"This implies that a sequence of uracils codes for phenylalanine, and our work suggests that it is probably a triplet of uracils." This was only a hypothesis when this paper was written and published, and now it is recognized as a biological fact. It's really crazy to see how quickly science can develop within just a few decades.

Author response:

The following is the authors’ response to the latest reviews:

"One remaining question is the interpretation of matching variants with very low stable posterior probabilities (~0), which the authors have analyzed in detail but without fully conclusive findings. I agree with the authors that this event is relatively rare and the current sample size is limited but this might be something to keep in mind for future studies."

Fine-mapping stability – on matching variants with very low stable posterior probability

We thank Reviewer 2 for encouraging us to think more about how low stable posterior probability matching variants can be interpreted. We describe a few plausible interpretations, even though – as Reviewer 2 and we have both acknowledged – our present experiments do not point to a clear and conclusive account.

One explanation is that the locus captured by the variant might not be well-resolved, in the sense that many correlated variants exist around the locus. Thus, the variant itself is unlikely causal, but the set of variants in high LD with it may contain the true causal variant, or it's possible that the causal variant itself was not sequenced but lies in that locus. A comparison of LD patterns across ancestries at the locus would be helpful here.

Another explanation rests on the following observation. For a variant to be matching between top and stable PICS and to also have very small stable PP, it has to have the largest PP after residualization on the ALL slice but also have positive PP with gene expression on many other slices. In other words, failing to control for potential confounders shrinks the PP. If one assumes that the matching variant is truly causal, then our observation points to an example of negative confounding (aka suppressor effect). This can occur when the confounders (PCs) are correlated with allele dosage at the causal variant in a different direction than their correlation with gene expression, so that the crude association between unresidualized gene expression and causal variant allele dosage is biased toward 0.

Although our present study does not allow us to systematically confirm either interpretation – since we found that matching variants were depleted in causal variants in our simulations, violating the second argument, but we also found functional enrichment in analyses of GEUVADIS data though only 17 matching variants with low stable PP were reported – we believe a larger-scale study using larger cohort sizes (at least 1000 individuals per ancestry) and many more simulations (to increase yield of such cases) would be insightful.

———

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

Reviewer #1:

Major comments:

(1) It would be interesting to see how much fine-mapping stability can improve the fine-mapping results in cross-population. One can simulate data using true genotype data and quantify the amount the fine-mapping methods improve utilizing the stability idea.

We agree, and have performed simulation studies where we assume that causal variants are shared across populations. Specifically, by mirroring the simulation approach described in Wang et al. (2020), we generated 2,400 synthetic gene expression phenotypes across 22 autosomes, using GEUVADIS gene expression metadata (i.e., gene transcription start site) to ensure largely cis expression phenotypes were simulated. We additionally generated 1,440 synthetic gene expression phenotypes that incorporate environmental heterogeneity, to motivate our pursuit of fine-mapping stability in the first place (see Response to Reviewer 2, Comment 6). These are described in Results section “Simulation study”:

We evaluated the performance of the PICS algorithm, specifically comparing the approach incorporating stability guidance against the residualization approach that is more commonly used — similar to our application to the real GEUVADIS data. We additionally investigated two ways of “combining” the residualization and stability guidance approaches: (1) running stability-guided PICS on residualized phenotypes; (2) prioritizing matching variants returned by both approaches. See Response to Reviewer 2, Comment 5.

(2) I would be very interested to see how other fine-mapping methods (FINEMAP, SuSiE, and CAVIAR) perform via the stability idea.

Thank you for this valuable comment. We ran SuSiE on the same set of simulated datasets. Specifically, we ran a version that uses residualized phenotypes (supposedly removing the effects of population structure), and also a version that incorporates stability. The second version is similar to how we incorporate stability in PICS. We investigated the performance of Stable SuSiE in a similar manner to our investigation of PICS. First we compared the performance relative to SuSiE that was run on residualized phenotypes. Motivated by our finding in PICS that prioritizing matching variants improves causal variant recovery, we did the same analysis for SuSiE. This analysis is described in Results section “Stability guidance improves causal variant recovery in SuSiE.”

We reported overall matching frequencies and causal variant recovery rates of top and stable variants for SuSiE in Figures 2C&D.

Frequencies with which Stable and Top SuSiE variants match, stratified by the simulation parameters, are summarized in Supplementary File 2C (reproduced for convenience in Response to Reviewer 2, Comment 3). Causal variant recovery rates split by the number of causal variants simulated, and stratified by both signal-to-noise ratio and the number of credible sets included, are reported in Figure 2—figure supplements 16-18. We reproduce Figure 2—figure supplement 18 (three causal variants scenario) below for convenience. Analogous recovery rates for matching versus non-matching top or stable variants are reported in Figure 2—figure supplements 19, 21 and 23.

(3) I am a little bit concerned about the PICS's assumption about one causal variant. The authors mentioned this assumption as one of their method limitations. However, given the utility of existing fine-mapping methods (FINEMAP and SuSiE), it is worth exploring this domain.

Thank you for raising this fair concern. We explored this domain, by considering simulations that include two and three causal variants (see Response to Reviewer 2, Comment 3). We looked at how well PICS recovers causal variants, and found that each potential set largely does not contain more than one causal variant (Figure 2—figure supplements 20 and 22). This can be explained by the fact that PICS potential sets are constructed from variants with a minimum linkage disequilibrium to a focal variant. On the other hand, in SuSiE, we observed multiple causal variants appearing in lower credible sets when applying stability guidance (Figure 2—figure supplements 21 and 23). A more extensive study involving more fine-mapping methods and metrics specific to violation of the one causal variant assumption could be pursued in future work.

Reviewer #2:

Aw et al. presents a new stability-guided fine-mapping method by extending the previously proposed PICS method. They applied their stability-based method to fine-map cis-eQTLs in the GEUVADIS dataset and compared it against what they call residualization-based method. They evaluated the performance of the proposed method using publicly available functional annotations and claimed the variants identified by their proposed stability-based method are more enriched for these functional annotations.

While the reviewer acknowledges the contribution of the present work, there are a couple of major concerns as described below.

Major:

(1) It is critical to evaluate the proposed method in simulation settings, where we know which variants are truly causal. While I acknowledge their empirical approach using the functional annotations, a more unbiased, comprehensive evaluation in simulations would be necessary to assess its performance against the existing methods.

Thank you for this point. We agree. We have performed a simulation study where we assume that causal variants are shared across populations (see response to Reviewer 1, Comment 1). Specifically, by mirroring the simulation approach described in Wang et al. (2020), we generated 2,400 synthetic gene expression phenotypes across 22 autosomes, using GEUVADIS gene expression metadata (i.e., gene transcription start site) to ensure cis expression phenotypes were simulated.

(2) Also, simulations would be required to assess how the method is sensitive to different parameters, e.g., LD threshold, resampling number, or number of potential sets.

Thank you for raising this point. The underlying PICS algorithm was not proposed by us, so we followed the default parameters set (LD threshold, r² \= 0.5; see Taylor et al., 2021 Bioinformatics) to focus on how stability considerations will impact the existing fine-mapping algorithm. We attempted to derive the asymptotic joint distribution of the p-values, but it was too difficult. Hence, we used 500 permutations because such a large number would allow large-sample asymptotics to kick in. However, following your critical suggestion we varied the number of potential sets in our analyses of simulated data. We briefly mention this in the Results.

“In the Supplement, we also describe findings from investigations into the impact of including more potential sets on matching frequency and causal variant recovery…”

A detailed write-up is provided in Supplementary File 1 Section S2 (p.2):

“The number of credible or potential sets is a parameter in many fine-mapping algorithms. Focusing on stability-guided approaches, we consider how including more potential sets for stable fine-mapping algorithms affects both causal variant recovery and matching frequency in simulations…

Causal variant recovery. We investigate both Stable PICS and Stable SuSiE. Focusing first on simulations with one causal variant, we observe a modest gain in causal variant recovery for both Stable PICS and Stable SuSiE, most noticeably when the number of sets was increased from 1 to 2 under the lowest signal-to-noise ratio setting…”

We observed that increasing the number of potential sets helps with recovering causal variants for Stable PICS (Figure 2—figure supplements 13-15). This observation also accounts for the comparable power that Stable PICS has with SuSiE in simulations with low signal-to-noise ratio (SNR), when we increase the number of credible sets or potential sets (Figure 2—figure supplements 10-12).

(3) Given the previous studies have identified multiple putative causal variants in both GWAS and eQTL, I think it's better to model multiple causal variants in any modern fine-mapping methods. At least, a simulation to assess its impact would be appreciated.

We agree. In our simulations we considered up to three causal variants in cis, and evaluated how well the top three Potential Sets recovered all causal variants (Figure 2—figure supplements 13-15; Figure 2—figure supplement 15). We also reported the frequency of variant matches between Top and Stable PICS stratified by the number of causal variants simulated in Supplementary File 2B and 2C. Note Supplementary File 2C is for results from SuSiE fine-mapping; see Response to Reviewer 1, Comment 2.

Supplementary File 2B. Frequencies with which Stable and Top PICS have matching variants for the same potential set. For each SNR/ “No. Causal Variants” scenario, the number of matching variants is reported in parentheses.

Supplementary File 2C. Frequencies with which Stable and Top SuSiE have matching variants for the same credible set. For each SNR/ “No. Causal Variants” scenario, the number of matching variants is reported in parentheses.

(4) Relatedly, I wonder what fraction of non-matching variants are due to the lack of multiple causal variant modeling.

PICS handles multiple causal variants by including more potential sets to return, owing to the important caveat that causal variants in high LD cannot be statistically distinguished. For example, if one believes there are three causal variants that are not too tightly linked, one could make PICS return three potential sets rather than just one. To answer the question using our simulation study, we subsetted our results to just scenarios where the top and stable variants do not match. This mimics the exact scenario of having modeled multiple causal variants but still not yielding matching variants, so we can investigate whether these non-matching variants are in fact enriched in the true causal variants.

Because we expect causal variants to appear in some potential set, we specifically considered whether these non-matching causal variants might match along different potential sets across the different methods. In other words, we compared the stable variant with the top variant from another potential set for the other approach (e.g., Stable PICS Potential Set 1 variant vs Top PICS Potential Set 2 variant). First, we computed the frequency with which such pairs of variants match. A high frequency would demonstrate that, even if the corresponding potential sets do not have a variant match, there could still be a match between non-corresponding potential sets across the two approaches, which shows that multiple causal variant modeling boosts identification of matching variants between both approaches — regardless of whether the matching variant is in fact causal.

Low frequencies were observed. For example, when restricting to simulations where Top and Stable PICS Potential Set 1 variants did not match, about 2-3% of variants matched between the Potential Set 1 variant in Stable PICS and Potential Sets 2 and 3 variants in Top PICS; or between the Potential Set 1 variant in Top PICS and Potential Sets 2 and 3 variants in Stable PICS (Supplementary File 2D). When looking at non-matching Potential Set 2 or Potential Set 3 variants, we do see an increase in matching frequencies (between 10-20%) between Potential Set 2 variants and other potential set variants between the different approaches. However, these percentages are still small compared to the matching frequencies we observed between corresponding potential sets (e.g., for simulations with one causal variant this was 70-90% between Top and Stable PICS Potential Set 1, and for simulations with two and three causal variants this was 55-78% and 57-79% respectively).

We next checked whether these “off-diagonal” matching variants corresponded to the true causal variants simulated. Here we find that the causal variant recovery rate is mostly less than the corresponding rate for diagonally matching variants, which together with the low matching frequency suggests that the enrichment of causal variants of “off-diagonal” matching variants is much weaker than in the diagonally matching approach. In other words, the fraction of non-matching (causal) variants due to the lack of multiple causal variant modeling is low.

We discuss these findings in Supplementary File 1 Section S2 (bottom of p.2).

(5) I wonder if you can combine the stability-based and the residualization-based approach, i.e., using the residualized phenotypes for the stability-based approach. Would that further improve the accuracy or not?

This is a good idea, thank you for suggesting it. We pursued this combined approach on simulated gene expression phenotypes, but did not observe significant gains in causal variant recovery (Figure 2B; Figure 2—figure supplements 2, 13 and 15). We reported this Results “Searching for matching variants between Top PICS and Stable PICS improves causal variant Recovery.”

“We thus explore ways to combine the residualization and stability-driven approaches, by considering (i) combining them into a single fine-mapping algorithm (we call the resulting procedure Combined PICS); and (ii) prioritizing matching variants between the two algorithms. Comparing the performance of Combined PICS against both Top and Stable PICS, however, we find no significant difference in its ability to recover causal variants (Figure 2B)...”

However, we also confirmed in our simulations that prioritizing matching variants between the two approaches led to gains in causal variant recovery (Figure 2D; Figure 2—figure supplements 4, 19, 20 and 22). We reported this Results “Searching for matching variants between Top PICS and Stable PICS improves causal variant Recovery.”

“On the other hand, matching variants between Top and Stable PICS are significantly more likely to be causal. Across all simulations, a matching variant in Potential Set 1 is 2.5X as likely to be causal than either a non-matching top or stable variant (Figure 2D) — a result that was qualitatively consistent even when we stratified simulations by SNR and number of causal variants simulated (Figure 2—figure supplements 19, 20 and 22)...”

This finding is consistent with our analysis of real GEUVADIS gene expression data, where we reported larger functional significance of matching variants relative to non-matching variants returned by either Top of Stable PICS.

(6) The authors state that confounding in cohorts with diverse ancestries poses potential difficulties in identifying the correct causal variants. However, I don't see that they directly address whether the stability approach is mitigating this. It is hard to say whether the stability approach is helping beyond what simpler post-hoc QC (e.g., thresholding) can do.

Thank you for raising this fair point. Here is a model we have in mind. Gene expression phenotypes (Y) can be explained by both genotypic effects (G, as in genotypic allelic dosage) and the environment (E): Y = G + E. However, both G and E depend on ancestry (A), so that Y = G|A+E|A. Suppose that the causal variants are shared across ancestries, so that (G|A=a)=G for all ancestries a. Suppose however that environments are heterogeneous by ancestry: (E|A=a) = e(a) for some function e that depends non-trivially on a. This would violate the exchangeability of exogenous E in the full sample, but by performing fine-mapping on each ancestry stratum, the exchangeability of exogenous E is preserved. This provides theoretical justification for the stability approach.

We next turned to simulations, where we investigated 1,440 simulated gene expression phenotypes capturing various ways in which ancestry induces heterogeneity in the exogenous E variable (simulation details in Lines 576-610 of Materials and Methods). We ran Stable PICS, as well as a version of PICS that did not residualize phenotypes or apply the stability principle. We observed that (i) causal variant recovery performance was not significantly different between the two approaches (Figure 2—figure supplements 24-32); but (ii) disagreement between the approaches can be considerable, especially when the signal-to-noise ratio is low (Supplementary File 2A). For example, in a set of simulations with three causal variants, with SNR = 0.11 and E heterogeneous by ancestry by letting E be drawn from N(2σ,σ²) for only GBR individuals (rest are N(0,σ²)), there was disagreement between Potential Set 1 and 2 variants in 25% of simulations — though recovery rates were similar (Probability of recovering at least one causal variant: 75% for Plain PICS and 80% for Stable PICS). These points suggest that confounding in cohorts can reduce power in methods not adjusting or accounting for ancestral heterogeneity, but can be remedied by approaches that do so. We report this analysis in Results “Simulations justify exploration of stability guidance”

In the current version of our work, we have evaluated, using both simulations and empirical evidence, different ways to combine approaches to boost causal variant recovery. Our simulation study shows that prioritizing matching variants across multiple methods improves causal variant recovery. On GEUVADIS data, where we might not know which variants are causal, we already demonstrated that matching variants are enriched for functional annotations. Therefore, our analyses justify that the adverse consequence of confounding on reducing fine-mapping accuracy can be mitigated by prioritizing matching variants between algorithms including those that account for stability.

(7) For non-matching variants, I wonder what the difference of posterior probabilities is between the stable and top variants in each method. If the difference is small, maybe it is due to noise rather than signal.

We have reported differences in posterior probabilities returned by Stable and Top PICS for GEUVADIS data; see Figure 3—figure supplement 1. For completeness, we compute the differences in posterior probabilities and summarize these differences both as histograms and as numerical summary statistics.

Potential Set 1

- Number of non-matching variants = 9,921

- Table of Summary Statistics of (Stable Posterior Probability – Top Posterior Probability)

Author response table 1.

- Histogram of (Stable Posterior Probability – Top Posterior Probability)

Author response image 1.

Potential Set 2

- Number of non-matching variants = 14,454

- Table of Summary Statistics of (Stable Posterior Probability – Top Posterior Probability)

Author response table 2.

- Histogram of (Stable Posterior Probability – Top Posterior Probability)

Author response image 2.

Potential Set 3

- Number of non-matching variants = 16,814

- Table of Summary Statistics of (Stable Posterior Probability – Top Posterior Probability)

Author response table 3.

- Histogram of (Stable Posterior Probability – Top Posterior Probability)

Author response image 3.

We also compared the difference in posterior probabilities between non-matching variants returned by Stable PICS and Top PICS for our 2,400 simulated gene expression phenotypes. Focusing on just Potential Set 1 variants, we find two equally likely scenarios, as demonstrated by two distinct clusters of points in a “posterior probability-posterior probability” plot. The first is, as pointed out, a small difference in posterior probability (points lying close to y=x). The second, however, reveals stable variants with very small posterior probability (of order 4 x 10^–5 to 0.05) but with a non-matching top variant taking on posterior probability well distributed along [0,1]. Moving down to Potential Sets 2 and 3, the distribution of pairs of posterior probabilities appears less clustered, indicating less tendency for posterior probability differences to be small ( Figure 2—figure supplement 8).

Here are the histograms and numerical summary statistics.

Potential Set 1

- Number of non-matching variants = 663 (out of 2,400)

- Table of Summary Statistics of (Stable Posterior Probability – Top Posterior Probability)

Author response table 4.

- Histogram of (Stable Posterior Probability – Top Posterior Probability)

Author response image 4.

Potential Set 2

Number of non-matching variants = 1,429 (out of 2,400)

- Table of Summary Statistics of (Stable Posterior Probability – Top Posterior Probability)

Author response table 5.

- Histogram of (Stable Posterior Probability – Top Posterior Probability)

Author response image 5.

Potential Set 3

- Number of non-matching variants = 1,810 (out of 2,400)

- Table of Summary Statistics of (Stable Posterior Probability – Top Posterior Probability)

Author response table 6.

- Histogram of (Stable Posterior Probability – Top Posterior Probability)

Author response image 6.

(8) It's a bit surprising that you observed matching variants with (stable) posterior probability ~ 0 (SFig. 1). What are the interpretations for these variants? Do you observe functional enrichment even for low posterior probability matching variants?

Thank you for this question. We have performed a thorough analysis of matching variants with very low stable posterior probability, which we define as having a posterior probability < 0.01 (Supplementary File 1 Section S11). Here, we briefly summarize the analysis and key findings.

Analysis

First, such variants occur very rarely — only 8 across all three potential sets in simulations, and 17 across all three potential sets for GEUVADIS (the latter variants are listed in Supplementary 2E). We begin interpreting these variants by looking at allele frequency heterogeneity by ancestry, support size — defined as the number of variants with positive posterior probability in the ALL slice* — and the number of slices including the stable variant (i.e., the stable variant reported positive posterior probability for the slice).

*Note that the stable variant posterior probability need not be at least 1/(Support Size). This is because the algorithm may have picked a SNP that has a lower posterior probability in the ALL slice (i.e., not the top variant) but happens to appear in the most number of other slices (i.e., a stable variant).

For variants arising from simulations, because we know the true causal variants, we check if these variants are causal. For GEUVADIS fine-mapped variants, we rely on functional annotations to compare their relative enrichment against other matching variants that did not have very low stable posterior probability.

Findings

While we caution against generalizing from observations reported here, which are based on very small sample sizes, we noticed the following. In simulations, matching variants with very low stable posterior probability are largely depleted in causal variants, although factors such as the number of slices including the stable variant may still be useful. In GEUVADIS, however, these variants can still be functionally enriched. We reported three examples in Supplementary File 1 Section S11 (pp. 8-9 of Supplement), where the variants were enriched in either VEP or biologically interpretable functional annotations, and were also reported in earlier studies. We partially reproduce our report below for convenience.

“However, we occasionally found variants that stand out for having large functional annotation scores. We list one below for each potential set.

- Potential Set 1 reported the variant rs12224894 from fine-mapping ENSG00000255284.1 (accession code AP006621.3) in Chromosome 11. This variant stood out for lying in the promoter flanking region of multiple cell types and being relatively enriched for GC content with a 75bp flanking region. This variant has been reported as a cis eQTL for AP006632 (using whole blood gene expression, rather than lymphoblastoid cell line gene expression in this study) in a clinical trial study of patients with systemic lupus erythematosus (Davenport et al., 2018). Its nearest gene is GATD1, a ubiquitously expressed gene that codes for a protein and is predicted to regulate enzymatic and catabolic activity. This variant appeared in all 6 slices, with a moderate support size of 23.

- Potential Set 2 reported the variant rs9912201 from fine-mapping ENSG00000108592.9 (mapped to FTSJ3) in Chromosome 17. Its FIRE score is 0.976, which is close to the maximum FIRE score reported across all Potential Set 2 matching variants. This variant has been reported as a SNP in high LD to a GWAS hit SNP rs7223966 in a pan-cancer study (Gong et al., 2018). This variant appeared in all 6 slices, with a moderate support size of 32.

- Potential Set 3 reported the variant rs625750 from fine-mapping ENSG00000254614.1 (mapped to CAPN1-AS1, an RNA gene) in Chromosome 11. Its FIRE score is 0.971 and its B statistic is 0.405 (region under selection), which lie at the extreme quantiles of the distributions of these scores for Potential Set 3 matching variants with stable posterior probability at least 0.01. Its associated mutation has been predicted to affect transcription factor binding, as computed using several position weight matrices (Kheradpour and Kellis, 2014). This variant appeared in just 3 slices, possibly owing to the considerable allele frequency difference between ancestries (maximum AF difference = 0.22). However, it has a small support size of 4 and a moderately high Top PICS posterior probability of 0.64.

To summarize, our analysis of GEUVADIS fine-mapped variants demonstrates that matching variants with very low stable posterior probability could still be functionally important, even for lower potential sets, conditional on supportive scores in interpretable features such as the number of slices containing the stable variant and the posterior probability support size…”

Student quote

fair point, we do need to bring media home again. i've made the switch in books early last year. Music up next.

The core argument

The essay argues that polling has become less reliable at the same time that it has become more powerful, and that this combination distorts democratic politics.

Polls:

increasingly fail to accurately measure public opinion

yet increasingly determine who gets attention, legitimacy, money, debate access, and media coverage

How Trump fits in

The piece opens with Donald Trump claiming he has no pollster and doesn’t tailor his message to polls. Lepore calls this disingenuous:

Trump may not have had a traditional campaign pollster

but his rise depended heavily on polls for visibility and validation

polls got him into debates, dictated stage placement, and fueled media coverage

So Trump is described as “a creature of the Sea of Polls,” not above it

Why modern polls are broken

The article explains in detail why polling has deteriorated:

1. People don’t answer anymore

Response rates used to be 60–90%

Now they’re often in the single digits

Most Americans refuse poll calls, creating non-response bias

1. Technology & law made it worse

Fewer landlines

Cell-phone autodialing is illegal

Internet polls are self-selected and skew younger and more liberal

Mixed-method polling still doesn’t work well

1. Samples are tiny and fragile

National election polls often rely on ~1,000–2,000 people

Statistical “weighting” tries to fix bias, but the lower the response rate, the shakier the results

Why polls now matter more than ever

Despite being unreliable, polls are used to:

decide who qualifies for debates

determine media attention

shape fundraising and momentum

create “winners” and “losers” long before anyone votes

Fox News using polls to select debate participants is presented as a major example of polling replacing democratic processes.

Historical background

The essay gives a history of polling:

Early “straw polls” by newspapers

The rise of George Gallup in the 1930s

Polling claimed to represent “the will of the people” scientifically

But:

Early polls systematically excluded Black Americans, the poor, and the disenfranchised

Polling mirrored and amplified existing inequalities

What was presented as “public opinion” was often the opinion of a privileged subset

Deeper philosophical critique

Lepore raises a fundamental question:

What if measuring public opinion isn’t good for democracy at all?

Key ideas:

Polls treat public opinion as the sum of individual answers, ignoring how opinions are formed socially

Polls can create opinion rather than measure it

Constant polling shifts politics from deliberation and leadership to reacting to numbers

Bottom line

The piece isn’t just saying “polls are inaccurate.”

It’s saying:

Polls shape reality instead of describing it

They weaken representative democracy

They reward spectacle, momentum, and media attention over governance

And they increasingly substitute statistical artifacts for actual voting

Obsidian often is known for protecting you against negative energies and psychic attacks in some religions, besides it being a sharp volcanic glass that can easily pierce victims of its wrath. It's also good for grounding you, or in other words, just keeping you in check with reality. As for jade, it can have several benefits in some cultures depending on the color. For example, green jade (usually seen in bangle form in countries like Vietnam and Myanmar/Burma) can represent wealth and luck. Just wanted to share because I am a massive fan of geology and crystal identification.

How’s this stereotype LMAO. It’s just a fact of biological reality

While this example is not on social media, I think that something similar is our use of plastic in our everyday lives. On the surface, it's just a bottle of water or a bag of chips, but the reality is that plastic has now permeated into our lives at a microscopic scale.

This gives a brief information of the user's information, the data such as likes and comments gives a brief idea that is this content worth to pay a close reading or not. Clearly this example is worthy! Also just one post it contains soo many information, it's kind of surprising.

I never realized how powerful metadata can be. It’s interesting that it’s not just about the content of the tweets, but also about information like when and how we collected them. That extra layer can really change how we understand and analyze data. It can reveal what time someone does things, trends, and behavior that we don't see behind the scenes.

It’s cool to see how images are really just grids of pixels with RGB values, and even something like microRGB fits into that same idea of breaking color down into tiny components. Thinking about images this way makes them feel a lot less mysterious and more like something you can actually work with in code.

It’s interesting to think about how sound is really just a list of numbers that tell a speaker how to move, moment by moment, to recreate a noise or a voice. Once you see it that way, audio feels a lot less abstract and more like something you can store, edit, and mess with just like any other data.

I've heard only three colors (RGB) are needed because these colors are enough to recreate what the human eye perceives. I think it’s interesting how such a wide range of colors and detailed images can be created just by changing the intensity of three simple values in each pixel.

This section makes it click that a “tweet” isn’t just one thing—it’s basically a bundle of lists (a list of images, a list of likes, a list of replies, etc.). Thinking of it that way also helps explain why social media data gets huge fast, because each post can point to multiple growing lists. It’s kind of wild that even something simple like “who liked this” is literally stored as a list of accounts behind the scenes.

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 #1

Evidence, reproducibility and clarity

Summary:

This study by Neupane et al. investigates modulators of α-synuclein aggregation, focusing on Ser129-phosphorylated α-synuclein (pSyn129), a pathological hallmark of Parkinson's disease (PD). The authors performed high-content image-based, arrayed CRISPR activation (CRISPRa) and knockout (CRISPRo) screens targeting > 2300 genes related to mitochondrial function, intracellular trafficking, and cytoskeletal reorganization. Using α-Syn overexpressing HEK293 cells, they identified OXR1 and EMC4 as novel modulators of pSyn129 abundance. Key findings were that activation of the mitochondrial protein OXR1 increased pSyn129 by decreasing ATP levels, while ablation of the ER-associated protein EMC4 reduced pSyn129 by enhancing autophagic flux and lysosomal clearance. These findings were validated in human iPSC-derived cortical and dopaminergic neurons.

My major comments have to do with statistical methods and with significance of their findings.

Major comments:

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

The claims and conclusions are generally well-supported by the presented data. The dual CRISPRa/CRISPRo screen provides a robust initial discovery platform, and the validation in iPSC-derived neurons strengthens the findings and their translational relevance. The mechanistic insights into OXR1 (ATP levels) and EMC4 (autophagic flux, lysosomal clearance) are supported by the described experiments. The use of two antibodies (81A and EP1536Y) for pSyn129 also enhances confidence in the measurements. I had a few questions about the statistical methods. The main concern I have about methodology for the screen is whether the authors have corrected for multiple hypotheses in their discovery screen. This is not clear from the text, methods, or legends (for Figures 2A/2B/2C).

• Figure 1B suggests a very large range of activation (multiple orders of magnitude) in the initial screen. What is the relationship between level of expression change and functional effect across the screen? How upregulated/downregulated are OXR1 and EMC4 at the mRNA and protein levels?
• Supplemental Figure S2D: Why do the non-targeting controls differ from the majority of the CRISPRa genes? If I am reading the figure correctly, it seems strange that the vast majority of the CRISPRa gene targets reduces pSyn pathology relative to the non-targeting controls (which is why I am wondering whether the level of increased expression correlates with the level of functional effect).
• In Figure 2A/B/C, is the p-value adjusted in any way for multiple comparisons? If so, this should be indicated in the legend. If not, why not? (The potential for false positives in a screen is very large and requires correction for multiple comparisons.)
• Figure 3: It's interesting that different seeding materials have different effects. However, it's quite surprising that the authors find less seeding with MSA-derived material in both the CRISPRa and CRISPRo context. This contradicts the work of Peng and coauthors (PMID 29743672) who find that MSA-derived material is much more potent in seeding aggregates in a number of different cell types. Do the authors have any thoughts about why this is the case?
• Figure 7A: pSyn129 image in the non-targeting control is poor - the very bright dots look like artifact. Not clear why the authors don't corroborate with EP1536Y antibody as they do in Figure 5.
• Overall methodology: Are the pSyn inclusions soluble? This could be easily determined by performing 1% TritonX extraction, for example, and it helps us understand how "pathological" the inclusions are.
• OPTIONAL: The authors perform some interesting experiments looking at genes affected downstream by, for example, OXR1 over-expression. It would be useful to understand whether the upstream effect is dependent on downstream effect. This could be tested by performing double perturbations (e.g. OXR1 overexpression and CCL8 knockout or ALDOC upregulation).
• OPTIONAL: The link between EMC4 ablation and enhanced ER-driven autophagic flux/lysosomal clearance could be corroborated with additional experiments. E.g.: Does EMC4 normally inhibit this pathway? Or only in the context of aSyn fibril seeding?

Are the suggested experiments realistic in terms of time and resources?

The OPTIONAL experiments are generally feasible as they employ methods that the lab is already using in this paper.

Are the experiments adequately replicated and statistical analysis adequate?

See comment about multiple hypothesis testing above.

Significance

This is a well-designed, difficult-to-accomplish study that expands the landscape of pS129Syn modulators. The validation of the primary hits identified in HEK293 cells in iPSC-derived neurons gives the findings greater relevance.

Strengths:

• Novelty: Using an unbiased and high-throughput approach, the study identifies two novel regulators of α-Syn aggregation, namely OXR1 and EMC4.
• Methodological Rigor: The use of arrayed CRISPRa/CRISPRo screens with high-content imaging is powerful and difficult to accomplish. Methodologically, this is a tour de force.
• Orthogonal Validation: The use of multiple α-Syn fibril polymorphs/strains and different antibodies (81A, EP1536Y) strengthens the robustness of the findings.

Limitations:

• It's not clear to me that pSyn129 is the ultimate readout. At a minimum, we should know something about the solubility of the inclusions. Some panels (e.g. Figure 7A) are not very informative in terms of what the authors are calling pSyn129+.
• The study relies on in vitro cellular models. While iPSC-derived neurons are relevant, the complexity of the brain environment, including glial cell interactions is not fully captured. This is fine for an initial report, but it does limit the significance.
• OXR1 and EMC4 seem to be very generic modulators. It's not clear to me that their effects are specific to aSyn or to PD in any way - they might just be effects on very basic cellular functions that would be applicable to a number of stressors or proteinopathies. Maybe that is fine (we probably need to get rid of tau aggregates, too!), but I don't think the authors can claim that they have identified "organelle-specific genetic nodes of aSyn pathology" since they biased their screen towards mitochondria and they don't test any other pathological aggregates. Moreover, from a translational perspective, it's not clear to me that implicating the antioxidant pathway or lysosomal/autophagosomal pathways in the pathogenesis of PD is new, and it's not clear that the specific genes identified would make good therapeutic targets.

The section on antagonistic bots was especially interesting to me. It’s concerning how bots can create the illusion of mass support or backlash, even when most real users don’t feel that strongly. This makes me think that bots don’t just add noise, but can actually change how people interpret public opinion.

As someone who's majoring in a creative field, I find it both incredibly interesting and concerning just how advanced AI is getting, and where this rapid innovation will take us in just a few years. It's so jarring to be watching a video on Tiktok or Instagram and fully believe it to be completely real, just to feel the need to dissect the video to see if it's really real. I can't begin to imagine how the job industry will change due to AI, but with innovation there (hopefully) comes opportunity.**

I think that tech giants are not invested in ethical changes because there is no real risk for them. At the end of the day, the goal is to make more money for shareholders, and if the well-being of users is not going to make them money, then it is not worth investing in. I believe that technology can be regulated at the same speed as its advancement. It's just, regulation will put up barriers to profit, so instead surface-level ethics are presented to us as a bandaid to ethical issues that are growing like a cancer at the same rate technology is advancing.

I feel this argument, against bullshit jobs, is much stronger than "capitalism has failed"... for once, because it doesn't necessitate a defeatist starting point, and can me framed as even a satirical position (CEOs don't do crap, is laughable), and for second, because just world hypothesis conservatism bias tells people this can't be the case. Messages online tell people "capitalism+democracy" is the way to go, or else dictatorships by the ultra rich (?). I find it amusing, that more of the same sells so nicely, but that's what people see, survivorship bias big companies employing lots of people, and capitalism lifting out pop stars through financial mobility, and the system delivering all the goods we can possible think of. It's become spectacular consumerism, and culture is everywhere. Culture works, it fucking does, but not your culture, rather Gmail's, and Meta, and OnlyFans, Roblox, etc. one.

But culture is health! A culture of health, of dancing, of being engaged in mentally challenging projects. Culture is education, it teaches, it creates communities. Culture might not be food per se, but it can help grow food, and cook it, collectively, with shared non-contaminating gardens, and recycling processes, through rites more often depicted in religion.

Culture is communication when misscomunication is rampant: It's not just posting and sharing the life of your children online, it's being aware of its impacts, checking before sending news, and feeling safe enough to talk about suicide and emotions, or to speak up about a close relative who is harassing you, regardless of gender! Is this not a progressivist culture that does not invisibilise these things to private life?

Or rather, arguably, culture could act as these things. Currently, mass consumption culture is at times an inhibating mass-sterelisation device, and at others, a way to propagate neo-colonialist monopolistic ideals who benefit a wealthy few. It also perpetuates the myth of equality and meritocracy, through ads, and its instrumentalist tokenised portrayal of diverse hires and rampant on your face corruption and ideals buying, ultimately makes change within the system almost impossible.

Yes, but there have been some welfare and rights advances! Think women and LGBTIQA+ rights. There is increasing cost because demographics have changed, it's not just automation and IA giving the capitalists excess capital gains. Polarisation and corruption are a byproduct of mass production, of noisy, post-truth production.

In my Intro to Information class, I recall my professor discussing the substantial impact that many researchers deem AI to have — an impact that is only comparable to that of the introduction of the Internet. It makes me think about how revolutionary AI feels to be in the midst of its development and how I often overlook this scale when thinking about the internet simply because it's all I've known throughout my upbringing. The digitization and automation of many once manual processes is so commonplace now, I can only imagine how much it'll change in the upcoming years

I only believe something when there is a hypothesis tested many times and the prediction comes true. Many old people get sick and struggle because they did not take care of themselves when they were young and used different things that made their bodies weak. My point is we can’t just keep predicting things. If we keep predicting, it’s just assuming about things we’re not really sure of yet.

When I first got here in America, there were certain things I believed because of my social background. My husband had a therapist, so I joined his therapy. At first, I just listened to other people’s problems and issues, until the therapist asked me about my own feelings about my family. I had a lot of beliefs like ‘my family is great,’ but when he asked me, I felt doomed because I didn’t know what to say and I didn’t even know the definition of feelings. As we kept working, I started to see the reality. I saw the true color, the true image of my past. I realized I have a lot of trauma, but at least now I can face it. I can see it is there, instead of lying to myself that it is not there. I know it’s not perfect and I still have a lot of issues and a lot of scramble in my life, but at least I can work on it now.

Discipline is and always will be part of any professional or academic career, personally I think that if you want to be successful you have to be organized and disciplined. If I feel it's more challenging, it's because it will help me push myself and see what I'm capable of and how I handle situations under pressure.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

The authors present a novel usage of fluorescence lifetime imaging microscopy (FLIM) to measure NAD(P)H autofluorescence in the Drosophila brain, as a proxy for cellular metabolic/redox states. This new method relies on the fact that both NADH and NADPH are autofluorescent, with a different excitation lifetime depending on whether they are free (indicating glycolysis) or protein-bound (indicating oxidative phosphorylation). The authors successfully use this method in Drosophila to measure changes in metabolic activity across different areas of the fly brain, with a particular focus on the main center for associative memory: the mushroom body.

Strengths:

The authors have made a commendable effort to explain the technical aspects of the method in accessible language. This clarity will benefit both non-experts seeking to understand the methodology and researchers interested in applying FLIM to Drosophila in other contexts.

Weaknesses:

(1) Despite being statistically significant, the learning-induced change in f-free in α/β Kenyon cells is minimal (a decrease from 0.76 to 0.73, with a high variability). The authors should provide justification for why they believe this small effect represents a meaningful shift in neuronal metabolic state.

We agree with the reviewer that the observed f_free shift averaged per individual, while statistically significant, is small. However, to our knowledge, this is the first study to investigate a physiological (i.e., not pharmacologically induced) variation in neuronal metabolism using FLIM. As such, there are no established expectations regarding the amplitude of the effect. In the revised manuscript, we have included an additional experiment involving the knockdown of ALAT in α/β Kenyon cells, which further supports our findings. We have also expanded the discussion to expose two potential reasons why this effect may appear modest.

(2) The lack of experiments examining the effects of long-term memory (after spaced or massed conditioning) seems like a missed opportunity. Such experiments could likely reveal more drastic changes in the metabolic profiles of KCs, as a consequence of memory consolidation processes.

We agree with the reviewer that investigating the effects of long-term memory on metabolism represent a valuable future path of investigation. An intrinsic caveat of autofluorescence measurement, however, is to identify the cellular origin of the observed changes. To this respect, long-term memory formation is not an ideal case study as its essential feature is expected to be a metabolic activation localized to Kenyon cells’ axons in the mushroom body vertical lobes (as shown in Comyn et al., 2024), where many different neuron subtypes send intricate processes. This is why we chose to first focus on middle-term memory, where changes at the level of the cell bodies could be expected from our previous work (Rabah et al., 2022). But our pioneer exploration of the applicability of NAD(P)H FLIM to brain metabolism monitoring in vivo now paves the way to extending it to the effect of other forms of memory.

(3) The discussion is mostly just a summary of the findings. It would be useful if the authors could discuss potential future applications of their method and new research questions that it could help address.

The discussion has been expanded by adding interpretations of the findings and remaining challenges.

Reviewer #2 (Public review):

This manuscript presents a compelling application of NAD(P)H fluorescence lifetime imaging (FLIM) to study metabolic activity in the Drosophila brain. The authors reveal regional differences in oxidative and glycolytic metabolism, with a particular focus on the mushroom body, a key structure involved in associative learning and memory. In particular, they identify metabolic shifts in α/β Kenyon cells following classical conditioning, consistent with their established role in energy-demanding middle- and long-term memories.

These results highlight the potential of label-free FLIM for in-vivo neural circuit studies, providing a powerful complement to genetically encoded sensors. This study is well-conducted and employs rigorous analysis, including careful curve fitting and well-designed controls, to ensure the robustness of its findings. It should serve as a valuable technical reference for researchers interested in using FLIM to study neural metabolism in vivo. Overall, this work represents an important step in the application of FLIM to study the interactions between metabolic processes, neural activity, and cognitive function.

Reviewer #3 (Public review):

This study investigates the characteristics of the autofluorescence signal excited by 740 nm 2-photon excitation, in the range of 420-500 nm, across the Drosophila brain. The fluorescence lifetime (FL) appears bi-exponential, with a short 0.4 ns time constant followed by a longer decay. The lifetime decay and the resulting parameter fits vary across the brain. The resulting maps reveal anatomical landmarks, which simultaneous imaging of genetically encoded fluorescent proteins helps to identify. Past work has shown that the autofluorescence decay time course reflects the balance of the redox enzyme NAD(P)H vs. its protein-bound form. The ratio of free-to-bound NADPH is thought to indicate relative glycolysis vs. oxidative phosphorylation, and thus shifts in the free-to-bound ratio may indicate shifts in metabolic pathways. The basics of this measure have been demonstrated in other organisms, and this study is the first to use the FLIM module of the STELLARIS 8 FALCON microscope from Leica to measure autofluorescence lifetime in the brain of the fly. Methods include registering the brains of different flies to a common template and masking out anatomical regions of interest using fluorescence proteins.

The analysis relies on fitting an FL decay model with two free parameters, f_free and t_bound. F_free is the fraction of the normalized curve contributed by a decaying exponential with a time constant of 0.4 ns, thought to represent the FL of free NADPH or NADH, which apparently cannot be distinguished. T_bound is the time constant of the second exponential, with scalar amplitude = (1-f_free). The T_bound fit is thought to represent the decay time constant of protein-bound NADPH but can differ depending on the protein. The study shows that across the brain, T_bound can range from 0 to >5 ns, whereas f_free can range from 0.5 to 0.9 (Figure 1a). These methods appear to be solid, the full range of fits are reported, including maximum likelihood quality parameters, and can be benchmarks for future studies.

The authors measure the properties of NADPH-related autofluorescence of Kenyon Cells(KCs) of the fly mushroom body. The results from the three main figures are:

(1) Somata and calyx of mushroom bodies have a longer average tau_bound than other regions (Figure 1e);

(2) The f_free fit is higher for the calyx (input synapses) region than for KC somata (Figure 2b);

(3) The average across flies of average f_free fits in alpha/beta KC somata decreases from 0.734 to 0.718. Based on the first two findings, an accurate title would be "Autofluorecense lifetime imaging reveals regional differences in NADPH state in Drosophila mushroom bodies."

The third finding is the basis for the title of the paper and the support for this claim is unconvincing. First, the difference in alpha/beta f_free (p-value of 4.98E-2) is small compared to the measured difference in f_free between somas and calyces. It's smaller even than the difference in average soma f_free across datasets (Figure 2b vs c). The metric is also quite derived; first, the model is fit to each (binned) voxel, then the distribution across voxels is averaged and then averaged across flies. If the voxel distributions of f_free are similar to those shown in Supplementary Figure 2, then the actual f_free fits could range between 0.6-0.8. A more convincing statistical test might be to compare the distributions across voxels between alpha/beta vs alpha'/beta' vs. gamma KCs, perhaps with bootstrapping and including appropriate controls for multiple comparisons.

The difference observed is indeed modest relative to the variability of f_free measurements in other contexts. The fact that the difference observed between the somata region and the calyx is larger is not necessarily surprising. Indeed, these areas have different anatomical compositions that may result in different basal metabolic profiles. This is suggested by Figure 1b which shows that the cortex and neuropile have different metabolic signatures. Differences in average f_free values in the somata region can indeed be observed between naive and conditioned flies. However, all comparisons in the article were performed between groups of flies imaged within the same experimental batches, ensuring that external factors were largely controlled for. This absence of control makes it difficult to extract meaningful information from the comparison between naive and conditioned flies.

We agree with the reviewer that the choice of the metric was indeed not well justified in the first manuscript. In the new manuscript, we have tried to illustrate the reasons for this choice with the example of the comparison of f_free in alpha/beta neurons between unpaired and paired conditioning (Dataset 8). First, the idea of averaging across voxels is supported by the fact that the distributions of decay parameters within a single image are predominantly unimodal. Examples for Dataset 8 are now provided in the new Sup. Figure 14. Second, an interpretable comparison between multiple groups of distributions is, to our knowledge, not straightforward to implement. It is now discussed in Supplementary information. To measure interpretable differences in the shapes of the distributions we computed the first three moments of distributions of f_free for Dataset 8 and compared the values obtained between conditions (see Supplementary information and new Sup. Figure 15). Third, averaging across individuals allows to give each experimental subject the same weight in the comparisons.

I recommend the authors address two concerns. First, what degree of fluctuation in autofluorescence decay can we expect over time, e.g. over circadian cycles? That would be helpful in evaluating the magnitude of changes following conditioning. And second, if the authors think that metabolism shifts to OXPHOS over glycolosis, are there further genetic manipulations they could make? They test LDH knockdown in gamma KCs, why not knock it down in alpha/beta neurons? The prediction might be that if it prevents the shift to OXPHOS, the shift in f_free distribution in alpha/beta KCs would be attenuated. The extensive library of genetic reagents is an advantage of working with flies, but it comes with a higher standard for corroborating claims.

In the present study, we used control groups to account for broad fluctuations induced by external factors such as the circadian cycle. We agree with the reviewer that a detailed characterization of circadian variations in the decay parameters would be valuable for assessing the magnitude of conditioning-induced shifts. We have integrated this relevant suggestion in the Discussion. Conducting such an investigation lies unfortunately beyond the scope and means of the current project.

In line with the suggestion of the reviewer, we have included a new experiment to test the influence of the knockdown of ALAT on the conditioning-induced shift measured in alpha/beta neurons. This choice is motivated in the new manuscript. The obtained result shows that no shift is detected in the mutant flies, in accordance with our hypothesis.

FLIM as a method is not yet widely prevalent in fly neuroscience, but recent demonstrations of its potential are likely to increase its use. Future efforts will benefit from the description of the properties of the autofluorescence signal to evaluate how autofluorescence may impact measures of FL of genetically engineered indicators.

Recommendations for the authors

Reviewer #1 (Recommendations for the authors):

(1) Y axes in Figures 1e, 2c, 3b,c are misleading. They must start at 0.

Although we agree that making the Y axes start at 0 is preferable, in our case it makes it difficult to observe the dispersion of the data at the same time (your next suggestion). To make it clearer to the reader that the axes do not start at 0, a broken Y-axis is now displayed in every concerned figure.

(2) These same plots should have individual data points represented, for increased clarity and transparency.

Individual data points were added on all boxplots.

Reviewer #2 (Recommendations for the authors):

I am evaluating this paper as a fly neuroscientist with experience in neurophysiology, including calcium imaging. I have little experience with FLIM but anticipate its use growing as more microscopes and killer apps are developed. From this perspective, I value the opportunity to dig into FLIM and try to understand this autofluorescence signal. I think the effort to show each piece of the analysis pipeline is valuable. The figures are quite beautiful and easy to follow. My main suggestion is to consider moving some of the supplemental data to the main figures. eLife allows unlimited figures, moving key pieces of the pipeline to the main figures would make for smoother reading and emphasize the technical care taken in this study.

We thank the reviewer for their feedback. Following their advice we have moved panels from the supplementary figures to the main text (see new Figure 2).

Unfortunately, the scientific questions and biological data do not rise to the typical standard in the field to support the claims in the title, "In vivo autofluorescence lifetime imaging of the Drosophila brain captures metabolic shifts associated with memory formation". The authors also clearly state what the next steps are: "hypothesis-driven approaches that rely on metabolite-specific sensors" (Intro). The advantage of fly neuroscience is the extensive library of genetic reagents that enable perturbations. The key manipulation in this study is the electric shock conditioning paradigm that subtly shifts the distribution of a parameter fit to an exponential decay in the somas of alpha/beta KCs vs others. This feels like an initial finding that deserves follow-up; but is it a large enough result to motivate a future student to pick this project up? The larger effect appears to be the gradients in f_free across KCs overall (Figure 2b). How does this change with conditioning?

We acknowledge that the observed metabolic shift is modest relative to the variability of f_free and agree that additional corroborating experiments would further strengthen this result. Nevertheless, we believe it remains a valid and valuable finding that will be of interest to researchers in the field. The reviewer is right in pointing out that the gradient across KCs is higher in magnitude, however, the fact that this technique can also report experience-dependent changes, in addition to innate heterogeneities across different cell types, is a major incentive for people who could be interested in applying NAD(P)H FLIM in the future. For this reason, we consider it appropriate to retain mention of the memory-induced shift in the title, while making it less assertive and adding a reference to the structural heterogeneities of f_free revealed in the study. We have also rephrased the abstract to adopt a more cautious tone and expanded the discussion to clarify why a low-magnitude shift in f_free can still carry biological significance in this context. Finally, we have added the results of a new set of data involving the knockdown of ALAT in Kenyon cells, to further support the relevance of our observation relative to memory formation, despite its small magnitude. We believe that these elements together form a good basis for future investigations and that the manuscript merits publication in its present form.

Together, I would recommend reshaping the paper as a methods paper that asks the question, what are the spatial properties of NADPH FL across the brain? The importance of this question is clear in the context of other work on energy metabolism in the MBs. 2P FLIM will likely always have to account for autofluorescence, so this will be of interest. The careful technical work that is the strength of the manuscript could be featured, and whether conditioning shifts f_free could be a curio that might entice future work.

By transferring panels of the supplementary figures to the main text (see new Figure 2) as suggested by Reviewer 2, we have reinforced the methodological part of the manuscript. For the reasons explained above, we however still mention the ‘biological’ findings in the title and abstract.

Minor recommendations on science:

Figure 2C. Plotting either individual data points or distributions would be more convincing.

Individual data points were added on all boxplots.

There are a few mentions of glia. What are the authors' expectations for metabolic pathways in glia vs. neurons? Are glia expected to use one more than the other? The work by Rabah suggests it should be different and perhaps complementary to neurons. Can a glial marker be used in addition to KC markers? This seems crucial to being able to distinguish metabolic changes in KC somata from those in glia.

Drosophila cortex glia are thought to play a similar role as astrocytes in vertebrates (see Introduction). In that perspective, we expect cortex glia to display a higher level of glycolysis than neurons. The work by Rabah et al. is coherent with this hypothesis. Reviewer 2 is right in pointing out that using a glial marker would be interesting. However, current technical limitations make such experiments challenging. These limitations are now exposed in the discussion.

The question of whether KC somata positions are stereotyped can probably be answered in other ways as well. For example, the KCs are in the FAFB connectomic data set and the hemibrain. How do the somata positions compare?

The reviewer’s suggestion is indeed interesting. However, the FAFB and hemibrain connectomic datasets are based on only two individual flies, which probably limits their suitability for assessing the stereotypy of KC subtype distributions. In addition, aligning our data with the FAFB dataset would represent substantial additional work.

The free parameter tau_bound is mysterious if it can be influenced by the identity of the protein. Are there candidate NADPH binding partners that have a spatial distribution in confocal images that could explain the difference between somas and calyx?

There are indeed dozens of NADH- or NADPH-binding proteins. For this reason, in all studies implementing exponential fitting of metabolic FLIM data, tau_bound is considered a complex combination of the contributions from many different proteins. In addition, one should keep in mind that the number of cell types contributing to the autofluorescence signal in the mushroom body calyx (Kenyon cells, astrocyte-like and ensheathing glia, APL neurons, olfactory projection neurons, dopamine neurons) is much higher than in the somas (only Kenyon cells and cortex glia). This could also participate in the observed difference. Hence, focusing on intracellular heterogeneities of potential NAD(P)H binding partners seems premature at that stage.

The phrase "noticeable but not statistically significant" is misleading.

We agree with the reviewer and have removed “noticeable but” from the sentence in the new version of the manuscript.

Minor recommendations on presentation:

The Introduction can be streamlined.

We agree that some parts of the Introduction can seem a bit long for experts of a particular field. However, we think that this level of detail makes the article easily accessible for neuroscientists working on Drosophila and other animal models but not necessarily with FLIM, as well as for experts in energy metabolism that may be familiar with FLIM but not with Drosophila neuroscience.

Reviewer #3 (Public review):

This paper applies a computational model to behavior in a probabilistic operant reward learning task (a 3-armed bandit) to uncover differences between individuals with temporomandibular disorder (TMD) compared with healthy controls. Integrating computational principles and models into pain research is an important direction, and the findings here suggest that TMD is associated with subtle changes in how uncertainty is represented over time as individuals learn to make choices that maximize reward. There are a number of strengths, including the comparison of a volatile Kalman filter (vKF) model to some standard base models (Rescorla Wagner with 1 or 2 learning rates) and parameter recovery analyses suggesting that the combination of task and vKF model may be able to capture some properties of learning and decision-making under uncertainty that may be altered in those suffering from chronic pain-related conditions.

I've focused my comments in four areas: (1) Questions about the patient population, (2) Questions about what the findings here mean in terms of underlying cognitive/motivational processes, (3) Questions about the broader implications for understanding individuals with TMD and other chronic pain-related disorders, and (4) Technical questions about the models and results.

(1) Patient population

This is a computational modelling study, so it is light on characterization of the population, but the patient characteristics could matter. The paper suggests they were hospitalized, but this is not a condition that requires hospitalization per se. It would be helpful to connect and compare the patient characteristics with large-scale studies of TMD, such as the OPPERA study led by Maixner, Fillingim, and Slade.

(2) What cognitive/motivational processes are altered in TMD

The study finds a pattern of alterations in TMD patients that seems clear in Figure 2. Healthy controls (HC) start the task with high estimates of volatility, uncertainty, and learning rate, which drop over the course of the task session. This is consistent with a learner that is initially uncertain about the structure of the environment (i.e., which options are rewarded and how the contingencies change over time) but learns that there is a fixed or slowly changing mean and stationary variance. The TMD patients start off with much lower volatility, uncertainty, and learning rate - which are actually all near 0 - and they remain stable over the course of learning. This is consistent with a learner who believes they know the structure of the environment and ignores new information.

What is surprising is that this pattern of changes over time was found in spite of null group differences in a number of aspects of performance: (1) stay rate, (2) switch rate, (3) win-stay/lose-switch behaviors, (4) overall performance (corrected for chance level), (5) response times, (6) autocorrelation, (7) correlations between participants' choice probability and each option's average reward rate, (7) choice consistency (though how operationalized is not described?), (8) win-stay-lose-shift patterns over time. I'm curious about how the patterns in Figure 2 would emerge if standard aspects of performance are essentially similar across groups (though the study cannot provide evidence in favor of the null). It will be important to replicate these patterns in larger, independent samples with preregistered analyses.

The authors believe that this pattern of findings reveals that TMD patients "maintain a chronically heightened sensitivity to environmental changes" and relate the findings to predictive processing, a hallmark of which (in its simplest form) is precision-weighted updating of priors. They also state that the findings are not related to reduced overall attentiveness or failure to understand the task, but describe them as deficits or impairments in calibrating uncertainty.

The pattern of differences could, in fact, result from differences in prior beliefs, conceptualization of the task, or learning. Unpacking these will be important steps for future work, along with direct measures of priors, cognitive processes during learning, and precision-weighted updating.

(3) Implications for understanding chronic pain

If the findings and conclusions of the paper are correct, individuals with TMD and perhaps other pain-related disorders may have fundamental alterations in the ways in which they make decisions about even simple monetary rewards. The broader questions for the field concern (1) how generalizable such alterations are across tasks, (2) how generalizable they are across patient groups and, conversely, how specific they are to TMD or chronic pain, (3) whether they are the result of neurological dysfunction, as opposed to (e.g.) adaptive strategies or assumptions about the environment/task structure.

It will be important to understand which features of patients' and/or controls' cognition are driving the changes. For example, could the performance differences observed here be attributable to a reduced or altered understanding of the task instructions, more uncertainty about the rules of the game, different assumptions about environments (i.e., that they are more volatile/uncertain or less so), or reduced attention or interest in optimizing performance? Are the controls OVERconfident in their understanding of the environment?

This set of questions will not be easy to answer and will be the work of many groups for many years to come. It is a judgment call how far any one paper must go to address them, but my view is that it is a collaborative effort. Start with a finding, replicate it across labs, take the replicable phenomena and work to unpack the underlying questions. The field must determine whether it is this particular task with this model that produces case-control differences (and why), or whether the findings generalize broadly. Would we see the same findings for monetary losses, sounds, and social rewards? Tasks with painful stimuli instead of rewards?

Another set of questions concerns the space of computational models tested, and whether their parameters are identifiable. An alteration in estimated volatility or learning rate, for example, can come from multiple sources. In one model, it might appear as a learning rate change and in another as a confirmation bias. It would be interesting in this regard to compare the "mechanisms" (parameters) of other models used in pain neuroscience, e.g., models by Seymour, Mancini, Jepma, Petzschner, Smith, Chen, and others (just to name a few).

One immediate next step here could be to formally compare the performance of both patients and controls to normatively optimal models of performance (e.g., Bayes optimal models under different assumptions). This could also help us understand whether the differences in patients reflect deficits and what further experiments we would need to pin that down.<br /> In addition, the volatility parameter in the computational model correlated with apathy. This is interesting. Is there a way to distinguish apathy as a particular clinical characteristic and feature of TMD from apathy in the sense of general disinterest in optimal performance that may characterize many groups?

If we know this, what actionable steps does it lead us to take? Could we take steps to reduce apathy and thus help TMD patients better calibrate to environmental uncertainty in their lives? Or take steps to recalibrate uncertainty (i.e., increase uncertainty adaptation), with benefits on apathy? A hallmark of a finding that the field can build off of is the questions it raises.

(4) Technical questions about the models and results

Clarification of some technical points would help interpret the paper and findings further:

(a) Was the reward probability truly random? Was the random walk different for each person, or constrained?

(b) When were self-report measures administered, and how?

(c) Pain assessments: What types of pain? Was a body map assessed? Widespreadness? Pain at the time of the test, or pain in general?

(d) Parameter recovery: As you point out, r = 0.47 seems very low for recovery of the true quantity, but this depends on noise levels and on how the parameter space is sampled. Is this noise-free recovery, and is it robust to noise? Are the examples of true parameters drawn from the space of participants, or do they otherwise systematically sample the space of true parameters?

(e) What are the covariances across parameter estimates and resultant confusability of parameter estimates (e.g., confusion matrix)?

(f) It would be helpful to have a direct statistical comparison of controls and TMD on model parameter estimates.

(g) Null statistical findings on differences in correlations should not be interpreted as a lack of a true effect. Bayes Factors could help, but an analysis of them will show that hundreds of people are needed before it is possible to say there are no differences with reasonable certainty. Some journals enforce rules around the kinds of language used to describe null statistical findings, and I think it would be helpful to adopt them more broadly.

(h) What is normatively optimal in this task? Are TMD patients less so, or not? The paper states "aberrant precision (uncertainty) weighting and misestimation of environmental volatility". But: are they misestimates?

(i) It's not clear how well the choice of prior variance for all parameters (6.25) is informed by previous research, as sensible values may be task- and context-dependent. Are the main findings robust to how priors are specified in the HBI model?

The author's purpose in writing this article is clearly laid out in the opening portion of this document. We can see that her main motivation is to uplift that trans community while educating the masses on the trans struggle due to the everlasting backlash that the trans community gets.

This text is about the use of scientific research to understand the unique and real trans experience using genetics, neurobiology, and endocrinology. Its main message is that the trans experience is far more complicated than many dehumanizing articles make it out to be.

The text cites ongoing U.S. debates over transgender rights, showing how scientific arguments are being used to justify legislation and social exclusion. While also showing the broader social and political context that online rhetoric has real-world consequences.

This small paragraph displays that the text is meant for college students and also individuals familiar with biology, and can understand scientific reasoning from research. We also know because most likely, those who are subscribing to the SCI AM are generally interested in or have a background in STEM/science

Exigence: The real world changes in policies and increase of hate crime was a primary motivator for the author writing this text. They highlight that this has been a longstanding issue with specifically the scientific community critiquing the existence of being transgender, though the "real world consequences" drove D Sun to write about this issue.

Reviewer #2 (Public review):

Summary:

The goal of the experiment was to identify the fMRI neural correlates of persistence and recovery of forgotten memories. A forgotten memory was defined behaviorally as successful learning, followed by failure in a recall format task, followed by next-day success in a recognition format task. The comparison is to memories that were not forgotten at any stage of the task. Various univariate, connectivity, and multivariate analyses were used to identify neural correlates of forgotten memories that were recovered, that remained forgotten, and successful memory. Some claims are made about how activity of the "episodic memory network" predicts the persistence of forgotten memories.

Strengths:

Studies on the persistence of forgotten memories in rodent models have been used to make some novel claims about the potential properties of engrams. Attempting similar research in humans is a laudable goal.

Patterns of behavioral responses are consistent across subjects.

Weaknesses:

I do not find that the fMRI results fit the narrative provided.

A major issue is that primary results do not replicate across the two fMRI datasets that were collected using the same task. For example, hippocampal activity associated with correct responses (confident and guess) was identified in the group receiving the fMRI scan that used a small FOV, but not in the group that received an fMRI scan of the whole brain, for both 30-min and 24-hr delays (lines 202-217). This suggests that the main findings are not even replicable internally within the same experiment. There is no reasonable justification for this.

Next, most of the reported fMRI findings do not meet reasonable thresholds for statistical significance. In many places, the authors acknowledge this in the text by saying that a difference in the fMRI metric "tended towards significant correlation" or that comparisons "revealed non-significant mean value comparisons". It is not clear why these non-significant findings are interpreted as though they are positive findings. Beyond that, many of the reported findings are not meeting the threshold (i.e., p=0.058), without any acknowledgement that they are marginal. Beyond that, the majority of comparisons that are interpreted in the main text are not significant based on the companion information provided in the supplementary tables. That is, they are totally non-significant when using FWE or FDR correction at either the cluster or peak levels.

Beyond this, the supplementary tables indicate that "clusters identified solely within white matter regions have been excluded." The fact that there are any findings in white matter to ignore indicates that the statistical thresholds are inappropriate. It's tantamount to seeing activation in the brain of a dead fish.

The overall picture based on these factors is that the statistical tests did not use sufficiently stringent safeguards against false positives given the multiple comparison problem that plagues fMRI. So, there are tons of false positives, which are being selectively interpreted to tell a particular story. That is, each comparison yields lots of findings in many brain area, and those that do not fit the particular narrative are being ignored (including those in white matter). What's more, when the small FOV fMRI scan is done, the imaging volume is centered on the hippocampus and its close network, so all false positives appear to be exactly in those brain regions about which the authors want to make conclusions. When throwing darts, you will always hit a bullseye if that is all that exists. The fact that the same comparisons done in the companion whole-brain dataset do not yield the same results is telling: the analysis plan is not sufficiently rigorous to yield findings that are replicable.

Further, I think that it is highly debatable whether the task measures the recovery of forgotten memories at all. Forgotten memories are defined as those that fail when tested using a recollection format but succeed when tested using a recognition format. The well-characterized distinction between recollection and recognition is thus being construed as telling us something about the fate of engrams. I think the much more likely alternative is that "forgotten" memories are just relatively weak memories that don't meet whatever criteria subjects typically use when making recollection judgments, and not some special category of memory. In terms of brain activation, they seem for the most part to follow the pattern of stronger memory, but weaker.

Finally, many hypotheses are used as though they are proven. For instance, fMRI activity patterns are called "engrams" even though there are no tests to determine whether they meet reasonable criteria that have been adopted in the engram literature (e.g., necessity, sufficiency). Whatever happens over the 24-hour delay is called "consolidation" even if there is no test that consolidation has occurred. Etc. It becomes hard to differentiate what is an assumption, versus a hypothesis, versus an inference/conclusion.

On Typewriters: Condition is King; Context is Queen.

It bears mentioning that an expert/professional repair person can only tell very little of the condition of a typewriter by photos. Does it look generally clean? Are the decals in tact? Does the segment look clean (a vague proxy for the potential condition of the internals)? Is anything obvious missing (knobs, return lever, keys)? Does it look cared for or has it been neglected in a barn for half a century? Most modern typewriters made after 1930 in unknown condition are worth about $5-25 and they peak at about $500 when purchased from a solid repair shop unless some Herculean additional restoration has taken place, they've got a rarer typeface, or are inherently actually rare. Hint: unless it's a pro repair shop or very high end collector with lots of experience, don't trust anyone saying that a typewriter is "rare", run the other direction. Run faster if they say it "works, but just needs a new ribbon" as—even at the most expensive—new ribbon is only $15 and their "rare" $600+ machine should have fresh, wet ribbon. The rule of thumb I use is that no one online selling a typewriter knows anything about it, including if it actually works. Worse, they've probably priced it at professional repair shop prices because they don't know that condition is king.

The least experienced typist will know far more about the condition of a machine by putting their hands on it and trying it out. Does it generally work? Does the carriage move the full length of its travel? Can you set the margins at the extremes and space reliably from one end to another? Does it skip? Is the inside clean or full of decades of dried oil, dust, and eraser crumbs? Does the margin release work? Does it backspace properly? If typing HHHhhhHHH are the letters all printed well and on the same baseline?

Presumably a typewriter at an antique store will meet these minimum conditions (though be aware that many don't as their proprietors have no idea about typewriters other than that if they wait long enough, some sucker will spend $150 on almost anything). They've done the work of finding a machine that (barely) works, housing it, and presenting it to the public for sale. This time and effort is worth something to the beginning typewriter enthusiast, but worth much, much less to the longer term practiced collector.

If everything is present and at least generally limping along, you've got yourself a $30 typewriter. Most people can spend a few hours watching YouTube videos and then manage to clean and lubricate a typewriter to get it functioning reasonably. You can always learn to do the adjustments from Youtube videos. (Or just take it to a repair shop and fork over $200-400 to get things squared.)

If you're getting into collecting, you'll make some useful mistakes by overpaying in the beginning and those mistakes will teach you a lot.

Maybe you're a tinkerer and looking for a project? If so, then find the cheapest machine you can get your hands on (maybe a Royal KMM for $9 at thrift) and work your way through a home study course.

Otherwise, if you're just buying one or two machines to use—by far—the best value you'll find is to purchase a cleaned, oiled, and well-adjusted machine from a repair shop. Sure it might cost $350-600, but what you'll save in time, effort, heartache, repair, etc. will more than outweigh the difference. Additionally you'll have a range of machines to choose from aesthetically and you can test out their feel to find something that works best for you.

Or, you could buy a reasonable machine like this for $70 and find out it needs cleaning, oiling, and adjusting and potentially a few repairs. The repair tab might run you an additional $450. Is it worth it when a repair shop would have sold you the same or a very similar machine in excellent condition for $350?

Remember in asking about the cost and value of a typewriter, you're actually attempting to maximize a wide variety of variables including, but not limited to: upfront money, information about the current state of the market, information/knowledge about the machine itself, information about how to clean it, information about oiling it, information about adjusting, information about repairing it, cost and availability of tools and repair parts, and the time involved for both learning and doing all of these. The more time you've spent learning and doing all of these, the better "deals" you'll find, but gaining this expertise is going to cost you a few years of life. What is all this "worth" when you just want to type on a machine that actually works?

Most of the prognostication you'll find in fora like this will be generally useless to you because you're not readily aware of the context and background of the respondents with respect to all of the variables above. Similarly they're working with no context about you, your situation, where you live, what's available in your area, your level of typewriter knowledge, or your budget. You don't know what you don't know. At the end of the day, you're assuredly just as well off to use a bit of your intuition and putting your hands on a machine and trying it out. Then ask: "What is it worth to you?"

If you're simply asking: "Is this highway robbery?", the answer is no.

More resources (and some of my own context) if you need them: https://boffosocko.com/research/typewriter-collection/

Happy typing.

Reply to u/NeverTheNess at https://reddit.com/r/typewriters/comments/1q7eho6/spotted_a_royal_at_an_antique_store_good/<br /> RE: run-of-the-mill late 70s plastic Litton/Royal typewriter

Reviewer #1 (Public review):

Summary:

This study was designed to manipulate and analyze the effects of chemosensory cues on visuomotor control. They approach this by analyzing how eye-body coordination and brain-wide activity are altered with specific chemosensation in larval zebrafish. After analyzing the dynamics of coupled saccade-tail coordination sequences - directionally linked and typically coupled to body turns - the authors investigated the effects of sensory cues shown to be either aversive or appetitive on freely swimming zebrafish on the eye-body coordination. Aversive chemicals lead to an increase in saccade-tail sequences in both number and dynamics, seemingly facilitating behaviors like escape. Brain-wide imaging led the authors to neurons in the telencephalic pallium as a target to study eye-body coordination. Pallium neuron activity correlated with both aversive chemicals and coupled saccade-tail movements.

Recommendations for improvement are minimal. So much of the data is ultimately tabular, and the figures are an impenetrable wall of datapoints. 1c is an excellent example: three concentrations are presented, but it's rare for the three averages to trend appropriately. The key point, which is that aversive odors are repulsive and attractive odors (sometimes) attractive just gets lost in showing the three concentrations individually; it also makes direct comparisons impossible. There are similar challenges abound in the violin plots in 4e-4h, the error bars on the "fits" in 4i-4m, and so on. We recommend selecting an illustrative subset of data to present to permit interpretation and putting the rest in a supplemental table. (Presenting) less is more (effective).

R0:

Reviewer #1:

I reviewed the manuscript titled ‘Awareness of anthrax disease and the knowledge of its transmission and symptoms identification: a cross sectional study among butchers in Ile-Ife’ and below are my observations: Abstract The authors were not consistent in the use of tenses. They should stick to the use of past tenses or continuous tenses. In line 43 they mentioned ‘were not aware’ but in 45 used ‘are aware’. Aware ‘of’ is more preferable than aware ‘about’. They were also not consistent in the use of singular or plural words. For instance, in line 46, they referred to ‘animal’ and then ‘animals’. They can stick to the former as a collective noun or the latter as plural. The authors’ conclusion that there was poor awareness is not consistent with the fact that there was no mention of scoring the questionnaire in the methods. In addition, the lack of mention of knowledge which is a part of the title in the abstract leaves a major void. Knowledge should be captured both in the methods and results. Otherwise, it should be deleted from the title. Introduction Remove the space between the punctuation and the citation in square bracket see lines 63, 79 and 112, for instance. Anthrax is a disease (not an agent) caused by Bacillus anthracis not ‘Bacillus Anthrax’. Line 75: Change ‘buy’ with ‘but’. Authors should stick to ‘human or ‘humans’ (Lines 74 and 76). The transition from one paragraph to the other is not smooth. For instance, lines 79 and 80 do not have any connection. In fact paragraph 4 has no place in its current position in the background of the study. The first sentence of paragraph 5 makes no sense and should be recast. The statement that the first case of anthrax in Nigeria was in 2023 is a lie! The authors should make use of the correct words: For instance, ‘body openings’ should be referred to as ‘body orifices’. Line 104: The authors seem to have changed from reporting a study to writing a proposal. Lines 105 – 108 require referencing. Lines 111 and 112: Change ‘has’ to ‘have’. Line 118: ‘Osun - State, South-Western’, should be written ‘Osun State, southwestern’. Line 121: Change ‘it's’ to ‘its’. Line 125: Change ‘accommodates’ to ‘accommodate’. The authors should draw a map of the area being described. Line 134: Change ‘accommodates’ to ‘accommodate’. The inclusion and exclusion criteria are confusing: ‘Butchers who were standing were included’. Does this mean that those butchers who were sitting were excluded? ‘Butchers who were sick were excluded’, therefore only the healthy were included. How did the authors identify healthy and sick butchers? What was the reason for the chosen criteria? Lines 140 - 149: The fact that the authors are contemplating calculating sample size and recruiting respondents for a study that is concluded is absurd. Lines 158 and 159: Change ‘was’ to ‘were’. Line 161: Which random sampling did the authors use to select the butchers? They should be specific in explaining how the sampling method used was applied in the work. The sampling was not well explained. The authors claimed to have used multi-stage sampling technique. What was the sampling frame in each stage and how were they prepared or accessed? The authors did not explain whether and how the questionnaire was validity and reliability tested. Was there a pilot study and how was it conducted? Without these, the instrument is not valid for a meaningful research. How many sections did the instrument contain and how many questions were there in each section? Was the questionnaire scored and how was it scored? Without scoring the questionnaire, the authors cannot determine the levels of awareness or knowledge. Results Line 167: Change ‘questionnaires’ to ‘questionnaire’ because only one questionnaire was used for the study. Line 174: Change ‘rare’ to ‘rear’. The authors claimed to have worked with only commercial butchers. How come most of them have become cattle rearers? Table 1: Change ‘live stock’ to ‘livestock’. Line 180 – 181. Recast the sentence. Discussion The authors seem not to understand that there is a difference between knowledge and awareness, Therefore, it may be worthwhile to define both words in the work. The authors’ claims on knowledge level may not be correct without scoring knowledge to determine the level among the participants. Comparisons made with some author, example Cadmus et al. (2024) do not rhyme.

Limitations Secondary education is not low level of literacy. Therefore, an area that has more than 70% of the population that attained secondary education cannot be regarded as having low level of literacy.

Reviewer #2:

The manuscript “Awareness of Anthrax Disease and the Knowledge of Its Transmission and Symptoms Identification: A Cross-Sectional Study Among Butchers in Ile-Ife” addresses an important public health issue, the awareness and knowledge of anthrax among butchers, that represents a high-risk occupational category in Nigeria. The topic is relevant, timely, also including a One Health perspective. The study provides useful descriptive data that could inform public health education and prevention strategies. The authors present clear findings about the low awareness of anthrax among butchers and the implications for public health. The study is quite good in methodology and analysis, but there are different areas for improvement, before the manuscript could be considered for publication. As regards the study design, it would be appropriate to clarify the sampling frame, randomization process, and justify the adequacy of the final sample size. How were markets and butchers selected? Was any sampling frame used? No basic inferential analyses (e.g., chi-square tests) were performed to identify factors associated with awareness and knowledge. Could you please, add this kind of analyses? Despite collecting rich demographic data and having explicit research objectives to assess awareness, no inferential analyses examine whether education, income, or animal sourcing practices associate with knowledge levels. Chi-square tests and logistic regression would identify high-risk subgroups requiring targeted interventions, fulfilling the study's stated aims and enhancing public health utility beyond simple prevalence reporting The results could be enriched by analysing possible relationships between the respondents' demographics (e.g., education, age) and their awareness or knowledge of anthrax. This would help contextualize the findings. The discussion could be strengthened by addressing any potential biases in the study (e.g., recall bias due to the use of self-reported data). The paper could explore in greater depth why the awareness was low (e.g., cultural factors, lack of effective public health messaging). While the conclusion addresses the need for improved awareness, it could be more specific about what measures should be taken. For example, the authors could suggest specific communication strategies, community-based interventions, or policy changes. The figures could benefit from more detailed captions that explain what each figure illustrates beyond just showing the data. Furthermore, the manuscript contains grammatical errors and spelling mistakes throughout such as “symtoms instead of symptoms in the short title” “Bacillus Anthrax (line 67)” instead of Bacillus anthracis, “unclothed blood (line 83)” instead of “unclotted blood”. Unclotted is better than un-clotted. Line 217 4got? Correct. Write B. anthracis in italics all over the manuscript. Some sentences are unclear or repetitive, especially in the Background and Discussion sections, so a professional English language editing is recommended.

Academic Editor:

The reviewers have raised a number of concerns that need attention. In particular, they request additional information on methodological aspects of the study, additional analysis, a clearly defined research question, and improvements to the quality of the language used.

As someone who grew up in a religious household, I often asked questions challenging this theory. It's interesting to think about what could be reprimanded or praised by your god(s)/religious circle, as long as it was written into the guidelines in a scripture or reading. Additionally, I do think that this thinking is dangerous, as it opens up the possibility for people within the religion to misinterpret or maliciously translate certain texts to push negative propaganda to a group of people, and the possibility of mistranslation is incredibly high as most of these texts were written hundreds of years ago.

I think this is an interesting concept to think about, as we are usually conditioned to think that the internet "isn't real", that most things online are fabricated, exaggerated, etc. However, I do think that just because this is common online, it's not to say that "real life" is a place where everyone is completely authentic and themselves, as some people may feel that they only want to share the good parts of their lives with their friends or family, while keeping anything that wouldn't be considered "good" to themselves, and vice versa. I do think it's hasty to say that all that we see on social media "is not real", as there are plenty of real people behind each account, but we must consider that because people are able to be behind potentially anonymous accounts, it is much easier to fabricate stories or life experiences, or to center one's entire online presence around a portion of their life they want the internet to see, essentially artificially creating an online persona that is not reflective of who they are in real life.

Kids are independent in the digital world before the physical one.

That flips the historical order. Earlier generations learned:

• physical world → social norms → abstract/digital spaces

Now it’s:

• abstract/digital spaces → simulated interaction → limited real-world agency

This matters because:

**Digital spaces forgive mistakes cheaply

Physical spaces teach consequences viscerally** - “Physical spaces teach consequences viscerally”

In physical space, mistakes hit the nervous system, not just the mind.

Run too fast → you fall. Ignore a curb → you trip. Misjudge a car → you don’t get a second try. Shout too loud → heads turn. Cross a boundary → someone reacts in front of you.

The feedback is:

Immediate

Embodied

Often irreversible

Digital spaces teach what you can get away with. Physical spaces teach what actually happens.

Both are necessary. But they are not interchangeable.

because you miss out on asking questions to other class mates and the instructor

Carla is setting her self up for failure because she dose not go to class and relies on other for notes and cant ask questions.

I think Carla is trying to do the "quicker way". But in reality it's not the fastest way it is in my opinion is the lazier way of going about things in a productive and efficient way.

In contrast to how people are saying "social media isn't real life," I think that people tend to be a more exaggerated version of themselves when engaging online. I think this might be because they don't feel an immediate consequence for poor or extreme behavior alongside the fact that it's very easy to just say whatever you want and click post. It's easy to post impulsive thoughts and not think about how others would react, especially when you're not physically seeing any effects in your real life. So in a way, I think that social media is a part of our real life, but in a much more exaggerated way now that social pressure is generally lightened.

I've probably read this post half a dozen times over the last 10 years.

This is the first time that I've noticed (and—now—am able to appreciate) that this paragraph is deliberately crafted to, in part, have the same effect as what it's actually describing.

Author response:

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

Public Reviews: 

Reviewer #1 (Public Review): 

Summary:

The manuscript submitted by Langenbacher et al., entitled " Rtf1-dependent transcriptional pausing regulates cardiogenesis", describes very interesting and highly impactful observations about the function of Rtf-1 in cardiac development. Over the last few years, the Chen lab has published novel insights into the genes involved in cardiac morphogenesis. Here, they used the mouse model, the zebrafish model, cellular assays, single cell transcription, chemical inhibition, and pathway analysis to provide a comprehensive view of Rtf1 in RNAPII (Pol2) transcription pausing during cardiac development. They also conducted knockdown-rescue experiments to dissect the functions of Rtf1 domains. 

Strengths:

The most interesting discovery is the connection between Rtf1 and CDK9 in regulating Pol2 pausing as an essential step in normal heart development. The design and execution of these experiments also demonstrate a thorough approach to revealing a previously underappreciated role of Pol2 transcription pausing in cardiac development. This study also highlights the potential amelioration of related cardiac deficiencies using small molecule inhibitors against cyclin dependent kinases, many of which are already clinically approved, while many other specific inhibitors are at various preclinical stages of development for the treatment of other human diseases. Thus, this work is impactful and highly significant. 

We thank the reviewer for appreciating our work.

Reviewer #2 (Public Review): 

Summary: 

Langenbacher at el. examine the requirement of Rtf1, a component of the PAF1C, which regulates transcriptional pausing in cardiac development. The authors first confirm their previous morphant study with newly generated rtf1 mutant alleles, which recapitulate the defects in cardiac progenitor and diUerentiation gene expression observed previously in morphants. They then examine the conservation of Rtf1 in mouse embryos and embryonic stem cell-derived cardiomyocytes. Conditional loss of Rtf1 in mesodermal lineages and depletion in murine ESCs demonstrates a failure to turn on cardiac progenitor and diUerentiation marker genes, supporting conservation of Rtf1 in promoting cardiac development. The authors subsequently employ bulk RNA-seq on flow-sorted hand2:GFP+ cells and multiomic single-cell RNA-seq on whole Rtf1-depleted embryos at the 10-12 stage. These experiments corroborate that genes associated with cardiac and muscle development are lost. Furthermore, the diUerentiation trajectories suggest that the expression of genes associated with cardiac maturation is not initiated.  Structure-function analysis supports that the Plus3 domain is necessary for its function in promoting cardiac progenitor formation. ChIP-seq for RNA Pol II on 1012 somite stage embryos suggests that Rtf1 is required for proper promoter pausing. This defect can partially be rescued through use of a pharmacological inhibitor for Cdk9, which inhibits elongation, can partially restore elongation in rtf1 mutants.  

Strengths: 

Many aspects of the data are strong, which support the basic conclusions of the authors that Rtf1 is required for transcriptional pausing and has a conserved requirement in vertebrate cardiac development. Areas of strength include the genetic data supporting the conserved requirement for Rtf1 in promoting cardiac development, the complementary bulk and single-cell RNA-sequencing approaches providing some insight into the gene expression changes of the cardiac progenitors, the structure-function analysis supporting the requirement of the Plus3 domain, and the pharmacological epistasis combined with the RNA Pol II ChIP-seq, supporting the mechanism implicating Cdk9 in the Rtf1 dependent mechanism of RNA Pol II pausing. 

We thank the reviewer for the summary and for recognizing many strengths of our work. 

Weaknesses: 

While most of the basic conclusions are supported by the data, there are a number of analyses that are confusing as to why they chose to perform the experiments the way they did and some places where the interpretations presently do not support the interpretations. One of the conclusions is that the phenotype aUects the maturation of the cardiomyocytes and they are arresting in an immature state. However, this seems to be mostly derived from picking a few candidates from the single cell data in Fig. 6. If that were the case, wouldn't the expectation be to observe relatively normal expression of earlier marker genes required for specification, such as Nkx2.5 and Gata5/6? The in situ expression analysis from fish and mice (Fig. 2 and Fig. 3) and bulk RNA-seq (Fig. 5) seems to suggest that there are pretty early specification and diUerentiation defects. While some genes associated with cardiac development are not changed, many of these are not specific to cardiomyocyte progenitors and expressed broadly throughout the ALPM. Similarly, it is not clear why a consistent set of cardiac progenitor genes (for instance mef2ca, nkx2.5, and tbx20) was analyzed for all the experiments, in particular with the single cell analysis. 

A major conclusion of our study is that Rtf1 deficiency impairs myocardial lineage differentiation from mesoderm, as suggested by the reviewer. Thus, the main goal of this study is to understand how Rtf1 drives cardiac differentiation from the LPM, rather than the maturation of cardiomyocytes.  Multiple lines of evidence support this conclusion:

(a) In situ hybridization showed that Rtf1 mutant embryos do not have nkx2.5+ cardiac progenitor cells and subsequently fail to produce cardiomyocytes (Figs. 2, 3).

(b) RT-PCR analysis showed that knockdown of Rtf1 in mouse embryonic stem cells causes a dramatic reduction of cardiac gene expression and production of significantly fewer beating patches (Fig.4).

(c) Bulk RNA sequencing revealed significant downregulation of cardiac lineage genes, including nkx2.5 (Fig. 5).

(d) Single cell RNA sequencing clearly showed that lateral plate mesoderm (LPM) cells are significantly more abundant in Rtf1 morphant,s whereas cardiac progenitors are less abundant (Fig. 6 and Fig.6 Supplement 1-5). 

When feasible, we used cardiac lineage restricted markers in our assays. Nkx2.5 and tbx5a are not highlighted in the single cell analysis because their expression in our sc-seq dataset was too low to examine in the clustering/trajectory analysis.  In this revised manuscript, we provide violin plots showing the low expression levels of these genes in single cells from Rtf1 deficient embryos (Figure 6 Supplement 5).

The point of the multiomic analysis is confusing. RNA- and ATAC-seq were apparently done at the same time. Yet, the focus of the analysis that is presented is on a small part of the RNA-seq data. This data set could have been more thoroughly analyzed, particularly in light of how chromatin changes may be associated with the transcriptional pausing. This seems to be a lost opportunity. Additionally, how the single cell data is covered in Supplemental Fig. 2 and 3 is confusing. There is no indication of what the diUerent clusters are in the Figure or the legend. 

In this study, we performed single cell multiome analysis and used both scRNAseq and scATACseq datasets to generate reliable clustering.  The scRNAseq analysis reveals how Rtf1 deficiency impacts cardiac differentiation from mesoderm, which inspired us to investigate the underlying mechanism and led to the discovery of defects in Rtf1-dependent transcriptional pause release.

We agree with the reviewer that deep examination of Rtf1-dependent chromatin changes would provide additional insights into how Rtf1 influences early development and careful examination of the scATACseq dataset is certainly a good future direction.  

In this revised manuscript, we have revised Fig.6 Supplement 1 to include the predicted cell types and provide an additional excel file showing the annotation of all 39 clusters (Supplementary Table 2). 

While the effect of Rtf1 loss on cardiomyocyte markers is certainly dramatic, it is not clear how well the mutant fish have been analyzed and how specific the eUect is to this population. It is interpreted that the eUects on cardiomyocytes are not due to "transfating" of other cell fates, yet supplemental Fig. 4 shows numerous eUects on potentially adjacent cell populations. Minimally, additional data needs to be provided showing the live fish at these stages and marker analysis to support these statements. In some images, it is not clear the embryos are the same stage (one can see pigmentation in the eyes of controls that is not in the mutants/morphants), causing some concern about developmental delay in the mutants. 

Single cell RNA sequencing showed an increased abundance of LPM cells and a reduced abundance of cardiac progenitors in Rtf1 morphants (Fig. 6 and Fig.6 Supplement 1-5). The reclustering of anterior lateral plate mesoderm (ALPM) cells and their derivatives further showed that cells representing undifferentiated ALPM were increased whereas cells representing all three ALPM derivatives were reduced. These findings indicate a defect in ALPM differentiation. 

The reviewer questioned whether we examined stage-matched embryos. In our assay, Rtf1 mutant embryos were collected from crosses of Rtf1 heterozygotes. Each clutch from these crosses consists of ¼ embryos showing rtf1 mutant phenotypes and ¾ embryos showing wild type phenotypes which were used as control. Mutants and their wild type siblings were fixed or analyzed at the same time.

The reviewer questioned the specificity of the Rtf1 deficient cardiac phenotype and pointed out that Rtf1 mutant embryos do not have pigment cells around the eye.  Rtf1 is a ubiquitously expressed transcriptional regulator.  Previous studies in zebrafish have shown that Rtf1 deficiency significantly impacts embryonic development. Rtf1 deficiency causes severe defects in cardiac lineage and neural crest cell development; consequently, Rtf1 deficient embryos do not have cardiomyocytes and pigmentation (Langenbacher et al., 2011, Akanuma et al., 2007, and Jurynec et al., 2019).  We now provide an image showing a 2-day-old Rtf1 mutant embryo and their wild type sibling to illustrate the cardiac, neural crest, and somitogenesis defects caused by loss of Rtf1 activity (Fig. 2 Supplement 1).

With respect to the transcriptional pausing defects in the Rtf1 deficient embryos, it is not clear from the data how this eUect relates to the expression of the cardiac markers. This could have been directly analyzed with some additional sequencing, such as PRO-seq, which would provide a direct analysis of transcriptional elongation. 

We showed that Rtf1 deficiency results in a nearly genome-wide decrease in promoterproximal pausing and downregulation of cardiac makers. Attenuating transcriptional pause release could restore cardiomyocyte formation in Rtf1 deficient embryos. In this revised manuscript, we provide additional RNAseq data showing that the expression levels of critical cardiac development genes such as nkx2.5, tbx5a, tbx20, mef2ca, mef2cb, ttn.2, and ryr2b are significantly rescued.  We agree with the reviewer that further analyses using the PRO-seq approach could provide additional insights, but it is beyond the scope of this manuscript. 

Some additional minor issues include the rationale that sequence conservation suggests an important requirement of a gene (line 137), which there are many examples this isn't the case, referencing figures panels out of order in Figs. 4, 7, and 8) as described in the text, and using the morphants for some experiments, such as the rescue, that could have been done in a blinded manner with the mutants. 

We have clarified the rationale in this revised manuscript and made the eRort to reference figures in order. 

The reviewer commented that rescue experiments “could have been done in a blinded manner with the mutants”. This was indeed how the flavopiridol rescue and cdk9 knockdown experiments were carried out. Embryos from crosses of Rtf1 heterozygotes were collected, fixed after treatment and subjected to in situ hybridization. Embryos were then scored for cardiac phenotype and genotyped (Fig.8 d-g). Morpholino knockdown was used in genomic experiments because our characterization of rtf1 morphants showed that they faithfully recapitulate the rtf1 mutant phenotype during the timeframe of interest (Fig. 2).

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors): 

This reviewer has a few suggestions below, aimed at improving the clarity and impact of the current study. Once these items are addressed, the manuscript should be of interest to the Elife reader. 

Item 1. Strengthening the interaction between Rfh1 and CDK9 on Pol2 pausing. 

The authors have convincingly shown that the chemical inhibition of CDK9 by flavopiridol can partially rescue the expression of cardiac genes in the zebrafish model. Although flavopiridol is FDA approved and has been a classical inhibitor for the dissection of CDK9 function, it also inhibits related CDKs (such as Flavopiridol (Alvocidib) competes with ATP to inhibit CDKs including CDK1, CDK2, CDK4, CDK6, and CDK9 with IC50 values in the 20-100 nM range) Therefore, this study could be more impactful if the authors can provide evidence on which of these CDKs may be most relevant during Rtf1-dependent cardiogenesis. To determine whether the observed cardiac defect indicates a preferential role for CDK9, or that other CDKs may also be able to provide partial rescue may be clarified using additional, more selective small molecules (e.g., BAY1251152, LDC000067 are commercially available). 

The reviewer raised a reasonable concern about the specificity of flavopiridol. We thank the reviewer for the insightful suggestion and share the concern about specificity. To address this question, we have used an orthogonal testing through morpholino inhibition where we directly targeted CDK9 and observed the same level of rescue, supporting a critical role of transcription pausing in cardiogenesis.

Item 2. Differences between CRISPR lines and morphants 

Much of the work presented used Rtf1 morphants while the authors have already generated 2 CRISPR lines. What is the diUerence between morphants and mutants? The authors should comment on the similarities and/or differences between using morphants or mutants in their study and whether the same Rtf1- CDK9 connection also occurs in the CRISPR lines. 

The morphology of our mutants (rtf1^LA2678 and rtf1^LA2679) resembles the morphants and the previously reported ENU-induced rtf1^KT641 allele. Extensive in situ hybridization analysis showed that the morphants faithfully recapitulate the mutant phenotypes (Fig.2). We have performed rescue experiments (flavopiridol and CDK9 morpholino) using Rtf1 mutant embryos and found that inhibiting Cdk9 restores cardiomyocyte formation (Fig.8). 

Item 3. Discuss the therapeutic relevance of study 

The authors have already generated a mouse model of Rtf1 Mesp1-Cre knockout where cardiac muscle development is severely derailed (Fig 3B). Thus, a demonstration of a conserved role for CDK9 inhibitor in rescuing cardiogenesis using mouse cells or the mouse model will provide important information on a conserved pathway function relevant to mammalian heart development. In the Discussion, how this underlying mechanistic role may be useful in the treatment of congenital heart disease should be provided.  

Thank you for the insight. We have incorporated your comments in the discussion. 

Item 4. Insights into the role of CDK9-Rtf1 in response to stress versus in cardiogenesis. 

In the Discussion, the authors commented on the role of additional stress-related stimuli such as heat shock and inflammation that have been linked to CDK9 activity. However, the current ms provides the first, endogenous role of Pol2 pausing in a critical developmental step during normal cardiogenesis. The authors should emphasize the novelty and significance of their work by providing a paragraph on the state of knowledge on the molecular mechanisms governing cardiogenesis, then placing their discovery within this framework. This minor addition will also clarify the significance of this work to the broad readership of eLife. 

Thank you for the suggestion. We have incorporated your comments and elaborate on the novelty and significance of our work in the discussion. 

Reviewer #2 (Recommendations For The Authors): 

(1) It is diUicult to assess what the overt defects are in the embryos at any stages. Images of live images were not included in the supplement. Do these have a small, malformed heart tube later or are the embryos just deteriorating due to broad defects? 

The Rtf1 deficient embryos do not produce nkx2.5+ cardiac progenitors. Consequently, we never observed a heart tube or detected cells expressing cardiomyocyte marker genes such as myl7. This finding is consistent with previous reports using rtf1 morphants and rtf^1KT64, an ENU-induced point mutation allele (Langenbacher et al., 2011 and Akanuma, 2007). In this revised manuscript, we provide a live image of 2-day-old wild type and rtf1^{LA2679/LA2679} embryos (Fig. 2 Supplement 1). After two days, rtf1 mutant embryos undergo broad cell death. 

(2) Fig. 2, although the in situs are convincing, there is not a quantitative assessment of expression changes for these genes. This could have been done for the bulk or single cell RNA-seq experiments, but was not and these genes weren't not included in the heat maps. A quantitative assessment of these genes would benefit the study. 

The top 40 most significantly differentially expressed genes are displayed in the heatmap presented in Fig.5d. The complete differential gene expression analysis results for our hand2 FACS-based comparison of rtf1 morphants and controls is presented in Supplementary Data File 1.  In this revised manuscript, we provide a new supplemental figure with violin plots showing the expression levels of genes of interest in our single cell sequencing dataset (Fig.6 Supplement 5).

(3) It doesn't not appear that any statistical tests were used for the comparisons in Fig. 2.

We now provide the statistical data in the legend and Fig.2 b, d, f, h and i.

(4) It's not clear the magnifications and orientations of the embryos in Fig. 3b are the same. 

Embryos shown in Fig.3b are at the same magnification. However, because Rtf1 mutant embryos display severe morphological defects, the orientation of mutant embryos was adjusted to examine the cardiac tissue.

(5) The n's for analysis of MLC2v in WT Rtf1 CKO embryos in Fig. 3b are only 1. At least a few more embryos should be analyzed to confirm that the phenotype is consistent. 

We have revised the figure and present the number of embryos analyzed and statistics in Fig.3c. 

(6) A number of figure panels are referred to out of order in the text. Fig. 4E-G are before Fig. 4C, D, Fig. 7C  before 7B, Fig. 8D-I before 8A ,B. In general, it is easier for the reader if the figures panels are presented in the order they are referred to in the text. 

Revised as suggested.

(7) While additional genes can be included, it is not clear why the same sets of genes are not examined in the bulk or single-cell RNA-seq as with the in situs or expression was analyzed in embryos. I suggest including the genes like nkx2.5, tbx20, myl7, in all the sequencing analysis. 

We used the same set of genes in all analyses when possible. However, the low expression of genes such as nkx2.5 and myl7 in our sc-seq dataset preclude them from the clustering/trajectory analysis. In this revised manuscript, we present violin plots showing their expression in wild type and rtf1 morphants (Fig. 6 Supplement 5).

(8) If a multiomic approach was used, why wasn't its analysis incorporated more into the manuscript? In general, a clearer presentation and deeper analysis of the single cell data would benefit the study. The integration of the RNA and ATAC would benefit the analysis.

As addressed in our response to the reviewer’s public review, both datasets were used in clustering. Examining changes in chromatin accessibility is certainly interesting, but beyond the scope of this study. 

(9) Many of the markers analyzed are not cardiac specific or it is not clear they are expressed in cardiac progenitors at the stage of the analysis. Hand2 has broader expression. Additional confirmation of some of the genes through in situ would help the interpretations. 

Markers used for the in situ hybridization analysis (myl7, mef2ca, nkx2.5, tbx5a, and tbx20) are known for their critical role in heart development. For sc-seq trajectory analyses, most displayed genes (sema3e, bmp6, ttn.2, mef2cb, tnnt2a, ryr2b, and myh7bb) were identified based on their differential expression along the LPM-cardiac progenitor pseudotime trajectory. Rather than selecting genes based on their cardiac specificity, our goal was to examine the progressive gene expression changes associated with cardiac progenitor formation and compare gene expression of wild type and rtf1 deficient embryos.

(10) Additional labels of the cell clusters are needed for Supplemental Figs. 2 and 3. 

The cluster IDs were presented on Supplementary Figures 2 and 3. In this revised version, we added predicted cell types to the UMAP (revised Fig.6 Supplement 1) and provided an excel file with this information (revised Supplementary Table 2). 

(11) On lines 101-102, the interpretation from the previous data is that diUerentiation of the LPM requires Rtf1. However, later from the single cell data the interpretation based on the markers is that Rtf1 loss aUects maturation. However, it is not clear this interpretation is correct or what changed from the single cell data. If that were the case, one would expect to see maintenance of more early marks and subsequent loss of maturation markers, which does not appear to the be the case from the presented data.

Our data suggests that cardiac progenitor formation is not accomplished by simultaneously switching on all cardiac marker genes. Our pseudotime trajectory analysis highlights tnnt2a, ryr2b, and myh7bb as genes that increase in expression in a lagged manner compared to mef2cb (Fig. 6). Thus, the abnormal activation of mef2cb without subsequent upregulation of tnnt2a, ryr2b, and myh7bb in rtf1 morphants suggests a requirement for rtf1 in the progressive gene expression changes required for proper cardiac progenitor differentiation. Our single cell experiment focuses on the process of cardiac progenitor differentiation and does not provide insights into cardiomyocyte maturation. We have edited the text to clarify these interpretations. 

(12) The interpretation that there is not "transfating" is not supported by the shown data. Analysis of markers in other tissues, again with in situ, to show spatially would benefit the study. 

As stated in our response to the reviewer’s public review, we observed a dramatic increase of ALPM cells, but a decrease of ALPM derivatives including the cardiac lineage. We did not observe the expansion of one ALPM-derived subpopulation at the expense of the others. These observations suggest a defect in ALPM differentiation and argue against the notion that the region of the ALPM that would normally give rise to cardiac progenitors is instead differentiating into another cell type.

(13) The rationale that sequence conservation means a gene is important (lines 137-139) is not really true. There are examples a lot of highly conserved genes whose mutants don't have defects. 

We have revised the text to avoid confusion. 

(14) The data showing that the 8 bp mutations do not aUect the RNA transcript is not shown or at least indicated in Fig. 7. It would seem that this experiment could have been done in the mutant embryos, in which case the experiment would have been semi-blinded as the genotyping would occur after imaging. 

The modified Rtf1 wt RNA (Rtf1 wt* in revised Fig. 7) robustly rescued nkx2.5 expression in rtf1 deficient embryos, demonstrating that the 8 bp modifications do not negatively impact the activity of the injected RNA. As stated previously, morpholino knockdown was used in some experiments because our characterization of rtf1 morphants showed that they faithfully recapitulate the rtf1 mutant phenotype during the timeframe of interest.

(15) Using a technique like PRO-seq at the same stage as the ChIP-seq would complement the ChIP-seq and allow a more detailed analysis of the transcriptional pausing on specific genes observed in WT and mutant embryos. 

As stated in our response to the reviewer’s public review, we appreciate the suggestion but PRO-seq is beyond the scope of this study.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

The authors assess the impact of E-cigarette smoke exposure on mouse lungs using single cell RNA sequencing. Air was used as control and several flavors (fruit, menthol, tobacco) were tested. Differentially expressed genes (DEGs) were identified for each group and compared against the air control. Changes in gene expression in either myeloid or lymphoid cells were identified for each flavor and the results varied by sex. The scRNAseq dataset will be of interest to the lung immunity and e-cig research communities and some of the observed effects could be important. Unfortunately, the revision did not address the reviewers' main concerns about low replicate numbers and lack of validations. The study remains preliminary, and no solid conclusions could be drawn about the effects of E-cig exposure as a whole or any flavor-specific phenotypes.

Strengths:

The study is the first to use scRNAseq to systematically analyze the impact of e-cigarettes on the lung. The dataset will be of broad interest.

Weaknesses:

scRNAseq studies may have low replicate numbers due to the high cost of studies but at least 2 or 3 biological replicates for each experimental group is required to ensure rigor of the interpretation. This study had only N=1 per sex per group and some sex-dependent effects were observed. This could have been remedied by validating key observations from the study using traditional methods such as flow cytometry and qPCR, but the limited number of validation experiments did not support the conclusions of the scRNA seq analysis. An important control group (PG:VG) had extremely low cell numbers and was basically not useful. Statistical analysis is lacking in almost all figures. Overall, this is a preliminary study with some potentially interesting observations, but no solid conclusions can be made from the data presented.

The only new validation experiment is the immunofluorescent staining of neutrophils in Figure 4. The images are very low resolution and low quality and it is not clear which cells are neutrophils. S100A8 (calprotectin) is highly abundant in neutrophils but not strictly neutrophil-specific. It's hard to distinguish positive cells from autofluorescence in both Ly6g and S100a8 channels. No statistical analysis in the quantification.

We thank the reviewer for identifying the strengths of this study and pointing out the gaps in knowledge. Overall, our purpose to present this data is to provide the scRNA seq results as a resource to a wider community. We have used techniques like flow cytometry, multianalyte cytokine array and immunofluorescence to validate some of the results. We agree with the reviewer that we were unable to rightly point out the significance of our findings with the immunofluorescent stain in the previous edit. We have revised the manuscript and included the quantification for both Ly6G+ and S100A8+ cells in e-cig aerosol exposed and control lung tissues. Briefly, we identified a marked decrease in the staining for S100A8 (marker for neutrophil activation) in tobacco-flavored e-cig exposed mouse lungs as compared to controls. Upon considering the corroborating evidence from scRNA seq and flow cytometry with regards to increased neutrophil percentages in experimental group and lowered staining for active neutrophils using immunofluorescence, we speculate that exposure to e-cig (tobacco) aerosols may alter the neutrophil dynamics within the lungs. Also, co-immunofluorescence identified a more prominent co-localization of the two markers in control samples as compared to the treatment group which points towards some changes in the innate immune milieu within the lungs upon exposures. Future work is required to validate these speculations.

We have now discussed all the above-mentioned points in the Discussion section of the revised manuscript and toned down our conclusions regarding sex-dependent changes from scRNA seq data.

It is unclear what the meaning of Fig. 3A and B is, since these numbers only reflect the number of cells captured in the scRNAseq experiment and are not biologically meaningful. Flow cytometry quantification is presented as cell counts, but the percentage of cells from the CD45+ gate should be shown. No statistical analysis is shown, and flow cytometry results do not support the conclusions of scRNAseq data.

We thank the reviewer for this question. However, we would like to highlight that scRNA seq and flow cytometry may show similar trends but cannot be identical as one relies on cell surface markers (protein) for identification of cell types, while other is dependent on the transcriptomic signatures to identify the cell types. In our data, for the myeloid cells (alveolar macrophages and neutrophils), the scRNA and flow cytometry data match in trend. However, the trends do not match with respect to the lymphoid cells being studied (CD4 and CD8 T cells). The possible explanation for such a finding could be possible high gene dropout rates in scRNA seq, different analytical resolution for the two techniques and pooling of samples in our single cell workflow. We realize these shortcomings in our analyses and mention it clearly in the discussion as limitation of our work. It is important to note also that cell frequencies identified in scRNA seq just provide wide and indistinct indications which need to be further validated, which we tried to accomplish in our work to some degree. Our flow-based results clearly highlight the sex-specific variations in the immune cell percentages (something we could not have anticipated earlier). In future studies, we will include more replicates to tease out sex-based variations upon acute and chronic exposure to e-cig aerosols.

We have now replotted the graphs in Fig 3A and B and plotted the flow quantification as the percentage of total CD45+ cells. The gating strategy for the flow plots is also included as Figure S6 in the revised manuscript.

Reviewer #2 (Public review):

This study provides some interesting observations on how different flavour e-cigarettes can affect lung immunology; however, there are numerous flaws, including a low replicate number and a lack of effective validation methods, meaning findings may not be repeated. This is a revised article but several weaknesses remain related to the analysis and interpretation of the data.

Strengths:

The strength of the study is the successful scRNA-seq experiment which gives some preliminary data that can be used to create new hypotheses in this area.

Weaknesses:

Although some text weaknesses have been addressed since resubmission, other specific weaknesses remain: The major weakness is the n-number and analysis methods. Two biological n per group is not acceptable to base any solid conclusions. Any validatory data was too little (only cell % data) and not always supporting the findings (e.g. figure 3D does not match 3B/4A). Other examples include:

There aren't enough cells to justify analysis - only 300-1500 myeloid cells per group with not many of these being neutrophils or the apparent 'Ly6G- neutrophils'.

We thank the reviewer for the comment, but we disagree with the reviewer in terms of the justification of analyses. All the flavored e-cig aerosol groups were compared with air controls to deduce the outcomes in the current study. We already acknowledge low sample quality for PGVG group and have only included the comparisons with PGVG upon reviewer’s request which is open to interpretation by the reader.

By that measure, each treatment group (except PGVG group) has over 1000 cells with 24777 genes being analyzed for each cell type, which by the standards of single cell is sufficient. We understand that this strategy should not be used for detection of rare cell populations, which was neither the purpose of this manuscript nor was attempted. We conduct comparisons of broader cell types and mention more samples need to be added in the Discussion section of the revised manuscript.

As for the Ly6G neutrophil category, we don’t only base our results on scRNA analyses but also perform co-immunofluorescence and multi-analyte analyses and use evidence from previous literature to back our outcome. To avoid over-stating our results we have revamped the whole manuscript and ensured to tone down our results with relation to the presence of Ly6G- neutrophils. We do understand that more work is required in the future, but our work clearly shows the shift in neutrophil dynamics upon exposure which should be reported, in our opinion.

The dynamic range of RNA measurement using scRNAseq is known to be limited - how do we know whether genes are not expressed or just didn't hit detection? This links into the Ly6G negative neutrophil comments, but in general the lack of gene expression in this kind of data should be viewed with caution, especially with a low n number and few cells. The data in the entire paper is not strong enough to base any solid conclusion - it is not just the RNA-sequencing data.

We acknowledge this to be a valid point and have revamped the manuscript and toned down our conclusions. However, such limitations exist with any scRNA seq dataset and so must be interpreted accordingly by the readers. We do understand that due to the low cell counts and the limitations with scRNA seq we should not perform DESeq2 analyses for Ly6G+ versus Ly6G- neutrophil categories, which was never attempted at the first place. However, our results with co-immunofluorescence, multianalyte assay and scRNA expression analyses in myeloid cluster do point towards a shift in neutrophil activation which needs to be further investigated. Furthermore, Ly6G deficiency has been linked to immature neutrophils in many previous studies and is not an unlikely outcome that needs to be treated with immense skepticism.

We wish to make this dataset available as a resource to influence future research. We are aware of its limitations and have been transparent with regards to our experimental design, capture strategy, the quality of obtained results, and possible caveats to make it is open for discussion by the readers.

There is no data supporting the presence of Ly6G negative neutrophils. In the flow cytometry only Ly6G+ cells are shown with no evidence of Ly6G negative neutrophils (assuming equal CD11b expression). There is no new data to support this claim since resubmission and the New figures 4C and D actually show there are no Ly6G negative cells - the cells that the authors deem Ly6G negative are actually positive - but the red overlay of S100A8 is so strong it blocks out the green signal - looking to the Ly6G single stains (green only) you can see that the reported S100A8+Ly6G- cells all have Ly6G (with different staining intensities).

We thank the reviewer for this query and do understand the skepticism. We have now quantified the data to provide more clarity for interpretation. As we were using paraffin embedded tissues, some autofluorescence is expected which could explain some of reviewer’s concerns. However we expect that the inclusion of better quality images and quantification must address some of the concerns raised by the reviewer.

Eosinophils are heavily involved in lung macrophage biology, but are missing from the analysis - it is highly likely the RNA-sequence picked out eosinophils as Ly6G- neutrophils rather than 'digestion issues' the authors claim

We thank the reviewer for raising a valid concern. However, the Ly6G- cluster cannot be eosinophils in our case. Literature suggests SiglecF as an important biomarker of eosinophils which was absent in the Ly6G- cluster our in scRNA seq analyses as shown in File S18 and Figure 6B of the revised manuscript. We have now provided a detailed explanation (Lines 476-488; 503-506) of the observed results pertaining to eosinophil population in the revised manuscript to further address some of the concerns raised by this reviewer.

After author comments, it appears the schematic in Figure 1A is misleading and there are not n=2/group/sex but actually only n=1/group/sex (as shown in Figure 6A). Meaning the n number is even lower than the previous assumption.

We concur with reviewers’ valid concern and so are willing to provide this data as a resource for a wider audience to assist future work. Pooling of samples have been practiced by many groups previously to save resources and expense. We did it for the very same reason. It may not be the preferred approach, but it still has its merit considering the vast amount of cell-specific data generated using this strategy. To avoid overstating our results we have ensured to maintain transparency in our reporting and acknowledge all the limitations of this study.

We do not believe that the strength of scRNA seq lies in drawing conclusive results, but to tease our possible targets and direction that need to be validated with more work. In that respect, our study does identify the target cell types and biological processes which could be of importance for future studies.

Reviewer #3 (Public review):

This work aims to establish cell-type specific changes in gene expression upon exposure to different flavors of commercial e-cigarette aerosols compared to control or vehicle. Kaur et al. conclude that immune cells are most affected, with the greatest dysregulation found in myeloid cells exposed to tobacco-flavored e-cigs and lymphoid cells exposed to fruit-flavored e-cigs. The up- and down-regulated genes are heavily associated with innate immune response. The authors suggest that a Ly6G-deficient subset of neutrophils is found to be increased in abundance for the treatment groups, while gene expression remains consistent, which could indicate impaired function. Increased expression of CD4+ and CD8+ T cells along with their associated markers for proliferation and cytotoxicity is thought to be a result of activation following this decline in neutrophil-mediated immune response.

Strengths:

Single cell sequencing data can be very valuable in identifying potential health risks and clinical pathologies of lung conditions associated with e-cigarettes considering they are still relatively new.

Not many studies have been performed on cell-type specific differential gene expression following exposure to e-cig aerosols.

The assays performed address several factors of e-cig exposure such as metal concentration in the liquid and condensate, coil composition, cotinine/nicotine levels in serum and the product itself, cell types affected, which genes are up- or down-regulated and what pathways they control.

Considerations were made to ensure clinical relevance such as selecting mice whose ages corresponded with human adolescents so that data collected was relevant.

Weaknesses:

The exposure period of 1 hour a day for 5 days is not representative of chronic use and this time point may be too short to see a full response in all cell types. The experimental design is not well-supported based on the literature available for similar mouse models. Clinical relevance of this short exposure remains unclear.

We thank the reviewer for this query. However, we would like to emphasize that chronic exposure was never the intention of this study. We wished to design a study for acute nose-only exposure owing to which the study duration was left shorter. Shorter durations limit the stress and discomfort to the animal. The in vivo study using nose-only exposure regimen is still developing with multiple exposure regimen being used by different groups. To our knowledge there is no gold standard of e-cig aerosol exposure which is widely accepted other than the CORESTA recommendations, which we followed. Also, we show in our study how the daily exposure to leached metals vary in a flavor-dependent manner thus validating that exposure regime does need more attention in terms of equal dosing, particle distribution and composition- something we have started doing in our future studies. We have included all the explanations in the revised manuscript (Lines 82-85, 425-435, 648-654).

Several claims lack supporting evidence or use data that is not statistically significant. In particular, there were no statistical analyses to compare results across sex, so conclusions stating there is a sex bias for things like Ly6G+ neutrophil percentage by condition are observational.

We agree with reviewer’s comment and have taken this into consideration. We have now revamped the whole manuscript and toned down most of the sex-based conclusions stated in this work. Having said that, it is important to note that most of the work relying solely on scRNA seq, as is the case for this study, is observational in nature and needs to be assessed bearing this in mind.

Overall, the paper and its discussion are relatively surface-level and do not delve into the significance of the findings or how they fit into the bigger picture of the field. It is not clear whether this paper is intended to be used as a resource for other researchers or as an original research article.

We have now reworked on the Discussion and tried to incorporate more in-depth discussion and the results providing our insights regarding the observations, discrepancies and the possible explanations. We have also made it clear that this paper is intended to be used as a resource by other researchers (Lines 577-579)

The manuscript has some validation of findings but not very comprehensive.

We have now revamped the manuscript. We have Included quantification for immunofluorescence data with better representation of the GO analyses. We have worked on the Results and Discussion sections to make this a useful resource for the scientific community.

This paper provides a strong foundation for follow-up experiments that take a closer look at the effects of e-cig exposure on innate immunity. There is still room to elaborate on the differential gene expression within and between various cell types.

We thank the reviewer for pointing out the strength of this paper. The reason why we refrained from elaborating of the differential gene expressions within and between various cell types was due to low sample number and sequencing depth for this study. However the raw data will be provided with the final publication, which should be freely accessible to the public to re-analyze the data set as they deem fit.

Comments on revisions:

The reviewers have addressed major concerns with better validation of data and improved organization of the paper. However, we still have some concerns and suggestions pertaining to the statistical analyses and justifications for experimental design.

We appreciate the nuance of this experimental design, and the reviewers have adequately commented on why they chose nose-only exposure over whole body exposure. However, the justification for the duration of the exposure, and the clinical relevance of a short exposure, have not been addressed in the revised manuscript.

We thank the editor for this query. We have now addressed this query briefly in Lines 82-85, 425-435, 648-654 of the revised manuscript. We would like to add, however, that we intend to design a study for acute nose-only exposure for this project. Shorter durations limit the stress and discomfort to the animal, owing to which a duration of 1hour per day was chosen. The in vivo study using nose-only exposure regimen is still developing with multiple exposure regimen being used by different groups. Ours is one such study in that direction just intended to identify cell-specific changes upon exposure. Considering our results in Figure 1B showing variations in the level of metals leached in each flavor per day, the appropriate exposure regimen to design a controlled, reproducible experiment needs to be discussed. There could be room for improvement in our strategy, but this was the best regimen that we found to be appropriate per the literature and our prior knowledge in the field.

The presentation of cell counts should be represented by a percentage/proportion rather than a raw number of cells. Without normalization to the total number of cells, comparisons cannot be made across groups/conditions. This comment applies to several figures.

We thank the editor for this comment and have now made the requested change in the revised manuscript.

We appreciate that the authors have taken the reviewers' advice to validate their findings. However, we have concerns regarding the immunofluorescent staining shown in Figure 4. If the red channel is showing a pan-neutrophil marker (S100A8) and the green channel is showing only a subset of neutrophils (LY6G+), then the green channel should have far less signal than the red channel. This expected pattern is not what is shown in the figure, with the Ly6G marker apparently showing more expression than S100A8. Additionally, the FACS data states that only 4-5% of cells are neutrophils, but the red channel co-localizes with far more than 4-5% of the DAPI stain, meaning this population is overrepresented, potentially due to background fluorescence (noise). In addition, some of the shapes in the staining pattern do not look like true neutrophils, although it is difficult to tell because there remains a lot of background staining. The authors need to verify that their S100A8 and Ly6G antibodies work and are specific to the populations they intend to target. It is possible that only the brightest spots are truly S100A8+ or Ly6G+.

We thank the editor for this comment and acknowledge that we may have made broad generalizations in our interpretation of our data previously. We have now revisited the data and quantified the two fluorescence for better interpretation of our results. We have also reassessed our conclusions from this data and reworded the manuscript accordingly. Briefly we believe that Ly6G deficiency could be an indication of the presence of immature neutrophils in the lungs. This is a common process of neutrophil maturation. An active neutrophil population has Ly6G and should also express S100A8 indicating a normal neutrophilic response against stressors. However, our results, despite some autofluorescence which is common with lung tissues, shows a marked decline in the S100A8+ cells in the lung of tobacco-flavored e-cig aerosol exposed mice as compared to air controls. We also do not see prominent co-localization of the two markers in exposed group thus proving a shift in neutrophil dynamics which requires further investigation. We would also like to mention here that S100A8 is predominantly expressed in neutrophils, but is also expressed by monocytes and macrophages, so that could explain the over-representation of these cells in our immunofluorescence results. We have now included this in the Discussion section (Lines 489- 538) of the revised manuscript.

Paraffin sections do not always yield the best immunostaining results and the images themselves are low magnification and low resolution.

We agree with the editor that paraffin sections may not yield best results, we have worked on the final figure to improve the quality of the displayed results and zoomed-in some parts of the merged image to show the differences in the co-localization patterns for the two markers in our treated and control groups for easier interpretation.

Please change the scale bars to white so they are more visible in each channel.

The merged image in Figure 6C now has a white scale bar.

We appreciate that this is a preliminary test used as a resource for the community, but there is interesting biology regarding immune cells that warrants DEG analysis by the authors. This computational analysis can be easily added with no additional experiments required.

We thank the editor for this comment and agree that interesting biology regarding immune cells could be explored upon performing the DEG analyses on individual immune populations. However, due to the small sample size, low sequencing depth and pooling of same sex animals in each treatment group, we refrained from performing that analyses fearing over-representation of our results. We will be providing the link to the raw data with this publication which will be freely accessible to public on NIH GEO resource to allow further analyses on this dataset by the judgement of the investigator who utilizes it as a resource.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(Minor) The pathway analyses in Fig. 6-8 have different fonts than what's used in all other figures.

We have now made the requested change in the revised manuscript.

I see this too. But it's a weird paragraph. Yes the automotive industry is behaving like dinosaurs in Germany, but the two examples (Mittelstand is not the automotive industry, and a medical device maker) don't connect to the rest.

compares AI dev and attempts to keep it for oneself to the dev of atomic bombs and containment

22:48 "It's the gravitas of the situation that I see, that frustrates me that other people don't see it. I have been "preparing" intentionally since Hurricane Katrina, but I grew up on a farm in upstate New York. I know how to hunt. I know how to butcher animals. I know how to grow food. I know row crops and gardens. I know foraging in the woods. I know how to fish and where to get water from. And I understand how to move in a rural environment, not just the topographical terrain, but the human terrain as well. Been doing that my whole life. One could say, I've been prepared for this by the hand of the Most High my whole life. And I I see it. I see it coming. And it... while I would love to be wrong, it bothers me that others who do see it, or pay lip service to seeing it, don't take it as seriously as they should."

preach. there is too much demoralization everywhere.

English (thorough explanation)

1) What “Caribou” literally means

Caribou is the common name for a large, hoofed, deer-like animal in the species Rangifer tarandus. In many parts of the world, the same species is called reindeer.

So in simple terms: Caribou = a large northern deer, closely related to (and often the same as) reindeer.

2) Caribou vs. reindeer (why two names?)

• “Caribou” is used mainly in North America, especially Canada and Alaska, and often for wild populations.
• “Reindeer” is used more in Europe and Asia, and it often refers to domesticated (herded) populations—though biologically they’re the same species.

Think of it like a naming convention:

• Caribou (North American / wild context)
• Reindeer (Eurasian / domesticated or general context)

3) Key features (what makes a caribou distinctive)

• Antlers: Both males and females can grow antlers (this is unusual among deer).
• Hooves: Wide hooves act like snowshoes, helping them walk on snow and soft ground.
• Cold adaptation: Thick fur and an efficient metabolism help them survive Arctic/subarctic winters.
• Migration: Many populations travel in large seasonal migrations.

4) Grammar: singular/plural form

Caribou is commonly used as both singular and plural:

• “I saw a caribou.” (one)
• “I saw three caribou.” (more than one)

Sometimes you’ll see caribous, but caribou is more common.

5) How the word might be used if it appears alone

If the excerpt is just “Caribou” by itself (like your example), it most likely serves as:

• a topic heading (a section about the animal),
• a label (e.g., a picture caption),
• a category name (wildlife list), or
• a symbolic reference (e.g., representing the North, wilderness, migration, resilience).

6) Pronunciation

• CARE-ih-boo (common English pronunciation) IPA often shown as /ˈkærɪbuː/

中文（详细解释）

1）“Caribou”的基本意思

Caribou 指一种生活在寒带/亚寒带的大型鹿科动物，中文常译为： 北美驯鹿 / 北美驯鹿（野生型） / 卡里布驯鹿（不同资料翻译略有差异）

它和 “reindeer（驯鹿）” 在生物学上通常是 同一种动物（同一物种：Rangifer tarandus），只是不同地区习惯叫法不同。

2）Caribou 和 Reindeer 的区别（名字上的区别）

• Caribou：多用于 北美语境（加拿大、阿拉斯加等），常指 野生种群
• Reindeer：多用于 欧洲/亚洲语境，也常指 被人类驯养、放牧的驯鹿

简单记忆：

• 北美野外常叫 caribou
• 欧亚与驯养常叫 reindeer

3）典型特征（为什么它很“特别”）

• 雌雄都有角：很多鹿只有雄鹿长角，但驯鹿/Caribou 的 雌性也常长角。
• 蹄子宽大：像“雪鞋”一样，适合走雪地和沼泽。
• 耐寒结构：毛厚、保温强，适应极冷环境。
• 迁徙行为：许多种群会进行 大规模季节性迁徙。

4）语法：单复数

英语里 caribou 常同时当 单数和复数：

• one caribou（一只）
• three caribou（三只）

也可能看到 caribous，但更常见还是 caribou。

5）单独出现 “Caribou” 可能表示什么

如果只出现一个词 Caribou（没有句子），它很可能是：

• 标题/小标题（这一段讲“驯鹿/北美驯鹿”）
• 图片说明（图下写 Caribou）
• 清单标签（野生动物列表里的一项）
• 象征意义（北境、荒野、迁徙、坚韧等意象）

6）发音

大致读作：“开-ri-bu”（CARE-ih-boo）

If you paste the sentence or paragraph around “Caribou”, I can explain the exact meaning, including whether it’s literal (the animal) or symbolic/metaphorical in that context, in both English and Chinese.

vibecoding original description by Andrej Karpathy

Quickly distorted to mean any code created w llm assistance. Note: [[Martijn Aslander p]] follows this dev quite closely (dictation, accept always, it mostly works)

The government reports highlight that cyberbullying is widespread and often chronic, affecting many youth for long periods.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Domínguez-Rodrigo and colleagues make a moderately convincing case for habitual elephant butchery by Early Pleistocene hominins at Olduvai Gorge (Tanzania), ca. 1.8-1.7 million years ago. They present this at the site scale (the EAK locality, which they excavated), as well as across the penecontemporaneous landscape, analyzing a series of findspots that contain stone tools and large-mammal bones. The latter are primarily elephants, but giraffids and bovids were also butchered in a few localities. The authors claim that this is the earliest well-documented evidence for elephant butchery; doing so requires debunking other purported cases of elephant butchery in the literature, or in one case, reinterpreting elephant bone manipulation as being nutritional (fracturing to obtain marrow) rather than technological (to make bone tools). The authors' critical discussion of these cases may not be consensual, but it surely advances the scientific discourse. The authors conclude by suggesting that an evolutionary threshold was achieved at ca. 1.8 ma, whereby regular elephant consumption rich in fats and perhaps food surplus, more advanced extractive technology (the Acheulian toolkit), and larger human group size had coincided.

The fieldwork and spatial statistics methods are presented in detail and are solid and helpful, especially the excellent description (all too rare in zooarchaeology papers) of bone conservation and preservation procedures. However, the methods of the zooarchaeological and taphonomic analysis - the core of the study - are peculiarly missing. Some of these are explained along the manuscript, but not in a standard Methods paragraph with suitable references and an explicit account of how the authors recorded bone-surface modifications and the mode of bone fragmentation. This seems more of a technical omission that can be easily fixed than a true shortcoming of the study. The results are detailed and clearly presented.

By and large, the authors achieved their aims, showcasing recurring elephant butchery in 1.8-1.7 million-year-old archaeological contexts. Nevertheless, some ambiguity surrounds the evolutionary significance part. The authors emphasize the temporal and spatial correlation of (1) elephant butchery, (2) Acheulian toolkits, and (3) larger sites, but do not actually discuss how these elements may be causally related. Is it not possible that larger group size or the adoption of Acheulian technology have nothing to do with megafaunal exploitation? Alternative hypotheses exist, and at least, the authors should try to defend the causation, not just put forward the correlation. The only exception is briefly mentioning food surplus as a "significant advantage", but how exactly, in the absence of food-preservation technologies? Moreover, in a landscape full of aggressive scavengers, such excess carcass parts may become a death trap for hominins, not an advantage. I do think that demonstrating habitual butchery bears very significant implications for human evolution, but more effort should be invested in explaining how this might have worked.

Overall, this is an interesting manuscript of broad interest that presents original data and interpretations from the Early Pleistocene archaeology of Olduvai Gorge. These observations and the authors' critical review of previously published evidence are an important contribution that will form the basis for building models of Early Pleistocene hominin adaptation.

This is a good example of the advantages of the eLife reviewing process. It has become much too common, among traditional peer-reviewing journals, to reject articles when there is no coincident agreement in the reviews, regardless of the heuristics (i.e., empirically-supported weight) of the arguments on both reviewers. Reviewers 1 and 2 provide contrasting evaluations, and the eLife dialogue between authors and reviewers enable us to address their comments differentially. Reviewer 1 (R1), whose evaluation is overall positive, remarks that the methods of the zooarchaeological and taphonomic analysis are missing. We have added them now in the revised version of our manuscript. R1 also remarks that our work highlights correlation of events, but not necessarily causation. We did not establish causation because such interpretations bear a considerable amount of speculation (and they might have fostered further criticism by R2); however, in the revised version, we expanded our discussion of these issues substantially. Establishing causation among the events described is impossible, but we certainly provide arguments to link them.

Reviewer #2 (Public review):

The authors argue that the Emiliano Aguirre Korongo (EAK) assemblage from the base of Bed II at Olduvai Gorge shows systematic exploitation of elephants by hominins about 1.78 million years ago. They describe it as the earliest clear case of proboscidean butchery at Olduvai and link it to a larger behavioral shift from the Oldowan to the Acheulean.

The paper includes detailed faunal and spatial data. The excavation and mapping methods appear to be careful, and the figures and tables effectively document the assemblage. The data presentation is strong, but the behavioral interpretation is not supported by the evidence.

The claim for butchery is based mainly on the presence of green-bone fractures and the proximity of bones and stone artifacts. These observations do not prove human activity. Fractures of this kind can form naturally when bones break while still fresh, and spatial overlap can result from post-depositional processes. The studies cited to support these points, including work by Haynes and colleagues, explain that such traces alone are not diagnostic of butchery, but this paper presents them as if they were.

The spatial analyses are technically correct, but their interpretation extends beyond what they can demonstrate. Clustering indicates proximity, not behavior. The claim that statistical results demonstrate a functional link between bones and artifacts is not justified. Other studies that use these methods combine them with direct modification evidence, which is lacking in this case.

The discussion treats different bodies of evidence unevenly. Well-documented cut-marked specimens from Nyayanga and other sites are described as uncertain, while less direct evidence at EAK is treated as decisive. This selective approach weakens the argument and creates inconsistency in how evidence is judged.

The broader evolutionary conclusions are not supported by the data. The paper presents EAK as marking the start of systematic megafaunal exploitation, but the evidence does not show this. The assemblage is described well, but the behavioral and evolutionary interpretations extend far beyond what can be demonstrated.

We disagree with the arguments provided by Reviewer 2 (R2). The arguments are based on two issues: bone breakage and spatial association. We will treat both separately here.

Bone breakage

R2 argues that:

“The claim for butchery is based mainly on the presence of green-bone fractures and the proximity of bones and stone artifacts. These observations do not prove human activity. Fractures of this kind can form naturally when bones break while still fresh, and spatial overlap can result from post-depositional processes. The studies cited to support these points, including work by Haynes and colleagues, explain that such traces alone are not diagnostic of butchery, but this paper presents them as if they were.”

In our manuscript, we argued that green-breakage provides an equally good (or even  better) taphonomic evidence of butchery if documented following clear taphonomic indicators. Not all green breaks are equal and not all “cut marks” are unambiguously identifiable as such. First, “natural” elephant long limb breaks have been documented only in pre/peri-mortem stages when an elephant breaks a leg. As a matter of fact, they have only been reported in publication on femora, the thinnest long bone (Haynes et al., 2021). Unfortunately, they have been studied many months after the death of the individuals, and the published diagnosis is made under the assumption that no other process intervened in the modification of those bones during this vast time span. Most of the breaks resulting from pre-mortem fractures produce long smooth, oblique/helical outlines. Occasionally, some flake scarring may occur on the cortical surface. This has been documented as uneven, small-sized, spaced, and we are not sure if it resulted from rubbing of broken fragments while the animal was alive and attempting to walk or some may have resulted from dessication of the bone after one year. When looking at them in detail, such breaks contain sometimes step-microfractures and angular (butterfly-like) outlines. Sometimes, they may be accompanied by pseudo-notches, which are distinct and not comparable to the deep notches that hammerstone breaking generates on the same types of bones. Commonly, the edges of the breaks show some polishing, probably from separate break planes rubbing against each other. It should be emphasized that the experimental work on hammerstone breaking documented by Haynes et al. (2021) is based on bone fracture properties of bones that are no longer completely green. The cracking documented in their hammerstone experimentation, with very irregular outlines differs from the cracking that we are documented in butchery of recently dead elephants.

All this contrasts with the overlapping notches and flake scars (mostly occurring on the medullary side of the bone), both of them bigger in size, with clear smooth, spiral and longitudinal trajectories, with a more intensive modification on the medullary surface, and with sharp break edges resulting from hammerstone breaking of the green bone. No “natural” break has been documented replicating the same morphologies displayed in the Supplementary File to our paper. We display specimens with inflection points, hackle marks on the breaks, overlapping scarring on the medullary surface, with several specimens displaying percussion marks and pitting (also most likely percussion marks). Most importantly, we document this patterned modification on elements other than femora, for which no example has been documented of purported morphological equifinality caused by pre-mortem “natural” breaking. In contrast, such morphologies are documented in hammerstone-broken completely green bones (work in progress). We cited the works of Haynes to support this, because they do not show otherwise. As a matter of fact, Haynes himself had the courtesy of making a thorough reading of our manuscript and did not encounter any contradiction with his work. 

Spatial association

R2 argues in this regard:

“The spatial analyses are technically correct, but their interpretation extends beyond what they can demonstrate. Clustering indicates proximity, not behavior. The claim that statistical results demonstrate a functional link between bones and artifacts is not justified. Other studies that use these methods combine them with direct modification evidence, which is lacking in this case.”

We should emphasize that there is some confusion in the use and interpretation of clustering by R2 when applied to EAK. R2 appears to interpret clustering as the typical naked-eye perception of the spatial association of different items. In contrast, we rely on the statistical concept of clustering, more specifically on spatial interdependence or covariance, which is different. Items may appear visually clustered but still be statistically independent. This could, for example, result from two independent depositional episodes that happen to overlap spatially. In such cases, the item-to-item relationship does not necessarily show any spatial interdependence between classes other than simple clustering (i.e., spatial coincidence in intensity).

Spatial statistical interdependence, on the other hand, reflects a spatial relationship or co-dependence between different items. This goes beyond the mere fact that classes appear clustered: items between classes may show specific spatial relationships — they may avoid each other or occupy distinct positions in space (regular co-dependence), or they may interact within the same spatial area (clustering co-dependence). Our tests indicate the latter for EAK.

Such patterns are difficult to explain when depositional events are unrelated, since the probability that two independent events would generate identical spatial patterns in the same loci is very low. They are also difficult to reconcile when post-depositional processes intervene and resediment part of the assemblage (Domínguez-Rodrigo et al. 2018).

Finally, R2 concludes:

“The discussion treats different bodies of evidence unevenly. Well-documented cut-marked specimens from Nyayanga and other sites are described as uncertain, while less direct evidence at EAK is treated as decisive. This selective approach weakens the argument and creates inconsistency in how evidence is judged.”

The Nyayanga hippo remains bearing modifications have not been well-documented cut marks. Neither R2 nor we can differentiate those marks from those inflicted by natural abrasive processes in coarse-grained sedimentary contexts, where the carcasses are found. The fact that the observable microscopic features (through low-quality photographs as appear in the original publication) differ between the cut marks documented on smaller animals and those inferred for the hippo remains makes them even more ambiguous. Nowhere in our manuscript do we treat the EAK evidence (or any other evidence) as decisive, but as the most likely given the methods used and the results reported.

References

Haynes G, Krasinski K, Wojtal P. 2021. A Study of Fractured Proboscidean Bones in Recent and Fossil Assemblages. Journal of Archaeological Method and Theory 28:956–1025.

Domínguez-Rodrigo, M., Cobo-Sánchez, L., yravedra, J., Uribelarrea, D., Arriaza, C., Organista, E., Baquedano, E. 2018. Fluvial spatial taphonomy: a new method for the study of post-depositional processes. Archaeological and Anthropological Sciences 10: 1769-1789.

Recommendations for authors:

Reviewer #1 (Recommendations for the authors):

I have several recommendations that, in my opinion, could enhance the communication of this study to the readers. The first point is the only crucial one.

(1) A detailed zooarchaeological methods section must be added, with explanations (or references to them) of precisely how the authors defined and recorded bone-surface modifications and mode of bone fragmentation.

This appears in the revised version of the manuscript in the form of a new sub-section within the Methods section.

(2) The title could be improved to better represent the contents of the paper. It contains two parts: the earliest evidence for elephant butchery (that's ok), and revealing the evolutionary impact of megafaunal exploitation. The latter point is not actually revealed in the manuscript, just alluded to here and there (see also below).

We have elaborated on this in the revised version, linking megafaunal exploitation and anatomical changes (which appear discussed in much more detail in the references indicated).

(3) The abstract does not make it clear whether the authors think that the megafaunal adaptation strongly correlates with the Acheulian technocomplex. It seems that they do, so please make this point apparent in the abstract.

From a functional point of view, we document the correlation, but do not believe in the causation, since most butchering tools around these megafaunal carcasses are typologically non Acheulian. We have indicated so in the abstract.

(4) Please define what you mean by "megafauna". How large should an animal be to be considered as megafauna in this particular context?

We have added this definition: we identify as “megafauna” those animals heavier than 800 kg.

(5) In the literature survey, consider also this Middle Pleistocene case-study of elephant butchery, including a probable bone tool: Rabinovich, R., Ackermann, O., Aladjem, E., Barkai, R., Biton, R., Milevski, I., Solodenko, N., and Marder, O., 2012. Elephants at the middle Pleistocene Acheulian open-air site of Revadim Quarry, Israel. Quaternary International, 276, pp.183-197.

Added to the revised version

(6) The paragraph in lines 123-160 is unclear. Do the authors argue that the lack of evidence for processing elephant carcasses for marrow and grease is universal? They bring forth a single example of a much later (MIS 5) site in Germany. Then, the authors state the huge importance of fats for foragers (when? Where? Surely not in all latitudes and ecosystems). This left me confused - what exactly are you trying to claim here?

We have explained this a little more in the revised text. What we pointed out was that most prehistoric (and modern) elephant butchery sites leave grease-containing long bones intact. Evidence of anthropogenic breakage of these elements is rather limited. The most probably reason is the overabundance of meat and fat from the rest of the carcass and the time-consuming effort needed to access the medullary cavity of elephant long bones.

(7) The paragraph in lines 174-187 disrupts the flow of the text, contains previously mentioned information, ends with an unclear sentence, and could be cut.

(8) Results: please provide the MNI for the EAK site (presumably 1, but this is never mentioned).

Done in the revised version.

(9) Lines 292 - 295: The authors found no traces of carnivoran activity (carnivoran remains, coprolites, or gnawing marks on the elephant bones), yet they attribute the absence of some non-dense skeletal elements to carnivore ravaging. I cannot understand this rationale, given that other density-mediated processes could have deleted the missing bones and epiphysis.

This interpretation stems from our observations of several elephant carcasses in the Okavango delta in Botswana. Those that were monitored showed deletion of remains (i.e., disappearance of certain bones, like feet) without necessarily imprinting damage on the rest of the carcass. Carnivore intervention in an elephant death site can result in deletion of a few remains without much damage (if any), or if hyena clans access the carcass, much more conspicuous damage can be documented. There is a whole range of carnivore signatures in between. We are currently working on our study of several elephant carcasses subjected to these highly variable degrees of carnivore impact.

(10) Lines 412 - 422: "The clustering of the elephant (and hippopotamus) carcasses in the areas containing the highest densities of landscape surface artifacts is suggestive of a hominin agency in at least part of their consumption and modification." - how so? It could equally suggest that both hominins and elephants were drawn to the same lush environments.

We agree. Both hominins and megafauna must have been drawn to the same ecological loci for interaction to emerge. However, the fact that the highest density clusters of artifacts coincide with the highest density of carcasses “showing evidence of having been broken”, is suggestive of hominin use and consumption.

(11) Discussion: I suggest starting the Discussion with a concise appraisal of the lines of evidence detailed in the Results and their interpretation, and only then, the critical reassessment of other studies. Similarly, a new topic starts in line 508, but without any subheading or an introductory sentence that could assist the readers.

We added the introductory lines of the former Conclusion section to the revised Discussion section, as suggested by R1.

(12) Line 607: Neumark-Nord are Late Pleistocene sites (MIS 5), not Middle Pleistocene.

Corrected.

(13) Regarding the ambiguity in how megafaunal exploitation may be causally related to the other features of the early Acheulian, the authors can develop the discussion. Alternatively, they should explicitly state that correlation is not causation, and that the present study adds the megafaunal exploitation element to be considered in future discussion of the shifts in lifestyles 1.8 million years ago.

We have done so.

Reviewer #2 (Recommendations for the authors):

The following detailed comments are provided to help clarify arguments, ensure accurate representation of cited literature, and strengthen the logical and methodological framing of the paper. Line numbers refer to the version provided for review.

(1) Line 55: Such concurrency (sometimes in conjunction with other variables)

The term "other variables" is very vague. I would suggest expanding on this or taking it out altogether.

(2) Line 146: Megafaunal long bone green breakage (linked to continuous spiral fractures on thick cortical bone) is probably a less ambiguous trace of butchery than "cut marks", since many of the latter could be equifinal and harder to identify, especially in contexts of high abrasion and trampling (Haynes et al., 2021, 2020).

This reasoning is not supported by the evidence or the cited sources. Green-bone spiral fractures only show that a bone broke while it was fresh and do not reveal who or what caused it. Carnivore feeding, trampling, and natural sediment pressure can all create the same patterns, so these fractures are not clearer evidence of butchery than cut marks. Cut marks, when they are preserved and morphologically clear, remain the most reliable indicator of human activity. The Haynes papers actually show the opposite of what is claimed here. They warn that spiral fractures and surface marks can form naturally and that fracture patterns alone cannot be used to infer butchery. This section should be revised to reflect what those studies actually demonstrate.

The reasoning referred to in line 146 is further explained below in the original text as follows:

“Despite the occurrence of green fractures on naturally-broken bones, such as those trampled by elephants (Haynes et al., 2020), those occurring through traumatic fracturing or gnawed by carnivores (Haynes and Hutson, 2020), these fail to reproduce the elongated, extensive, or helicoidal spiral fractures (uninterrupted by stepped sections), accompanied by the overlapping conchoidal scars (both cortical and medullary), the reflected scarring, the inflection points, or the impact hackled break surfaces and flakes typical of dynamic percussive breakage. Evidence of this type of green breakage had not been documented earlier for the Early Pleistocene proboscidean or hippopotamid carcasses, beyond the documentation of flaked bone with the purpose of elaboration of bone tools (Backwell and d’Errico, 2004; Pante et al., 2020; Sano et al., 2020).”

The problem in the way that R2 uses Haynes et al.´s works is that R2 uses features separately. Natural breaks occurring while the bone is green can generate spiral smooth breaks, for example, but it is not the presence of a single feature that invalidates the diagnosis of agency or that is taphonomically relevant, but the concurrence of several of them. The best example of a naturally (pre-mortem) broken bone was published by Haynes et al.

The natural break shows helical fractures, subjugated to linear (angular) fracture outlines. Notice how the crack displays a zig-zag. The break is smooth but most damage occurs on the cortical surface, with flaking adjacent to the break and step micro-fracturing on the edges. The cortical scarring is discontinuous (almost marginal) and very small, almost limited to the very edge of the break. No modification occurs on the medullary surface. No extensive conchoidal fractures are documented, and certainly none inside the medullary surface of the break.

Compare with Figure S8, S10, S17 and S34 (all specimens are shown in their medullary surface):

In these examples, we see clearly modified medullary surfaces with multiple green breaks and large-sized step fractures, accompanied in some examples by hackle marks. Some show large overlapping scars (of substantially bigger size than those documented in the natural break image). Not a single example of naturally-broken bones has been documented displaying these morphologies simultaneously. It is the comprehensive analysis of the co-occurrence of these features and not their marginal and isolated occurrence in naturally-broken bones that make a difference in the attribution of agency. Likewise, no example of naturally-broken bone has been published that could mimic any of the two green-broken bones documented at EAK. In contrast, we do have bones from our on-going experimentation with green elephant carcasses that jointly reproduce these features. See also Figure 6 of the article to find another example without any modern referent in the naturally-broken bones documented.

We should emphasize that R2 is inaccurately portraying what Haynes et al.´s results really document. Contrary to R2´s assertion, trampling does not reproduce any of the examples shown above. Neither do carnivores. It should be stressed that Haynes & Harrod only document similar overlapping scarring on the medullary surface of bones, when using much smaller animals. In all the carnivore damage repertoire that they document for elephants, durophagous spotted hyenas can only inflict furrowing on the ends of the biggest long bones, especially if they are adults. Long bone midshafts remain inaccessible to them. The mid-shaft portions of bones that we document in our Supplementary File and at EAK cannot be the result of hyena (or carnivore damage) for this reason, and also because their intense gnawing on elephant bones leaves tooth marking on most of the elements that they modify, being absent in our sample.

(3) Line 176: other than hominins accessed them in different taphonomically-defined stages- stages - the "Stages" is repeated twice

Defined in the revised version

(4) Line 174: Regardless of the type of butchery evidence - and with the taphonomic caveat that no unambiguous evidence exists to confirm that megafaunal carcasses were hunted or scavenged other than hominins accessed them in different taphonomically-defined stages- stages - the principal reasons for exploring megafaunal consumption in early human evolution is its origin, its episodic or temporally-patterned occurrence, its impact on hominin adaptation to certain landscapes, and its reflection on hominin group size and site functionality.

This sentence is confusing and needs to be rewritten for clarity. It tries to combine too many ideas at once, and the phrasing makes it hard to tell what the main point is. The taphonomic caveat in the middle interrupts the sentence and obscures the argument. It should be broken into separate, clearer statements that distinguish what evidence exists, what remains uncertain, and what the broader goals of the discussion are.

We believe the ideas are displayed clearly

(5) Line 179: landscapes, and its reflection on hominin group size and site functionality. If hominins actively sought the exploitation of megafauna, especially if targeting early stages of carcass consumption, the recovery of an apparent surplus of resources reflects a substantially different behavior from the small-group/small-site pattern documented at several earlier Oldowan anthropogenic sites (Domínguez-Rodrigo et al., 2019) -or some modern foragers, like the Hadza, who only exploit megafaunal carcasses very sporadically, mostly upon opportunistic encounters (Marlowe, 2010; O'Connell et al., 1992; Wood, 2010; Wood and Marlowe, 2013).

This sentence makes a reasonable point, but is written in a confusing way. The idea that early, deliberate access to megafauna would represent a different behavioral pattern from smaller Oldowan or modern foraging contexts is valid, but the sentence is awkward and hard to follow. It should be rephrased to make the logic clearer and more direct.

We believe the ideas are displayed clearly

(6) Line 186: When the process started of becoming megafaunal commensal started has major implications for human evolution.

This sentence is awkward and needs to be rewritten for clarity. The phrasing "when the process started of becoming megafaunal commensal started" is confusing and grammatically incorrect. It could be revised to something like "Determining when hominins first began to interact regularly with megafauna has major implications for human evolution," or another version that clearly identifies the process being discussed.

Modified in the revised version

(7) Line189: The multiple taphonomic biases intervening in the palimpsestic nature of most of these butchery sites often prevent the detection of the causal traces linking megafaunal carcasses and hominins. Functional links have commonly been assumed through the spatial concurrence of tools and carcass remains; however, this perception may be utterly unjustified as we argued above. Functional association of both archaeological elements can more securely be detected through objective spatial statistical methods. This has been argued to be foundational for heuristic interpretations of proboscidean butchery sites (Giusti, 2021). Such an approach removes ambiguity and solidifies spatial functional association, as demonstrated at sites like Marathousa 1 (Konidaris et al., 2018) or TK Sivatherium (Panera et al., 2019). This method will play a major role in the present study.

This section overstates what spatial analysis can demonstrate and misrepresents the cited studies. The works by Giusti (2021), Konidaris et al. (2018), and Panera et al. (2019) do use spatial statistics to examine relationships between artifacts and faunal remains, but they explicitly caution that spatial overlap alone does not prove functional or behavioral association. These studies argue that clustering can support such interpretations only when combined with detailed taphonomic and stratigraphic evidence. None of them claims that spatial analysis "removes ambiguity" or "solidifies" functional links. The text should be revised to reflect the more qualified conclusions of those papers and to avoid implying that spatial statistics can establish behavioral causation on their own.

We disagree. Both works (Giusti and Panera) use spatial statistical tools to create an inferential basis reinforcing a functional association of lithics and bones. In both cases, the anthropogenic agency inferred is based on that. We should stress that this only provides a basis for argumentation, not a definitive causation. Again, those analyses show much more than just apparent visual clustering.

(8) Line 200: Here, we present the discovery of a new elephant butchery site (Emiliano Aguirre Korongo, EAK), dated to 1.78 Ma, from the base of Bed II at Olduvai Gorge. It is the oldest unambiguous proboscidean butchery site at Olduvai.

It is fine to state the main finding in the introduction, but the phrasing here is too strong. Calling EAK "the oldest unambiguous proboscidean butchery site" asserts certainty before the evidence is presented. The claim should be stated more cautiously, for example, "a new site that provides early evidence for proboscidean butchery," so that the language reflects the strength of the data rather than pre-judging it.

We understand the caution by R2, but in this case, EAK is the oldest taphonomically-supported evidence of elephant butchery at Olduvai (see discussion about FLK North in the text). Whether this is declared at the beginning or the end of the text is irrelevant.

(9) Line 224: The drying that characterizes Bed II had not yet taken place during this moment.

This sentence reads like a literal translation. It should be rewritten for clarity.

Modified in the revised version

(10) Line 233: During the recent Holocene, the EAK site was affected by a small landslide which displaced the...

This section contains far more geological detail than is needed for the argument. The reader only needs to know that the site block was displaced by a small Holocene landslide but retains its stratigraphic integrity. The extended discussion of regional faults, seismicity, and slope processes goes well beyond what is necessary for context and distracts from the main focus of the paper.

We disagree. The geological information is what is most commonly missing from most archaeological reports. Here, it is relevant because of the atypical process and because it has been documented only twice with elephant butchery sites. Explaining the dynamic geological process that shaped the site helps to understand its spatial properties.

(11) Line 264: In June 2022, a partial elephant carcass was found at EAK on a fragmented stratigraphic block...

This section reads like field notes rather than a formal site description. Most of the details about the discovery sequence, trench setup, and excavation process are unnecessary for the main text. Only the basic contextual information about the find location, stratigraphic position, and anatomical composition is needed. The rest could be condensed or moved to the methods or supplementary material.

We disagree. See reply above.

(12) Line 291: hominins or other carnivores. Ongoing restoration work will provide an accurate estimate of well-preserved and modified fractions of the assemblage.

This sentence is unclear and needs to specify what kind of restoration work is being done and what is meant by well-preserved and modified fractions. It is not clear whether modified refers to surface marks, diagenetic alteration, or something else. If the bones are still being cleaned or prepared, the analysis is incomplete, and the counts cannot be considered final. If restoration only means conservation or stabilization, that should be stated clearly so the reader understands that it does not affect the results. As written, it is not clear whether the data presented here are preliminary or complete.

We added: For this reason, until restoration is concluded, we cannot produce any asssertion about the presence or absence of bone surface modifications.

(13) Line 294: The tibiae were well preserved, but the epiphyseal portions of the femora were missing, probably removed by carnivores, which would also explain why a large portion of the rib cage and almost all vertebrae are missing.

This explanation is not well supported. The missing elements could be the result of other forms of density-mediated destruction, such as sediment compaction or post-depositional fragmentation, especially since no tooth marks were found. Given the low density of ribs, vertebrae, and femoral epiphyses, these processes are more likely explanations than carnivore removal. The text should acknowledge these alternatives rather than attributing the pattern to carnivore activity without direct evidence.

Sediment compaction and post-depositional can break bones but cannot make them disappear. Our excavation process was careful enough to detect bone if present. Their absence indicates two possibilities: erosion through the years at the front of the excavation or carnivore intervention. Carnivores can take elephant bones without impacting the remaining assemblage (see our reply above to a similar comment).

(14) Line 304: The fact that the carcass was moved while encased in its sedimentary context, along with the close association of stone tools with the elephant bones, is in agreement with the inference that the animal was butchered by hominins. A more objective way to assess this association is through spatial statistical analysis.

The authors state that "the carcass was moved while encased in its sedimentary context, along with the close association of stone tools with the elephant bones, is in agreement with the inference that the animal was butchered by hominins." This does not logically follow. Movement of the block explains why the bones and tools remain together, not how that association was created. The preserved association alone does not demonstrate butchery, especially in the absence of cut marks or other direct evidence of hominin activity.

Again, we are sorry that R2 is completely overlooking the strong signal detected by the spatial statistical analysis. The way that the block moved, it preserved the original association of bones and tools. This statement is meant to clarify that despite the allochthonous nature of the block, the original autochthonous depositional process of both types of archaeological materials has been preserved. The spatial association, as statistically demonstrated, indicates that the functional link is more likely than any other alternative process. The additional fact that nowhere else in that portion of the outcrop do we identify scatters of tools (all appear clustered at a landscape scale with the elephant) adds more support to this interpretation. This would have been further supported by the presence of cut marks, no doubt, but their absence does not indicate lack of functional association, since as Haynes´ works have clearly shown, most bulk defleshing of modern elephant leaves no traces on most bones.

(15) Line 370: This also shows that the functional connection between the elephant bones and the tools has been maintained despite the block post-sedimentary movement.

The spatial analyses appear to have been carried out appropriately, and the interpretations of clustering and segregation are consistent with the reported results. However, the conclusion that the "functional connection" between bones and tools has been maintained goes beyond what spatial correlation alone can demonstrate. These analyses show spatial proximity and scale-dependent clustering but cannot, by themselves, confirm a behavioral or functional link.

R2 is making this comment repeatedly and we have addressed it more than once above. We disagree and we refer to our replies above to sustain it.

(16) Line 412: The clustering of the elephant (and hippopotamus) carcasses in the areas containing the highest densities of landscape surface artifacts is suggestive of a hominin agency in at least part of their consumption and modification. The presence of green broken elephant long bone elements in the area surveyed is only documented within such clusters, both for lower and upper Bed II. This constitutes inverse negative evidence for natural breaks occurring on those carcasses through natural (i.e., non-hominin) pre- and peri-mortem limb breaking (Haynes et al., 2021, 2020; Haynes and Hutson, 2020). In this latter case, it would be expected for green-broken bones to show a more random landscape distribution, and occur in similar frequencies in areas with intense hominin landscape use (as documented in high density artifact deposition) and those with marginal or non-hominin intervention (mostly devoid of anthropogenic lithic remains).

The clustering of green-bone fractures with stone tools is intriguing but should be interpreted cautiously. The Haynes references are misrepresented here. Those studies address both cut marks and green-bone (spiral) fractures, emphasizing that each can arise through non-hominin processes such as trampling, carcass collapse, and sediment loading. They do not treat green fractures as clearer evidence of butchery; in fact, they caution that such breakage patterns can occur naturally and even form clustered distributions in areas of repeated animal activity. The claim that these studies support spiral fractures as unambiguous indicators of hominin activity, or that natural breaks would be randomly distributed, is not accurate.

We would like to emphasize again that the Haynes´references are not misrepresented here. See our extensive reply above. If R2 can provide evidence of natural breakage patterns resulting from pre-mortem limb breaking or post-mortem trampling resulting in all limb bones being affected by these processes and resulting in smooth spiral breaks, accompanied with extensive and overlapping scarring on the medullary surface, in conjunction with the other features described in our replies above, then we would be willing to reconsider. With the evidence reported until now, that does not occur simultaneously on specimens resulting from studies on modern elephant bones.

R2 seems to contradict him(her)self here by saying that Haynes studies show that cut marks are not reliable because they can also be reproduced via trampling. Until this point, R2 had been saying that only cut marks could demonstrate a functional link and support butchery. Haynes´ studies do not deal experimentally with sediment loading.

(17) Line 424: This indicates that from lower Bed II (1.78 Ma) onwards, there is ample documented evidence of anthropogenic agency in the modification of proboscidean bones across the Olduvai paleolandscapes. The discovery of EAK constitutes, in this respect, the oldest evidence thereof at the gorge. The taphonomic evidence of dynamic proboscidean bone breaking across time and space supports, therefore, the inferences made by the spatial statistical analyses of bones and lithics at the site.

This conclusion is overstated. The claim of "ample documented evidence of anthropogenic agency" is too strong, given that the main support comes from indirect indicators like green-bone fractures and spatial clustering rather than clear butchery marks. It would be more accurate to say that the evidence suggests or is consistent with possible hominin involvement. The final sentence also conflates association with causation; spatial and taphonomic data can indicate a relationship, but do not confirm that the carcasses were butchered by hominins.

The evidence is based on spatially clustering (at a landscape scale) of tools and elephant (and other megafaunal taxa) bones, in conjunction with a large amount of green-broken elements. This interpretation, if we compare it against modern referents is supported even stronger. In the past few years, we have been conducting work on modern naturally dead elephant carcasses in Botswana and Zambia, and of the several carcasses that we have seen, we have not identified a single case of long bone shaft breaks like those described by Haynes as natural or like those we describe here as anthropogenic. This probably means that they are highly unlikely or marginal occurrences at a landscape scale. This seems to be supported by Haynes´ work too. Out of the hundreds of elephant carcasses that he has monitored and studied over the years for different works, we have managed to identify only two instances where he described natural pre-mortem breaks. This certainly qualifies as extremely marginal. 

Most of the Results section is clearly descriptive, but beginning with "The clustering of the elephant (and hippopotamus) carcasses..." the text shifts from reporting observations to drawing behavioral conclusions. From this point on, it interprets the data as evidence of hominin activity rather than simply describing the patterns. This part would be more appropriate for the Discussion, or should be rewritten in a neutral, descriptive way if it is meant to stay in the Results.

This appears extensively discussed in the Discussion section, but the data presented in the results is also interpreted in that section, following a clear argumental chain.

(18) Line 433: A recent discovery of a couple of hippopotamus partial carcasses at the 3.0-2.6 Ma site of Nyayanga (Kenya), spatially concurrent with stone artifacts, has been argued to be causally linked by the presence of cut marks on some bones (Plummer et al., 2023). The only evidence published thereof is a series of bone surface modifications on a hippo rib and a tibial crest, which we suggest may be the result of byproduct of abiotic abrasive processes; the marks contrast noticeably with the well-defined cut marks found on smaller mammal bones (Plummer et al. ́s 2023: Figure 3C, D) associated with the hippo remains (Plummer et al., 2023).

The authors suggest that the Nyayanga marks could result from abiotic abrasion, but this claim does not engage with the detailed evidence presented by Plummer et al. (2023). Plummer and colleagues documented well-defined, morphologically consistent cut marks and considered the sedimentary context in their interpretation. Raising abrasion as a general possibility without addressing that analysis gives the impression of selective skepticism rather than an evaluation grounded in the published data.

We disagree again on this matter. R2 does not clarify what he/she means by well-defined or morphologically consistent. We provide an alternative interpretation of those marks that fit their morphology and features and that Plummer at al did not successfully exclude. We also emphasize that the interpretation of the Nyayanga marks was made descriptively, without any analytical approach and with a high degree of subjectivity by the researcher. All of this disqualifies the approach as well defined and keeps casting an old look at modern taphonomy. Descriptive taphonomy is a thing of the 1980´s. Today there are a plethora of analytical methods, from multivariate statistics, to geometric morphometrics to AI computer vision (so far the most reliable) which represent how taphonomy (and more specifically, analysis of bone surface modifications) should be conducted in the XXI century. This approaches would reinforce interpretations as preliminarily published by Plummer et al, provided they reject alternative explanations like those that we have provided.

(19) Line 459: It would have been essential to document that the FLK N6 tools associated with the elephant were either on the same depositional surface as the elephant bones and/or on the same vertical position. The ambiguity about the FLK N6 elephant renders EAK the oldest secure proboscidean butchery evidence at Olduvai, and also probably one of the oldest in the early Pleistocene elsewhere in Africa.

The concern about vertical mixing is fair, but the tone makes it sound like the association is definitely not real. It would be more accurate to say that the evidence is ambiguous, not that it should be dismissed altogether.

We have precisely done so. We do not dismiss it, but we cannot take it for anything solid since we excavated the site and show how easily one could make functional associations if forgetting about the third dimension. It is not a secure butchery site. This is what we said and we stick to this statement.

(20) Line 479: In all cases, these wet environments must have been preferred places for water-dependent megafauna, like elephants and hippos, and their overlapping ecological niches are reflected in the spatial co-occurrence of their carcasses. Both types of megafauna show traces of hominin use through either cutmarked or percussed bones, green-broken bones, or both (Supplementary Information).

The environmental part is good, but the behavioral interpretation is too strong. Saying elephants and hippos "must have been" drawn to these areas is too certain, and claiming that both "show traces of hominin use" makes it sound like every carcass was modified. It should be clearer that only some have possible evidence of this.

The sentence only refers to both types of fauna taxonomically. No inference can be drawn therefor that all carcasses are modified.

(21) Line 496: In most green-broken limb bones, we document the presence of a medullary cavity, despite the continuous presence of trabecular bone tissue on its walls.

This sentence is confusing and doesn't seem to add anything meaningful. All limb bones naturally have a medullary cavity lined with trabecular bone, so it's unclear why this is noted as significant. The authors should clarify what they mean here or remove it if it's simply describing normal bone structure.

No. Modern elephant long bones do not have a hollow medullary cavity. All the medullary volume is composed of trabecular tissue. Some elephants in the past had hollow medullary cavities, which probably contained larger amounts of marrow and fat. 

(22) Line 518: We are not confident that the artefacts reported by de la Torre et al are indeed tools.

While I generally agree with this statement, the paragraph reads as defensive rather than comparative. It would help if they briefly summarized what de la Torre et al. actually argued before explaining why they disagree.

We devote two full pages of the Discussion section to do so precisely.

(23) Lines 518-574: They are similar to the green-broken specimens that we have reported here...

This part is very detailed but inconsistent. They argue that the T69 marks could come from natural processes, but they use similar evidence (green fractures, overlapping scars) to argue for human activity at EAK. If equifinality applies to one, it applies to both.

We are confused by this misinterpretation. Features like green fractures and overlapping scars (among others) can be used to detect anthropogenic agency in elephant bone breaking; that is, any given specimen can be determined to have been an “artifact” (in the sense of human-created item), but going from there to interpreting an artifact as a tool, there is a large distance. Whereas an artifact (something made by a human) can be created indirectly through several processes (for example, demarrowing a bone resulting in long bone fragments), a tool suggest either intentional manufacture and use or both. That is the difference between de la Torre et al.´s interpretation and ours. We believe that they are showing anthropogenically-made items, but they have provided no proof that they were tools.

(24) Line 576: A final argument used by the authors to justify the intentional artifactual nature of their bone implements is that the bone tools were found in situ within a single stratigraphic horizon securely dated to 1.5 million years ago, indicating systematic production rather than episodic use. This is taphonomically unjustified.

The reasoning here feels uneven in how clustering evidence is used. At EAK, clustering of bones and artifacts is taken as meaningful evidence of hominin activity, but here the same pattern at T69 is treated as a natural by-product of butchery or carnivore activity. If clustering alone cannot distinguish between intentional and incidental association, the authors should clarify why it is interpreted as diagnostic in one case but not in the other.

Again, we are confused by this misinterpretation. It applies to two different scenarios/questions:

a) is there a functional link between tools and bones at EAK and T69? We have statistically demonstrated that at EAK and we think de la Torre et al. is trying to do the same for T69, although using a different method. 

b) Are the purported tools at T69 tools? Are those that we report here tools? In this regard there is no evidence for either case and given that several bones from T69 come from animals smaller than elephants, we do not discard that carnivores might have been responsible for those, whereas hominin butchery might have been responsible for the intense long limb breaking at that site. It remains to be seen how many (if any) of those specimens were tools.

(25) Line 600: If such a bone implement was a tool, it would be the oldest bone tool documented to date (>1.7 Ma).

The comparison to prior studies is useful, and the point about missing use-wear traces is well taken. However, the last lines feel speculative. If no clear use evidence has been found, it's premature to suggest that one specimen "would be the oldest bone tool." That claim should be either removed or clearly stated as hypothetical.

It clearly reads as hypothetical.

(26) Line 606: Evidence documents that the oldest systematic anthropogenic exploitation of proboscidean carcasses are documented (at several paleolandscape scales) in the Middle Pleistocene sites of Neumark-Nord (Germany)(Gaudzinski-Windheuser et al., 2023a, 2023b).

This is the first and only mention of Neumark-Nord in the paper, and it appears without any prior discussion or connection to the rest of the study. If this site is being used for comparison or as part of a broader temporal framework, it needs to be introduced and contextualized earlier. As written, it feels out of place and disconnected from the rest of the argument.

This is a Late Pleistocene site and we do not see the need to present it earlier, given that the scope of this work is Early Pleistocene.

(27) Line 608: Evidence of at least episodic access to proboscidean remains goes back in time (see review in Agam and Barkai, 2018; Ben-Dor et al., 2011; Haynes, 2022).

The distinction between "systematic" and "episodic" exploitation is useful, but the authors should clarify what criteria define each. The phrase "episodic access...goes back in time" is vague and could be replaced with a clearer statement summarizing the nature of the earlier evidence.

It is self-explanatory

(28) Line 610: Redundant megafaunal exploitation is well documented at some early Pleistocene sites from Olduvai Gorge (Domínguez-Rodrigo et al., 2014a, 2014b; Organista et al., 2019, 2017, 2016).

The phrase "redundant megafaunal exploitation" needs clarification. "Redundant" is not standard terminology in this context. Does this mean repeated, consistent, or overlapping behaviors? Also, while these same Olduvai sites are mentioned earlier, this phrasing also introduces new interpretive language not used before and implies a broader behavioral generalization than what the data actually show.

Webster: Redundant means repetitive, occurring multiple times.

(29) Line 612: At the very same sites, the stone artifactual assemblages, as well as the site dimensions, are substantially larger than those documented in the Bed I Oldowan sites (Diez-Martín et al., 2024, 2017, 2014, 2009).

The placement and logic of this comparison are unclear. The discussion moves from Middle Pleistocene Neumark-Nord to early Pleistocene Olduvai sites, then to Bed I Oldowan contexts without clearly signaling the temporal or geographic transitions. If the intent is to contrast Acheulean vs. Oldowan site scale or organization, that connection needs to be made explicit. As written, it reads as a disjointed shift rather than a continuation of the argument.

We disagree. Here, we finalize by bringing in some more recent assemblages where hominin agency is not in question.

(30) Line 616: Here, we have reported a significant change in hominin foraging behaviors during Bed I and Bed II times, roughly coinciding with the replacement of Oldowan industries by Acheulian tool kits -although during Bed II, both industries co-existed for a substantial amount of time (Domínguez-Rodrigo et al., 2023; Uribelarrea et al., 2019, 2017).

This section should be restructured for flow. The reference to behavioral change during Bed I-II and the overlap of Oldowan and Acheulean industries is important, but feels buried after a long detour. Consider moving this earlier or rephrasing so the main conclusion (behavioral change across Beds I-II) is clearly stated first, followed by supporting examples.

It is not within the scope of this work and is properly described in the references mentioned.

(31) Line 620: The evidence presented here, together with that documented by de la Torre et al. (2025), represents the most geographically extensive documentation of repeated access to proboscidean and other megafaunal remains at a single fossil locality.

The phrase "most geographically extensive documentation of repeated access" overstates what has been demonstrated. The evidence presented is site-specific and does not justify such a broad superlative. This should be toned down or supported with comparative quantitative data.

We disagree. There is no other example where such an abundant record of green-broken elements from megafauna is documented. Neumark-Nord is more similar because it shows extensive evidence of butchery, but not so much about degreasing.

(32) Line 623: The transition from Oldowan sites, where lithic and archaeofaunal assemblages are typically concentrated within 30-40 m2 clusters, to Acheulean sites that span hundreds or even over 1000 m2 (as in BK), with distinct internal spatial organization and redundancy in space use across multiple archaeological layers spanning meters of stratigraphic sequence (Domínguez-Rodrigo et al., 2014a, 2009b; Organista et al., 2017), reflects significant behavioral and technological shifts.

This sentence about site size and spatial organization repeats earlier claims without adding new insight. If it's meant as a synthesis, it should explicitly say how the spatial expansion relates to changes in behavior or mobility, not just describe the difference.

In the Conclusion section these correlations have been explained in more detail to add some causation.

(33) Line 628: This pattern likely signifies critical innovations in human evolution, coinciding with major anatomical and physiological transformations in early hominins (Dembitzer et al., 2022; Domínguez-Rodrigo et al., 2021, 2012).

The conclusion that this "signifies critical innovations in human evolution" is too sweeping, given the data presented. It introduces physiological and anatomical transformation without connecting it to any evidence in this paper. Either cite the relevant findings or limit the claim to behavioral implications.

The references cited elaboration in extension this. The revised version of the Conclusion section also elaborates on this.

Overall, the conclusions section reads as a loosely connected set of assertions rather than a focused synthesis. It introduces new interpretations and terminology not supported or developed earlier in the paper, and the argument jumps across temporal and geographic scales without clear transitions. The discussion should be restructured to summarize key results, clarify the scope of interpretation, and avoid speculative or overstated claims about evolutionary significance.

We have done so, supported by the references used in addition to extending some of the arguments

(34) Line 639: The systematic excavation of the stratigraphic layers involved a small crew.

This sentence is not necessary.

No comment

(35) Line 643: The orientation and inclination of the artifacts were recorded using a compass and an inclinometer, respectively.

What were these measurements used for (e.g., post-depositional movement analysis, spatial patterning)? A short note on the purpose would make this more meaningful.

Fabric analysis has been added to the revised version.

(36) Line 659: Restoration of the EAK elephant bones

This section could be streamlined and clarified. It includes procedural detail that doesn't contribute to scientific replicability (e.g., the texture of gauze, number of consolidant applications), while omitting some key information (such as how restoration may have affected analytical results). It also contains interpretive comments ("most of the assemblage has been successfully studied") that don't belong in Methods.

No comment

(37) Line 689: In the field laboratory, cleaning of the bone remains was carried out, along with adhesion of fragments and their consolidation when necessary.

Clarify whether cleaning or adhesion treatments might obscure or alter bone surface modifications, as this has analytical implications.

These protocols do not impact bone like that anymore.

(38) Line 711: (b) Percussion Tools - Includes hammerstones or cobbles exhibiting diagnostic battering, pitting, and/or impact scars consistent with percussive activities.

Define how diagnostic features (battering, pitting) were identified - visual inspection, magnification, or quantitative criteria?

Both macro and microscopically

(39) Line 734: We conducted the analysis in three different ways after selecting the spatial window, i.e., the analysed excavated area (52.56 m2).

Clarify why the 52.56 m² spatial window was chosen. Was this the total excavated area or a selected portion?

It was what was left of the elephant accumulation after erosion.

(40) Line 728: The spatial statistical analyses of EAK.

Adding one or two sentences at the start explaining the analytical objective, such as testing spatial association between faunal and lithic materials, would help readers understand how each analysis relates to the broader research questions.

This is well explained in the main text

(41) Line 782: An intensive survey seeking stratigraphically-associated megafaunal bones was carried out in the months of June 2023 and 2024.

It would help to specify whether the same areas were resurveyed in both field seasons or if different zones were covered each year. This information is important for understanding sampling consistency and potential spatial bias.

Both areas were surveyed in both field seasons. We were very consistent.

(42) Line 787: We focused on proboscidean bones and used hippopotamus bones, some of the most abundant in the megafaunal fossils, as a spatial control.

Clarify how the hippopotamus remains functional as a "spatial control." Are they used as a proxy for water-associated taxa to test habitat patterning, or as a baseline for comparing carcass distribution? The meaning of "control" in this context is ambiguous.

As a proxy for megafaunal distribution given their greater abundance over any other megafaunal taxa.

(43) Line 789: Stratigraphic association was carried out by direct observation of the geological context and with the presence of a Quaternary geologist during the whole survey.

This is good methodological practice, but it would be helpful to describe how stratigraphic boundaries were identified in the field (for example, by reference to tuffs or marker beds). That information would make the geological framework more replicable.

This is basic geological work. Of course, both tuffs and marker beds were followed.

(44) Line 791: When fossils found were ambiguously associated with specific strata, these were excluded from the present analysis.

You might specify what proportion of the total finds were excluded due to uncertain stratigraphic association. Reporting this would indicate the strength of the stratigraphic control.

This was not quantified but it was a very small amount compared to those whose stratigraphic provenience was certain.

(45) Line 799: The goals of this survey were: a) collect a spatial sample of proboscidean and megafaunal bones enabling us to understand if carcasses on the Olduvai paleolandscapes were randomly deposited or associated to specific habitats.

You might clarify how randomness or habitat association was tested.

Randomness was tested spatially and comparing density according to ecotone. Same for habitat association.

(46) The Methods section provides detailed information about excavation, restoration, and spatial analyses but omits critical details about the zooarchaeological and taphonomic procedures. There is no explanation of how faunal remains were analyzed once recovered, including how cut marks, percussion marks, or green bone fractures were identified or what magnification or diagnostic criteria were used. The authors also do not specify the analytical unit used for faunal quantification (e.g., NISP, MNI, MNE, or other), making it unclear how specimen counts were generated for spatial or taphonomic analyses. Even if these details are provided in the Supplementary Information, the main text should include at least a concise summary describing the analytical framework, the criteria for identifying surface modifications and fracture morphology, and the quantification system employed. This information is essential for transparency, replicability, and proper evaluation of the behavioral interpretations.

See reply above. There is a new subsection on taphonomic methods now.

Supplementary information:

(47) The Supplementary Information includes a large number of green-broken proboscidean specimens from other Olduvai localities (BK, LAS, SC, FLK West), but it is never explained why these are shown or how they relate to the EAK study. The main analysis focuses entirely on the EAK elephant, including so much unrelated material without any stated purpose, which makes the supplement confusing. If these examples are meant only to illustrate the appearance of green fractures, that should be stated. Otherwise, the extensive inclusion of non-EAK material gives the impression that they were part of the analyzed assemblage when they were not.

This is stated in the opening paragraph to the section.

(48) Line 96: A small collection of green-broken elephant bones was retrieved from the lower and upper Bed II units.

It would help to clarify whether these specimens are part of the EAK assemblage or derive from other Bed II localities. As written, it is not clear whether this description refers to material analyzed in the main text or to comparative examples shown only in the Supplementary Information.

No, EAK only occupies the lower Bed II section. They belong in the Bed II paleolandscape units.

(49) Line 97: One of them, a proximal femoral shaft found within the LAS unit, has all the traces of having been used as a tool (Figure 6).

This says the bone tool in Figure 6 is from LAS, but the main text caption identifies it as from EAK. If I am not mistaken, EAK is a site at the base of Bed II, and LAS is a separate stratigraphic unit higher in the sequence, so the authors should clarify which is correct.

Our mistake. It provenience is from LAS in the vicinity of EAK.

(50) Line 186: Figure S20. Example of other megafaunal long bone shafts showing green breaks.

Not cited in text or SI narrative. No indication where these bones come from or why they are relevant.

It appears justified in the revised version.

(51) Line 474: Figure S28-S30. Hyena-ravaged giraffe bones from Chobe (Botswana).

These figures are not discussed in the text or SI, and their relevance to the study is unclear. The authors should explain why these modern comparative examples were included and how they inform interpretations of the Olduvai assemblages.

It appears justified in the revised version.

(52) Line 498: Figure S31. Bos/Bison bone from Bois Roche (France).

This figure is not mentioned in the text or Supplementary Information. The authors should specify why this specimen is shown and how it contributes to the study's taphonomic or behavioral comparisons.

It appears justified in the revised version.

(53) Line 504: Figure S32. Miocene Gomphotherium femur from Spain.

This figure is never referenced in the paper. The authors should clarify the purpose of including a Miocene specimen from outside Africa and explain what it adds to the interpretation of Bed II material.

It appears justified in the revised version.

(54) Line 508: Figure S33. Elephant femoral shaft from BK (Olduvai).

This figure appears to show comparative material but is not cited or discussed in the text. The authors should explain why the BK material is presented here and how it relates to EAK or the broader analysis.

There are two figures labeled S33.

It appears justified in the revised version.

(55) Line 515: Figure S33. Tibia fragment from a large medium-sized bovid displaying multiple overlapping scars on both breakage planes inflicted by carnivore damage.

Because this figure repeats the S33 label and is not cited or explained in the text, it is unclear why this specimen is included or how it contributes to the study. The authors should correct the duplicate numbering and clarify the purpose of this figure.

It appears justified in the revised version.

(56) Line 522: Same specimen as shown in Figure S30, viewed on its medial side.

This is not the same bone as S30. This figure is not discussed in the text or Supplementary Information. The authors should clarify why it is included and how it relates to the rest of the analysis.

It appears justified in the revised version.

Author response:

Public Reviews: 

Reviewer #1 (Public review): 

Summary: 

This study aims to explore how different forms of "fragile nucleosomes" facilitate RNA Polymerase II (Pol II) transcription along gene bodies in human cells. The authors propose that pan-acetylated, pan-phosphorylated, tailless, and combined acetylated/phosphorylated nucleosomes represent distinct fragile states that enable eFicient transcription elongation. Using CUT&Tagseq, RNA-seq, and DRB inhibition assays in HEK293T cells, they report a genome-wide correlation between histone pan-acetylation/phosphorylation and active Pol II occupancy, concluding that these modifications are essential for Pol II elongation. 

Strengths: 

(1) The manuscript tackles an important and long-standing question about how Pol II overcomes nucleosomal barriers during transcription. 

(2) The use of genome-wide CUT&Tag-seq for multiple histone marks (H3K9ac, H4K12ac, H3S10ph, H4S1ph) alongside active Pol II mapping provides a valuable dataset for the community. 

(3) The integration of inhibition (DRB) and recovery experiments oFers insight into the coupling between Pol II activity and chromatin modifications. 

(4) The concept of "fragile nucleosomes" as a unifying framework is potentially appealing and could stimulate further mechanistic studies. 

Really appreciate the positive or affirmative comments from the reviewer.

Weaknesses: 

(1)  Misrepresentation of prior literature 

The introduction incorrectly describes findings from Bintu et al., 2012. The cited work demonstrated that pan-acetylated or tailless nucleosomes reduce the nucleosomal barrier for Pol II passage, rather than showing no improvement. This misstatement undermines the rationale for the current study and should be corrected to accurately reflect prior evidence. 

What we said is according to the original report in the publication (Bintu et al., Cell, 2012). Here is the citation from the report:

Page 739,(Bintu, L. et al., Cell, 2012)(PMID: 23141536)

“Overall transcription through tailless and acetylated nucleosomes is slightly faster than through unmodified nucleosomes (Figure 1C), with crossing times that are generally under 1 min (39.5 ± 5.7 and 45.3 ± 7.6 s, respectively). Both the removal and acetylation of the tails increase eFiciency of NPS passage:71% for tailless nucleosomes and 63% for acetylated nucleosomes (Figures 1C and S1), in agreement with results obtained using bulk assays of transcription (Ujva´ ri et al., 2008).”

We will cite this original sentence in our revision.

(2) Incorrect statement regarding hexasome fragility

The authors claim that hexasome nucleosomes "are not fragile," citing older in vitro work. However, recent studies clearly showed that hexasomes exist in cells (e.g., PMID 35597239) and that they markedly reduce the barrier to Pol II (e.g., PMID 40412388). These studies need to be acknowledged and discussed. 

“hexasome” was introduced in the transcription field four decades ago. Later, several groups claimed that “hexasome” is fragile and could be generated in transcription elongation of Pol II. However, their original definition was based on the detection of ~100 bps DNA fragments (MNase resistant) in vivo by Micrococcal nuclease sequencing (MNase-seq), which is the right length to wrap up one hexasome histone subunit (two H3/4 and one H2A/2B) to form the sub-nucleosome of a hexasome. As we should all agree that acetylation or phosphorylation of the tails of histone nucleosomes will lead to the compromised interaction between DNA and histone subunits, which could lead to the intact naïve nucleosome being fragile and easy to disassemble, and easy to access by MNase. Fragile nucleosomes lead to better accessibility of MNase to DNA that wraps around the histone octamer, producing shorter DNA fragments (~100 bps instead of ~140 bps). In this regard, we believe that these ~100 bps fragments are the products of fragile nucleosomes (fragile nucleosome --> hexasome), instead of the other way around (hexasome --> fragile). 

Actually, two early reports from Dr. David J.  Clark’s group from NIH raised questions about the existence of hexasomes in vivo (PMID: 28157509) (PMID: 25348398).

From the report of PMID:35597239, depletion of INO80 leads to the reduction of “hexasome” for a group of genes, and the distribution of both “nucleosomes” and “hexasomes” with the gene bodies gets fuzzier (less signal to noise). In a recent theoretical model (PMID: 41425263), the corresponding PI found that chromatin remodelers could act as drivers of histone modification complexes to carry out different modifications along gene bodies. The PI found that INO80 could drive NuA3 (a H3 acetyltransferase) to carry out pan-acetylation of H3 and possibly H2B as well in the later runs of transcription of Pol II for a group of genes (SAGA-dependent). It suggests that the depletion of INO80 will affect (reduce) the pan-acetylation of nucleosomes, which leads to the drop of pan-acetylated fragile nucleosomes, subsequently the drop of “hexasomes”. This explains why depletion of INO80 leads to the fuzzier results of either nucleosomes or “hexasomes” in PMID: 35597239. The result of PMID: 35597239 could be a strong piece of evidence to support the model proposed by the corresponding PI (PMID: 41425263).

From a recent report: PMID:40412388, the authors claimed that FACT could bind to nucleosomes to generate “hexasomes”, which are fragile for Pol II to overcome the resistance of nucleosomes. It was well established that FACT enhances the processivity of Pol II in vivo via its chaperonin property. However, the exact working mechanism of FACT still remains ambiguous. A report from Dr. Cramer’s group showed that FACT enhances the elongation of regular genes but works just opposite for pausing-regulated genes (PMID: 38810649). An excellent review by Drs. Tim Formosa and Fred Winston showed that FACT is not required for the survival of a group of differentiated cells (PMID: 33104782), suggesting that FACT is not always required for transcription. It is quite tricky to generate naïve hexasomes in vitro according to early reports from the late Dr. Widom’s group. Most importantly, the new data (the speed of Pol II, the best one on bare DNA is ~27 bps/s) from the report of PMID: 40412388, which is much slower than the speed of Pol II in vivo: ~2.5 kbs/min or ~40 bps/s. From our recovering experiments (Fig. 4C, as mentioned by reviewer #3), in 20 minutes (the period between 10 minutes and 30 minutes, due to the property of CUT-&TAG-seq, of which Pol II still active after cells are collected, there is a big delay of complete stop of Pol II during the procedure of CUT&TAG experiments, so the first period of time does not actually reflect the speed of Pol II, which is ~5 kb/min), all Pol IIs move at a uniform speed of ~2.5 kbs/min in vivo. Interestingly, a recent report from Dr. Shixin Liu’s group (PMID: 41310264) showed that adding SPT4/5 to the transcription system with bare DNA (in vitro), the speed of Pol II reaches ~2.5kbs/min, exactly the same one as we derived in vivo. Similar to the original report (PMID: 23141536), the current report of PMID:40412388 does not mimic the conditions in vivo exactly.

There is an urgent need for a revisit of the current definition of “hexasome”, which is claimed to be fragile and could be generated during the elongation of Pol II in vivo. MNase is an enzyme that only works when the substrate is accessible. In inactive regions of the genome, due to the tight packing of chromatin, MNase is not accessible to individual nucleosomes within the bodies of a gene or upstream of promoters, which is why we only see phased/spacing or clear distribution of nucleosomes at the transcription start sites, but it becomes fuzzy downstream or upstream of promoters. On the other hand, for fragile nucleosomes, the accessibility to MNase should increase dramatically, which leads to the ~100 bps fragments. Based on the uniform rate (2.5 kbs/min) of Pol II for all genes derived from human 293T cells and the similar rate (2.5 kbs/min) of Pol II on bare DNA in vitro, it is unlikely for Pol II to pause in the middle of nucleosomes to generate “hexasomes” to continue during elongation along gene bodies. Similar to RNAPs in bacterial (no nucleosomes) and Archaea (tailless nucleosomes), there should be no resistance when Pol IIs transcribe along all fragile nucleosomes within gene bodies in all eukaryotes, as we characterized in this manuscript. 

(3)  Inaccurate mechanistic interpretation of DRB 

The Results section states that DRB causes a "complete shutdown of transcription initiation (Ser5-CTD phosphorylation)." DRB is primarily a CDK9 inhibitor that blocks Pol II release from promoter-proximal pausing. While recent work (PMID: 40315851) suggests that CDK9 can contribute to CTD Ser5/Ser2 di-phosphorylation, the manuscript's claim of initiation shutdown by DRB should be revised to better align with the literature. The data in Figure 4A indicate that 1 M DRB fully inhibits Pol II activity, yet much higher concentrations (10-100 ) are needed to alter H3K9ac and H4K12ac levels. The authors should address this discrepancy by discussing the differential sensitivities of CTD phosphorylation versus histone modification turnover. 

Yes, it was reported that DRB is also an inhibitor of CDK9. However, if the reviewer agrees with us and the current view in the field, the phosphorylation of Ser5-CTD of Pol II is the initiation of transcription for all Pol II-regulated genes in eukaryotes. CDK9 is only required to work on the already phosphorylated Ser5-CTD of Pol II to release the paused Pol II, which only happens in metazoans. From a series of works by us and others: CDK9 is unique in metazoans, required only for the pausing-regulated genes but not for regular genes. We found that CDK9 works on initiated Pol II (Ser5-CTD phosphorylated Pol II) and generates a unique phosphorylation pattern on CTD of Pol II (Ser2ph-Ser2ph-Ser5ph-CTD of Pol II), which is required to recruit JMJD5 (via CID domain) to generate a tailless nucleosome at +1 from TSS to release paused Pol II (PMID: 32747552). Interestingly, the report from Dr. Jesper Svejstrup’s group (PMID: 40315851) showed that CDK9 could generate a unique phosphorylation pattern (Ser2ph-Ser5ph-CTD of Pol II), which is not responsive to the popular 3E10 antibody that recognizes the single Ser2phCTD of Pol II.  This interesting result is consistent with our early report showing the unique phosphorylation pattern (Ser2ph-Ser2ph-Ser5ph-CTD of Pol II) is specifically generated by CDK9 in animals, which is not recognized by 3E10 either (PMID: 32747552). Actually, an early report from Dr. Dick Eick’s group (PMID: 26799765) showed the difference in the phosphorylation pattern of the CTD of Pol II between animal cells and yeast cells.  We have characterized how CDK9 is released from 7SK snRNP and recruited onto paused Pol II via the coupling of JMJD6 and BRD4 (PMID: 32048991), which was published on eLIFE. It is well established that CDK9 works after CDK7 or CDK8. From our PRO-seq data (Fig. 3) and CUT&TAG-seq data of active Pol II (Fig. 4), adding DRB completely shuts down all genes via inhibiting the initiation of Pol II (generation of Ser5ph-CTD of Pol II). Due to the uniqueness of CDK9 only in metazoans, it is not required for the activation of CDK12 or CDK13 (they are orthologs of CTK1 in yeast), as we demonstrated recently (PMID: 41377501). Instead, we found that CDK11/10 acts as the ortholog of Bur1 kinase from yeast, is essential for the phosphorylation of Spt5, the link of CTD of Pol II, and CDK12 (PMID: 41377501). 

(4) Insufficient resolution of genome-wide correlations 

Figure 1 presents only low-resolution maps, which are Insufficient o determine whether pan-acetylation and pan-phosphorylation correlate with Pol II at promoters or gene bodies. The authors should provide normalized metagene plots (from TSS to TTS) across different subgroups to visualize modification patterns at higher resolution. In addition, the genome-wide distribution of another histone PTM with a diFerent localization pattern should be included as a negative control. 

A popular view in the field is that the majority of genomes are inactive since they do not contain coding RNAs, which are responsible for ~20,000 protein candidates characterized in animals. However, our genomewide characterization using the four histone modification marks, active Pol II, and RNA-seq, shows a different story. Figure 1 shows that most of the human genome of HEK293T is active in producing not only protein-coding RNAs but also non-coding RNAs (the majority of them). We believe that Figure 1 could change our current view of the activity of the entire genome, and should be of great interest to general readers as well as researchers on genomics. Furthermore, it is a basis for Figure 2, which is a zoom-in of Figure 1.  

(5) Conceptual framing 

The manuscript frequently extrapolates correlative genome-wide data to mechanistic conclusions (e.g., that pan-acetylation/phosphorylation "generate" fragile nucleosomes). Without direct biochemical or structural evidence. Such causality statements should be toned down.  

The reviewer is right, we should tone down the strong sentences. However, we believe that our data is strong enough to derive the general conclusion. The reviewer may agree with us that the entire field of transcription and epigenetics has been stagnant in recent decades, but there is an urgent need for fresh ideas to change the current situation. Our novel discoveries, for sure, additional supporting data are needed, should open up a brand new avenue for people to explore. We believe that a new era of transcription will emerge based on our novel discoveries. We hope that this manuscript will attract more people to these topics. As Reviewer #3 pointed out, this story establishes the connection between transcription and epigenetics in the field. 

Reviewer #2 (Public review): 

Summary: 

In this manuscript, the authors use various genomics approaches to examine nucleosome acetylation, phosphorylation, and PolII-CTD phosphorylation marks. The results are synthesized into a hypothesis that 'fragile' nucleosomes are associated with active regions of PolII transcription. 

Strengths: 

The manuscript contains a lot of genome-wide analyses of histone acetylation, histone phosphorylation, and PolII-CTD phosphorylation. 

Weaknesses: 

This reviewer's main research expertise is in the in vitro study of transcription and its regulation in purified, reconstituted systems. 

Actually, the pioneering work of the establishment of in vitro transcription assays at Dr. Robert Roeder’s group led to numerous groundbreaking discoveries in the transcription field. The contributions of in vitro work in the transcription field are the key for us to explore the complexity of transcription in eukaryotes in the early times and remain important currently.

I am not an expert at the genomics approaches and their interpretation, and overall, I had a very hard time understanding and interpreting the data that are presented in this manuscript.  I believe this is due to a problem with the manuscript, in that the presentation of the data is not explained in a way that's understandable and interpretable to a non-expert.

Thanks for your suggestions. You are right, we have problems expressing our ideas clearly in this manuscript, which could confuse. We will make modifications accordingly per your suggestions.

For example: 

(1) Figure 1 shows genome-wide distributions of H3K9ac, H4K12ac, Ser2phPolII, mRNA, H3S10ph, and H4S1ph, but does not demonstrate correlations/coupling - it is not clear from these data that pan-acetylation and pan-phosphorylation are coupled with Pol II transcription. 

Figure 1 shows the overall distribution of the four major histone modifications, active Pol II, and mRNA genome-wide in human HEK293T cells. It tells general readers that the entire genome is quite active and far more than people predicted that most of the genome is inactive, since just a small portion of the genome expresses coding RNAs (~20,000 in animals). Figure 1 shows that the majority of the genome is active and expresses not only coded mRNA but also non-coding RNAs. After all, it is the basis of Figure 2, which is a zoom-in of Figure 1. However, it is beyond the scope of this manuscript to discuss the non-coding RNAs. 

(2) Figure 2 - It's not clear to me what Figure 2 is supposed to be showing. 

(A) Needs better explanation - what is the meaning of the labels at the top of the gel lanes? 

Figure 2 is a zoom-in for the individual gene, which shows how histone modifications are coupled with Pol II activity on the individual gene. We will give a more detailed explanation of the figure per the reviewer’s suggestions.

(B) This reviewer is not familiar with this technique, its visualization, or its interpretation - more explanation is needed. What is the meaning of the quantitation graphs shown at the top? How were these calculated (what is on the y-axis)? 

Good suggestions, we will do some modifications.

(3) To my knowledge, the initial observation of DRB eFects on RNA synthesis also concluded that DRB inhibited initiation of RNA chains (pmid:982026) - this needs to be acknowledged. 

Thanks for the reference, which is the first report to show the DRB inhibits initiation of Pol II in vivo. We will cite it in the revision.  

(4) Again, Figures 4B, 4C, 5, and 6 are very difficult to understand - what is shown in these heat maps, and what is shown in the quantitation graphs on top? 

Thanks for the suggestions, we will give a more detailed description of the Figures.  

Reviewer #3 (Public review): 

Summary: 

Li et al. investigated the prevalence of acetylated and phosphorylated histones (using H3K9ac, H4K12ac, H3S10ph & H4S1ph as representative examples) across the gene body of human HEK293T cells, as well as mapping elongating Pol II and mRNA. They found that histone acetylation and phosphorylation were dominant in gene bodies of actively transcribing genes. Genes with acetylation/phosphorylation restricted to the promoter region were also observed. Furthermore, they investigated and reported a correlation between histone modifications and Pol II activity, finding that inhibition of Pol II activity reduced acetylation/phosphorylation levels, while resuming Pol II activity restored them. The authors then proposed a model in which panacetylation or pan-phosphorylation of histones generates fragile nucleosomes; the first round of transcription is accompanied by panacetylation, while subsequent rounds are accompanied by panphosphorylation. 

Strengths: 

This study addresses a highly significant problem in gene regulation. The author provided riveting evidence that certain histone acetylation and/or phosphorylation within the gene body is correlated with Pol II transcription. The author furthermore made a compelling case that such transcriptionally correlated histone modification is dynamic and can be regulated by Pol II activity. This work has provided a clearer view of the connection between epigenetics and Pol II transcription. 

Thanks for the insightful comments, which are exactly what we want to present in this manuscript. 

Weaknesses: 

The title of the manuscript, "Fragile nucleosomes are essential for RNA Polymerase II to transcribe in eukaryotes", suggests that fragile nucleosomes lead to transcription. While this study shows a correlation between histone modifications in gene bodies and transcription elongation, a causal relationship between the two has not been demonstrated. 

Thanks for the suggestions. What we want to express is that the generation of fragile nucleosomes precedes transcription, or, more specifically, transcription elongation. The corresponding PI wrote a hypothetical model on how pan-acetylation is generated by the coupling of chromatin remodelers and acetyltransferase complexes along gene bodies, in which chromatin remodelers act as drivers to carry acetyltransferases along gene bodies to generate pan-acetylation of nucleosomes (PMID: 41425263). We have a series of work to show how “tailless nucleosomes” at +1 from transcription start sites are generated to release paused Pol II in metazoans (PMID: 28847961) (PMID: 29459673) (PMID: 32747552) (PMID: 32048991).   We still do not know how pan-phosphorylation along gene bodies is generated. It should be one of the focuses of our future research.

Universal pre-school education aims to ensure that all children enter Grade 1 with basic language, cognitive, and social readiness. In India, this is feasible by strengthening and integrating the existing Anganwadi system with structured early childhood curricula and teacher training. Global examples from Finland, France, and the UK show that universal early childhood education reduces early learning gaps and improves long-term educational outcomes, especially for disadvantaged children.

Why are Anganwadi reforms challenging? 1. Anganwadis were designed for nutrition & care, not education. Hence, the centers are not properly equipped nor is the staff. And if the early schooling is done in an incorrect manner it can lead to a major damage in child's curiosity

1. Anganwadi workers are overburdened and undertrained Reality on the ground

An Anganwadi worker today often:

• Handles nutrition distribution
• Maintains health records
• Supports surveys and elections
• Manages multiple government schemes

Now we expect them to: * Teach early literacy * Build number sense * Do classroom management * Track learning progress

Without: * Deep training * Time * Support staff

• This isn’t resistance - it’s capacity mismatch.

• Early childhood pedagogy is deceptively hard Biggest misconception that teaching children is easy. This involves knowing how child's brain develops, designing play that secretly builds skills, managing attention spans, language scaffolding through language (Language scaffolding through conversation means helping a child develop language step-by-step by talking with them in a guided way, instead of just teaching words or letters directly.)

• Coordination problem - Anganwadis operate at the intersection of the women and child development, health, and education systems, but are governed primarily as welfare units rather than educational institutions. As a result, there is no clear ownership or accountability for learning outcomes.

Fitzgerald's stories reference family money situations sometimes. I'm thinking his first novel Tender is the Night but I can't be sure its consistent across the span of his short stories.

Booth Tarkington's The Magnificent Ambersons, while I haven't read the book, initiates the downfall of that family with bad investments by Minaford Sr.

Both aren't detailed about it, sure, but they do continue this trend in a diminished way through works published around the first quarter of the 20th century and I find it interesting their generation was still culturally guided by this motif. As well as all the others of the 19th century novel. Like what another contemporary Edith Wharton accomplished novelistically as well.

Reviewer #1 (Public review):

Summary:

This study investigates how the brain processes facial expressions across development by analyzing intracranial EEG (iEEG) data from children (ages 5-10) and post-childhood individuals (ages 13-55). The researchers used a short film containing emotional facial expressions and applied AI-based models to decode brain responses to facial emotions. They found that in children, facial emotion information is represented primarily in the posterior superior temporal cortex (pSTC)-a sensory processing area-but not in the dorsolateral prefrontal cortex (DLPFC), which is involved in higher-level social cognition. In contrast, post-childhood individuals showed emotion encoding in both regions. Importantly, the complexity of emotions encoded in the pSTC increased with age, particularly for socially nuanced emotions like embarrassment, guilt, and pride.The authors claim that these findings suggest that emotion recognition matures through increasing involvement of the prefrontal cortex, supporting a developmental trajectory where top-down modulation enhances understanding of complex emotions as children grow older.

Strengths:

(1) The inclusion of pediatric iEEG makes this study uniquely positioned to offer high-resolution temporal and spatial insights into neural development compared to non-invasive approaches, e.g., fMRI, scalp EEG, etc.

(2) Using a naturalistic film paradigm enhances ecological validity compared to static image tasks often used in emotion studies.

(3) The idea of using state-of-the-art AI models to extract facial emotion features allows for high-dimensional and dynamic emotion labeling in real time.

Weaknesses:

(1) The study has notable limitations that constrain the generalizability and depth of its conclusions. The sample size was very small, with only nine children included and just two having sufficient electrode coverage in the posterior superior temporal cortex (pSTC), which weakens the reliability and statistical power of the findings, especially for analyses involving age. Authors pointed out that a similar sample size has been used in previous iEEG studies, but the cited works focus on adults and do not look at the developmental perspectives. Similar work looking at developmental changes in iEEG signals usually includes many more subjects (e.g., n = 101 children from Cross ZR et al., Nature Human Behavior, 2025) to account for inter-subject variabilities.

(2) Electrode coverage was also uneven across brain regions, with not all participants having electrodes in both the dorsolateral prefrontal cortex (DLPFC) and pSTC, making the conclusion regarding the different developmental changes between DLPFC and pSTC hard to interpret (related to point 3 below). It is understood that it is rare to have such iEEG data collected in this age group, and the electrode location is only determined by clinical needs. However, the scientific rigor should not be compromised by the limited data access. It's the authors' decision whether such an approach is valid and appropriate to address the scientific questions, here the developmental changes in the brain, given all the advantages and constraints of the data modality.

(3) The developmental differences observed were based on cross-sectional comparisons rather than longitudinal data, reducing the ability to draw causal conclusions about developmental trajectories. Also, see comments in point 2.

(4) Moreover, the analysis focused narrowly on DLPFC, neglecting other relevant prefrontal areas such as the orbitofrontal cortex (OFC) and anterior cingulate cortex (ACC), which play key roles in emotion and social processing. Agree that this might be beyond the scope of this paper, but a discussion section might be insightful.

(5) Although the use of a naturalistic film stimulus enhances ecological validity, it comes at the cost of experimental control, with no behavioral confirmation of the emotions perceived by participants and uncertain model validity for complex emotional expressions in children. A non-facial music block that could have served as a control was available but not analyzed. The validation of AI model's emotional output needs to be tested. It is understood that we cannot collect these behavioral data retrospectively within the recorded subjects. Maybe potential post-hoc experiments and analyses could be done, e.g., collect behavioral, emotional perception data from age-matched healthy subjects.

(6) Generalizability is further limited by the fact that all participants were neurosurgical patients, potentially with neurological conditions such as epilepsy that may influence brain responses. At least some behavioral measures between the patient population and the healthy groups should be done to ensure the perception of emotions is similar.

(7) Additionally, the high temporal resolution of intracranial EEG was not fully utilized, as data were downsampled and averaged in 500-ms windows. It seems like the authors are trying to compromise the iEEG data analyses to match up with the AI's output resolution, which is 2Hz. It is not clear then why not directly use fMRI, which is non-invasive and seems to meet the needs here already. The advantages of using iEEG in this study are missing here.

(8) Finally, the absence of behavioral measures or eye-tracking data makes it difficult to directly link neural activity to emotional understanding or determine which facial features participants attended to. Related to point 5 as well.

Comments on revisions:

A behavioral measurement will help address a lot of these questions. If the data continues collecting, additional subjects with iEEG recording and also behavioral measurements would be valuable.

Author response:

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

eLife Assessment

This study examines a valuable question regarding the developmental trajectory of neural mechanisms supporting facial expression processing. Leveraging a rare intracranial EEG (iEEG) dataset including both children and adults, the authors reported that facial expression recognition mainly engaged the posterior superior temporal cortex (pSTC) among children, while both pSTC and the prefrontal cortex were engaged among adults. However, the sample size is relatively small, with analyses appearing incomplete to fully support the primary claims. 

Public Reviews:

Reviewer #1 (Public review):

Summary:

This study investigates how the brain processes facial expressions across development by analyzing intracranial EEG (iEEG) data from children (ages 5-10) and post-childhood individuals (ages 13-55). The researchers used a short film containing emotional facial expressions and applied AI-based models to decode brain responses to facial emotions. They found that in children, facial emotion information is represented primarily in the posterior superior temporal cortex (pSTC) - a sensory processing area - but not in the dorsolateral prefrontal cortex (DLPFC), which is involved in higher-level social cognition. In contrast, post-childhood individuals showed emotion encoding in both regions. Importantly, the complexity of emotions encoded in the pSTC increased with age, particularly for socially nuanced emotions like embarrassment, guilt, and pride. The authors claim that these findings suggest that emotion recognition matures through increasing involvement of the prefrontal cortex, supporting a developmental trajectory where top-down modulation enhances understanding of complex emotions as children grow older.

Strengths:

(1) The inclusion of pediatric iEEG makes this study uniquely positioned to offer high-resolution temporal and spatial insights into neural development compared to non-invasive approaches, e.g., fMRI, scalp EEG, etc.

(2) Using a naturalistic film paradigm enhances ecological validity compared to static image tasks often used in emotion studies.

(3) The idea of using state-of-the-art AI models to extract facial emotion features allows for high-dimensional and dynamic emotion labeling in real time

Weaknesses:

(1) The study has notable limitations that constrain the generalizability and depth of its conclusions. The sample size was very small, with only nine children included and just two having sufficient electrode coverage in the posterior superior temporal cortex (pSTC), which weakens the reliability and statistical power of the findings, especially for analyses involving age

We appreciated the reviewer’s point regarding the constrained sample size.

As an invasive method, iEEG recordings can only be obtained from patients undergoing electrode implantation for clinical purposes. Thus, iEEG data from young children are extremely rare,  and rapidly increasing the sample size within a few years is not feasible. However, we are confident in the reliability of our main conclusions. Specifically, 8 children (53 recording contacts in total) and 13 control participants (99 recording contacts in total) with electrode coverage in the DLPFC are included in our DLPFC analysis. This sample size is comparable to other iEEG studies with similar experiment designs [1-3]. 

For pSTC, we returned to the data set and found another two children who had pSTC coverage. After involving these children’s data, the group-level analysis using permutation test showed that children’s pSTC significantly encode facial emotion in naturalistic contexts (Figure 3B). Notably, the two new children’s (S33 and S49) responses were highly consistent with our previous observations. Moreover, the averaged prediction accuracy in children’s pSTC (r_speech=0.1565) was highly comparable to that in post-childhood group (r_speech=0.1515).

(1) Zheng, J. et al. Multiplexing of Theta and Alpha Rhythms in the Amygdala-Hippocampal Circuit Supports Pafern Separation of Emotional Information. Neuron 102, 887-898.e5 (2019).

(2) Diamond, J. M. et al. Focal seizures induce spatiotemporally organized spiking activity in the human cortex. Nat. Commun. 15, 7075 (2024).

(3) Schrouff, J. et al. Fast temporal dynamics and causal relevance of face processing in the human temporal cortex. Nat. Commun. 11, 656 (2020).

(2) Electrode coverage was also uneven across brain regions, with not all participants having electrodes in both the dorsolateral prefrontal cortex (DLPFC) and pSTC, and most coverage limited to the left hemisphere-hindering within-subject comparisons and limiting insights into lateralization.

The electrode coverage in each patient is determined entirely by the clinical needs. Only a few patients have electrodes in both DLPFC and pSTC because these two regions are far apart, so it’s rare for a single patient’s suspected seizure network to span such a large territory. However, it does not affect our results, as most iEEG studies combine data from multiple patients to achieve sufficient electrode coverage in each target brain area. As our data are mainly from left hemisphere (due to the clinical needs), this study was not designed to examine whether there is a difference between hemispheres in emotion encoding. Nevertheless, lateralization remains an interesting question that should be addressed in future research, and we have noted this limitation in the Discussion (Page 8, in the last paragraph of the Discussion).

(3) The developmental differences observed were based on cross-sectional comparisons rather than longitudinal data, reducing the ability to draw causal conclusions about developmental trajectories.  

In the context of pediatric intracranial EEG, longitudinal data collection is not feasible due to the invasive nature of electrode implantation. We have added this point to the Discussion to acknowledge that while our results reveal robust age-related differences in the cortical encoding of facial emotions, longitudinal studies using non-invasive methods will be essential to directly track developmental trajectories (Page 8, in the last paragraph of Discussion). In addition, we revised our manuscript to avoid emphasis causal conclusions about developmental trajectories in the current study (For example, we use “imply” instead of “suggest” in the fifth paragraph of Discussion).

(4) Moreover, the analysis focused narrowly on DLPFC, neglecting other relevant prefrontal areas such as the orbitofrontal cortex (OFC) and anterior cingulate cortex (ACC), which play key roles in emotion and social processing.

We agree that both OFC and ACC are critically involved in emotion and social processing. However, we have no recordings from these areas because ECoG rarely covers the ACC or OFC due to technical constraints. We have noted this limitation in the Discussion(Page 8, in the last paragraph of Discussion). Future follow-up studies using sEEG or non-invasive imaging methods could be used to examine developmental patterns in these regions.

(5) Although the use of a naturalistic film stimulus enhances ecological validity, it comes at the cost of experimental control, with no behavioral confirmation of the emotions perceived by participants and uncertain model validity for complex emotional expressions in children. A nonfacial music block that could have served as a control was available but not analyzed. 

The facial emotion features used in our encoding models were extracted by Hume AI models, which were trained on human intensity ratings of large-scale, experimentally controlled emotional expression data[1-2]. Thus, the outputs of Hume AI model reflect what typical facial expressions convey, that is, the presented facial emotion. Our goal of the present study was to examine how facial emotions presented in the videos are encoded in the human brain at different developmental stages. We agree that children’s interpretation of complex emotions may differ from that of adults, resulting in different perceived emotion (i.e., the emotion that the observer subjectively interprets). Behavioral ratings are necessary to study the encoding of subjectively perceived emotion, which is a very interesting direction but beyond the scope of the present work. We have added a paragraph in the Discussion (see Page 8) to explicitly note that our study focused on the encoding of presented emotion.

We appreciated the reviewer’s point regarding the value of non-facial music blocks. However,  although there are segments in music condition that have no faces presented, these cannot be used as a control condition to test whether the encoding model’s prediction accuracy in pSTC or DLPFC drops to chance when no facial emotion is present. This is because, in the absence of faces, no extracted emotion features are available to be used for the construction of encoding model (see Author response image 1 below).  Thus, we chose to use a different control analysis for the present work. For children’s pSTC, we shuffled facial emotion feature in time to generate a null distribution, which was then used to test the statistical significance of the encoding models (see Methods/Encoding model fitting for details).

(1) Brooks, J. A. et al. Deep learning reveals what facial expressions mean to people in different cultures. iScience 27, 109175 (2024).

(2) Brooks, J. A. et al. Deep learning reveals what vocal bursts express in different cultures. Nat. Hum. Behav. 7, 240–250 (2023).

Author response image 1.

Time courses of Hume AI extracted facial expression features for the first block of music condition. Only top 5 facial expressions were shown here to due to space limitation.

(6) Generalizability is further limited by the fact that all participants were neurosurgical patients, potentially with neurological conditions such as epilepsy that may influence brain responses. 

We appreciated the reviewer’s point. However, iEEG data can only be obtained from clinical populations (usually epilepsy patients) who have electrodes implantation.  Given current knowledge about focal epilepsy and its potential effects on brain activity, researchers believe that epilepsy-affected brains can serve as a reasonable proxy for normal human brains when confounding influences are minimized through rigorous procedures[1]. In our study, we took several steps to ensure data quality: (1) all data segments containing epileptiform discharges were identified and removed at the very beginning of preprocessing, (2) patients were asked to participate the experiment several hours outside the window of seizures. Please see Method for data quality check description (Page 9/ Experimental procedures and iEEG data processing). 

(1) Parvizi J, Kastner S. 2018. Promises and limitations of human intracranial electroencephalography. Nat Neurosci 21:474–483. doi:10.1038/s41593-018-0108-2

(7) Additionally, the high temporal resolution of intracranial EEG was not fully utilized, as data were down-sampled and averaged in 500-ms windows.  

We agree that one of the major advantages of iEEG is its millisecond-level temporal resolution. In our case, the main reason for down-sampling was that the time series of facial emotion features extracted from the videos had a temporal resolution of 2 Hz, which were used for the modelling neural responses. In naturalistic contexts, facial emotion features do not change on a millisecond timescale, so a 500 ms window is sufficient to capture the relevant dynamics. Another advantage of iEEG is its tolerance to motion, which is excessive in young children (e.g., 5-year-olds). This makes our dataset uniquely valuable, suggesting robust representation in the pSTC but not in the DLPFC in young children. Moreover, since our method framework (Figure 1) does not rely on high temporal resolution method, so it can be transferred to non-invasive modalities such as fMRI, enabling future studies to test these developmental patterns in larger populations.

(8) Finally, the absence of behavioral measures or eye-tracking data makes it difficult to directly link neural activity to emotional understanding or determine which facial features participants afended to.  

We appreciated this point. Part of our rationale is presented in our response to (5) for the absence of behavioral measures. Following the same rationale, identifying which facial features participants attended to is not necessary for testing our main hypotheses because our analyses examined responses to the overall emotional content of the faces. However, we agree and recommend future studies use eye-tracking and corresponding behavioral measures in studies of subjective emotional understanding. 

Reviewer #2 (Public review):

Summary:

In this paper, Fan et al. aim to characterize how neural representations of facial emotions evolve from childhood to adulthood. Using intracranial EEG recordings from participants aged 5 to 55, the authors assess the encoding of emotional content in high-level cortical regions. They report that while both the posterior superior temporal cortex (pSTC) and dorsolateral prefrontal cortex (DLPFC) are involved in representing facial emotions in older individuals, only the pSTC shows significant encoding in children. Moreover, the encoding of complex emotions in the pSTC appears to strengthen with age. These findings lead the authors to suggest that young children rely more on low-level sensory areas and propose a developmental shiZ from reliance on lower-level sensory areas in early childhood to increased top-down modulation by the prefrontal cortex as individuals mature.

Strengths: 

(1) Rare and valuable dataset: The use of intracranial EEG recordings in a developmental sample is highly unusual and provides a unique opportunity to investigate neural dynamics with both high spatial and temporal resolution. 

(2) Developmentally relevant design: The broad age range and cross-sectional design are well-suited to explore age-related changes in neural representations. 

(3) Ecological validity: The use of naturalistic stimuli (movie clips) increases the ecological relevance of the findings. 

(4) Feature-based analysis: The authors employ AIbased tools to extract emotion-related features from naturalistic stimuli, which enables a data-driven approach to decoding neural representations of emotional content. This method allows for a more fine-grained analysis of emotion processing beyond traditional categorical labels. 

Weaknesses: 

(1) The emotional stimuli included facial expressions embedded in speech or music, making it difficult to isolate neural responses to facial emotion per se from those related to speech content or music-induced emotion. 

We thank the reviewer for their raising this important point. We agree that in naturalistic settings, face often co-occur with speech, and that these sources of emotion can overlap. However, background music induced emotions have distinct temporal dynamics which are separable from facial emotion (See the Author response image 2 (A) and (B) below). In addition, face can convey a wide range of emotions (48 categories in Hume AI model), whereas music conveys far fewer (13 categories reported by a recent study [1]). Thus, when using facial emotion feature time series as regressors (with 48 emotion categories and rapid temporal dynamics), the model performance will reflect neural encoding of facial emotion in the music condition, rather than the slower and lower-dimensional emotion from music. 

For the speech condition, we acknowledge that it is difficult to fully isolate neural responses to facial emotion from those to speech when the emotional content from faces and speech highly overlaps. However, in our study, (1) the time courses of emotion features from face and voice are still different (Author response image 2 (C) and (D)), (2) our main finding that DLPFC encodes facial expression information in postchildhood individuals but not in young children was found in both speech and music condition (Figure 2B and 2C). In music condition, neural responses to facial emotion are not affected by speech. Thus, we have included the DLPFC results from the music condition in the revised manuscript (Figure 2C), and we acknowledge that this issue should be carefully considered in future studies using videos with speech, as we have indicated in the future directions in the last paragraph of Discussion.

(1) Cowen, A. S., Fang, X., Sauter, D. & Keltner, D. What music makes us feel: At least 13 dimensions organize subjective experiences associated with music across different cultures. Proc Natl Acad Sci USA 117, 1924–1934 (2020).

Author response image 2.

Time courses of the amusement. (A) and (B) Amusement conveyed by face or music in a 30-s music block. Facial emotion features are extracted by Hume AI. For emotion from music, we approximated the amusement time course using a weighted combination of low-level acoustic features (RMS energy, spectral centroid, MFCCs), which capture intensity, brightness, and timbre cues linked to amusement. Notice that music continues when there are no faces presented. (C) and (D) Amusement conveyed by face or voice in a 30-s speech block. From 0 to 5 seconds, a girl is introducing her friend to a stranger. The camera focuses on the friend, who appears nervous, while the girl’s voice sounds cheerful. This mismatch explains why the shapes of the two time series differ at the beginning. Such situations occur frequently in naturalistic movies

(2) While the authors leveraged Hume AI to extract facial expression features from the video stimuli, they did not provide any validation of the tool's accuracy or reliability in the context of their dataset. It remains unclear how well the AI-derived emotion ratings align with human perception, particularly given the complexity and variability of naturalistic stimuli. Without such validation, it is difficult to assess the interpretability and robustness of the decoding results based on these features.  

Hume AI models were trained and validated by human intensity ratings of large-scale, experimentally controlled emotional expression data [1-2]. The training process used both manual annotations from human raters and deep neural networks. Over 3000 human raters categorized facial expressions into emotion categories and rated on a 1-100 intensity scale. Thus, the outputs of Hume AI model reflect what typical facial expressions convey (based on how people actually interpret them), that is, the presented facial emotion. Our goal of the present study was to examine how facial emotions presented in the videos are encoded in the human brain at different developmental stages. We agree that the interpretation of facial emotions may be different in individual participants, resulting in different perceived emotion (i.e., the emotion that the observer subjectively interprets). Behavioral ratings are necessary to study the encoding of subjectively perceived emotion, which is a very interesting direction but beyond the scope of the present work. We have added text in the Discussion to explicitly note that our study focused on the encoding of presented emotion (second paragraph in Page 8).

(1) Brooks, J. A. et al. Deep learning reveals what facial expressions mean to people in different cultures. iScience 27, 109175 (2024).

(2) Brooks, J. A. et al. Deep learning reveals what vocal bursts express in different cultures. Nat. Hum. Behav. 7, 240–250 (2023).

(3) Only two children had relevant pSTC coverage, severely limiting the reliability and generalizability of results.  

We appreciated this point and agreed with both reviewers who raised it as a significant concern. As described in response to reviewer 1 (comment 1), we have added data from another two children who have pSTC coverage. Group-level analysis using permutation test showed that children’s pSTC significantly encode facial emotion in naturalistic contexts (Figure 3B). Because iEEG data from young children are extremely rare, rapidly increasing the sample size within a few years is not feasible. However, we are confident in the reliability of our conclusion that children’s pSTC can encode facial emotion. First,  the two new children’s responses (S33 and S49) from pSTC were highly consistent with our previous observations (see individual data in Figure 3B). Second, the averaged prediction accuracy in children’s pSTC (r_speech=0.1565) was highly comparable to that in post-childhood group (r_speech=0.1515).

(4) The rationale for focusing exclusively on high-frequency activity for decoding emotion representations is not provided, nor are results from other frequency bands explored.   

We focused on high-frequency broadband (HFB) activity because it is widely considered to reflect the responses of local neuronal populations near the recording electrode, whereas low-frequency oscillations in the theta, alpha, and beta ranges are thought to serve as carrier frequencies for long-range communication across distributed networks[1-2]. Since our study aimed to examine the representation of facial emotion in localized cortical regions (DLPFC and pSTC), HFB activity provides the most direct measure of the relevant neural responses. We have added this rationale to the manuscript (Page 3).

(1) Parvizi, J. & Kastner, S. Promises and limitations of human intracranial electroencephalography. Nat. Neurosci. 21, 474–483 (2018).

(2) Buzsaki, G. Rhythms of the Brain. (Oxford University Press, Oxford, 200ti).

(5) The hypothesis of developmental emergence of top-down prefrontal modulation is not directly tested. No connectivity or co-activation analyses are reported, and the number of participants with simultaneous coverage of pSTC and DLPFC is not specified.  

Directional connectivity analysis results were not shown because only one child has simultaneous coverage of pSTC and DLPFC. However, the  Granger Causality results from post-childhood group (N=7) clearly showed that the influence in the alpha/beta band from DLPFC to pSTC (top-down) is gradually increased above the onset of face presentation (Author response image 3, below left, plotted in red). By comparison, the influence in the alpha/beta band from pSTC to DLPFC (bottom-up) is gradually decreased after the onset of face presentation (Author response image 3, below left, blue curve). The influence in alpha/beta band from DLPFC to pSTC was significantly increased at 750 and 1250 ms after the face presentation (face vs nonface, paired t-test, Bonferroni  corrected P=0.005, 0.006), suggesting an enhanced top-down modulation in the post-childhood group during watching emotional faces. Interestingly, this top-down influence appears very different in the 8-year-old child at 1250 ms after the face presentation (Author response image 3, below left, black curve).

As we cannot draw direct conclusions from the single-subject sample presented here, the top-down hypothesis is introduced only as a possible explanation for our current results. We have removed potentially misleading statements, and we plan to test this hypothesis directly using MEG in the future.

Author response image 3.

Difference of Granger causality indices (face – nonface) in alpha/beta and gamma band for both directions. We identified a series of face onset in the movie that paticipant watched. Each trial was defined as -0.1 to 1.5 s relative to the onset. For the non-face control trials, we used houses, animals and scenes. Granger causality was calculated for 0-0.5 s, 0.5-1 s and 1-1.5 s time window. For the post-childhood group, GC indices were averaged across participants. Error bar is sem.

(6) The "post-childhood" group spans ages 13-55, conflating adolescence, young adulthood, and middle age. Developmental conclusions would benefit from finer age stratification.  

We appreciate this insightful comment. Our current sample size does not allow such stratification. But we plan to address this important issue in future MEG studies with larger cohorts.

(7) The so-called "complex emotions" (e.g., embarrassment, pride, guilt, interest) used in the study often require contextual information, such as speech or narrative cues, for accurate interpretation, and are not typically discernible from facial expressions alone. As such, the observed age-related increase in neural encoding of these emotions may reflect not solely the maturation of facial emotion perception, but rather the development of integrative processing that combines facial, linguistic, and contextual cues. This raises the possibility that the reported effects are driven in part by language comprehension or broader social-cognitive integration, rather than by changes in facial expression processing per se.  

We agree with this interpretation. Indeed, our results already show that speech influences the encoding of facial emotion in the DLPFC differently in the childhood and post-childhood groups (Figure 2D), suggesting that children’s ability to integrate multiple cues is still developing. Future studies are needed to systematically examine how linguistic cues and prior experiences contribute to the understanding of complex emotions from faces, which we have added to our future directions section (last paragraph in Discussion, Page 8-9 ).

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors): 

In the introduction: "These neuroimaging data imply that social and emotional experiences shape the prefrontal cortex's involvement in processing the emotional meaning of faces throughout development, probably through top-down modulation of early sensory areas." Aren't these supposed to be iEEG data instead of neuroimaging? 

Corrected.

Reviewer #2 (Recommendations for the authors):

This manuscript would benefit from several improvements to strengthen the validity and interpretability of the findings:

(1) Increase the sample size, especially for children with pSTC coverage. 

We added data from another two children who have pSTC coverage. Please see our response to reviewer 2’s comment 3 and reviewer 1’s comment 1.

(2) Include directional connectivity analyses to test the proposed top-down modulation from DLPFC to pSTC. 

Thanks for the suggestion. Please see our response to reviewer 2’s comment 5.

(3) Use controlled stimuli in an additional experiment to separate the effects of facial expression, speech, and music. 

This is an excellent point. However, iEEG data collection from children is an exceptionally rare opportunity and typically requires many years, so we are unable to add a controlled-stimulus experiment to the current study. We plan to consider using controlled stimuli to study the processing of complex emotion using non-invasive method in the future. In addition, please see our response to reviewer 2’s comment 1 for a description of how neural responses to facial expression and music are separated in our study.

Owing to the body-mind duality myth, that is. This logical argument chain makes sense insofar as people could perform as black and then be black, much in line with TERF anti-trans ideologies. Yet, this gets rid of the complexities of the process and accumulated lived experiences, basically strawmaning "I want to be a girl" or "I must abort" as if they were trends.

Now, even if instrumentally acknowledging it as an aesthetic-identity privilege "entitlement" of some white people who transition (though note most are vulnerable and face many barriers, institutional, economical, and familiar, from an already poor household), the logic disregards the impacts of trends and long term, only focusing on the immediate after. It fearmongs in an infantilising about the "correct (hegemonic) development" for an adolescent, but most importantly, it decenters the loss of privilege and the abuse and backlash faced from following this process, highligting the statist objectivist biases that engendered binaryist stereotypes.

One can't simply impersonate and live someone else's life. Fantasy can approach us to it, and be very vivid at doing so, but the approach will be a momentary peak of attention that is not usually maintained as it is not continuously lived, embodied.

Identity might not be a right, sure, but oftentimes, but the right-wind reframing displaces the debate here... we are not visiting Jakarta or the North Pole as exotic tourists, we are commiting to a change of reality because over multiple years of questioning and trying changes, we've decided it's the way to go. This is not about white people having a right to racialised bodies like fast clothing or food, this is about acknoweldging other ways of living as valid through an ethics of care that decenters the falsely "efficient" white male default. It's about saying yes to diverse ways of living that don't exploit the world, because some "being" blind, asexual, autistic, indigenous, and why not, fat, is not somethign that one usually "choses", and thereby, without relying on naturalistic fallacies but on a decentering anti-monopoly axiology, we should provide safe spaces for these to flourish.

I hope it's not just American soldiers... and wydm just soldiers? If This War of Mine showed us something, it's that soldiers are not the only victims of war.

Reviewer #1 (Public review):

Summary

The strength of this manuscript lies in the behavior: mice use a continuous auditory background (pink vs brown noise) to set a rule for interpreting an identical single-whisker deflection (lick in W+ and withhold in W− contexts) while always licking to a brief 10 kHz tone. Behaviorally, animals acquire the rule and switch rapidly at block transitions and take a few trials to fully integrate the context cue. What's nice about this behavior is the separate auditory cue, which shows the animals remain engaged in the task, so it's not just that the mice check out (i.e., become disengaged in the W- context). The authors then use optical tools, combining cortex-wide optogenetic inactivation (using localized inhibition in a grid-like fashion) with widefield calcium imaging to map what regions are necessary for the task and what the local and global dynamics are. Classic whisker sensorimotor nodes (wS1/wS2/wM/ALM) behave as expected with silencing reducing whisker-evoked licking. Retrosplenial cortex (RSC) emerges as a somewhat unexpected, context-specific node: silencing RSC (and tjS1) increases licking selectively in W−, arguing that these regions contribute to applying the "don't lick" policy in that context. I say somewhat because work from the Delamater group points to this possibility, albeit in a Pavlovian conditioning task and without neural data. I would still recommend the authors of the current manuscript review that work to see whether there is a relevant framework or concept (Castiello, Zhang, Delamater, 'The retrosplenial cortex as a possible 'sensory integration' area: a neural network modeling approach of the differential outcomes effect of negative patterning', 2021, Neurobiology of Learning and Memory).

The widefield imaging shows that RSC is the earliest dorsal cortical area to show W+ vs W− divergence after the whisker stimulus, preceding whisker motor cortex, consistent with RSC injecting context into the sensorimotor flow. A "Context Off" control (continuous white noise; same block structure) impairs context discrimination, indicating the continuous background is actually used to set the rule (an important addition!) Pre-stimulus functional-connectivity analyses suggest that there is some activity correlation that maps to the context presumably due to the continuous background auditory context. Simultaneous opto+imaging projects perturbations into a low-dimensional subspace that separates lick vs no-lick trajectories in an interpretable way.

In my view, this is a clear, rigorous systems-level study that identifies an important role for RSC in context-dependent sensorimotor transformation, thereby expanding RSC's involvement beyond navigation/memory into active sensing and action selection. The behavioral paradigm is thoughtfully designed, the claims related to the imaging are well defended, and the causal mapping is strong. I have a few suggestions for clarity that may require a bit of data analysis. I also outline one key limitation that should be discussed, but is likely beyond the scope of this manuscript.

Major strengths

(1) The task is a major strength. It asks the animal to generate differential motor output to the same sensory stimulus, does so in a block-based manner, and the Context-Off condition convincingly shows that the continuous contextual cue is necessary. The auditory tone control ensures this is more than a 'motivational' context but is decision-related. In fact, the slightly higher bias to lick on the catch trials in the W+ context is further evidence for this.

(2) The dorsal-cortex optogenetic grid avoids a 'look-where-we-expect' approach and lets RSC fall out as a key node. The authors then follow this up with pharmacology and latency analyses to rule out simple motor confounds. Overall, this is rigorous and thoughtfully done.

(3) While the mesoscale imaging doesn't allow for cellular resolution, it allows for mapping of the flow of information. It places RSC early in the context-specific divergence after whisker onset, a valuable piece that complements prior work.

(4) The baseline (pre-stim) functional connectivity and the opto-perturbation projections into a task subspace increase the significance of the work by moving beyond local correlates.

Key limitation

The current optogenetic window begins ~10 ms before the sensory cue and extends 1s after, which is ideal for perturbing within-trial dynamics but cannot isolate whether RSC is required to maintain the context-specific rule during the baseline. Because context is continuously available, it makes me wonder whether RSC is the locus maintaining or, instead, gating the context signal. The paper's results are fully consistent with that possibility, but causality in the pre-stimulus window remains an open question. (As a pointer for future work, pre-stimulus-only inactivation, silencing around block switches, or context-omission probe trials (e.g., removing the background noise unexpectedly within a W+ or W- context block), could help separate 'holding' from 'gating' of the rule. But I'm not suggesting these are needed for this manuscript, but would be interesting for future studies.)

Free to Download Sweet Venom (Vipers, #2)

by Rina Kent

Download Now

Overview :

From the New York Times & USA Today bestselling author Rina Kent comes a dangerously dark stalker hockey romance.Can I outrun his merciless obsession?I accidentally witnessed a brutal murder.I froze, pretended I saw nothing, hoping I could leave it behind.But my plan backfired, and my life spiraled downward.Now, I’m the target of cold-blooded revenge.Jude Callahan isn’t just a hockey god—he’s a devil no one dares to cross.My existence disrupts his stardom, prestige, and possible serial killer career choice.And he’s set out to make me pay for that moment of silence.No matter how much I run or hide, he finds me, watching from the shadows.Like a predator.I thought he’d stop at the stalking.Or even better, he’d kill me and finally end my misery.But Jude has other plans.He says I can’t die. I have to pay for my sins.And just like that, he drags me into his depraved world, kicking and screaming.This book can be read on its own but for better understanding of the world, it's recommended to read Beautiful Venom first. The pacing of the book reflects a careful consideration of reader engagement. Moments of intensity are balanced with quieter scenes that provide context and reflection. This rhythm keeps the story dynamic without becoming overwhelming. This book stands out for its ability to convey complex ideas in a way that feels natural and easy to follow. Rather than relying on excessive exposition, the author allows the story to evolve organically through dialogue and action. Readers often appreciate how the pacing remains consistent, avoiding unnecessary detours while still providing enough detail to fully understand the motivations behind each character. This approach creates a reading experience that feels both thoughtful and satisfying. The narrative voice used throughout the book feels confident and well-defined. The author’s tone remains steady, helping readers develop a sense of familiarity with the storytelling style. This consistency makes it easier to follow the plot and understand the underlying messages woven into the text. The result is a cohesive reading experience that feels deliberate and carefully crafted.

Include Format in : √pdf, √e-book, √book, √ePub

Chat with Gemini:<br /> Lee:

I'm taking an online course of 3D graphics. Geometry (as it pertains to 3D graphics on computers) is the current focus. The text just said:

If B is a unit vector, the operation A⋅B yields ∥A∥cos(θ), signifying the magnitude of A's projection in B's direction, with a negative sign if the direction is reversed. This is termed the scalar projection of A onto B. Can you explain this better to me? What does "magnitude of A's (a non-unit vector) projection in B's (a unit vector) direction " mean?

Gemini:<br /> This is one of the most fundamental concepts in 3D graphics, but the math jargon can sometimes obscure the simple visual reality.

To understand "the magnitude of A's projection in B's direction," the best tool is the Shadow Analogy.

1. The Shadow Analogy

Imagine Vector A is a stick floating in space. Vector B represents the ground (or a specific direction along the ground).

If you were to shine a light directly from above (perpendicular to the ground B), the stick (A) would cast a shadow onto the ground.

• The Projection: The "shadow" itself is the projection.
• The Magnitude: The length of that shadow is the "magnitude of the projection."

So, when the text says "magnitude of A's projection in B's direction," it is simply asking: "How much of Vector A is pointing in the same direction as Vector B?"

2. Why does B have to be a Unit Vector?

This is the crucial part that often trips people up.

In 3D graphics, a Unit Vector has a length of exactly 1. It is used purely to point the way; it contains direction information but no "scale" or "weight."

The formula for the dot product is:

** MISSING FORMULAS HERE **

If B is a unit vector, then . The formula simplifies to:

If B wasn't a unit vector (say, it was 5 units long), the result would be the length of the shadow multiplied by 5. That gives you a useless number if you just wanted to measure the shadow. By ensuring B is length 1, the math gives you the exact, unscaled length of the shadow of A.

3. Visualizing the Angles (The "Negative" Sign)

The text mentioned a "negative sign if the direction is reversed." This happens based on the angle () between the two vectors.

• Acute Angle (): The vectors point generally in the same direction. The shadow falls forward along B. The result is Positive.
• Right Angle (): The vectors are perpendicular. There is no shadow (it's just a dot). The result is Zero.
• Obtuse Angle (): The vectors point generally away from each other. The shadow falls backward behind the start of B. The result is Negative.

Summary Table

``` | Concept | The Visual | The Math | | ----------------- | ---------------------------------------- | ------------- | | Vector A | The object casting the shadow. | ∥A∥ | | Vector B (Unit) | The ground/direction we measure against. | ∥B∥=1 | | Dot Product (A⋅B) | The length of the shadow. | A⋅B=∥A∥cos(θ) |

```

Why do you need this in 3D Graphics?

You will use this constantly. Here are two immediate examples:

1. Lighting: If B is the surface normal (pointing out of a polygon) and A is the light direction, the dot product tells you how directly the light hits the surface. High projection = Bright light. Negative projection = The light is hitting the back of the polygon (it's dark).<br />
2. Mario running up a slope: If B is the direction of the slope and A is Mario's velocity, the projection tells you how fast he is actually moving up the hill vs just moving forward.

Lee:

So if B was pointing directly along the +X axis (X=1, Y=0, Z=0) and A was angled up from that and longer (X=2, Y=2, Z=0), A's magnitude would be ~2.83. But A⋅B would be 2 (It's X coordinate in this specific scenario). If you made a triangle with A being one side and B specifying the direction of the adjacent side, and drew a straight line from the end of A straight down to and perpendicular to the direction B, the length of that "B" side would be A⋅B assuming ∥B∥ was 1. Does that sound correct?

Gemini:<br /> Yes, that is exactly correct. You have perfectly grasped the geometric interpretation of the dot product.

Your example scenario is spot on. Here is the breakdown of why your visualization works, mapping your description to the standard trigonometry terms.

1. The Math Check

Using your numbers:

• Vector A: (Magnitude )
• Vector B: (Unit vector along X)

The calculation is:

You correctly identified that because B is aligned perfectly with the X-axis, the "shadow" of A is simply its X-coordinate (2).

2. The Triangle Visualization

You described making a triangle. This is the exact geometric definition used in physics and graphics.

• The Hypotenuse: This is your Vector A (length ).
• The "Drop" Line: The line you drew "straight down to and perpendicular to B" is the opposite side of the triangle.
• The Adjacent Side: The side lying along the direction of B is the Scalar Projection. Its length is exactly the dot product (2).

In trigonometry terms:

3. One Crucial Detail

You added the condition: "assuming ∥B∥ was 1."

This is the most important part of your insight. If Vector B had been (2, 0, 0) (length of 2), the dot product would have been:

(2×2) + (2×0) + (0×0) = 4

The math would return 4, but the "shadow" is physically still only 2 units long. The dot product scales the result by the magnitude of B. That is why we must normalize B (make it a unit vector) before using it as a "measuring stick."

You have the concept of Scalar Projection (the length of the shadow) down cold.

Chat with Gemini:<br /> Lee:

Here's another block of text in that same lesson. Can you help me visualize what it means?

When both vectors are normalized, the arc cosine (cos⁻¹) of their dot product reveals the angle θ between them: θ=cos⁻¹((A⋅B)/(∥A∥ ∥B∥)) or cos⁻¹(Â⋅B̂), where cos⁻¹ denotes the inverse cosine function, commonly represented as acos() in programming languages.

Gemini:<br /> This block of text is explaining how to "reverse-engineer" an angle using the dot product. In 3D graphics, you often know the coordinates of two objects (like a player and an enemy), but you don't know the angle between them. This formula is how you find it.

The Logic: Working Backwards

Think of the dot product formula we just discussed:

A⋅B = ∥A∥∥B∥cos(θ)

The text is simply rearranging that equation to solve for .

1. Isolate the Cosine: Divide both sides by the magnitudes (). A⋅B cos(θ) = ------ ∥A∥∥B∥
2. Find the Angle: To get θ by itself, you use the inverse cosine (acos). / A⋅B \ θ = arccos( ------ ) \ ∥A∥∥B∥ /

Visualizing the "Normalized" Shortcut

The text mentions: θ=cos(Â⋅B̂). (The "hat" symbol  means it is a normalized unit vector).

This is the "pro-gamer move" in graphics programming. If you normalize both vectors first (meaning you force their lengths to be 1), then ∥A∥ and ∥B∥ both become 1. The denominator in our formula disappears!

• If lengths are 1: The dot product is the cosine of the angle.
• To get the angle: You just plug that dot product into acos().

A Practical Example: "Field of View"

Imagine you are coding a stealth game. You have a Guard and a Player.

• Vector A: The direction the Guard is looking.
• Vector B: The direction from the Guard to the Player.

If you normalize both vectors and calculate (A⋅B), you get the exact angle (e.g., 30°). If the Guard's peripheral vision is only 45°, you now know the Guard can see the player!

Important "Gotcha" for Programmers

When you use acos() in languages like C#, C++, or Python, the result is usually in radians, not degrees.

• Dot Product = 1: Angle is 0 (Vectors point same way).
• Dot Product = 0: Angle is 1.57 radians (90°).
• Dot Product = -1: Angle is 3.14 radians (180°).

Lee:

You said If you normalize both vectors and calculate $acos(A⋅B), you get the exact angle (e.g., 30°). If the Guard's peripheral vision is only 45°, you now know the Guard can see the player! By "only 45°", is that from left to right of the guard's visibility? So, from straight ahead to the far right of his vision would only be 22.5°, thus the player is not visible at a 30° again from the guard? Or is that 45° from straight ahead to the far right of the guard's vision (for a total viewable angle of 90°)?

Gemini:<br /> That is a fantastic catch. In game development and 3D graphics, this distinction is a common source of bugs!

When you use the dot product to check visibility, the angle θ you calculate is the offset from the center.

1. The "Half-Angle" Concept

The dot product measures the angle between two specific lines: where the guard is looking (the "forward vector") and where the player is. It doesn't know about "left" or "right"; it only knows "how far away from my nose is this object?"

• If your calculation acos(A⋅B) returns 30°, it means the player is 30° away from the center line.
• If the guard has a 45° Field of View (FOV), usually that refers to the total horizontal arc (22.5° to the left and 22.5° to the right).

In your specific scenario:

• Total FOV: 45°
• Allowed "Half-Angle": 22.5°
• Player's Angle: 30°
• Result: Not Visible. The player is "outside the cone."

2. How it's usually coded

To avoid doing the math every single frame, programmers usually define the guard's "View Cone" as a threshold value.

If the guard can see 45° to the left and 45° to the right (a 90° total FOV), the threshold is:<br /> cos(45°) ≈ 0.707 The code then looks like this:

```csharp // If dot product is greater than 0.707, the player is within the 90-degree cone if (DotProduct(GuardForward, DirectionToPlayer) > 0.707) { // I see you! }

```

3. Why the Dot Product is "Circular"

One interesting thing to visualize: because the dot product only cares about the angle between two vectors, the "visibility" it creates is actually a cone, not a flat triangle.

If you say "anything within 30° of my forward vector is visible," that includes 30° up, 30° down, 30° left, and 30° right. It creates a circular spotlight of vision.

Summary Table: FOV vs. Dot Product

| Total FOV | Angle from Center | Dot Product Threshold (Approx) | | ------------------ | ----------------- | ------------------------------ | | 180° (Flat wall) | 90° | 0.0 | | 90° (Standard) | 45° | 0.707 | | 60° (Narrow) | 30° | 0.866 | | 2° (Sniper Scope)| 1° | 0.999 |

All these factors are achievable I think, or will be soonish. Smaller models, better sourced data sets, niche models, etc. But not with current actors as mentioned at the end.

Cloud hosting can become an expensive abstraction layer quickly. I also think there's an entire generation of coders / engineers who treat silo'd cloudhosting as a given, without considering other options and their benefits. Large window for selfhosting in which postgres almost always falls

Sees use for silo'd postgres hosting on the extremes of the spectrum: when you start without knowledge and are vibecoding, so you can treat the database as just another API, and when you are megacorp (outsourcing looks cheaper quickly if you have to otherwise pay multiple FTE salaries otherwise), or/and have to prove regulatory compliance.

AWS RDS is not much else as open source postgres with operational tooling that is not complex itself.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary: 

Zhang and colleagues examine neural representations underlying abstract navigation in the entorhinal cortex (EC) and hippocampus (HC) using fMRI. This paper replicates a previously identified hexagonal modulation of abstract navigation vectors in abstract space in EC in a novel task involving navigating in a conceptual Greeble space. In HC, the authors claim to identify a three-fold signal of the navigation angle. They also use a novel analysis technique (spectral analysis) to look at spatial patterns in these two areas and identify phase coupling between HC and EC. Finally, the authors propose an EC-HPC PhaseSync Model to understand how the EC and HC construct cognitive maps. While the wide array of techniques used is impressive and their creativity in analysis is admirable, overall, I found the paper a bit confusing and unconvincing. I recommend a significant rewrite of their paper to motivate their methods and clarify what they actually did and why. The claim of three-fold modulation in HC, while potentially highly interesting to the community, needs more background to motivate why they did the analysis in the first place, more interpretation as to why this would emerge in biology, and more care taken to consider alternative hypotheses seeped in existing models of HC function. I think this paper does have potential to be interesting and impactful, but I would like to see these issues improved first.

General comments:

(1) Some of the terminology used does not match the terminology used in previous relevant literature (e.g., sinusoidal analysis, 1D directional domain).

We thank the reviewer for this valuable suggestion, which helps to improve the consistency of our terminology with previous literature and to reduce potential ambiguity. Accordingly, we have replaced “sinusoidal analysis” with “sinusoidal modulation” (Doeller et al., 2010; Bao et al., 2019; Raithel et al., 2023) and “1D directional domain” with “angular domain of path directions” throughout the manuscript.

(2) Throughout the paper, novel methods and ideas are introduced without adequate explanation (e.g., the spectral analysis and three-fold periodicity of HC).

We thank the reviewer for raising this important point. In the revised manuscript, we have substantially extended the Introduction (paragraphs 2–4) to clarify our hypothesis, explicitly explaining why the three primary axes of the hexagonal grid cell code may manifest as vector fields. We have also revised the first paragraph of the “3-fold periodicity in the HPC” section in the Results to clarify the rationale for using spectral analysis. Please refer to our responses to comment 2 and 3 below for details.

Reviewer #2 (Public review):

The authors report results from behavioral data, fMRI recordings, and computer simulations during a conceptual navigation task. They report 3-fold symmetry in behavioral and simulated model performance, 3-fold symmetry in hippocampal activity, and 6-fold symmetry in entorhinal activity (all as a function of movement directions in conceptual space). The analyses are thoroughly done, and the results and simulations are very interesting.

We sincerely thank the reviewer for the positive and encouraging comments on our study.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) This paper has quite a few spelling and grammatical mistakes, making it difficult to understand at times.

We apologize for the wordings and grammatical errors. We have thoroughly re-read and carefully edited the entire manuscript to correct typographical and grammatical errors, ensuring improved clarity and readability.

(2) Introduction - It's not clear why the three primary axes of hexagonal grid cell code would manifest as vector fields.

We thank the reviewer for raising this important point. In the revised Introduction (paragraphs 2, 3, and 4), we now explicitly explain the rationale behind our hypothesis that the three primary axes of the hexagonal grid cell code manifest as vector fields.

In paragraph 2, we present empirical evidence from rodent, bat, and human studies demonstrating that mental simulation of prospective paths relies on vectorial representations in the hippocampus (Sarel et al., 2017; Ormond and O’Keefe, 2022; Muhle-Karbe et al., 2023).

In paragraphs 3 and 4, we introduce our central hypothesis: vectorial representations may originate from population-level projections of entorhinal grid cell activity, based on three key considerations:

(1) The EC serves as the major source of hippocampal input (Witter and Amaral, 1991; van Groen et al., 2003; Garcia and Buffalo, 2020).

(2) Grid codes exhibit nearly invariant spatial orientations (Hafting et al., 2005; Gardner et al., 2022), which makes it plausible that their spatially periodic activity can be detected using fMRI.

(3) A model-based inference: for example, in the simplest case, when one mentally simulates a straight pathway aligned with the grid orientation, a subpopulation of grid cells would be activated. The resulting population activity would form a near-perfect vectorial representation, with constant activation strength along the path. In contrast, if the simulated path is misaligned with the grid orientation, the population response becomes a distorted vectorial code. Consequently, simulating all possible straight paths spanning 0°–360° results in 3-fold periodicity in the activity patterns—due to the 180° rotational symmetry of the hexagonal grid, orientations separated by 180° are indistinguishable.

We therefore speculate that vectorial representations embedded in grid cell activity exhibit 3-fold periodicity across spatial orientations and serve as a periodic structure to represent spatial direction. Supporting this view, reorientation paradigms in both rodents and young children have shown that subjects search equally in two opposite directions, reflecting successful orientation encoding but a failure to integrate absolute spatial direction (Hermer and Spelke, 1994; Julian et al., 2015; Gallistel, 2017; Julian et al., 2018).

(3) It took me a few reads to understand what the spectral analysis was. After understanding, I do think this is quite clever. However, this paper needs more motivation to understand why you are performing this analysis. E.g., why not just take the average regressor at the 10º, 70º, etc. bins and compare it to the average regressor at 40º, 100º bins? What does the Fourier transform buy you?

We are sorry for the confusion. we outline the rationale for employing Fast Fourier Transform (FFT) analysis to identify neural periodicity. In the revised manuscript, we have added these clarifications into the first paragraph of the “3-fold periodicity in the HPC” subsection in the Results.

First, FFT serves as an independent approach to cross-validate the sinusoidal modulation results, providing complementary evidence for the 6-fold periodicity in EC and the 3-fold periodicity in HPC.

Second, FFT enables unbiased detection of multiple candidate periodicities (e.g., 3–7-fold) simultaneously without requiring prior assumptions about spatial phase (orientation). By contrast, directly comparing “aligned” versus “misaligned” angular bins (e.g., 10°/70° vs. 40°/100°) would implicitly assume knowledge of the phase offset, which was not known a priori.

Finally, FFT uniquely allows periodicity analysis of behavioral performance, which is not feasible with standard sinusoidal GLM approaches. This methodological consistency makes it possible to directly compare periodicities across neural and behavioral domains.

(4) A more minor point: at one point, you say it’s a spectral analysis of the BOLD signals, but the methods description makes it sound like you estimated regressors at each of the bins before performing FFT. Please clarify. 

We apologize for the confusion. In our manuscript, we use the term spectral analysis to distinguish this approach from sinusoidal modulation analysis. Conceptually, our spectral analysis involves a three-level procedure:

(1) First level: We estimated direction-dependent activity maps using a general linear model (GLM), which included 36 regressors corresponding to path directions, down-sampled in 10° increments.

(2) Second level: We applied a Fast Fourier Transform (FFT) to the direction-dependent activity maps derived from the GLM to examine the spectral magnitude of potential spatial periodicities.

(3) Third level: We conducted group-level statistical analyses across participants to assess the consistency of the observed periodicities.

We have revised the “Spectral analysis of MRI BOLD signals” subsection in the Methods to clarify this multi-level procedure.

(5) Figure 4a:

Why do the phases go all the way to 2*pi if periodicity is either three-fold or six-fold? 

When performing correlation between phases, you should perform a circular-circular correlation instead of a Pearson's correlation.

We thank the reviewer for raising this important point. In the original Figure 4a, both EC and HPC phases spanned 0–2π because their sinusoidal phase estimates were projected into a common angular space by scaling them according to their symmetry factors (i.e., multiplying the 3-fold phase by 3 and the 6-fold phase by 6), followed by taking the modulo 2π. However, this projection forced signals with distinct intrinsic periodicities (120° vs. 60° cycles) into a shared 360° space, thereby distorting their relative angular distances and disrupting the one-to-one correspondence between physical directions and phase values. Consequently, this transformation could bias the estimation of their phase relationship.

In the revised analysis and Figure 4a, we retained the original phase estimates derived from the sinusoidal modulation within their native periodic ranges (0–120° for 3-fold and 0–60° for 6-fold) by applying modulo operations directly. Following your suggestion, the relationship between EC and HPC phases was then quantified using circular–circular correlation (Jammalamadaka & Sengupta, 2001), as implemented in the CircStat MATLAB toolbox. This updated analysis avoids the rescaling artifact and provides a statistically stronger and conceptually clearer characterization of the phase correspondence between EC and HPC.

(6) Figure 4d needs additional clarification:

Phase-locking is typically used to describe data with a high temporal precision. I understand you adopted an EEG analysis technique to this reconstructed fMRI time-series data, but it should be described differently to avoid confusion. This needs additional control analyses (especially given that 3 is a multiple of 6) to confirm that this result is specific to the periodicities found in the paper.

We thank the reviewer for this insightful comment. We have extensively revised the description of the Figure 4 to avoid confusion with EEG-based phase-locking techniques. The revised text now explicitly clarifies that our approach quantifies spatial-domain periodic coupling across path directions, rather than temporal synchronization of neural signals.

To further address the reviewer’s concern about potential effects of the integer multiple relationship between the 3-fold HPC and 6-fold EC periodicities, we additionally performed two control analyses using the 9-fold and 12-fold EC components, both of which are also integer multiples of the 3-fold HPC periodicity. Neither control analysis showed significant coupling (p > 0.05), confirming that the observed 3-fold–6-fold coupling was specific and not driven by their harmonic relationship.

The description of the revised Figure 4 has been updated in the “Phase Synchronization Between HPC and EC Activity” subsection of the Results.

(7) Figure 5a is misleading. In the text, you say you test for propagation to egocentric cortical areas, but I don’t see any analyses done that test this. This feels more like a possible extension/future direction of your work that may be better placed in the discussion.

We are sorry for the confusion. Figure 5a was intended as a hypothesis-driven illustration to motivate our analysis of behavioral periodicity based on participants’ task performance. However, we agree with the reviewer that, on its own, Figure 5a could be misleading, as it does not directly present supporting analyses.

To provide empirical support for the interpretation depicted in Figure 5a, we conducted a whole-brain analysis (Figure S8), which revealed significant 3-fold periodic signals in egocentric cortical regions, including the parietal cortex (PC), precuneus (PCU), and motor regions.

To avoid potential misinterpretation, we have revised the main text to include these results and explicitly referenced Figure S8 in connection with Figure 5a.

The updated description in the “3-fold periodicity in human behavior” subsection in the Results is as follows:

“Considering the reciprocal connectivity between the medial temporal lobe (MTL), where the EC and HPC reside, and the parietal cortex implicated in visuospatial perception and action, together with the observed 3-fold periodicity within the DMN (including the PC and PCu; Fig. S8), we hypothesized that the 3-fold periodic representations of path directions extend beyond the MTL to the egocentric cortical areas, such as the PC, thereby influencing participants' visuospatial task performance (Fig. 5a)”.

Additionally, Figure 5a has been modified to more clearly highlight the hypothesized link between activity periodicity and behavioral periodicity, rather than suggesting a direct anatomical pathway.

(8) PhaseSync model: I am not an expert in this type of modeling, so please put a lower weight on this comment (especially compared to some of the other reviewers). While the PhaseSync model seems interesting, it’s not clear from the discussion how this compares to current models. E.g., Does it support them by adding the three-fold HC periodicity? Does it demonstrate that some of them can't be correct because they don't include this three-fold periodicity?

We thank the reviewer for the insightful comment regarding the PhaseSync model. We agree that further clarifying its relationship to existing computational frameworks is important.

The EC–HPC PhaseSync model is not intended to replace or contradict existing grid–place cell models of navigation (e.g., Bicanski and Burgess, 2019; Whittington et al., 2020; Edvardsen et al., 2020). Instead, it offers a hierarchical extension by proposing that vectorial representations in the hippocampus emerge from the projections of periodic grid codes in the entorhinal cortex. Specifically, the model suggests that grid cell populations encode integrated path information, forming a vectorial gradient toward goal locations.

To simplify the theoretical account, our model was implemented in an idealized square layout. In more complex real-world environments, hippocampal 3-fold periodicity may interact with additional spatial variables, such as distance, movement speed, and environmental boundaries.

We have revised the final two paragraphs of the Discussion to clarify this conceptual framework and emphasize the importance of future studies in exploring how periodic activity in the EC–HPC circuit interacts with environmental features to support navigation.

Reviewer #2 (Recommendations for the authors):

(1) Please show a histogram of movement direction sampling for each participant.

We thank the reviewer for this helpful suggestion. We have added a new supplementary figure (Figure S2) showing histograms of path direction sampling for each participant (36 bins of 10°). The figure is also included. Rayleigh tests for circular uniformity revealed no significant deviations from uniformity (all ps > 0.05, Bonferroni-corrected across participants), confirming that path directions were sampled evenly across 0°–360°.

(2) Why didn’t you use participants’ original trajectories (instead of the trajectories inferred from the movement start and end points) for the hexadirectional analyses? 

In our paradigm, participants used two MRI-compatible 2-button response boxes (one for each hand) to adjust the two features of the greebles. As a result, the raw adjustment path contained only four cardinal directions (up, down, left, right). If we were to use the raw stepwise trajectories, the analysis would be restricted to these four directions, which would severely limit the angular resolution. By instead defining direction as the vector from the start to the end position in feature space, we can expand the effective range of directions to the full 0–360°. This approach follows previous literature on abstract grid-like coding in humans (e.g., Constantinescu et al., 2016), where direction was similarly defined by the relative change between two feature dimensions rather than the literal stepwise path. We have added this clarification in the “Sinusoidal modulation” subsection of the revised method.

(3) Legend of Figure 2: the statement "localizing grid cell activity" seems too strong because it is still not clear whether hexadirectional signals indeed result from grid-cell activity (e.g., Bin Khalid et al., eLife, 2024). I would suggest rephrasing this statement (here and elsewhere). 

Thank you for this helpful suggestion. We have removed the statement “localizing grid cell activity” to avoid ambiguity and revised the legend of Figure 2a to more explicitly highlight its main purpose—defining how path directions and the aligned/misaligned conditions were constructed in the 6-fold modulation. We have also modified similar expressions throughout the manuscript to ensure consistency and clarity.

(4) Legend of Figure 2: “cluster-based SVC correction for multiple comparisons” - what is the small volume you are using for the correction? Bilateral EC?

For both Figure 2 and Figure 3, the anatomical mask of the bilateral medial temporal lobe (MTL), as defined by the AAL atlas, was used as the small volume for correction. This has been clarified in the revised Statistical Analysis section of the Methods as “… with small-volume correction (SVC) applied within the bilateral MTL”.

(5) Legend of Figure 2: "ROI-based analysis" - what kind of ROI are you using? "corrected for multiple comparisons" - which comparisons are you referring to? Different symmetries and also the right/left hemisphere?

In Figure 2b, the ROI was defined as a functional mask derived from the significant activation cluster in the right entorhinal cortex (EC). Since no robust clusters were observed in the left EC, the functional ROI was restricted to the right hemisphere. We indeed included Figure 2c to illustrate this point; however, we recognize that our description in the text was not sufficiently clear.

Regarding the correction for multiple comparisons, this refers specifically to the comparisons across different rotational symmetries (3-, 4-, 5-, 6-, and 7-fold). Only the 6-fold symmetry survived correction, whereas no significant effects were detected for the other symmetries.

We have clarified these points in the “6-fold periodicity in the EC” subsection of the result as “… The ROI was defined as a functional mask of the right EC identified in the voxel-based analysis and further restricted within the anatomical EC. These analyses revealed significant periodic modulation only at 6-fold (Figure  2c; t(32) = 3.56, p = 0.006, two-tailed, corrected for multiple comparisons across rotational symmetries; Cohen’s d = 0.62) …”.

We have also revised the “3-fold periodicity in the HPC” subsection of the result as “… ROI analysis, using a functional mask of the HPC identified in the spectral analysis and further restricted within the anatomical HPC, indicated that HPC activity selectively fluctuated at 3-fold periodicity (Figure 3e; t(32) = 3.94, p = 0.002, corrected for multiple comparisons across rotational symmetries; Cohen’s d = 0.70) …”.

(6) Figure 2d: Did you rotationally align 0{degree sign} across participants? Please state explicitly whether (or not) 0{degree sign} aligns with the x-axis in Greeble space.

We thank the reviewer for this helpful question. Yes, before reconstructing the directional tuning curve in Figure 2d, path directions were rotationally aligned for each participant by subtracting the participant-specific grid orientation (ϕ) estimated from the independent dataset (odd sessions). We have now made this description explicit in the revised manuscript in the “6-fold periodicity in the EC” subsection of the Results, stating “… To account for individual difference in spatial phase, path directions were calibrated by subtracting the participant-specific grid orientation estimated from the odd sessions ...”.

(7) Clustering of grid orientations in 30 participants: What does “Bonferroni corrected” refer to? Also, the Rayleigh test is sensitive to the number of voxels - do you obtain the same results when using pair-wise phase consistency? 

“Bonferroni corrected” here refers to correction across participants. We have clarified this in the first paragraph of the “6-fold periodicity in the EC” subsection of the Result and in the legend of Supplementary Figure S5 as “Bonferroni-corrected across participants.”

To examine whether our findings were sensitive to the number of voxels, we followed the reviewer’s guidance to compute pairwise phase consistency (PPC; Vinck et al., 2010) for each participant. The PPC results replicated those obtained with the Rayleigh test. We have updated the new results into the Supplementary Figure S5. We also updated the “Statistical Analysis” subsection of the Methods to describe PPC as “For the PPC (Vinck et al., 2010), significance was tested using 5,000 permutations of uniformly distributed random phases (0–2π) to generate a null distribution for comparison with the observed PPC”.

(8) 6-fold periodicity in the EC: Do you compute an average grid orientation across all EC voxels, or do you compute voxel-specific grid orientations?

Following the protocol originally described by Doeller et al. (2010), we estimated voxel-wise grid orientations within the EC and then obtained a participant-specific orientation by averaging across voxels within a hand-drawn bilateral EC mask. The procedure is described in detail in the “Sinusoidal modulation” subsection of the Methods.

(9) Hand-drawn bilateral EC mask: What was your procedure for drawing this mask? What results do you get with a standard mask, for example, from Freesurfer or SPM? Why do you perform this analysis bilaterally, given that the earlier analysis identified 6-fold symmetry only in the right EC? What do you mean by "permutation corrected for multiple comparisons"?

We thank the reviewer for raising these important methodological points. To our knowledge, no standard volumetric atlas provides an anatomically defined entorhinal cortex (EC) mask. For example, the built-in Harvard–Oxford cortical structural atlas in FSL contains only a parahippocampal region that encompasses, but does not isolate, the EC. The AAL atlas likewise does not contain an EC region. In FreeSurfer, an EC label is available, but only in the fsaverage surface space, which is not directly compatible with MNI-based volumetric group-level analyses.

Therefore, we constructed a bilateral EC mask by manually delineating the EC according to the detailed anatomical landmarks described by Insausti et al. (1998). Masks were created using ITK-SNAP (Version 3.8, www.itksnap.org). For transparency and reproducibility, the mask has been made publicly available at the Science Data Bank (link: https://www.scidb.cn/s/NBriAn), as indicated in the revised Data and Code availability section.

Regarding the use of a bilateral EC mask despite voxel-wise effects being strongest in the right EC. First, we did not have any a priori hypothesis regarding laterality of EC involvement before performing analyses. Second, previous studies estimated grid orientation using a bilateral EC mask in their sinusoidal analyses (Doeller et al., 2010; Constantinescu et al., 2016; Bao et al., 2019; Wagner et al., 2023; Raithel et al., 2023). We therefore followed this established approach to estimate grid orientation.

By “permutation corrected for multiple comparisons” we refer to the family-wise error correction applied to the reconstructed directional tuning curves (Figure 2d for the EC, Figure 3f for the HPC). Specifically, directional labels were randomly shuffled 5,000 times, and an FFT was applied to each shuffled dataset to compute spectral power at each fold. This procedure generated null distributions of spectral power for each symmetry. For each fold, the 95th percentile of the maximal power across permutations was used as the uncorrected threshold. To correct across folds, the 95th percentile of the maximal suprathreshold power across all symmetries was taken as the family-wise error–corrected threshold. We have clarified this procedure in the revised “Statistical Analysis” subsection of the Methods.

(10) Figures 3b and 3d: Why do different hippocampal voxels show significance for the sinusoidal versus spectral analysis? Shouldn’t the analyses be redundant and, thus, identify the same significant voxels? 

We thank the reviewer for this insightful question. Although both sinusoidal modulation and spectral analysis aim to detect periodic neural activity, the two approaches are methodologically distinct and are therefore not expected to identify exactly the same significant voxels.

Sinusoidal modulation relies on a GLM with sine and cosine regressors to test for phase-aligned periodicity (e.g., 3-fold or 6-fold), calibrated according to the estimated grid orientation. This approach is highly specific but critically depends on accurate orientation estimation. In contrast, spectral analysis applies Fourier decomposition to the directional tuning profile, enabling the detection of periodic components without requiring orientation calibration.

Accordingly, the two analyses are not redundant but complementary. The FFT approach allows for an unbiased exploration of multiple candidate periodicities (e.g., 3–7-fold) without predefined assumptions, thereby providing a critical cross-validation of the sinusoidal GLM results. This strengthens the evidence for 6-fold periodicity in EC and 3-fold periodicity in HPC. Furthermore, FFT uniquely facilitates the analysis of periodicities in behavioral performance data, which is not feasible with standard sinusoidal GLM approaches. This methodological consistency enables direct comparison of periodicities across neural and behavioral domains.

Additionally, the anatomical distributions of the HPC clusters appear more similar between Figure 3b and Figure 3d after re-plotting Figure 3d using the peak voxel coordinates (x = –24, y = –18), which are closer to those used for Figure 3b (x = –24, y = –20), as shown in the revised Figure 3.

Taken together, the two analyses serve distinct but complementary purposes.

(11) 3-fold sinusoidal analysis in hippocampus: What kind of small volume are you using to correct for multiple comparisons?

We thank the reviewer for this comment. The same small volume correction procedure was applied as described in R4. Specifically, the anatomical mask of the bilateral medial temporal lobe (MTL), as defined by the AAL atlas, was used as the small volume for correction. This procedure has been clarified in the revised Statistical Analysis section of the Methods as following: “… with small-volume correction (SVC) applied within the bilateral MTL.”

(12) Figure S5: “right HPC” – isn’t the cluster in the left hippocampus? 

We are sorry for the confusion. The brain image was present in radiological orientation (i.e., the left and right orientations are flipped). We also checked the figure and confirmed that the cluster shown in the original Figure S5 (i.e., Figure S6 in the revised manuscript) is correctly labeled as the right hippocampus, as indicated by the MNI coordinate (x = 22), where positive x values denote the right hemisphere. To avoid potential confusion, we have explicitly added the statement “Volumetric results are displayed in radiological orientation” to the figure legends of all volume-based results.

(13) Figure S5: Why are the significant voxels different from the 3-fold symmetry analysis using 10{degree sign} bins?

As shown in R10, the apparent differences largely reflect variation in MNI coordinates. After adjusting for display coordinates, the anatomical locations of the significant clusters are in fact highly similar between the 10°-binned (Figure 3d, shown above) and the 20°-binned results (Figure S6).

Although both analyses rely on sinusoidal modulation, they differ in the resolution of the input angular bins (10° vs. 20°). Combined with the inherent noise in fMRI data, this makes it unlikely that the two approaches would yield exactly the same set of significant voxels. Importantly, both analyses consistently reveal robust 3-fold periodicity in the hippocampus, indicating that the observed effect is not dependent on angular bin size.

(14) Figure 4a and corresponding text: What is the unit? Phase at which frequency? Are you using a circular-circular correlation to test for the relationship?

We thank the reviewer for raising this important point. In the revised manuscript, we have clarified that the unit of the phase values is radians, corresponding to the 6-fold periodic component in the EC and the 3-fold periodic component in the HPC. In the original Figure 4a, both EC and HPC phases—estimated from sinusoidal modulation—were analyzed using Pearson correlation. We have since realized issues with this approach, as also noted R5 to Reviewer #1.

In the revised analysis and Figure 4a (as shown above), we re-evaluated the relationship between EC and HPC phases using a circular–circular correlation (Jammalamadaka & Sengupta, 2001), implemented in the CircStat MATLAB toolbox. The “Phase synchronization between the HPC and EC activity” subsection of the Result has been accordingly updated as following:

“To examine whether the spatial phase structure in one region could predict that in another, we tested whether the orientations of the 6-fold EC and 3-fold HPC periodic activities, estimated from odd-numbered sessions using sinusoidal modulation with rotationally symmetric parameters (in radians), were correlated across participants. A cross-participant circular–circular correlation was conducted between the spatial phases of the two areas to quantify the spatial correspondence of their activity patterns (EC: purple dots; HPC: green dots) (Jammalamadaka & Sengupta, 2001). The analysis revealed a significant circular correlation (Figure 4a; r = 0.42, p < 0.001) …”.

In the “Statistical analysis” subsection of the method:

“… The relationship between EC and HPC phases was evaluated using the circular–circular correlation (Jammalamadaka & Sengupta, 2001) implemented in the CircStat MATLAB toolbox …”.

(15) Paragraph following “We further examined amplitude-phase coupling...” - please clarify what data goes into this analysis.

We thank the reviewer for this helpful comment. In this analysis, the input data consisted of hippocampal (HPC) phase and entorhinal (EC) amplitude, both extracted using the Hilbert transform from the reconstructed BOLD signals of the EC and HPC derived through sinusoidal modulation. We have substantially revised the description of the amplitude–phase coupling analysis in the third paragraph of the “Phase Synchronization Between HPC and EC Activity” subsection of the Results to clarify this procedure.

(16) Alignment between EC 6-fold phases and HC 3-fold phases: Why don't you simply test whether the preferred 6-fold orientations in EC are similar to the preferred 3-fold phases in HC? The phase-amplitude coupling analyses seem sophisticated but are complex, so it is somewhat difficult to judge to what extent they are correct. 

We thank the reviewer for this thoughtful comment. We employed two complementary analyses to examine the relationship between EC and HPC activity. In the revised Figure 4 (as shown in Figure 4 for Reviewer #1), Figure 4a provides a direct and intuitive measure of the phase relationship between the two regions using circular–circular correlation. Figure 4b–c examines whether the activity peaks of the two regions are aligned across path directions using cross-frequency amplitude–phase coupling, given our hypothesis that the spatial phase of the HPC depends on EC projections. These two analyses are complementary: a phase correlation does not necessarily imply peak-to-peak alignment, and conversely, peak alignment does not always yield a statistically significant phase correlation. We therefore combined multiple analytical approaches as a cross-validation across methods, providing convergent evidence for robust EC–HPC coupling.

(17) Figure 5: Do these results hold when you estimate performance just based on “deviation from the goal to ending locations” (without taking path length into account)? 

We thank the reviewer for this thoughtful suggestion. Following the reviewer’s advice, we re-estimated behavioral performance using the deviation between the goal and ending locations (i.e., error size) and path length independently. As shown in the new Figure S9, no significant periodicity was observed in error size (p > 0.05), whereas a robust 3-fold periodicity was found for path length (p < 0.05, corrected for multiple comparisons).

We employed two behavioral metrics,(1) path length and (2) error size, for complementary reasons. In our task, participants navigated using four discrete keys corresponding to the cardinal directions (north, south, east, and west). This design inherently induces a 4-fold bias in path directions, as described in the “Behavioral performance” subsection of the Methods. To minimize this artifact, we computed the objectively optimal path length and used it to calibrate participants’ path lengths. However, error size could not be corrected in the same manner and retained a residual 4-fold tendency (see Figure S9d).

Given that both path length and error size are behaviorally relevant and capture distinct aspects of task performance, we decided to retain both measures when quantifying behavioral periodicity. This clarification has been incorporated into the “Behavioral performance” subsection of the Methods, and the 2^nd paragraph of the “3-fold periodicity in human behavior” subsection of the Results.

(18) Phase locking between behavioral performance and hippocampal activity: What is your way of creating surrogates here?

We thank the reviewer for this helpful question. Surrogate datasets were generated by circularly shifting the signal series along the direction axis across all possible offsets (following Canolty et al., 2006). This procedure preserves the internal phase structure within each domain while disrupting consistent phase alignment, thereby removing any systematic coupling between the two signals. Each surrogate dataset underwent identical filtering and coherence computation to generate a null distribution, and the observed coherence strength was compared with this distribution using paired t-tests across participants. The statistical analysis section has been systematically revised to incorporate these methodological details.

(19) I could not follow why the authors equate 3-fold symmetry with vectorial representations. This includes statements such as “these empirical findings provide a potential explanation for the formation of vectorial representation observed in the HPC.” Please clarify.

We thank the reviewer for raising this point. Please refer to our response to R2 for Reviewer #1 and the revised Introduction (paragraphs 2–4), where we explicitly explain why the three primary axes of the hexagonal grid cell code can manifest as vector fields.

(20) It was unclear whether the sentence “The EC provides a foundation for the formation of periodic representations in the HPC” is based on the authors’ observations or on other findings. If based on the authors’ findings, this statement seems too strong, given that no other studies have reported periodic representations in the hippocampus to date (to the best of my knowledge).

We thank the reviewer for this comment. We agree that the original wording lacked sufficient rigor. We have extensively revised the 3rd paragraph of the Discussion section with more cautious language by reducing overinterpretation and emphasizing the consistency of our findings with prior empirical evidence, as follows: “The EC–HPC PhaseSync model demonstrates how a vectorial representation may emerge in the HPC from the projections of populations of periodic grid codes in the EC. The model was motivated by two observations. First, the EC intrinsically serves as the major source of hippocampal input (Witter and Amaral, 1991; van Groen et al., 2003; Garcia and Buffalo, 2020), and grid codes exhibit nearly invariant spatial orientations (Hafting et al., 2005; Gardner et al., 2022). Second, mental planning, characterized by “forward replay” (Dragoi and Tonegawa, 2011; Pfeiffer, 2020), has the capacity to activate populations of grid cells that represent sequential experiences in the absence of actual physical movement (Nyberg et al., 2022). We hypothesize that an integrated path code of sequential experiences may eventually be generated in the HPC, providing a vectorial gradient toward the goal location. The path code exhibits regular, vector-like representations when the path direction aligns with the orientations of grid axes, and becomes irregular when they misalign. This explanation is consistent with the band-like representations observed in the dorsomedial EC (Krupic et al., 2012) and the irregular activity fields of trace cells in the HPC (Poulter et al., 2021). ”

Like myself Alper is his own most frequent reader of his blog. Mentions realising how much he did in a week earlier in his life. Same for me, things that now might be a big thing in a week, were Tuesday afternoon 15yrs ago. It's not just age I suspect, but also an overall attenuation that Covid brought?

Author response:

The following is the authors’ response to the original reviews

A point by point response included below. Before we turn to that we want to note one change that we decided to introduce, related to generalization on unseen tissues/cell types (Figure 3a in the original submission and related question by Reviewer #2 below). This analysis was based on adding a latent “RBP state” representation during learning of condition/tissue specific splicing. The “RBP state” per condition is captured by a dedicated encoder. Our original plan was to have a paper describing a new RBP-AE model we developed in parallel, which also served as the base to capture this “RBP State”. However, we got delayed in getting this second paper finalized (it was led by other lab members, some of whom have already left the lab). This delay affected the TrASPr manuscript as TrASPr’s code should be available and analysis reproducible upon publication. After much deliberation, we decided that in order to comply with reproducibility standards while not self scooping the RBP-AE paper, we eventually decided to take out the RBP-AE and replace it with a vanilla PCA based embedding for the “RBP-State”. The PCA approach is simpler and reproducible, based on linear transformation of the RBPs expression vector into a lower dimension. The qualitative results included in Figure 3a still hold, and we also produced the new results suggested by Reviewer #2 in other GTEX tissues with this PCA based embedding (below). 

We don’t believe the switch to PCA based embedding should have any bearing on the current manuscript evaluation but wanted to take this opportunity to explain the reasoning behind this additional change.

Public Reviews:

Reviewer #1 (Public review):

Summary:

The authors propose a transformer-based model for the prediction of condition - or tissue-specific alternative splicing and demonstrate its utility in the design of RNAs with desired splicing outcomes, which is a novel application. The model is compared to relevant existing approaches (Pangolin and SpliceAI) and the authors clearly demonstrate its advantage. Overall, a compelling method that is well thought out and evaluated.

Strengths:

(1) The model is well thought out: rather than modeling a cassette exon using a single generic deep learning model as has been done e.g. in SpliceAI and related work, the authors propose a modular architecture that focuses on different regions around a potential exon skipping event, which enables the model to learn representations that are specific to those regions. Because each component in the model focuses on a fixed length short sequence segment, the model can learn position-specific features. Another difference compared to Pangolin and SpliceAI which are focused on modeling individual splice junctions is the focus on modeling a complete alternative splicing event.

(2) The model is evaluated in a rigorous way - it is compared to the most relevant state-of-the-art models, uses machine learning best practices, and an ablation study demonstrates the contribution of each component of the architecture.

(3) Experimental work supports the computational predictions.     

(4) The authors use their model for sequence design to optimize splicing outcomes, which is a novel application.

We wholeheartedly thank Reviewer #1 for these positive comments regarding the modeling approach we took to this task and the evaluations we performed. We have put a lot of work and thought into this and it is gratifying to see the results of that work acknowledged like this.

Weaknesses:

No weaknesses were identified by this reviewer, but I have the following comments:

(1) I would be curious to see evidence that the model is learning position-specific representations.

This is an excellent suggestion to further assess what the model is learning. To get a better sense of the position-specific representation we performed the following analyses:

(1) Switching the transformers relative order: All transformers are pretrained on 3’ and 5’ splice site regions before fine-tunning for the PSI and dPSI prediction task. We hypothesized that if relative position is important, switching the order of the transformers would make a large difference on prediction accuracy. Indeed if we switch the 3’ and 5’ we see as expected a severe drop in performance, with Pearson correlation on test data dropping from 0.82 to 0.11. Next, we switched the two 5’ and 3’ transformers, observing a drop to 0.65 and 0.78 respectively. When focusing only on changing events the drop was from 0.66 to 0.54 (for 3’ SS transformers), 0.48 (for 5’ SS transformers), and 0.13 (when the 3’ and 5’ transformers flanking the alternative exon were switched). 

(2) Position specific effect of RBPs: We wanted to test whether the model is able to learn position specific effects for RBPs. For this we focused on two RBPs, FOX (a family of three highly related RBPs), and QKI, both have a relatively well defined motif, known condition and position specific effect identified via RBP KD experiments combined with CLIP experiments (e.g. PMID: 23525800, PMID: 24637117, PMID: 32728246). For each, we randomly selected 40 highly and 40 lowly included cassette exons sequences. We then ran in-silico mutagenesis experiments where we replaced small windows of sequences with the RBP motifs (80 for RBFOX and 80 for QKI), then compared TrASPR’s predictions for the average predictions for 5 random sequences inserted in the same location. The results of this are now shown in Figure 4 Supp 3, where the y-axis represents the dPSI effect per position (x-axis), and the color represents the percentile of observed effects over inserting motifs in that position across all 80 sequences tested. We see that both RBPs have strong positional preferences for exerting a strong effect on the alternative exon. We also see differences between binding upstream and downstream of the alternative exon. These results, learned by the model from natural tissue-specific variations, recapitulate nicely the results derived from high-throughput experimental assays. However, we also note that effects were highly sequence specific. For example, RBFOX is generally expected to increase inclusion when binding downstream of the alternative exon and decrease inclusion when binding upstream. While we do observe such a trend we also see cases where the opposite effects are observed. These sequence specific effects have been reported in the literature but may also represent cases where the model errs in the effect’s direction. We discuss these new results in the revised text.

(3) Assessing BOS sequence edits to achieve tissue-specific splicing: Here we decided to test whether BOS edits in intronic regions (at least 8b away from the nearest splice site) are important for the tissue-specific effect. The results are now included in Figure 6 Supp 1, clearly demonstrating that most of the neuronal specific changes achieved by BOS were based on changing the introns, with a strong effect observed for both up and downstream intron edits.

(2) The transformer encoders in TrASPr model sequences with a rather limited sequence size of 200 bp; therefore, for long introns, the model will not have good coverage of the intronic sequence. This is not expected to be an issue for exons.

The reviewer is raising a good question here. On one hand, one may hypothesize that, as the reviewer seems to suggest, TrASPr may not do well on long introns as it lacks the full intronic sequence.

Conversely, one may also hypothesize that for long introns, where the flanking exons are outside the window of SpliceAI/Pangolin, TrASPr may have an advantage.

Given this good question and a related one by Reviewer #2, we divided prediction accuracy by intron length and the alternative exon length.

For short exons  (<100bp) we find TrASPr and Pangolin perform similarly, but for longer exons, especially those > 200, TrASPr results are better. When dividing samples by the total length of the upstream and downstream intron, we find TrASPr outperform all other models for introns of combined length up to 6K, but Pangolin gets better results when the combined intron length is over 10K. This latter result is interesting as it means that contrary to the second hypothesis laid out above, Pangolin’s performance did not degrade for events where the flanking exons were outside its field of view. We note that all of the above holds whether we assess all events or just cases of tissue specific changes. It is interesting to think about the mechanistic causes for this. For example, it is possible that cassette exons involving very long introns evoke a different splicing mechanism where the flanking exons are not as critical and/or there is more signal in the introns which is missed by TrASPr. We include these new results now as Figure 2 - Supp 1,2 and discuss these in the main text.

(3) In the context of sequence design, creating a desired tissue- or condition-specific effect would likely require disrupting or creating motifs for splicing regulatory proteins. In your experiments for neuronal-specific Daam1 exon 16, have you seen evidence for that? Most of the edits are close to splice junctions, but a few are further away.

That is another good question. Regarding Daam1 exon 16, in the original paper describing the mutation locations some motif similarities were noted to PTB (CU) and CUG/Mbnl-like elements (Barash et al Nature 2010). In order to explore this question beyond this specific case we assessed the importance of intronic edits by BOS to achieve a tissue specific splicing profile - see above.

(4) For sequence design, of tissue- or condition-specific effect in neuronal-specific Daam1 exon 16 the upstream exonic splice junction had the most sequence edits. Is that a general observation? How about the relative importance of the four transformer regions in TrASPr prediction performance?

This is another excellent question. Please see new experiments described above for RBP positional effect and BOS edits in intronic regions which attempt to give at least partial answers to these questions. We believe a much more systematic analysis can be done to explore these questions but such evaluation is beyond the scope of this work.

(5) The idea of lightweight transformer models is compelling, and is widely applicable. It has been used elsewhere. One paper that came to mind in the protein realm:

Singh, Rohit, et al. "Learning the language of antibody hypervariability." Proceedings of the National Academy of Sciences 122.1 (2025): e2418918121.

We definitely do not make any claim this approach of using lighter, dedicated models instead of a large ‘foundation’ model has not been taken before. We believe Rohit et al mentioned above represents a somewhat different approach, where their model (AbMAP) fine-tunes large general protein foundational models (PLM) for antibody-sequence inputs by supervising on antibody structure and binding specificity examples. We added a description of this modeling approach citing the above work and another one which specifically handles RNA splicing (intron retention, PMID: 39792954).

Reviewer #2 (Public review):

Summary:

The authors present a transformer-based model, TrASPr, for the task of tissue-specific splicing prediction (with experiments primarily focused on the case of cassette exon inclusion) as well as an optimization framework (BOS) for the task of designing RNA sequences for desired splicing outcomes.

For the first task, the main methodological contribution is to train four transformer-based models on the 400bp regions surrounding each splice site, the rationale being that this is where most splicing regulatory information is. In contrast, previous work trained one model on a long genomic region. This new design should help the model capture more easily interactions between splice sites. It should also help in cases of very long introns, which are relatively common in the human genome.

TrASPr's performance is evaluated in comparison to previous models (SpliceAI, Pangolin, and SpliceTransformer) on numerous tasks including splicing predictions on GTEx tissues, ENCODE cell lines, RBP KD data, and mutagenesis data. The scope of these evaluations is ambitious; however, significant details on most of the analyses are missing, making it difficult to evaluate the strength of the evidence. Additionally, state-of-the-art models (SpliceAI and Pangolin) are reported to perform extremely poorly in some tasks, which is surprising in light of previous reports of their overall good prediction accuracy; the reasoning for this lack of performance compared to TrASPr is not explored.

In the second task, the authors combine Latent Space Bayesian Optimization (LSBO) with a Transformer-based variational autoencoder to optimize RNA sequences for a given splicing-related objective function. This method (BOS) appears to be a novel application of LSBO, with promising results on several computational evaluations and the potential to be impactful on sequence design for both splicing-related objectives and other tasks.

We thank Reviewer #2 for this detailed summary and positive view of our work. It seems the main issue raised in this summary regards the evaluations: The reviewer finds details of the evaluations missing and the fact that SpliceAI and Pangolin perform poorly on some of the tasks to be surprising. We made a concise effort to include the required details, including code and data tables. In short, some of the concerns were addressed by adding additional evaluations, some by clarifying missing details, and some by better explaining where Pangolin and SpliceAI may excel vs. settings where these may not do as well. More details are given below. 

Strengths:

(1) A novel machine learning model for an important problem in RNA biology with excellent prediction accuracy.

(2) Instead of being based on a generic design as in previous work, the proposed model incorporates biological domain knowledge (that regulatory information is concentrated around splice sites). This way of using inductive bias can be important to future work on other sequence-based prediction tasks.

Weaknesses:

(1) Most of the analyses presented in the manuscript are described in broad strokes and are often confusing. As a result, it is difficult to assess the significance of the contribution.

We made an effort to make the tasks be specific and detailed,  including making the code and data of those available. We believe this helped improve clarity in the revised version.

(2) As more and more models are being proposed for splicing prediction (SpliceAI, Pangolin, SpliceTransformer, TrASPr), there is a need for establishing standard benchmarks, similar to those in computer vision (ImageNet). Without such benchmarks, it is exceedingly difficult to compare models. For instance, Pangolin was apparently trained on a different dataset (Cardoso-Moreira et al. 2019), and using a different processing pipeline (based on SpliSER) than the ones used in this submission. As a result, the inferior performance of Pangolin reported here could potentially be due to subtle distribution shifts. The authors should add a discussion of the differences in the training set, and whether they affect your comparisons (e.g., in Figure 2). They should also consider adding a table summarizing the various datasets used in their previous work for training and testing. Publishing their training and testing datasets in an easy-to-use format would be a fantastic contribution to the community, establishing a common benchmark to be used by others.

There are several good points to unpack here. Starting from the last one, we very much agree that a standard benchmark will be useful to include. For tissue specific splicing quantification we used the GTEx dataset from which we select six representative human tissues (heart, cerebellum, lung, liver, spleen, and EBV-transformed lymphocytes). In total, we collected 38394 cassette exon events quantified across 15 samples (here a ‘sample’ is a cassette exon quantified in two tissues) from the GTEx dataset with high-confidence quantification for their PSIs based on MAJIQ. A detailed description of how this data was derived is now included in the Methods section, and the data itself is made available via the bitbucket repository with the code.

Next, regarding the usage of different data and distribution shifts for Pangolin: The reviewer is right to note there are many differences between how Pangolin and TrASPr were trained. This makes it hard to determine whether the improvements we saw are not just a result of different training data/labels. To address this issue, we first tried to finetune the pre-trained Pangolin with MAJIQ’s PSI dataset: we use the subset of the GTEx dataset described above, focusing on the three tissues analyzed in Pangolin’s paper—heart, cerebellum, and liver—for a fair comparison. In total, we obtained 17,218 events, and we followed the same training and test split as reported in the Pangolin paper. We got Pearson: 0.78 Spearman: 0.68 which are values similar to what we got without this extra fine tuning. Next, we retrained Pangolin from scratch, with the full tissues and training set used for TrASPr, which was derived from MAJIQ’s quantifications. Since our model only trained on human data with 6 tissues at the same time, we modified Pangolin from original 4 splice site usage outputs to 6 PSI outputs. We tried to take the sequence centered with the first or the second splice site of the mid exon. This test resulted in low performance (3’ SS: pearson 0.21 5’ SS: 0.26.). 

The above tests are obviously not exhaustive but their results suggest that the differences we observe are unlikely to be driven by distribution shifts. Notably, the original Pangolin was trained on much more data (four species, four tissues each, and sliding windows across the entire genome). This training seems to be important for performance while the fact we switched from Pangolin’s splice site usage to MAJIQ’s PSI was not a major contributor. Other potential reasons for the improvements we observed include the architecture, target function, and side information (see below) but a complete delineation of those is beyond the scope of this work. 

(3) Related to the previous point, as discussed in the manuscript, SpliceAI, and Pangolin are not designed to predict PSI of cassette exons. Instead, they assign a "splice site probability" to each nucleotide. Converting this to a PSI prediction is not obvious, and the method chosen by the authors (averaging the two probabilities (?)) is likely not optimal. It would be interesting to see what happens if an MLP is used on top of the four predictions (or the outputs of the top layers) from SpliceAI/Pangolin. This could also indicate where the improvement in TrASPr comes from: is it because TrASPr combines information from all four splice sites? Also, consider fine-tuning Pangolin on cassette exons only (as you do for your model).

Please see the above response. We did not investigate more sophisticated models that adjust Pangolin’s architecture further as such modifications constitute new models which are beyond the scope of this work.

(4) L141, "TrASPr can handle cassette exons spanning a wide range of window sizes from 181 to 329,227 bases - thanks to its multi-transformer architecture." This is reported to be one of the primary advantages compared to existing models. Additional analysis should be included on how TrASPr performs across varying exon and intron sizes, with comparison to SpliceAI, etc.

This was a good suggestion, related to another comment made by Reviewer #1. Please see above our response to them with a breakdown by exon/intron length.

(5) L171, "training it on cassette exons". This seems like an important point: previous models were trained mostly on constitutive exons, whereas here the model is trained specifically on cassette exons. This should be discussed in more detail.

Previous models were not trained exclusively on constitutive exons and Pangolin specifically was trained with their version of junction usage across tissues. That said, the reviewer’s point is valid (and similar to ones made above) about a need to have a matched training/testing and potential distribution shifts. Please see response and evaluations described above. 

(6) L214, ablations of individual features are missing.

These were now added to the table which we moved to the main text (see table also below).

(7) L230, "ENCODE cell lines", it is not clear why other tissues from GTEx were not included.

Good question. The task here was to assess predictions in unseen conditions, hence we opted to test on completely different data of human cell lines rather than additional tissue samples. Following the reviewers suggestion we also evaluated predictions on two additional GTEx tissues, Cortex and Adrenal Gland. These new results, as well as the previous ones for ENCODE, were updated to use the PCA based embedding of “RBP-State” as described above. We also compared the predictions using the PCA based embedding of the “RBP-State” to training directly on data (not the test data of course) from these tissues. See updated Figure 3a,b. Figure 3 Supp 1,2.

(8) L239, it is surprising that SpliceAI performs so badly, and might suggest a mistake in the analysis. Additional analysis and possible explanations should be provided to support these claims. Similarly, the complete failure of SpliceAI and Pangolin is shown in Figure 4d.

Line 239 refers to predicting relative inclusion levels between competing 3’ and 5’ splice sites. We admit we too expected this to be better for SpliceAI and Pangolin but we were not able to find bugs in our analysis (which is all made available for readers and reviewers alike). Regarding this expectation to perform better, first we note that we are not aware of a similar assessment being done for either of those algorithms (i.e. relative inclusion for 3’ and 5’ alternative splice site events). Instead, our initial expectation, and likely the reviewer’s as well, was based on their detection of splice site strengthening/weakening due to mutations, including cryptic splice site activation. More generally though, it is worth noting in this context that given how SpliceAI, Pangolin and other algorithms have been presented in papers/media/scientific discussions, we believe there is a potential misperception regarding tasks that SpliceAI and Pangolin excel at vs other tasks where they should not necessarily be expected to excel. Both algorithms focus on cryptic splice site creation/disruption. This has been the focus of those papers and subsequent applications.  While Pangolin added tissue specificity to SpliceAI training, the authors themselves admit “...predicting differential splicing across tissues from sequence alone is possible but remains a considerable challenge and requires further investigation”. The actual performance on this task is not included in Pangolin’s main text, but we refer Reviewer #2 to supplementary figure S4 in the Pangolin manuscript to get a sense of Pangolin’s reported performance on this task. Similar to that, Figure 4d in our manuscript is for predicting ‘tissue specific’ regulators. We do not think it is surprising that SpliceAI (tissue agnostic) and Pangolin (slight improvement compared to SpliceAI in tissue specific predictions) do not perform well on this task. Similarly, we do not find the results in Figure 4C surprising either. These are for mutations that slightly alter inclusion level of an exon, not something SpliceAI was trained on - SpiceAI was trained on genomic splice sites with yes/no labels across the genome. As noted elsewhere in our response, re-training Pangolin on this mutagenesis dataset results in performance much closer to that of TrASPr. That is to be expected as well - Pangolin is constructed to capture changes in PSI (or splice site usage as defined by the authors), those changes are not even tissue specific for the CD19 data and the model has no problem/lack of capacity to generalize from the training set just like TrASPr does. In fact, if you only use combinations of known mutations seen during training a simple regression model gives correlation of ~92-95% (Cortés-López et al 2022). In summary, we believe that better understanding of what one can realistically expect from models such as SpliceAI, Pangolin, and TrASPr will go a long way to have them better understood and used effectively. We have tried to make this more clear in the revision.

(9) BOS seems like a separate contribution that belongs in a separate publication. Instead, consider providing more details on TrASPr.

We thank the reviewer for the suggestion. We agree those are two distinct contributions/algorithms and we indeed considered having them as two separate papers. However, there is strong coupling between the design algorithm (BOS) and the predictor that enables it (TrASPr). This coupling is both conceptual (TrASPr as a “teacher”) and practical in terms of evaluations. While we use experimental data (experiments done involving Daam1 exon 16, CD19 exon 2) we still rely heavily on evaluations by TrASPr itself. A completely independent evaluation would have required a high-throughput experimental system to assess designs, which is beyond the scope of the current paper. For those reasons we eventually decided to make it into what we hope is a more compelling combined story about generative models for prediction and design of RNA splicing.

(10) The authors should consider evaluating BOS using Pangolin or SpliceTransformer as the oracle, in order to measure the contribution to the sequence generation task provided by BOS vs TrASPr.

We can definitely see the logic behind trying BOS with different predictors. That said, as we note above most of BOS evaluations are based on the “teacher”. As such, it is unclear what value replacing the teacher would bring. We also note that given this limitation we focus mostly on evaluations in comparison to existing approaches (genetic algorithm or random mutations as a strawman). 

Recommendations for the authors: 

Reviewer #1 (Recommendations for the authors):

Additional comments:

(1) Is your model picking up transcription factor binding sites in addition to RBPs? TFs have been recently shown to have a role in splicing regulation:

Daoud, Ahmed, and Asa Ben-Hur. "The role of chromatin state in intron retention: A case study in leveraging large scale deep learning models." PLOS Computational Biology 21.1 (2025): e1012755.

We agree this is an interesting point to explore, especially given the series of works from the Ben-Hur’s group. We note though that these works focus on intron retention (IR) which we haven’t focused on here, and we only cover short intronic regions flanking the exons. We leave this as a future direction as we believe the scope of this paper is already quite extensive.

(2) SpliceNouveau is a recently published algorithm for the splicing design problem:

Wilkins, Oscar G., et al. "Creation of de novo cryptic splicing for ALS and FTD precision medicine." Science 386.6717 (2024): 61-69.

Thank you for pointing out Wilkins et al recent publication, we now refer to it as well. 

(3) Please discuss the relationship between your model and this deep learning model. You will also need to change the following sentence: "Since the splicing sequence design task is novel, there are no prior implementations to reference."

We revised this statement and now refer to several recent publications that propose similar design tasks.  

(4) I would suggest adding a histogram of PSI values - they appear to be mostly close to 1 or 0.

PSI values are indeed typically close to either 0 or 1. This is a known phenomenon illustrated in previous studies of splicing (e.g. Shen et al NAR 2012 ). We are not sure what is meant by the comment to add a histogram but we made sure to point this out in the main text: 

“...Still, those statistics are dominated by extreme values, such that 33.2\% are smaller than 0.15 and 56.0\% are higher than 0.85. Furthermore, most cassette exons do not change between a given tissue pair (only 14.0\% of the samples in the dataset, \ie a cassette exon measured across two tissues, exhibit ΔΨ| ≥ 0.15).”

(5) Part of the improvement of TrASPr over Pangolin could be the result of a more extensive dataset.

Please see above responses and new analysis.

(6) In the discussion of the roles of alternative splicing, protein diversity is mentioned, but I suggest you also mention the importance of alternative splicing as a regulatory mechanism:

Lewis, Benjamin P., Richard E. Green, and Steven E. Brenner. "Evidence for the widespread coupling of alternative splicing and nonsense-mediated mRNA decay in humans." Proceedings of the National Academy of Sciences 100.1 (2003): 189-192.

Thank you for the suggestion. We added that point and citation. 

(7) Line 96: You use dPSI without defining it (although quite clear that it should be Delta PSI).

Fixed.

(8) Pretrained transformers: Have you trained separate transformers on acceptor and donor sites, or a single splice junction transformer?

Single splice junction pre-training.

(9) "TrASPr measures the probability that the splice site in the center of Se is included in some tissue" - that's not my understanding of what TrASPr is designed to do.

We revised the above sentence to make it more precise: “Given a genomic sequence context S_e = (s_e,...,s_e), made of  a cassette exon e and flanking intronic/exonic regions, TrASPr predicts for tissue c the fraction of transcripts where exon e is included or skipped over, ΔΨ-_e,c,c’.”

(10) Please include the version of the human genome annotations that you used. 

We used GENCODE v40 human genome hg38- this is now included in the Data section. 

(11) I did not see a description of the RBP-AE component in the methods section. A bit more detail on the model would be useful as well.

Please see above details about replacing RBP-AE with a simpler linear PCA “RBP-State” encoding. We added details about how the PCA was performed to the Methods section.

(12) Typos, grammar:

-   Fix the following sentence: ATP13A2, a lysosomal transmembrane cation transporter, linked to an early-onset form of Parkinson's Disease (PD) when 306 loss-of-function mutations disrupt its function.

Sentence was fixed to now read: “The first example is of a brain cerebellum-specific cassette exon skipping event predicted by TrASPr in the ATP13A2 gene (aka PARK9). ATP13A2 is a lysosomal transmembrane cation transporter, for which loss of function mutation has been linked to early-onset of Parkinson’s Disease (PD)”.

-   Line 501: "was set to 4e−4"(the - is a superscript). 

Fixed

-   A couple of citations are missing in lines 580 and 581.

Thank you for catching this error. Citations in line 580, 581 were fixed.

(13) Paper title: Generative modeling for RNA splicing predictions and design - it would read better as "Generative modeling for RNA splicing prediction and design", as you are solving the problems of splicing prediction and splicing design.  

Thank you for the suggestion. We updated the title and removed the plural form.

Reviewer #2 (Recommendations for the authors):

(1) Appendices are not very common in biology journals. It is also not clear what purpose the appendix serves exactly - it seems to repeat some of the things said earlier. Consider merging it into the methods or the main text. 

We merged the appendices into the Methods section and removed redundancy.

(2) L112, "For instance, the model could be tasked with designing a new version of the cassette exon, restricted to no more than N edit locations and M total base changes." How are N and M different? Is there a difference between an edit location and a base change? 

Yes, N is the number of locations (one can think of it as a start position) of various lengths (e.g. a SNP is of length 1) and the total number of positions edited is M. The text now reads “For instance, the model could be tasked with designing a new version of the cassette exon, restricted to no more than  $N$ edit locations (\ie start position of one or more consecutive bases) and $M$ total base changes.”

(3) L122: "DEN was developed for a distinct problem". What prevents one from adapting DEN to your sequence design task? The method should be generic. I do not see what "differs substantially" means here. (Finally, wasn't DEN developed for the task you later refer to as "alternative splice site" (as opposed to "splice site selection")? Use consistent terminology. And in L236 you use "splice site variation" - is that also the same?).

Indeed, our original description was not clear/precise enough. DEN was designed and trained for two tasks: APA, and 5’ alternative splice site usage. The terms “selection”, “usage”, and “variation” were indeed used interchangeably in different locations and the reviewer was right, noting the lack of precision. We have now revised the text to make sure the term “relative usage” is used. 

Nonetheless, we hold DEN was indeed defined for different tasks. See figures from Figure 2A, 6A of Linder et al 2020 (the reference was also incorrect as we cited the preprint and not the final paper):

In both cases DEN is trying to optimize a short region for selecting an alternative PA site (left) or a 5’ splice site (right). This work focused on an MPRA dataset of short synthetic sequences inserted in the designated region for train/test. We hold this is indeed a different type of data and task then the one we focus on here. Yes, one can potentially adopt DEN for our task, but this is beyond the scope of this paper. Finally, we note that a more closely related algorithm recently proposed is Ledidi (Schreiber et al 2025) which was posted as a pre-print. Similar to BOS, Ledidi tries to optimize a given sequence and adopt it with a few edits for a given task. Regardless, we updated the main text to make the differences between DEN and the task we defined here for BOS more clear, and we also added a reference to Ledidi and other recent works in the discussion section.

(4) L203, exons with DeltaPSI very close to 0.15 are going to be nearly impossible to classify (or even impossible, considering that the DeltaPSI measurements are not perfect). Consider removing such exons to make the task more feasible.

Yes, this is how it was done. As described in more details below, we defined changing samples as ones where the change was >= 0.15 and non-changing as ones where the change in PSI was < 0.05 to avoid ambiguous cases affecting the classification task.  

(5) L230, RBP-AE is not explained in sufficient detail (and does not appear in the methods, apparently). It is not clear how exactly it is trained on each new cellular condition.

Please see response in the opening of this document and Q11 from

Reviewer 1 

(6) L230, "significantly improving": the r value actually got worse; it is therefore not clear you can claim any significant improvement. Please mention that fact in the text.

This is a fair point. We note that we view the “a” statistic as potentially more interesting/relevant here as the Pearson “r” is dominated by points being generally close to 0/1.  Regardless, revisiting this we realized one can also make a point that the term “significant” is imprecise/misplaced since there is no statistical test done here (side note: given the amount of points, a simple null of same distribution yes/no would pass significance but we don’t think this is an interesting/relevant test here). Also, we note that with the transition to PCA instead of RBP-AE we actually get improvements in both a and r values, both for the ENCODE samples shown in Figure 3a and the two new GTEX tissues we tested (see above). We now changed the text to simply state: 

“...As shown in Figure 3a, this latent space representation allows TrSAPr to generalize from the six GTEX tissues to unseen conditions, including unseen GTEX tissues (top row), and ENCODE cell lines (bottom row). It improves prediction accuracy compared to TrASPr lacking PCA (eg a=88.5% vs a=82.3% for ENCODE cell lines), though naturally training on the additional GTEX and ENCODE conditions can lead to better performance  (eg a=91.7%, for ENCODE, Figure 3a left column).”

(7) L233, "Notably, previous splicing codes focused solely on cassette exons", Rosenberg et al. focused solely on alternative splice site choice.

Right - we removed that sentence.. 

(8) L236, "trained TrASPr on datasets for 3' and 5' splice site variations". Please provide more details on this task. What is the input to TrASPr and what is the prediction target (splice site usage, PSI of alternative isoforms)? What datasets are used for this task?

The data for this data was the same GTEx tissue data processed, just for alternative 3’ and 5’ splice sites events. We revised the description of this task in the main task and added information in the Methods section. The data is also included in the repo.

(9) L243, "directly from genomic sequences", and conservation?

Yes, we changed the sentence to read “...directly from genomic sequences combined with related features” 

(10) L262, what is the threshold for significant splicing changes?

The threshold is 0.15 We updated the main text to read the following:

The total number of mutations hitting each of the 1198 genomic positions across the 6106 sequences is shown in \FIG{mut_effect}b (left), while the distribution of effects ($|\Delta \Psi|$) observed across those 6106 samples is shown in \FIG{mut_effect}b (right). To this data we applied three testing schemes. The first is a standard 5-fold CV where 20\% of combinations of point mutations were hidden in every fold while the second test involved 'unseen mutation' (UM) where we hide any sample that includes mutations in specific positions for a total of 1480 test samples. As illustrated by the CDF in \FIG{mut_effect}b, most samples (each sample may involve multiple positions mutated) do not involve significant splicing changes. Thus, we also performed a third test using only  the 883 samples were mutations cause significant changes ($|\Delta \Psi|\geq 0.15 $). 

(11) L266, Pangolin performance is only provided for one of the settings (and it is not clear which). Please provide details of its performance in all settings.

The description was indeed not clear. Pangolin’s performance was similar to SpliceAI as mentioned above but retraining it on the CD19 data yielded much closer performance to TrASPr. We include all the matching tests for Pangolin after retraining in Figure 4 Supp Figure 1. 

(12) Please specify "n=" in all relevant plots. 

Fixed.

(13) Figure 3a, "The tissues were first represented as tokens, and new cell line results were predicted based on the average over conditions during training." Please explain this procedure in more detail. What are these tokens and how are they provided to the model? Are the cell line predictions the average of the predictions for the training tissues?

Yes, we compared to simply the average over the predictions for the training tissues for that specific event as baseline to assess improvements (see related work pointing for the need to have similar baselines in DL for genomics in https://pubmed.ncbi.nlm.nih.gov/33213499/). Regarding the tokens - we encode each tissue type as a possible value and feed the two tissues as two tokens to the transformer.

(14) Figure 4b, the total count in the histogram is much greater than 6106. Please explain the dataset you're using in more detail, and what exactly is shown here.

We updated the text to read: 

“...we used 6106 sequence samples where each sample may have multiple positions mutated (\ie mutation combinations) in exon 2 of CD19 and its flanking introns and exons (Cortes et al 2022). The total number of mutations hitting each of the 1198 genomic positions across the 6106 sequences is shown in Figure 4b (left).”

(15) Figure 5a, how are the prediction thresholds (TrASPr passed, TrASPr stringent, and TrASPr very stringent) defined?

Passed: dpsi>0.1, Stringent: dpsi>0.15, Very stringent: dpsi>0.2 This is now included in the main text.

(16) L417, please include more detail on the relative size of TrASPr compared to other models (e.g. number of parameters, required compute, etc.).

SpliceAI is a general-purpose splicing predictor with 32-layer deep residual neural network to capture long-range dependencies in genomic sequences. Pangolin is a deep learning model specifically designed for predicting tissue-specific splicing with similar architecture as SpliceAI. The implementation of SpliceAI that can be found here https://huggingface.co/multimolecule/spliceai involves an ensemble of 5 such models for a total of ~3.5M parameters. TrASPr, has 4 BERT transformers (each 6 layers and 12 heads) and MLP a top of those for a total of ~189M parameters. Evo 2, a genomic ‘foundation’ model has 40B parameters, DNABERT has ~86M (a single BERT with 12 layers and 12 heads), and Borzoi has 186M parameters (as stated in https://www.biorxiv.org/content/10.1101/2025.05.26.656171v2).  We note that the difference here is not just in model size but also the amount of data used to train the model. We edited the original L417 to reflect that.

(17) L546, please provide more detail on the VAE. What is the dimension of the latent representation?

We added more details in the Methods section like the missing dimension (256) and definitions for P(Z) and P(S). 

(18) Consider citing (and possibly comparing BOS to) Ghari et al., NeurIPS 2024 ("GFlowNet Assisted Biological Sequence Editing").

Added.

(19) Appendix Figure 2, and corresponding main text: it is not clear what is shown here. What is dPSI+ and dPSI-? What pairs of tissues are you comparing? Spearman correlation is reported instead of Pearson, which is the primary metric used throughout the text.

The dPSI+ and dPSI- sets were indeed not well defined in the original submission. Moreover, we found our own code lacked consistency due to different tests executed at different times/by different people. We apologize for this lack of consistency and clarity which we worked to remedy in the revised version. To answer the reviewer’s question, given two tissues ($c,c'$), dPSI+ and dPSI- is for correctly classifying the exons that are significantly differentially included or excluded. Specifically, differential included exons are those for which  $\Delta \Psi_{e,c1,c2} = \Psi_\Psi_{e,c1} - \Psi_{e,c2}  \geq 0.15$, compared to those that are not  ($\Delta \Psi_{e,c1,c2} < 0.05). Similarly, dPSI- is for correctly classifying the exons that are significantly differentially excluded in the first tissue or included in the second tissue ($\Delta \Psi_{e,c1,c2} = \Psi_\Psi_{e,c1} - \Psi_{e,c2}  \leq -0.15$) compared to those that are not  ($\Delta \Psi_{e,c1,c2} > -0.05). This means dPSI+ and dPSI- are dependent on the order of c1, c2. In addition, we also define a direction/order agnostic test for changing vs non changing events i.e. $|\Delta \Psi_{e,c1,c2}| \geq 0.15$ vs $|\Delta \Psi_{e,c1,c2}| < 0.05$. These test definitions are consistent with previous publications (e.g. Barash et al Nature 2010, Jha et al 2017) and also answer different biological questions: For example “Exons that go up in brain” and “Exons that go up in Liver” can reflect distinct mechanisms, while changing exons capture a model’s ability to identify regulated exons even if the direction of prediction may be wrong. The updated Appendix Figure 2 is now in the main text as Figure 2d and uses Pearson, while AUPRC and AUROC refer to the changing vs no-changing classification task described above such that we avoid dPSI+ and dPSI- when summarizing in this table over 3 pairs of tissues . Finally, we note that making sure all tests comply with the above definition also resulted in an update to Figure 2b/c labels and values, where TrASPr’s improvements over Pangolin reaches up to 1.8fold in AUPRC compared to 2.4fold in the earlier version. We again apologize for having a lack of clarity and consistent evaluations in the original submission.

(20) Minor typographical comments:

-   Some plots could use more polishing (e.g., thicker stroke, bigger font size, consistent style (compare 4a to the other plots)...).

Agreed. While not critical for the science itself we worked to improve figure polishing in the revision to make those more readable and pleasant. 

-   Consider using 2-dimensional histograms instead of the current kernel density plots, which tend to over-smooth the data and hide potentially important details. 

We were not sure what the exact suggestion is here and opted to leave the plots as is.

-   L53: dPSI_{e, c, c'} is never formally defined. Is it PSI_{e, c} - PSI_{e, c'} or vice versa?  

Definition now included (see above).

-   L91: Define/explain "transformer" and provide reference. 

We added the explanation and related reference of the transformer in the introduction section and BERT in the method section.  

-   L94: exons are short. Are you referring here to the flanking introns? Please explain. 

We apologize for the lack of clarity. We are referring to a cassette exon alternative splicing event as is commonly defined by the splice junctions involved that is from the 5’ SS of the upstream exon to the 3’ SS of the downstream exon. The text now reads:

“...In contrast, 24% of the cassette exons analyzed in this study span a region between the flanking exons' upstream 3' and downstream 5' splice sites that are larger than 10 kb.”

-   L132: It's unclear whether a single, shared transformer or four different transformers (one for each splice site) are being pre-trained. One would at least expect 5' and 3' splice sites to have a different transformer. In Methods, L506, it seems that each transformer is pre-trained separately. 

We updated the text to read:

“We then center a dedicated transformer around each of the splice sites of the cassette exon and its upstream and downstream (competing) exons (four separate transformers for four splice sites in total).”

-   L471: You explain here that it is unclear what tasks 'foundation' models are good for. Also in L128, you explain that you are not using a 'foundation' model. But then in L492, you describe the BERT model you're using as a foundation model! 

Line 492 was simply a poor choice of wording as “foundation” is meant here simply as the “base component”. We changed it accordingly.

-   L169, "pre-training ... BERT", explain what exactly this means. Is it using masking? Is it self-supervised learning? How many splice sites do you provide? Also explain more about the BERT architecture and provide references. 

We added more details about the BERT architecture and training in the Methods section.

-   L186 and later, the values for a and r provided here and in the below do not correspond to what is shown in Figure 2. 

Fixed, thank you for noticing this.

-   L187,188: What exactly do you mean by "events" and "samples"? Are they the same thing? If so, are they (exon, tissue) pairs? Please use consistent terminology. Moreover, when you say "changing between two conditions": do you take all six tissues whenever there is a 0.15 spread in PSI among them? Or do you take just the smallest PSI tissue and the largest PSI tissue when there is a 0.15 spread between them? Or something else altogether?

Reviewer #2 is yet again correct that the definitions were not precise. A “sample” involves a specific exon skipping “event” measured in two tissues.  The text now reads: 

“....most cassette exons do not change between a given tissue pair (only 14.0% of the samples in the dataset, i.e., a cassette exon measured across two tissues, exhibit |∆Ψ| ≥ 0.15). Thus, when we repeat this analysis only for samples involving exons that exhibited a change in inclusion (|∆Ψ| ≥ 0.15) between at least two tissues, performance degrades for all three models, but the differences between them become more striking (Figure 2a, right column).”

-   Figure 1a, explain the colors in the figure legend. The 3D effect is not needed and is confusing (ditto in panel C).

Color explanation is now added: “exons and introns are shown as blue rectangles and black lines. The blue dashed line indicates the inclusive pattern and the red junction indicates an alternative splicing pattern.” 

These are not 3D effects but stacks to indicate multiple events/cases. We agree these are not needed in Fig1a to illustrate types of AS and removed those. However, in Fig1c and matching caption we use the stacks to  indicate HT data captures many such LSVs over which ML algorithms can be trained. 

-   Figure 1b, this cartoon seems unnecessary and gives the wrong impression that this paper explores mechanistic aspects of splicing. The only relevant fact (RBPs serving as splicing factors) can be explained in the text (and is anyway not really shown in this figure).

We removed Figure 1b cartoon.

-   Figure 1c, what is being shown by the exon label "8"? 

This was meant to convey exon ID, now removed to simplify the figure. 

-   Figure 1e, left, write "Intron Len" in one line. What features are included under "..."? Based on the text, I did not expect more features.

Also, the arrows emanating from the features do not make sense. Is "Embedding" a layer? I don't think so. Do not show it as a thin stripe. Finally, what are dPSI'+ and dPSI'-? are those separate outputs? are those logits of a classification task?

We agree this description was not good and have updated it in the revised version. 

-   Figure 1e, the right-hand side should go to a separate figure much later, when you introduce BOS.

We appreciate the suggestion. However, we feel that Figure 1e serves as a visual representation of the entire framework. Just like we opted to not turn this work into two separate papers (though we fully agree it is a valid option that would also increase our publication count), we also prefer to leave this unified visual representation as is.

-   Figure 2, does the n=2456 refer to the number of (exons, tissues) pairs? So each exon contributes potentially six times to this plot? Typo "approximately". 

The “n” refers to the number of samples which is a cassette event measured in two tissues. The same cassette event may appear in multiple samples if it was confidently quantified in more than two tissues. We updated the caption to reflect this and corrected the typo.

-   Figure 2b, typo "differentially included (dPSI+) or excluded" .

Fixed.

-   L221, "the DNABERT" => "DNABERT".

Fixed.

-   L232, missing percent sign.

-    

Fixed.

-   L246, "see Appendix Section 2 for details" seems to instead refer to the third section of the appendix.

We do not have this as an Appendix, the reference has been updated.

-   Figure 3, bottom panels, PSI should be "splice site usage"? 

PSI is correct here - we hope the revised text/definitions make it more clear now.

-   Figure 3b: typo: "when applied to alternative alternative 3'".

Fixed.

-   p252, "polypyrimidine" (no capitalization).

Fixed.

-   Strange capitalization of tissue names (e.g., "Brain-Cerebellum"). The tissue is called "cerebellum" without capitalization.

We used EBV (capital) for the abbreviation and lower case for the rest.

-   Figure 4c: "predicted usage" on the left but "predicted PSI" on the right. 

Right. We opted to leave it as is since Pangolin and SpliceAI do predict their definition of “usage” and not directly PSI, we just measure correlations to observed PSI as many works have done in the past. 

-   Figure 4 legend typo: "two three".

Fixed.

-   L351, typo: "an (unsupervised)" (and no need to capitalize Transformer).

Fixed.

-   L384, "compared to other tissues at least" => "compared to other tissues of at least".

Fixed.

-   L549, P(Z) and P(S) are not defined in the text.

Fixed.

-   L572, remove "Subsequently". Add missing citations at the end of the paragraph.

Fixed.

-   L580-581, citations missing.

Fixed.

-   L584-585, typo: "high confidince predictions"

Fixed.

-   L659-660, BW-M and B-WM are both used. Typo?

Fixed.

-   L895, "calculating the average of these two", not clear; please rewrite.

Fixed.

-   L897, "Transformer" and "BERT", do these refer to the same thing? Be consistent.  

BOS is a transformer and not a BERT but TrASPr uses the BERT architecture. BERT is a type of transformer as the reviewer is surely well aware so the sentence is correct. Still, to follow the reviewer’s recommendation for consistency/clarity we changed it here to state BERT.

-   Appendix Figure 5: The term dPSI appears to be overloaded to also represent the difference between predicted PSI and measured PSI, which is inconsistent with previous definitions. 

Indeed! We thank the reviewer again for their sharp eye and attention to details that we missed. We changed Supp Figure 5, now Figure 4 Supplementary Figure 2, to |PSI’-PSI| and defined those as the difference between TrASPr’s predictions (PSI’) and MAJIQ based PSI quantifications.

Joint Public Review:

Summary:

This is an excellent, timely study investigating and characterizing the underlying neural activity that generates the neuroendocrine GnRH and LH surges that are responsible for triggering ovulation. Abundant evidence accumulated over the past 20 years implicated the population of kisspeptin neurons in the hypothalamic RP3V region (also referred to as the POA or AVPV/PeN kisspeptin neurons) as being involved in driving the GnRH surge in response to elevated estradiol (E2), also known as the "estrogen positive feedback". However, while former studies used Cfos coexpression as a marker of RP3V kisspeptin neuron activation at specific times and found this correlates with the timing of the LH surge, detailed examination of the live in vivo activity of these neurons before, during, and after the LH surge remained elusive due to technical challenges.

Here, Zhou and colleagues use fiber photometry to measure the long-term synchronous activity of RP3V kisspeptin neurons across different stages of the mouse estrous cycle, including on proestrus when the LH surge occurs, as well as in a well-established OVX+E2 mouse model of the LH surge.

The authors report that RP3V kisspeptin neuron activity is low on estrous and diestrus, but increases on proestrus several hours before the late afternoon LH surge, mirroring prior reports of rising GnRH neuron activity in proestrus female mice. The measured increase in RP3V kisspeptin activation is long, spanning ~13 hours in proestrus females and extending well beyond the end of the LH secretion, and is shown by the authors to be E2 dependent.

For this work, Kiss-Cre female mice received a Cre-dependent AAV injection, containing GCaMP6, to measure the neuronal activation of RP3V Kiss1 cells. Females exhibited periods of increased neuronal activation on the day of proestrus, beginning several hours prior to the LH surge and lasting for about 12 hours. Though oscillations in the pattern of GCaMP fluorescence were occasionally observed throughout the ovarian cycle, the frequency, duration, and amplitude of these oscillations were significantly higher on the day of proestrus. This increase in RP3V Kiss1 neuronal activation that precedes the increase in LH supports the hypothesis that these neurons are critical in regulating the LH surge. The authors compare this data to new data showing a similar increased activation pattern in GnRH neurons just prior to the LH surge, further supporting the hypothesis that RP3V Kiss1 cell activation causes the release of kisspeptin to stimulate GnRH neurons and produce the LH surge.

Strengths:

This study provides compelling data demonstrating that RP3V kisspeptin neuronal activity changes throughout the ovarian cycle, likely in response to changes in estradiol levels, and that neuronal activation increases on the day of the LH surge.

The observed increase in RP3V kisspeptin neuronal activation precedes the LH surge, which lends support to the hypothesis that these neurons play a role in regulating the estradiol-induced LH surge. Continuing to examine the complexities of the LH surge and the neuronal populations involved, as done in this study, is critical for developing therapeutic treatments for women's reproductive disorders.

This innovative study uses a within-subject design to examine neuronal activation in vivo across multiple hormone milieus, providing a thorough examination of the changes in activation of these neurons. The variability in neuronal activity surrounding the LH surge across ovarian cycles in the same animals is interesting and could not be achieved without this within-subjects design. The inclusion and comparison of ovary-intact females and OVX+E2 females is valuable to help test mechanisms under these two valuable LH surge conditions, and allows for further future studies to tease apart minor differences in the LH surge pattern between these 2 conditions.

This study provides an excellent experimental setup able to monitor the daily activity of preoptic kisspeptin neurons in freely moving female mice. It will be a valuable tool to assess the putative role of these kisspeptin neurons in various aspects of altered female fertility (aging, pathologies...). This approach also offers novel and useful insights into the impact of E2 and circadian cues on the electrical activity of RP3V kisspeptin neurons.

An intriguing cyclical oscillation in kisspeptin neural activity every 90 minutes exists, which may offer critical insight into how the RP3V kisspeptin system operates. Interestingly, there was also variability in the onset and duration of RP3V Kisspeptin neuron activity between and within mice in naturally cycling females. Preoptic kisspeptin neurons show an increased activity around the light/dark transition only on the day of proestrus, and this is associated with an increase in LH secretion. An original finding is the observation that the peak of kisspeptin neuron activation continues a few hours past the peak of LH, and the authors hypothesize that this prolonged activity could drive female sexual behaviors, which usually appear after the LH surge.

The authors demonstrated that ovariectomy resulted in very little neuronal activity in RP3V kisspeptin neurons. When these ovarietomized females were treated with estradiol benzoate (EB) and an LH surge was induced, there was an increase in RP3V kisspeptin neuronal activation, as was seen during proestrus. However, the magnitude of the change in activity was greater during proestrus than during the EB-induced LH surge. Interestingly, the authors noted a consistent peak in activity about 90 minutes prior to lights out on each day of the ovarian cycle and during EB treatment, but not in ovariectomized females. The functional purpose of this consistent neuronal activity at this time remains to be determined.

Though not part of this study, the comparison of neuronal activation of GnRH neurons during the LH surge to the current data was convincing, demonstrating a similar pattern of increased activation that precedes the LH surge.

In summary, the study is well-designed, uses proper controls and analyses, has robust data, and the paper is nicely organized and written. The data from these experiments is compelling, and the authors' claims and conclusions are nicely supported and justified by the data. The data support the hypothesis in the field that these RP3V neurons regulate the LH surge. Overall, these findings are important and novel, and lend valuable insight into the underlying neural mechanisms for neuroendocrine control of ovulation.

Weaknesses:

(1) LH levels were not measured in many mice or in robust temporal detail, such as every 30 or 60 min, to allow a more detailed comparison between the fine-scale timing of RP3V neuron activation with onset and timing of LH surge dynamics.

(2) The authors report that the peak LH value occurred 3.5 hours after the first RP3V kisspeptin neuron oscillation. However, it is likely, and indeed evident from the 2 example LH patterns shown in Figures 3A-B, that LH values start to increase several hours before the peak LH. This earlier rise in LH levels ("onset" of the surge) occurs much closer in time to the first RP3V kisspeptin neuron oscillatory activation, and as such, the ensuing LH secretion may not be as delayed as the authors suggest.

(3) The authors nicely show that there is some variation (~2 hours) in the peak of the first oscillation in proestrus females. Was this same variability present in OVX+E2 females, or was the variability smaller or absent in OVX+E2 versus proestrus? It is possible that the variability in proestrus mice is due to variability in the timing and magnitude of rising E2 levels, which would, in theory, be more tightly controlled and similar among mice in the OVX+E2 model. If so, the OVX+E2 mice may have less variability between mice for the onset of RP3V kisspeptin activity.

(4) One concern regarding this study is the lack of data showing the specificity of the AAV and the GCaMP6s signals. There are no data showing that GCaMP6s is limited to the RP3V and is not expressed in other Kiss1 populations in the brain. Given that 2ul of the AAV was injected, which seems like a lot considering it was close to the ventricle, it is important to show that the signal and measured activity are specific to the RP3V region. Though the authors discuss potential reasons for the low co-expression of GCaMP6 and kisspeptin immunoreactivity, it does raise some concern regarding the interpretation of these results. The low co-expression makes it difficult to confirm the Kiss1 cell-specificity of the Cre-dependent AAV injections. In addition, if GFP (GCaMP6s) and kisspeptin protein co-localization is low, it is possible that the activation of these neurons does not coincide with changes in kisspeptin or that these neurons are even expressing Kiss1 or kisspeptin at the time of activation. It is important to remember that the study measures activation of the kisspeptin neuron, and it does not reveal anything specific about the activity of the kisspeptin protein.

(5) One additional minor concern is that LH levels were not measured in the ovariectomized females during the expected time of the LH surge. The authors suggest that the lower magnitude of activation during the LH surge in these females, in comparison to proestrus females, may be the result of lower LH levels. It's hard to interpret the difference in magnitude of neuronal activation between EB-treated and proestrus females without knowing LH levels. In addition, it's possible that an LH surge did not occur in all EB-treated females, and thus, having LH levels would confirm the success of the EB treatment.

(6) This kisspeptin neuron peak activity is abolished in ovariectomized mice, and estradiol replacement restored this activity, but only partially. Circulating levels of estradiol were not measured in these different setups, but the authors hypothesize that the lack of full restoration may be due to the absence of other ovarian signals, possibly progesterone.

(7) Recordings in several mice show inter- and intra-variability in the time of peak onset. It is not shown whether this variability is associated with a similar variability in the timing of the LH surge onset in the recorded mice. The authors hypothesized that this variability indicates a poor involvement of the circadian input. However, no experiments were done to investigate the role of the (vasopressinergic-driven) circadian input on the kisspeptin neuron activation at the light/dark transition. Thus, we suggest that the authors be more tentative about this hypothesis.

12:48 "I have a hypothesis about why society is broken right now ... 1913 fiat currency ... printing money ... inflation ... steal from people ... hyper inequality ... 10% of Americans own 93% of assets ... unfair ... envy ... hollowing out of the middle class ... wealth inequality is reaching levels like in the French revolution ... we actually are headed towards a French revolution, but it's economic in nature."

no. fiat money is just another tool to control idiots, just like religion or politics. the actual drivers of this global policy are: overpopulation, degeneration, ecocide, resource depletion.

so yes, the problem is "economic in nature" in the sense that we have too many humans and too few planets.

the obvious solution is global depopulation by 90% of today's population, but that is obviously not a popular topic, because only 10% will survive this cull.

They aren't LITERALLY competing against each other in the auction. It's not that each is setting a bid and then trying to one up each other. It's that if one KW has weaker relevance it might bid really high, resulting in a higher ad rank & winning the same click for a more expensive price, just because the structure wasn't clean enough. We also run the risk of Google favoring click heavy, but low CvR ad ad group placements.

I wish people's works were more online—more accessible (preferably, with stable, citable URLs). But to say that the people themselves should be more public in the sense that the author means here is just a continuation-without-lessons-learned of the modern (Facebook and onwards) social networking era.

It's great if all of a person's contributions to, say, mailing lists are preserved and available—and aren't just holes, missing from the record.* That's different from whether or not it's great to be able to click on that person's name, find a profile page for them, and then encounter an exhaustive, reverse chronological feed of all of their activity across all mailing lists. Mailing lists rarely enable this, but virtually every modern social network does, and they're actually built around it.

The former is topic-based indexing, and the latter is actor- (person-) based indexing. Actor-based indexing is bad, and we know that it's bad.

Actor-based indexing is like running into someone you know (or a stranger, even) at the post office and then, through some mechanism where their physical presence wired to some metaverse data source, being able to perform some tap/gesture at the virtualized floating bubble over their heads that lets you see all the public places where you would have seen them earlier that day if you had been at any one of those, and then having a log of every interaction for the day prior, and the day before that, and so on, stretching back over their entire life, including the grocery store, the restaurant, the houseparty they were at, their work, etc. This would be bad. That means it's not good. And it's not good "online", either, for the exact same reasons.

* as unfortunately, many Mastodon (and other ActivityPub-powered) interactions turn out; Mastodon happens to achieve the worst of both worlds!

Not really, it does not favor scale, as it's progressive compliance. The next sentence says as much. One platform's bifurcation is the same as having two smaller ones, who have less compliance costs and thus won't be crushed. Which is already the case even, global platforms already cater to diff regulatory regimes (and morally questionable at that). The underlying faulty assumption is that of global platforms for everyone and everything being the desired outcome at all. SV thinking and funding is the root cause. Other paths exist, just not in their world. Zebra's not unicorns. [[Zebra bedrijven zijn beter 20190907063530]] Vgl physical e.g. the German industrial base actually is one of many medium sized orgs being market leaders in some niche, not the car manufacturers usually mentioned as such.

The cost of lack of skills is not just in the individual knowledge worker, it gets externalised to others to fix it, or multiplied in groups waiting on you to get something working. The incompetence spreads out.

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

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

Summary: In this study, the authors used proximity proteomics in U2OS cells to identify several E3 ubiquitin ligases recruited to stress granules (SGs), and they focused on MKRN2 as a novel regulator. They show that MKRN2 localization to SGs requires active ubiquitination via UBA1. Functional experiments demonstrated that MKRN2 knockdown increases the number of SG condensates, reduces their size, slightly raises SG liquidity during assembly, and slows disassembly after heat shock. Overexpression of MKRN2-GFP combined with confocal imaging revealed co-localization of MKRN2 and ubiquitin in SGs. By perturbing ubiquitination (using a UBA1 inhibitor) and inducing defective ribosomal products (DRiPs) with O-propargyl puromycin, they found that both ubiquitination inhibition and MKRN2 depletion lead to increased accumulation of DRiPs in SGs. The authors conclude that MKRN2 supports granulostasis, the maintenance of SG homeostasis , through its ubiquitin ligase activity, preventing pathological DRiP accumulation within SGs.

Major comments: - Are the key conclusions convincing? The key conclusions are partially convincing. The data supporting the role of ubiquitination and MKRN2 in regulating SG condensate dynamics are coherent, well controlled, and consistent with previous literature, making this part of the study solid and credible. However, the conclusions regarding the ubiquitin-dependent recruitment of MKRN2 to SGs, its relationship with UBA1 activity, the functional impact of the MKRN2 knockdown for DRiP accumulation are less thoroughly supported. These aspects would benefit from additional mechanistic evidence, validation in complementary model systems, or the use of alternative methodological approaches to strengthen the causal connections drawn by the authors. - Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? The authors should qualify some of their claims as preliminary. 1) MKRN2 recruitment to SGs (ubiquitin-dependent): The proteomics and IF data are a reasonable starting point, but they do not yet establish that MKRN2 is recruited from its physiological localization to SGs in a ubiquitin-dependent manner. To avoid overstating this point the authors should qualify the claim and/or provide additional controls: show baseline localization of endogenous MKRN2 under non-stress conditions (which is reported in literature to be nuclear and cytoplasmatic), include quantification of nuclear/cytoplasmic distribution, and demonstrate a shift into bona fide SG compartments after heat shock. Moreover, co-localization of overexpressed GFP-MKRN2 with poly-Ub (FK2) should be compared to a non-stress control and to UBA1-inhibition conditions to support claims of stress- and ubiquitination-dependent recruitment. *

Authors: We will stain cells for endogenous MKRN2 and quantify nuc/cyto ratio of MKRN2 without heat stress, without heat stress + TAK243, with HS and with HS + TAK243. We will do the same in the MKRN2-GFP overexpressing line while also staining for FK2.

*2) Use and interpretation of UBA1 inhibition: UBA1 inhibition effectively blocks ubiquitination globally, but it is non-selective. The manuscript should explicitly acknowledge this limitation when interpreting results from both proteomics and functional assays. Proteomics hits identified under UBA1 inhibition should be discussed as UBA1-dependent associations rather than as evidence for specific E3 ligase recruitment. The authors should consider orthogonal approaches before concluding specificity. *

Authors: We have acknowledged the limitation of using only TAK243 in our study by rephrasing statements about dependency on “ubiquitination” to “UBA1-dependent associations”.

* 3) DRiP accumulation and imaging quality: The evidence presented in Figure 5 is sufficient to substantiate the claim that DRiPs accumulate in SGs upon ubiquitination inhibition or MKRN2 depletion but to show that the event of the SGs localization and their clearance from SGs during stress is promoted by MKRN3 ubiquitin ligase activity more experiments would be needed. *

Authors: We have acknowledged the fact that our experiments do not include DRiP and SG dynamics assays using ligase-dead mutants of MKRN2 by altering our statement regarding MKRN2-mediated ubiquitination of DRiPs in the text (as proposed by reviewer 1).

*- Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation. Yes, a few targeted experiments would strengthen the conclusions without requiring the authors to open new lines of investigation. 1) Baseline localization of MKRN2: It would be important to show the baseline localization of endogenous and over-expressed MKRN2 (nuclear and cytoplasmic) under non-stress conditions and prior to ubiquitination inhibition. This would provide a reference to quantify redistribution into SGs and demonstrate recruitment in response to heat stress or ubiquitination-dependent mechanisms. *

Authors: We thank the reviewer for bringing this important control. We will address it in revisions.

We will quantify the nuclear/cytoplasmic distribution of endogenous and GFP-MKRN2 under control, TAK243, heat shock, and combined conditions, and assess MKRN2–ubiquitin colocalization by FK2 staining in unstressed cells.

* 2) Specificity of MKRN2 ubiquitin ligase activity: to address the non-specific effects of UBA1 inhibition and validate that observed phenotypes depend on MKRN2's ligase activity, the authors could employ a catalytically inactive MKRN2 mutant in rescue experiments. Comparing wild-type and catalytic-dead MKRN2 in the knockdown background would clarify the causal role of MKRN2 activity in SG dynamics and DRiP clearance. *

Authors: We thank the reviewer for this suggestion and have altered the phrasing of some of our statements in the text accordingly.

* 3) Ubiquitination linkage and SG marker levels: While the specific ubiquitin linkage type remains unknown, examining whether MKRN2 knockdown or overexpression affects total levels of key SG marker proteins would be informative. This could be done via Western blotting of SG markers along with ubiquitin staining, to assess whether MKRN2 influences protein stability or turnover through degradative or non-degradative ubiquitination. Such data would strengthen the mechanistic interpretation while remaining within the current study's scope. *

Authors: We thank the reviewer for requesting and will address it by performing MKRN2 KD and perform Western blot for G3BP1.

*

• Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments. The experiments suggested in points 1 and 3 are realistic and should not require substantial additional resources beyond those already used in the study. • Point 1 (baseline localization of MKRN2): This involves adding two control conditions (no stress and no ubiquitination inhibition) for microscopy imaging. The setup is essentially the same as in the current experiments, with time requirements mainly dependent on cell culture growth and imaging. Overall, this could be completed within a few weeks. • Point 3 (SG marker levels and ubiquitination): This entails repeating the existing experiment and adding a Western blot for SG markers and ubiquitin. The lab should already have the necessary antibodies, and the experiment could reasonably be performed within a couple of weeks. • Point 2 (catalytically inactive MKRN2 mutant and rescue experiments): This is likely more time-consuming. Designing an effective catalytic-dead mutant depends on structural knowledge of MKRN2 and may require additional validation to confirm loss of catalytic activity. If this expertise is not already present in the lab, it could significantly extend the timeline. Therefore, this experiment should be considered only if similarly recommended by other reviewers, as it represents a higher resource and time investment.

Overall, points 1 and 3 are highly feasible, while point 2 is more substantial and may require careful planning.

• Are the data and the methods presented in such a way that they can be reproduced? Yes. The methodologies used in this study to analyze SG dynamics and DRiP accumulation are well-established in the field and should be reproducible, particularly by researchers experienced in stress granule biology. Techniques such as SG assembly and disassembly assays, use of G3BP1 markers, and UBA1 inhibition are standard and clearly described. The data are generally presented in a reproducible manner; however, as noted above, some results would benefit from additional controls or complementary experiments to fully support specific conclusions.

• Are the experiments adequately replicated and statistical analysis adequate? Overall, the experiments in the manuscript appear to be adequately replicated, with most assays repeated between three and five times, as indicated in the supplementary materials. The statistical analyses used are appropriate and correctly applied to the datasets presented. However, for Figure 5 the number of experimental replicates is not reported. This should be clarified, and if the experiment was not repeated sufficiently, additional biological replicates should be performed. Given that this figure provides central evidence supporting the conclusion that DRiP accumulation depends on ubiquitination-and partly on MKRN2's ubiquitin ligase activity-adequate replication is essential. *

Authors: We thank the reviewer for noting this accidental omission. We now clarify in the legend of Figure 5 that the experiments with DRiPs were replicated three times.

Minor comments: - Specific experimental issues that are easily addressable. • For the generation and the validation of MKRN2 knockdown in UOS2 cells data are not presented in the results or in the methods sections to demonstrate the effective knockdown of the protein of interest. This point is quite essential to demonstrate the validity of the system used

Authors: We thank the reviewer for requesting and will address it by performing MKRN2 KD and perform Western blot and RT-qPCR.

• * In the supplementary figure 2 it would be useful to mention if the Western Blot represent the input (total cell lysates) before the APEX-pulldown or if it is the APEX-pulldown loaded for WB. There is no consistence in the difference of biotynilation between different replicates shown in the 2 blots. For example in R1 and R2 G3BP1-APX TAK243 the biotynilation is one if the strongest condition while on the left blot, in the same condition comparison samples R3 and R4 are less biotinilated compared to others. It would be useful to provide an explanation for that to avoid any confusion for the readers. * Authors: We have added a mention in the legend of Figure S2 that these are total cell lysates before pulldown. The apparent differences in biotin staining are small and not sufficient to question the results of our APEX-proteomics.

• * In Figure 2D, endogenous MKRN2 localization to SGs appears reduced following UBA1 inhibition. However, it is not clear whether this reduction reflects a true relocalization or a decrease in total MKRN2 protein levels. To support the interpretation that UBA1 inhibition specifically affects MKRN2 recruitment to SGs rather than its overall expression, the authors should provide data showing total MKRN2 levels remain unchanged under UBA1 inhibition, for example via Western blot of total cell lysates. * Authors: Based on first principles in regulation of gene expression, it is unlikely that total MKRN2 expression levels would decrease appreciably through transcriptional or translational regulation within the short timescale of these experiments (1 h TAK243 pretreatment followed by 90 min of heat stress).

• * DRIPs accumulation is followed during assembly but in the introduction is highlighted the fact that ubiquitination events, other reported E3 ligases and in this study data on MKRN2 showed that they play a crucial role in the disassembly of SGs which is also related with cleareance of DRIPs. Authors could add tracking DRIPs accumulation during disassembly to be added to Figure 5. I am not sure about the timeline required for this but I am just adding as optional if could be addressed easily. * Authors: We thank the reviewer for proposing this experimental direction. However, in a previous study (Ganassi et al., 2016; 10.1016/j.molcel.2016.07.021), we demonstrated that DRiP accumulation during the stress granule assembly phase drives conversion to a solid-like state and delays stress granule disassembly. It is therefore critical to assess DRiP enrichment within stress granules immediately after their formation, rather than during the stress recovery phase, as done here.

• * The authors should clarify in the text why the cutoff used for the quantification in Figure 5D (PC > 3) differs from the cutoff used elsewhere in the paper (PC > 1.5). Providing a rationale for this choice will help the reader understand the methodological consistency and ensure that differences in thresholds do not confound interpretation of the results. * Authors: We thank the reviewer for this question. The population of SGs with a DRiP enrichment > 1.5 represents SGs with a significant DRiP enrichment compared to the surrounding (background) signal. As explained in the methods, the intensity of DRiPs inside each SG is corrected by the intensity of DRiPs two pixels outside of each SG. Thus, differences in thresholds between independent experimental conditions (5B versus 5D) do not confound interpretation of the results but depend on overall staining intensity that can different between different experimental conditions. Choosing the cut-off > 3 allows to specifically highlight the population of SGs that are strongly enriched with DRiPs. MKRN2 silencing caused a strong DRiP enrichment in the majority of the SGs analyzed and therefore we chose this way of data representation. Note that the results represent the average of the analysis of 3 independent experiments with high numbers of SGs automatically segmented and analyzed/experiment. Figure 5A, B: n = 3 independent experiments; number of SGs analyzed per experiment: HS + OP-puro (695; 1216; 952); TAK-243 + HS + OP-puro (1852; 2214; 1774). Figure 5C, D: n = 3 independent experiments; number of SGs analyzed per experiment: siRNA control, HS + OP-puro (1984; 1400; 1708); siRNA MKRN2, HS + OP-puro (912; 1074; 1532).

• * For Figure 3G, the authors use over-expressed MKRN2-GFP to assess co-localization with ubiquitin in SGs. Given that a reliable antibody for endogenous MKRN2 is available and that a validated MKRN2 knockdown line exists as an appropriate control, this experiment would gain significantly in robustness and interpretability if co-localization were demonstrated using endogenous MKRN2. In the current over-expression system, MKRN2-GFP is also present in the nucleus, whereas the endogenous protein does not appear nuclear under the conditions shown. This discrepancy raises concerns about potential over-expression artifacts or mislocalization. Demonstrating co-localization using endogenous MKRN2 would avoid confounding effects associated with over-expression. If feasible, this would be a relatively straightforward experiment to implement, as it relies on tools (antibody and knockdown line) already described in the manuscript.

* Authors: We thank the reviewer for requesting and will address it by performing MKRN2 KD, FK2 immunofluorescence microscopy and perform SG partition coefficient analysis.

* - Are prior studies referenced appropriately? • From line 54 to line 67, the manuscript in total cites eight papers regarding the role of ubiquitination in SG disassembly. However, given the use of UBA1 inhibition in the initial MS-APEX experiment and the extensive prior literature on ubiquitination in SG assembly and disassembly under various stress conditions, the manuscript would benefit from citing additional relevant studies to provide more specifc examples. Expanding the references would provide stronger context, better connect the current findings to prior work, and emphasize the significance of the study in relation to established literature *

Authors: We have added citations for the relevant studies.

• *

At line 59, it would be helpful to note that G3BP1 is ubiquitinated by TRIM21 through a Lys63-linked ubiquitin chain. This information provides important mechanistic context, suggesting that ubiquitination of SG proteins in these pathways is likely non-degradative and related to functional regulation of SG dynamics rather than protein turnover. * Authors: The reviewer is correct. We have added to the text that G3BP1 is ubiquitinated through a Lys63-linked ubiquitin chain.

• *

When citing references 16 and 17, which report that the E3 ligases TRIM21 and HECT regulate SG formation, the authors should provide a plausible explanation for why these specific E3 ligases were not detected in their proteomics experiments. Differences could arise from the stress stimulus used, cell type, or experimental conditions. Similarly, since MKRN2 and other E3 ligases identified in this study have not been reported in previous works, discussing these methodological or biological differences would help prevent readers from questioning the credibility of the findings. It would also be valuable to clarify in the Conclusion that different types of stress may activate distinct ubiquitination pathways, highlighting context-dependent regulation of SG assembly and disassembly. * Authors: We thank the reviewer for this suggestion. We added to the discussion plausible explanations for why our study identified new E3 ligases.

• *

Line 59-60: when referring to the HECT family of E3 ligases involved in ubiquitination and SG disassembly, it would be more precise to report the specific E3 ligase identified in the cited studies rather than only the class of ligase. This would provide clearer mechanistic context and improve accuracy for readers. * Authors: We have added this detail to the discussion.

• *

The specific statement on line 182 "SG E3 ligases that depend on UBA1 activity are RBULs" should be supported by reference. * Authors: We have added citations to back up our claim that ZNF598, CNOT4, MKRN2, TRIM25 and TRIM26 exhibit RNA-binding activity.

*- Are the text and figures clear and accurate?

• In Supplementary Figure 1, DMSO is shown in green and the treatment in red, whereas in the main figures (Figure 1B and 1F) the colours in the legend are inverted. To avoid confusion, the colour coding in figure legends should be consistent across all figures throughout the manuscript. *

Authors: We have made the colors consistent across the main and supplementary figures.

• *

At line 79, the manuscript states that "inhibition of ubiquitination delayed fluorescence recovery dynamics of G3BP1-mCherry, relative to HS-treated cells (Figure 1F, Supplementary Fig. 6A)." However, the data shown in Figure 1F appear to indicate the opposite effect: the TAK243-treated condition (green curve) shows a faster fluorescence recovery compared to the control (red curve). This discrepancy between the text and the figure should be corrected or clarified, as it may affect the interpretation of the role of ubiquitination in SG dynamics. * Authors: Good catch. We now fixed the graphical mistake (Figure 1F and S6).

• * Line 86: adjust a missing bracket * Authors: Thank you, we fixed it.

• *

There appears to be an error in the legend of Supplementary Figure 3: the legend states that the red condition (MKRN2) forms larger aggregates, but both the main Figure 3C of the confocal images and the text indicate that MKRN2 (red) forms smaller aggregates. Please correct the legend and any corresponding labels so they are consistent with the main figure and the text. The authors should also double-check that the figure panel order, color coding, and statistical annotations match the legend and the descriptions in the Results section to avoid reader confusion.

* Authors: This unfortunate graphical mistake has been corrected.

• * At lines 129-130, the manuscript states that "FRAP analysis demonstrated that MKRN2 KD resulted in a slight increase in SG liquidity (Fig. 3F, Supplementary Fig. 6B)." However, the data shown in Figure 3F appear to indicate the opposite trend: the MKRN2 KD condition (red curve) exhibits a faster fluorescence recovery compared to the control (green curve). This discrepancy between the text and the figure should be corrected or clarified, as it directly affects the interpretation of MKRN2's role in SG disassembly. Ensuring consistency between the written description and the plotted FRAP data is essential for accurate interpretation. * Authors: We thank the reviewer and clarify in the legend of Figure 3F and the Results the correct labels: indeed faster fluorescence recovery seen in MKRN2 KD is correctly interpreted as increased liquidity in the text.

• *

At lines 132-133, the manuscript states: "Then, to further test the impact of MKRN2 on SG dynamics, we overexpressed MKRN2-GFP and observed that it was recruited to SG (Fig. 3G)." This description should be corrected or clarified, as the over-expressed MKRN2-GFP also appears to localize to the nucleus. * Authors: The text has been modified to reflect both the study of MKRN2 localization to SGs and of nuclear localization.

• *

At lines 134-135, the manuscript states that the FK2 antibody detects "free ubiquitin." This is incorrect. FK2 does not detect free ubiquitin; it recognizes only ubiquitin conjugates, including mono-ubiquitinated and poly-ubiquitinated proteins. The text should be corrected accordingly to avoid misinterpretation of the immunostaining data. * Authors: Thank you for pointing out this error. We have corrected it.

• * Figure 5A suffers from poor resolution, and no scale bar is provided, which limits interpretability. Additionally, the ROI selected for the green channel (DRIPs) appears to capture unspecific background staining, while the most obvious DRIP spots are localized in the nucleus. The authors should clarify this in the text, improve the image quality if possible, and ensure that the ROI accurately represents DRIP accumulation - in SGs rather than background signal. * Authors: We thank the reviewer for pointing the sub-optimal presentation of this figure. We modified Figure 5A to improve image quality and interpretation. Concerning the comment that “the most obvious DRIP spots are localized in the nucleus”, this is in line with our previous findings demonstrating that a fraction of DRiPs accumulates in nucleoli (Mediani et al. 2019 10.15252/embj.2018101341). To avoid misinterpretation, we modified Figure 5A as follows: (i) we provide a different image for control cells, exposed to heat shock and OP-puro; (ii) we select a ROI that only shows a few stress granules; (iii) we added arrowheads to indicate the nucleoli that are strongly enriched for DRiPs; (iv) we include a dotted line to show the nuclear membrane, helping to distinguish cytoplasm and nucleus in the red and green channel. We also include the scale bars (5 µm) in the image.

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

• In the first paragraph following the APEX proteomics results, the authors present validation data exclusively for MKRN2, justifying this early focus by stating that MKRN2 is the most SG-depleted E3 ligase. However, in the subsequent paragraph they introduce the RBULs and present knockdown data for MKRN2 along with two additional E3 ligases identified in the screen, before once again emphasizing that MKRN2 is the most SG-depleted ligase and therefore the main focus of the study. For clarity and logical flow, the manuscript would benefit from reordering the narrative. Specifically, the authors should first present the validation data for all three selected E3 ligases, and only then justify the decision to focus on MKRN2 for in-depth characterization. In addition to the extent of its SG depletion, the authors may also consider providing biologically relevant reasons for prioritizing MKRN2 (e.g., domain architecture, known roles in stress responses, or prior evidence of ubiquitination-related functions). Reorganizing this section would improve readability and better guide the reader through the rationale for the study's focus.*

Authors: We thank the reviewer for this suggested improvement to our “storyline”. As suggested by the reviewer, we have moved the IF validation of MKRN2 to the following paragraph in order to improve the flow of the manuscript. We added additional justification to prioritizing MKRN2 citing (Youn et al. 2018 and Markmiller et al. 2018).

• *

At lines 137-138, the manuscript states: "Together these data indicate that MKRN2 regulates the assembly dynamics of SGs by promoting their coalescence during HS and can increase SG ubiquitin content." While Figure 3G shows some co-localization of MKRN2 with ubiquitin, immunofluorescence alone is insufficient to claim an increase in SG ubiquitin content. This conclusion should be supported by orthogonal experiments, such as Western blotting, in vitro ubiquitination assays, or immunoprecipitation of SG components. Including a control under no-stress conditions would also help demonstrate that ubiquitination increases specifically in response to stress. The second part of the statement should therefore be rephrased to avoid overinterpretation, for example:"...and may be associated with increased ubiquitination within SGs, as suggested by co-localization, pending further validation by complementary assays." * Authors: The statement has been rephrased in a softer way as suggested by the reviewer.

• At line 157, the statement: "Therefore, we conclude that MKRN2 ubiquitinates a subset of DRiPs, avoiding their accumulation inside SGs" should be rephrased as a preliminary observation. While the data support a role for MKRN2 in SG disassembly and a reduction of DRIPs, direct ubiquitination of DRIPs by MKRN2 has not been demonstrated. A more cautious phrasing would better reflect the current evidence and avoid overinterpretation. * * *Authors: We thank the reviewer for this suggestion and have altered the phrasing of this statement accordingly.

*Reviewer #1 (Significance (Required)):

General assessment: provide a summary of the strengths and limitations of the study. What are the strongest and most important aspects? What aspects of the study should be improved or could be developed?

• This study provides a valuable advancement in understanding the role of ubiquitination in stress granule (SG) dynamics and the clearance of SGs formed under heat stress. A major strength is the demonstration of how E3 ligases identified through proteomic screening, particularly MKRN2, influence SG assembly and disassembly in a ubiquitination- and heat stress-dependent manner. The combination of proteomics, imaging, and functional assays provides a coherent mechanistic framework linking ubiquitination to SG homeostasis. Limitations of the study include the exclusive use of a single model system (U2OS cells), which may limit generalizability. Additionally, some observations-such as MKRN2-dependent ubiquitination within SGs and changes in DRIP accumulation under different conditions-would benefit from orthogonal validation experiments (e.g., Western blotting, immunoprecipitation, or in vitro assays) to confirm and strengthen these findings. Addressing these points would enhance the robustness and broader applicability of the conclusions.

Advance: compare the study to the closest related results in the literature or highlight results reported for the first time to your knowledge; does the study extend the knowledge in the field and in which way? Describe the nature of the advance and the resulting insights (for example: conceptual, technical, clinical, mechanistic, functional,...).

• The closest related result in literature is - Yang, Cuiwei et al. "Stress granule homeostasis is modulated by TRIM21-mediated ubiquitination of G3BP1 and autophagy-dependent elimination of stress granules." Autophagy vol. 19,7 (2023): 1934-1951. doi:10.1080/15548627.2022.2164427 - demonstrating that TRIM21, an E3 ubiquitin ligase, catalyzes K63-linked ubiquitination of G3BP1, a core SG nucleator, under oxidative stress. This ubiquitination by TRIM21 inhibits SG formation, likely by altering G3BP1's propensity for phase separation. In contrast, the MKRN2 study identifies a different E3 (MKRN2) that regulates SG dynamics under heat stress and appears to influence both assembly and disassembly. This expands the role of ubiquitin ligases in SG regulation beyond those previously studied (like TRIM21).

• Gwon and colleagues (Gwon Y, Maxwell BA, Kolaitis RM, Zhang P, Kim HJ, Taylor JP. Ubiquitination of G3BP1 mediates stress granule disassembly in a context-specific manner. Science. 2021;372(6549):eabf6548. doi:10.1126/science.abf6548) have shown that K63-linked ubiquitination of G3BP1 is required for SG disassembly after heat stress. This ubiquitinated G3BP1 recruits the segregase VCP/p97, which helps extract G3BP1 from SGs for disassembly. The MKRN2 paper builds on this by linking UBA1-dependent ubiquitination and MKRN2's activity to SG disassembly. Specifically, they show MKRN2 knockdown affects disassembly, and suggest MKRN2 helps prevent accumulation of defective ribosomal products (DRiPs) in SGs, adding a new layer to the ubiquitin-VCP model.

• Ubiquitination's impact is highly stress- and context-dependent (different chain types, ubiquitin linkages, and recruitment of E3s). The MKRN2 work conceptually strengthens this idea: by showing that MKRN2's engagement with SGs depends on active ubiquitination via UBA1, and by demonstrating functional consequences (SG dynamics + DRIP accumulation), the study highlights how cellular context (e.g., heat stress) can recruit specific ubiquitin ligases to SGs and modulate their behavior.

• There is a gap in the literature: very few (if any) studies explicitly combine the biology of DRIPs, stress granules, and E3 ligase mediated ubiquitination, especially in mammalian cells. There are relevant works about DRIP biology in stress granules, but those studies focus on chaperone-based quality control, not ubiquitin ligase-mediated ubiquitination of DRIPs. This study seems to be one of the first to make that connection in mammalian (or human-like) SG biology. A work on the plant DRIP-E3 ligase TaSAP5 (Zhang N, Yin Y, Liu X, et al. The E3 Ligase TaSAP5 Alters Drought Stress Responses by Promoting the Degradation of DRIP Proteins. Plant Physiol. 2017;175(4):1878-1892. doi:10.1104/pp.17.01319 ) shows that DRIPs can be directly ubiquitinated by E3s in other biological systems - which supports the plausibility of the MKRN2 mechanism, but it's not the same context.

• A very recent review (Yuan, Lin et al. "Stress granules: emerging players in neurodegenerative diseases." Translational neurodegeneration vol. 14,1 22. 12 May. 2025, doi:10.1186/s40035-025-00482-9) summarizes and reinforces the relationship among SGs and the pathogenesis of different neurodegenerative diseases (NDDs). By identifying MKRN2 as a new ubiquitin regulator in SGs, the current study could have relevance for neurodegeneration and proteotoxic diseases, providing a new candidate to explore in disease models.

Audience: describe the type of audience ("specialized", "broad", "basic research", "translational/clinical", etc...) that will be interested or influenced by this research; how will this research be used by others; will it be of interest beyond the specific field?

The audience for this paper is primarily specialized, including researchers in stress granule biology, ubiquitin signaling, protein quality control, ribosome biology, and cellular stress responses. The findings will also be of interest to scientists working on granulostasis, nascent protein surveillance, and proteostasis mechanisms. Beyond these specific fields, the study provides preliminary evidence linking ubiquitination to DRIP handling and SG dynamics, which may stimulate new research directions and collaborative efforts across complementary areas of cell biology and molecular biology.

• Please define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.

I work in ubiquitin biology, focusing on ubiquitination signaling in physiological and disease contexts, with particular expertise in the identification of E3 ligases and their substrates across different cellular systems and in vivo models. I have less expertise in stress granule dynamics and DRiP biology, so my evaluation of those aspects is more limited and relies on interpretation of the data presented in the manuscript.

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

This study identifies the E3 ubiquitin ligase Makorin 2 (MKRN2) as a novel regulator of stress granule (SG) dynamics and proteostasis. Using APEX proximity proteomics, the authors demonstrate that inhibition of the ubiquitin-activating enzyme UBA1 with TAK243 alters the SG proteome, leading to depletion of several E3 ligases, chaperones, and VCP cofactors. Detailed characterization of MKRN2 reveals that it localizes to SGs in a ubiquitination-dependent manner and is required for proper SG assembly, coalescence, and disassembly. Functionally, MKRN2 prevents the accumulation of defective ribosomal products (DRiPs) within SGs, thereby maintaining granulostasis. The study provides compelling evidence that ubiquitination, mediated specifically by MKRN2, plays a critical role in surveilling stress-damaged proteins within SGs and maintaining their dynamic liquid-like properties. Major issues: 1. Figures 1-2: Temporal dynamics of ubiquitination in SGs. The APEX proteomics was performed at a single timepoint (90 min heat stress), yet the live imaging data show that SG dynamics and TAK243 effects vary considerably over time: • The peak or SG nucleation was actually at 10-30 min (Figure 1B). • TAK243 treatment causes earlier SG nucleation (Figure 1B) but delayed disassembly (Figure 1A-B, D). A temporal proteomic analysis at multiple timepoints (e.g., 30 min, 60 min, 90 min of heat stress, and during recovery) would reveal whether MKRN2 and other ubiquitination-dependent proteins are recruited to SGs dynamically during the stress response. It would also delineate whether different E3 ligases predominate at different stages of the SG lifecycle. While such experiments may be beyond the scope of the current study, the authors should at minimum discuss this limitation and acknowledge that the single-timepoint analysis may miss dynamic changes in SG composition. *

Authors: We thank the reviewer for identifying this caveat in our methodology. We now discuss this limitation and acknowledge that the single-timepoint analysis may miss dynamic changes in SG composition.

* Figures 2D-E, 3G: MKRN2 localization mechanism requires clarification. The authors demonstrate that MKRN2 localization to SGs is dependent on active ubiquitination, as TAK243 treatment significantly reduces MKRN2 partitioning into SGs (Figure 2D-E). However, several mechanistic questions remain: • Does MKRN2 localize to SGs through binding to ubiquitinated substrates within SGs, or does MKRN2 require its own ubiquitination activity to enter SGs? • The observation that MKRN2 overexpression increases SG ubiquitin content (Figure 3G-H) could indicate either: (a) MKRN2 actively ubiquitinates substrates within SGs, or (b) MKRN2 recruitment brings along pre-ubiquitinated substrates from the cytoplasm. • Is MKRN2 localization to SGs dependent on its E3 ligase activity? A catalytically inactive mutant of MKRN2 would help distinguish whether MKRN2 must actively ubiquitinate proteins to remain in SGs or whether it binds to ubiquitinated proteins independently of its catalytic activity. The authors should clarify whether MKRN2's SG localization depends on its catalytic activity or on binding to ubiquitinated proteins, as this would fundamentally affect the interpretation of its role in SG dynamics. *

Authors: We thank the reviewer for this experimental suggestion. We will perform an analysis of the SG partitioning coefficient between WT-MKRN2 and a RING mutant of MKRN2.

* Figures 3-4: Discrepancy between assembly and disassembly phenotypes. MKRN2 knockdown produces distinct phenotypes during SG assembly versus disassembly. During assembly: smaller, more numerous SGs that fail to coalesce (Figure 3A-E), while during disassembly: delayed SG clearance (Figure 4A-D). These phenotypes may reflect different roles for MKRN2 at different stages, but the mechanism underlying this stage-specificity is unclear: • Does MKRN2 have different substrates or utilize different ubiquitin chain types during assembly versus disassembly? • The increased SG liquidity upon MKRN2 depletion (Figure 3F) seems paradoxical with delayed disassembly- typically more liquid condensates disassemble faster. The authors interpret this as decreased coalescence into "dense and mature SGs," but this requires clarification. • How does prevention of DRiP accumulation relate to the assembly defect? One would predict that DRiP accumulation would primarily affect disassembly (by reducing liquidity), yet MKRN2 depletion impacts both assembly dynamics and DRiP accumulation. The authors should discuss how MKRN2's role in preventing DRiP accumulation mechanistically connects to both the assembly and disassembly phenotypes. *

Authors: We thank the reviewer and will add to the Discussion a mention of a precedent for this precise phenotype from our previous work (Seguin et al., 2014).

* Figure 5: Incomplete characterization of MKRN2 substrates. While the authors convincingly demonstrate that MKRN2 prevents DRiP accumulation in SGs (Figure 5C-D), the direct substrates of MKRN2 remain unknown. The authors acknowledge in the limitations that "the direct MKRN2 substrates and ubiquitin-chain types (K63/K48) are currently unknown." However, several approaches could strengthen the mechanistic understanding: • Do DRiPs represent direct MKRN2 substrates? Co-immunoprecipitation of MKRN2 followed by ubiquitin-chain specific antibodies (K48 vs K63) could reveal whether MKRN2 mediates degradative (K48) or non-degradative (K63) ubiquitination. *

Authors: The DRiPs generated in the study represent truncated versions of all the proteins that were in the process of being synthesized by the cell at the moment of the stress, and therefore include both MKRN2 specific substrates and MKRN2 independent substrates. Identifying specific MKRN2 substrates, while interesting as a new research avenue, is not within the scope of the present study.

• * Given that VCP cofactors (such as UFD1L, PLAA) are depleted from SGs upon UBA1 inhibition (Figure 2C) and these cofactors recognize ubiquitinated substrates, does MKRN2 function upstream of VCP recruitment? Testing whether MKRN2 depletion affects VCP cofactor localization to SGs would clarify this pathway. * Authors: We thank the reviewer for requesting and will address it by performing MKRN2 KD, VCP immunofluorescence microscopy and perform SG partition coefficient analysis.

• * The authors note that MKRN2 knockdown produces a phenotype reminiscent of VCP inhibition-smaller, more numerous SGs with increased DRiP partitioning. This similarity suggests MKRN2 may function in the same pathway as VCP. Direct epistasis experiments would strengthen this connection. * Authors: This study is conditional results of the above study. If VCP partitioning to SGs is reduced upon MKRN2 KD, which we do not know at this point, then MKRN2/VCP double KD experiment will be performed to strengthen this connection.

* Alternative explanations for the phenotype of delayed disassembly with TAK243 or MKRN2 depletion- the authors attribute this to DRiP accumulation, but TAK243 affects global ubiquitination. Could impaired degradation of other SG proteins (not just DRiPs) contribute to delayed disassembly? Does proteasome inhibition (MG-132 treatment) phenocopy the MKRN2 depletion phenotype? This would support that MKRN2-mediated proteasomal degradation (via K48 ubiquitin chains) is key to the phenotype. *

Authors: We are happy to provide alternative explanations in the Discussion in line with Reviewer #2 suggestion. The role of the proteosome is out of the scope of our study.

• Comparison with other E3 ligases (Supplementary Figure 5): The authors show that CNOT4 and ZNF598 depletion also affect SG dynamics, though to lesser extents than MKRN2. However: • Do these E3 ligases also prevent DRiP accumulation in SGs? Testing OP-puro partitioning in CNOT4- or ZNF598-depleted cells would reveal whether DRiP clearance is a general feature of SG-localized E3 ligases or specific to MKRN2. *

• * Are there redundant or compensatory relationships between these E3 ligases? Do double knockdowns have additive effects? * Authors: Our paper presents a study of the E3 ligase MKRN2. Generalizing these observations to ZNF598, CNOT4 and perhaps an even longer list of E3s, may be an interesting question, outside the scope of our mission.

• * The authors note that MKRN2 is "the most highly SG-depleted E3 upon TAK243 treatment"-does this mean MKRN2 has the strongest dependence on active ubiquitination for its SG localization, or simply that it has the highest basal level of SG partitioning? * Authors: We thank the reviewer for this smart question. MKRN2 has the strongest dependence on active ubiquitination as we now clarify better in the Results.

*Reviewer #2 (Significance (Required)):

This is a well-executed study that identifies MKRN2 as an important regulator of stress granule dynamics and proteostasis. The combination of proximity proteomics, live imaging, and functional assays provides strong evidence for MKRN2's role in preventing DRiP accumulation and maintaining granulostasis. However, key mechanistic questions remain, particularly regarding MKRN2's direct substrates, the ubiquitin chain types it generates, and how its enzymatic activity specifically prevents DRiP accumulation while promoting both SG coalescence and disassembly. Addressing the suggested revisions, particularly those related to MKRN2's mechanism of SG localization and substrate specificity, would significantly strengthen the manuscript and provide clearer insights into how ubiquitination maintains the dynamic properties of stress granules under proteotoxic stress.

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

In this paper, Amzallag et al. investigate the relationship between ubiquitination and the dynamics of stress granules (SGs). They utilize proximity ligation coupled mass spectrometry to identify SG components under conditions where the proteasome is inhibited by a small drug that targets UBiquitin-like modifier Activating enzyme 1 (UBA1), which is crucial for the initial step in the ubiquitination of misfolded proteins. Their findings reveal that the E3 ligase Makorin2 (MKRN2) is a novel component of SGs. Additionally, their data suggest that MKRN2 is necessary for processing damaged ribosome-associated proteins (DRIPs) during heat shock (HS). In the absence of MKRN2, DRIPs accumulate in SGs, which affects their dynamics. Major comments: Assess the knockdown efficiency (KD) for CNOT1, ZNF598, and MKRN2 to determine if the significant effect observed on SG dynamics upon MKRN2 depletion is due to the protein's function rather than any possible differences in KD efficiency. *

Authors: To address potential variability in knockdown efficiency, we will quantify CNOT4, ZNF598, and MKRN2 mRNA levels by RT-qPCR following siRNA knockdown.

* Since HS-induced stress granules (SGs) are influenced by the presence of TAK-243 or MKRN2 depletion, could it be that these granules become more mature and thus acquire more defective ribosomal products (DRIPs)? Do HS cells reach the same level of DRIPs, as assessed by OP-Puro staining, at a later time point? *

Authors: an interesting question. Mateju et al. carefully characterized the time course of DRiP accumulation in stress granules during heat shock, decreasing after the 90 minutes point (Appendix Figure S7; 10.15252/embj.201695957). We therefore interpret DRiP accumulation in stress granules following TAK243 treatment as a pathological state, reflecting impaired removal and degradation of DRiPs, rather than a normal, more “mature” stress granule state.

* Incorporating OP-Puro can lead to premature translation termination, potentially confounding results. Consider treating cells with a short pulse (i.e., 5 minutes) of OP-Puro just before fixation. *

Authors: Thank you for this suggestion. Treating the cell with a short pulse of OP-Puro just before fixation will lead to the labelling of a small amount of proteins, likely undetectable using conventional microscopy or Western blotting. Furthermore, it will lead to the unwanted labeling of stress responsive proteins that are translated with non canonical cap-independent mechanisms upon stress.

* Is MKRN2's dependence limited to HS-induced SGs? *

Authors: We will test sodium arsenite–induced stress and use immunofluorescence at discrete time points to assess whether the heat shock–related observations generalize to other stress types.

*

Minor comments: Abstract: Introduce UBA1. Introduction: The reference [2] should be replaced with 25719440. Results: Line 70, 'G3BP1 and 2 genes,' is somewhat misleading. Consider rephrasing into 'G3BP1 and G3BP2 genes'. Line 103: considers rephrasing 'we orthogonally validated the ubiquitin-dependent interaction' to 'we orthogonally validated the ubiquitin-dependent stress granule localization'. Line 125: '(fig.3C, EI Supplementary fig. 3)' Remove 'I'. Methods: line 260: the reference is not linked (it should be ref. [26]). Line 225: Are all the KDs being performed using the same method? Please specify. *

Authors: The text has been altered to reflect the reviewer’s suggestions.

*Fig.2C: Consider adding 'DEPLETED' on top of the scheme.

Reviewer #3 (Significance (Required)):

The study offers valuable insights into the degradative processes associated with SGs. The figures are clear, and the experimental quality is high. The authors do not overstate or overinterpret their findings, and the results effectively support their claims. However, the study lacks orthogonal methods to validate the findings and enhance the results. For instance, incorporating biochemical and reporter-based methods to measure degradation-related intermediate products (DRIPs) would be beneficial. Additionally, utilizing multiple methods to block ubiquitination, studying the dynamics of MKRN2 on SGs, and examining the consequences of excessive DRIPs on the cell fitness of SGs would further strengthen the research. *

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 #1

Evidence, reproducibility and clarity

Summary:

In this study, the authors used proximity proteomics in U2OS cells to identify several E3 ubiquitin ligases recruited to stress granules (SGs), and they focused on MKRN2 as a novel regulator. They show that MKRN2 localization to SGs requires active ubiquitination via UBA1. Functional experiments demonstrated that MKRN2 knockdown increases the number of SG condensates, reduces their size, slightly raises SG liquidity during assembly, and slows disassembly after heat shock. Overexpression of MKRN2-GFP combined with confocal imaging revealed co-localization of MKRN2 and ubiquitin in SGs. By perturbing ubiquitination (using a UBA1 inhibitor) and inducing defective ribosomal products (DRiPs) with O-propargyl puromycin, they found that both ubiquitination inhibition and MKRN2 depletion lead to increased accumulation of DRiPs in SGs. The authors conclude that MKRN2 supports granulostasis, the maintenance of SG homeostasis , through its ubiquitin ligase activity, preventing pathological DRiP accumulation within SGs.

Major comments:

• Are the key conclusions convincing?

The key conclusions are partially convincing. The data supporting the role of ubiquitination and MKRN2 in regulating SG condensate dynamics are coherent, well controlled, and consistent with previous literature, making this part of the study solid and credible. However, the conclusions regarding the ubiquitin-dependent recruitment of MKRN2 to SGs, its relationship with UBA1 activity, the functional impact of the MKRN2 knockdown for DRiP accumulation are less thoroughly supported. These aspects would benefit from additional mechanistic evidence, validation in complementary model systems, or the use of alternative methodological approaches to strengthen the causal connections drawn by the authors. - Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? The authors should qualify some of their claims as preliminary.

1) MKRN2 recruitment to SGs (ubiquitin-dependent): The proteomics and IF data are a reasonable starting point, but they do not yet establish that MKRN2 is recruited from its physiological localization to SGs in a ubiquitin-dependent manner. To avoid overstating this point the authors should qualify the claim and/or provide additional controls: show baseline localization of endogenous MKRN2 under non-stress conditions (which is reported in literature to be nuclear and cytoplasmatic), include quantification of nuclear/cytoplasmic distribution, and demonstrate a shift into bona fide SG compartments after heat shock. Moreover, co-localization of overexpressed GFP-MKRN2 with poly-Ub (FK2) should be compared to a non-stress control and to UBA1-inhibition conditions to support claims of stress- and ubiquitination-dependent recruitment.

2) Use and interpretation of UBA1 inhibition: UBA1 inhibition effectively blocks ubiquitination globally, but it is non-selective. The manuscript should explicitly acknowledge this limitation when interpreting results from both proteomics and functional assays. Proteomics hits identified under UBA1 inhibition should be discussed as UBA1-dependent associations rather than as evidence for specific E3 ligase recruitment. The authors should consider orthogonal approaches before concluding specificity.

3) DRiP accumulation and imaging quality: The evidence presented in Figure 5 is sufficient to substantiate the claim that DRiPs accumulate in SGs upon ubiquitination inhibition or MKRN2 depletion but to show that the event of the SGs localization and their clearance from SGs during stress is promoted by MKRN3 ubiquitin ligase activity more experiments would be needed. - Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation. Yes, a few targeted experiments would strengthen the conclusions without requiring the authors to open new lines of investigation.

1) Baseline localization of MKRN2: It would be important to show the baseline localization of endogenous and over-expressed MKRN2 (nuclear and cytoplasmic) under non-stress conditions and prior to ubiquitination inhibition. This would provide a reference to quantify redistribution into SGs and demonstrate recruitment in response to heat stress or ubiquitination-dependent mechanisms.

2) Specificity of MKRN2 ubiquitin ligase activity: to address the non-specific effects of UBA1 inhibition and validate that observed phenotypes depend on MKRN2's ligase activity, the authors could employ a catalytically inactive MKRN2 mutant in rescue experiments. Comparing wild-type and catalytic-dead MKRN2 in the knockdown background would clarify the causal role of MKRN2 activity in SG dynamics and DRiP clearance.

3) Ubiquitination linkage and SG marker levels: While the specific ubiquitin linkage type remains unknown, examining whether MKRN2 knockdown or overexpression affects total levels of key SG marker proteins would be informative. This could be done via Western blotting of SG markers along with ubiquitin staining, to assess whether MKRN2 influences protein stability or turnover through degradative or non-degradative ubiquitination. Such data would strengthen the mechanistic interpretation while remaining within the current study's scope. - Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments. The experiments suggested in points 1 and 3 are realistic and should not require substantial additional resources beyond those already used in the study. - Point 1 (baseline localization of MKRN2): This involves adding two control conditions (no stress and no ubiquitination inhibition) for microscopy imaging. The setup is essentially the same as in the current experiments, with time requirements mainly dependent on cell culture growth and imaging. Overall, this could be completed within a few weeks. - Point 3 (SG marker levels and ubiquitination): This entails repeating the existing experiment and adding a Western blot for SG markers and ubiquitin. The lab should already have the necessary antibodies, and the experiment could reasonably be performed within a couple of weeks. - Point 2 (catalytically inactive MKRN2 mutant and rescue experiments): This is likely more time-consuming. Designing an effective catalytic-dead mutant depends on structural knowledge of MKRN2 and may require additional validation to confirm loss of catalytic activity. If this expertise is not already present in the lab, it could significantly extend the timeline. Therefore, this experiment should be considered only if similarly recommended by other reviewers, as it represents a higher resource and time investment.

Overall, points 1 and 3 are highly feasible, while point 2 is more substantial and may require careful planning. - Are the data and the methods presented in such a way that they can be reproduced?

Yes. The methodologies used in this study to analyze SG dynamics and DRiP accumulation are well-established in the field and should be reproducible, particularly by researchers experienced in stress granule biology. Techniques such as SG assembly and disassembly assays, use of G3BP1 markers, and UBA1 inhibition are standard and clearly described. The data are generally presented in a reproducible manner; however, as noted above, some results would benefit from additional controls or complementary experiments to fully support specific conclusions. - Are the experiments adequately replicated and statistical analysis adequate?

Overall, the experiments in the manuscript appear to be adequately replicated, with most assays repeated between three and five times, as indicated in the supplementary materials. The statistical analyses used are appropriate and correctly applied to the datasets presented. However, for Figure 5 the number of experimental replicates is not reported. This should be clarified, and if the experiment was not repeated sufficiently, additional biological replicates should be performed. Given that this figure provides central evidence supporting the conclusion that DRiP accumulation depends on ubiquitination-and partly on MKRN2's ubiquitin ligase activity-adequate replication is essential.

Minor comments:

• Specific experimental issues that are easily addressable.
  • For the generation and the validation of MKRN2 knockdown in UOS2 cells data are not presented in the results or in the methods sections to demonstrate the effective knockdown of the protein of interest. This point is quite essential to demonstrate the validity of the system used
  • In the supplementary figure 2 it would be useful to mention if the Western Blot represent the input (total cell lysates) before the APEX-pulldown or if it is the APEX-pulldown loaded for WB. There is no consistence in the difference of biotynilation between different replicates shown in the 2 blots. For example in R1 and R2 G3BP1-APX TAK243 the biotynilation is one if the strongest condition while on the left blot, in the same condition comparison samples R3 and R4 are less biotinilated compared to others. It would be useful to provide an explanation for that to avoid any confusion for the readers.
  • In Figure 2D, endogenous MKRN2 localization to SGs appears reduced following UBA1 inhibition. However, it is not clear whether this reduction reflects a true relocalization or a decrease in total MKRN2 protein levels. To support the interpretation that UBA1 inhibition specifically affects MKRN2 recruitment to SGs rather than its overall expression, the authors should provide data showing total MKRN2 levels remain unchanged under UBA1 inhibition, for example via Western blot of total cell lysates.
  • DRIPs accumulation is followed during assembly but in the introduction is highlighted the fact that ubiquitination events, other reported E3 ligases and in this study data on MKRN2 showed that they play a crucial role in the disassembly of SGs which is also related with cleareance of DRIPs. Authors could add tracking DRIPs accumulation during disassembly to be added to Figure 5. I am not sure about the timeline required for this but I am just adding as optional if could be addressed easily.
  • The authors should clarify in the text why the cutoff used for the quantification in Figure 5D (PC > 3) differs from the cutoff used elsewhere in the paper (PC > 1.5). Providing a rationale for this choice will help the reader understand the methodological consistency and ensure that differences in thresholds do not confound interpretation of the results.
  • For Figure 3G, the authors use over-expressed MKRN2-GFP to assess co-localization with ubiquitin in SGs. Given that a reliable antibody for endogenous MKRN2 is available and that a validated MKRN2 knockdown line exists as an appropriate control, this experiment would gain significantly in robustness and interpretability if co-localization were demonstrated using endogenous MKRN2. In the current over-expression system, MKRN2-GFP is also present in the nucleus, whereas the endogenous protein does not appear nuclear under the conditions shown. This discrepancy raises concerns about potential over-expression artifacts or mislocalization. Demonstrating co-localization using endogenous MKRN2 would avoid confounding effects associated with over-expression. If feasible, this would be a relatively straightforward experiment to implement, as it relies on tools (antibody and knockdown line) already described in the manuscript.

• Are prior studies referenced appropriately?

  • From line 54 to line 67, the manuscript in total cites eight papers regarding the role of ubiquitination in SG disassembly. However, given the use of UBA1 inhibition in the initial MS-APEX experiment and the extensive prior literature on ubiquitination in SG assembly and disassembly under various stress conditions, the manuscript would benefit from citing additional relevant studies to provide more specifc examples. Expanding the references would provide stronger context, better connect the current findings to prior work, and emphasize the significance of the study in relation to established literature
  • At line 59, it would be helpful to note that G3BP1 is ubiquitinated by TRIM21 through a Lys63-linked ubiquitin chain. This information provides important mechanistic context, suggesting that ubiquitination of SG proteins in these pathways is likely non-degradative and related to functional regulation of SG dynamics rather than protein turnover.
  • When citing references 16 and 17, which report that the E3 ligases TRIM21 and HECT regulate SG formation, the authors should provide a plausible explanation for why these specific E3 ligases were not detected in their proteomics experiments. Differences could arise from the stress stimulus used, cell type, or experimental conditions. Similarly, since MKRN2 and other E3 ligases identified in this study have not been reported in previous works, discussing these methodological or biological differences would help prevent readers from questioning the credibility of the findings. It would also be valuable to clarify in the Conclusion that different types of stress may activate distinct ubiquitination pathways, highlighting context-dependent regulation of SG assembly and disassembly.
  • Line 59-60: when referring to the HECT family of E3 ligases involved in ubiquitination and SG disassembly, it would be more precise to report the specific E3 ligase identified in the cited studies rather than only the class of ligase. This would provide clearer mechanistic context and improve accuracy for readers.
  • The specific statement on line 182 "SG E3 ligases that depend on UBA1 activity are RBULs" should be supported by reference.
  • Are the text and figures clear and accurate?
  • In Supplementary Figure 1, DMSO is shown in green and the treatment in red, whereas in the main figures (Figure 1B and 1F) the colours in the legend are inverted. To avoid confusion, the colour coding in figure legends should be consistent across all figures throughout the manuscript.
  • At line 79, the manuscript states that "inhibition of ubiquitination delayed fluorescence recovery dynamics of G3BP1-mCherry, relative to HS-treated cells (Figure 1F, Supplementary Fig. 6A)." However, the data shown in Figure 1F appear to indicate the opposite effect: the TAK243-treated condition (green curve) shows a faster fluorescence recovery compared to the control (red curve). This discrepancy between the text and the figure should be corrected or clarified, as it may affect the interpretation of the role of ubiquitination in SG dynamics.
  • Line 86: adjust a missing bracket
  • There appears to be an error in the legend of Supplementary Figure 3: the legend states that the red condition (MKRN2) forms larger aggregates, but both the main Figure 3C of the confocal images and the text indicate that MKRN2 (red) forms smaller aggregates. Please correct the legend and any corresponding labels so they are consistent with the main figure and the text. The authors should also double-check that the figure panel order, color coding, and statistical annotations match the legend and the descriptions in the Results section to avoid reader confusion.
  • At lines 129-130, the manuscript states that "FRAP analysis demonstrated that MKRN2 KD resulted in a slight increase in SG liquidity (Fig. 3F, Supplementary Fig. 6B)." However, the data shown in Figure 3F appear to indicate the opposite trend: the MKRN2 KD condition (red curve) exhibits a faster fluorescence recovery compared to the control (green curve). This discrepancy between the text and the figure should be corrected or clarified, as it directly affects the interpretation of MKRN2's role in SG disassembly. Ensuring consistency between the written description and the plotted FRAP data is essential for accurate interpretation.
  • At lines 132-133, the manuscript states: "Then, to further test the impact of MKRN2 on SG dynamics, we overexpressed MKRN2-GFP and observed that it was recruited to SG (Fig. 3G)." This description should be corrected or clarified, as the over-expressed MKRN2-GFP also appears to localize to the nucleus.
  • At lines 134-135, the manuscript states that the FK2 antibody detects "free ubiquitin." This is incorrect. FK2 does not detect free ubiquitin; it recognizes only ubiquitin conjugates, including mono-ubiquitinated and poly-ubiquitinated proteins. The text should be corrected accordingly to avoid misinterpretation of the immunostaining data.
  • Figure 5A suffers from poor resolution, and no scale bar is provided, which limits interpretability. Additionally, the ROI selected for the green channel (DRIPs) appears to capture unspecific background staining, while the most obvious DRIP spots are localized in the nucleus. The authors should clarify this in the text, improve the image quality if possible, and ensure that the ROI accurately represents DRIP accumulation - in SGs rather than background signal.

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

• In the first paragraph following the APEX proteomics results, the authors present validation data exclusively for MKRN2, justifying this early focus by stating that MKRN2 is the most SG-depleted E3 ligase. However, in the subsequent paragraph they introduce the RBULs and present knockdown data for MKRN2 along with two additional E3 ligases identified in the screen, before once again emphasizing that MKRN2 is the most SG-depleted ligase and therefore the main focus of the study. For clarity and logical flow, the manuscript would benefit from reordering the narrative. Specifically, the authors should first present the validation data for all three selected E3 ligases, and only then justify the decision to focus on MKRN2 for in-depth characterization. In addition to the extent of its SG depletion, the authors may also consider providing biologically relevant reasons for prioritizing MKRN2 (e.g., domain architecture, known roles in stress responses, or prior evidence of ubiquitination-related functions). Reorganizing this section would improve readability and better guide the reader through the rationale for the study's focus.
• At lines 137-138, the manuscript states: "Together these data indicate that MKRN2 regulates the assembly dynamics of SGs by promoting their coalescence during HS and can increase SG ubiquitin content." While Figure 3G shows some co-localization of MKRN2 with ubiquitin, immunofluorescence alone is insufficient to claim an increase in SG ubiquitin content. This conclusion should be supported by orthogonal experiments, such as Western blotting, in vitro ubiquitination assays, or immunoprecipitation of SG components. Including a control under no-stress conditions would also help demonstrate that ubiquitination increases specifically in response to stress. The second part of the statement should therefore be rephrased to avoid overinterpretation, for example:"...and may be associated with increased ubiquitination within SGs, as suggested by co-localization, pending further validation by complementary assays."
• At line 157, the statement: "Therefore, we conclude that MKRN2 ubiquitinates a subset of DRiPs, avoiding their accumulation inside SGs" should be rephrased as a preliminary observation. While the data support a role for MKRN2 in SG disassembly and a reduction of DRIPs, direct ubiquitination of DRIPs by MKRN2 has not been demonstrated. A more cautious phrasing would better reflect the current evidence and avoid overinterpretation.

Significance

General assessment: provide a summary of the strengths and limitations of the study. What are the strongest and most important aspects? What aspects of the study should be improved or could be developed?

• This study provides a valuable advancement in understanding the role of ubiquitination in stress granule (SG) dynamics and the clearance of SGs formed under heat stress. A major strength is the demonstration of how E3 ligases identified through proteomic screening, particularly MKRN2, influence SG assembly and disassembly in a ubiquitination- and heat stress-dependent manner. The combination of proteomics, imaging, and functional assays provides a coherent mechanistic framework linking ubiquitination to SG homeostasis. Limitations of the study include the exclusive use of a single model system (U2OS cells), which may limit generalizability. Additionally, some observations-such as MKRN2-dependent ubiquitination within SGs and changes in DRIP accumulation under different conditions-would benefit from orthogonal validation experiments (e.g., Western blotting, immunoprecipitation, or in vitro assays) to confirm and strengthen these findings. Addressing these points would enhance the robustness and broader applicability of the conclusions.

Advance: compare the study to the closest related results in the literature or highlight results reported for the first time to your knowledge; does the study extend the knowledge in the field and in which way? Describe the nature of the advance and the resulting insights (for example: conceptual, technical, clinical, mechanistic, functional,...).

• The closest related result in literature is - Yang, Cuiwei et al. "Stress granule homeostasis is modulated by TRIM21-mediated ubiquitination of G3BP1 and autophagy-dependent elimination of stress granules." Autophagy vol. 19,7 (2023): 1934-1951. doi:10.1080/15548627.2022.2164427 - demonstrating that TRIM21, an E3 ubiquitin ligase, catalyzes K63-linked ubiquitination of G3BP1, a core SG nucleator, under oxidative stress. This ubiquitination by TRIM21 inhibits SG formation, likely by altering G3BP1's propensity for phase separation. In contrast, the MKRN2 study identifies a different E3 (MKRN2) that regulates SG dynamics under heat stress and appears to influence both assembly and disassembly. This expands the role of ubiquitin ligases in SG regulation beyond those previously studied (like TRIM21).
• Gwon and colleagues (Gwon Y, Maxwell BA, Kolaitis RM, Zhang P, Kim HJ, Taylor JP. Ubiquitination of G3BP1 mediates stress granule disassembly in a context-specific manner. Science. 2021;372(6549):eabf6548. doi:10.1126/science.abf6548) have shown that K63-linked ubiquitination of G3BP1 is required for SG disassembly after heat stress. This ubiquitinated G3BP1 recruits the segregase VCP/p97, which helps extract G3BP1 from SGs for disassembly. The MKRN2 paper builds on this by linking UBA1-dependent ubiquitination and MKRN2's activity to SG disassembly. Specifically, they show MKRN2 knockdown affects disassembly, and suggest MKRN2 helps prevent accumulation of defective ribosomal products (DRiPs) in SGs, adding a new layer to the ubiquitin-VCP model.
• Ubiquitination's impact is highly stress- and context-dependent (different chain types, ubiquitin linkages, and recruitment of E3s). The MKRN2 work conceptually strengthens this idea: by showing that MKRN2's engagement with SGs depends on active ubiquitination via UBA1, and by demonstrating functional consequences (SG dynamics + DRIP accumulation), the study highlights how cellular context (e.g., heat stress) can recruit specific ubiquitin ligases to SGs and modulate their behavior.
• There is a gap in the literature: very few (if any) studies explicitly combine the biology of DRIPs, stress granules, and E3 ligase mediated ubiquitination, especially in mammalian cells. There are relevant works about DRIP biology in stress granules, but those studies focus on chaperone-based quality control, not ubiquitin ligase-mediated ubiquitination of DRIPs. This study seems to be one of the first to make that connection in mammalian (or human-like) SG biology. A work on the plant DRIP-E3 ligase TaSAP5 (Zhang N, Yin Y, Liu X, et al. The E3 Ligase TaSAP5 Alters Drought Stress Responses by Promoting the Degradation of DRIP Proteins. Plant Physiol. 2017;175(4):1878-1892. doi:10.1104/pp.17.01319 ) shows that DRIPs can be directly ubiquitinated by E3s in other biological systems - which supports the plausibility of the MKRN2 mechanism, but it's not the same context.
• A very recent review (Yuan, Lin et al. "Stress granules: emerging players in neurodegenerative diseases." Translational neurodegeneration vol. 14,1 22. 12 May. 2025, doi:10.1186/s40035-025-00482-9) summarizes and reinforces the relationship among SGs and the pathogenesis of different neurodegenerative diseases (NDDs). By identifying MKRN2 as a new ubiquitin regulator in SGs, the current study could have relevance for neurodegeneration and proteotoxic diseases, providing a new candidate to explore in disease models.

Audience: describe the type of audience ("specialized", "broad", "basic research", "translational/clinical", etc...) that will be interested or influenced by this research; how will this research be used by others; will it be of interest beyond the specific field?

The audience for this paper is primarily specialized, including researchers in stress granule biology, ubiquitin signaling, protein quality control, ribosome biology, and cellular stress responses. The findings will also be of interest to scientists working on granulostasis, nascent protein surveillance, and proteostasis mechanisms. Beyond these specific fields, the study provides preliminary evidence linking ubiquitination to DRIP handling and SG dynamics, which may stimulate new research directions and collaborative efforts across complementary areas of cell biology and molecular biology.

Please define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.

I work in ubiquitin biology, focusing on ubiquitination signaling in physiological and disease contexts, with particular expertise in the identification of E3 ligases and their substrates across different cellular systems and in vivo models. I have less expertise in stress granule dynamics and DRiP biology, so my evaluation of those aspects is more limited and relies on interpretation of the data presented in the manuscript.

Reviewer #1 (Public review):

Summary:

Zhou and colleagues developed a computational model of replay that heavily builds on cognitive models of memory in context (e.g., the context-maintenance and retrieval model), which have been successfully used to explain memory phenomena in the past. Their model produces results that mirror previous empirical findings in rodents and offers a new computational framework for thinking about replay.

Strengths:

The model is compelling and seems to explain a number of findings from the rodent literature. It is commendable that the authors implement commonly used algorithms from wakefulness to model sleep/rest, thereby linking wake and sleep phenomena in a parsimonious way. Additionally, the manuscript's comprehensive perspective on replay, bridging humans and non-human animals, enhanced its theoretical contribution.

Weaknesses:

This reviewer is not a computational neuroscientist by training, so some comments may stem from misunderstandings. I hope the authors would see those instances as opportunities to clarify their findings for broader audiences.

(1) The model predicts that temporally close items will be co-reactivated, yet evidence from humans suggests that temporal context doesn't guide sleep benefits (instead, semantic connections seem to be of more importance; Liu and Ranganath 2021, Schechtman et al 2023). Could these findings be reconciled with the model or is this a limitation of the current framework?

(2) During replay, the model is set so that the next reactivated item is sampled without replacement (i.e., the model cannot get "stuck" on a single item). I'm not sure what the biological backing behind this is and why the brain can't reactivate the same item consistently. Furthermore, I'm afraid that such a rule may artificially generate sequential reactivation of items regardless of wake training. Could the authors explain this better or show that this isn't the case?

(3) If I understand correctly, there are two ways in which novelty (i.e., less exposure) is accounted for in the model. The first and more talked about is the suppression mechanism (lines 639-646). The second is a change in learning rates (lines 593-595). It's unclear to me why both procedures are needed, how they differ, and whether these are two different mechanisms that the model implements. Also, since the authors controlled the extent to which each item was experienced during wakefulness, it's not entirely clear to me which of the simulations manipulated novelty on an individual item level, as described in lines 593-595 (if any).

As to the first mechanism - experience-based suppression - I find it challenging to think of a biological mechanism that would achieve this and is selectively activated immediately before sleep (somehow anticipating its onset). In fact, the prominent synaptic homeostasis hypothesis suggests that such suppression, at least on a synaptic level, is exactly what sleep itself does (i.e., prune or weaken synapses that were enhanced due to learning during the day). This begs the question of whether certain sleep stages (or ultradian cycles) may be involved in pruning, whereas others leverage its results for reactivation (e.g., a sequential hypothesis; Rasch & Born, 2013). That could be a compelling synthesis of this literature. Regardless of whether the authors agree, I believe that this point is a major caveat to the current model. It is addressed in the discussion, but perhaps it would be beneficial to explicitly state to what extent the results rely on the assumption of a pre-sleep suppression mechanism.

(4) As the manuscript mentions, the only difference between sleep and wake in the model is the initial conditions (a0). This is an obvious simplification, especially given the last author's recent models discussing the very different roles of REM vs NREM. Could the authors suggest how different sleep stages may relate to the model or how it could be developed to interact with other successful models such as the ones the last author has developed (e.g., C-HORSE)? Finally, I wonder how the model would explain findings (including the authors') showing a preference for reactivation of weaker memories. The literature seems to suggest that it isn't just a matter of novelty or exposure, but encoding strength. Can the model explain this? Or would it require additional assumptions or some mechanism for selective endogenous reactivation during sleep and rest?

(5) Lines 186-200 - Perhaps I'm misunderstanding, but wouldn't it be trivial that an external cue at the end-item of Figure 7a would result in backward replay, simply because there is no potential for forward replay for sequences starting at the last item (there simply aren't any subsequent items)? The opposite is true, of course, for the first-item replay, which can't go backward. More generally, my understanding of the literature on forward vs backward replay is that neither is linked to the rodent's location. Both commonly happen at a resting station that is further away from the track. It seems as though the model's result may not hold if replay occurs away from the track (i.e. if a0 would be equal for both pre- and post-run).

(6) The manuscript describes a study by Bendor & Wilson (2012) and tightly mimics their results. However, notably, that study did not find triggered replay immediately following sound presentation, but rather a general bias toward reactivation of the cued sequence over longer stretches of time. In other words, it seems that the model's results don't fully mirror the empirical results. One idea that came to mind is that perhaps it is the R/L context - not the first R/L item - that is cued in this study. This is in line with other TMR studies showing what may be seen as contextual reactivation. If the authors think that such a simulation may better mirror the empirical results, I encourage them to try. If not, however, this limitation should be discussed.

(7) There is some discussion about replay's benefit to memory. One point of interest could be whether this benefit changes between wake and sleep. Relatedly, it would be interesting to see whether the proportion of forward replay, backward replay, or both correlated with memory benefits. I encourage the authors to extend the section on the function of replay and explore these questions.

(8) Replay has been mostly studied in rodents, with few exceptions, whereas CMR and similar models have mostly been used in humans. Although replay is considered a good model of episodic memory, it is still limited due to limited findings of sequential replay in humans and its reliance on very structured and inherently autocorrelated items (i.e., place fields). I'm wondering if the authors could speak to the implications of those limitations on the generalizability of their model. Relatedly, I wonder if the model could or does lead to generalization to some extent in a way that would align with the complementary learning systems framework.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Zhou and colleagues developed a computational model of replay that heavily builds on cognitive models of memory in context (e.g., the context-maintenance and retrieval model), which have been successfully used to explain memory phenomena in the past. Their model produces results that mirror previous empirical findings in rodents and offers a new computational framework for thinking about replay.

Strengths:

The model is compelling and seems to explain a number of findings from the rodent literature. It is commendable that the authors implement commonly used algorithms from wakefulness to model sleep/rest, thereby linking wake and sleep phenomena in a parsimonious way. Additionally, the manuscript's comprehensive perspective on replay, bridging humans and non-human animals, enhanced its theoretical contribution.

Weaknesses:

This reviewer is not a computational neuroscientist by training, so some comments may stem from misunderstandings. I hope the authors would see those instances as opportunities to clarify their findings for broader audiences.

(1) The model predicts that temporally close items will be co-reactivated, yet evidence from humans suggests that temporal context doesn't guide sleep benefits (instead, semantic connections seem to be of more importance; Liu and Ranganath 2021, Schechtman et al 2023). Could these findings be reconciled with the model or is this a limitation of the current framework?

We appreciate the encouragement to discuss this connection. Our framework can accommodate semantic associations as determinants of sleep-dependent consolidation, which can in principle outweigh temporal associations. Indeed, prior models in this lineage have extensively simulated how semantic associations support encoding and retrieval alongside temporal associations. It would therefore be straightforward to extend our model to simulate how semantic associations guide sleep benefits, and to compare their contribution against that conferred by temporal associations across different experimental paradigms. In the revised manuscript, we have added a discussion of how our framework may simulate the role of semantic associations in sleep-dependent consolidation.

“Several recent studies have argued for dominance of semantic associations over temporal associations in the process of human sleep-dependent consolidation (Schechtman et al., 2023; Liu and Ranganath 2021; Sherman et al., 2025), with one study observing no role at all for temporal associations (Schechtman et al., 2023). At first glance, these findings appear in tension with our model, where temporal associations drive offline consolidation. Indeed, prior models have accounted for these findings by suppressing temporal context during sleep (Liu and Ranganath 2024; Sherman et al., 2025). However, earlier models in the CMR lineage have successfully captured the joint contributions of semantic and temporal associations to encoding and retrieval (Polyn et al., 2009), and these processes could extend naturally to offline replay. In a paradigm where semantic associations are especially salient during awake learning, the model could weight these associations more and account for greater co-reactivation and sleep-dependent memory benefits for semantically related than temporally related items. Consistent with this idea, Schechtman et al. (2023) speculated that their null temporal effects likely reflected the task’s emphasis on semantic associations. When temporal associations are more salient and task-relevant, sleep-related benefits for temporally contiguous items are more likely to emerge (e.g., Drosopoulos et al., 2007; King et al., 2017).”

The reviewer’s comment points to fruitful directions for future work that could employ our framework to dissect the relative contributions of semantic and temporal associations to memory consolidation.

(2) During replay, the model is set so that the next reactivated item is sampled without replacement (i.e., the model cannot get "stuck" on a single item). I'm not sure what the biological backing behind this is and why the brain can't reactivate the same item consistently.

Furthermore, I'm afraid that such a rule may artificially generate sequential reactivation of items regardless of wake training. Could the authors explain this better or show that this isn't the case?

We appreciate the opportunity to clarify this aspect of the model. We first note that this mechanism has long been a fundamental component of this class of models (Howard & Kahana 2002). Many classic memory models (Brown et al., 2000; Burgess & Hitch, 1991; Lewandowsky & Murdock 1989) incorporate response suppression, in which activated items are temporarily inhibited. The simplest implementation, which we use here, removes activated items from the pool of candidate items. Alternative implementations achieve this through transient inhibition, often conceptualized as neuronal fatigue (Burgess & Hitch, 1991; Grossberg 1978). Our model adopts a similar perspective, interpreting this mechanism as mimicking a brief refractory period that renders reactivated neurons unlikely to fire again within a short physiological event such as a sharp-wave ripple. Importantly, this approach does not generate spurious sequences. Instead, the model’s ability to preserve the structure of wake experience during replay depends entirely on the learned associations between items (without these associations, item order would be random). Similar assumptions are also common in models of replay. For example, reinforcement learning models of replay incorporate mechanisms such as inhibition to prevent repeated reactivations (e.g., Diekmann & Cheng, 2023) or prioritize reactivation based on ranking to limit items to a single replay (e.g., Mattar & Daw, 2018). We now discuss these points in the section titled “A context model of memory replay”

“This mechanism of sampling without replacement, akin to response suppression in established context memory models (Howard & Kahana 2002), could be implemented by neuronal fatigue or refractory dynamics (Burgess & Hitch, 1991; Grossberg 1978). Non-repetition during reactivation is also a common assumption in replay models that regulate reactivation through inhibition or prioritization (Diekmann & Cheng 2023; Mattar & Daw 2018; Singh et al., 2022).”

(3) If I understand correctly, there are two ways in which novelty (i.e., less exposure) is accounted for in the model. The first and more talked about is the suppression mechanism (lines 639-646). The second is a change in learning rates (lines 593-595). It's unclear to me why both procedures are needed, how they differ, and whether these are two different mechanisms that the model implements. Also, since the authors controlled the extent to which each item was experienced during wakefulness, it's not entirely clear to me which of the simulations manipulated novelty on an individual item level, as described in lines 593-595 (if any).

We agree that these mechanisms and their relationships would benefit from clarification. As noted, novelty influences learning through two distinct mechanisms. First, the suppression mechanism is essential for capturing the inverse relationship between the amount of wake experience and the frequency of replay, as observed in several studies. This mechanism ensures that items with high wake activity are less likely to dominate replay. Second, the decrease in learning rates with repetition is crucial for preserving the stochasticity of replay. Without this mechanism, the model would increase weights linearly, leading to an exponential increase in the probability of successive wake items being reactivated back-to-back due to the use of a softmax choice rule. This would result in deterministic replay patterns, which are inconsistent with experimental observations.

We have revised the Methods section to explicitly distinguish these two mechanisms:

“This experience-dependent suppression mechanism is distinct from the reduction of learning rates through repetition; it does not modulate the update of memory associations but exclusively governs which items are most likely to initiate replay.”

We have also clarified our rationale for including a learning rate reduction mechanism:

“The reduction in learning rates with repetition is important for maintaining a degree of stochasticity in the model’s replay during task repetition, since linearly increasing weights would, through the softmax choice rule, exponentially amplify differences in item reactivation probabilities, sharply reducing variability in replay.”

Finally, we now specify exactly where the learning-rate reduction applied, namely in simulations where sequences are repeated across multiple sessions:

“In this simulation, the learning rates progressively decrease across sessions, as described above.“

As to the first mechanism - experience-based suppression - I find it challenging to think of a biological mechanism that would achieve this and is selectively activated immediately before sleep (somehow anticipating its onset). In fact, the prominent synaptic homeostasis hypothesis suggests that such suppression, at least on a synaptic level, is exactly what sleep itself does (i.e., prune or weaken synapses that were enhanced due to learning during the day). This begs the question of whether certain sleep stages (or ultradian cycles) may be involved in pruning, whereas others leverage its results for reactivation (e.g., a sequential hypothesis; Rasch & Born, 2013). That could be a compelling synthesis of this literature. Regardless of whether the authors agree, I believe that this point is a major caveat to the current model. It is addressed in the discussion, but perhaps it would be beneficial to explicitly state to what extent the results rely on the assumption of a pre-sleep suppression mechanism.

We appreciate the reviewer raising this important point. Unlike the mechanism proposed by the synaptic homeostasis hypothesis, the suppression mechanism in our model does not suppress items based on synapse strength, nor does it modify synaptic weights. Instead, it determines the level of suppression for each item based on activity during awake experience. The brain could implement such a mechanism by tagging each item according to its activity level during wakefulness. During subsequent consolidation, the initial reactivation of an item during replay would reflect this tag, influencing how easily it can be reactivated.

A related hypothesis has been proposed in recent work, suggesting that replay avoids recently active trajectories due to spike frequency adaptation in neurons (Mallory et al., 2024). Similarly, the suppression mechanism in our model is critical for explaining the observed negative relationship between the amount of recent wake experience and the degree of replay.

We discuss the biological plausibility of this mechanism and its relationship with existing models in the Introduction. In the section titled “The influence of experience”, we have added the following:

“Our model implements an activity‑dependent suppression mechanism that, at the onset of each offline replay event, assigns each item a selection probability inversely proportional to its activation during preceding wakefulness. The brain could implement this by tagging each memory trace in proportion to its recent activation; during consolidation, that tag would then regulate starting replay probability, making highly active items less likely to be reactivated. A recent paper found that replay avoids recently traversed trajectories through awake spike‑frequency adaptation (Mallory et al., 2025), which could implement this kind of mechanism. In our simulations, this suppression is essential for capturing the inverse relationship between replay frequency and prior experience. Note that, unlike the synaptic homeostasis hypothesis (Tononi & Cirelli 2006), which proposes that the brain globally downscales synaptic weights during sleep, this mechanism leaves synaptic weights unchanged and instead biases the selection process during replay.”

(4) As the manuscript mentions, the only difference between sleep and wake in the model is the initial conditions (a0). This is an obvious simplification, especially given the last author's recent models discussing the very different roles of REM vs NREM. Could the authors suggest how different sleep stages may relate to the model or how it could be developed to interact with other successful models such as the ones the last author has developed (e.g., C-HORSE)? 

We appreciate the encouragement to comment on the roles of different sleep stages in the manuscript, especially since, as noted, the lab is very interested in this and has explored it in other work. We chose to focus on NREM in this work because the vast majority of electrophysiological studies of sleep replay have identified these events during NREM. In addition, our lab’s theory of the role of REM (Singh et al., 2022, PNAS) is that it is a time for the neocortex to replay remote memories, in complement to the more recent memories replayed during NREM. The experiments we simulate all involve recent memories. Indeed, our view is that part of the reason that there is so little data on REM replay may be that experimenters are almost always looking for traces of recent memories (for good practical and technical reasons).

Regarding the simplicity of the distinction between simulated wake and sleep replay, we view it as an asset of the model that it can account for many of the different characteristics of awake and NREM replay with very simple assumptions about differences in the initial conditions. There are of course many other differences between the states that could be relevant to the impact of replay, but the current target empirical data did not necessitate us taking those into account. This allows us to argue that differences in initial conditions should play a substantial role in an account of the differences between wake and sleep replay.

We have added discussion of these ideas and how they might be incorporated into future versions of the model in the Discussion section:

“Our current simulations have focused on NREM, since the vast majority of electrophysiological studies of sleep replay have identified replay events in this stage. We have proposed in other work that replay during REM sleep may provide a complementary role to NREM sleep, allowing neocortical areas to reinstate remote, already-consolidated memories that need to be integrated with the memories that were recently encoded in the hippocampus and replayed during NREM (Singh et al., 2022). An extension of our model could undertake this kind of continual learning setup, where the student but not teacher network retains remote memories, and the driver of replay alternates between hippocampus (NREM) and cortex (REM) over the course of a night of simulated sleep. Other differences between stages of sleep and between sleep and wake states are likely to become important for a full account of how replay impacts memory. Our current model parsimoniously explains a range of differences between awake and sleep replay by assuming simple differences in initial conditions, but we expect many more characteristics of these states (e.g., neural activity levels, oscillatory profiles, neurotransmitter levels, etc.) will be useful to incorporate in the future.”

Finally, I wonder how the model would explain findings (including the authors') showing a preference for reactivation of weaker memories. The literature seems to suggest that it isn't just a matter of novelty or exposure, but encoding strength. Can the model explain this? Or would it require additional assumptions or some mechanism for selective endogenous reactivation during sleep and rest?

We appreciate the encouragement to discuss this, as we do think the model could explain findings showing a preference for reactivation of weaker memories, as in Schapiro et al. (2018). In our framework, memory strength is reflected in the magnitude of each memory’s associated synaptic weights, so that stronger memories yield higher retrieved‑context activity during wake encoding than weaker ones. Because the model’s suppression mechanism reduces an item’s replay probability in proportion to its retrieved‑context activity, items with larger weights (strong memories) are more heavily suppressed at the onset of replay, while those with smaller weights (weaker memories) receive less suppression. When items have matched reward exposure, this dynamic would bias offline replay toward weaker memories, therefore preferentially reactivating weak memories. 

In the section titled “The influence of experience”, we updated a sentence to discuss this idea more explicitly: 

“Such a suppression mechanism may be adaptive, allowing replay to benefit not only the most recently or strongly encoded items but also to provide opportunities for the consolidation of weaker or older memories, consistent with empirical evidence (e.g., Schapiro et al. 2018; Yu et al., 2024).”

(5) Lines 186-200 - Perhaps I'm misunderstanding, but wouldn't it be trivial that an external cue at the end-item of Figure 7a would result in backward replay, simply because there is no potential for forward replay for sequences starting at the last item (there simply aren't any subsequent items)? The opposite is true, of course, for the first-item replay, which can't go backward. More generally, my understanding of the literature on forward vs backward replay is that neither is linked to the rodent's location. Both commonly happen at a resting station that is further away from the track. It seems as though the model's result may not hold if replay occurs away from the track (i.e. if a0 would be equal for both pre- and post-run).

In studies where animals run back and forth on a linear track, replay events are decoded separately for left and right runs, identifying both forward and reverse sequences for each direction, for example using direction-specific place cell sequence templates. Accordingly, in our simulation of, e.g., Ambrose et al. (2016), we use two independent sequences, one for left runs and one for right runs (an approach that has been taken in prior replay modeling work). Crucially, our model assumes a context reset between running episodes, preventing the final item of one traversal from acquiring contextual associations with the first item of the next. As a result, learning in the two sequences remains independent, and when an external cue is presented at the track’s end, replay predominantly unfolds in the backward direction, only occasionally producing forward segments when the cue briefly reactivates an earlier sequence item before proceeding forward.

We added a note to the section titled “The context-dependency of memory replay” to clarify this:

“In our model, these patterns are identical to those in our simulation of Ambrose et al. (2016), which uses two independent sequences to mimic the two run directions. This is because the drifting context resets before each run sequence is encoded, with the pause between runs acting as an event boundary that prevents the final item of one traversal from associating with the first item of the next, thereby keeping learning in each direction independent.”

To our knowledge, no study has observed a similar asymmetry when animals are fully removed from the track, although both types of replay can be observed when animals are away from the track. For example, Gupta et al. (2010) demonstrated that when animals replay trajectories far from their current location, the ratio of forward vs. backward replay appears more balanced. We now highlight this result in the manuscript and explain how it aligns with the predictions of our model:

“For example, in tasks where the goal is positioned in the middle of an arm rather than at its end, CMR-replay predicts a more balanced ratio of forward and reverse replay, whereas the EVB model still predicts a dominance of reverse replay due to backward gain propagation from the reward. This contrast aligns with empirical findings showing that when the goal is located in the middle of an arm, replay events are more evenly split between forward and reverse directions (Gupta et al., 2010), whereas placing the goal at the end of a track produces a stronger bias toward reverse replay (Diba & Buzsaki 2007).” 

Although no studies, to our knowledge, have observed a context-dependent asymmetry between forward and backward replay when the animal is away from the track, our model does posit conditions under which it could. Specifically, it predicts that deliberation on a specific memory, such as during planning, could generate an internal context input that biases replay: actively recalling the first item of a sequence may favor forward replay, while thinking about the last item may promote backward replay, even when the individual is physically distant from the track.

We now discuss this prediction in the section titled “The context-dependency of memory replay”:

“Our model also predicts that deliberation on a specific memory, such as during planning, could serve to elicit an internal context cue that biases replay: actively recalling the first item of a sequence may favor forward replay, while thinking about the last item may promote backward replay, even when the individual is physically distant from the track. While not explored here, this mechanism presents a potential avenue for future modeling and empirical work.”

(6) The manuscript describes a study by Bendor & Wilson (2012) and tightly mimics their results. However, notably, that study did not find triggered replay immediately following sound presentation, but rather a general bias toward reactivation of the cued sequence over longer stretches of time. In other words, it seems that the model's results don't fully mirror the empirical results. One idea that came to mind is that perhaps it is the R/L context - not the first R/L item - that is cued in this study. This is in line with other TMR studies showing what may be seen as contextual reactivation. If the authors think that such a simulation may better mirror the empirical results, I encourage them to try. If not, however, this limitation should be discussed.

Although our model predicts that replay is triggered immediately by the sound cue, it also predicts a sustained bias toward the cued sequence. Replay in our model unfolds across the rest phase as multiple successive events, so the bias observed in our sleep simulations indeed reflects a prolonged preference for the cued sequence.

We now discuss this issue, acknowledging the discrepancy:

“Bendor and Wilson (2012) found that sound cues during sleep did not trigger immediate replay, but instead biased reactivation toward the cued sequence over an extended period of time. While the model does exhibit some replay triggered immediately by the cue, it also captures the sustained bias toward the cued sequence over an extended period.”

Second, within this framework, context is modeled as a weighted average of the features associated with items. As a result, cueing the model with the first R/L item produces qualitatively similar outcomes as cueing it with a more extended R/L cue that incorporates features of additional items. This is because both approaches ultimately use context features unique to the two sides.

(7) There is some discussion about replay's benefit to memory. One point of interest could be whether this benefit changes between wake and sleep. Relatedly, it would be interesting to see whether the proportion of forward replay, backward replay, or both correlated with memory benefits. I encourage the authors to extend the section on the function of replay and explore these questions.

We thank the reviewer for this suggestion. Regarding differences in the contribution of wake and sleep to memory, our current simulations predict that compared to rest in the task environment, sleep is less biased toward initiating replay at specific items, leading to a more uniform benefit across all memories. Regarding the contributions of forward and backward replay, our model predicts that both strengthen bidirectional associations between items and contexts, benefiting memory in qualitatively similar ways. Furthermore, we suggest that the offline learning captured  by our teacher-student simulations reflects consolidation processes that are specific to sleep.

We have expanded the section titled “The influence of experience” to discuss these predictions of the model: 

“The results outlined above arise from the model's assumption that replay strengthens bidirectional associations between items and contexts to benefit memory. This assumption leads to several predictions about differences across replay types. First, the model predicts that sleep yields different memory benefits compared to rest in the task environment: Sleep is less biased toward initiating replay at specific items, resulting in a more uniform benefit across all memories. Second, the model predicts that forward and backward replay contribute to memory in qualitatively similar ways but tend to benefit different memories. This divergence arises because forward and backward replay exhibit distinct item preferences, with backward replay being more likely to include rewarded items, thereby preferentially benefiting those memories.”

We also updated the “The function of replay” section to include our teacher-student speculation:

“We speculate that the offline learning observed in these simulations corresponds to consolidation processes that operate specifically during sleep, when hippocampal-neocortical dynamics are especially tightly coupled (Klinzing et al., 2019).”

(8) Replay has been mostly studied in rodents, with few exceptions, whereas CMR and similar models have mostly been used in humans. Although replay is considered a good model of episodic memory, it is still limited due to limited findings of sequential replay in humans and its reliance on very structured and inherently autocorrelated items (i.e., place fields). I'm wondering if the authors could speak to the implications of those limitations on the generalizability of their model. Relatedly, I wonder if the model could or does lead to generalization to some extent in a way that would align with the complementary learning systems framework.

We appreciate these insightful comments. Traditionally, replay studies have focused on spatial tasks with autocorrelated item representations (e.g., place fields). However, an increasing number of human studies have demonstrated sequential replay using stimuli with distinct, unrelated representations. Our model is designed to accommodate both scenarios. In our current simulations, we employ orthogonal item representations while leveraging a shared, temporally autocorrelated context to link successive items. We anticipate that incorporating autocorrelated item representations would further enhance sequence memory by increasing the similarity between successive contexts. Overall, we believe that the model generalizes across a broad range of experimental settings, regardless of the degree of autocorrelation between items. Moreover, the underlying framework has been successfully applied to explain sequential memory in both spatial domains, explaining place cell firing properties (e.g., Howard et al., 2004), and in non-spatial domains, such as free recall experiments where items are arbitrarily related. 

In the section titled “A context model of memory replay”, we added this comment to address this point:

“Its contiguity bias stems from its use of shared, temporally autocorrelated context to link successive items, despite the orthogonal nature of individual item representations. This bias would be even stronger if items had overlapping representations, as observed in place fields.”

Since CMR-replay learns distributed context representations where overlap across context vectors captures associative structure, and replay helps strengthen that overlap, this could indeed be viewed as consonant with complementary learning systems integration processes. 

Reviewer #2 (Public Review):

This manuscript proposes a model of replay that focuses on the relation between an item and its context, without considering the value of the item. The model simulates awake learning, awake replay, and sleep replay, and demonstrates parallels between memory phenomenon driven by encoding strength, replay of sequence learning, and activation of nearest neighbor to infer causality. There is some discussion of the importance of suppression/inhibition to reduce activation of only dominant memories to be replayed, potentially boosting memories that are weakly encoded. Very nice replications of several key replay findings including the effect of reward and remote replay, demonstrating the equally salient cue of context for offline memory consolidation.

I have no suggestions for the main body of the study, including methods and simulations, as the work is comprehensive, transparent, and well-described. However, I would like to understand how the CMRreplay model fits with the current understanding of the importance of excitation vs inhibition, remembering vs forgetting, activation vs deactivation, strengthening vs elimination of synapses, and even NREM vs REM as Schapiro has modeled. There seems to be a strong association with the efforts of the model to instantiate a memory as well as how that reinstantiation changes across time. But that is not all this is to consolidation. The specific roles of different brain states and how they might change replay is also an important consideration.

We are gratified that the reviewer appreciated the work, and we agree that the paper would benefit from comment on the connections to these other features of consolidation.

Excitation vs. inhibition: CMR-replay does not model variations in the excitation-inhibition balance across brain states (as in other models, e.g., Chenkov et al., 2017), since it does not include inhibitory connections. However, we posit that the experience-dependent suppression mechanism in the model might, in the brain, involve inhibitory processes. Supporting this idea, studies have observed increased inhibition with task repetition (Berners-Lee et al., 2022). We hypothesize that such mechanisms may underlie the observed inverse relationship between task experience and replay frequency in many studies. We discuss this in the section titled “A context model of memory replay”:

“The proposal that a suppression mechanism plays a role in replay aligns with models that regulate place cell reactivation via inhibition (Malerba et al., 2016) and with empirical observations of increased hippocampal inhibitory interneuron activity with experience (Berners-Lee et al., 2022). Our model assumes the presence of such inhibitory mechanisms but does not explicitly model them.”

Remembering/forgetting, activation/deactivation, and strengthening/elimination of synapses: The model does not simulate synaptic weight reduction or pruning, so it does not forget memories through the weakening of associated weights. However, forgetting can occur when a memory is replayed less frequently than others, leading to reduced activation of that memory compared to its competitors during context-driven retrieval. In the Discussion section, we acknowledge that a biologically implausible aspect of our model is that it implements only synaptic strengthening: 

“Aspects of the model, such as its lack of regulation of the cumulative positive weight changes that can accrue through repeated replay, are biologically implausible (as biological learning results in both increases and decreases in synaptic weights) and limit the ability to engage with certain forms of low level neural data (e.g., changes in spine density over sleep periods; de Vivo et al., 2017; Maret et al., 2011). It will be useful for future work to explore model variants with more elements of biological plausibility.” Different brain states and NREM vs REM: Reviewer 1 also raised this important issue (see above). We have added the following thoughts on differences between these states and the relationship to our prior work to the Discussion section:

“Our current simulations have focused on NREM, since the vast majority of electrophysiological studies of sleep replay have identified replay events in this stage. We have proposed in other work that replay during REM sleep may provide a complementary role to NREM sleep, allowing neocortical areas to reinstate remote, already-consolidated memories that need to be integrated with the memories that were recently encoded in the hippocampus and replayed during NREM (Singh et al., 2022). An extension of our model could undertake this kind of continual learning setup, where the student but not teacher network retains remote memories, and the driver of replay alternates between hippocampus (NREM) and cortex (REM) over the course of a night of simulated sleep. Other differences between stages of sleep and between sleep and wake states are likely to become important for a full account of how replay impacts memory. Our current model parsimoniously explains a range of differences between awake and sleep replay by assuming simple differences in initial conditions, but we expect many more characteristics of these states (e.g., neural activity levels, oscillatory profiles, neurotransmitter levels, etc.) will be useful to incorporate in the future.”

We hope these points clarify the model’s scope and its potential for future extensions.

Do the authors suggest that these replay systems are more universal to offline processes beyond episodic memory? What about procedural memories and working memory?

We thank the reviewer for raising this important question. We have clarified in the manuscript:

“We focus on the model as a formulation of hippocampal replay, capturing how the hippocampus may replay past experiences through simple and interpretable mechanisms.”

With respect to other forms of memory, we now note that:

“This motor memory simulation using a model of hippocampal replay is consistent with evidence that hippocampal replay can contribute to consolidating memories that are not hippocampally dependent at encoding (Schapiro et al., 2019; Sawangjit et al., 2018). It is possible that replay in other, more domain-specific areas could also contribute (Eichenlaub et al., 2020).”

Though this is not a biophysical model per se, can the authors speak to the neuromodulatory milieus that give rise to the different types of replay?

Our work aligns with the perspective proposed by Hasselmo (1999), which suggests that waking and sleep states differ in the degree to which hippocampal activity is driven by external inputs. Specifically, high acetylcholine levels during waking bias activity to flow into the hippocampus, while low acetylcholine levels during sleep allow hippocampal activity to influence other brain regions. Consistent with this view, our model posits that wake replay is more biased toward items associated with the current resting location due to the presence of external input during waking states. In the Discussion section, we have added a comment on this point:

“Our view aligns with the theory proposed by Hasselmo (1999), which suggests that the degree of hippocampal activity driven by external inputs differs between waking and sleep states: High acetylcholine levels during wakefulness bias activity into the hippocampus, while low acetylcholine levels during slow-wave sleep allow hippocampal activity to influence other brain regions.”

Reviewer #3 (Public Review):

In this manuscript, Zhou et al. present a computational model of memory replay. Their model (CMR-replay) draws from temporal context models of human memory (e.g., TCM, CMR) and claims replay may be another instance of a context-guided memory process. During awake learning, CMR replay (like its predecessors) encodes items alongside a drifting mental context that maintains a recency-weighted history of recently encoded contexts/items. In this way, the presently encoded item becomes associated with other recently learned items via their shared context representation - giving rise to typical effects in recall such as primacy, recency, and contiguity. Unlike its predecessors, CMR-replay has built-in replay periods. These replay periods are designed to approximate sleep or wakeful quiescence, in which an item is spontaneously reactivated, causing a subsequent cascade of item-context reactivations that further update the model's item-context associations.

Using this model of replay, Zhou et al. were able to reproduce a variety of empirical findings in the replay literature: e.g., greater forward replay at the beginning of a track and more backward replay at the end; more replay for rewarded events; the occurrence of remote replay; reduced replay for repeated items, etc. Furthermore, the model diverges considerably (in implementation and predictions) from other prominent models of replay that, instead, emphasize replay as a way of predicting value from a reinforcement learning framing (i.e., EVB, expected value backup).

Overall, I found the manuscript clear and easy to follow, despite not being a computational modeller myself. (Which is pretty commendable, I'd say). The model also was effective at capturing several important empirical results from the replay literature while relying on a concise set of mechanisms - which will have implications for subsequent theory-building in the field.

With respect to weaknesses, additional details for some of the methods and results would help the readers better evaluate the data presented here (e.g., explicitly defining how the various 'proportion of replay' DVs were calculated).

For example, for many of the simulations, the y-axis scale differs from the empirical data despite using comparable units, like the proportion of replay events (e.g., Figures 1B and C). Presumably, this was done to emphasize the similarity between the empirical and model data. But, as a reader, I often found myself doing the mental manipulation myself anyway to better evaluate how the model compared to the empirical data. Please consider using comparable y-axis ranges across empirical and simulated data wherever possible.

We appreciate this point. As in many replay modeling studies, our primary goal is to provide a qualitative fit that demonstrates the general direction of differences between our model and empirical data, without engaging in detailed parameter fitting for a precise quantitative fit. Still, we agree that where possible, it is useful to better match the axes. We have updated figures 2B and 2C so that the y-axis scales are more directly comparable between the empirical and simulated data. 

In a similar vein to the above point, while the DVs in the simulations/empirical data made intuitive sense, I wasn't always sure precisely how they were calculated. Consider the "proportion of replay" in Figure 1A. In the Methods (perhaps under Task Simulations), it should specify exactly how this proportion was calculated (e.g., proportions of all replay events, both forwards and backwards, combining across all simulations from Pre- and Post-run rest periods). In many of the examples, the proportions seem to possibly sum to 1 (e.g., Figure 1A), but in other cases, this doesn't seem to be true (e.g., Figure 3A). More clarity here is critical to help readers evaluate these data. Furthermore, sometimes the labels themselves are not the most informative. For example, in Figure 1A, the y-axis is "Proportion of replay" and in 1C it is the "Proportion of events". I presumed those were the same thing - the proportion of replay events - but it would be best if the axis labels were consistent across figures in this manuscript when they reflect the same DV.

We appreciate these useful suggestions. We have revised the Methods section to explain in detail how DVs are calculated for each simulation. The revisions clarify the differences between related measures, such as those shown in Figures 1A and 1C, so that readers can more easily see how the DVs are defined and interpreted in each case. 

Reviewer #4/Reviewing Editor (Public Review):

Summary:

With their 'CMR-replay' model, Zhou et al. demonstrate that the use of spontaneous neural cascades in a context-maintenance and retrieval (CMR) model significantly expands the range of captured memory phenomena.

Strengths:

The proposed model compellingly outperforms its CMR predecessor and, thus, makes important strides towards understanding the empirical memory literature, as well as highlighting a cognitive function of replay.

Weaknesses:

Competing accounts of replay are acknowledged but there are no formal comparisons and only CMR-replay predictions are visualized. Indeed, other than the CMR model, only one alternative account is given serious consideration: A variant of the 'Dyna-replay' architecture, originally developed in the machine learning literature (Sutton, 1990; Moore & Atkeson, 1993) and modified by Mattar et al (2018) such that previously experienced event-sequences get replayed based on their relevance to future gain. Mattar et al acknowledged that a realistic Dyna-replay mechanism would require a learned representation of transitions between perceptual and motor events, i.e., a 'cognitive map'. While Zhou et al. note that the CMR-replay model might provide such a complementary mechanism, they emphasize that their account captures replay characteristics that Dyna-replay does not (though it is unclear to what extent the reverse is also true).

We thank the reviewer for these thoughtful comments and appreciate the opportunity to clarify our approach. Our goal in this work is to contrast two dominant perspectives in replay research: replay as a mechanism for learning reward predictions and replay as a process for memory consolidation. These models were chosen as representatives of their classes of models because they use simple and interpretable mechanisms that can simulate a wide range of replay phenomena, making them ideal for contrasting these two perspectives.

Although we implemented CMR-replay as a straightforward example of the memory-focused view, we believe the proposed mechanisms could be extended to other architectures, such as recurrent neural networks, to produce similar results. We now discuss this possibility in the revised manuscript (see below). However, given our primary goal of providing a broad and qualitative contrast of these two broad perspectives, we decided not to undertake simulations with additional individual models for this paper.

Regarding the Mattar & Daw model, it is true that a mechanistic implementation would require a mechanism that avoids precomputing priorities before replay. However, the "need" component of their model already incorporates learned expectations of transitions between actions and events. Thus, the model's limitations are not due to the absence of a cognitive map.

In contrast, while CMR-replay also accumulates memory associations that reflect experienced transitions among events, it generates several qualitatively distinct predictions compared to the Mattar & Daw model. As we note in the manuscript, these distinctions make CMR-replay a contrasting rather than complementary perspective.

Another important consideration, however, is how CMR replay compares to alternative mechanistic accounts of cognitive maps. For example, Recurrent Neural Networks are adept at detecting spatial and temporal dependencies in sequential input; these networks are being increasingly used to capture psychological and neuroscientific data (e.g., Zhang et al, 2020; Spoerer et al, 2020), including hippocampal replay specifically (Haga & Fukai, 2018). Another relevant framework is provided by Associative Learning Theory, in which bidirectional associations between static and transient stimulus elements are commonly used to explain contextual and cue-based phenomena, including associative retrieval of absent events (McLaren et al, 1989; Harris, 2006; Kokkola et al, 2019). Without proper integration with these modeling approaches, it is difficult to gauge the innovation and significance of CMR-replay, particularly since the model is applied post hoc to the relatively narrow domain of rodent maze navigation.

First, we would like to clarify our principal aim in this work is to characterize the nature of replay, rather than to model cognitive maps per se. Accordingly, CMR‑replay is not designed to simulate head‐direction signals, perform path integration, or explain the spatial firing properties of neurons during navigation. Instead, it focuses squarely on sequential replay phenomena, simulating classic rodent maze reactivation studies and human sequence‐learning tasks. These simulations span a broad array of replay experimental paradigms to ensure extensive coverage of the replay findings reported across the literature. As such, the contribution of this work is in explaining the mechanisms and functional roles of replay, and demonstrating that a model that employs simple and interpretable memory mechanisms not only explains replay phenomena traditionally interpreted through a value-based lens but also accounts for findings not addressed by other memory-focused models.

As the reviewer notes, CMR-replay shares features with other memory-focused models. However, to our knowledge, none of these related approaches have yet captured the full suite of empirical replay phenomena, suggesting the combination of mechanisms employed in CMR-replay is essential for explaining these phenomena. In the Discussion section, we now discuss the similarities between CMR-replay and related memory models and the possibility of integrating these approaches:

“Our theory builds on a lineage of memory-focused models, demonstrating the power of this perspective in explaining phenomena that have often been attributed to the optimization of value-based predictions. In this work, we focus on CMR-replay, which exemplifies the memory-centric approach through a set of simple and interpretable mechanisms that we believe are broadly applicable across memory domains. Elements of CMR-replay share similarities with other models that adopt a memory-focused perspective. The model learns distributed context representations whose overlaps encodes associations among items, echoing associative learning theories in which overlapping patterns capture stimulus similarity and learned associations (McLaren & Mackintosh 2002). Context evolves through bidirectional interactions between items and their contextual representations, mirroring the dynamics found in recurrent neural networks (Haga & Futai 2018; Levenstein et al., 2024). However, these related approaches have not been shown to account for the present set of replay findings and lack mechanisms—such as reward-modulated encoding and experience-dependent suppression—that our simulations suggest are essential for capturing these phenomena. While not explored here, we believe these mechanisms could be integrated into architectures like recurrent neural networks (Levenstein et al., 2024) to support a broader range of replay dynamics.”

Recommendations For The Authors

Reviewer #1 (Recommendations For The Authors):

(1) Lines 94-96: These lines may be better positioned earlier in the paragraph.

We now introduce these lines earlier in the paragraph.

(2) Line 103 - It's unclear to me what is meant by the statement that "the current context contains contexts associated with previous items". I understand why a slowly drifting context will coincide and therefore link with multiple items that progress rapidly in time, so multiple items will be linked to the same context and each item will be linked to multiple contexts. Is that the idea conveyed here or am I missing something? I'm similarly confused by line 129, which mentions that a context is updated by incorporating other items' contexts. How could a context contain other contexts?

In the model, each item has an associated context that can be retrieved via Mfc. This is true even before learning, since Mfc is initialized as an identity matrix. During learning and replay, we have a drifting context c that is updated each time an item is presented. At each timestep, the model first retrieves the current item’s associated context cf by Mfc, and incorporates it into c. Equation #2 in the Methods section illustrates this procedure in detail. Because of this procedure, the drifting context c is a weighted sum of past items’ associated contexts. 

We recognize that these descriptions can be confusing. We have updated the Results section to better distinguish the drifting context from items’ associated context. For example, we note that:

“We represent the drifting context during learning and replay with c and an item's associated context with cf.”

We have also updated our description of the context drift procedure to distinguish these two quantities: 

“During awake encoding of a sequence of items, for each item f, the model retrieves its associated context cf via Mfc. The drifting context c incorporates the item's associated context cf and downweights its representation of previous items' associated contexts (Figure 1c). Thus, the context layer maintains a recency weighted sum of past and present items' associated contexts.”

(3) Figure 1b and 1d - please clarify which axis in the association matrices represents the item and the context.

We have added labels to show what the axes represent in Figure 1.

(4) The terms "experience" and "item" are used interchangeably and it may be best to stick to one term.

We now use the term “item” wherever we describe the model results. 

(5) The manuscript describes Figure 6 ahead of earlier figures - the authors may want to reorder their figures to improve readability.

We appreciate this suggestion. We decided to keep the current figure organization since it allows us to group results into different themes and avoid redundancy. 

(6) Lines 662-664 are repeated with a different ending, this is likely an error.

We have fixed this error.

Reviewer #3 (Recommendations For The Authors):

Below, I have outlined some additional points that came to mind in reviewing the manuscript - in no particular order.

(1) Figure 1: I found the ordering of panels a bit confusing in this figure, as the reading direction changes a couple of times in going from A to F. Would perhaps putting panel C in the bottom left corner and then D at the top right, with E and F below (also on the right) work?

We agree that this improves the figure. We have restructured the ordering of panels in this figure. 

(2) Simulation 1: When reading the intro/results for the first simulation (Figure 2a; Diba & Buszaki, 2007; "When animals traverse a linear track...", page 6, line 186). It wasn't clear to me why pre-run rest would have any forward replay, particularly if pre-run implied that the animal had no experience with the track yet. But in the Methods this becomes clearer, as the model encodes the track eight times prior to the rest periods. Making this explicit in the text would make it easier to follow. Also, was there any reason why specifically eight sessions of awake learning, in particular, were used?

We now make more explicit that the animals have experience with the track before pre-run rest recording:

“Animals first acquire experience with a linear track by traversing it to collect a reward. Then, during the pre-run rest recording, forward replay predominates.”

We included eight sessions of awake learning to match with the number of sessions in Shin et al. (2017), since this simulation attempts to explain data from that study. After each repetition, the model engages in rest. We have revised the Methods section to indicate the motivation for this choice: 

“In the simulation that examines context-dependent forward and backward replay through experience (Figs. 2a and 5a), CMR-replay encodes an input sequence shown in Fig. 7a, which simulates a linear track run with no ambiguity in the direction of inputs, over eight awake learning sessions (as in Shin et al. 2019)”

(3) Frequency of remote replay events: In the simulation based on Gupta et al, how frequently overall does remote replay occur? In the main text, the authors mention the mean frequency with which shortcut replay occurs (i.e., the mean proportion of replay events that contain a shortcut sequence = 0.0046), which was helpful. But, it also made me wonder about the likelihood of remote replay events. I would imagine that remote replay events are infrequent as well - given that it is considerably more likely to replay sequences from the local track, given the recency-weighted mental context. Reporting the above mean proportion for remote and local replay events would be helpful context for the reader.

In Figure 4c, we report the proportion of remote replay in the two experimental conditions of Gupta et al. that we simulate. 

(4) Point of clarification re: backwards replay: Is backwards replay less likely to occur than forward replay overall because of the forward asymmetry associated with these models? For example, for a backwards replay event to occur, the context would need to drift backwards at least five times in a row, in spite of a higher probability of moving one step forward at each of those steps. Am I getting that right?

The reviewer’s interpretation is correct: CMR-replay is more likely to produce forward than backward replay in sleep because of its forward asymmetry. We note that this forward asymmetry leads to high likelihood of forward replay in the section titled “The context-dependency of memory replay”: 

“As with prior retrieved context models (Howard & Kahana 2002; Polyn et al., 2009), CMR-replay encodes stronger forward than backward associations. This asymmetry exists because, during the first encoding of a sequence, an item's associated context contributes only to its ensuing items' encoding contexts. Therefore, after encoding, bringing back an item's associated context is more likely to reactivate its ensuing than preceding items, leading to forward asymmetric replay (Fig. 6d left).”

(5) On terminating a replay period: "At any t, the replay period ends with a probability of 0.1 or if a task-irrelevant item is reactivated." (Figure 1 caption; see also pg 18, line 635). How was the 0.1 decided upon? Also, could you please add some detail as to what a 'task-irrelevant item' would be? From what I understood, the model only learns sequences that represent the points in a track - wouldn't all the points in the track be task-relevant?

This value was arbitrarily chosen as a small value that allows probabilistic stopping. It was not motivated by prior modeling or a systematic search. We have added: “At each timestep, the replay period ends either with a stop probability of 0.1 or if a task-irrelevant item becomes reactivated. (The choice of the value 0.1 was arbitrary; future work could explore the implications of varying this parameter).” 

In addition, we now explain in the paper that task irrelevant items “do not appear as inputs during awake encoding, but compete with task-relevant items for reactivation during replay, simulating the idea that other experiences likely compete with current experiences during periods of retrieval and reactivation.”

(6) Minor typos:

Turn all instances of "nonlocal" into "non-local", or vice versa

"For rest at the end of a run, cexternal is the context associated with the final item in the sequence. For rest at the end of a run, cexternal is the context associated with the start item." (pg 20, line 663) - I believe this is a typo and that the second sentence should begin with "For rest at the START of a run".

We have updated the manuscript to correct these typos. 

(7) Code availability: I may have missed it, but it doesn't seem like the code is currently available for these simulations. Including the commented code in a public repository (Github, OSF) would be very useful in this case.

We now include a Github link to our simulation code: https://github.com/schapirolab/CMR-replay.

I bet Civil Liberties as a concept is not in the average liberal's lexicon these days. The state must force WOC's and gays into everything.

I believe that the Gay stuff is all about population control, it's much more moral than the Nazi's so I am not complaining.

Like Historically it does seem to just popped up and taken over institutions.

The "Empire of Love" as Moldbug calls it.

Говорится, что делегирование AI ключевых мыслительных задач (даже с последующим редактированием) подрывает развитие собственного критического мышления и креативности.

Just as the previous term, this one is charged with a derogatory connotation, so much as it is preferred to refer to it with the euphemistic expression "N-word", which emcompasses both this word and its -er variant. The phenomenon that allowed the erosion of the -er ending is called r-dropping: the -r is replaced by a schwa (ə), an indistinct vowel pronounced "uh". These terms have always been linked with white supremacy, racism and white power. Even though the two terms may seem synonyms, in fact there is a difference: the -er ending word is strictly connected with the all-encompassing hatred and contempt towards black people, whereas the second one is perceived as a term of endearment when uttered by someone belonging to the Black community. Indeed, starting from the 1980s, the word has underwent a process of reclaiming (also called semantic inversion or looping) which corresponds to "taking a word meant as a slur and reappropriating it as a term of endearment" (https://www.washingtonpost.com/sf/national/2014/11/09/the-n-word-an-entrenched-racial-slur-now-more-prevalent-than-ever/?utm_term=.1590a4928864). This strategy allows the originally oppressive term to be re-semantized (that is, to acquire a new meaning) and used to celebrate the community's unique identity and humanity as "an act of redemption by black folk. The word survives on the conditions that black folks have inscribed for it and nobody else can take that. And it becomes violent when other people try to take it and use it." Indeed, white people "have created the word in the first place, but […] they have lost the power to use it with impunity, they have lost the power to reclaim it." […] "If you understand the history of the word and how it's been used, it's not for white people to use […] So if you're not black you can't do that. You actually can't use the word in the way that we use it. It's not possible, because you're not in that space. So any other usage of it is completely wrong." (https://www.bbc.com/news/stories-53749800). https://www.merriam-webster.com/dictionary/N-word https://share.google/2p6rElVA4Vin0v2cC https://www.dailydot.com/irl/how-not-to-use-the-n-word/

Just as the previous term, this one is charged with a derogatory connotation, so much as it is preferred to refer to it with the euphemistic expression "N-word", which emcompasses both this word and its -er variant. The phenomenon that allowed the erosion of the -er ending is called r-dropping: the -r is replaced by a schwa (ə), an indistinct vowel pronounced "uh". These terms have always been linked with white supremacy, racism and white power. Even though the two terms may seem synonyms, in fact there is a difference: the -er ending word is strictly connected with the all-encompassing hatred and contempt towards black people, whereas the second one is perceived as a term of endearment when uttered by someone belonging to the Black community. Indeed, starting from the 1980s, the word has underwent a process of reclaiming (also called semantic inversion or looping) which corresponds to "taking a word meant as a slur and reappropriating it as a term of endearment" (https://www.washingtonpost.com/sf/national/2014/11/09/the-n-word-an-entrenched-racial-slur-now-more-prevalent-than-ever/?utm_term=.1590a4928864). This strategy allows the originally oppressive term to be re-semantized (that is, to acquire a new meaning) and used to celebrate the community's unique identity and humanity as "an act of redemption by black folk. The word survives on the conditions that black folks have inscribed for it and nobody else can take that. And it becomes violent when other people try to take it and use it." Indeed, white people "have created the word in the first place, but […] they have lost the power to use it with impunity, they have lost the power to reclaim it." […] "If you understand the history of the word and how it's been used, it's not for white people to use […] So if you're not black you can't do that. You actually can't use the word in the way that we use it. It's not possible, because you're not in that space. So any other usage of it is completely wrong." (https://www.bbc.com/news/stories-53749800). https://www.merriam-webster.com/dictionary/N-word https://share.google/2p6rElVA4Vin0v2cC https://www.dailydot.com/irl/how-not-to-use-the-n-word/

By def, we're not talking about community then. The behaviour mentioned is that of those who do not think they're part of a bigger whole here. Then by def whatever output is there to just use, as there is no social contract involved. Social asymmetry then is a given, and thus a breakdown of commons.

Llimitations are low concurrency (not an issue, unless I write from multiple applications/scripts to the same thing) local only/mostly, fine too user management, not an issue, just me scalability, not suited to large amounts. The premise is that I won't have lots of data ALTER TABLE limitations, this may mean rebulds/redesigns as things evolve?

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary

This is a strong paper that presents a clear advance in multi-animal tracking. The authors introduce an updated version of idtracker.ai that reframes identity assignment as a contrastive learning problem rather than a classification task requiring global fragments. This change leads to gains in speed and accuracy. The method eliminates a known bottleneck in the original system, and the benchmarking across species is comprehensive and well executed. I think the results are convincing and the work is significant.

Strengths

The main strengths are the conceptual shift from classification to representation learning, the clear performance gains, and the fact that the new version is more robust. Removing the need for global fragments makes the software more flexible in practice, and the accuracy and speed improvements are well demonstrated. The software appears thoughtfully implemented, with GUI updates and integration with pose estimators.

Weaknesses

I don't have any major criticisms, but I have identified a few points that should be addressed to improve the clarity and accuracy of the claims made in the paper.

(1) The title begins with "New idtracker.ai," which may not age well and sounds more promotional than scientific. The strength of the work is the conceptual shift to contrastive representation learning, and it might be more helpful to emphasize that in the title rather than branding it as "new."

We considered using “Contrastive idtracker.ai”. However, we thought that readers could then think that we believe they could use both the old idtracker.ai or this contrastive version. But we want to say that the new version is the one to use as it is better in both accuracy and tracking times. We think “New idtracker.ai” communicates better that this version is the version we recommend.

(2) Several technical points regarding the comparison between TRex (a system evaluated in the paper) and idtracker.ai should be addressed to ensure the evaluation is fair and readers are fully informed.

(2.1) Lines 158-160: The description of TRex as based on "Protocol 2 of idtracker.ai" overlooks several key additions in TRex, such as posture image normalization, tracklet subsampling, and the use of uniqueness feedback during training. These features are not acknowledged, and it's unclear whether TRex was properly configured - particularly regarding posture estimation, which appears to have been omitted but isn't discussed. Without knowing the actual parameters used to make comparisons, it's difficult to dassess how the method was evaluated.

We added the information about the key additions of TRex in the section “The new idtracker.ai uses representation learning”, lines 153-157. Posture estimation in TRex was not explicitly used but neither disabled during the benchmark; we clarified this in the last paragraph of “Benchmark of accuracy and tracking time”, lines 492-495.

(2.2) Lines 162-163: The paper implies that TRex gains speed by avoiding Protocol 3, but in practice, idtracker.ai also typically avoids using Protocol 3 due to its extremely long runtime. This part of the framing feels more like a rhetorical contrast than an informative one.

We removed this, see new lines 153-157.

(2.3) Lines 277-280: The contrastive loss function is written using the label l, but since it refers to a pair of images, it would be clearer and more precise to write it as l_{I,J}. This would help readers unfamiliar with contrastive learning understand the formulation more easily.

We added this change in lines 613-620.

(2.4) Lines 333-334: The manuscript states that TRex can fail to track certain videos, but this may be inaccurate depending on how the authors classify failures. TRex may return low uniqueness scores if training does not converge well, but this isn't equivalent to tracking failure. Moreover, the metric reported by TRex is uniqueness, not accuracy. Equating the two could mislead readers. If the authors did compare outputs to human-validated data, that should be stated more explicitly.

We observed TRex crashing without outputting any trajectories on some occasions (Appendix 1—figure 1), and this is what we labeled as “failure”. These failures happened in the most difficult videos of our benchmark, that’s why we treated them the same way as idtracker.ai going to P3. We clarified this in new lines 464-469.

The accuracy measured in our benchmark is not estimated but it is human-validated (see section Computation of tracking accuracy in Appendix 1). Both softwares report some quality estimators at the end of a tracking (“estimated accuracy” for idtracker.ai and "uniqueness” for TRex) but these were not used in the benchmark.

(2.5) Lines 339-341: The evaluation approach defines a "successful run" and then sums the runtime across all attempts up to that point. If success is defined as simply producing any output, this may not reflect how experienced users actually interact with the software, where parameters are iteratively refined to improve quality.

Yes, our benchmark was designed to be agnostic to the different experiences of the user. Also, our benchmark was designed for users that do not inspect the trajectories to choose parameters again not to leave room for potential subjectivity.

(2.6) Lines 344-346: The simulation process involves sampling tracking parameters 10,000 times and selecting the first "successful" run. If parameter tuning is randomized rather than informed by expert knowledge, this could skew the results in favor of tools that require fewer or simpler adjustments. TRex relies on more tunable behavior, such as longer fragments improving training time, which this approach may not capture.

We precisely used the TRex parameter track_max_speed to elongate fragments for optimal tracking. Rather than randomized parameter tuning, we defined the “valid range” for this parameter so that all values in it would produce a decent fragment structure. We used this procedure to avoid worsening those methods that use more parameters.

(2.7) Line 354 onward: TRex was evaluated using two varying parameters (threshold and track_max_speed), while idtracker.ai used only one (intensity_threshold). With a fixed number of samples, this asymmetry could bias results against TRex. In addition, users typically set these parameters based on domain knowledge rather than random exploration.

idtracker.ai and TRex have several parameters. Some of them have a single correct value (e.g. number of animals) or the default value that the system computes is already good (e.g. minimum blob size). For a second type of parameters, the system finds a value that is in general not as good, so users need to modify them. In general, users find that for this second type of parameter there is a valid interval of possible values, from which they need to choose a single value to run the system. idtracker.ai has intensity_threshold as the only parameter of this second type and TRex has two: threshold and track_max_speed. For these parameters, choosing one value or another within the valid interval can give different tracking results. Therefore, when we model a user that wants to run the system once except if it goes to P3 (idtracker.ai) or except if it crashes (TRex), it is these parameters we sample from within the valid interval to get a different value for each run of the system. We clarify this in lines 452-469 of the section “Benchmark of accuracy and tracking time”.

Note that if we chose to simply run old idtracker.ai (v4 or v5) or TRex a single time, this would benefit the new idtracker.ai (v6). This is because old idtracker.ai can enter the very slow protocol 3 and TRex can fail to track. So running old idtracker.ai or TRex up to 5 times until old idtracker.ai does not use Protocol 3 and TRex does not fail is to make them as good as they can be with respect to the new idtracker.ai. 

(2.8) Figure 2-figure supplement 3: The memory usage comparison lacks detail. It's unclear whether RAM or VRAM was measured, whether shared or compressed memory was included, or how memory was sampled. Since both tools dynamically adjust to system resources, the relevance of this comparison is questionable without more technical detail.

We modified the text in the caption (new Figure 1-figure supplement 2) adding the kind of memory we measured (RAM) and how we measured it. We already have a disclaimer for this plot saying that memory management depends on the machine's available resources. We agree that this is a simple analysis of the usage of computer resources.

(3) While the authors cite several key papers on contrastive learning, they do not use the introduction or discussion to effectively situate their approach within related fields where similar strategies have been widely adopted. For example, contrastive embedding methods form the backbone of modern facial recognition and other image similarity systems, where the goal is to map images into a latent space that separates identities or classes through clustering. This connection would help emphasize the conceptual strength of the approach and align the work with well-established applications. Similarly, there is a growing literature on animal re-identification (ReID), which often involves learning identity-preserving representations across time or appearance changes. Referencing these bodies of work would help readers connect the proposed method with adjacent areas using similar ideas, and show that the authors are aware of and building on this wider context.

We have now added a new section in Appendix 3, “Differences with previous work in contrastive/metric learning” (lines 792-841) to include references to previous work and a description of what we do differently.

(4) Some sections of the Results text (e.g., lines 48-74) read more like extended figure captions than part of the main narrative. They include detailed explanations of figure elements, sorting procedures, and video naming conventions that may be better placed in the actual figure captions or moved to supplementary notes. Streamlining this section in the main text would improve readability and help the central ideas stand out more clear

Thank you for pointing this out. We have rewritten the Results, for example streamlining the old lines 48-74 (new lines 42-48)  by moving the comments about names, files and order of videos to the caption of Figure 1.

Overall, though, this is a high-quality paper. The improvements to idtracker.ai are well justified and practically significant. Addressing the above comments will strengthen the work, particularly by clarifying the evaluation and comparisons.

We thank the reviewer for the detailed suggestions. We believe we have taken all of them into consideration to improve the ms.

Reviewer #2 (Public review):

Summary:

This work introduces a new version of the state-of-the-art idtracker.ai software for tracking multiple unmarked animals. The authors aimed to solve a critical limitation of their previous software, which relied on the existence of "global fragments" (video segments where all animals are simultaneously visible) to train an identification classifier network, in addition to addressing concerns with runtime speed. To do this, the authors have both re-implemented the backend of their software in PyTorch (in addition to numerous other performance optimizations) as well as moving from a supervised classification framework to a self-supervised, contrastive representation learning approach that no longer requires global fragments to function. By defining positive training pairs as different images from the same fragment and negative pairs as images from any two co-existing fragments, the system cleverly takes advantage of partial (but high-confidence) tracklets to learn a powerful representation of animal identity without direct human supervision. Their formulation of contrastive learning is carefully thought out and comprises a series of empirically validated design choices that are both creative and technically sound. This methodological advance is significant and directly leads to the software's major strengths, including exceptional performance improvements in speed and accuracy and a newfound robustness to occlusion (even in severe cases where no global fragments can be detected). Benchmark comparisons show the new software is, on average, 44 times faster (up to 440 times faster on difficult videos) while also achieving higher accuracy across a range of species and group sizes. This new version of idtracker.ai is shown to consistently outperform the closely related TRex software (Walter & Couzin, 2021\), which, together with the engineering innovations and usability enhancements (e.g., outputs convenient for downstream pose estimation), positions this tool as an advancement on the state-of-the-art for multi-animal tracking, especially for collective behavior studies.

Despite these advances, we note a number of weaknesses and limitations that are not well addressed in the present version of this paper:

Weaknesses

(1) The contrastive representation learning formulation. Contrastive representation learning using deep neural networks has long been used for problems in the multi-object tracking domain, popularized through ReID approaches like DML (Yi et al., 2014\) and DeepReID (Li et al., 2014). More recently, contrastive learning has become more popular as an approach for scalable self-supervised representation learning for open-ended vision tasks, as exemplified by approaches like SimCLR (Chen et al., 2020), SimSiam (Chen et al., 2020\), and MAE (He et al., 2021\) and instantiated in foundation models for image embedding like DINOv2 (Oquab et al., 2023). Given their prevalence, it is useful to contrast the formulation of contrastive learning described here relative to these widely adopted approaches (and why this reviewer feels it is appropriate):

(1.1) No rotations or other image augmentations are performed to generate positive examples. These are not necessary with this approach since the pairs are sampled from heuristically tracked fragments (which produces sufficient training data, though see weaknesses discussed below) and the crops are pre-aligned egocentrically (mitigating the need for rotational invariance).

(1.2) There is no projection head in the architecture, like in SimCLR. Since classification/clustering is the only task that the system is intended to solve, the more general "nuisance" image features that this architectural detail normally affords are not necessary here.

(1.3) There is no stop gradient operator like in BYOL (Grill et al., 2020\) or SimSiam. Since the heuristic tracking implicitly produces plenty of negative pairs from the fragments, there is no need to prevent representational collapse due to class asymmetry. Some care is still needed, but the authors address this well through a pair sampling strategy (discussed below).

(1.4) Euclidean distance is used as the distance metric in the loss rather than cosine similarity as in most contrastive learning works. While cosine similarity coupled with L2-normalized unit hypersphere embeddings has proven to be a successful recipe to deal with the curse of dimensionality (with the added benefit of bounded distance limits), the authors address this through a cleverly constructed loss function that essentially allows direct control over the intra- and inter-cluster distance (D\_pos and D\_neg). This is a clever formulation that aligns well with the use of K-means for the downstream assignment step.

No concerns here, just clarifications for readers who dig into the review. Referencing the above literature would enhance the presentation of the paper to align with the broader computer vision literature.

Thank you for this detailed comparison. We have now added a new section in Appendix 3, “Differences with previous work in contrastive/metric learning” (lines 792-841) to include references to previous work and a description of what we do differently, including the points raised by the reviewer.

(2) Network architecture for image feature extraction backbone. As most of the computations that drive up processing time happen in the network backbone, the authors explored a variety of architectures to assess speed, accuracy, and memory requirements. They land on ResNet18 due to its empirically determined performance. While the experiments that support this choice are solid, the rationale behind the architecture selection is somewhat weak. The authors state that: "We tested 23 networks from 8 different families of state-of-the-art convolutional neural network architectures, selected for their compatibility with consumer-grade GPUs and ability to handle small input images (20 × 20 to 100 × 100 pixels) typical in collective animal behavior videos."

(2.1) Most modern architectures have variants that are compatible with consumer-grade GPUs. This is true of, for example, HRNet (Wang et al., 2019), ViT (Dosovitskiy et al., 2020), SwinT (Liu et al., 2021), or ConvNeXt (Liu et al., 2022), all of which report single GPU training and fast runtime speeds through lightweight configuration or subsequent variants, e.g., MobileViT (Mehta et al., 2021). The authors may consider revising that statement or providing additional support for that claim (e.g., empirical experiments) given that these have been reported to outperform ResNet18 across tasks.

Following the recommendation of the reviewer, we tested the architectures SwinT, ConvNeXt and ViT. We found out that none of them outperformed ResNet18 since they all showed a slower learning curve. This would result in higher tracking times. These tests are now included in the section “Network architecture” (lines 550-611).

(2.2) The compatibility of different architectures with small image sizes is configurable. Most convolutional architectures can be readily adapted to work with smaller image sizes, including 20x20 crops. With their default configuration, they lose feature map resolution through repeated pooling and downsampling steps, but this can be readily mitigated by swapping out standard convolutions with dilated convolutions and/or by setting the stride of pooling layers to 1, preserving feature map resolution across blocks. While these are fairly straightforward modifications (and are even compatible with using pretrained weights), an even more trivial approach is to pad and/or resize the crops to the default image size, which is likely to improve accuracy at a possibly minimal memory and runtime cost. These techniques may even improve the performance with the architectures that the authors did test out.

The only two tested architectures that require a minimum image size are AlexNet and DenseNet. DenseNet proved to underperform ResNet18 in the videos where the images are sufficiently large. We have tested AlexNet with padded images to see that it also performs worse than ResNet18 (see Appendix 3—figure 1).

We also tested the initialization of ResNet18 with pre-trained weights from ImageNet (in Appendix 3—figure 2) and it proved to bring no benefit to the training speed (added in lines 591-592).

(2.3) The authors do not report whether the architecture experiments were done with pretrained or randomly initialized weights.

We adapted the text to make it clear that the networks are always randomly initialized (lines 591-592, lines 608-609 and the captions of Appendix 3—figure 1 and 2).

(2.4) The authors do not report some details about their ResNet18 design, specifically whether a global pooling layer is used and whether the output fully connected layer has any activation function. Additionally, they do not report the version of ResNet18 employed here, namely, whether the BatchNorm and ReLU are applied after (v1) or before (v2) the conv layers in the residual path.

We use ResNet18 v1 with no activation function nor bias in its last layer (this has been clarified in the lines 606-608). Also, by design, ResNet has a global average pool right before the last fully connected layer which we did not remove. In response to the reviewer, Resnet18 v2 was tested and its performance is the same as that of v1 (see Appendix 3—figure 1 and lines 590-591).

(3) Pair sampling strategy. The authors devised a clever approach for sampling positive and negative pairs that is tailored to the nature of the formulation. First, since the positive and negative labels are derived from the co-existence of pretracked fragments, selection has to be done at the level of fragments rather than individual images. This would not be the case if one of the newer approaches for contrastive learning were employed, but it serves as a strength here (assuming that fragment generation/first pass heuristic tracking is achievable and reliable in the dataset). Second, a clever weighted sampling scheme assigns sampling weights to the fragments that are designed to balance "exploration and exploitation". They weigh samples both by fragment length and by the loss associated with that fragment to bias towards different and more difficult examples.

(3.1) The formulation described here resembles and uses elements of online hard example mining (Shrivastava et al., 2016), hard negative sampling (Robinson et al., 2020\), and curriculum learning more broadly. The authors may consider referencing this literature (particularly Robinson et al., 2020\) for inspiration and to inform the interpretation of the current empirical results on positive/negative balancing.

Following this recommendation, we added references of hard negative mining in the new section “Differences with previous work in contrastive/metric learning”, lines 792-841. Regarding curriculum learning, even though in spirit it might have parallels with our sampling method in the sense that there is a guided training of the network, we believe the approach is more similar to an exploration-exploitation paradigm.

(4) Speed and accuracy improvements. The authors report considerable improvements in speed and accuracy of the new idTracker (v6) over the original idTracker (v4?) and TRex. It's a bit unclear, however, which of these are attributable to the engineering optimizations (v5?) versus the representation learning formulation.

(4.1) Why is there an improvement in accuracy in idTracker v5 (L77-81)? This is described as a port to PyTorch and improvements largely related to the memory and data loading efficiency. This is particularly notable given that the progression went from 97.52% (v4; original) to 99.58% (v5; engineering enhancements) to 99.92% (v6; representation learning), i.e., most of the new improvement in accuracy owes to the "optimizations" which are not the central emphasis of the systematic evaluations reported in this paper.

V5 was a two year-effort designed to improve time efficiency of v4. It was also a surprise to us that accuracy was higher, but that likely comes from the fact that the substituted code from v4 contained some small bug/s. The improvements in v5 are retained in v6 (contrastive learning) and v6 has higher accuracy and shorter tracking times. The difference in v6 for this extra accuracy and shorter tracking times is contrastive learning.

(4.2) What about the speed improvements? Relative to the original (v4), the authors report average speed-ups of 13.6x in v5 and 44x in v6. Presumably, the drastic speed-up in v6 comes from a lower Protocol 2 failure rate, but v6 is not evaluated in Figure 2 - figure supplement 2.

Idtracker.ai v5 runs an optimized Protocol 2 and, sometimes, the Protocol 3. But v6 doesn’t run either of them. While P2 is still present in v6 as a fallback protocol when contrastive fails, in our v6 benchmark P2 was never needed. So the v6 speedup comes from replacing both P2 and P3 with the contrastive algorithm.

(5) Robustness to occlusion. A major innovation enabled by the contrastive representation learning approach is the ability to tolerate the absence of a global fragment (contiguous frames where all animals are visible) by requiring only co-existing pairs of fragments owing to the paired sampling formulation. While this removes a major limitation of the previous versions of idtracker.ai, its evaluation could be strengthened. The authors describe an ablation experiment where an arc of the arena is masked out to assess the accuracy under artificially difficult conditions. They find that the v6 works robustly up to significant proportions of occlusions, even when doing so eliminates global fragments.

(5.1) The experiment setup needs to be more carefully described.

(5.1.1) What does the masking procedure entail? Are the pixels masked out in the original video or are detections removed after segmentation and first pass tracking is done?

The mask is defined as a region of interest in the software. This means that it is applied at the segmentation step where the video frame is converted to a foreground-background binary image. The region of interest is applied here, converting to background all pixels not inside of it. We clarified this in the newly added section Occlusion tests, lines 240-244.

(5.1.2) What happens at the boundary of the mask? (Partial segmentation masks would throw off the centroids, and doing it after original segmentation does not realistically model the conditions of entering an occlusion area.)

Animals at the boundaries of the mask are partially detected. This can change the location of their detected centroid. That’s why, when computing the ground-truth accuracy for these videos, only the groundtruth centroids that were at minimum 15 pixels further from the mask were considered. We clarified this in the newly added section Occlusion tests, lines 248-251.

(5.1.3) Are fragments still linked for animals that enter and then exit the mask area?

No artificial fragment linking was added in these videos. Detected fragments are linked the usual way. If one animal hides into the mask, the animal disappears so the fragment breaks.  We clarified this in the newly added section Occlusion tests, lines 245-247.

(5.1.4) How is the evaluation done? Is it computed with or without the masked region detections?

The groundtruth used to validate these videos contains the positions of all animals at all times. But only the positions outside the mask at each frame were considered to compute the tracking accuracy. We clarified this in the newly added section Occlusion tests, lines 248-251.

(5.2) The circular masking is perhaps not the most appropriate for the mouse data, which is collected in a rectangular arena.

We wanted to show the same proof of concept in different videos. For that reason, we used to cover the arena parametrized by an angle. In the rectangular arena the circular masking uses an external circle, so it is covering the rectangle parametrized by an angle.

(5.3) The number of co-existing fragments, which seems to be the main determinant of performance that the authors derive from this experiment, should be reported for these experiments. In particular, a "number of co-existing fragments" vs accuracy plot would support the use of the 0.25(N-1) heuristic and would be especially informative for users seeking to optimize experimental and cage design. Additionally, the number of co-existing fragments can be artificially reduced in other ways other than a fixed occlusion, including random dropout, which would disambiguate it from potential allocentric positional confounds (particularly relevant in arenas where egocentric pose is correlated with allocentric position).

We included the requested analysis about the fragment connectivity in Figure 3-figure supplement 1. We agree that there can be additional ways of reducing co-existing fragments, but we think the occlusion tests have the additional value that there are many real experiments similar to this test.

(6) Robustness to imaging conditions. The authors state that "the new idtracker.ai can work well with lower resolutions, blur and video compression, and with inhomogeneous light (Figure 2 - figure supplement 4)." (L156). Despite this claim, there are no speed or accuracy results reported for the artificially corrupted data, only examples of these image manipulations in the supplementary figure.

We added this information in the same image, new Figure 1 - figure supplement 3.

(7) Robustness across longitudinal or multi-session experiments. The authors reference idmatcher.ai as a compatible tool for this use case (matching identities across sessions or long-term monitoring across chunked videos), however, no performance data is presented to support its usage. This is relevant as the innovations described here may interact with this setting. While deep metric learning and contrastive learning for ReID were originally motivated by these types of problems (especially individuals leaving and entering the FOV), it is not clear that the current formulation is ideally suited for this use case. Namely, the design decisions described in point 1 of this review are at times at odds with the idea of learning generalizable representations owing to the feature extractor backbone (less scalable), low-dimensional embedding size (less representational capacity), and Euclidean distance metric without hypersphere embedding (possible sensitivity to drift). It's possible that data to support point 6 can mitigate these concerns through empirical results on variations in illumination, but a stronger experiment would be to artificially split up a longer video into shorter segments and evaluate how generalizable and stable the representations learned in one segment are across contiguous ("longitudinal") or discontiguous ("multi-session") segments.

We have now added a test to prove the reliability of idmatcher.ai in v6. In this test, 14 videos are taken from the benchmark and split in two non-overlapping parts (with a 200 frames gap in between). idmatcher.ai is run between the two parts presenting a 100% accuracy identity matching across all of them (see section “Validity of idmatcher.ai in the new idtracker.ai”, lines 969-1008).

We thank the reviewer for the detailed suggestions. We believe we have taken all of them into consideration to improve the ms.

Reviewer #3 (Public review):

Summary

The authors propose a new version of idTracker.ai for animal tracking. Specifically, they apply contrastive learning to embed cropped images of animals into a feature space where clusters correspond to individual animal identities.

Strengths

By doing this, the new software alleviates the requirement for so-called global fragments - segments of the video, in which all entities are visible/detected at the same time - which was necessary in the previous version of the method. In general, the new method reduces the tracking time compared to the previous versions, while also increasing the average accuracy of assigning the identity labels.

Weaknesses

The general impression of the paper is that, in its current form, it is difficult to disentangle the old from the new method and understand the method in detail. The manuscript would benefit from a major reorganization and rewriting of its parts. There are also certain concerns about the accuracy metric and reducing the computational time.

We have made the following modifications in the presentation:

(1) We have added section tiles to the main text so it is clearer what tracking system we are referring to. For example, we now have sections “Limitation of the original idtracker.ai”, “Optimizing idtracker.ai without changes in the learning method” and “The new idtracker.ai uses representation learning”.

(2) We have completely rewritten all the text of the ms until we start with contrastive learning. Old L20-89 is now L20-L66, much shorter and easier to read.

(3) We have rewritten the first 3 paragraphs in the section “The new idtracker.ai uses representation learning” (lines 68-92).

(4) We now expanded Appendix 3 to discuss the details of our approach  (lines 539-897).  It discusses in detail the steps of the algorithm, the network architecture, the loss function, the sampling strategy, the clustering and identity assignment, and the stopping criteria in training

(5) To cite previous work in detail and explain what we do differently, we have now added in Appendix 3 the new section “Differences with previous work in contrastive/metric learning” (lines 792-841).

Regarding accuracy metrics, we have replaced our accuracy metric with the standard metric IDF1. IDF1 is the standard metric that is applied to systems in which the goal is to maintain consistent identities across time. See also the section in Appendix 1 "Computation of tracking accuracy” (lines 414-436) explaining IDF1 and why this is an appropriate metric for our goal.

Using IDF1 we obtain slightly higher accuracies for the idtracker.ai systems. This is the comparison of mean accuracy over all our benchmark for our previous accuracy score and the new one for the full trajectories:

v4:   97.42% -> 98.24%

v5:   99.41% -> 99.49%

v6:   99.74% -> 99.82%

trex: 97.89% -> 97.89%

We thank the reviewer for the suggestions about presentation and about the use of more standard metrics.

Recommendations for the authors:

Reviewer #2 (Recommendations for the authors):

(1) Figure 1a: A graphical legend inset would make it more readable since there are multiple colors, line styles, and connecting lines to parse out.

Following this recommendation, we added a graphical legend in the old Figure 1 (new Figure 2).

(2) L46: "have images" → "has images".

We applied this correction. Line 35.

(3) L52: "videos start with a letter for the species (z,**f**,m)", but "d" is used for fly videos.

We applied this correction in the caption of Figure 1.

(4) L62: "with Protocol 3 a two-step process" → "with Protocol 3 being a two-step process".

We rewrote this paragraph without mentioning Protocol 3, lines 37-41.

(5) L82-89: This is the main statement of the problems that are being addressed here (speed and relaxing the need for global fragments). This could be moved up, emphasized, and made clearer without the long preamble and results on the engineering optimizations in v5. This lack of linearity in the narrative is also evident in the fact that after Figure 1a is cited, inline citations skip to Figure 2 before returning to Figure 1 once the contrastive learning is introduced.

We have rewritten all the text until the contrastive learning, (old lines 20-89 are now lines 20-66). The text is shorter, more linear and easier to read.

(6) L114: "pairs until the distance D_{pos}" → "pairs until the distance approximates D_{pos}".

We rewrote as “ pairs until the distance 𝐷pos (or 𝐷neg) is reached” in line 107.

(7) L570: Missing a right parenthesis in the equation.

We no longer have this equation in the ms.

(8) L705: "In order to identify fragments we, not only need" → "In order to identify fragments, we not only need".

We applied this correction, Line 775.

(9) L819: "probably distribution" → "probability distribution".

We applied this correction, Line 776.

(10) L833: "produced the best decrease the time required" → "produced the best decrease of the time required".

We applied this correction, Line 746.

Reviewer #3 (Recommendations for the authors):

(1) We recommend rewriting and restructuring the manuscript. The paper includes a detailed explanation of the previous approaches (idTracker and idTracker.ai) and their limitations. In contrast, the description of the proposed method is short and unstructured, which makes it difficult to distinguish between the old and new methods as well as to understand the proposed method in general. Here are a few examples illustrating the problem. 

(1.1) Only in line 90 do the authors start to describe the work done in this manuscript. The previous 3 pages list limitations of the original method.

We have now divided the main text into sections, so it is clearer what is the previous method (“Limitation of the original idtracker.ai”, lines 28-51), the new optimization we did of this method (“Optimizing idtracker.ai without changes in the learning method”, lines 52-66) and the new contrastive approach that also includes the optimizations (“The new idtracker.ai uses representation learning”, lines 66-164). Also, the new text has now been streamlined until the contrastive section, following your suggestion. You can see that in the new writing the three sections are 25 , 15 and 99 lines. The more detailed section is the new system, the other two are needed as reference, to describe which problem we are solving and the extra new optimizations.  

(1.2) The new method does not have a distinct name, and it is hard to follow which idtracker.ai is a specific part of the text referring to. Not naming the new method makes it difficult to understand.

We use the name new idtracker.ai (v6) so it becomes the current default version. v5 is now obsolete, as well as v4. And from the point of view of the end user, no new name is needed since v6 is just an evolution of the same software they have been using. Also, we added sections in the main text to clarify the ideas in there and indicate the version of idtracker.ai we are referring to.

(1.3) There are "Protocol 2" and "Protocol 3" mixed with various versions of the software scattered throughout the text, which makes it hard to follow. There should be some systematic naming of approaches and a listing of results introduced.

Following this recommendation we no longer talk about the specific protocols of the old version of idtracker.ai in the main text. We rewritten the explanation of these versions in a more clear and straightforward way, lines 29-36.

(2) To this end, the authors leave some important concepts either underexplained or only referenced indirectly via prior work. For example, the explanation of how the fragments are created (line 15) is only explained by the "video structure" and the algorithm that is responsible for resolving the identities during crossings is not detailed (see lines 46-47, 149-150). Including summaries of these elements would improve the paper's clarity and accessibility.

We listed the specific sections from our previous publication where the reader can find information about the entire tracking pipeline (lines 539-549). This way, we keep the ms clear and focused on the new identification algorithm while indicating where to find such information.

(3) Accuracy metrics are not clear. In line 319, the authors define it as based on "proportion of errors in the trajectory". This proportion is not explained. How is the error calculated if a trajectory is lost or there are identity swaps? Multi-object tracking has a range of accuracy metrics that account for such events but none of those are used by the authors. Estimating metrics that are common for MOT literature, for example, IDF1, MOTA, and MOTP, would allow for better method performance understanding and comparison.

In the new ms, we replaced our accuracy metric with the standard metric IDF1. IDF1 is the standard metric that is applied to systems in which the goal is to maintain consistent identities across time. See also the section in Appendix 1 "Computation of tracking accuracy” explaining why IDF1 and not MOTA or MOTP is the adequate metric for a system that wants to give correct tracking by identification in time. See lines 416-436.

Using IDF1 we obtain slightly higher accuracies for the idtracker.ai systems. This is the comparison of mean accuracy four our previous accuracy and the new one for the full trajectories:

v4:   97.42% -> 98.24%

v5:   99.41% -> 99.49%

v6:   99.74% -> 99.82%

trex: 97.89% -> 97.89%

(4) Additionally, the authors distinguish between tracking with and without crossings, but do not provide statistics on the frequency of crossings per video. It is also unclear how the crossings are considered for the final output. Including information such as the frame rate of the videos would help to better understand the temporal resolution and the differences between consecutive frames of the videos.

We added this information in the Appendix 1 “Benchmark of accuracy and tracking time”, lines 445-451. The framerate in our benchmark videos goes from 25 to 60 fps (average of 37 fps). On average 2.6% of the blobs are crossings (1.1% for zebrafish 0.7% for drosophila 9.4% for mice).

(5) In the description of the dataset used for evaluation (lines 349-365), the authors describe the random sampling of parameter values for each tracking run. However, it is unclear whether the same values were used across methods. Without this clarification, comparisons between the proposed method, older versions, and TRex might be biased due to lucky parameter combinations. In addition, the ranges from which the values were randomly sampled were also not described.

Only one parameter is shared between idtracker.ai and TRex: intensity_threshold (in idtracker.ai) and threshold (in TRex). Both are conceptually equivalent but differ in their numerical values since they affect different algorithms. V4, v5, and TRex each required the same process of independent expert visual inspection of the segmentation to select the valid value range. Since versions 5 and 6 use exactly the same segmentation algorithm, they share the same parameter ranges.

All the ranges of valid values used in our benchmark are public here https://drive.google.com/drive/folders/1tFxdtFUudl02ICS99vYKrZLeF28TiYpZ as stated in the section “Data availability”, lines 227-228.

(6) Lines 122-123, Figure 1c. "batches" - is an imprecise metric of training time as there is no information about the batch size.

We clarified the Figure caption, new Figure 2c.

(7) Line 145 - "we run some steps... For example..." leaves the method description somewhat unclear. It would help if you could provide more details about how the assignments are carried out and which metrics are being used.

Following this recommendation, we listed the specific sections from our previous publication where the reader can find information about the entire tracking pipeline (lines 539-549). This way, we keep the ms clear and focused on the new identification algorithm while indicating where to find such information.

(8) Figure 3. How is tracking accuracy assessed with occlusions? Are the individuals correctly recognized when they reappear from the occluded area?

The groundtruth for this video contains the positions of all animals at all times. Only the groundtruth points inside the region of interest are taken into account when computing the accuracy. When the tracking reaches high accuracy, it means that animals are successfully relabeled every time they enter the non-masked region. Note that this software works all the time by identification of animals, so crossings and occlusion are treated the same way. What is new here is that the occlusions are so large that there are no global fragments. We clarified this in the new section “Occlusion tests” in Methods, lines 239-251.

(9) Lines 185-187 this part of the sentence is not clear.

We rewrote this part in a clearer way, lines 180-182.

(10) The authors also highlight the improved runtime performance. However, they do not provide a detailed breakdown of the time spent on each component of the tracking/training pipeline. A timing breakdown would help to compare the training duration with the other components. For example, the calculation of the Silhouette Score alone can be time-consuming and could be a bottleneck in the training process. Including this information would provide a clearer picture of the overall efficiency of the method.

We measured that the training of ResNet takes on average in our benchmark 47% of the tracking time (we added this information line 551 section “Network Architecture”). In this training stage the bottleneck becomes the network forward and backward pass, limited by the GPU performance. All other processes happening during training have been deeply optimized and parallelized when needed so their contribution to the training time is minimal. Apart from the training, we also measured 24.4% of the total tracking time spent in reading and segmenting the video files and 11.1% in processing the identification images and detecting crossings.

(11) An important part of the computational cost is related to model training. It would be interesting to test whether a model trained on one video of a specific animal type (e.g., zebrafish_5) generalizes to another video of the same type (e.g., zebrafish_7). This would assess the model's generalizability across different videos of the same species and spare a lot of compute. Alternatively, instead of training a model from scratch for each video, the authors could also consider training a base model on a superset of images from different videos and then fine-tuning it with a lower learning rate for each specific video. This could potentially save time and resources while still achieving good performance.

Already before v6, there was the possibility for the user to start training the identification network by copying the final weights from another tracking session. This knowledge transfer feature is still present in v6 and it still decreases the training times significatively. This information has been added in Appendix 4, lines 906-909.

We have already begun working on the interesting idea of a general base model but it brings some complex challenges. It could be a very useful new feature for future idtracker.ai releases.

We thank the reviewer for the many suggestions. We have implemented all of them.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

Artiushin et al. establish a comprehensive 3D atlas of the brain of the orb-web building spider Uloborus diversus. First, they use immunohistochemistry detection of synapsin to mark and reconstruct the neuropils of the brain of six specimens and they generate a standard brain by averaging these brains. Onto this standard 3D brain, they plot immunohistochemical stainings of major transmitters to detect cholinergic, serotonergic, octopaminergic/taryminergic and GABAergic neurons, respectively. Further, they add information on the expression of a number of neuropeptides (Proctolin, AllatostatinA, CCAP, and FMRFamide). Based on this data and 3D reconstructions, they extensively describe the morphology of the entire synganglion, the discernible neuropils, and their neurotransmitter/neuromodulator content.

Strengths:

While 3D reconstruction of spider brains and the detection of some neuroactive substances have been published before, this seems to be the most comprehensive analysis so far, both in terms of the number of substances tested and the ambition to analyze the entire synganglion. Interestingly, besides the previously described neuropils, they detect a novel brain structure, which they call the tonsillar neuropil.<br /> Immunohistochemistry, imaging, and 3D reconstruction are convincingly done, and the data are extensively visualized in figures, schemes, and very useful films, which allow the reader to work with the data. Due to its comprehensiveness, this dataset will be a valuable reference for researchers working on spider brains or on the evolution of arthropod brains.

Weaknesses:

As expected for such a descriptive groundwork, new insights or hypotheses are limited, apart from the first description of the tonsillar neuropil. A more comprehensive labeling in the panels of the mentioned structures would help to follow the descriptions. The reconstruction of the main tracts of the brain would be a very valuable complementary piece of data.

Reviewer #2 (Public review):

Summary

Artiushin et al. created the first three-dimensional atlas of a synganglion in the hackled orb-weaver spider, which is becoming a popular model for web-building behavior. Immunohistochemical analysis with an impressive array of antisera reveals subcompartments of neuroanatomical structures described in other spider species as well as two previously undescribed arachnid structures, the protocerebral bridge, hagstone, and paired tonsillar neuropils. The authors describe the spider's neuroanatomy in detail and discuss similarities and differences from other spider species. The final section of the discussion examines the homology between onychophoran and chelicerate arcuate bodies and mandibulate central bodies.

Strengths

The authors set out to create a detailed 3D atlas and accomplished this goal.

Exceptional tissue clearing and imaging of the nervous system reveal the three-dimensional relationships between neuropils and some connectivity that would not be apparent in sectioned brains.

A detailed anatomical description makes it easy to reference structures described between the text and figures.

The authors used a large palette of antisera which may be investigated in future studies for function in the spider nervous system and may be compared across species.

Weaknesses

It would be useful for non-specialists if the authors would introduce each neuropil with some orientation about its function or what kind of input/output it receives, if this is known for other species. Especially those structures that are not described in other arthropods, like the opisthosomal neuropil. Are there implications for neuroanatomical findings in this paper on the understanding of how web-building behaviors are mediated by the brain?

Likewise, where possible, it would be helpful to have some discussion of the implications of certain neurotransmitters/neuropeptides being enriched in different areas. For example, GABA would signal areas of inhibitory connections, such as inhibitory input to mushroom bodies, as described in other arthropods. In the discussion section on relationships between spider and insect midline neuropils, are there similarities in expression patterns between those described here and in insects?

Reviewer #3 (Public review):

Summary:

This is an impressive paper that offers a much-needed 3D standardized brain atlas for the hackled-orb weaving spider Uloborus diversus, an emerging organism of study in neuroethology. The authors used a detailed immunohistological whole-mount staining method that allowed them to localize a wide range of common neurotransmitters and neuropeptides and map them on a common brain atlas. Through this approach, they discovered groups of cells that may form parts of neuropils that had not previously been described, such as the 'tonsillar neuropil', which might be part of a larger insect-like central complex. Further, this work provides unique insights into the previously underappreciated complexity of higher-order neuropils in spiders, particularly the arcuate body, and hints at a potentially important role for the mushroom bodies in vibratory processing for web-building spiders.

Strengths:

To understand brain function, data from many experiments on brain structure must be compiled to serve as a reference and foundation for future work. As demonstrated by the overwhelming success in genetically tractable laboratory animals, 3D standardized brain atlases are invaluable tools - especially as increasing amounts of data are obtained at the gross morphological, synaptic, and genetic levels, and as functional data from electrophysiology and imaging are integrated. Among 'non-model' organisms, such approaches have included global silver staining and confocal microscopy, MRI, and, more recently, micro-computed tomography (X-ray) scans used to image multiple brains and average them into a composite reference. In this study, the authors used synapsin immunoreactivity to generate an averaged spider brain as a scaffold for mapping immunoreactivity to other neuromodulators. Using this framework, they describe many previously known spider brain structures and also identify some previously undescribed regions. They argue that the arcuate body - a midline neuropil thought to have diverged evolutionarily from the insect central complex - shows structural similarities that may support its role in path integration and navigation.

Having diverged from insects such as the fruit fly Drosophila melanogaster over 400 million years ago, spiders are an important group for study - particularly due to their elegant web-building behavior, which is thought to have contributed to their remarkable evolutionary success. How such exquisitely complex behavior is supported by a relatively small brain remains unclear. A rich tradition of spider neuroanatomy emerged in the previous century through the work of comparative zoologists, who used reduced silver and Golgi stains to reveal remarkable detail about gross neuroanatomy. Yet, these techniques cannot uncover the brain's neurochemical landscape, highlighting the need for more modern approaches-such as those employed in the present study.

A key insight from this study involves two prominent higher-order neuropils of the protocerebrum: the arcuate body and the mushroom bodies. The authors show that the arcuate body has a more complex structure and lamination than previously recognized, suggesting it is insect central complex-like and may support functions such as path integration and navigation, which are critical during web building. They also report strong synapsin immunoreactivity in the mushroom bodies and speculate that these structures contribute to vibratory processing during sensory feedback, particularly in the context of web building and prey localization. These findings align with prior work that noted the complex architecture of both neuropils in spiders and their resemblance (and in some cases greater complexity) compared to their insect counterparts. Additionally, the authors describe previously unrecognized neuropils, such as the 'tonsillar neuropil,' whose function remains unknown but may belong to a larger central complex. The diverse patterns of neuromodulator immunoreactivity further suggest that plasticity plays a substantial role in central circuits.

Weaknesses:

My major concern, however, is that some of the authors' neuroanatomical descriptions rely too heavily on inference rather than what is currently resolvable from their immunohistochemistry stains alone.

We would like to thank the reviewers for their time and effort in carefully reading our manuscript and providing helpful feedback, and particularly for their appreciation and realistic understanding of the scope of this study and its context within the existing spider neuroanatomical literature.

Regarding the limitations and potential additions to this study, we believe these to be well-reasoned and are in agreement. We plan to address some of these shortcomings in future publications.

As multiple reviewers remarked, a mapping of the major tracts of the brain would be a welcome addition to understanding the neuroanatomy of U. diversus. This is something which we are actively working on and hope to provide in a forthcoming publication. Given the length of this paper as is, we considered that a treatment of the tracts would be better served as an additional paper. Likewise, mapping of the immunoreactive somata of the currently investigated targets is a component which we would like to describe as part of a separate paper, keeping the focus of the current one on neuropils, in order to leverage our aligned volumes to describe co-expression patterns, which is not as useful for the more widely dispersed somata. Furthermore, while we often see somata through immunostaining, the presence and intensity of the signal is variable among immunoreactive populations. We are finding that these populations are more consistently and comprehensively revealed thru fluorescent in situ hybridization.

We appreciate the desire of the reviewers for further information regarding the connectivity and function of the described neuropils, and where possible we have added additional statements and references. That being said, where this context remains sparse is largely a reflection of the lack of information in the literature. This is particularly the case for functional roles for spider neuropils, especially higher order ones of the protocerebrum, which are essentially unexamined. As summarized in the quite recent update to Foelix’s Spider Neuroanatomy, a functional understanding for protocerebral neuropil is really only available for the visual pathway. Consequently, it is therefore also difficult to speak of the implications for presence or absence of particular signaling elements in these neuropils, if no further information about the circuitry or behavioral correlates are available. Finally, multiple reviewers suggested that it might be worthwhile to explore a comparison of the arcuate body layer innervation to that of the central bodies of insects, of which there is a richer literature. This is an idea which we were also initially attracted to, and have now added some lines to the discussion section. Our position on this is a cautious one, as a series of more recent comparative studies spanning many insect species using the same antibody, reveals a considerable amount of variation in central body layering even within this clade, which has given us pause in interpreting how substantive similarities and differences to the far more distant spiders would be. Still, this is an interesting avenue which merits an eventual comprehensive analysis, one which would certainly benefit from having additional examples from more spider species, in order to not overstate conclusions based on the currently limited neuroanatomical representation.

Given our framing for the impetus to advance neuroanatomical knowledge in orb-web builders, the question of whether the present findings inform the circuitry controlling web-building is one that naturally follows. While we are unable with this dataset alone to define which brain areas mediate web-building - something which would likely be beyond any anatomical dataset lacking complementary functional data – the process of assembling the atlas has revealed structures and defined innervation patterns in previously ambiguous sectors of the spider brain, particularly in the protocerebrum. A simplistic proposal is that such regions, which are more conspicuous by our techniques and in this model species, would be good candidates for further inquiries into web-building circuitry, as their absence or oversight in past work could be attributable to the different behavioral styles of those model species. Regardless, granted that such a hypothesis cannot be readily refuted by the existing neuroanatomical literature, underscores the need to have more finely refined models of the spider brain, to which we hope that we have positively contributed to and are gratified by the reviewer’s enthusiasm for the strengths of this study.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) Brenneis 2022 has done a very nice and comprehensive study focused on the visual system - this might be worth including.

Thank you, we have included this reference on Line 34.

(2) L 29: When talking about "connectivity maps", the emerging connectomes based on EM data could be mentioned.