Cell size-dependent mRNA transcription drives proteome remodeling. 2025.10.30.685141 doi:10.1101/2025.10.30.685141). Indeed, CCND1 shRNA cells have a slightly elongated G1 phase due to a ~30% reduction in Cyclin D1 concentration: the G1 fraction changes from ~70% in wild-type to ~80% in CCND1 shRNA cells, which could potentially affect the ferroptosis susceptibility, but the additional results obtained on synchronized RPE-1 cells, described above (Fig. 2C-E), support the conclusion that the primary effect on Era2 sensitivity is due to cell size.

Reviewer #1: "Can the authors address the meaningfulness of the FACS-based size-scaling results in cases where cell-to-cell variability is very large? For example, in Fig 4D&G, the results are so variable even in identically sized cells that the importance of the size-scaling pattern seems questionable."

We do observe variability in fluorescent probe-based measurements of GSH and lipid oxidation, which could be due to biological (natural cell heterogeneity) and/or technical (low sensitivity of the probes) reasons. However, when we look at binned data and compare the mean values {plus minus} s.e.m. for each bin, we observe a robust and reproducible trend (black line with dark-grey shaded area), even though the SD is quite broad (lighter shaded area). We believe such trends are meaningful when describing cell death in probabilistic terms as we do. I.e., the GSH measurement might not be precise enough to predict cell death for a given individual cell, but the statistical trend is clear and these measurements help predict cell death probabilities for cells of different sizes.

Reviewer #1: "In Figs 4B-D, the cell size axis seems to have over 4-fold size variability, but when the authors show the analysis of this data (Figs 4E-G) the variability is only 2-fold. What was excluded and on what basis?"

To address this point, we have now clarified in the Methods section how the data were processed and what data points we excluded from this analysis:

Line 671: "For all binned flow cytometry data plots, the cells below the 2nd and above the 98th cell size percentiles were excluded to remove the extreme outliers. Then, the remaining data were binned by size and plotted as background-corrected average fluorescence intensity for each bin against the bin's average cell size. Bins with fewer than 200 cells were excluded from the analysis to reduce noise."

Typically, such pre-processing reduces the size range, mostly from the large-cell end, because of the long right tail of the size distribution containing a few very large cells.

Reviewer #1: "Based on the methods section & figure legends of Fig 4B-I, the RPE cells were not pre-sorted to include only G1 cells, nor did the assay account for cell cycle differences. How can these data be used to explain results from earlier figures, where analyses were exclusively focused on size differences in G1?"

This is a valid point: Cells in the GSH measurement experiment were not gated by Hoechst signal for G1 phase because the channel normally used for Hoechst staining was in this case occupied by the MCB probe. However, given the data in Fig. 4A,B showing that the GSH production machinery is superscaling when measured specifically in G1-phase cells, we believe the flow cytometry data in Fig. 4C-J showing GSH concentration increasing with cell size across the whole cell cycle is very likely true for G1 cells as well.

Reviewer #1: "Minor comments:

I recommend clarifying in the early introduction that all size changes discussed are in the absence of DNA content increase."

We have now clarified this in the introduction (Line 41 and Line 81).

Reviewer #1: "The introduction seems to cite primary research and review paper in the same sentences, which is a bit misleading as the reviews don't seem to add new evidence."

We have removed review citations where they did not provide additional context.

Reviewer #1: "*OPTIONAL* In the second introduction paragraph, consider the classification/description of the three different mechanisms. Currently, it seems that these mechanisms are not independent of each other, and the details provided about each mechanism are inconsistent."

We have now modified this paragraph to make the description more consistent.

Reviewer #1: "Please provide statistics for the IC50 values reported based on Fig 1C. Were small and large cells statistically different? Are the IC50 values reported as +/- standard deviation or some other metric?"

This has now been clarified in the text as follows:

"For example, at the 72 h time point, the Era2 IC50 was 28 {plus minus} 11 µM (mean {plus minus} SD) for large cells versus 2.0 {plus minus} 1.4 µM for small cells (Student's t-test: p = 0.039) (Fig. 1C)."

Reviewer #1: "*OPTIONAL* Providing more insight into why Era2 and RSL3 treatments yield more opposite responses would be of great interest to the field."

We agree this is an important point that should be discussed in more detail. In the field of ferroptosis, context-dependent (i.e., cell type-specific) effects are common and multiple groups including our own (Dixon) have published extensively on genes and mechanisms that can lead to differences between erastin2 and RSL3 sensitivity. For example, there are studies showing that the mTOR pathway or the p53 pathway can either prevent or promote ferroptosis, depending on the cell type and/or other currently unknown variables. To address more specifically the differences between Era2 and RSL3 in the context our observed cell-size-dependent response, we have now added more data and discussion. In the Results section we added panel 4B and the following text:

Line 359: "While the upregulation of GSH biosynthesis may promote the resistance of larger cells to ferroptosis, such an upregulation alone cannot explain why larger cells become more resistant to ferroptosis induced by the cystine import inhibitor Era2, but not, for example, by the GPX4 inhibitor RSL3 (Chan et al, 2025) (Figs. 2B, S1B). We found previously that upon mTORC1 inhibition cells can evade cystine deprivation-induced ferroptosis by uptake and catabolism of cysteine-rich extracellular proteins, mostly albumin (Armenta et al, 2022) (Fig. S3C). This process involves albumin degradation in lysosomes, predominantly by cathepsin B (CatB), and subsequent export of cystine from lysosomes to fuel the synthesis of glutathione. Large cells undergo proteome rearrangements similar to those occurring upon mTORC1 inhibition (Zatulovskiy et al, 2022). This suggests that large cells may upregulate CatB expression to bypass the Era2-induced cystine import inhibition via system xc-. To test this hypothesis, we used flow cytometry to measure how the expression of cathepsin B and the system xc- cystine/glutamate transporter SLC7A11 (xCT) scales with cell size (Fig. 4B). We found that SLC7A11 concentration modestly decreases, while CatB concentration significantly increases with cell size (Fig. 4B). This shift in the ratio between SLC7A11 and CatB supports the hypothesis that larger cells may rely less on cystine import via system xc- and thus become more resistant to system xc- inhibition by Era2."

Additionally, in the Discussion we added the following:

Line 578: "We show that large cells may become resistant specifically to Era2 but not RSL3 through the upregulation of lysosomal function, particularly cathepsin B expression, which enables the uptake and catabolism of cysteine-rich extracellular proteins. A size-dependent shift in the ratio between SLC7A11 and cathepsin B makes large cells less dependent on cystine import via system xc-, and thus, more resistant to Era2. In addition to this, it was reported that RSL3 can induce ferroptosis independently of GPX4 and may target other selenoproteins (DeAngelo et al, 2025; Cheff et al, 2023), which could also contribute to the difference in size-dependent responses to RSL3 and Era2."

Reviewer #1: "Is the BODIPY-C11 labeling specific to plasma membrane, as suggested by the writing of the authors, or do the results shown integrate signals over all cell membranes?"

We thank the reviewer for pointing this out. BODIPY-C11 581/591 stains many membranes in the cell, not just the plasma membrane. We have changed the wording in the manuscript to reflect this.

Reviewer #1: "How exactly is gating done for the flow cytometry samples? Especially when analyzing size-scaling, the results are likely to be sensitive to outliers, such as those seen in Fig 4C (a subpopulation of very low CFSE stained cells). Can the authors clarify their methods and/or display supplementary figures with gating examples?"

We have now specified our gating strategy in the Methods section (Line 663) and added a corresponding Supplementary Figure S5:

"Single cells were gated based on FSC-A vs SSC-A, then FSC-A vs FSC-H, then SSC-A vs SSC-W plots. From this population of single cells, G1 cells were selected using Hoechst-A vs FSC-A plot for subsequent scaling analysis"

Reviewer #1: "In Fig 4, total protein staining was used as a control, whereas Fig 5B b-actin was used as a control. Why did the authors rely on different controls approaches for essentially the same measurements? Are these controls comparable?"

In our flow cytometry experiments, we consistently use live-cell total protein stain (CFSE) for live cells, and anti-Tubulin immunofluorescent staining for fixed cells, both of which scale in proportion to cell volume and act as a read-out for total cellular protein content (Lanz and Zatulovskiy et al., Mol Cell 2022; Berenson et al. MBoC 2019), which we use to calculate concentrations of other cellular components (analogous to loading controls). In Fig. 5B, beta-Actin is used as a reference - a protein whose concentration does not change with cell size, as opposed to ACSL4 whose concentration decreases with cell size. In this plot, both ACSL4 and beta-Actin amounts were normalized to alpha-Tubulin, which is analogous to a concentration calculation using loading control. This is now explained in more detail in the Figure legend.

Reviewer #2:

"Zatulovskiy et al. demonstrate that cell size modulates susceptibility to ferroptosis, a form of iron-dependent cell death driven by lipid peroxidation. Using human cell lines (HMEC, HT-1080, RPE-1), the authors examined cell size through FACS sorting, CDK4/6 inhibition and inducible cyclin D1 knockdown. They found that larger cells are more resistant to ferroptosis induced by system xc⁻ inhibition (erastin2), but more sensitive to GPX4 inhibition (RSL3), highlighting pathway-specific size dependencies.

Mechanistically, larger cells exhibited:

• Higher glutathione levels, supporting lipid peroxide detoxification
• Increased ferritin expression, promoting iron sequestration
• Lower ACSL4 levels, reducing incorporation of peroxidation-prone lipids These findings were supported by high-throughput microscopy, flow cytometry (BODIPY-C11 lipid peroxidation assays), and proteomic analyses. The study concludes that cell size influences proteome composition and metabolic capacity, thereby shaping cell death decisions, an insight with implications for aging, cancer, and ferroptosis-based therapies.

Major Comments

1. Direct evaluation of SLC7A11 abundance and function is needed The opposite size-dependent effects of erastin2 and RSL3 strongly suggest a role for SLC7A11/system xc⁻ activity in size-dependent ferroptosis resistance. However, SLC7A11 levels were not quantified due to insufficient peptide detection in the proteomic data.

o Direct measurement of SLC7A11 protein levels (immunoblotting or flow cytometry) in small vs large cells would test whether its expression scales with size.

o Functional perturbation (siRNA/CRISPR knockdown) followed by erastin2 treatment would provide mechanistic validation.

o Use of additional SLC7A11 inhibitors (e.g., sulfasalazine, sorafenib) could further test whether the size resistance phenotype is xc⁻-specific."

We agree that the difference in size-dependent responses to RSL3 and Era2 is an important point that needs further investigation and discussion, as other reviewers also pointed out. To address more specifically the differences between Era2 and RSL3 in the context of cell-size-dependent response, we have now added more data and discussion. In the Results section we added panel 4B measuring SLC7A11 and Cathepsin B scaling with cell size and the following text:

Line 359: "While the upregulation of GSH biosynthesis may promote the resistance of larger cells to ferroptosis, such an upregulation alone cannot explain why larger cells become more resistant to ferroptosis induced by the cystine import inhibitor Era2, but not, for example, by the GPX4 inhibitor RSL3 (Chan et al, 2025) (Figs. 2B, S1B). We found previously that upon mTORC1 inhibition cells can evade cystine deprivation-induced ferroptosis by uptake and catabolism of cysteine-rich extracellular proteins, mostly albumin (Armenta et al, 2022) (Fig. S3C). This process involves albumin degradation in lysosomes, predominantly by cathepsin B (CatB), and subsequent export of cystine from lysosomes to fuel the synthesis of glutathione. Large cells undergo proteome rearrangements similar to those occurring upon mTORC1 inhibition (Zatulovskiy et al, 2022). This suggests that large cells may upregulate CatB expression to bypass the Era2-induced cystine import inhibition via system xc-. To test this hypothesis, we used flow cytometry to measure how the expression of cathepsin B and the system xc- cystine/glutamate transporter SLC7A11 (xCT) scales with cell size (Fig. 4B). We found that SLC7A11 concentration modestly decreases, while CatB concentration significantly increases with cell size (Fig. 4B). This shift in the ratio between SLC7A11 and CatB supports the hypothesis that larger cells may rely less on cystine import via system xc- and thus become more resistant to system xc- inhibition by Era2."

Additionally, in the Discussion we added the following:

Line 578: "We show that large cells may become resistant specifically to Era2 but not RSL3 through the upregulation of lysosomal function, particularly cathepsin B expression, which enables the uptake and catabolism of cysteine-rich extracellular proteins. A size-dependent shift in the ratio between SLC7A11 and cathepsin B makes large cells less dependent on cystine import via system xc-, and thus, more resistant to Era2. In addition to this, it was reported that RSL3 can induce ferroptosis independently of GPX4 and may target other selenoproteins (DeAngelo et al, 2025; Cheff et al, 2023), which could also contribute to the difference in size-dependent responses to RSL3 and Era2."

Reviewer #2: "2. Functional tests of ferritin contribution to resistance are needed

Although elevated ferritin (FTH1/FTL) levels in larger cells represent a strong correlational signal, definitive experimental evidence establishing causality is currently lacking.

o Measuring the labile iron pool directly in size-stratified populations would strengthen the link.

o Knockdown of FTH1 or FTL could reveal whether ferritin upregulation is necessary for the resistance of large cells to ferroptosis."

We thank the reviewer for raising this point. We have now completed additional experiments, as suggested by the reviewer, and found that iron chelation is unlikely to mediate the size-dependent response to Era2. We have modified the manuscript accordingly and added the following data and discussion to address this point:

Line 296: "The observed increase in ferritin concentration with cell size could therefore lead to additional Fe2+ ion chelation, which in turn would protect large cells from iron-dependent lipid peroxidation and ferroptosis. However, when we measured the concentration of labile intracellular Fe2+ using a fluorescent probe FerroOrange (Hirayama et al, 2020), we did not observe any size-dependent decrease in labile iron concentration (Fig. S2A). Previous work suggests a link between increased sequestration of ferrous iron in lysosomes and resistance to ferroptosis. It was reported that senescent cells, which are also large (Fig. S3A,B), gain resistance to ferroptosis through lysosomal alkalinization and sequestration of ferrous iron in lysosomes (Loo et al, 2025). We therefore tested whether the superscaling of lysosomes observed in large cells (Lanz et al, 2022; You et al, 2025) promotes Era2 resistance through lysosomal iron sequestration. To do this, we stained the cells with the lysosomal iron detection probe Lyso-FerroRed (Saimoto et al, 2025) and measured its scaling using flow cytometry (Fig. S2B). We observed that the amount of Lyso-FerroRed, and therefore, the amount of lysosomal iron, scaled in direct proportion to cell size, just like the total cellular protein content (Fig. S2B). These results indicate that iron chelation by ferritin and its sequestration in lysosomes are unlikely to play a crucial role in size-dependent decrease in Era2 sensitivity."

Reviewer #2: "3. Relevance to senescence should be addressed experimentally or explicitly discussed

Given that senescent cells are enlarged and accumulate in aged and tumour tissues, testing senescent models for erastin2 resistance would greatly strengthen the physiological significance."

We agree that an increase in cell size contributing to the resistance of senescent cells to ferroptosis is intriguing. We have now added a Supplementary Figure S3 and discussion of this point in the manuscript as follows:

Discussion line 552: "our data suggest that previously reported resistance of senescent cells to ferroptosis can at least partially be due to the increased cell size, a well-established hallmark of senescence."

Reviewer #2: "Minor Comments

1. Mechanistic nuance regarding RSL3 should be included RSL3 has been reported to induce ferroptosis independently of GPX4 (PMID: 37087975, PMID: 40392234) and may target other selenoproteins such as TXNRD1. This nuance would help explain the observed divergence between RSL3 and erastin2 sensitivity across sizes."

We have now added this in the Discussion as suggested by the reviewer (line 583):

"In addition to this, it was reported that RSL3 can induce ferroptosis independently of GPX4 and may target other selenoproteins (DeAngelo et al, 2025; Cheff et al, 2023), which could also contribute to the difference in size-dependent responses to RSL3 and Era2."

Reviewer #2: "2. Dynamic range of BODIPY-C11 assays needs commentary

Despite high erastin2 doses, the oxidized BODIPY signal remains close to DMSO levels. The authors should comment on whether this reflects high GSH buffering capacity, probe limitations, or other factors."

We believe there are both technical (narrow dynamic range of the probe) and biological reasons for the relatively small (2-3 fold) difference in Oxidized-to-Non-oxidized BODIPY-C11 ratios between DMSO and Era2-treated cells. The biological reason is that the cells continue producing GSH until they fully deplete the cystine pool, which happens ~20-24 h after Era2 addition. Once the cystine pool is depleted, the cells very rapidly deplete GSH and initiate cell death. Therefore, there is only a short time window where cells are strongly depleted of GSH before dying. We see this small fraction of cells with a high Oxidized BODIPY-C11 signal in our flow cytometry experiments and in previous microscopy analysis of BODIPY-C11 (Murray et al., Protocol for detection of ferroptosis in cultured cells. STAR Protoc. 2023), but at our chosen time point (20h Era2) most cells are not as bright because we aimed to analyze the population before the onset of widespread cell death.

Reviewer #2: "3. Western blot for shCycD1 depletion should be included

CycD1 depletion usually causes cells to stop proliferating, which is not the case here. Therefore, depletion must be partial. The level of depletion should be shown by immunblotting."

The CCND1 manipulation model is extensively characterized in our recent work cited in this manuscript (You et al. (2025), Cell size-dependent mRNA transcription drives proteome remodeling. 2025.10.30.685141 doi:10.1101/2025.10.30.685141). CCND1 shRNA cells do not fully arrest in G0/G1 because the concentration of Cyclin D1 protein in this system is only partially decreased, as the reviewer noted. As a result, the cells have a slightly elongated G1 phase due to a ~30% reduction in Cyclin D1 concentration, but continue to proliferate. The G1 fraction changes from ~70% in wild-type to ~80% in CCND1 shRNA cells.

Reviewer #3:

"In this manuscript, Zatulovskiy and colleagues elaborate on their previous work describing cell size-dependent changes in the proteome by investigating whether these changes can be correlated in differences in cell physiology. Using a cleverly-designed high throughput screen, they searched for compounds that differently-sized cells display differential sensitivity towards. Their primary hit, Era2, is involved in the ferroptosis pathway and serves as the starting point for a detailed study of how excess cell size protects cells from ferroptosis-induced cell death via: 1) lower concentrations of ACSL4 (which produces peroxidation-prone PUFAs), 2) increased ferritin concentrations, and 3) increased GSH concentrations.

Overall, the experiments in this manuscript are well-designed and interpreted. It is an extremely well-written manuscript with a clear trajectory of logic. I have only a few major concerns that should be addressed before publication:"

We thank Reviewer #3 for their careful reading of the manuscript and for the clear summary of our study and its central findings. We appreciate their positive assessment of the experimental design, interpretation, and overall clarity of the writing and logical flow. We are also grateful for their constructive feedback and take their major concerns seriously; we have addressed each point in detail below.

Reviewer #3: "Major concerns:

1) In Figure 3E, the authors gate their flow cytometry data using SYTOX so that they are only analyzing live cells. Based on their gating scheme, it seems like there are really a lot of dead cells. Presumably the cells that died were the most sensitive to Era2, so it seems an oversight to discard these cells. Of course, it is not appropriate to analyze dead cells, but this could potentially be solved by using a shorter treatment duration than 24 hours wherein fewer cells die."

This is a good point. To address it, we have now replaced this panel with a time point where most cells are still alive (20 h, 0.2 µM Era2), as suggested by the reviewer (Fig. 3E,F). This did not change the conclusion that BODIPY-C11 oxidation decreases with cell size.

Reviewer #3: "2) In Figure 5, are the small, medium, and large bins for ACSL4 KO cells the same as for WT cells? If the ACSL4 KO cells are just bigger to begin with, this could explain why the "small" bin has greater cell survival than the WT small bin. Moreover, is the overlap between the three bins the same in the WT and KO cells?"

This is an important point that we now address with data shown in Fig. S4B. We have now added a Supplementary Figure S4B to show the relative size of small, medium, and large WT and ACSL4 KO HMEC cells. As seen from this graph, the ACSL4 KO cells are not bigger than WT cells. Importantly, the fold-range between the small and large FACS-sorted cells is similar (~1.9 to 2-fold).

Reviewer #3: "3) Loo, et al. Nat Comms 2025 similarly found that senescent cells (which are enlarged) are resistant to ferroptosis using the same inhibitor as the authors. In contrast to the authors, they show that this is due to lysosomal alkalinization and sequestration of ferrous iron in lysosomes. Given that Lanz et al. 2022 found that lysosomal components super-scale with cell size, it seems like this would be an important hypothesis to address. Free lysosomal iron can be easily measured with the LysoRhoNox stain. Loo et al. was able to restore ferroptosis sensitivity in senescent cells using the V-ATPase activator EN6, so it would be important for the authors to address whether this (or similar) treatment would have the same effect in enlarged cells."

This is an excellent point. We have now performed this experiment and added it to the manuscript, as suggested by the reviewer. Based on the Lyso-FerroRed staining (another brand name for the LysoRhoNox probe), we do not see an increase in lysosomal iron sequestration in large cells (Fig. S2B) - see the graph and the corresponding discussion below:

Line 301: "Previous work suggests a link between increased sequestration of ferrous iron in lysosomes and resistance to ferroptosis. It was reported that senescent cells, which are also large (Fig. S3A,B), gain resistance to ferroptosis through lysosomal alkalinization and sequestration of ferrous iron in lysosomes (Loo et al, 2025). We therefore tested whether the superscaling of lysosomes observed in large cells (Lanz et al, 2022; You et al, 2025) promotes Era2 resistance through lysosomal iron sequestration. To do this, we stained the cells with the lysosomal iron detection probe Lyso-FerroRed (Saimoto et al, 2025) and measured its scaling using flow cytometry (Fig. S2B). We observed that the amount of Lyso-FerroRed, and therefore, the amount of lysosomal iron, scaled in direct proportion to cell size, just like the total cellular protein content (Fig. S2B). These results indicate that iron chelation by ferritin and its sequestration in lysosomes are unlikely to play a crucial role in size-dependent decrease in Era2 sensitivity."

Reviewer #3: "Minor concerns:

1) It would be helpful if this manuscript were re-submitted with line numbers to more easily reference the text."

We have added line numbers for convenience.

Reviewer #3: "2) In Figure 5A and other figures that reproduce data from Lanz et al. 2022, it would be helpful to have a summary curve for the overall abundance of each protein rather than only the individual peptide curves. These plots (particularly Figure 5A) are difficult to interpret since some peptides were presumably more abundant / measured with higher confidence than others."

We have added the average ACSL4 protein slope line to Fig. 5A.

Reviewer #3: "3) In Figure 5, the authors show the validation of the ACSL4 KO HT-1080 cell line but not HMEC, even though both are used in this figure. It would be useful to show both. Additionally, the authors switch back and forth between the two cell lines for this figure, and it is not clear why."

We have added the HMEC ACSL4 KO validation Western blot in Fig. S4A (see below).

For the BODIPY oxidation experiment (Fig. 5D), we used HT-1080 instead of HMEC because HT1080 cells are sensitive to lower concentrations of Era2, and therefore, we could better optimize the Era2 concentrations and treatment durations to measure BODIPY oxidation at the time point when most cells are still alive but demonstrate a pronounced oxidized BODIPY signal.

Reviewer #3: "4) In Figure 5B, the authors use antibody-based staining of ACSL4 and flow cytometry to correlate a loss of ACSL4 expression with increased cell size, validating the proteomics data in Figure 5A. This does not seem like a good way to do this. Firstly, fixing cells with formaldehyde alters their size (is this proportional across differently sized cells? It's impossible to know), which makes it inappropriate to use SSC as a proxy for size in this particular situation. Secondly, the normalization scheme here doesn't make sense. If actin was used as a reference protein, why was tubulin used to normalize ACSL4 abundance? Overall, this seems like a very round-about experiment that could have just been addressed by doing a simple western blot with the four size bins sorted from live cells (as it was in the proteomics). If the issue is that ACSL4 is not detectable by western in the HMEC cells, another solution would be plating the live, sorted bins on coverslips and measuring by IF (or using the HT-1080 cells)."

We prefer IF flow cytometry to Western blotting for protein scaling analysis because it is more quantitative and provides cell size and protein content information for each individual cell. While in principle, different-sized cells might change their size differently during fixation, the cells that were larger or smaller prior to the fixation remain larger or smaller after fixation as well. Therefore, the SSC measurement after fixation still provides reliable information on size ranking, even if SSC does not perfectly linearly scale with cell volume. We do not use the SSC information to calculate protein concentrations here. Instead, we divide the amount of our protein of interest in the cell by the amount of constitutively-expressed Tubulin, which acts as an analogue of a loading control in this experiment. In Fig. 5B, both ACSL4 and Actin were normalized to Tubulin to estimate their concentrations. Actin is used just as a reference protein to show how the concentration of a perfectly scaling protein remains constant across cell size, as opposed to the sub-scaling ACSL4. Tubulin in this case was used as a proxy for total cellular protein content, which scales linearly in proportion to cell volume. This approach for determining the scaling behaviors of different proteins was previously validated in Lanz et al., Mol Cell 2022.

Reviewer #3: "5) In Figure 5E/5F, the authors pre-arrest the cells in G1 with palbociclib before size-sorting them. The pre-arrest is not done in other experiments using this cell line for size-sorting, so it would be important for the authors to comment on why this was done for this experiment but not others."

As we found in Fig. 2B-E, the cell cycle has confounding effects on size-dependent ferroptosis susceptibility measurements (as discussed in detail in our response to the first major point of Reviewer #1 above). Briefly, to avoid these confounding effects and isolate the effects of cell size from the effects of the cell cycle, we pre-synchronized the cells with 24 h treatment with palbociclib in Fig. 5E,F. This is now better clarified in the text, as follows:

Line 456: "In this experiment, we synchronized cells in G1 phase using palbociclib prior to cell sorting and also incubated the sorted cells in the presence of palbociclib during Era2 treatment to isolate cell size effects from the previously observed confounding effects of the cell cycle on ferroptosis (Fig. 2B,E)."

Reviewer #3: "6) Conceptually, it is difficult for me to understand why large cell size sensitizes cells to GPX4 inhibition but confers resistance to Era2 treatment. Particularly given the pathway described in Figure 3A, I am having trouble understanding why these would convey such opposing phenotypes. Shouldn't the extra ferritin in the bigger cells also help them cope with GPX4 inhibition if, as the authors state in the discussion, the increased sensitivity to the GPX4 inhibitor is reported to be mediated by (among other things) iron accumulation? A deeper discussion of this seeming-incongruity would be helpful for contextualizing the broader role of cell size in determining ferroptosis sensitivity."

We agree this is an important point, which was also raised by the other reviewers. As such, we note that context-dependent (i.e., cell type-specific) effects are common in the ferroptosis field, and multiple groups including our own (Dixon) have published extensively on genes and mechanisms that can lead to differences between erastin2 and RSL3. For example, there are studies showing that the mTOR pathway or the p53 pathway can both prevent and promote ferroptosis, depending on the cell type or some other hidden variable.

To better address the differences between Era2 and RSL3 in the context of the cell-size-dependent response, we have now added more data and discussion. In the Results section we added panel 4B and the following text:

Line 359: "While the upregulation of GSH biosynthesis may promote the resistance of larger cells to ferroptosis, such an upregulation alone cannot explain why larger cells become more resistant to ferroptosis induced by the cystine import inhibitor Era2, but not, for example, by the GPX4 inhibitor RSL3 (Chan et al, 2025) (Figs. 2B, S1B). We found previously that upon mTORC1 inhibition cells can evade cystine deprivation-induced ferroptosis by uptake and catabolism of cysteine-rich extracellular proteins, mostly albumin (Armenta et al, 2022) (Fig. S3C). This process involves albumin degradation in lysosomes, predominantly by cathepsin B (CatB), and subsequent export of cystine from lysosomes to fuel the synthesis of glutathione. Large cells undergo proteome rearrangements similar to those occurring upon mTORC1 inhibition (Zatulovskiy et al, 2022). This suggests that large cells may upregulate CatB expression to bypass the Era2-induced cystine import inhibition via system xc-. To test this hypothesis, we used flow cytometry to measure how the expression of cathepsin B and the system xc- cystine/glutamate transporter SLC7A11 (xCT) scales with cell size (Fig. 4B). We found that SLC7A11 concentration modestly decreases, while CatB concentration significantly increases with cell size (Fig. 4B). This shift in the ratio between SLC7A11 and CatB supports the hypothesis that larger cells may rely less on cystine import via system xc- and thus become more resistant to system xc- inhibition by Era2."

Figure 4. (B) Flow-cytometry-based measurement of cystine/glutamate transporter SLC7A11 (xCT) and cathepsin B (CatB) concentrations in G1-phase RPE-1 cells demonstrates a modest decrease in SLC7A11 and a significant increase in Cathepsin B concentrations with cell size. To calculate the concentrations of SLC7A11 and CatB, their amounts were measured with flow cytometry using immunofluorescence and normalized to the amounts of α-Tubulin. The data were binned by cell size, and mean values for each bin were plotted against normalized cell size (solid blue line for SLC7A11 and red line for CatB). Shaded areas denote the s.e.m. for each bin.

Additionally, in the Discussion we added the following:

Line 578: "We show that large cells may become resistant specifically to Era2 but not RSL3 through the upregulation of lysosomal function, particularly cathepsin B expression, which enables the uptake and catabolism of cysteine-rich extracellular proteins. A size-dependent shift in the ratio between SLC7A11 and cathepsin B makes large cells less dependent on cystine import via system xc-, and thus, more resistant to Era2. In addition to this, it was reported that RSL3 can induce ferroptosis independently of GPX4 and may target other selenoproteins (DeAngelo et al, 2025; Cheff et al, 2023), which could also contribute to the difference in size-dependent responses to RSL3 and Era2."

logos: This is logos because it presents research findings and evidence.

Logos: presents research findings and evidence

¿Permite el co-diseño agenciamiento o es solo co-creación?

Esto rompe con el paradigma de la tesis como producto lineal y propone verla como un producto de diseño en evolución en el que se aprovechan las tensiones o fisuras como puentes para el aprendizaje

Um reforço: incluir o Ecommerce na Prática em ecossistema, mostrando que oferecemos não só infraestrutura. Somos a parte educativa, o que ajuda a dar o próximo passo independente do estágio do lojista. Todo lojista tem acesso (alguns a mais áreas, outros a menos) e não sabe disso.

+ Não tenho certeza se é uma big bet, pra mim é algo que deve ser recorrente, seguindo o caminho dos marketplaces.

Dúvida: a gente sabe como foi a performance das lojas no período da última Copa? Eu vejo que devemos ter cuidado em relacionar com Black Friday. Iria menos por esse gancho, salvo se tivermos dados internos de que as lojas tiveram pico durante o período. E lembrando que a copa foi em outro período: a última copa do mundo foi em nov/dez e a BF desse ano no e-commerce teve uma queda de 21%, em média (dado de mercado, não sei na Nuvem). Poucos setores tiveram alta

Aqui pra mim é continuar com a ideia de Lumi, seguir ativações, e usar Copa como gancho. Dá para testar vários... exemplo: Sua equipe para para ver o brasil jogar? o Lumi não.

Gosto evento online como um demo day e o EnP pode fazer o passo a passo. Mas vejo valor em também ter um evento físico, pra imprensa e pra lojistas, com apresentação da novidade, como ocorre com nuvemcommerce

Eu não sei o nível que tá o Lumi, mas como ref: teve um evento há uns 2 anos onde a IA de uma empresa debatia com uma pessoa real sobre temas de inclusão. Eu esqueci o nome da empresa, mas na época foi algo super atrativo porque mostava a lucidez das respostas da IA da marca. Seria legal algo assim de demo.

Esse conceito da Carta de Demissão é comum do mercado de infoproduto. Eu gosto bastante, acho provocativo. Consigo visualizar um vídeo de campanha bem interessante, talvez até com lojistas "tirando do ar" vagas no Linkedin - e aqui só entender se o tom não geraria desconforto demais. Do ponto de vista do lojista, ter tempo pra dedicar à gestão e estratégia do negócio é incrível. Minha dúvida é mais sobre o tom, pois se não tomar cuidado por soar como a "IA está substituindo pessoas" e gerar um buzz reverso. Não sei até que ponto Nuvemshop está disposta a este posicionamento, Vivi certamente pode falar mais sobre isso.

Na raíz, o que estamos dando pro empreendedor é tempo. Com Lumi, ele pode dedicar o tempo para gerir e implementar/revisar estratégias para o negócio crescer. As mesmas 24h de antes, só que agora ele não está sozinho.

Uma outra ideia de abordagem surge disso: início do turno 24/7, algo nesta linha. Brinca um pouco com a discussão do mercado sobre escala de trabalho...

Um caminho foca na dor, outro no ganho

Sería bueno tener una tabla de caracterización por carrera para, por ejemplo, sabe la composición por genero en Ed. Parvulari o el % de colegios particulares en trabajo social, etc.

Esta es una pregunta central en la inteligencia artificial: si la inteligencia es realmente pensamiento o simplemente cálculo avanzado.

Durante el entrenamiento el modelo ajusta millones o miles de millones de parámetros para mejorar sus predicciones.

Se señala uno de los problemas actuales de internet: la rapidez en la circulación de información muchas veces supera los mecanismos de verificación o control.

Mosaic fue uno de los primeros navegadores gráficos importantes, y en él se experimentó con la idea de permitir a los usuarios hacer anotaciones o comentarios sobre las páginas web. Mosaic

Este ejemplo es muy grafico ya que demuestra como cualquier persona con acceso a internet puede publicar información que sin querer o con intención puede lograr tener un impacto global.

分析句子结构：The people elected Bill Clinton President.

这句话是一个典型的主谓宾补（SVOC）结构。它的核心逻辑是“主语动作了宾语，并使宾语处于某种状态或身份”。

下面是该句子的详细拆解：

1. 语法成分分析

• The people (主语 - Subject): 执行动作的主体。
• elected (谓语 - Verb): 动作。这是一个及物动词，且在此处作为不完全及物动词使用，因为它需要补足语才能表达完整意思。
• Bill Clinton (宾语 - Object): 动作的承受者。
• President (宾语补足语 - Object Complement): 重点就在这里。这个名词用来补充说明宾语 Bill Clinton 的身份。

2. 结构公式

该句子的结构可以抽象为：

Subject + Verb + Object + Object Complement (S + V + O + C)

3. 深度解析：为什么是“补语”而不是“双宾语”？

很多人会把这个结构跟“授予”类动词（如 Give me a book）混淆。辨别的方法很简单：

• 看逻辑关系： 在本句中，宾语和补足语之间存在逻辑上的“主系表”关系。即：Bill Clinton IS President.（克林顿是总统）。
• 对比双宾语： 在 Give me a book 中，你不能说 I am a book。

4. 词法小贴士

• 冠词省略： 在担任“唯一职位”或“头衔”的宾补位置上，名词前的冠词（如 the）通常可以省略。所以直接用 President 而不是 the President。

你想再尝试分析一个类似的结构，还是想看看这个句子如何转换成被动语态？

那这两句又怎么说： John's father gave him a dog.  John's father called him a dog.

如果用上面的逻辑关系分析方法，He is a dog，都说的通。

这是一个非常经典的语法陷阱！你敏锐地察觉到了“逻辑关系”的相似性，但在这里，我们需要引入“语义逻辑”和“动词属性”来做最终裁决。

虽然从字面上看都能凑出 $He = a\ dog$，但它们在语法深层结构上完全不同：

1. John's father gave him a dog. (双宾语结构)

• 结构： 主语 + 谓语 + 间接宾语 (IO) + 直接宾语 (DO)
• 逻辑： 这里的 gave 是一个授予动词（Ditative Verb）。它表示的是“所有权的转移”。
• 为什么不是补语？ 这里的 him 和 a dog 是两个独立的实体。虽然你可以开玩笑说“他是一条狗”，但在正常的语义逻辑下，他（人）不等于狗（动物）。父亲把“狗”这个东西给了“他”。
• 验证方法： 间接宾语和直接宾语通常可以用 to 或 for 改写：

  John's father gave a dog to him. (成立)

2. John's father called him a dog. (宾补结构)

• 结构： 主语 + 谓语 + 宾语 (O) + 宾语补足语 (C)
• 逻辑： 这里的 called 是一个命名/定义动词。它不涉及任何实物的传递，而是涉及“认知”或“称呼”。
• 为什么是补语？ 这里的 a dog 是用来定义 him 的特征或身份的。在父亲的语言定义里，him = a dog（这里的狗通常是指骂人的隐喻）。
• 验证方法： 1. 无法改写： 你不能说 John's father called a dog to him（这变成了把狗叫到他身边，意思全变了）。
• 插入 to be： 在宾语和补语之间插入 to be 依然通顺：...called him to be a dog.

对比总结表

| 句子 | 动词类型 | 逻辑关系 | 结构名称 | | --- | --- | --- | --- | | Give him a dog | 授予动词 | 他 拥有 狗 (Ownership) | 双宾语 (S+V+IO+DO) | | Call him a dog | 定义动词 | 他 就是 狗 (Identity) | 宾补 (S+V+O+C) |

💡 幽默警示

这就是英语有趣的地方：

• 如果你说 "Make me a sandwich," 你是在要吃的（双宾语：给我做个三明治）。
• 如果你遇到一个会魔法的杠精，他可能会说 "Poof! You are a sandwich!"（宾补：把你变成了三明治）。

既然聊到了这种容易混淆的结构，你想看看怎么通过“被动语态”的变换来一眼识别它们吗？（提示：其中一个句子有两个被动变法，另一个只有一个）

既然你准备好了，咱们就用“被动语态测试法”来给这两个结构做个“亲子鉴定”。

这个方法非常硬核，能让你在考场或写作时瞬间清醒：

1. 双宾语 (Give him a dog) —— 左右逢源

因为有两个宾语（人和物），所以它理论上有两种变法：

• 变法 A（以人为中心）： He was given a dog by John's father. (非常通顺)
• 变法 B（以物为中心）： A dog was given to him by John's father. (也通顺)
• 结论： 既然两个词都能独立出来当主语，说明它们是两个平等的实体（两个宾语）。

2. 宾补 (Call him a dog) —— 只有一条路

因为 a dog 只是 him 的一个“标签”，它不能独立门户：

• 变法 A（以人为中心）： He was called a dog by John's father. (完全正确，他被骂成是狗)
• 变法 B（以标签为中心）： A dog was called him by John's father. (错误！ 这变成了“一只狗被他父亲称呼为‘他’”，语义完全崩塌)
• 结论： 补足语就像影子，必须跟着宾语走。一旦宾语变成主语，补语就变成了主语补足语。

⚡️ 进阶挑战

看看下面这两个句子，你能一眼看出哪个是“双宾语”，哪个是“宾补”吗？

1. They found him a job.
2. They found him a hero.

提示： 试试看能不能改成 “He was found to be a...”？如果你觉得有难度，可以告诉我你的直觉！

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• Vide que a regra constitucional, quanto à responsabilidade do empregador por acidentes de trabalho, é a responsabilidade subjetiva, exigindo-se demonstração de culpa ou dolo.
• No entanto, sobretudo diante o recente Tema 932/STF, é constitucional a atribuição de responsabilidade objetiva ao empregador em hipóteses legais ou situações em que se impõe um risco de trabalho mais acentuado.
• Por fim, importante frisar que não é correto atribuir indiscriminadamente responsabilidade objetiva ao empregador por todo e qualquer acidente de trabalho, na forma já pontuada.

• Informativo 969
• RE 828040 / DF
• Órgão julgador: Tribunal Pleno
• Relator(a): Min. ALEXANDRE DE MORAES
• Julgamento: 12/03/2020 (Presencial)
• Ramo do Direito: Civil, Constitucional
• Matéria: Responsabilidade Civil - Direitos Sociais

Responsabilidade civil objetiva e acidente de trabalho

• O artigo 927, parágrafo único, do Código Civil é compatível com o artigo 7º, XXVIII, da Constituição Federal, sendo constitucional a responsabilização objetiva do empregador por danos decorrentes de acidentes de trabalho, nos casos especificados em lei, ou quando a atividade normalmente desenvolvida, por sua natureza, apresentar exposição habitual a risco especial, com potencialidade lesiva e implicar ao trabalhador ônus maior do que aos demais membros da coletividade.

Resumo - É admissível — nos casos especificados por lei, ou em razão do risco inerente à própria atividade — a responsabilização objetiva do empregador por danos decorrentes de acidentes de trabalho.

• O art. 927, parágrafo único, do Código Civil (CC) (1) é compatível com o art. 7º, XXVIII, da Constituição Federal (CF) (2), sendo constitucional a responsabilização objetiva do empregador por danos decorrentes de acidentes de trabalho nos casos especificados em lei ou quando a atividade normalmente desenvolvida, por sua natureza, apresentar exposição habitual a risco especial, com potencialidade lesiva, e implicar ao trabalhador ônus maior do que aos demais membros da coletividade.

• Essa é a tese do Tema 932 da repercussão geral, fixada pelo Plenário, por maioria, ao negar provimento a recurso extraordinário (Informativo 950).

Vencido o ministro Marco Aurélio.(1) CC/2002: “Art. 927. Aquele que, por ato ilícito (arts. 186 e 187), causar dano a outrem, fica obrigado a repará-lo. Parágrafo único. Haverá obrigação de reparar o dano, independentemente de culpa, nos casos especificados em lei, ou quando a atividade normalmente desenvolvida pelo autor do dano implicar, por sua natureza, risco para os direitos de outrem.” (2) CF/1988: “Art. 7º São direitos dos trabalhadores urbanos e rurais, além de outros que visem à melhoria de sua condição social: (...) XXVIII – seguro contra acidentes de trabalho, a cargo do empregador, sem excluir a indenização a que este está obrigado, quando incorrer em dolo ou culpa;”

Legislação: CC/2002, art. 927. CF, art. 7º, XXVIII.

Consultar todos os resumos relacionados ao processo (2)

• O termo inicial do prazo decadencial para o direito de revisar/invalidar a tutela de urgência antecedente se constitui na data da ciência da decisão que extinguiu o processo.

• Ou seja, <u>não</u> se deve contar da data da decisão que extinguiu o processo, nem mesmo da data da sua publicação; mas sim na data em que se tomou ciência inequívoca da referida decisão.

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hermeticism%20role%20in%20heuristics%20-%20Ask%20Brave%20(12_10_2025%201：08：31%20PM).html

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DOI: 10.1073/pnas.2209531119

Resource: RRID:BDSC_26018

Curator: @maulamb

SciCrunch record: RRID:BDSC_26018

What is this?

Traçar um perfil psicológico é muito importante em uma investigação criminal para entender quem é o agente do crime, se ele pode cometer outros crimes e qual a pena ideal para cada caso

IMRaD format is used in this article. There is an Introduction, Methods, Results, and Discussion sections

Author response:

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

I thank the authors for their clarifications. The manuscript is much improved now, in my opinion. The new power spectral density plots and revised Figure 1 are much appreciated. However, there is one remaining point that I am unclear about. In the rebuttal, the authors state the following: "To directly address the question of whether the auditory signal was distracting, we conducted a follow-up MEG experiment. In this study, we observed a significant reduction in visual accuracy during the second block when the distractor was present (see Fig. 7B and Suppl. Fig. 1B), providing clear evidence of a distractor cost under conditions where performance was not saturated." 

I am very confused by this statement, because both Fig. 7B and Suppl. Fig. 1B show that the visual- (i.e., visual target presented alone) has a lower accuracy and longer reaction time than visual+ (i.e., visual target presented with distractor). In fact, Suppl. Fig. 1B legend states the following: "accuracy: auditory- - auditory+: M = 7.2 %; SD = 7.5; p = .001; t(25) = 4.9; visual- - visual+: M = -7.6%; SD = 10.80; p < .01; t(25) = -3.59; Reaction time: auditory- - auditory +: M = -20.64 ms; SD = 57.6; n.s.: p = .08; t(25) = -1.83; visual- - visual+: M = 60.1 ms ; SD = 58.52; p < .001; t(25) = 5.23)." 

These statements appear to directly contradict each other. I appreciate that the difficulty of auditory and visual trials in block 2 of MEG experiments are matched, but this does not address the question of whether the distractor was actually distracting (and thus needed to be inhibited by occipital alpha). Please clarify.

We apologize for mixing up the visual and auditory distractor cost in our rebuttal. The reviewer is right in that our two statements contradict each other.

To clarify: In the EEG experiment, we see significant distractor cost for auditory distractors in the accuracy (which can be seen in SUPPL Fig. 1A). We also see a faster reaction time with auditory distractors, which may speak to intersensory facilitation. As we used the same distractors for both experiments, it can be assumed that they were distracting in both experiments.

In our follow-up MEG-experiment, as the reviewer stated, performance in block 2 was higher than in block 1, even though there were distractors present. In this experiment, distractor cost and learning effects are difficult to disentangle. It is possible that participants improved over time for the visual discrimination task in Block 1, as performance at the beginning was quite low. To illustrate this, we divided the trials of each condition into bins of 10 and plotted the mean accuracy in these bins over time (see Author response image 1). Here it can be seen that in Block 2, there is a more or less stable performance over time with a variation < 10 %. In Block 1, both for visual as well as auditory trials, an improvement over time can be seen. This is especially strong for visual trials, which span a difference of > 20%. Note that the mean performance for the 80-90 trial bin was higher than any mean performance observed in Block 2. 

Additionally, the same paradigm has been applied in previous investigations, which also found distractor costs for the here-used auditory stimuli in blocked and non-blocked designs. See:

Mazaheri, A., van Schouwenburg, M. R., Dimitrijevic, A., Denys, D., Cools, R., & Jensen, O. (2014). Region-specific modulations in oscillatory alpha activity serve to facilitate processing in the visual and auditory modalities. NeuroImage, 87, 356–362. https://doi.org/10.1016/j.neuroimage.2013.10.052

Van Diepen, R & Mazaheri, A 2017, 'Cross-sensory modulation of alpha oscillatory activity: suppression, idling and default resource allocation', European Journal of Neuroscience, vol. 45, no. 11, pp. 1431-1438. https://doi.org/10.1111/ejn.13570

Author response image 1.

Accuracy development over time in the MEG experiment. During block 1, a performance increase over time can be observed for visual as well as for auditory stimuli. During Block 2, performance is stable over time. Data are presented as mean ± SEM. N = 27 (one participant was excluded from this analysis, as their trial count in at least one condition was below 90 trials).

The following is the authors’ response to the previous reviews

Reviewer #1 (Public review):

In this study, Brickwedde et al. leveraged a cross-modal task where visual cues indicated whether upcoming targets required visual or auditory discrimination. Visual and auditory targets were paired with auditory and visual distractors, respectively. The authors found that during the cue-to-target interval, posterior alpha activity increased along with auditory and visual frequency-tagged activity when subjects were anticipating auditory targets. The authors conclude that their results disprove the alpha inhibition hypothesis, and instead implies that alpha "regulates downstream information transfer." However, as I detail below, I do not think the presented data irrefutably disproves the alpha inhibition hypothesis. Moreover, the evidence for the alternative hypothesis of alpha as an orchestrator for downstream signal transmission is weak. Their data serves to refute only the most extreme and physiologically implausible version of the alpha inhibition hypothesis, which assumes that alpha completely disengages the entire brain area, inhibiting all neuronal activity.

We thank the reviewer for taking the time to provide additional feedback and suggestions and we improved our manuscript accordingly.

(1) Authors assign specific meanings to specific frequencies (8-12 Hz alpha, 4 Hz intermodulation frequency, 36 Hz visual tagging activity, 40 Hz auditory tagging activity), but the results show that spectral power increases in all of these frequencies towards the end of the cue-to-target interval. This result is consistent with a broadband increase, which could simply be due to additional attention required when anticipating auditory target (since behavioral performance was lower with auditory targets, we can say auditory discrimination was more difficult). To rule this out, authors will need to show a power spectral density curve with specific increases around each frequency band of interest. In addition, it would be more convincing if there was a bump in the alpha band, and distinct bumps for 4 vs 36 vs 40 Hz band.

This is an interesting point with several aspects, which we will address separately

Broadband Increase vs. Frequency-Specific Effects:

The suggestion that the observed spectral power increases may reflect a broadband effect rather than frequency-specific tagging is important. However, Supplementary Figure 11 shows no difference between expecting an auditory or visual target at 44 Hz. This demonstrates that (1) there is no uniform increase across all frequencies, and (2) the separation between our stimulation frequencies was sufficient to allow differentiation using our method.

Task Difficulty and Performance Differences:

The reviewer suggests that the observed effects may be due to differences in task difficulty, citing lower performance when anticipating auditory targets in the EEG study. This issue was explicitly addressed in our follow-up MEG study, where stimulus difficulty was calibrated. In the second block—used for analysis—accuracy between auditory and visual targets was matched (see Fig. 7B). The replication of our findings under these controlled conditions directly rules out task difficulty as the sole explanation. This point is clearly presented in the manuscript.

Power Spectrum Analysis:

The reviewer’s suggestion that our analysis lacks evidence of frequency-specific effects is addressed directly in the manuscript. While we initially used the Hilbert method to track the time course of power fluctuations, we also included spectral analyses to confirm distinct peaks at the stimulation frequencies. Specifically, when averaging over the alpha cluster, we observed a significant difference at 10 Hz between auditory and visual target expectation, with no significant differences at 36 or 40 Hz in that cluster. Conversely, in the sensor cluster showing significant 36 Hz activity, alpha power did not differ, but both 36 Hz and 40 Hz tagging frequencies showed significant effects These findings clearly demonstrate frequency-specific modulation and are already presented in the manuscript.

(2) For visual target discrimination, behavioral performance with and without the distractor is not statistically different. Moreover, the reaction time is faster with distractor. Is there any evidence that the added auditory signal was actually distracting?