This echoes Anzaldúa (2022): “The act of writing is the act of making soul.” As well as theory in the flesh where lived stories produce political knowledge (Anzaldúa and Moraga, 2022).

This showcases standpoint theory. Knowledge is created from lived experience, not detached objectivity. Knowledge should begin from the experiences of marginalized communities. Harding (1987) argues that marginalized standpoints produce “stronger objectivity.” TallBear (2014) also critiques extractive research and instead argues knowledge must be grounded in relationships and obligations, not neutrality.

The thesis of this article argues that decolonization is not only public activism, but something lived through intimate relationships, kinship, and everyday interactions. The authors reject the idea that decolonization equals symbolic gestures; instead, they centre it in the home, family, friendship, and embodied queer life. Decolonization happens in family, intimacy, conflict, and daily life.

private argument

Ex. of languages used based on situations

Diglossia = community officially uses one language for formal stuff and another for everyday

people switch for control of the conversation or to show power

Studies looked at when and why people choose certain languages

psycholinguists look at how the brain handles switching

people disagree because there are a lot of ways to classify switching

Switching between languages while speaking and writing

This might be intended as an insult but I think it is a sign of humility

He was doing a lot of good. This is the true side of Christianity

Truly dedicated

my god, I knew isps were shit but I didn't know they were THIS shit! once again, big mega corporations not caring for poor workers and all.

(unrelated but finally feel better to post a few annotations for once, my god everything has sucked over the weeks mentally, financially, and whatnot...)

This reading is perhaps one of the most normal ones covering orientalism, but it is still rife with stereotypes. We see the typical fascination with women and splendor. It also reveals the intentions of the westerners as it is essentially a guide for business and exploitation.

So sassy

Legitimizing European fantasies

This misconception again

Of course, the mysterious oriental women

Because a European is assumed to be of high rank, right

I feel as though what really contributes to the stereotypes and mysticism is the fact that they write what what are essentially proto-ethnographies with a voice of absolute bewilderment, turning behavior into a complex and absolute thing and not a living, normal thing

This paragraph is highlighting the idea that oriental countries are sort of stuck in the past or in a different time entirely. To the Europeans Persia is still sort of the Persia it has always been, or at least very close.

So he is self aware of the orientalist fixation, but he believes that it is reasonable

To be fair if you were trying to see extravagance and beauty you would find it here - the Alhambra is quite beautiful

Already showing extravagance

He should be very knowledgeable and have a realistic depiction of Persia.

I appreciate how Chiang reframes the fear of AI “taking over” by comparing it to management-consulting logic rather than superintelligence. His argument that powerful institutions often use technology as a justification for harmful decisions — rather than technology making those decisions itself — really stuck with me. It made me think about how often companies claim, “The algorithm says we have to do this,” the same way executives once said, “McKinsey says we have to cut costs.”

A Luddite was a 19th-century textile worker who was part of a movement against the use of certain machines that, if used, would have saved companies money. They would destroy some of the machines that caused the loss of jobs, and they were eventually met with military force.

We've always talked about artificial intelligence as some kind of neutral "intelligent tool," but in reality, it's more like a consulting firm, employed by people to improve efficiency and cut costs—usually at the expense of workers' interests. Chiang's points that technology doesn't automatically improve people's lives impresses me most because it explains why so many "innovations" feel disconnected from the actual needs of ordinary people.

I think this kind of moral courage is undervalued in discussions of “tech ethics.” Too often we focus on the abstract risks of algorithmic bias, misinformation, privacy — but forget that behind every system is a person (or a team) making design choices. The chapter’s call for potential tech workers to reflect on these issues — and to be ready to push back — feels deeply important.

This would be an interesting question to discuss with the class whether that was ethical or not

Metaphor for the intimate, sensuous contact of nature with his body; the wind is personified as a lover.

He remains present in the everyday world, in the earth beneath us.

The power of words.

Nature seems to dare him; playful challenge from the world.

Metaphor for universal connection and order in nature.

Indicates innocence or lack of awareness; the young men are passive or unconcerned with the observer.

Transpire = emerge or rise from.

Grass grows from the bodies of the dead.

Old terms for different ethnic and social groups: 1. Kanuck: Canadian 2. Tuckahoe: A term for people from the American South 3. Cuff: A racist old term for a Black man The point is that the grass grows the same for all.

A universal symbol; grass reveals the same truth everywhere, regardless of race or class.

A metaphor, grass as a divine token or reminder (“remembrancer”), as if left by God with His “name in the corners.

Emphasizes the poet’s connection to the American land; a theme of belonging.

Metaphor for the intimate, sensuous contact of nature with his body; the wind is personified as a lover.

A metaphor elevating the natural scent of the body above religious ritual.

The whole point about sequence and precedence is erroneous. The solution the author has in mind (despite the contradictory phrasing and code) seems to be to put the entity_ruler BEFORE ner. Although this works here, it is NOT deterministic and NOT the standard way of solving the problem.

• If you put the entity_ruler BEFORE ner, you just suggest a label to the NER model. The NER model can potentially override your rule-based matches if it has strong predictions.
• If you put the entity_ruler AFTER ner, your rules have the final say and override any conflicting NER predictions. Note, however, that for this behaviour to work you have to set overwrite_ents to True in a configuration argument. E.g. ruler = nlp.add_pipe("entity_ruler", config={"overwrite_ents": True})

Have we? I don’t think so.

Code mixing= identity and belonging

early focus on when and why people switch (power, formality, education)

Shows structured code-switching in communities

Researchers classify code-switching differently, by sentence length, frequency, and meaning. Helps explain why definitions are different

switching between two or more languages or dialects

Note 1 - Different areas of the page should have different backgrounds to follow the existing style of the site. And backgrounds should contrast with adjacent ones. The background above is the same color just with a pattern, so make the next section a different color.

Note 2 - I would move this lower on the page. Perhaps follow this order, each bullet being a separate section: -Leave the top general Family Camp paragraph right under the event cover photo -Theme and Verse Info and Note about Jr Camp -Welcome from Cheeks -Pricing -Register Button

I think my views have changed in that I understand the mechanics of how social media works better than I did before. I also now have a better idea of the different kinds of social media, and what they do to generate money. From this, I think I will be even more wary of what I see on social media.

Helped mitigate splits

!!!!!

eLife Assessment

The study introduces a valuable dataset for investigating the relationship between vision and language in the brain. The authors provide convincing evidence that decoders trained on brain responses to both images and captions outperform those trained on responses to a single modality. The dataset and decoder results will be of interest to communities studying brain and machine decoding.

Reviewer #2 (Public review):

Summary:

This work presents a modality-agnostic decoder trained on a large fMRI dataset (SemReps-8K), in which subjects viewed natural images and corresponding captions. The decoder predicts stimulus content from brain activity irrespective of the input modality and performs on par with-or even outperforms-modality-specific decoders. Its success depends more on the diversity of brain data (multimodal vs. unimodal) than on whether the feature-extraction models are visual, linguistic, or multimodal. Particularly, the decoder shows strong performance in decoding imagery content. These results suggest that the modality-agnostic decoder effectively leverages shared brain information across image and caption tasks.

Strengths:

(1) The modality-agnostic decoder compellingly leverages multimodal brain information, improving decoding accuracy-particularly for non-sensory input such as captions-showing high methodological and application value.

(2) The dataset is a substantial and well-controlled contribution, with >8,000 image-caption trials per subject and careful matching of stimuli across modalities-an essential resource for testing theories about different representational modalities.

Weakness:

In the searchlight analysis aimed at identifying modality-invariant representations, although the combined use of four decoding conditions represents a relatively strict approach, the underlying logic remains unclear. The modality-agnostic decoder has demonstrated strong sensitivity in decoding brain activity, as shown earlier in the paper, whereas the cross-decoding with modality-specific decoders is inherently more conservative. If, as the authors note, the modality-agnostic decoder might have learned to leverage different features to project stimuli from different modalities, then taking the union of conditions would seem more appropriate. Conversely, if the goal is to obtain a more conservative result, why not focus solely on the cross-decoding conditions? The relationships among the four decoding conditions are not clearly delineated, and the contrasts between them might themselves yield valuable insights. As it stands, however, the logic of the current approach is not straightforward.

Reviewer #3 (Public review):

Summary:

The authors recorded brain responses while participants viewed images and captions. The images and captions were taken from the COCO dataset, so each image has a corresponding caption and each caption has a corresponding image. This enabled the authors to extract features from either the presented stimulus or the corresponding stimulus in the other modality. The authors trained linear decoders to take brain responses and predict stimulus features. "Modality-specific" decoders were trained on brain responses to either images or captions while "modality-agnostic" decoders were trained on brain responses to both stimulus modalities. The decoders were evaluated on brain responses while the participants viewed and imagined new stimuli, and prediction performance was quantified using pairwise accuracy. The authors reported the following results:

(1) Decoders trained on brain responses to both images and captions can predict new brain responses to either modality.

(2) Decoders trained on brain responses to both images and captions outperform decoders trained on brain responses to a single modality.

(3) Many cortical regions represent the same concepts in vision and language.

(4) Decoders trained on brain responses to both images and captions can decode brain responses to imagined scenes.

Strengths:

This is an interesting study that addresses important questions about modality-agnostic representations. Previous work has shown that decoders trained on brain responses to one modality can be used to decode brain responses to another modality. The authors build on these findings by collecting a new multimodal dataset and training decoders on brain responses to both modalities.

To my knowledge, SemReps-8K is the first dataset of brain responses to vision and language where each stimulus item has a corresponding stimulus item in the other modality. This means that brain responses to a stimulus item can be modeled using visual features of the image, linguistic features of the caption, or multimodal features derived from both the image and the caption. The authors also employed a multimodal one-back matching task which forces the participants to activate modality-agnostic representations. Overall, SemReps-8K is a valuable resource that will help researchers answer more questions about modality-agnostic representations.

The analyses are also very comprehensive. The authors trained decoders on brain responses to images, captions, and both modalities, and they tested the decoders on brain responses to images, caption, and imagined scenes. They extracted stimulus features using a range of visual, linguistic, and multimodal models. The modeling framework appears rigorous and the results offer new insights into the relationship between vision, language, and imagery. In particular, the authors found that decoders trained on brain responses to both images and captions were more effective at decoding brain responses to imagined scenes than decoders trained on brain responses to either modality in isolation. The authors also found that imagined scenes can be decoded from a broad network of cortical regions.

Weaknesses:

The characterization of "modality-agnostic" and "modality-specific" decoders seems a bit contradictory. There are three major choices when fitting a decoder: the modality of the training stimuli, the modality of the testing stimuli, and the model used to extract stimulus features. However, the authors characterize their decoders based on only the first choice-"modality-specific" decoders were trained on brain responses to either images or captions while "modality-agnostic" decoders were trained on brain responses to both stimulus modalities. I think that this leads to some instances where the conclusions are inconsistent with the methods and results.

First, the authors suggest that "modality-specific decoders are not explicitly encouraged to pick up on modality-agnostic features during training" (line 137) while "modality-agnostic decoders may be more likely to leverage representations that are modality-agnostic" (line 140). However, whether a decoder is required to learn modality-agnostic representations depends on both the training responses and the stimulus features. Consider the case where the stimuli are represented using linguistic features of the captions. When you train a "modality-specific" decoder on image responses, the decoder is forced to rely on modality-agnostic information that is shared between the image responses and the caption features. On the other hand, when you train a "modality-agnostic" decoder on both image responses and caption responses, the decoder has access to the modality-specific information that is shared by the caption responses and the caption features, so it is not explicitly required to learn modality-agnostic features. As a result, while the authors show that "modality-agnostic" decoders outperform "modality-specific" decoders in most conditions, I am not convinced that this is because they are forced to learn more modality-agnostic features.

Second, the authors claim that "modality-specific decoders can be applied only in the modality that they were trained on" while "modality-agnostic decoders can be applied to decode stimuli from multiple modalities, even without knowing a priori the modality the stimulus was presented in" (line 47). While "modality-agnostic" decoders do outperform "modality-specific" decoders in the cross-modality conditions, it is important to note that "modality-specific" decoders still perform better than expected by chance (figure 5). It is also important to note that knowing about the input modality still improves decoding performance even for "modality-agnostic" decoders, since it determines the optimal feature space-it is better to decode brain responses to images using decoders trained on image features, and it is better to decode brain responses to captions using decoders trained on caption features.

Comments on revised version:

The revised version benefits from clearer claims and more precise terminology (i.e. classifying the decoders as "modality-agnostic" or "modality-specific" while classifying the representations as "modality-invariant" or "modality-dependent").

While the modality-agnostic decoders outperform the modality-specific decoders, I am still not convinced that this is because they are "explicitly trained to leverage the shared information in modality-invariant patterns of the brain activity". On one hand, the high-level feature spaces may each contain some amount of modality-invariant information, so even modality-specific decoders can capture some modality-invariant information. On the other hand, I do not see how training the modality-agnostic decoders on responses to both modalities necessitates that they learn modality-invariant representations beyond those that are learned by the modality-specific decoders.

Author response:

The following is the authors’ response to the original reviews

We would like to thank all reviewers for their constructive and in-depth reviews. Thanks to your feedback, we realized that the main objective of the paper was not presented clearly enough, and that our use of the same “modality-agnostic” terminology for both decoders and representations caused confusion. We addressed these two major points as outlined in the following. 

In the revised manuscript, we highlight that the main contribution of this paper is to introduce modality-agnostic decoders. Apart from introducing this new decoder type, we put forward their advantages in comparison to modality-specific decoders in terms of decoding performance and analyze the modality-invariant representations (cf. updated terminology in the following paragraph) that these decoders rely on. The dataset that these analyses are based on is released as part of this paper, in the spirit of open science (but this dataset is only a secondary contribution for our paper). 

Regarding the terminology, we clearly define modality-agnostic decoders as decoders that are trained on brain imaging data from subjects exposed to stimuli in multiple modalities. The decoder is not given any information on which modality a stimulus was presented in, and is therefore trained to operate in a modality-agnostic way. In contrast, modality-specific decoders are trained only on data from a single stimulus modality. These terms are explained in Figure 2. While these terms describe different ways of how decoders can be trained, there are also different ways to evaluate them afterwards (see also Figure 3); but obviously, this test-time evaluation does not change the nature of the decoder, i.e., there is no contradiction in applying a modality-specific decoder to brain data from a different modality.

Further, we identify representations that are relevant for modality-agnostic decoders using the searchlight analysis. We realized that our choice of using the same “modality-agnostic” term to describe these brain representations created unnecessary debate and confusion. In order to not conflate the terminology, in the updated manuscript we call these representations modality-invariant (and the opposite modality-dependent). Our methodology does not allow us to distinguish whether certain representations merely share representational structure to a certain degree, or are truly representations that abstract away from any modality-dependent information. However, in order to be useful for modality-agnostic decoding, a significant degree of shared representational structure is sufficient, and it is this property of brain representations that we now define as “modality-invariant”. 

We updated the manuscript in line with this new terminology and focus: in particular, the first Related Work section on Modality-invariant brain representations, as well as the Introduction and Discussion.

Public Reviews:

Reviewer #1 (Public review):

Summary:

The authors introduce a densely-sampled dataset where 6 participants viewed images and sentence descriptions derived from the MS Coco database over the course of 10 scanning sessions. The authors further showcase how image and sentence decoders can be used to predict which images or descriptions were seen, using pairwise decoding across a set of 120 test images. The authors find decodable information widely distributed across the brain, with a left-lateralized focus. The results further showed that modality-agnostic models generally outperformed modality-specific models, and that data based on captions was not explained better by caption-based models but by modality-agnostic models. Finally, the authors decoded imagined scenes.

Strengths:

(1) The dataset presents a potentially very valuable resource for investigating visual and semantic representations and their interplay.

(2) The introduction and discussion are very well written in the context of trying to understand the nature of multimodal representations and present a comprehensive and very useful review of the current literature on the topic.

Weaknesses:

(1) The paper is framed as presenting a dataset, yet most of it revolves around the presentation of findings in relation to what the authors call modality-agnostic representations, and in part around mental imagery. This makes it very difficult to assess the manuscript, whether the authors have achieved their aims, and whether the results support the conclusions.

Thanks for this insightful remark. The dataset release is only a secondary contribution of our study; this was not clear enough in the previous version. We updated the manuscript to make the main objective of the paper more clear, as outlined in our general response to the reviews (see above).

(2) While the authors have presented a potential use case for such a dataset, there is currently far too little detail regarding data quality metrics expected from the introduction of similar datasets, including the absence of head-motion estimates, quality of intersession alignment, or noise ceilings of all individuals.

As already mentioned in the general response, the main focus of the paper is to introduce modality-agnostic decoders. The dataset is released in addition, this is why we did not focus on reporting extensive quality metrics in the original manuscript. To respond to your request, we updated the appendix of the manuscript to include a range of data quality metrics. 

The updated appendix includes head motion estimates in the form of realignment parameters and framewise displacement, as well as a metric to assess the quality of intersession alignment. More detailed descriptions can be found in Appendix 1 of the updated manuscript.

Estimating noise ceilings based on repeated presentations of stimuli (as for example done in Allen et al. (2022)) requires multiple betas for each stimulus. All training stimuli were only presented once, so this could only be done for the test stimuli which were presented repeatedly. However, during our preprocessing procedure we directly calculated stimulus-specific betas based on data from all sessions using one single GLM, which means that we did not obtain separate betas for repeated presentations of the same stimulus. We will however share the raw data publicly, so that such noise ceilings can be calculated using an adapted preprocessing procedure if required.

Allen, E. J., St-Yves, G., Wu, Y., Breedlove, J. L., Prince, J. S., Dowdle, L. T., Nau, M., Caron, B., Pestilli, F., Charest, I., Hutchinson, J. B., Naselaris, T., & Kay, K. (2022). A massive 7T fMRI dataset to bridge cognitive neuroscience and artificial intelligence. Nature Neuroscience, 25(1), 116–126. https://doi.org/10.1038/s41593-021-00962-x

(3) The exact methods and statistical analyses used are still opaque, making it hard for a reader to understand how the authors achieved their results. More detail in the manuscript would be helpful, specifically regarding the exact statistical procedures, what tests were performed across, or how data were pooled across participants.

In the updated manuscript, we improved the level of detail for the descriptions of statistical analyses wherever possible (see also our response to your “Recommendations for the authors”, Point 6).

Regarding data pooling across participants: 

Figure 8 shows averaged results across all subjects (as indicated in the caption)

Regarding data pooling for the estimation of the significance threshold of the searchlight analysis for modality-invariant regions: We updated the manuscript to clarify that we performed a permutation test, combined with a bootstrapping procedure to estimate a group-level null distribution: “For each subject, we evaluated the decoders 100 times with shuffled labels to create per-subject chance-level results. Then, we randomly selected one of the 100 chance-level results for each of the 6 subjects and calculated group-level statistics (TFCE values) the exact same way as described in the preceding paragraph. We repeated this procedure 10,000 times resulting in 10,000 permuted group-level results.”

Additionally, we indicated that the same permutation testing methods were applied to assess the significance threshold for the imagery decoding searchlight maps (Figure 10). 

(4) Many findings (e.g., Figure 6) are still qualitative but could be supported by quantitative measures.

The Figures 6 and 7 are intentionally qualitative results to support the quantitative decoding results presented in Figures 4 and 5. (see also Reviewer 2 Comment 2)

Figures 4 and 5 show pairwise decoding accuracy as a quantitative measure for evaluation of the decoders. This metric is the main metric we used to compare different decoder types and features. Based on the finding that modality-agnostic decoders using imagebind features achieve the best score on this metric, we performed the additional qualitative analysis presented in Figures 6 and 7. (Note that we expanded the candidate set for the qualitative analysis in order to have a larger and more diverse set of images.)

(5) Results are significant in regions that typically lack responses to visual stimuli, indicating potential bias in the classifier. This is relevant for the interpretation of the findings. A classification approach less sensitive to outliers (e.g., 70-way classification) could avoid this issue. Given the extreme collinearity of the experimental design, regressors in close temporal proximity will be highly similar, which could lead to leakage effects.

It is true that our searchlight analysis revealed significant activity in regions outside of the visual cortex. However, it is assumed that the processing of visual information does not stop at the border of the visual cortex. The integration of information such as the semantics of the image is progressively processed in other higher-level regions of the brain. Recent studies have shown that activity in large areas of the cortex (including many outside of the visual cortex) can be related to visual stimulation (Solomon et al. 2024; Raugel et al. 2025). Our work confirms this finding and we therefore do not see reason to believe that this is due to a bias in our decoders.

Further, you are suggesting that we could replace our regression approach with a 70-way classification. However, this is difficult using our fMRI data as we do not see a straightforward way to assign the training and testing stimuli with class labels (the two datasets consist of non-overlapping sets of naturalistic images).

To address your concerns regarding the collinearity of the experimental design and possible leakage effects, we trained and evaluated a decoder for one subject after running a “null-hypothesis” adapted preprocessing. More specifically, for all sessions, we shifted the functional data of all runs by one run (moving the data of the last run to the very front), but leaving the design matrices in place. Thereby, we destroyed the relationship of stimuli and brain activity but kept the original data and design with its collinearity (and possible biases). We preprocessed this adapted data for subject 1, and ran a whole-brain decoding using Imagebind features and verified that the decoding performance was at chance level:  Pairwise accuracy (captions): 0.43 | Pairwise accuracy (images): 0.47 | Pairwise accuracy (imagery): 0.50. This result provides evidence against the notion that potential collinearity or biases in our experimental design or evaluation procedure could have led to inflated results.

Raugel, J., Szafraniec, M., Vo, H.V., Couprie, C., Labatut, P., Bojanowski, P., Wyart, V. and King, J.R. (2025). Disentangling the Factors of Convergence between Brains and Computer Vision Models. arXiv preprint arXiv:2508.18226.

Solomon, S. H., Kay, K., & Schapiro, A. C. (2024). Semantic plasticity across timescales in the human brain. bioRxiv, 2024-02.

(6) The manuscript currently lacks a limitations section, specifically regarding the design of the experiment. This involves the use of the overly homogenous dataset Coco, which invites overfitting, the mixing of sentence descriptions and visual images, which invites imagery of previously seen content, and the use of a 1-back task, which can lead to carry-over effects to the subsequent trial.

Regarding the dataset CoCo: We agree that CoCo is somewhat homogenous, it is however much more diverse and naturalistic than the smaller datasets used in previous fMRI experiments with multimodal stimuli. Additionally, CoCo has been widely adopted as a benchmark dataset in the Machine Learning community, and features rich annotations for each image (e.g. object labels, segmentations, additional captions, people’s keypoints) facilitating many more future analyses based on our data.

Regarding the mixing of sentence descriptions and images: Subjects were not asked to visualize sentences and different techniques for the one-back tasks might have been used. Generally, we do not see it as problematic if subjects are performing visual imagery to some degree while reading sentences, and this might even be the case during normal reading as well. A more targeted experiment comparing reading with and without interleaved visual stimulation in the form of images and a one-back task would be required to assess this, but this was not the focus of our study. For now, it is true that we can not be sure that our results generalize to cases in which subjects are just reading and are less incentivized to perform mental imagery.

Regarding the use of a 1-back task: It was necessary to make some design choices in order to realize this large-scale data collection with approximately 10 hours of recording per subject. Specifically, the 1-back task was included in the experimental setup in order to assure continuous engagement of the participant during the rather long sessions of 1 hour. The subjects did indeed need to remember the previous stimulus to succeed at the 1-back task, which means that some brain activity during the presentation of a stimulus is likely to be related to the previous stimulus. We aimed to account for this confound during the preprocessing stage when fitting the GLM, which was fit to capture only the response to the presented image/caption, not the preceding one. Still, it might have picked up on some of the activity from preceding stimuli, causing some decrease of the final decoding performance.

We added a limitations section to the updated manuscript to discuss these important issues.

(7) I would urge the authors to clarify whether the primary aim is the introduction of a dataset and showing the use of it, or whether it is the set of results presented. This includes the title of this manuscript. While the decoding approach is very interesting and potentially very valuable, I believe that the results in the current form are rather descriptive, and I'm wondering what specifically they add beyond what is known from other related work. This includes imagery-related results. This is completely fine! It just highlights that a stronger framing as a dataset is probably advantageous for improving the significance of this work.

Thanks a lot for pointing this out. Based on this comment and feedback from the other reviewers we restructured the abstract, introduction and discussion section of the paper to better reflect the primary aim. (cf. general response above).

You further mention that it is not clear what our results add beyond what is known from related work. We list the main contributions here:

A single modality-agnostic decoder can decode the semantics of visual and linguistic stimuli irrespective of the presentation modality with a performance that is not lagging behind modality-specific decoders.

Modality-agnostic decoders outperform modality-specific decoders for decoding captions and mental imagery.

Modality-invariant representations are widespread across the cortex (a range of previous work has suggested they were much more localized (Bright et al. 2004; Jung et al. 2018; Man et al. 2012; Simanova et al. 2014).

Regions that are useful for imagery are largely overlapping with modality-invariant regions

Bright, P., Moss, H., & Tyler, L. K. (2004). Unitary vs multiple semantics: PET studies of word and picture processing. Brain and language, 89(3), 417-432.

Jung, Y., Larsen, B., & Walther, D. B. (2018). Modality-Independent Coding of Scene Categories in Prefrontal Cortex. Journal of Neuroscience, 38(26), 5969–5981.

Liuzzi, A. G., Bruffaerts, R., Peeters, R., Adamczuk, K., Keuleers, E., De Deyne, S., Storms, G., Dupont, P., & Vandenberghe, R. (2017). Cross-modal representation of spoken and written word meaning in left pars triangularis. NeuroImage, 150, 292–307. https://doi.org/10.1016/j.neuroimage.2017.02.032

Man, K., Kaplan, J. T., Damasio, A., & Meyer, K. (2012). Sight and Sound Converge to Form Modality-Invariant Representations in Temporoparietal Cortex. Journal of Neuroscience, 32(47), 16629–16636.

Simanova, I., Hagoort, P., Oostenveld, R., & van Gerven, M. A. J. (2014). Modality-Independent Decoding of Semantic Information from the Human Brain. Cerebral Cortex, 24(2), 426–434.

Reviewer #2 (Public review):

Summary:

This study introduces SemReps-8K, a large multimodal fMRI dataset collected while subjects viewed natural images and matched captions, and performed mental imagery based on textual cues. The authors aim to train modality-agnostic decoders--models that can predict neural representations independently of the input modality - and use these models to identify brain regions containing modality-agnostic information. They find that such decoders perform comparably or better than modality-specific decoders and generalize to imagery trials.

Strengths:

(1) The dataset is a substantial and well-controlled contribution, with >8,000 image-caption trials per subject and careful matching of stimuli across modalities - an essential resource for testing theories of abstract and amodal representation.

(2) The authors systematically compare unimodal, multimodal, and cross-modal decoders using a wide range of deep learning models, demonstrating thoughtful experimental design and thorough benchmarking.

(3) Their decoding pipeline is rigorous, with informative performance metrics and whole-brain searchlight analyses, offering valuable insights into the cortical distribution of shared representations.

(4) Extension to mental imagery decoding is a strong addition, aligning with theoretical predictions about the overlap between perception and imagery.

Weaknesses:

While the decoding results are robust, several critical limitations prevent the current findings from conclusively demonstrating truly modality-agnostic representations:

(1) Shared decoding ≠ abstraction: Successful decoding across modalities does not necessarily imply abstraction or modality-agnostic coding. Participants may engage in modality-specific processes (e.g., visual imagery when reading, inner speech when viewing images) that produce overlapping neural patterns. The analyses do not clearly disambiguate shared representational structure from genuinely modality-independent representations. Furthermore, in Figure 5, the modality-agnostic encoder did not perform better than the modality-specific decoder trained on images (in decoding images), but outperformed the modality-specific decoder trained on captions (in decoding captions). This asymmetry contradicts the premise of a truly "modality-agnostic" encoder. Additionally, given the similar performance between modality-agnostic decoders based on multimodal versus unimodal features, it remains unclear why neural representations did not preferentially align with multimodal features if they were truly modality-independent.

We agree that successful modality-agnostic and cross-modal decoding does not necessarily imply that abstract patterns were decoded. In the updated manuscript, we therefore refer to these representations as modality-invariant (see also the updated terminology explained in the general response above).

If participants are performing mental imagery when reading, and this is allowing us to perform cross-decoding, then this means that modality-invariant representations are formed during this mental imagery process, i.e. that the representations formed during this form of mental imagery are compatible with representations during visual perception (or, in your words, produce overlapping neural patterns). While we can not know to what extent people were performing mental imagery while reading (or having inner speech while viewing images), our results demonstrate that their brain activity allows for decoding across modalities, which implies that modality-invariant representations are present.

It is true that our current analyses can not disambiguate modality-invariant representations (or, in your words, shared representational structure) from abstract representations (in your words, genuinely modality-independent representations). As the main goal of the paper was to build modality-agnostic decoders, and these only require what we call “modality-invariant” representations (see our updated terminology in the general reviewer response above), we leave this question open for future work. We do however discuss this important limitation in the Discussion section of the updated manuscript.

Regarding the asymmetry of decoding results when comparing modality-agnostic decoders with the two respective modality-specific decoders for captions and images: We do not believe that this asymmetry contradicts the premise of a modality-agnostic decoder. Multiple explanations for this result are possible: (1) The modality-specific decoder for images might benefit from the more readily decodable lower-level modality-dependent neural activity patterns in response to images, which are less useful for the modality-agnostic decoder because they are not useful for decoding caption trials. The modality-specific decoders for captions might not be able to pick up on low-level modality-dependent neural activity patterns as these might be less easily decodable. 

The signal-to-noise ratio for caption trials might be lower than for image trials (cf. generally lower caption decoding performance), therefore the addition of training data (even if it is from another modality) improves the decoding performance for captions, but not for images (which might be at ceiling already).

Regarding the similar performance between modality-agnostic decoders based on multimodal versus unimodal features: Unimodal features are based on rather high-level features of the respective modality (e.g. last-layer features of a model trained for semantic image classification), which can be already modality-invariant to some degree. Additionally, as already mentioned before, in the updated manuscript we only require representations to be modality-invariant and not necessarily abstract.

(2) The current analysis cannot definitively conclude that the decoder itself is modality-agnostic, making "Qualitative Decoding Results" difficult to interpret in this context. This section currently provides illustrative examples, but lacks systematic quantitative analyses.

The qualitative decoding results in Figures 6 and 7 present exemplary qualitative results for the quantitative results presented in Figures 4 and 5 (see also Reviewer 1 Comment 4).

Figures 4 and 5 show pairwise decoding accuracy as a quantitative measure for evaluation of the decoders. This metric is the main metric we used to compare different decoder types and features. Based on the finding that modality-agnostic decoders using imagebind features achieve the best score on this metric, we performed the additional qualitative analysis presented in Figures 6 and 7. (Note that we expanded the candidate set for the qualitative analysis in order to have a larger and more diverse set of images.)

(3) The use of mental imagery as evidence for modality-agnostic decoding is problematic.

Imagery involves subjective, variable experiences and likely draws on semantic and perceptual networks in flexible ways. Strong decoding in imagery trials could reflect semantic overlap or task strategies rather than evidence of abstraction.

It is true that mental imagery does not necessarily rely on modality-agnostic representations. In the updated manuscript we revised our terminology and refer to the analyzed representations as modality-invariant, which we define as “representations that significantly overlap between modalities”. 

The manuscript presents a methodologically sophisticated and timely investigation into shared neural representations across modalities. However, the current evidence does not clearly distinguish between shared semantics, overlapping unimodal processes, and true modality-independent representations. A more cautious interpretation is warranted.

Nonetheless, the dataset and methodological framework represent a valuable resource for the field.

We fully agree with these observations, and updated our terminology as outlined in the general response.

Reviewer #3 (Public review):

Summary:

The authors recorded brain responses while participants viewed images and captions. The images and captions were taken from the COCO dataset, so each image has a corresponding caption, and each caption has a corresponding image. This enabled the authors to extract features from either the presented stimulus or the corresponding stimulus in the other modality.

The authors trained linear decoders to take brain responses and predict stimulus features.

"Modality-specific" decoders were trained on brain responses to either images or captions, while "modality-agnostic" decoders were trained on brain responses to both stimulus modalities. The decoders were evaluated on brain responses while the participants viewed and imagined new stimuli, and prediction performance was quantified using pairwise accuracy. The authors reported the following results:

(1) Decoders trained on brain responses to both images and captions can predict new brain responses to either modality.

(2) Decoders trained on brain responses to both images and captions outperform decoders trained on brain responses to a single modality.

(3) Many cortical regions represent the same concepts in vision and language.

(4) Decoders trained on brain responses to both images and captions can decode brain responses to imagined scenes.

Strengths:

This is an interesting study that addresses important questions about modality-agnostic representations. Previous work has shown that decoders trained on brain responses to one modality can be used to decode brain responses to another modality. The authors build on these findings by collecting a new multimodal dataset and training decoders on brain responses to both modalities.

To my knowledge, SemReps-8K is the first dataset of brain responses to vision and language where each stimulus item has a corresponding stimulus item in the other modality. This means that brain responses to a stimulus item can be modeled using visual features of the image, linguistic features of the caption, or multimodal features derived from both the image and the caption. The authors also employed a multimodal one-back matching task, which forces the participants to activate modality-agnostic representations. Overall, SemReps-8K is a valuable resource that will help researchers answer more questions about modality-agnostic representations.

The analyses are also very comprehensive. The authors trained decoders on brain responses to images, captions, and both modalities, and they tested the decoders on brain responses to images, captions, and imagined scenes. They extracted stimulus features using a range of visual, linguistic, and multimodal models. The modeling framework appears rigorous, and the results offer new insights into the relationship between vision, language, and imagery. In particular, the authors found that decoders trained on brain responses to both images and captions were more effective at decoding brain responses to imagined scenes than decoders trained on brain responses to either modality in isolation. The authors also found that imagined scenes can be decoded from a broad network of cortical regions.

Weaknesses:

The characterization of "modality-agnostic" and "modality-specific" decoders seems a bit contradictory. There are three major choices when fitting a decoder: the modality of the training stimuli, the modality of the testing stimuli, and the model used to extract stimulus features. However, the authors characterize their decoders based on only the first choice-"modality-specific" decoders were trained on brain responses to either images or captions, while "modality-agnostic" decoders were trained on brain responses to both stimulus modalities. I think that this leads to some instances where the conclusions are inconsistent with the methods and results.

In our analysis setup, a decoder is entirely determined by two factors: (1) the modality of the stimuli that the subject was exposed to, and (2) the machine learning model used to extract stimulus features.

The modality of the testing stimuli defines whether we are evaluating the decoder in a within-modality or cross-modality setting, but is not an inherent characteristic of a trained decoder

First, the authors suggest that "modality-specific decoders are not explicitly encouraged to pick up on modality-agnostic features during training" (line 137) while "modality-agnostic decoders may be more likely to leverage representations that are modality-agnostic" (line 140). However, whether a decoder is required to learn modality-agnostic representations depends on both the training responses and the stimulus features. Consider the case where the stimuli are represented using linguistic features of the captions. When you train a "modality-specific" decoder on image responses, the decoder is forced to rely on modality-agnostic information that is shared between the image responses and the caption features. On the other hand, when you train a "modality-agnostic" decoder on both image responses and caption responses, the decoder has access to the modality-specific information that is shared by the caption responses and the caption features, so it is not explicitly required to learn modality-agnostic features. As a result, while the authors show that "modality-agnostic" decoders outperform "modality-specific" decoders in most conditions, I am not convinced that this is because they are forced to learn more modality-agnostic features.

It is true that for example a modality-specific decoder trained on fmri data from images with stimulus features extracted from captions might also rely on modality-invariant features. We still call this decoder modality-specific, as it has been trained to decode brain activity recorded from a specific stimulus modality. In the updated manuscript we corrected the statement that “modality-specific decoders are not explicitly encouraged to pick up on modality-invariant features during training” to include the case of decoders trained on features from the other modality which might also rely on modality-invariant features.

It is true that a modality-agnostic decoder can also have access to modality-dependent information for captions and images. However, as it is trained jointly with both modalities and the modality-dependent features are not compatible, it is encouraged to rely on modality-invariant features. The result that modality-agnostic decoders are outperforming modality-specific decoders trained on captions for decoding captions confirms this, because if the decoder was only relying on modality-dependent features the addition of additional training data from another stimulus modality could not increase the performance. (Also, the lack of a performance drop compared to modality-specific decoders trained on images is only possible thanks to the reliance on modality-invariant features. If the decoder only relied on modality-dependent features the addition of data from another modality would equal an addition of noise to the training data which must result in a performance drop at test time.). We can not exclude the possibility that modality-agnostic decoders are also relying on modality-dependent features, but our results suggest that they are relying at least to some degree on modality-invariant features.

Second, the authors claim that "modality-specific decoders can be applied only in the modality that they were trained on, while "modality-agnostic decoders can be applied to decode stimuli from multiple modalities, even without knowing a priori the modality the stimulus was presented in" (line 47). While "modality-agnostic" decoders do outperform "modality-specific" decoders in the cross-modality conditions, it is important to note that "modality-specific" decoders still perform better than expected by chance (figure 5). It is also important to note that knowing about the input modality still improves decoding performance even for "modality-agnostic" decoders, since it determines the optimal feature space-it is better to decode brain responses to images using decoders trained on image features, and it is better to decode brain responses to captions using decoders trained on caption features.

Thanks for this important remark. We corrected this statement and now say that “modality-specific decoders that are trained to be applied only in the modality that they were trained on”, highlighting that their training process optimizes them for decoding in a specific modality. They can indeed be applied to the other modality at test time, this however results in a substantial performance drop.

It is true that knowing the input modality can improve performance even for modality-agnostic decoders. This can most likely be explained by the fact that in that case the decoder can leverage both, modality-invariant and modality-dependent features. We will not further focus on this result however as the main motivation to build modality-agnostic decoders is to be able to decode stimuli without knowing the stimulus modality a priori. 

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

I will list additional recommendations below in no specific order:

(1) I find the term "modality agnostic" quite unusual, and I believe I haven't seen it used outside of the ML community. I would urge the authors to change the terminology to be more common, or at least very early explain why the term is much better suited than the range of existing terms. A modality agnostic representation implies that it is not committed to a specific modality, but it seems that a representation cannot be committed to something.

In the updated manuscript we now refer to the identified brain patterns as modality-invariant, which has previously been used in the literature (Man et al. 2012; Devereux et al. 2013; Patterson et al. 2016; Deniz et al. 2019, Nakai et al. 2021) (see also the general response on top and the Introduction and Related Work sections of the updated manuscript).

We continue to refer to the decoders as modality-agnostic, as this is a new type of decoder, and describes the fact that they are trained in a way that abstracts away from the modality of the stimuli. We chose this term as we are not aware of any work in which brain decoders were trained jointly on multiple stimulus modalities and in order not to risk contradictions/confusions with other definitions.

Deniz, F., Nunez-Elizalde, A. O., Huth, A. G., & Gallant, J. L. (2019). The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality. Journal of Neuroscience, 39(39), 7722–7736. https://doi.org/10.1523/JNEUROSCI.0675-19.2019

Devereux, B. J., Clarke, A., Marouchos, A., & Tyler, L. K. (2013). Representational Similarity Analysis Reveals Commonalities and Differences in the Semantic Processing of Words and Objects. The Journal of Neuroscience, 33(48).

Nakai, T., Yamaguchi, H. Q., & Nishimoto, S. (2021). Convergence of Modality Invariance and Attention Selectivity in the Cortical Semantic Circuit. Cerebral Cortex, 31(10), 4825–4839. https://doi.org/10.1093/cercor/bhab125

Man, K., Kaplan, J. T., Damasio, A., & Meyer, K. (2012). Sight and Sound Converge to Form Modality-Invariant Representations in Temporoparietal Cortex. Journal of Neuroscience, 32(47), 16629–16636.

Patterson, K., & Lambon Ralph, M. A. (2016). The Hub-and-Spoke Hypothesis of Semantic Memory. In Neurobiology of Language (pp. 765–775). Elsevier. https://doi.org/10.1016/B978-0-12-407794-2.00061-4

(2) The table in Figure 1B would benefit from also highlighting the number of stimuli that have overlapping captions and images.

The number of overlapping stimuli is rather small (153-211 stimuli depending on the subject). We added this information to Table 1B. 

(3) The authors wrote that training stimuli were presented only once, yet they used a one-back task. Did the authors also exclude the first presentation of these stimuli?

Thanks for pointing this out. It is indeed true that some training stimuli were presented more than once, but only for the case of one-back target trials. In these cases the second presentation of the stimulus was excluded, but not the first. As the subject can not be aware of the fact that the upcoming presentation is going to be a one-back target, the first presentation can not be affected by the presence of the subsequent repeated presentation. We updated the manuscript to clarify this issue.

(4) Coco has roughly 80-90 categories, so many image captions will be extremely similar (e.g., "a giraffe walking", "a surfer on a wave", etc.). How can people keep these apart?

It is true that some captions and images are highly similar even though they are not matching in the dataset. This might result in several false button presses because the subjects identified an image-caption pair as matching when in fact it wasn't intended to. However, as there was no feedback given on the task performance, this issue should not have had a major influence on the brain activity of the participants.

(5) Footnotes for statistics are quite unusual - could the authors integrate statistics into the text?

Thanks for this remark, in the updated manuscript all statistics are part of the main text.

(6) It may be difficult to achieve the assumptions of a permutation test - exchangeability, which may bias statistical results. It is not uncommon for densely sampled datasets to use bootstrap sampling on the predictions of the test data to identify if a given percentile of that distribution crosses 0. The lowest p-value is given by the number of bootstrap samples (e.g., if all 10,000 bootstrap samples are above chance, then p < 0.0001). This may turn out to be more effective.

Thanks for this comment. Our statistical procedure was in fact involving a bootstrapping procedure to generate a null distribution on the group-level. We updated the manuscript to describe this method in more detail. Here is the updated paragraph: “To estimate the statistical significance of the resulting clusters we performed a permutation test, combined with a bootstrapping procedure to estimate a group-level null distribution see also Stelzer et al., 2013). For each subject, we evaluated the decoders 100 times with shuffled labels to create per-subject chance-level results. Then, we randomly selected one of the 100 chance-level results for each of the 6 subjects and calculated group-level statistics (TFCE values) the exact same way as described in the preceding paragraph. We repeated this procedure 10,000 times resulting in 10,000 permuted group-level results. We ensured that every permutation was unique, i.e. no two permutations were based on the same combination of selected chance-level results. Based on this null distribution, we calculated p-values for each vertex by calculating the proportion of sampled permutations where the TFCE value was greater than the observed TFCE value. To control for multiple comparisons across space, we always considered the maximum TFCE score across vertices for each group-level permutation (Smith and Nichols, 2009).”

(7) The authors present no statistical evidence for some of their claims (e.g., lines 335-337). It would be good if they could complement this in their description. Further, the visualization in Figure 4 is rather opaque. It would help if the authors could add a separate bar for the average modality-specific and modality-agnostic decoders or present results in a scatter plot, showing modality-specific on the x-axis and modality-agnostic on the y-axis and color-code the modality (i.e., making it two scatter colors, one for images, one for captions). All points will end up above the diagonal.

We updated the manuscript and added statistical evidence for the claims made:

We now report results for the claim that when considering the average decoding performance for images and captions, modality-agnostic decoders perform better than modality-specific decoders, irrespective of the features that the decoders were trained on.

Additionally, we report the average modality-agnostic and modality-specific decoding accuracies corresponding to Figure 4. For modality-agnostic decoders the average value is 81.86\%, for modality-specific decoders trained on images 78.15\%, and for modality-specific decoders trained on captions 72.52\%. We did not add a separate bar to Figure 4 as this would add additional information to a Figure which is already very dense in its information content (cf. Reviewers 2’s recommendations for the authors). We therefore believe it is more useful to report the average values in the text and provide results for a statistical test comparing the decoder types. A scatter plot would make it difficult to include detailed information on the features, which we believe is crucial.

We further provide statistical evidence for the observation regarding the directionality of cross-modal decoding.

Reviewer #2 (Recommendations for the authors):

For achieving more evidence to support modality-agnostic representations in the brain, I suggest more thorough analyses, for example:

(1) Traditional searchlight RSA using different deep learning models. Through this approach, it might identify different brain areas that are sensitive to different formats of information (visual, text, multimodal); subsequently, compare the decoding performance using these ROIs.

(2) Build more dissociable decoders for information of different modality formats, if possible. While I do not have a concrete proposal, more targeted decoder designs might better dissociate representational formats (i.e., unimodal vs. modality-agnostic).

(3) A more detailed exploration of the "qualitative decoding results"--for example, quantitatively examining error types produced by modality-agnostic versus modality-specific decoders--would be informative for clarifying what specific content the decoder captures, potentially providing stronger evidence for modality-agnostic representations.

Thanks for these suggestions. As the main goal of the paper is to introduce modality-agnostic decoders (which should be more clear from the updated manuscript, see also the general response to reviews), we did not include alternative methods for identifying modality-invariant regions. Nonetheless, we agree that in order to obtain more in-depth insight into the nature of representations that were recorded, performing analyses with additional methods such as RSA, comparisons with more targeted decoder designs in terms of their target features will be indispensable, as well as more in-depth error type analyses. We leave these analyses as promising directions for future work.

The writing could be further improved in the introduction and, accordingly, the discussion. The authors listed a series of theories about conceptual representations; however, they did not systematically explain the relationships and controversies between them, and it seems that they did not aim to address the issues raised by these theories anyway. Thus, the extraction of core ideas is suggested. The difference between "modality-agnostic" and terms like "modality-independent," "modality-invariant," "abstract," "amodal," or "supramodal," and the necessity for a novel term should be articulated.

The updated manuscript includes an improved introduction and discussion section that highlight the main focus and contributions of the study.

We believe that a systematic comparison of theories on conceptual representations involving their relationships and controversies would require a dedicated review paper. Here, we focused on the aspects that are relevant for the study at hand (modality-invariant representations), for which we find that none of the considered theories can be rejected based on our results.

Regarding the terminology (modality-agnostic vs. modality-invariant, ..) please refer to the general response.

The figures also have room to improve. For example, Figures 4 and 5 present dense bar plots comparing multiple decoding settings (e.g., modality-specific vs. modality-agnostic decoders, feature space, within-modal vs. cross-modal, etc.); while comprehensive, they would benefit from clearer labels or separated subplots to aid interpretation. All figures are recommended to be optimized for greater clarity and directness in future revisions.

Thanks for this remark. We agree that the figures are quite dense in information. However, splitting them up into subplots (e.g. separate subplots for different decoder types) would make it much less straightforward to compare the accuracy scores between conditions. As the main goal of these figures is to compare features and decoder types, we believe that it is useful to keep all information in the same plot. 

You are also suggesting to improve the clarity of the labels. It is true that the top left legend of Figures 4 and 5 was mixing information about decoder type and broad classes of features  (vision/language/multimodal). To improve clarity, we updated the figures and clearly separated information on decoder type (the hue of different bars) and features (x-axis labels).  The broad classes of features (vision/language/multimodal) are distinguished by alternating light gray background colors and additional labels at the very bottom of the plots.

The new plots allow for easy performance comparison of the different decoder types and additionally provide information on confidence intervals for the performance of modality-specific decoders, which was not available in the previous figures.

Reviewer #3 (Recommendations for the authors):

(1) As discussed in the Public Review, I think the paper would greatly benefit from clearer terminology. Instead of describing the decoders as "modality-agnostic" and "modality-specific", perhaps the authors could describe the decoding conditions based on the train and test modalities (e.g., "image-to-image", "caption-to-image", "multimodal-to-image") or using the terminology from Figure 3 (e.g., "within-modality", "cross-modality", "modality-agnostic").

We updated our terminology to be clearer and more accurate, as outlined in the general response. The terms modality-agnostic and modality-specific refer to the training conditions, and the test conditions are described in Figure 3 and are used throughout the paper.

(2) Line 244: I think the multimodal one-back task is an important aspect of the dataset that is worth highlighting. It seems to be a relatively novel paradigm, and it might help ensure that the participants are activating modality-agnostic representations.

It is true that the multimodal one-back task could play an important role for the activation of modality-invariant representations. Future work could investigate to what degree the presence of widespread modality-invariant representations is dependent on such a paradigm.

(3) Line 253: Could the authors elaborate on why they chose a random set of training stimuli for each participant? Is it to make the searchlight analyses more robust?

A random set of training stimuli was chosen in order to maximize the diversity of the training sets, i.e. to avoid bias based on a specific subsample of the CoCo dataset. Between-subject comparisons can still be made based on the test set which was shared for all subjects, with the limitation that performance differences due to individual differences or to the different training sets can not be disentangled. However, the main goal of the data collection was not to make between-subject comparisons based on common training sets, but rather to make group-level analyses based on a large and maximally diverse dataset. 

(4) Figure 4: Could the authors comment more on the patterns of decoding performance in Figure 5? For instance, it is interesting that ResNet is a better target than ViT, and BERT-base is a better target than BERT-large.

A multitude of factors influence the decoding performance, such as features dimensionality, model architecture, training data, and training objective(s) (Conwell et al. 2023; Raugel et al. 2025). Bert-base might be better than bert-large because the extracted features are of lower dimension. Resnet might be better than ViT because of its architecture (CNN vs. Transformer). To dive deeper into these differences further controlled analysis would be necessary, but this is not the focus of this paper. The main objective of the feature comparison was to provide a broad overview over visual/linguistic/multimodal feature spaces and to identify the most suitable features for modality-agnostic decoding.

Conwell, C., Prince, J. S., Kay, K. N., Alvarez, G. A., & Konkle, T. (2023). What can 1.8 billion regressions tell us about the pressures shaping high-level visual representation in brains and machines? (p. 2022.03.28.485868). bioRxiv. https://doi.org/10.1101/2022.03.28.485868

Raugel, J., Szafraniec, M., Vo, H.V., Couprie, C., Labatut, P., Bojanowski, P., Wyart, V. and King, J.R. (2025). Disentangling the Factors of Convergence between Brains and Computer Vision Models. arXiv preprint arXiv:2508.18226.

(5) Figure 7: It is interesting that the modality-agnostic decoder predictions mostly appear traffic-related. Is there a possibility that the model always produces traffic-related predictions, making it trivially correct for the presented stimuli that are actually traffic-related? It could be helpful to include some examples where the decoder produces other types of predictions to dispel this concern.

The presented qualitative examples were randomly selected. To make sure that the decoder is not always predicting traffic-related content, we included 5 additional randomly selected examples in Figures 6 and 7 of the updated manuscript. In only one of the 5 new examples the decoder was predicting traffic-related content, and in this case the stimulus had actually been traffic-related (a bus).

I do prefer round for border-image but this is good!

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Reply to the reviewers

Manuscript number: RC-2025-03174

Corresponding author(s): Cristina, Tocchini and Susan, Mango

1. General Statements

We thank the reviewers for their thoughtful and constructive comments. We were pleased that the reviewers found our study “rigorous”, “well presented”, “technically strong”, and “novel”. We are also grateful for their recognition that our work identifies a function for a HOT region in gene regulation and provides new insights into the role of the uHOT in controlling dlg-1 expression.

Point-by-point description of the revisions

We have addressed the reviewers’ concerns by clarifying and refining the text, particularly regarding the intron 1 results, improving the quantitation and statistical analyses, and making adjustments and additions to text and figures.

Specific responses to each point are provided below in blue.

Reviewer #1

1. • The results fully support the authors conclusions regarding the significant role of the upstream HOT region ("uHOT") with strong fluorescence activity and substantial phenotypic effects (i.e., the animals have very low brood sizes and rarely progress through hatching). This data is well presented and technically well done.* Thank you.

2. In my view, their conclusions regarding the intronic HOT region are speculative and unconvincing. See below for main criticisms.*

We agree, and have made changes throughout the manuscript to make this point clearer. Specifically, we contextualize the role of intron 1 as a putative enhancer in reporter assays, but not in endogenous, physiological conditions. Some examples are:

Abstract: “(…) In contrast, the intronic region displays weak enhancer-like activity when tested in transcriptional reporter assays but is dispensable in transcriptional control when studied at the endogenous locus. Our findings reveal how HOT regions contribute to gene regulation during animal development and illustrate how regulatory potential identified in isolated contexts can be selectively deployed or buffered within the native genomic architecture.”

Background: “(…) The HOT region in the first intron possesses weak transcriptional capabilities that are restricted to epidermal cells as observed in transcriptional reporters, but seem to not be employed in physiological contexts.” As it will become clear reading this updated version of the manuscript, we cannot exclude at present a functional role during non-physiological conditions (e.g., stress)

Results and discussion: “(…) This is in contrast with what the reporter experiments showed, where intron 1 alone was permissive for transcription and slightly enhanced the FL transgene expression levels (Figure 1F,G and S4). (…)”

Other changes can be found highlighted in yellow in the manuscript.

• Furthermore, their conclusions about interactions between the two tested regions is speculative and they show no strong evidence for this claim.*

We thank the reviewer for raising this concern. To avoid overstating our conclusions, we now frame the potential interaction between the two studied HOT regions strictly in the context of previously published ARC-C data (Huang et al., 2022). We clarify in the revised text that these interactions have been observed in earlier work during larval stages (Huang et al., 2022), but remain to be validated during embryogenesis, and we present them solely as contextual information rather than as a central conclusion.

In Results and discussion section we wrote: “(…) Although the presence of a fountain at this locus remains to be confirmed during embryogenesis, Accessible Region Conformation Capture (ARC-C), a method that maps chromatin contacts anchored at accessible regulatory elements, showed that the putative HOT region interacts with other DNA sequences, including the first intron of dlg-1 (1). (…)”

* The authors claim that not all the phenotypic effects seen from deleting the uHOT region are specific to the dlg-1 gene. This is an interesting model, but the authors show essentially no data to support this or any explanation of what other gene might be regulated.*

We appreciate the reviewer’s comment and have revised the manuscript to ensure that the possibility of additional regulatory effects from the uHOT region is presented as a hypothesis rather than a claim. Our study was designed to investigate HOT-region–based transcriptional regulation rather than chromatin interactions, and we now make this scope more explicit in the text. The revised discussion highlights that, although ARC-C data suggest the uHOT region may contact other loci, the idea that these interactions contribute to the observed phenotypes remains speculative and will require dedicated future work.

In Results and discussion section we wrote: “(…) Because, as previously shown, the upstream HOT region exhibits chromatin interactions with other genomic loci (1), its depletion might affect gene expression of beyond dlg-1 alone. An intriguing hypothesis is that these phenotypes do not arise only from the reduction in dlg-1 mRNA and DLG-1 protein levels, but also from synergistic, partial loss-of-function phenotypes involving other genes (24). (…)”

* Finally, some of the hypotheses in the text could be more accurately framed by the authors. They claim HOT regions are often considered non-functional (lines 189-191). Also, they claim that correct expression levels and patterning is usually regulation by elements within a few hundred basepairs of the CDS (lines 78-80). These claims are not generally accepted in the field, despite a relatively compact genome. Notably, both claims were tested and disproven by Chen et al (2014), Genome Research, where the authors specifically showed strong transcriptional activity from 10 out of 10 HOT regions located up to 4.7 kb upstream of their nearest gene. Chen et al. 2014 is cited by Tocchini et al. and it is, therefore, surprisingly inconsistent with the claims in this manuscript.*

We thank the reviewer for this comment and have revised the text to clarify our intended meaning and avoid framing discussion points as absolute claims. We changed “often” to “frequently” in both sentences so that they better reflect general trends rather than universal rules.

The revised text now reads: “Controversially, C. elegans sequences that dictate correct expression levels and patterning are frequently located within a few hundred base-pairs (bp) (maximum around 1,000–1,500 bp) from a gene’s CDS (3,13–15),”;

And: “HOT regions in C. elegans, as well as other systems, have been predominantly associated with promoters and were frequently considered non-functional or simply reflective of accessible chromatin (25).”

Regarding the comparison to Chen et al., 2014, we note that their reporters did not include a reference baseline for “strong” transcriptional activity, and only five of the ten tested HOT regions were located more than 1.5 kb from the nearest TSS. Therefore, our phrasing is consistent with their findings while describing general trends observed in the C. elegans genome rather than absolute rules. We have also ensured that these sentences are presented as discussion points rather than definitive claims. We hope these revisions make the framing and context clearer to the reader. The fluorescence expression from the intronic HOT region is not visible by eye and the quantification shows very little expression, suggestive of background fluorescence. Although the authors show statistical significance in Figure 1G, I would argue this is possibly based on inappropriate comparisons and/or a wrong choice statistical test. The fluorescence levels should be compared to a non-transgenic animal and/or to a transgenic animal with the tested region shuffled but in an equivalent

We understand the reviewer’s concern regarding the low fluorescence levels observed for the intronic HOT reporter. To address this, we have now included a Figure S4 with higher-exposure versions of the embryos shown in Figure 1. These panels confirm that the nuclear signal is genuine: embryos without a functional transcriptional transgene do not display any comparable fluorescence, aside from the characteristic cytoplasmic granules associated with embryonic autofluorescence. Similar reference images have also been added to Figure S3 to clarify the appearance of autofluorescence under the same imaging conditions.

Regarding the quantitation analyses, as suggested by the reviewers, we now consistently quantify fluorescence by calculating the mean intensity for each embryo (biological replicates) and performing statistical analyses on these values. This approach ensures that the statistical tests are applied to independent biological measurements.

* I would suggest the authors remove their claims about the intronic enhancer and the interaction between the two regions. And I would suggest softening the claims about the uHOT regulation of another putatitive gene.*

We have revised the manuscript to avoid definitive claims regarding the presence of an interaction between the two studied HOT regions. These points are now presented strictly as hypotheses within the discussion, suggested by previously published ARC-C data rather than by our own experimental evidence. Likewise, we have softened our statements regarding the possibility that the uHOT region may regulate additional gene(s). This idea is now framed as a speculative model that will require dedicated future studies, rather than as a conclusion of the present work. Quotes can be found in the previous points (#3 and #4) raised by Reviewer 1.

* The authors would need to demonstrate several things to support their current claims. The major experiments necessary are:*

1. • Insert single-copy transgene with a minimal promoter and the intronic sequence scrambled to generate a proper baseline control. It is very possible that the intronic sequence does drive some expression, but the current control is not appropriate for statistical comparison (e.g., only the transgene with intron 1 contains the minimal promoter from pes-10, which may have baseline transcriptional activity even without the intron placed in front of the transgene).* We thank the reviewer for this suggestion. We agree that a scrambled-sequence control can be informative in some contexts; however, in this case we believe the existing data already address the concern. In our dataset, all uHOT reporter constructs—each containing the same minimal promoter—show consistent background levels in the absence of regulatory input, providing an internal baseline for comparison. For this reason, we consider the current controls sufficient to interpret the effects of the intronic region in reporter assays.

In general, the minimal Δpes-10 promoter is specifically designed to have negligible basal transcriptional activity on its own, and this property has been extensively validated in previous studies (reference included in the revised manuscript).

* It is not very clear why the authors did not test intron 1 within the H2B of the transgene and just the minimal promoter in front of the transgene, but only in the context of the full-length promoter. The authors show a minor difference in expression levels for the full-length (FL) and full-length with intron 1 (FL-INT1) but show a large statistical differnce. The authors use an inappropriate statistical test (T-test) for this experiment and treat many datapoints from the same embryo as independent, which is clearly not the case. Even minor differences in staging, transgene silencing in early development, or variability would potentially bias their data collection.*

We thank the reviewer for this comment. Our goal was to assess the potential contribution of intron 1 in two complementary contexts: (i) on its own, upstream of a minimal promoter, to test whether it can in principle support transcription, and (ii) within the full-length promoter construct, which more closely reflects the endogenous configuration. For this reason, we did not generate an additional construct placing intron 1 within the H2B reporter driven only by the minimal promoter, as we considered this redundant with the information provided by the existing INT1 and FL-INT1 reporters.

Regarding the statistical analysis, we agree that treating multiple measurements from the same embryo as independent is not appropriate. In the revised manuscript, we now use the mean fluorescence intensity per embryo as a single biological replicate and perform all statistical tests on these independent values. This approach avoids pseudo-replication and ensures that the analysis is robust to variability in staging or transgene behavior. The conclusions remain the same.

* The authors claim, based on ARC-C data previously published by their lab (Huang et al. 2022) that the dlg-1 HOT region interacts with "other" genomic regions. This is potentially interesting but the evidence for this should be included in the manuscript itself, perhaps by re-analyzing data from the 2022 manuscript?*

We thank the reviewer for this suggestion. The chromatin-interaction data referred to in the manuscript originate from the work of Huang et al., 2022, published by the Ahringer lab. As these ARC-C datasets are already publicly available and thoroughly analyzed in the original publication, we felt that reproducing them in our manuscript was not necessary for supporting the limited contextual point we make. Our intent is simply to note that previous work reported contacts between the uHOT region and additional loci. To address the reviewer’s concern, we have revised the manuscript to make clear that we are referencing previously published ARC-C observations and that we do not present these interactions as new findings from our study.

For example, in Results and discussion section we wrote: “(…) Because, as previously shown, the upstream HOT region exhibits chromatin interactions with other genomic loci (1), its depletion might affect gene expression beyond dlg-1 alone. An intriguing hypothesis is that these phenotypes do not arise only from the reduction in dlg-1 mRNA and DLG-1 protein levels, but also from a synergistic, partial loss-of-function phenotypes involving other genes (24). (…)”

* The fluorescence quantification is difficult to interpret from the attached data file (Table S1). For the invidividual values, it is unclear how many indpendent experiments (different embryos) were conducted. The authors should clarify if every data value is from an independent embryo or if they used several values from the same embryo. If they did use several values from the same embryo, how did they do this? Did they take very cell? Or did they focus on specific cells? How did they ensure embryo staging?*

We thank the reviewer for pointing this out. To clarify the quantification procedure, we have expanded the description in the Methods section (“Live imaging: microscopy, quantitation, and analysis”). The revised text now specifies that each data point represents the normalized fluorescence value obtained from three nuclei (or five junctions, depending on the construct), all taken from the same anatomical positions across embryos. Two independent biological replicates were performed for each experiment, with each embryo contributing a single averaged value.

As noted in the figure legends, the specific nuclei used for quantification are indicated in each panel (with dashed outlines), and a reference nucleus marked with an asterisk allows unambiguous identification of the same positions across all conditions. We are happy to further refine this description if additional clarification is needed.

* The authors also do not describe how they validated single-copy insertions (partial transgene deletions in integrants are not infrequent and they only appear to use a single insertion for each strain). This should be described and or added as a caveat if no validation was performed.*

The authors also do not describe any validation for the CRISPR alleles, either deletions or insertion of the synthetic intron into dlg-1. How were accurate gene edits verified.

We thank the reviewer for highlighting the importance of validating the genetic constructs. We have now clarified this more explicitly in the revised Methods section and in Table S1. All single-copy transgene insertions and all CRISPR-generated alleles were verified by genotyping and Sanger sequencing to confirm correct integration and the absence of unintended rearrangements.

• *

I am not convinced the statistical analysis of the fluorescence data is correct. Unless the authors show that every datapoint in the fluorescence quantification is independent, then I would argue they vastly overestimate the statistical significance. Even small differences are shown to have "***" levels of significance, which does not appear empirically plausible.

We thank the reviewer for highlighting this point. To ensure that each data point represents an independent measurement, we now calculate the mean fluorescence per embryo (from three nuclei or five junctions) and use these per-embryo means as biological replicates for statistical testing. Two independent experiments were performed for each condition. Statistical differences were evaluated using a one-tailed t-test on the per-embryo means, as indicated in the revised Methods section.

After this adjustment, the differences remain statistically significant, although less extreme than in the initial analysis (now p * *

This study is so closely related to the Chen et al study, that I believe this study should be discussed in more detail to put the data into context.

We thank the reviewer for this suggestion. While we refer to Chen et al., 2014 as a relevant prior study for context, we believe that our work addresses distinct questions and experimental approaches. Specifically, our study focuses on HOT region-based transcriptional regulation in the dlg-1 locus and its functional dissection in vivo, which is conceptually and methodologically different from the scope of Chen et al., 2014 where the author tested the functionality of HOT region-containing promoters in the context of single-copy integrated transcriptional reporters. We hope this is clearer to the reader in the revised manuscript.

* Add H2B to the mNG in Figure 1 in order to understand where the first intron was inserted.*

We thank the reviewer for this suggestion. A schematic representation of the transgene is already provided above the corresponding images to indicate the location of the first intron.

For additional clarity, we have now added the following sentence in the main text: “In the other, intron 1 was inserted in the FL transgene within the H2B coding sequence (at position 25 from the ATG), preserving the canonical splice junctions with AG at the end of the first exon and a G at the beginning of the second exon, so that it acted as a bona fide intron (FL-INT1) (Figure 1F).”

This should help readers understand the placement of the intron without requiring modifications to the figure itself.__ __

Reviewer #2

1) The authors suggest that the region upstream of the dlg-1 gene is a HOT region. Although they highlight that other broad studies pick up this region as a HOT region, it would be good that the authors dive into the HOT identity of the region and characterize it, as it is a major part of their study. In addition to multiple TFs binding to the site, there are different criteria by which a region would be considered a HOT region. E.g. is there increased signal on this region in the IgG ChIP-seq tracks? Is the area CpG dense?

We thank the reviewer for this suggestion. In the manuscript and Figure S1, we show several features of HOT regions, including transcription factor binding and chromatin marks. To further characterize the dlg-1 uHOT region, we have added the following sentence to the text: “The conserved region is positioned approximately four Kb from the CDS of dlg-1 in a CpG-dense sequence (2), and is overlapping and bordered by chromatin marks typically found in enhancers (5,16).”

This addition provides additional evidence supporting the identity of the region as a HOT region, complementing the features already presented.

* 2) When describing the HOT region, they refer to Pol II binding as 'confirming its role as a promoter': non-promoter regions can also have Pol II binding, especially enhancers. Having binding of Pol II does not confirm its role as promoter. On the contrary, seeing the K27ac and K4me1 would point towards it being an enhancer.*

The sentence has been revised to clarify the interpretation of Pol II binding: “This HOT site also contains RNA Pol II peaks during embryogenesis (Figure S1C), supporting its role as a promoter or enhancer (9).” This wording avoids overinterpreting Pol II binding alone, while acknowledging that the HOT region may have both promoter and enhancer characteristics.

We would like to note that the relevant chromatin marks (H3K27ac and H3K4me1), which are indicative of enhancer activity, are described in the text: “(…) Specifically, it is enriched in acetylated lysine 27 (H3K27ac) and mono- and di-methylated lysine 4 of histone H3 (H3K4me1/2), and depleted from tri-methylated lysine 4 of histone H3 (H3K4me3) (Figure S1D) (5,16). (…)”

These changes clarify that the HOT region may have enhancer characteristics and avoid overinterpreting the Pol II signal.

* 3) In S1B, the authors show TF binding tracks. They also have a diagram of the region subsets (HOT1-4) that were later tested. What is their criteria for dividing the HOT region into those fragments? From looking at Fig S1, the 'proper' HOT region (ie. Where protein binding occurs) seems to be divided into two (one chunk as part of HOT3 and one chunk as part of HOT4). Can the authors comment on the effects of this division?*

To clarify the criteria for dividing the HOT region into subregions, we have added the following sentence to the main text: “The subregions were chosen taking into account (i) enrichment of putative TF binding sites (uHOT1 for PHA-4, uHOT2 for YAP-1 and NHR-25, uHOT3 for ELT-3, and uHOT4 for PHA-4 and others (e.g., ELT-1 and ELT-3)), (ii) Pol II binding peaks, and (iii) histone modification peaks (Fig. S1C,D).”

This description explains the rationale behind the division and clarifies why the HOT region was split into these four fragments for functional testing.

* 4) For the reporter experiments, the first experiments carry the histone H2B sequence and the second set of experiments (where the HOT region is dissected) carry a minimal promoter Δ*pes-10 (MINp). The results could be affected by the addition of these sequences. Is there a reason for this difference? Can the authors please justify it?

The difference in reporter design reflects the distinct goals of the two sets of experiments. The H2B sequence, coupled to mNG, is used as a coding sequence throughout the first part of the study (reporter analysis). This is commonly used to (i) concentrate the fluorescence signal (mNG) into nuclei (H2B) and (ii) be able to identify specific cells more accurately for quantitation reasons (intensity and consistency). The Δpes-10 promoter is instead used to analyze whether specific sequences possess enhancer potential: this promoter alone possesses the sequences that can allow transcription only in the presence of transcription factors that bind to the studied sequence placed upstream it.

To clarify this distinction in the manuscript, we have added the following sentence: “(…) Each region was paired with the minimal promoter Δpes-10 (MINp) (Figure 1D) and generated four transcriptional reporters. Δpes-10 is commonly used to generate transcriptional reporter aimed at assessing candidate regulatory enhancer sequences (20). The minimal promoter drives expression only when transcription factors bind to the tested upstream sequence and test enhancer activity. (…)”

5) Regarding the H2B sequence: ' 137: first intron [...] inserted in the FL transgene within the H2B sequence, acting as an actual intron (FL-INT1)': how was the location of the insertion chosen? Does it disrupt H2B? can it be that the H2B sequence contributed to dampening down the expression of mNG and disrupting it makes it stronger? It would be important to run the first experiments with minimal promoters and not with the H2B sequence.

The location of the intron insertion within the H2B coding sequence was chosen to preserve proper splicing and avoid disrupting H2B protein. We added the following sentence to clarify this point: “(…) In the other, the intron was inserted in the FL transgene within the H2B coding sequence (at position 25 from the ATG), preserving the canonical splice junctions with AG at the end of the first exon and a G at the beginning of the second exon, so that it acted as a bona fide intron (FL-INT1) (Figure 1F). (…)”

* 6) Have the authors explored the features of the sequences underlying the different HOT subregions? (e.g. running a motif enrichment analysis)? Is there anything special about HOT3 that could make it a functional region? It would be good to compare uHOT3 vs the others that do not drive the correct pattern. Since it's a HOT region, it may not have a special feature, but it is important to look into it.*

We thank the reviewer for this suggestion. To clarify the rationale for dividing the HOT region into four subregions, we have added the following sentence to the main text: “(…) The subregions were chosen taking into account (i) enrichment of putative TF binding sites (uHOT1 for PHA-4, uHOT2 for YAP-1 and NHR-25, uHOT3 for ELT-3, and uHOT4 for PHA-4 and others (e.g., ELT-1 and ELT-3)), (ii) Pol II binding peaks, and (iii) histone modification peaks (Fig. S1C,D). (…)”

While uHOT3 does not appear to possess unique sequence features beyond these general HOT-region characteristics, this approach allowed us to systematically test which fragments contribute to transcriptional activity and patterning.

7) For comparisons, the authors run t-tests. Is the data parametric? Otherwise, it would be more suitable to use a non-parametric test.

To ensure that each data point represents an independent biological replicate, we now calculate the mean fluorescence intensity per embryo and perform statistical tests on these per-embryo means. The data meet the assumptions of parametric tests, and we use a one-tailed t-test as indicated in the Methods.

* 1) The authors work with C. elegans embryos at comma stage, according to the methods section. It would be good if the authors mentioned it in the main text so that the reader is informed.*

Thanks for this suggestion. We added this sentence in the main text: “(…) Live imaging and quantitation analyses on embryos at the comma stage (used throughout the study for consistency purposes) showed (…)”.

* 2) 'Notably, the upstream HOT region is located more than four kilo-bases (Kb) away the CDS, and the one in the first intron contains enhancer sites, too.': what do they mean by 'enhance sites, too'. Is the region known as a functional enhancer? If so, could you please provide the reference?*

Here the clarification from the revised text: “(…) Notably, the upstream HOT region is located more than four kilo-bases (Kb) away the CDS, and the one in the first intron does not only contain two TSS but also three enhancer sites (8). (…)”

* 3) 'We hypothesized the upstream HOT region is the main driver of dlg-1 transcriptional regulation.': this sentence needs more reasoning. What led to this hypothesis? Is it the fact of seeing multiple TFs binding there? The chromatin marks?*

The reasoning behind the hypothesis is described in the preceding paragraph, and to make this connection clearer, we have revised the sentence to begin with: “Considering all of this information, we hypothesized the upstream HOT region is the main driver of dlg-1 transcriptional regulation. (…)”.

This change explicitly links the hypothesis to the observed TF binding and chromatin marks described above.

* 4) The labels of S1B are too wide, as if they have stretched the image. Could the authors please correct this?*

Yes, we agree with Reviewer 2. We corrected this.

* 5) This sentence does not flow with the rest of the text '84 - cohesins have been shown to organize the DNA in a way that active enhancers make contacts in the 3D space forming "fountains" detectable in Hi-C data (17,18).': is there a reason to explain this? I would remove it if not, as it can confuse the reader.*

We thank the reviewer for this comment. We agree that the sentence could potentially interrupt the flow; however, it is important for introducing the concept of “fountains” in 3D genome organization, which is necessary to understand the subsequent statement: “(…) Although the presence of a fountain at this locus remains to be confirmed during embryogenesis, Accessible Region Conformation Capture (ARC-C), a method that maps chromatin contacts anchored at accessible regulatory elements, showed that the putative HOT region interacts with other DNA sequences, including the first intron of dlg-1 (1). (…)”.

Therefore, we have retained this sentence to provide the necessary background for readers.

* 6) The authors mentioned that 'ARC-C data showed the putative HOT region interacts with other DNA sequences, including the first intron of dlg': have the authors analysed the data from the previous paper? A figure with the relevant data could illustrate this interaction so that the reader knows which specific region has been shown to interact with which. This would also bring clarity as to why they chose intron1 for additional experiments.*

We thank the reviewer for this suggestion. We have examined the relevant ARC-C data from the previous publication (Huang et al., 2022). However, as these results are already published, we do not feel it is necessary to reproduce them in our manuscript. The mentioning of these interactions is intended only to introduce the concept for discussion and to provide context for why intron 1 was considered in subsequent experiments

* 7) 'two deletion sequences spanning from the beginning (uHOT) or the end (Short) of the HOT region until the dlg-1 CDS': From the diagrams of the figure, I understand that uHOT has the distal region deleted, and the short HOT has the distal and the upstream regions deleted. Is this correct? Could you clarify this in the text? E.g. 'we designed two reporters - one containing the sequence starting at the HOT region and ending at the dlg-1 CDS, and the other without the HOT region, but rather starting downstream of it until the dlg-1 CDS'.*

To clarify the design of the reporters, we have revised the text as follows: “(…) To test this idea, we generated three single-copy, integrated transcriptional reporters carrying a histone H2B sequence fused to an mNeon-Green (mNG) fluorescent protein sequence under the transcriptional control of the following dlg-1 upstream regions: (i) a full-length sequence (“FL” = Distal + uHOT + Proximal sequences), (ii) one spanning from the beginning of the HOT region to the dlg-1 CDS (“uHOT” = uHOT + Proximal sequences), and (iii) one starting at the end of the HOT region and ending at the dlg-1 CDS (“Short” = Proximal sequence) (Figure 1A-C). (…)”

This description clarifies which parts of the upstream region are included in each reporter and matches the schematics in Figure 1.

* 8) 'Specifically, it spanned from bp 5,475,070 to 5,475,709 on chromosome X and removed HOT2 and HOT2 sequences' - this is unclear to me. What sequences are removed? HOT2 and 3?*

Thanks for spotting this typo. It has now been corrected.

* 9) 'ARC-C' is not introduced. Please spell out what this is. Accessible Region Conformation Capture (ARC-C). It would be helpful to include a sentence of what it is, as it will not be known by many readers.*

You are right, we changed into: “(…) Although the presence of a fountain at this locus remains to be confirmed during embryogenesis, Accessible Region Conformation Capture (ARC-C), a method that maps chromatin contacts anchored at accessible regulatory elements, showed that the putative HOT region interacts with other DNA sequences, including the first intron of dlg-1 (1). (...)”

* 10) Fig 1 B, diagram on the right: the H2B sequence is missing. I see that is indicated in the legend as part of mNG but this can be misleading. Could the authors add it to the diagram for clarification?*

Yes, you are right. We added this in the figure.__ __

Reviewer #3

The authors' claims are generally supported by the data, thoug the last sentence of the abstract was a bit overstated. They state that they "reveal the function of HOT regions in animals development...."; it would be more accurate to state that they linked the role of an upstream HOT region to dlg-1 regulation, and their findings hint that this element could have additional regulatory functions. The authors can either temper their conclusions or try RNA-seq experiments to find additional genes that are misregulated by the delta-uHOT deletion allele. [OPTIONAL]. Another [OPTIONAL] experiment that would strengthen the claims is to perform RNAi knockdown or DLG-1 protein depletion and link that to phenotype to show that the dlg-1 mRNA and DLG-1 protein changes seen in the uHOT mutant do not explain the lethality observed.

We thank the reviewer for this comment. We have studied HOT region function in the context of a model organism, C. elegans; therefore, we believe that describing our findings as revealing a function of HOT regions in animal development is accurate. The sentence aims at noting that these observations may provide broader insights into HOT region regulation. We changed the last sentence of the abstract into: “(…) Our findings reveal how HOT regions contribute to gene regulation during animal development and illustrate how regulatory potential identified in isolated contexts can be selectively deployed or buffered within the native genomic architecture. (…)”.

We note that RNA-seq is beyond the scope of this study; our discussion of potential effects on other genes is intended only as a hypothesis for future work. RNAi of dlg-1 has been previously reported and is cited in the manuscript, providing context for the phenotypes observed and discussed.

1. * When printed out I cannot read what the tracks are in Fig S1. Adding larger text to indicate what those tracks are is necessary.* Yes, you are right. We changed this in the figure.

2. *

3. Line 79. I would change the word "usually" to "frequently" in the discussion about regulatory element position. While promoters ranging from a few hundred to 2000 basepairs are frequently used, there are numerous examples where important enhancers can be further away.*

Corrected.

* Line 93-95. The description of the reporters was very confusing. When referring to the deletion sequences it sounds like that is what is missing rather than what is included. Rather, if I understand correctly the uHOT is the sequence from the start of the uHOT to the CDS and Short starts at the end of uHOT (omitting it). Adding the promoter fragments to the figure would improve clarity.*

To clarify the design of the reporters, we have revised the text as follows: “(…) To test this idea, we generated three single-copy, integrated transcriptional reporters carrying a histone H2B sequence fused to an mNeon-Green (mNG) fluorescent protein sequence under the transcriptional control of the following dlg-1 upstream regions: (i) a full-length sequence (“FL” = Distal + uHOT + Proximal sequences), (ii) one spanning from the beginning of the HOT region to the dlg-1 CDS (“uHOT” = uHOT + Proximal sequences), and (iii) one starting at the end of the HOT region and ending at the dlg-1 CDS (“Short” = Proximal sequence) (Figure 1A-C). (…)”

This description clarifies which parts of the upstream region are included in each reporter and matches the schematics in Figure 1.

* Line 108. Re-work the phrase "increase majorly". Majorly increase would be better.*

We thank the reviewer for this suggestion. The verb is used here as an infinitive (“to increase majorly”), and in standard English the infinitive is usually not split. Therefore, we have kept the phrasing as it currently appears in the manuscript.

* Line 153-154. The deletion indicates that HOT2 and HOT2 were removed. Was one supposed to be HOT3?*

Thanks for spotting this typo. It has now been corrected.

* In the figure legends the number of animals scored and the number of biological repeats is missing.*

Added.

* Figure 1 title in the legend. Should read "main driver" not "man driver".*

Thanks for spotting this typo. It has now been corrected.

* The references need to be gone through carefully and cleaned up. There are numerous gene and species names that are not italicized. There are also extra elements added by the reference manager such as [Internet].*

Thanks for pointing it out. We used Zotero and the requested formatting from the journal of our choice. We will discuss with their team how to go through this issue.

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Referee #3

Evidence, reproducibility and clarity

High occupancy target (HOT) regions are genomic sequences in C. elegans that are bound by large numbers of transcription factors and emerged from systematic ChIP-seq studies. Whether they play physiologically important roles in gene regulation is not clear. in In this manuscript, Tocchini et al. examine the function of two HOT regions using a combination of promoter reporters, genome editing, and smFISH. One HOT region is upstream of the dlg-1 gene and other is in the first intron of dlg-1.

The claims about the impact of the upstream HOT region on dlg-1 expression are convincing. Omitting the sequence in a promoter reporter reduces expression, the element is sufficient to drive expression from a MINp::mNG reporter, and deletion of the element reduces dlg-1 expression and causes developmental defects. The claims about the intronic HOT region need to be tempered slightly. The element drives weak expression in a MINp::mNG reporter but the replacement of the dlg-1 first intron with a syntron had no effect on expression, limiting the claims that be made about this regulatory element. The authors' claims are generally supported by the data, thoug the last sentence of the abstract was a bit overstated. They state that they "reveal the function of HOT regions in animals development...."; it would be more accurate to state that they linked the role of an upstream HOT region to dlg-1 regulation, and their findings hint that this element could have additional regulatory functions. The authors can either temper their conclusions or try RNA-seq experiments to find additional genes that are misregulated by the delta-uHOT deletion allele. [OPTIONAL]. Another [OPTIONAL] experiment that would strengthen the claims is to perform RNAi knockdown or DLG-1 protein depletion and link that to phenotype to show that the dlg-1 mRNA and DLG-1 protein changes seen in the uHOT mutant do not explain the lethality observed.

There are elements of the manuscript that must be improved for clarity/accuracy.

1. When printed out I cannot read what the tracks are in Fig S1. Adding larger text to indicate what those tracks are is necessary.
2. Line 79. I would change the word "usually" to "frequently" in the discussion about regulatory element position. While promoters ranging from a few hundred to 2000 basepairs are frequently used, there are numerous examples where important enhancers can be further away.
3. Line 93-95. The description of the reporters was very confusing. When referring to the deletion sequences it sounds like that is what is missing rather than what is included. Rather, if I understand correctly the uHOT is the sequence from the start of the uHOT to the CDS and Short starts at the end of uHOT (omitting it). Adding the promoter fragments to the figure would improve clarity.
4. Line 108. Re-work the phrase "increase majorly". Majorly increase would be better.
5. Line 153-154. The deletion indicates that HOT2 and HOT2 were removed. Was one supposed to be HOT3?
6. In the figure legends the number of animals scored and the number of biological repeats is missing.
7. Figure 1 title in the legend. Should read "main driver" not "man driver",
8. The references need to be gone through carefully and cleaned up. There are numerous gene and species names that are not italicized. There are also extra elements added by the reference manager such as [Internet].

Referee cross-commenting

I agree with the comments from the previous reviewers. The suggested experiments are reasonable. Reviewer 1's point about the Chen et al 2014 Genome Res paper is really important. I put the revision as unknown as it depended on whether they did the optional experiments I suggested. If they revise their text, tempering claims, adjusting statistical analyses, then that could be 1-3 months. If they did the RNA-seq that I suggested, that would be a longer timeline.

Significance

The study is generally rigorously done. Strengths are that this work finds a function for a HOT region in gene regulation. Limitations are that the work is currently very thorough regulatory element bashing. They convincingly demonstrate the role of uHOT in regulating dlg-1 and suggest that the reduction of DLG-1 levels does not explain the phenotype. This finding is of interest to basic researchers in gene regulation. Without going into that discrepancy more, the significance is limited. Linking HOT regions to novel regulatory mechanisms controlling multiple genes would be broadly interesting to the gene regulation and developmental biology.

I am a C. elegans molecular biologist with expertise in gene regulatory networks.

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Referee #2

Evidence, reproducibility and clarity

Summary:

The authors investigate the functionality of a HOT region located upstream of the dlg-1 gene in Caenorhabditis elegans. This region is bound by multiple proteins and enriched for H3K27ac and H3K4me1, features characteristic of enhancers. Using reporter assays, they dissect the region and identify a sub-fragment, HOT3, as responsible for driving gene expression in epidermis, with a pattern similar to that of dlg-1 itself. Deletion of this region leads to downregulation of dlg-1 and lethality before or shortly after hatching, in contrast to complete dlg-1 knockouts, which die at mid-embryogenesis. They further examine the role of the gene's first intron, previously reported to physically interact with the HOT region. Incorporating intron 1 into the reporter construct slightly increases expression, suggesting an additive regulatory effect. However, replacing intron 1 with a synthetic sequence at the endogenous locus does not cause major changes. Overall, this study demonstrates that HOT regions can play a functional role in gene regulation, challenging the prevailing view that they are largely non-functional.

Major comments:

Overall, the paper lacks to explain their reasoning on choosing certain conditions and it also lacks on discussions on relevant topics, highlighted below.

1) The authors suggest that the region upstream of the dlg-1 gene is a HOT region. Although they highlight that other broad studies pick up this region as a HOT region, it would be good that the authors dive into the HOT identity of the region and characterize it, as it is a major part of their study. In addition to multiple TFs binding to the site, there are different criteria by which a region would be considered a HOT region. E.g. is there increased signal on this region in the IgG ChIP-seq tracks? Is the area CpG dense?

2) When describing the HOT region, they refer to Pol II binding as 'confirming its role as a promoter': non-promoter regions can also have Pol II binding, especially enhancers. Having binding of Pol II does not confirm its role as promoter. On the contrary, seeing the K27ac and K4me1 would point towards it being an enhancer.

3) In S1B, the authors show TF binding tracks. They also have a diagram of the region subsets (HOT1-4) that were later tested. What is their criteria for dividing the HOT region into those fragments? From looking at Fig S1, the 'proper' HOT region (ie. Where protein binding occurs) seems to be divided into two (one chunk as part of HOT3 and one chunk as part of HOT4). Can the authors comment on the effects of this division?

4) For the reporter experiments, the first experiments carry the histone H2B sequence and the second set of experiments (where the HOT region is dissected) carry a minimal promoter Δpes-10 (MINp). The results could be affected by the addition of these sequences. Is there a reason for this difference? Can the authors please justify it?

5) Regarding the H2B sequence: ' 137: first intron [...] inserted in the FL transgene within the H2B sequence, acting as an actual intron (FL-INT1)': how was the location of the insertion chosen? Does it disrupt H2B? can it be that the H2B sequence contributed to dampening down the expression of mNG and disrupting it makes it stronger? It would be important to run the first experiments with minimal promoters and not with the H2B sequence.

6) Have the authors explored the features of the sequences underlying the different HOT subregions? (e.g. running a motif enrichment analysis)? Is there anything special about HOT3 that could make it a functional region? It would be good to compare uHOT3 vs the others that do not drive the correct pattern. Since it's a HOT region, it may not have a special feature, but it is important to look into it.

7) For comparisons, the authors run t-tests. Is the data parametric? Otherwise, it would be more suitable to use a non-parametric test.

Minor comments:

1) The authors work with C. elegans embryos at comma stage, according to the methods section. It would be good if the authors mentioned it in the main text so that the reader is informed.

2) 'Notably, the upstream HOT region is located more than four kilo-bases (Kb) away the CDS, and the one in the first intron contains enhancer sites, too.': what do they mean by 'enhance sites, too'. Is the region known as a functional enhancer? If so, could you please provide the reference?

3) 'We hypothesized the upstream HOT region is the main driver of dlg-1 transcriptional regulation.': this sentence needs more reasoning. What led to this hypothesis? Is it the fact of seeing multiple TFs binding there? The chromatin marks?

4) The labels of S1B are too wide, as if they have stretched the image. Could the authors please correct this?

5) This sentence does not flow with the rest of the text '84 - cohesins have been shown to organize the DNA in a way that active enhancers make contacts in the 3D space forming "fountains" detectable in Hi-C data (17,18).': is there a reason to explain this? I would remove it if not, as it can confuse the reader.

6) The authors mentioned that 'ARC-C data showed the putative HOT region interacts with other DNA sequences, including the first intron of dlg': have the authors analysed the data from the previous paper? A figure with the relevant data could illustrate this interaction so that the reader knows which specific region has been shown to interact with which. This would also bring clarity as to why they chose intron1 for additional experiments.

7) 'two deletion sequences spanning from the beginning (uHOT) or the end (Short) of the HOT region until the dlg-1 CDS': From the diagrams of the figure, I understand that uHOT has the distal region deleted, and the short HOT has the distal and the upstream regions deleted. Is this correct? Could you clarify this in the text? E.g. 'we designed two reporters - one containing the sequence starting at the HOT region and ending at the dlg-1 CDS, and the other without the HOT region, but rather starting downstream of it until the dlg-1 CDS'.

8) 'Specifically, it spanned from bp 5,475,070 to 5,475,709 on chromosome X and removed HOT2 and HOT2 sequences' - this is unclear to me. What sequences are removed? HOT2 and 3?

9) 'ARC-C' is not introduced. Please spell out what this is. Accessible Region Conformation Capture (ARC-C). It would be helpful to include a sentence of what it is, as it will not be known by many readers.

10) Fig 1 B, diagram on the right: the H2B sequence is missing. I see that is indicated in the legend as part of mNG but this can be misleading. Could the authors add it to the diagram for clarification?

Significance

HOT regions are thought to be artifacts from ChIP-seq experiments. This study provides evidence that at least some HOT regions can have a functional role in gene regulation, emphasizing that they should not be dismissed outright.

The findings will be of interest to researchers investigating the biological nature of HOT regions, as well as to those who have encountered HOT regions in their own sequencing datasets. In addition, researchers studying the regulation of dlg-1 in C. elegans may find this work particularly relevant. I work on gene regulation during embryonic development and my technical expertise is omics and fluorescence microscopy. Since I do not work in C. elegans, I cannot evaluate if the patterns/location of the signal is where they claim it to be, I do not know if the cells marked are epidermal cells.

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Referee #1

Evidence, reproducibility and clarity

Summary:

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

In this manuscript, Tocchini et al. characterize two enhancer regions, one distal and one intronic, of the gene dlg-1 in C. elegans. The two enhancers are termed high-occupancy target (HOT) regions as defined by their binding of most transcription factors, as identified by the modENCODE project. The authors test transcriptional activity of the two HOT regions using single-copy transgene assays and assay their functional relevance by deleting the regions using CRISPR/Cas9 genome editing. The authors observe robust transcriptional activity and functional effects of the distal regulatory element and little evidence for enhancer activity from the intronic enhancer. From these assays, the authors conclude that the distal and intronic enhancers coordinate to fine tune gene expression in a cell-type specific manner.

Major comments:

• Are the key conclusions convincing?

• The results fully support the authors conclusions regarding the significant role of the upstream HOT region ("uHOT") with strong fluorescence activity and substantial phenotypic effects (i.e., the animals have very low brood sizes and rarely progress through hatching). This data is well presented and technically well done.

• In my view, their conclusions regarding the intronic HOT region are speculative and unconvincing. See below for main criticisms.
• Furthermore, their conclusions about interactions between the two tested regions is speculative and they show no strong evidence for this claim.
• The authors claim that not all the phenotypic effects seen from deleting the uHOT region are specific to the dlg-1 gene. This is an interesting model, but the authors show essentially no data to support this or any explanation of what other gene might be regulated.
• Finally, some of the hypotheses in the text could be more accurately framed by the authors. They claim HOT regions are often considered non-functional (lines 189-191). Also, they claim that correct expression levels and patterning is usually regulation by elements within a few hundred basepairs of the CDS (lines 78-80). These claims are not generally accepted in the field, despite a relatively compact genome. Notably, both claims were tested and disproven by Chen et al (2014), Genome Research, where the authors specifically showed strong transcriptional activity from 10 out of 10 HOT regions located up to 4.7 kb upstream of their nearest gene. Chen et al. 2014 is cited by Tocchini et al. and it is, therefore, surprisingly inconsistent with the claims in this manuscript.

The fluorescence expression from the intronic HOT region is not visible by eye and the quantification shows very little expression, suggestive of background fluorescence. Although the authors show statistical significance in Figure 1G, I would argue this is possibly based on inappropriate comparisons and/or a wrong choice statistical test. The fluorescence levels should be compared to a non-transgenic animal and/or to a transgenic animal with the tested region shuffled but in an equivalent - Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?

Yes, I would suggest the authors remove their claims about the intronic enhancer and the interaction between the two regions. And I would suggest softening the claims about the uHOT regulation of another putatitive gene. - 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, the authors would need to demonstrate several things to support their current claims. The major experiments necessary are:

1. Insert single-copy transgene with a minimal promoter and the intronic sequence scrambled to generate a proper baseline control. It is very possible that the intronic sequence does drive some expression, but the current control is not appropriate for statistical comparison (e.g., only the transgene with intron 1 contains the minimal promoter from pes-10, which may have baseline transcriptional activity even without the intron placed in front of the transgene).
2. It is not very clear why the authors did not test intron 1 within the H2B of the transgene and just the minimal promoter in front of the transgene, but only in the context of the full-length promoter. The authors show a minor difference in expression levels for the full-length (FL) and full-length with intron 1 (FL-INT1) but show a large statistical differnce. The authors use an inappropriate statistical test (T-test) for this experiment and treat many datapoints from the same embryo as independent, which is clearly not the case. Even minor differences in staging, transgene silencing in early development, or variability would potentially bias their data collection.
3. The authors claim, based on ARC-C data previously published by their lab (Huang et al. 2022) that the dlg-1 HOT region interacts with "other" genomic regions. This is potentially interesting but the evidence for this should be included in the manuscript itself, perhaps by re-analyzing data from the 2022 manuscript?
4. 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.

These experiments are not costly (two transgenes inserted by single-copy transgenesis) nor particularly time-consuming. With cloning, injection, and microscopy, these experiments can be conducted in 6 weeks with relatively few "hands on" hours. The cost should be very reasonably (reagents surely less than €500). - Are the data and the methods presented in such a way that they can be reproduced?

The data are not entirely clear and could benefit from additional details. This is a partial list but shows the general concern.

The fluorescence quantification is difficult to interpret from the attached data file (Table S1). For the invidividual values, it is unclear how many indpendent experiments (different embryos) were conducted. The authors should clarify if every data value is from an independent embryo or if they used several values from the same embryo. If they did use several values from the same embryo, how did they do this? Did they take very cell? Or did they focus on specific cells? How did they ensure embryo staging?

The authors also do not describe how they validated single-copy insertions (partial transgene deletions in integrants are not infrequent and they only appear to use a single insertion for each strain). This should be described and or added as a caveat if no validation was performed.

The authors also do not describe any validation for the CRISPR alleles, either deletions or insertion of the synthetic intron into dlg-1. How were accurate gene edits verified. - Are the experiments adequately replicated and statistical analysis adequate?

I am not convinced the statistical analysis of the fluorescence data is correct. Unless the authors show that every datapoint in the fluorescence quantification is independent, then I would argue they vastly overestimate the statistical significance. Even small differences are shown to have "***" levels of significance, which does not appear empirically plausible.

Minor comments:

• Specific experimental issues that are easily addressable.
• Are prior studies referenced appropriately?

This study is so closely related to the Chen et al study, that I believe this study should be discussed in more detail to put the data into context. - Are the text and figures clear and accurate?

Yes, the text and figurea are clear - Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

Add H2B to the mNG in Figure 1 in order to understand where the first intron was inserted.

Significance

• Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.
• Place the work in the context of the existing literature (provide references, where appropriate).
• State what audience might be interested in and influenced by the reported findings.
• 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.

This manuscript shows an incremental advance in our understanding of HOT regions in C. elegans. The authors replicate similar data presented previously (enhancer assays on HOT regions, PMID: 24653213). Importantly, the authors funcationally validate their data with smFISH and CRISPR-mediated deletion of two enhancers (including the substitution of the intron for a synthetic intron), which is, to my knowledge, novel and advances the field. As such, the data presented validate and increase our confidence in prior results on HOT regions. Unfortunately, the more interesting conclusions about HOT region interactions and synergy to direct expression are less well supported. The work will likely be mainly of interest to C. elegans researchers working on transcriptional regulation. My own field of expertise is C. elegans gene regulation and my lab frequently uses transcriptional transgene assays to determine gene expression.

1. Prophet Nuh

You have to tick "Set a default namespace in the new file"

wrong picture

eLife Assessment

This study presents a large, systematically curated catalog of non-canonical open reading frames (ncORFs) in human and mouse by reanalyzing nearly 400 Ribo-seq datasets using a standardized pipeline; the resulting atlas consolidates ncORF annotations across tissues and provides a valuable reference for understanding non-canonical translation and ORF emergence. The main conclusions are supported by consistent data processing and multiple computational measures of translation and conservation. While the pipeline is transparent and robust, several downstream analyses are descriptive, and some evolutionary interpretations remain correlative; dataset heterogeneity, uneven tissue representation, and limited experimental validation also constrain the strength of a subset of the findings. Overall, the evidence is solid, and the resource will be broadly used by the community.

Reviewer #1 (Public review):

This work compiles a comprehensive atlas of ncORFs across mammalian tissues and cell types, derived from reanalysis of ~400 public ribosome profiling datasets. The authors then evaluate cross-species conservation and functional signatures, proposing that evolutionarily ancient ncORFs tend to have higher translation potential, stronger expression, and closer relationships with canonical coding sequences.

Strengths:

In general, the study provides a large-scale and timely resource of annotated ncORFs, which could be broadly useful for the community. The authors collected ~400 public ribosome profiling datasets for annotations of ncORFs, which, to my best knowledge, is the largest collection of data for such a purpose. The catalog could facilitate future investigations into ncORF biology and broaden understanding of the coding potential of the "non-coding" genome.

Weaknesses:

Based on the ncORF catalog, some of the analyses were not properly done. Some of the results are descriptive.

(1) Bias and representations of the data source. Public ribo-seq datasets are unevenly distributed across tissues and cell lines, raising concerns about heterogeneity and underrepresentation of certain contexts. This may limit the generalizability of the catalog.

(2) The discussion on modular domains of ncORFs is unclear, and the claim that they may originate via TE-related mechanisms is not well supported. Stronger evidence or clearer reasoning is needed.

(3) The conservation comparisons are not fully convincing. Figure S7 shows only mild differences between ncORFs and CDS, and statistical significance is not clearly demonstrated.<br /> Comparisons with other non-coding RNAs should be added, and overlapping sequences between ncORFs and CDS should be excluded to avoid bias.

(4) Figure 3 indicates that some ncORFs are subject to evolutionary constraints. This is not surprising. The authors should provide further analyses on more detailed features of these "conserved" ncORFs vs. the "non-conserved" ones. Some pretty informative works have been done in Drosophila, worms, mice, and humans. Figure 3 suggests some ncORFs are under evolutionary constraint, but this is not unexpected. More granular analyses contrasting "conserved" versus "non-conserved" ncORFs would be informative. In fact, small ORFs, especially uORFs, have been extensively studied for their functions and cross-species conservation. The authors should explicitly show what is new here in their analyses.

(5) Translation levels are reported using RPF counts. However, translation efficiency (normalized by RNA expression) is a more appropriate measure to account for expression heterogeneity.

(6) The correlation analyses between ncORF translation levels and PhyloCSF are confusing and largely descriptive. These sections need sharper framing and clearer conclusions.

(7) Public ribo-seq datasets, generated by different research labs, are known for their strong batch effects. Representations of tissues and cells are also very unbalanced. Therefore, the co-translation analysis between ncORFs and canonical CDS is not well controlled. This should be done by referring to a recent large-scale ribo-seq meta-analysis (Nat Biotechnol. 2025. doi: 10.1038/s41587-025-02718-5).

Reviewer #2 (Public review):

Summary:

Chang et al. attempted to analyze a large number of ribo-seq datasets through a standardized pipeline, identifying novel non-canonical ORFs and elucidating their evolutionary and expression characteristics.

Strengths:

(1) The datasets analyzed by the authors are sufficiently comprehensive, and the use of standardized pipelines ensures excellent analytical consistency.

(2) Their analyses of ORF evolution and co-expression further deepen our understanding of these ORFs.

Weaknesses:

(1) The authors primarily conducted analyses through bioinformatics, lacking sufficient wet-lab experimental evidence.

(2) Regarding the evolution of non-canonical ORFs, a considerable amount of prior work already exists. The authors need to further clarify what new insights and discoveries they have made based on the analysis of such a large dataset.

Обилие информации и лёгкий доступ делают особенно важными способности оценивать, фильтровать, критически мыслить.

Даже самый продвинутый ИИ пока лишён понимания человеческого контекста, эмоций и морали

Для учебного процесса важно сохранять активное участие мозга, иначе навыки не развиваются.

Вполне верное раскрытие тезиса Неоднозначника. Мудрый человек.

Введение.

Человеческий мозг в рамках Байесовского вывода работает лучше самого этого вывода

рассказывается о гланой сути статьи "Искуственный интеллект и человеческое познание"

Человек, используя ИИ, сознательно "губит" свою креативность и критическое мышление, создавая что-то стандартно, "машинно", шаблонно, что приводит к печальным последствиям

если ИИ может сэкономить вам время, которое вы можете потратить на более серьёзное обучение, это плюс.

ИИ может как заменять ученика/студента, так и быть его помощником, советником

Метафора "совы на плече" подчеркивает роль человека как главного действующего лица.

Как студенты злоупотребляют ИИ

Погружение в тему бездумного использования Искусственного Интеллекта

Подчёркивается, что обучение требует активной умственной работы. Если студент просто копирует ответ от ИИ, реального усвоения знаний не происходит.

Важно помнить - ИИ не обладает человеческим опытом и эмпатией. Он может обрабатывать данные, но не способен на настоящие рассуждения в сложных социальных ситуациях.

I do not know why the program says this code is hardcoded. Could you please fix this?

this is probably the main impediment to rail and specifically electric rail's success

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Reply to the reviewers

__Reviewer #1 (Evidence, reproducibility and clarity (Required)): __

This study explores chromatin organization around trans-splicing acceptor sites (TASs) in the trypanosomatid parasites Trypanosoma cruzi, T. brucei and Leishmania major. By systematically re-analyzing MNase-seq and MNase-ChIP-seq datasets, the authors conclude that TASs are protected by an MNase-sensitive complex that is, at least in part, histone-based, and that single-copy and multi-copy genes display differential chromatin accessibility. Altogether, the data suggest a common chromatin landscape at TASs and imply that chromatin may modulate transcript maturation, adding a new regulatory layer to an unusual gene-expression system.

I value integrative studies of this kind and appreciate the careful, consistent data analysis the authors implemented to extract novel insights. That said, several aspects require clarification or revision before the conclusions can be robustly supported. My main concerns are listed below, organized by topic/result section.

TAS prediction * Why were TAS predictions derived only from insect-stage RNA-seq data? Restricting TAS calls to one life stage risks biasing predictions toward transcripts that are highly expressed in that stage and may reduce annotation accuracy for lowly expressed or stage-specific genes. Please justify this choice and, if possible, evaluate TAS robustness using additional transcriptomes or explicitly state the limitation.

TAS predictions derived only from insect-stage RNA-seq data because in a previous study it was shown that there are no significant differences between stages in the 5'UTR procesing in T. cruzi life stages (https://doi.org/10.3389/fgene.2020.00166) We are not testing an additional transcriptome here, because the robustness of the software was already probed in the original article were UTRme was described (Radio S, 2018 doi:10.3389/fgene.2018.00671).

Results - "There is a distinctive average nucleosome arrangement at the TASs in TriTryps": * You state that "In the case of L. major the samples are less digested." However, Supplementary Fig. S1 suggests that replicate 1 of L. major is less digested than the T. brucei samples, while replicate 2 of L. major looks similarly digested. Please clarify which replicates you reference and correct the statement if needed.

The reviewer has a good point. We made our statement based on the value of the maximum peak of the sequenced DNA molecules, which in general is a good indicative of the extension of the digestion achieved by the sample (Cole H, NAR, 2011).

As the reviewer correctly points, we should have also considered the length of the DNA molecules in each percentile. However, in this case both, T. brucei's and L major's samples were gel purified before sequencing and it is hard to know exactly what fragments were left behind in each case. Therefore, it is better not to over conclude on that regard.

We have now comment on this in the main manuscript, and we have clarified in the figure legends which data set we used in each case in the figure legends and in Table S1.

* It appears you plot one replicate in Fig. 1b and the other in Suppl. Fig. S2. Please indicate explicitly which replicate is in each plot. For T. brucei, the NDR upstream of the TAS is clearer in Suppl. Fig. S2 while the TAS protection is less prominent; based on your digestion argument, this should correspond to the more-digested replicate. Please confirm.

The replicates used for the construction of each figure are explicitly indicated in Table S1. Although we have detailed in the table the original publication, the project and accession number for each data set, the reviewer is correct that in this case it was still not completely clear to which length distribution heatmap was each sample associated with. To avoid this confusion, we have now added the accession number for each data set to the figure legends and also clarified in Table S1. Regarding the reviewer's comment on the correspondence between the observed TAS protection and the extent of samples digestion, he/she is correct that for a more digested sample we would expect a clearer NDR. In this case, the difference in the extent of digestion between these two samples is minor, as observed the length of the main peak in the length distribution histogram for sequenced DNA molecules is the same. These two samples GSM5363006, represented in Fig1 b, and GSM5363007, represented in S2, belong to the same original paper (Maree et al 2017), and both were gel purified before sequencing. Therefore, any difference between them could not only be the result of a minor difference in the digestion level achieved in each experiment but could be also biased by the fragments included or not during gel purification. Therefore, I would not over conclude about TAS protection from this comparison. We have now included a brief comment on this, in the figure discussion

* The protected region around the TAS appears centered on the TAS in T. brucei but upstream in L. major. This is an interesting difference. If it is technical (different digestion or TAS prediction offset), explain why; if likely biological, discuss possible mechanisms and implications.

We appreciate the reviewer suggestion. We cannot assure if it is due to technical or biological reasons, but there is evidence that L. major 's genome has a different dinucleotide content and it might have an impact on nucleosome assembly. We have now added a comment about this observation in the final discussion of the manuscript.

Additionally, we analyzed DRIP-seq data for L. major, recently published doi: 10.1038/s41467-025-56785-y, and we observed that the R-loop footprint co-localized with the MNase-protected region upstream of the TAS (new S5 Fig), suggesting that the shift is not related to the MNase-seq technique.

Results - "An MNase sensitive complex occupies the TASs in T. brucei": * The definition of "MNase activity" and the ordering of samples into Low/Intermediate/High digestion are unclear. Did you infer digestion levels from fragment distributions rather than from controlled experimental timepoints? In Suppl. Fig. S3a it is not obvious how "Low digestion" was defined; that sample's fragment distribution appears intermediate. Please provide objective metrics (e.g., median fragment length, fraction 120-180 bp) used to classify digestion levels.

As the reviewer suggests, the ideal experiment would be to perform a time course of MNase reaction with all the samples in parallel, or to work with a fixed time point adding increasing amounts of MNase. However, even when making controlled experimental timepoints, you need to check the length distribution histogram of sequenced DNA molecules to be sure which level of digestion you have achieved.

In this particular case, we used public available data sets to make this analysis. We made an arbitrary definition of low, intermediate and high level of digestion, not as an absolute level of digestion, but as a comparative output among the tested samples. We based our definition on the comparison of __the main peak in length distribution heatmaps because this parameter is the best metric to estimate the level of digestion of a given sample. It represents the percentage of the total DNA sequenced that contains the predominant length in the sample tested. __Hence, we considered:

low digestion: when the main peak is longer than the expected protection for a nucleosome (longer than 150 bp). We expect this sample to contain additional longer bands that correspond to less digested material.

intermediate digestion, when the main peak is the expected for the nucleosome core-protection (˜146-150bp).

high digestion, when the main peak is shorter than that (shorter than 146 bp). This case, is normally accompanied by a bigger dispersion in fragment sizes.

To do this analysis, we chose samples that render different MNase protection of the TAS when plotting all the sequenced DNA molecules relative to this point and we used this protection as a predictor of the extent of sample digestion (Figure 2). To corroborate our hypothesis, that the degree of TAS protection was indeed related to the extent of the MNase digestion of a given sample, we looked at the length distribution histogram of the sequenced DNA molecules in each case. It is the best measurement of the extent of the digestion achieved, especially, when sequencing the whole sample without any gel purification and representing all the reads in the analysis as we did. The only caveat is with the sample called "intermediate digestion 1" that belongs to the original work of Mareé 2017, since only this data set was gel purified. To avoid this problem, we decided to remove this data from figures 2 and S3. In summary, the 3 remaining samples comes from the same lab, and belong to the same publication (Mareé 2022). These sample are the inputs of native MNase ChIp-seq, obtain the same way, totally comparable among each other.

* Several fragment distributions show a sharp cutoff at ~100-125 bp. Was this due to gel purification or bioinformatic filtering? State this clearly in Methods. If gel purification occurred, that can explain why some datasets preserve the MNase-sensitive region.

The sharp cutoff is neither due to gel purification or bioinformatic filtering, it is just due to the length of the paired-end read used in each case. In earlier works the most common was to sequence only 50bp, with the improvement of technologies it went up to 75,100 or 125 bp. We have now clarified in Table S1 the length of the paired-reads used in each case when possible.

* Please reconcile cases where samples labeled as more-digested contain a larger proportion of >200 bp fragments than supposedly less-digested samples; this ordering affects the inference that digestion level determines the loss/preservation of TAS protection. Based on the distributions I see, "Intermediate digestion 1" appears most consistent with an expected MNase curve - please confirm and correct the manuscript accordingly.

As explained above, it's a common observation in MNase digestion of chromatin that more extensive digestion can still result in a broad range of fragment sizes, including some longer fragments. This seemingly counter-intuitive result is primarily due to the non-uniform accessibility of chromatin and the sequence preference of the MNase enzyme, which has a preference for AT reach sequences.

The rationale of this is as follows: when you digest chromatin with MNase and the objective is to map nucleosomes genome-wide, the ideal situation would be to get the whole material contained in the mononucleosome band. Given that MNase is less efficient to digest protected DNA but, if the reaction proceeds further, it always ends up destroying part of it, the result is always far from perfect. The better situation we can get, is to obtain samples were ˜80% of the material is contained in the mononucloesome band. __And here comes the main point: __even in the best scenario, you always get some additional longer bands, such as those for di or tri nucleosomes. If you keep digesting, you will get less than 80 % in the nucleosome band and, those remaining DNA fragments that use to contain di and tri nucleosomes start getting digested as well, originating a bigger dispersion in fragments sizes. How do we explain persistence of Long Fragments? The longest fragments (di-, tri-nucleosomes) that persist in a highly digested sample are the ones that were originally most highly protected by proteins or higher-order structure, or by containing a poor AT sequence content, making their linker DNA extremely resistant to initial cleavage. Once the majority of the genome is fragmented, these few resistant longer fragments become a more visible component of the remaining population, contributing to a broader size dispersion. Hence, you end up observing a bigger dispersion in length distributions in the final material. Bottom line, it is not a good practice to work with under or over digested samples. Our main point, is to emphasize that especially when comparing samples, it important to compare those with comparable levels of digestion. Otherwise, a different sampling of the genome will be represented in the remaining sequenced DNA.

Results - "The MNase sensitive complexes protecting the TASs in T. brucei and T. cruzi are at least partly composed of histones": * The evidence that histones are part of the MNase-sensitive complex relies on H3 MNase-ChIP signal in subnucleosomal fragment bins. This seems to conflict with the observation (Fig. 1) that fragments protecting TASs are often nucleosome-sized. Please reconcile these points: are H3 signals confined to subnucleosomal fragments flanking the TAS while the TAS itself is depleted of H3? Provide plots that compare MNase-seq and H3 ChIP signals stratified by consistent fragment-size bins to clarify this.

What we learned from other eukaryotic organisms that were deeply studied, such as yeast, is that NDRs are normally generated at regulatory points in the genome. In this sense, yeast tRNA genes have a complex with a bootprint smaller than a nucleosome formed by TFIIIC-TFIIB (Nagarajavel, doi: 10.1093/nar/gkt611). On the other hand, many promotor regions have an MNase-sensitive complex with a nucleosome-size footprint, but it does not contain histones (Chereji, et al 2017, doi:10.1016/j.molcel.2016.12.009). The reviewer is right that from Figure 1 and S2 we could observe that the footprint of whatever occupies the TAS region, especially in T. brucei, is nucleosome-size. However, it only shows the size, but it doesn't prove the nature of its components. Nevertheless, those are only MNase-seq data sets. Since it does not include a precipitation with specific antibodies, we cannot confirm the protecting complex is made up by histones. In parallel, a complementary study by Wedel 2017, from Siegel's lab, shows that using a properly digested sample and further immunoprecipitating with a-H3 antibody, the TAS is not protected by nucleosomes at least not when analyzing nucleosome size-DNA molecules. Besides, Briggs et. al 2018 (doi: 10.1093/nar/gky928) showed that at least at intergenic regions H3 occupancy goes down while R-loops accumulation increases. We have now added a new figure 4 replotting R-loops and MNase-ChIP-seq for H3 relative to our predicted TAS showing this anti-correlation and how it partly correlates with MNase protection as well. As a control we show that Rpb9 trends resembles H3 as Siegel's lab have shown in Wedel 2018. Moreover, we analyzed redate from a recently published paper (doi: 10.1038/s41467-025-56785-y) added a new supplemental figure 5 showing that a similar correlation between MNase protection and R-loop footprint occurs in L. major (S5 Fig).

* Please indicate which datasets are used for each panel in Suppl. Fig. S4 (e.g., Wedel et al., Maree et al.), and avoid calling data from different labs "replicates" unless they are true replicates.

In most of our analysis we used real replicated experiments. Such is the case MNase-seq data used in Figure 1, with the corresponding replicate experiments used in Figure S2; T. cruzi MNase-ChIP-seq data used in Figure 3b and 4a with the respective replicate used in Figures S4 and S5 (now S6 in the revised manuscript). The only case in which we used experiments coming from two different laboratories, is in the case of MNase-ChIP-seq for H3 from T. brucei. Unfortunately, there are only two public data sets coming each of them from different laboratories. The samples used in Fig 3 (from Siegel's lab) whether the IP from H3 represented in S4 and S5 (S6 n the updated version) comes from another lab (Patterton's). To be more rigorous, we now call them data 1 and 2 when comparing these particular case.

The reviewer is right that in this particular case one is native chromatin (Pattertons') while the other one is crosslinked (Siegel's). We have now clarified it in the main text that unfortunately we do not count on a replicate but even under both condition the result remains the same, and this is compatible with my own experience, were crosslinking does not affect the global nucleosome patterns (compared nucleosome organization from crosslinked chromatin MNAse-seq inputs Chereji, Mol Cell, 2017 doi: 10.1016/j.molcel.2016.12.009 and native MNase-seq from Ocampo, NAR, 2016 doi: 10.1093/nar/gkw068).

* Several datasets show a sharp lower bound on fragment size in the subnucleosomal range (e.g., ~80-100 bp). Is this a filtering artifact or a gel-size selection? Clarify in Methods and, if this is an artifact, consider replotting after removing the cutoff.

We have only filtered adapter dimmer or overrepresented sequences when needed. In Figures 2 and S3 we represented all the sequenced reads. In other figures when we sort fragments sizes in silico, such as nucleosome range, dinucleosome or subnucleosome size, we make a note in the figure legends. What the reviewer points is related to the length of the sequence DNA fragment in each experiment. As we explained above, the older data-sets were performed with 50 bp paired-end reads, the newer ones are 75, 100 or 125bp. This is information is now clarified in Table S1.

__Results - "The TASs of single and multi-copy genes are differentially protected by nucleosomes": __

__ __* Please include T. brucei RNA-seq data in Suppl. Fig. S5b as you did for T. cruzi.

We have shown chromatin organization for T. brucei in previous S5b to illustrate that there is a similar trend. Unfortunately, we did not get a robust list of multi-copy genes for T. brucei as we did get for T. cruzi, therefore we do not want to over conclude showing the RNA-seq for these subsets of genes. The limitation is related to the fact that UTRme restrict the search and is extremely strict when calling sites at repetitive regions. Additionally, attending to the request of one reviewer we have now changed the UTR predictions for T. brucei using a different RNA-seq data set from Lister 427(detail in method section). Given that with the new predictions it was even harder to obtain the list of multicopy genes for T. brucei, we decided to remove that figure in the updated version of the manuscript.

* Discuss how low or absent expression of multigene families affects TAS annotation (which relies on RNA-seq) and whether annotation inaccuracies could bias the observed chromatin differences.

The mapping of occurrence and annotations that belong to repetitive regions has great complexity. UTRme is specially designed to avoid overcalling those sites. In other words, there is a chance that we could be underestimating the number of predicted TASs at multi-copy genes. Regarding the impact on chromatin analysis, we cannot rule out that it might have an impact, but the observation favors our conclusion, since even when some TASs at multi-copy genes can remain elusive, we observe more nucleosome density at those places.

* The statement that multi-copy genes show an "oscillation" between AT and GC dinucleotides is not clearly supported: the multi-copy average appears noisier and is based on fewer loci. Please tone down this claim or provide statistical support that the pattern is periodic rather than noisy.

We have fixed this now in the preliminary revised version

* How were multi-copy genes defined in T. brucei? Include the classification method in Methods.

This classification was done the same way it was explained for T. cruzi. However, decided to remove the supplemental figure that included this sorting.

Genomes and annotations: * If transcriptomic data for the Y strain was used for T. cruzi, please explain why a Y strain genome was not used (e.g., Wang et al. 2021 GCA_015033655.1), or justify the choice. For T. brucei, consider the more recent Lister 427 assembly (Tb427_2018) from TriTrypDB. Use strain-matched genomes and transcriptomes when possible, or discuss limitations.

The most appropriate way to analyze high throughput data, is to aline it to the same genome were the experiments were conducted. This was clearly illustrated in a previous publication from our group were we explained how should be analyzed data from the hybrid CL Brener strain. A common practice in the past was to use only Esmeraldo-like genome for simplicity, but this resulted in output artifacts. Therefore, we aligned it to CL Brener genome, and then focused the main analysis on the Esmeraldo haplotype (Beati Plos ONE, 2023). Ideally, we should have counted on transcriptomic data for the same strain (CL Brener or Esmeraldo). Since this was not the case at that moment, we used data from Y strain that belongs to the same DTU with Esmeraldo.

In the case of T. brucei, when we started our analysis and the software code for UTRme was written, the previous version of the genome was available. Upon 2018 version came up, we checked chromatin parameters and observed that it did not change the main observations. Therefore, we continue working with our previous setups.

Reproducibility and broader integration: * Please share the full analysis pipeline (ideally on GitHub/Zenodo) so the results are reproducible from raw reads to plots.

We are preparing a full pipeline in GitHub. We will make it available before manuscript full revision

* As an optional but helpful expansion, consider including additional datasets (other life stages, BSF MNase-seq, ATAC-seq, DRIP-seq) where available to strengthen comparative claims.

We are now including a new figure 4 and a supplemental figure 5 including DRIP-seq and Rp9 ChIP-seq for T. brucei (revised Fig 4) and DRIP-seq for L. major (S5 Fig). Additionally, we added FAIRE-seq data to previous Fig 4 now Fig 5 (revised Fig 5C).

We are analyzing ATAC-seq data for T. brucei.

Regarding BSF MNase-seq, the original article by Mareé 2017 claims that there is not significant difference for average chromatin organization between the two life forms; therefore, is not worth including that analysis.

Optional analyses that would strengthen the study: * Stratify single-copy genes by expression (high / medium / low) and examine average nucleosome occupancy at TASs for each group; a correlation between expression and NDR depth would strengthen the functional link to maturation.

We have now included a panel in suplemental figure 5 (now revised S6), showing the concordance for chromatin organization of stratified genes by RNA-seq levels relative to TAS.

__Minor / editorial comments: __ * In the Introduction, the sentence "transcription is initiated from dispersed promoters and in general they coincide with divergent strand switch regions" should be qualified: such initiation sites also include single transcription start regions.

We have clarified this in the preliminary revised version

* Define the dotted line in length distribution plots (if it is not the median, please clarify) and consider placing it at 147 bp across plots to ease comparison.

The dotted line is just to indicate where the maximum peak is located. It is now clarified in figure legends.

* In Suppl. Fig. 4b "Replicate2" the x-axis ticks are misaligned with labels - please fix.

We have now fixed the figure. Thanks for noticing this mistake.

* Typo in the Introduction: "remodellingremodeling" → "remodeling

Thanks for noticing this mistake, it is fixed in the current version of the manuscript

**Referee cross-commenting** Comment 1: I think Reviewer #2 and Reviewer #3 missed that they authors of this manuscript do cite and consider the results from Wedel at al. 2017. They even re-analysed their data (e.g. Figure 3a). I second Reviewer #2 comment indicating that the inclusion of a schematic figure to help readers visualize and better understand the findings would be an important addition.

Comment 2: I agree with Reviewer #3 that the use of different MNase digestion procedures in the different datasets have to be considered. On the other hand, I don't think there is a problem with figure 1 showing an MNase-protected TAS for T. brucei as it is based on MNase-seq data and reproduces the reported results (Maree et al. 2017). What the Siegel lab did in Wedel et al. 2017 was MNase-ChIPseq of H3 showing nucleosome depletion at TAS, but both results are not necessary contradictory: There could still be something else (which does not contain H3) sitting on the TAS protecting it from MNase digestion.

Reviewer #1 (Significance (Required)):

This study provides a systematic comparative analysis of chromatin landscapes at trans-splicing acceptor sites (TASs) in trypanosomatids, an area that has been relatively underexplored. By re-analyzing and harmonizing existing MNase-seq and MNase-ChIP-seq datasets, the authors highlight conserved and divergent features of nucleosome occupancy around TASs and propose that chromatin contributes to the fidelity of transcript maturation. The significance lies in three aspects: 1. Conceptual advance: It broadens our understanding of gene regulation in organisms where transcription initiation is unusual and largely constitutive, suggesting that chromatin can still modulate post-transcriptional processes such as trans-splicing. 2. Integrative perspective: Bringing together data from T. cruzi, T. brucei and L. major provides a comparative framework that may inspire further mechanistic studies across kinetoplastids. 3. Hypothesis generation: The findings open testable avenues about the role of chromatin in coordinating transcript maturation, the contribution of DNA sequence composition, and potential interactions with R-loops or RNA-binding proteins. Researchers in parasitology, chromatin biology, and RNA processing will find it a useful resource and a stimulus for targeted experimental follow-up.

My expertise is in gene regulation in eukaryotic parasites, with a focus on bioinformatic analysis of high-throughput sequencing data

__Reviewer #2 (Evidence, reproducibility and clarity (Required)): __

Siri et al. perform a comparative analysis using publicly available MNase-seq data from three trypanosomatids (T. brucei, T. cruzi, and Leishmania), showing that a similar chromatin profile is observed at TAS (trans-splicing acceptor site) regions. The original studies had already demonstrated that the nucleosome profile at TAS differs from the rest of the genome; however, this work fills an important gap in the literature by providing the most reliable cross-species comparison of nucleosome profiles among the tritryps. To achieve this, the authors applied the same computational analysis pipeline and carefully evaluated MNase digestion levels, which are known to influence nucleosome profiling outcomes.

In my view, the main conclusion is that the profiles are indeed similar-even when comparing T. brucei and T. cruzi. This was not clear in previous studies (and even appeared contradictory, reporting nucleosome depletion versus enrichment) largely due to differences in chromatin digestion across these organisms. The manuscript could be improved with some clarifications and adjustments:

1. The authors state from the beginning that available MNase data indicate altered nucleosome occupancy around the TAS. However, they could also emphasize that the conclusions across the different trypanosomatids are inconsistent and even contradictory: NDR in T. cruzi versus protection-in different locations-in T. brucei and Leishmania.

We start our manuscript by referring to the first MNase-seq data sets publicly available for each TriTryp and we point that one of the main observations, in each of them, is the occurrence of a change in nucleosome density or occupancy at intergenic regions. In T. cruzi, in a previous publication from our group, we stablished that this intergenic drop in nucleosome density occurs near the trans-splicing acceptor site. In this work, we extend our study to the other members of TriTryps: T. brucei and L. major.

In T. brucei the papers from Patterton's lab and Siegel's lab came out almost simultaneously in 2017. Hence, they do not comment on each other's work. The first one claims the presence of a well-positioned nucleosome at the TAS by using MNase-seq, while the second one, shows an NDR at the TAS by using MNase-ChIP-seq. However, we do not think they are contradictory, or they have inconsistency. We brought them together along the manuscript because we think these works can provide complementary information.

On one hand, we infer data from Pattertons lab is slightly less digested than the sample from Siegel's lab. Therefore, we discuss that this moderate digestion must be the reason why they managed to detect an MNase protecting complex sitting at the TAS (Figure 1). On the other hand, Sigel's lab includes an additional step by performing MNase-ChIP-seq, showing that when analyzing nucleosome size fragments, histones are not detected at the TAS. Here, we go further in this analysis on figure 3, showing that only when looking at subnucleosome-size fragments, we can detect histone H3. And this is also true for T. cruzi.

By integrating every analysis in this work and the previous ones, we propose that TASs are protected by an MNase-sensitive complex (proved in Figure 2). This complex most likely is only partly formed by histones, since only when analyzing sub-nucleosomes size DNA molecules we can detect histone H3 (Figure 3). To be sure that the complex is not entirely made up by histones, future studies should perform an MNse-ChIP-seq with less digested samples. However, it was previously shown that R-loops are enriched at those intergenic NDRs (Briggs, 2018 doi: 10.1093/nar/gky928) and that R-loops have plenty of interacting proteins (Girasol, 2023 10.1093/nar/gkad836). Therefore, most likely, this MNase-sensitive complexed have a hybrid nature made up by H3 and some other regulatory molecules, possibly involved in trans-splicing. We have now added a new figure 4 showing R-loop co-localization with the NDR.

Regarding the comparison between different organisms, after explaining the sensitivity to MNase of the TAS protecting complex, we discuss that when comparing equally digested samples T. cruzi and T. brucei display a similar chromatin landscape with a mild NDR at the TAS (See T. cruzi represented in Figure 1 compared to T. brucei represented in Intermediate digestion 2 in Figure 2, intermediate digestion in the revised manuscript). Unfortunately, we cannot make a good comparison with L. major, since we do not count on a similar level of digestion. However, by analyzing a recently published DRIP-seq data-set for L. major we show that R-loop signal co localize with MNase-protection in a similar way (new S5 Fig).

Another point that requires clarification concerns what the authors mean in the introduction and discussion when they write that trypanosomes have "...poorly organized chromatin with nucleosomes that are not strikingly positioned or phased." On the other hand, they also cite evidence of organization: "...well-positioned nucleosome at the spliced-out region.. in Leishmania (ref 34)"; "...a well-positioned nucleosome at the TASs for internal genes (ref37)"; "...a nucleosome depletion was observed upstream of every gene (ref 35)." Aren't these examples of organized chromatin with at least a few phased nucleosomes? In addition, in ref 37, figure 4 shows at least two (possibly three to four) nucleosomes that appear phased. In my opinion, the authors should first define more precisely what they mean by "poorly organized chromatin" and clarify that this interpretation does not contradict the findings highlighted in the cited literature.

For a better understanding of nucleosome positioning and phasing I recommend the review: Clark 2010 doi:10.1080/073911010010524945, Figure 4. Briefly, in a cell population there are different alternative positions that a given nucleosome can adopt. However, some are more favorable. When talking about favorable positions, we refer to the coordinates in the genome that are most likely covered by a nucleosome and are predominant in the cell population. Additionally, nucleosomes could be phased or not. This refers not only the position in the genome, but to the distance relative to a given point. In yeast, or in highly transcribed genes of more complex eukaryotes, nucleosomes are regularly spaced and phased relative to the transcription start site (TSS) or to the +1 nucleosome (Ocampo, NAR, 2016, doi:10.1093/nar/gkw068). In trypanosomes, nucleosomes have some regular distribution when making a browser inspection but, given that they are not properly phased with respect to any point, it is almost impossible to make a spacing estimation from paired-end data. This is also consistent with a chromatin that is transcribed in an almost constitutive manner.

As the reviewer mention, we do site evidence of organization. We think the original observations are correct, but we do not fully agree with some of the original statements. In this manuscript our aim is to take the best we learned from their original works and to make a constructive contribution adding to the original discussions. In this regard, in trypanosomes there are some conserved patterns in the chromatin landscape, but their nucleosomes are far from being well-positioned or phased. For a better understanding, compare the variations observed in the y axis when representing av. nucleosome occupancy in yeast with those observed in trypanosomes and you will see that the troughs and peaks are much more prominent in yeast than the ones observed in any TryTryp member.

Following the reviewer's suggestion we have now clarified this in the main text.

The paper would also benefit from the inclusion of a schematic figure to help readers visualize and better understand the findings. What is the biological impact of having nucleosomes, di-nucleosomes, or sub-nucleosomes at TAS? This is not obvious to readers outside the chromatin field. For example, the following statement is not intuitive: "We observed that, when analyzing nucleosome-size (120-180 bp) DNA molecules or longer fragments (180-300 bp), the TASs of either T. cruzi or T. brucei are mostly nucleosome-depleted. However, when representing fragments smaller than a nucleosome-size (50-120 bp) some histone protection is unmasked (Fig. 3 and Fig. S4). This observation suggests that the MNase sensitive complex sitting at the TASs is at least partly composed of histones." Please clarify.

We appreciate the reviewer's suggestion to make a schematic figure. We have now added a new Figure 6.

Regarding the biological impact of having mono, di or subnucleosome fragments, it is important to unveil the fragment size of the protected DNA to infer the nature of the protecting complex. In the case of tRNA genes in yeast, at pol III promoters they found footprints smaller than a nucleosome size that ended up being TFIIB-TFIIC (Nagarajavel, doi: 10.1093/nar/gkt611). Therefore, detecting something smaller than a nucleosome might suggest the binding of trans-acting factors different than histones or involving histones in a mixed complex. These mixed complexes are also observed, and that is the case of the centromeric nucleosome which has a very peculiar composition (Ocampo and Clark, Cells Reports, 2015). On the other hand, if instead we detect bigger fragments, it could be indicative of the presence of bigger protecting molecules or that those regions are part of higher order chromatin organization still inaccessible for MNase linker digestions.

Here we show on 2Dplots, that complex or components protecting the TAS have nucleosome size, but we cannot assure they are entirely made up by histones, since, only when looking at subnucleosome-size fragments, we are able to detect histone H3. We have now added part of this explanation to the discussion.

By integrating every analysis in this work and the previous ones, we propose that the TAS is protected by an MNase-sensitive complex (Figure 2). This complex most likely is only partly formed by histones, since only when analyzing sub-nucleosomes size DNA molecules we can detect histone H3 (Figure 3). As explained above, to be sure that the complex is not entirely made up by histones, future studies should perform an MNse-ChIP-seq with less digested samples. However, it was previously shown that R-loops are enriched at those intergenic NDRs (Briggs 2018) and that R-loops have plenty of interacting proteins (Girasol, 2023). Therefore, most likely, this MNase-sensitive complexed have a hybrid nature made up by H3 and some other regulatory molecules. We have now added a new figure 4 showing R-loop partial co-localization with MNase protection.

Some references are missing or incorrect:

we will make a thorough revision

"In trypanosomes, there are no canonical promoter regions." - please check Cordon-Obras et al. (Navarro's group). Thank you for the appropiate suggestion.

Thank you for the appropriate suggestion. We have now added this reference

Please, cite the study by Wedel et al. (Siegel's group), which also performed MNase-seq analysis in T. brucei.

We understand that reviewer number 2# missed that we cited this reference and that we did used the raw data from the manuscript of Wedel et. al 2017 form Siegel's group. We used the MNase-ChIP-seq data set of histone H3 in our analysis for Figures 3, S4 and S6 (in the revised version), also detailed in table S1. To be even more explicit, we have now included the accession number of each data set in the figure legends.

Figure-specific comments: Fig. S3: Why does the number of larger fragments increase with greater MNase digestion? Shouldn't the opposite be expected?

This a good observation. As we also explained to reviewer#1:

It's a common observation in MNase digestion of chromatin that more extensive digestion can still result in a broad range of fragment sizes, including some longer fragments. This seemingly counter-intuitive result is primarily due to the non-uniform accessibility of chromatin and the sequence preference of the MNase enzyme.

The rationale of this is as follows: when you digest chromatin with MNase and the objective is to map nucleosomes genome-wide, the ideal situation would get the whole material contained in the mononucleosome band. Given that MNase is less efficient to digest protected DNA but, if the reaction proceeds further, it always ends up destroying part of it, the result is always far from perfect. The better situation we can get, is to obtain samples were ˜80% of the material is contained in the mononucloesome band. __And here comes the main point: __even in the best scenario, you always have some additional longer bands, such as those for di or tri nucleosomes. If you keep digesting, you will get less than 80 % in the nucleosome band and, those remaining DNA fragments that use to contain di and tri nucleosomes start getting digested as well originating a bigger dispersion in fragments sizes. How do we explain persistence of Long Fragments? The longest fragments (di-, tri-nucleosomes) that persist in a highly digested sample are the ones that were originally most highly protected by proteins or higher-order structure, making their linker DNA extremely resistant to initial cleavage. Once most of the genome is fragmented, these few resistant longer fragments become a more visible component of the remaining population, contributing to a broader size dispersion. Hence, there you end up having a bigger dispersion in length distributions in the final material. Bottom line, it is not a good practice to work with under or overdirected samples. Our main point is to emphasize that especially when comparing samples, it important to compare those with comparable levels of digestion. Otherwise, a different sampling of the genome will be represented in the remaining sequenced DNA.

Minor points:

There are several typos throughout the manuscript.

Thanks for the observation. We will check carefully.

Methods: "Dinucelotide frecuency calculation."

We will add a code in GitHub

Reviewer #2 (Significance (Required)):

In my view, the main conclusion is that the profiles are indeed similar-even when comparing T. brucei and T. cruzi. This was not clear in previous studies (and even appeared contradictory, reporting nucleosome depletion versus enrichment) largely due to differences in chromatin digestion across these organisms. Audience: basic science and specialized readers.

Expertise: epigenetics and gene expression in trypanosomatids.

__Reviewer #3 (Evidence, reproducibility and clarity (Required)): __

The authors analysed publicly accessible MNase-seq data in TriTryps parasites, focusing on the chromatin structure around trans-splicing acceptor sites (TASs), which are vital for processing gene transcripts. They describe a mild nucleosome depletion at the TAS of T. cruzi and L. major, whereas a histone-containing complex protects the TASs of T. brucei. In the subsequent analysis of T. brucei, they suggest that a Mnase-sensitive complex is localised at the TASs. For single-copy versus multi-copy genes, the authors show different di-nucleotide patterns and chromatin structures. Accordingly, they propose this difference could be a novel mechanism to ensure the accuracy of trans-splicing in these parasites.

Before providing an in- depth review of the manuscript, I note that some missing information would have helped in assessing the study more thoroughly; however, in the light of the available information, I provide the following comments for consideration.

The numbering of the figures, including the figure legends, is missing in the PDF file. This is essential for assessing the provided information.

We apologized for not including the figure numbers in the main text, although they are located in the right place when called in the text. The omission was unwillingly made when figure legends were moved to the bottom of the main text. This is now fixed in the updated version of the manuscript.

The publicly available Mnase- seq data are manyfold, with multiple datasets available for T. cruzi, for example. It is unclear from the manuscript which dataset was used for which figure. This must be clarified.

This was detailed in Table S1. We have now replaced the table by an improved version, and we have also included the accession number of each data set used in the figure legends.

Why do the authors start in figure 1 with the description of an MNase- protected TAS for T.brucei, given that it has been clearly shown by the Siegel lab that there is a nucleosome depletion similar to other parasites?

We did not want to ignore the paper from Patterton's lab because it was the first one to map nucleosomes genome-wide in T. brucei and the main finding of that paper claimed the existence of a well-positioned nucleosome at intergenic regions, what we though constitutes a point worth to be discussed. While Patterton's work use MNase-seq from gel-purified samples and provides replicated experiments sequenced in really good depth; Siegel's lab uses MNase-ChIP-seq of histone H3 but performs only one experiment and its input was not sequenced. So, each work has its own caveats and provides different information that together contributes to make a more comprehensive study. We think that bringing up both data sets to the discussion, as we have done in Figures 1 and 3, helps us and the community working in the field to enrich the discussion.

If the authors re- analyse the data, they should compare their pipeline to those used in the other studies, highlighting differences and potential improvements.

We are working on this point. We will provide a more detail description in the final revision.

Since many figures resemble those in already published studies, there seems little reason to repeat and compare without a detailed comparison of the pipelines and their differences.

Following the reviewer advice, we are now working on highlighting the main differences that justify analyzing the data the way we did and will be added in the finally revised method section.

At a first glance, some of the figures might look similar when looking at the original manuscripts comparing with ours. However, with a careful and detailed reading of our manuscripts you can notice that we have added several analyses that allow to unveil information that was not disclosed before.

First, we perform a systematic comparison analyzing every data set the same way from beginning to end, being the main difference with previous studies the thorough and precise prediction of TAS for the three organisms. Second, we represent the average chromatin organization relative to those predicted TASs for TriTryps and discuss their global patterns. Third, by representing the average chromatin into heatmaps, we show for the very first time, that those average nucleosome landscape are not just an average, they keep a similar organization in most of the genome. These was not done in any of the previous manuscripts except for our own (Beati, PLOS One 2023). Additionally, we introduce the discussion of how the extension of MNase reaction can affect the output of these experiments and we show 2D-plots and length distribution heatmaps to discuss this point (a point completely ignored in all the chromatin literature for trypanosomes). Furthermore, we made a far-reaching analysis by considering the contributions of each publish work even when addressed by different techniques. Finally, we discuss our findings in the context of a topic of current interest in the field, such as TriTryp's genome compartmentalization.

Several previous Mnase- seq analysis studies addressing chromatin accessibility emphasized the importance of using varying degrees of chromatin digestion, from low to high digestion (30496478, 38959309, 27151365).

The reviewer is correct, and this point is exactly what we intended to illustrate in figure number 2. We appreciate he/she suggests these references that we are now citing in the final discussion. Just to clarify, using varying degrees of chromatin digestion is useful to make conclusions about a given organism but when comparing samples, strains, histone marks, etc. It is extremely important to do it upon selection of similar digested samples.

No information on the extent of DNA hydrolysis is provided in the original Mnase- seq studies. This key information can not be inferred from the length distribution of the sequenced reads.

The reviewer is correct that "No information on the extent of DNA hydrolysis is provided in the original Mnase-seq studies" and this is another reason why our analysis is so important to be published and discussed by the scientific community working in trypanosomes. We disagree with the reviewer in the second statement, since the level of digestion of a sequenced sample is actually tested by representing the length distribution of the total DNA sequenced. It is true that before sequencing you can, and should, check the level of digestion of the purified samples in an agarose gel and/or in a bioanalyzer. It could be also tested after library preparation, but before sequencing, expecting to observe the samples sizes incremented in size by the addition of the library adapters. But, the final test of success when working with MNase digested samples is to analyze length of DNA molecules by representing the histograms with length distribution of the sequenced DNA molecules. Remarkably, on occasions different samples might look very similar when run in a gel, but they render different length distribution histograms and this is because the nucleosome core could be intact but they might have suffered a differential trimming of the linker DNA associated to it or even be chewed inside (see Cole Hope 2011, section 5.2, doi: 10.1016/B978-0-12-391938-0.00006-9, for a detailed explanation).

As the input material are selected, in part gel- purified mono- nucleosomal DNA bands. Furthermore the datasets are not directly comparable, as some use native MNase, while others employ MNase after crosslinking; some involve short digestion times at 37 {degree sign} C, while others involve longer digestion at lower temperatures. Combining these datasets to support the idea of an MNase- sensitive complex at the TAS of T. brucei therefore may not be appropriate, and additional experiments using consistent methodologies would strengthen the study's conclusions.

In my opinion, describing an MNase- sensitive complex based solely on these data is not feasible. It requires specifically designed experiments using a consistent method and well- defined MNase digestion kinetics.

As the reviewer suggests, the ideal experiment would be to perform a time course of MNase reaction with all the samples in parallel, or to work with a fix time point adding increasing amounts of MNase. However, the information obtained from the detail analysis of the length distribution histogram of sequenced DNA molecules the best test of the real outcome. In fact, those samples with different digestion levels were probably not generated on purpose.

The only data sets that were gel purified are those from Mareé 2017 (Patterton's lab), used in Figures 1, S1 and S2 and those from L. major shown in Fig 1. It was a common practice during those years, then we learned that is not necessary to gel purify, since we can sort fragment sizes later in silico when needed.

As we explained to reviewer #1, to avoid this conflict, we decided to remove this data from figures 2 and S3. In summary, the 3 remaining samples comes from the same lab, and belong to the same publication (Mareé 2022). These sample are the inputs of native MNase ChIp-seq, obtain the same way, totally comparable among each other.

Reviewer #3 (Significance (Required)):

Due to the lack of controlled MNase digestion, use of heterogeneous datasets, and absence of benchmarking against previous studies, the conclusions regarding MNase-sensitive complexes and their functional significance remain speculative. With standardized MNase digestion and clearly annotated datasets, this study could provide a valuable contribution to understanding chromatin regulation in TriTryps parasites.

As we have explained in the previous point our conclusions are valid since we do not compare in any figure samples coming from different treatments. The only exception to this comment could be in figure 3 when talking about MNase-ChIP-seq. We have now added a clear and explicit comment in the section and the discussion that despite having subtle differences in experimental procedures we arrive to the same results. This is the case for T. cruzi IP, run from crosslinked chromatin, compared to T. brucei's IP, run from native chromatin.

Along the years it was observed in the chromatin field that nucleosomes are so tightly bound to DNA that crosslinking is not necessary. However, it is still a common practice specially when performing IPs. In our own hands, we did not observe any difference at the global level neither in T. cruzi (unpublished) nor in my previous work with yeast (compared nucleosome organization from crosslinked chromatin MNAse-seq inputs Chereji, Mol Cell, 2017 doi:10.1016/j.molcel.2016.12.009 and native MNase-seq from Ocampo, NAR, 2016 doi: 10.1093/nar/gkw068).

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Referee #3

Evidence, reproducibility and clarity

The authors analysed publicly accessible MNase-seq data in TriTryps parasites, focusing on the chromatin structure around trans-splicing acceptor sites (TASs), which are vital for processing gene transcripts. They describe a mild nucleosome depletion at the TAS of T. cruzi and L. major, whereas a histone-containing complex protects the TASs of T. brucei. In the subsequent analysis of T. brucei, they suggest that a Mnase-sensitive complex is localised at the TASs. For single-copy versus multi-copy genes, the authors show different di-nucleotide patterns and chromatin structures. Accordingly, they propose this difference could be a novel mechanism to ensure the accuracy of trans-splicing in these parasites.

Before providing an in- depth review of the manuscript, I note that some missing information would have helped in assessing the study more thoroughly; however, in the light of the available information, I provide the following comments for consideration.

The numbering of the figures, including the figure legends, is missing in the PDF file. This is essential for assessing the provided information. The publicly available Mnase- seq data are manyfold, with multiple datasets available for T. cruzi, for example. It is unclear from the manuscript which dataset was used for which figure. This must be clarified. Why do the authors start in figure 1 with the description of an MNase- protected TAS for T.brucei, given that it has been clearly shown by the Siegel lab that there is a nucleosome depletion similar to other parasites? If the authors re- analyse the data, they should compare their pipeline to those used in the other studies, highlighting differences and potential improvements. Since many figures resemble those in already published studies, there seems little reason to repeat and compare without a detailed comparison of the pipelines and their differences. Several previous Mnase- seq analysis studies addressing chromatin accessibility emphasised the importance of using varying degrees of chromatin digestion, from low to high digestion (30496478, 38959309, 27151365). No information on the extent of DNA hydrolysis is provided in the original Mnase- seq studies. This key information can not be inferred from the length distribution of the sequenced reads. As the input material are selected, in part gel- purified mono- nucleosomal DNA bands. Furthermore the datasets are not directly comparable, as some use native MNase, while others employ MNase after crosslinking; some involve short digestion times at 37 {degree sign} C, while others involve longer digestion at lower temperatures. Combining these datasets to support the idea of an MNase- sensitive complex at the TAS of T. brucei therefore may not be appropriate, and additional experiments using consistent methodologies would strengthen the study's conclusions. In my opinion, describing an MNase- sensitive complex based solely on these data is not feasible. It requires specifically designed experiments using a consistent method and well- defined MNase digestion kinetics.

Significance

Due to the lack of controlled MNase digestion, use of heterogeneous datasets, and absence of benchmarking against previous studies, the conclusions regarding MNase-sensitive complexes and their functional significance remain speculative. With standardized MNase digestion and clearly annotated datasets, this study could provide a valuable contribution to understanding chromatin regulation in TriTryps parasites.

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Referee #2

Evidence, reproducibility and clarity

Siri et al. perform a comparative analysis using publicly available MNase-seq data from three trypanosomatids (T. brucei, T. cruzi, and Leishmania), showing that a similar chromatin profile is observed at TAS (trans-splicing acceptor site) regions. The original studies had already demonstrated that the nucleosome profile at TAS differs from the rest of the genome; however, this work fills an important gap in the literature by providing the most reliable cross-species comparison of nucleosome profiles among the tritryps. To achieve this, the authors applied the same computational analysis pipeline and carefully evaluated MNase digestion levels, which are known to influence nucleosome profiling outcomes.

In my view, the main conclusion is that the profiles are indeed similar-even when comparing T. brucei and T. cruzi. This was not clear in previous studies (and even appeared contradictory, reporting nucleosome depletion versus enrichment) largely due to differences in chromatin digestion across these organisms. The manuscript could be improved with some clarifications and adjustments:

1. The authors state from the beginning that available MNase data indicate altered nucleosome occupancy around the TAS. However, they could also emphasize that the conclusions across the different trypanosomatids are inconsistent and even contradictory: NDR in T. cruzi versus protection-in different locations-in T. brucei and Leishmania.
2. Another point that requires clarification concerns what the authors mean in the introduction and discussion when they write that trypanosomes have "...poorly organized chromatin with nucleosomes that are not strikingly positioned or phased." On the other hand, they also cite evidence of organization: "...well-positioned nucleosome at the spliced-out region.. in Leishmania (ref 34)"; "...a well-positioned nucleosome at the TASs for internal genes (ref37)"; "...a nucleosome depletion was observed upstream of every gene (ref 35)." Aren't these examples of organized chromatin with at least a few phased nucleosomes? In addition, in ref 37, figure 4 shows at least two (possibly three to four) nucleosomes that appear phased. In my opinion, the authors should first define more precisely what they mean by "poorly organized chromatin" and clarify that this interpretation does not contradict the findings highlighted in the cited literature.
3. The paper would also benefit from the inclusion of a schematic figure to help readers visualize and better understand the findings. What is the biological impact of having nucleosomes, di-nucleosomes, or sub-nucleosomes at TAS? This is not obvious to readers outside the chromatin field. For example, the following statement is not intuitive: "We observed that, when analyzing nucleosome-size (120-180 bp) DNA molecules or longer fragments (180-300 bp), the TASs of either T. cruzi or T. brucei are mostly nucleosome-depleted. However, when representing fragments smaller than a nucleosome-size (50-120 bp) some histone protection is unmasked (Fig. 3 and Fig. S4). This observation suggests that the MNase sensitive complex sitting at the TASs is at least partly composed of histones." Please clarify. Some references are missing or incorrect:

"In trypanosomes, there are no canonical promoter regions." - please check Cordon-Obras et al. (Navarro's group).

Please, cite the study by Wedel et al. (Siegel's group), which also performed MNase-seq analysis in T. brucei.

Figure-specific comments:

Fig. S3: Why does the number of larger fragments increase with greater MNase digestion? Shouldn't the opposite be expected?

Fig. S5B: Why not use MNase conditions under which T. cruzi and T. brucei display comparable profiles at TAS? This would facilitate interpretation.

Minor points:

There are several typos throughout the manuscript.

Methods: "Dinucelotide frecuency calculation."

Significance

In my view, the main conclusion is that the profiles are indeed similar-even when comparing T. brucei and T. cruzi. This was not clear in previous studies (and even appeared contradictory, reporting nucleosome depletion versus enrichment) largely due to differences in chromatin digestion across these organisms.

Audience: basic science and specialized readers.

Expertise: epigenetics and gene expression in trypanosomatids.

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Referee #1

Evidence, reproducibility and clarity

This study explores chromatin organization around trans-splicing acceptor sites (TASs) in the trypanosomatid parasites Trypanosoma cruzi, T. brucei and Leishmania major. By systematically re-analyzing MNase-seq and MNase-ChIP-seq datasets, the authors conclude that TASs are protected by an MNase-sensitive complex that is, at least in part, histone-based, and that single-copy and multi-copy genes display differential chromatin accessibility. Altogether, the data suggest a common chromatin landscape at TASs and imply that chromatin may modulate transcript maturation, adding a new regulatory layer to an unusual gene-expression system.

I value integrative studies of this kind and appreciate the careful, consistent data analysis the authors implemented to extract novel insights. That said, several aspects require clarification or revision before the conclusions can be robustly supported. My main concerns are listed below, organized by topic/result section.

TAS prediction:

• Why were TAS predictions derived only from insect-stage RNA-seq data? Restricting TAS calls to one life stage risks biasing predictions toward transcripts that are highly expressed in that stage and may reduce annotation accuracy for lowly expressed or stage-specific genes. Please justify this choice and, if possible, evaluate TAS robustness using additional transcriptomes or explicitly state the limitation.

Results

• "There is a distinctive average nucleosome arrangement at the TASs in TriTryps":
• You state that "In the case of L. major the samples are less digested." However, Supplementary Fig. S1 suggests that replicate 1 of L. major is less digested than the T. brucei samples, while replicate 2 of L. major looks similarly digested. Please clarify which replicates you reference and correct the statement if needed.
• It appears you plot one replicate in Fig. 1b and the other in Suppl. Fig. S2. Please indicate explicitly which replicate is in each plot. For T. brucei, the NDR upstream of the TAS is clearer in Suppl. Fig. S2 while the TAS protection is less prominent; based on your digestion argument, this should correspond to the more-digested replicate. Please confirm. The protected region around the TAS appears centered on the TAS in T. brucei but upstream in L. major. This is an interesting difference. If it is technical (different digestion or TAS prediction offset), explain why; if likely biological, discuss possible mechanisms and implications.

Results

• "An MNase sensitive complex occupies the TASs in T. brucei":
• The definition of "MNase activity" and the ordering of samples into Low/Intermediate/High digestion are unclear. Did you infer digestion levels from fragment distributions rather than from controlled experimental timepoints? In Suppl. Fig. S3a it is not obvious how "Low digestion" was defined; that sample's fragment distribution appears intermediate. Please provide objective metrics (e.g., median fragment length, fraction 120-180 bp) used to classify digestion levels.
• Several fragment distributions show a sharp cutoff at ~100-125 bp. Was this due to gel purification or bioinformatic filtering? State this clearly in Methods. If gel purification occurred, that can explain why some datasets preserve the MNase-sensitive region.
• Please reconcile cases where samples labeled as more-digested contain a larger proportion of >200 bp fragments than supposedly less-digested samples; this ordering affects the inference that digestion level determines the loss/preservation of TAS protection. Based on the distributions I see, "Intermediate digestion 1" appears most consistent with an expected MNase curve - please confirm and correct the manuscript accordingly. Results - "The MNase sensitive complexes protecting the TASs in T. brucei and T. cruzi are at least partly composed of histones":
• The evidence that histones are part of the MNase-sensitive complex relies on H3 MNase-ChIP signal in subnucleosomal fragment bins. This seems to conflict with the observation (Fig. 1) that fragments protecting TASs are often nucleosome-sized. Please reconcile these points: are H3 signals confined to subnucleosomal fragments flanking the TAS while the TAS itself is depleted of H3? Provide plots that compare MNase-seq and H3 ChIP signals stratified by consistent fragment-size bins to clarify this.
• Please indicate which datasets are used for each panel in Suppl. Fig. S4 (e.g., Wedel et al., Maree et al.), and avoid calling data from different labs "replicates" unless they are true replicates.
• Several datasets show a sharp lower bound on fragment size in the subnucleosomal range (e.g., ~80-100 bp). Is this a filtering artifact or a gel-size selection? Clarify in Methods and, if this is an artifact, consider replotting after removing the cutoff. Results - "The TASs of single and multi-copy genes are differentially protected by nucleosomes":
• Please include T. brucei RNA-seq data in Suppl. Fig. S5b as you did for T. cruzi.
• Discuss how low or absent expression of multigene families affects TAS annotation (which relies on RNA-seq) and whether annotation inaccuracies could bias the observed chromatin differences.
• The statement that multi-copy genes show an "oscillation" between AT and GC dinucleotides is not clearly supported: the multi-copy average appears noisier and is based on fewer loci. Please tone down this claim or provide statistical support that the pattern is periodic rather than noisy.
• How were multi-copy genes defined in T. brucei? Include the classification method in Methods.

Genomes and annotations:

• If transcriptomic data for the Y strain was used for T. cruzi, please explain why a Y strain genome was not used (e.g., Wang et al. 2021 GCA_015033655.1), or justify the choice. For T. brucei, consider the more recent Lister 427 assembly (Tb427_2018) from TriTrypDB. Use strain-matched genomes and transcriptomes when possible, or discuss limitations.

Reproducibility and broader integration:

• Please share the full analysis pipeline (ideally on GitHub/Zenodo) so the results are reproducible from raw reads to plots.
• As an optional but helpful expansion, consider including additional datasets (other life stages, BSF MNase-seq, ATAC-seq, DRIP-seq) where available to strengthen comparative claims. Optional analyses that would strengthen the study:
• Stratify single-copy genes by expression (high / medium / low) and examine average nucleosome occupancy at TASs for each group; a correlation between expression and NDR depth would strengthen the functional link to maturation.

Minor / editorial comments:

• In the Introduction, the sentence "transcription is initiated from dispersed promoters and in general they coincide with divergent strand switch regions" should be qualified: such initiation sites also include single transcription start regions.
• Define the dotted line in length distribution plots (if it is not the median, please clarify) and consider placing it at 147 bp across plots to ease comparison.
• In Suppl. Fig. 4b "Replicate2" the x-axis ticks are misaligned with labels - please fix.
• Typo in the Introduction: "remodellingremodeling" → "remodeling."

Referee cross-commenting

Comment 1: I think Reviewer #2 and Reviewer #3 missed that they authors of this manuscript do cite and consider the results from Wedel at al. 2017. They even re-analysed their data (e.g. Figure 3a). I second Reviewer #2 comment indicating that the inclusion of a schematic figure to help readers visualize and better understand the findings would be an important addition.

Comment 2: I agree with Reviewer #3 that the use of different MNase digestion procedures in the different datasets have to be considered. On the other hand, I don't think there is a problem with figure 1 showing an MNase-protected TAS for T. brucei as it is based on MNase-seq data and reproduces the reported results (Maree et al. 2017). What the Siegel lab did in Wedel et al. 2017 was MNase-ChIPseq of H3 showing nucleosome depletion at TAS, but both results are not necessary contradictory: There could still be something else (which does not contain H3) sitting on the TAS protecting it from MNase digestion.

Significance

This study provides a systematic comparative analysis of chromatin landscapes at trans-splicing acceptor sites (TASs) in trypanosomatids, an area that has been relatively underexplored. By re-analyzing and harmonizing existing MNase-seq and MNase-ChIP-seq datasets, the authors highlight conserved and divergent features of nucleosome occupancy around TASs and propose that chromatin contributes to the fidelity of transcript maturation.

The significance lies in three aspects:

1. Conceptual advance: It broadens our understanding of gene regulation in organisms where transcription initiation is unusual and largely constitutive, suggesting that chromatin can still modulate post-transcriptional processes such as trans-splicing.
2. Integrative perspective: Bringing together data from T. cruzi, T. brucei and L. major provides a comparative framework that may inspire further mechanistic studies across kinetoplastids.
3. Hypothesis generation: The findings open testable avenues about the role of chromatin in coordinating transcript maturation, the contribution of DNA sequence composition, and potential interactions with R-loops or RNA-binding proteins. Researchers in parasitology, chromatin biology, and RNA processing will find it a useful resource and a stimulus for targeted experimental follow-up.

My expertise is in gene regulation in eukaryotic parasites, with a focus on bioinformatic analysis of high-throughput sequencing data

This is a test

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Reply to the reviewers

The authors do not wish to provide a response at this time.

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Referee #4

Evidence, reproducibility and clarity

Major criticisms

The manuscript by Chapa-y-Lazo et al. is confusing. It does not provide precise information about the three photostable monomers developed by different research groups. Please read the review (ref. 17) carefully. The monomeric version analyzed in this study was developed by Ivorra-Molla et al. and should be referred to as StayGold-E138D. This variant excels in dispersibility (monomericity), photostability, and molecular brightness (the product of the molar extinction coefficient and the fluorescence quantum yield). However, when analyzed in animal cells, StayGold-E138D is practically dim, and its brightness is poor. This can be seen in Figures 2, 3, S5, and S6 of the manuscript. The maturation efficiency of the chromophore is not so good in fly embryos. On the other hand, Ando et al. independently developed a monomeric version of StayGold called mStayGold at FPbase and Addgene. Therefore, I think that the authors should acknowledge that their analysis of StayGold monomer behavior is still incomplete. Additionally, the evolution tree of StayGold shown in Figure S2 is incorrect. The side-by side comparison of the three monomeric variants of StayGold, including StayGold-E138D and mStayGold, is documented in a recent preprint. Comparison of monomeric variants of StayGold | bioRxiv

Minor comments

Line 84 z-stacks were acquired using a spinning disc confocal microscope. Line 100 we collected a z-stack through each embryo. Line373 We analyzed the slices from 7 µm to 20.5 µm depth. Line 390 Depth 9 µm to 21 µm was analyzed. It is not clear what "z-stack" means in these sentences.

Significance

Nothing in particular.

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Referee #3

Evidence, reproducibility and clarity

Chapa-y-Lazo and colleagues report the detailed characterization of a number of different genetically-encoded fluorescent proteins in Drosophila embryos. The screening and selection of an appropriate fluorescent protein for imaging tasks is an important and often neglected part of experimental design, and datasets such as this one will be extremely useful in guiding decision making for other users. The manuscript is well-written and carefully controlled for different developmental stages and nicely compares the most pertinent properties of FPs such as brightness, photobleaching, and folding time. There would be a couple of additional experiments that would be nice to see but are not strictly necessary for improving the paper as-is, but might be helpful points to include in the discussion.

Comments:

1) All fluorophores in this study were fused to H2Av, at the same insertion site, which makes for a nice and easy comparison between lines. However, histone-binding proteins can sometimes behave unpredictably when tagged with different things and in addition it would be interesting to see if the fusion protein affects the FP properties in anyway. I.e. would sfGFP be brighter than mEmerald when bound to a CAAX sequence or some other organelle? It would be impractical for this study to re-do all the FPs, but the top two hits could be interesting and would potentially be quite interesting if there is a significant difference in behaviour between FPs when bound to different proteins/cellular compartments. Else maybe a mention in the discussion?

2) Another way to compare the fluorophore folding time would be to selectively bleach a portion of the embryo at the same developmental stage and measure the time it takes for each FP to recover to the same intensity as the rest of the embryo. This could potentially control for any delay for developmental reasons.

3) Some of the lines in the figure plots could be a bit thicker - purple and pink when overlapping are hard to distinguish.

Significance

This manuscript will be quite useful for those who are deciding between which fluorescent protein or combination to use for their live-imaging work, and additionally has created a number of useful fly strains in the process. It will hopefully also start a discussion about proper characterization and quantification of fluorescent reporters under different conditions, ideally before all the effort to generate an entirely new genetically modified animal is performed.

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Referee #2

Evidence, reproducibility and clarity

In this manuscript Saunders and colleagues benchmark the brightness, folding speed and photostability of a variety of red (8 versions) and green fluorescent proteins (9 versions), which have been widely used for in vivo imaging. They fused each protein to histone2Av, cloned the fusion into attP constructs and inserted them in the Drosophila genome at the same genetic location. Thus, expression levels can be compared. Nuclei at embryonic cycle 14 were imaged, segmented and fluorescence was quantified. At this early stage the maturation kinetics of the fluorophore can particularly influence its fluorescence intensity.

Additionally, stage 15-16 embryos were imaged at the dorsal side to quantify brightness. As the histone promoter is active in all cells, the fluorescence in the nuclei of all cell types can be quantified. Brightness differences between the different proteins vary a bit between both experiments, likely taking folding versus brightness into account. Generally, sfGFP, mEGFP, mEmerald as well as mStrawberry and mScarlet are the brightest. Next, developmental movies were recorded starting at gastrulation to estimate the folding rates of the different proteins. No large differences of the relative fluorescence increase over time were reported. To estimate photostability, embryos were imaged ventrally shortly before the onset of gastrulation for 2 or 4 hours with high laser intensity and the fluorescence intensity was recorded. Consistent with data in the literature, StayGold is the most photostable green protein, although it is not the brightest from the start, likely to also slower folding. From the red proteins mRFP and mCherry are good choices for long-term imaging.

In summary, these results do not bring huge surprises but are still valuable for future choice of protein tagging for imaging. Best green proteins are mEGFP, mNeonGreen, mStayGold with differences in brightness vs stability. For red, no protein is the clear winner, mScarlet-I is good in folding and brightness but others are better for photostability.

Major comments:

1. Form the methods, it is not clear which promoter is used to drive expression of the histone2Av fusions. I assume this is not UAS but the histone promotor/enhancer. Please clarify.
2. From text is not always what the purpose of the experiment is. For example, it is not mentioned that developmental movies were recorded for the data related to Figure 3 to calculate folding, while bleaching was measured in the movies related to Figure 4. In contrast to simple single time points in Figures 1 and 2.

Minor comments:

1. Please add time to movie 2 and rotate it such that anterior is to the left and dorsal it up.
2. Lines 141 - 144 should refer to Figure 3D not 4D.
3. Movies 3 and 4, please insert time.

Significance

Experiments are well performed and the finding are useful to guide the future choice of fluorophores in Drosophila and possibly other model organisms. Results are not very surprising, as the major finding that StayGold is photostable (but not the brightest) is not entirely new but still reassuring. It is particularly nice to have the differences confirmed by well controlled side-by-side measurements in Drosophila. This will likely guide many Drosophila researchers to tag their favourite protein with StayGold in the future.

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Referee #1

Evidence, reproducibility and clarity

Summary:

This is an outstanding study of high practical value, which provided the systematic performance evaluation of in vivo fluorophores under the same condition in the field of Drosophila developmental biology. By site-integrating 17 green and red fluorescent proteins into the same genomic locus and evaluating their fluorescent intensity (early/late embryos), folding time and photostability on the same imaging platform, this provides a powerful database for researchers.

Major comments:

Q1: All fluorescent proteins are fused with histone H2Av. Will this nuclear localization expression pattern mask the performance differences of fluorescent proteins in other subcellular structures (such as cell membranes, cytoplasm, and cytoskeleton)?

Q2: How do the authors ensure precise developmental synchrony among different embryos to avoid the influence of developmental time differences on fluorescence intensity and folding curves?

Q3: In this study, the authors conduct a quantitative screen of nine green and eight red fluorophore lines in Drosophila. A logical and valuable extension of this work would be a systematic evaluation of newer fluorescent proteins, including promising candidates like mBaoJin, mScarlet3, and mScarlet3-H.

Q4：The study does not discuss the potential for fluorescent proteins to interfere with biological function. Although the proteins were expressed from the identical genomic location, variations in their size, structure, or fusion design may influence the target protein's localization or activity.

Lines 145-147 "The only profile that did not fit well to this phenomenological function was mStayGold which did not display a clear reduction in its rate of intensity increase". What is the reason that causes mStayGold to fail to fit well? Is it related to the unique structure?

Lines 154-156 "For the red fluorophores, the intensity profiles were more varied (Fig. 3E). They could not be reduced to a single curve, unlike the green fluorophores (Fig. S7B). The phenomenological function I(t) did not fit the curves well". Compared with green fluorophores, the intensity profiles of red fluorophores vary greatly, what's the major factors drive this difference?

Lines 206 "Fluorophores including mEGFP and mEmerald displayed a secondary peak in intensity around an hour after experiment initiation. This is consistent with a change in the rate of protein production". What is the mechanism behind the secondary peak, and why is it distinctly observed only in mEGFP and mEmerald?

Minor comments:

Line 143 "curve I(t) = I0 tanh (t-tin/ts) (Fig. 4D, Methods)". It's not Fig. 4D, but Fig. 3D.

Line 145 "time is smallest for Superfolder GFP and longest for mNeonGreen (Fig. 4D)". Not Fig. 4D, but Fig. 3D.

Line159 "mScarlet" must be replaced with "mScarlet-I".

Significance

The systematic performance evaluation of in vivo fluorophores under the same condition will give a comprehensive guidence when choosing fluorescent proteins in the field of Drosophila developmental biology.

eLife Assessment

Recent studies have shown that mRNA can be acetylated (ac4c), altering mRNA stability and translation efficiency; however, the role of mRNA acetylation in the brain remains unexplored. In this valuable study, the authors demonstrate that ac4c occurs in synaptically localised mRNAs, mediated by NAT10. Conditional reduction of NAT10 protein levels led to decreases in ac4c of mRNAs and deficits in synaptic plasticity and memory. These solid results suggest that mRNA acetylation may play a role in memory consolidation.

Reviewer #1 (Public review):

Summary:

RNA modification has emerged as an important modulator of protein synthesis. Recent studies found that mRNA can be acetylated (ac4c), which can alter mRNA stability and translation efficiency. The role of ac4c mRNA in the brain has not been studied. In this paper, the authors convincingly show that ac4c occurs selectively on mRNAs localized at synapses, but not cell-wide. The ac4c "writer" NAT10 is highly expressed in hippocampal excitatory neurons. Using NAT10 conditional KO mice, decreasing levels of NAT10 resulted in decreases in ac4c of mRNAs and also showed deficits in LTP and spatial memory. These results reveal a potential role for ac4c mRNA in memory consolidation.

This is a new type of mRNA regulation that seems to act specifically at synapses, which may help elucidate the mechanisms of local protein synthesis in memory consolidation. Overall, the studies are well carried out and presented. There is some confusion over training/learning vs memory, and the precise mRNAs that require ac4c to carry out memory consolidation are not clear. The specificity of changes occurring only at the end of training, rather than after each day of training, is interesting and warrants some investigation. This timeframe is puzzling because the authors show that ac4c can dynamically increase within 1 hour after cLTP.

Strengths:

(1) The studies show that mRNA acetylation (ac4c) occurs selectively at mRNAs localized to synaptic compartments (using synaptoneurosome preps).

(2) The authors identify a few key mRNAs acetylated and involved in plasticity and memory - e.g., Arc.

(3) The authors show that Ac4c is induced by learning and neuronal activity (cLTP).

(4) The studies show that the ac4c "writer" NAT10 is expressed in hippocampal excitatory neurons and may be relocated to synapses after cLTP/learning induction.

(5) The authors used floxed NAT10 mice injected with AAV-Cre in the hippocampus (NAT10 cKO) to show that NAT10 may play a role in LTP maintenance and memory consolidation (using the Morris Water Maze).

Weaknesses:

(1) The authors use a confusing timeline for their behavioral experiments, i.e, day 1 is the first day of training in the MWM, and day 6 is the probe trial, but in reality, day 6 is the first day after the last training day. So this is really day 1 post-training, and day 20 is 14 days post-training.

(2) The authors inaccurately use memory as a term. During the training period in the MWM, the animals are learning, while memory is only probed on day 6 (after learning). Thus, day 6 reflects memory consolidation processes after learning has taken place.

(3) The NAT10 cKO mice are useful to test the causal role of NAT10 in ac4a and plasticity/memory, but all the experiments used AAV-CRE injections in the dorsal hippocampus that showed somewhat modest decreases in total NAT10 protein levels. For these experiments, it would be better to cross the NAT10 floxed animals to CRE lines where a better knockdown of NAT10 can be achieved, with less variability.

(4) Because knockdown is only modest (~50%), it is not clear if the remaining ac4c on mRNAs is due to remaining NAT10 protein or due to an alternative writer (as the authors pose).

Reviewer #2 (Public review):

This is an interesting study that shows that mRNA acetylation at synapses is dynamically regulated at synapses by spatial memory in the mouse hippocampus. The dynamic changes of ac4C-mRNAs regulated by memory were validated by methods including ac4C dot-blot and liquid 13 chromatography-tandem mass spectrometry (LC-MS/MS).

Here are some comments for consideration by readers and authors:

(1) It is known that synaptosomes are contaminated with glial tissue. In the study, the authors also show that NAT0 is expressed in glia. So the candidate mRNAs identified by acRIP-seq might also be mixed with glial mRNAs. Are the GO BP terms shown in Figure 3A specifically chosen, or unbiasedly listed for all top ones?

(2) Where does NAT10-mediated mRNA acetylation take place within cells generally? Is there evidence that NAT10 can catalyze mRNA acetylation in the cytoplasm?

(3) "The NAT10 proteins were significantly reduced in the cytoplasm (S2 fraction) but increased in the PSD fraction at day 6 after memory (Figures 5J and 5K)." The authors argue that the translocation of NAT10 from soma to synapses accounts for these changes. The increase of NAT10 protein in the PSD fraction can be understood. However, it is quite surprising that the NAT10 proteins were significantly reduced in the cytoplasm (S2 fraction), considering the amount of NAT10 in soma is much more abundant in synapses. The small increase in synaptic NAT10 might not be enough to cause a decrease in soma NAT10 protein level.

(4) It is difficult to separate the effect on mRNA acetylation and protein mRNA acetylation when doing the loss of function of NAT10.

Author response:

Reviewer #1:

Comment 1: The authors use a confusing timeline for their behavioral experiments, i.e., day 1 is the first day of training in the MWM, and day 6 is the probe trial, but in reality, day 6 is the first day after the last training day. So this is really day 1 post-training, and day 20 is 14 days post-training.

We thank this reviewer for pointing out the issue of the behavioral timeline. We will revise the behavioral timeline as suggested by this reviewer. Days 1–5 will be labeled as “Training phase day 1–5”. Day 6 will be labeled as the “Day 1 post-training” and Day 20 will be labeled as the “Day 14 post-training”.

Comment 2: The authors inaccurately use memory as a term. During the training period in the MWM, the animals are learning, while memory is only probed on day 6 (after learning). Thus, day 6 reflects memory consolidation processes after learning has taken place.

We will revise the manuscript to distinguish between "learning" and "memory." We will refer to the performance during the 5-day training period as "spatial learning" and restrict the term "memory" to the probe tests on Day 6, which reflect memory processes after learning has taken place.

Comment 3: The NAT10 cKO mice are useful... but all the experiments used AAV-CRE injections in the dorsal hippocampus that showed somewhat modest decreases... For these experiments, it would be better to cross the NAT10 floxed animals to CRE lines where a better knockdown of NAT10 can be achieved, with less variability.

We want to clarify the reason for using AAV-Cre injection rather than Cre lines. Indeed, we attempted to generate Nat10 conditional knockouts by crossing Nat10^flox/flox mice with several CNS-specific Cre lines. Crossing with Nestin-Cre and Emx1-Cre resulted in embryonic and premature lethality, respectively, consistent with the essential housekeeping function of NAT10 during neurodevelopment. We are currently using the Camk2α-Cre line which starts to express Cre after postnatal 3 weeks specifically in hippocampal pyramidal neurons (Tsien et al., 1996).

Comment 4: Because knockdown is only modest (~50%), it is not clear if the remaining ac4c on mRNAs is due to remaining NAT10 protein or due to an alternative writer (as the authors pose).

Our results suggest the existence of alternative writers. As shown in Figure 6D, we identified a population of "NAT10-independent" MISA mRNAs (present in MISA but not downregulated in NASA). Remarkably, these mRNAs possess a consensus motif (RGGGCACTAACY) that is fundamentally different from the canonical NAT10 motif (AGCAGCTG). This distinct motif usage suggests that the residual ac4C signals are not merely due to incomplete knockdown of NAT10, but reflect the activity of other, as-yet-unidentified ac4C writers. Nonetheless, we think that generation of a Nat10 knockout line with completely loss of NAT10 proteins is useful to address this reviewer’s concern.

Reviewer #2:

Comment 1: It is known that synaptosomes are contaminated with glial tissue... So the candidate mRNAs identified by acRIP-seq might also be mixed with glial mRNAs. Are the GO BP terms shown in Figure 3A specifically chosen, or unbiasedly listed for all top ones?

It is true that some ac4C-mRNAs identified by acRIP-seq from the synaptosomes are highly expressed in astrocyte, such as Aldh1l1, ApoE, Sox9 and Aqp4 (Table S3, Fig. S6H). In agreement, we found that NAT10 was also expressed in astrocyte in addition to neurons. We will show representative image for the expression of NAT10-Cre in astrocytes in the revised MS. The BP items shown in Fig. 3A were chosen from top 30 and highly related with synaptic plasticity and memory. We will show the full list of significant BP items for MISA in the revised MS.

Comment 2: Where does NAT10-mediated mRNA acetylation take place within cells generally? Is there evidence that NAT10 can catalyze mRNA acetylation in the cytoplasm?

The previous studies from non-neuronal cells showed that NAT10 can catalyze mRNA acetylation in the cytoplasm and enhance translational efficiency (Arango et al., 2018; Arango et al., 2022). In this study, we showed that mRNA acetylation occurred both in the homogenates and synapses (see ac4C-mRNA lists in Table S2 and S3). However, spatial memory upregulated mRNA acetylation mainly in the synapses rather than in the homogenates (Fig. 2 and Fig. S2).

Comment 3: "The NAT10 proteins were significantly reduced in the cytoplasm (S2 fraction) but increased in the PSD fraction..." The small increase in synaptic NAT10 might not be enough to cause a decrease in soma NAT10 protein level.

We showed that the NAT10 protein levels were increased by one-fold in the PSD fraction, but were reduced by about 50% in the cytoplasm after memory formation (Fig. 5J and K). The protein levels of NAT10 in the homogenates and nucleus were not altered after memory formation (Fig. 5F and I). Due to these facts, we hypothesized that NAT10 proteins may have a relocation from cytoplasm to synapses after memory formation, which was also supported by the immunofluorescent results from cultured neurons (Fig. S4). However, we agree with this reviewer that drawing such a conclusion may require the time-lapse imaging of NAT10 protein trafficking in living animals, which is technically challenging at this moment.

Comment 4: It is difficult to separate the effect on mRNA acetylation and protein mRNA acetylation when doing the loss of function of NAT10.

This is a good point. We agree with this reviewer that NAT10 may acetylate both mRNA and proteins. We examined the acetylation levels of -tubulin and histone H3, two substrate proteins of NAT10 in the hippocampus of Nat10 cKO mice. As shown in Fig S5C, E, and F, the acetylation levels of -tubulin and histone H3 remained unchanged in the Nat10 cKO mice, likely due to the compensation by other protein acetyltransferases. In contrast, mRNA ac4C levels were significantly decreased in the Nat10 cKO mice (Figure S5G–H). These results suggest that the memory deficits seen in Nat10 cKO mice may be largely due to the impaired mRNA acetylation. Nonetheless, we believe that developing a new technology which enables selective erasure of mRNA acetylation would be helpful to address the function of mRNA. We discussed these points in the MS (line 585-592).

References

Arango, D., Sturgill, D., Alhusaini, N., Dillman, A. A., Sweet, T. J., Hanson, G., Hosogane, M., Sinclair, W. R., Nanan, K. K., & Mandler, M. D. (2018). Acetylation of cytidine in mRNA promotes translation efficiency. Cell, 175(7), 1872-1886. e1824.

Arango, D., Sturgill, D., Yang, R., Kanai, T., Bauer, P., Roy, J., Wang, Z., Hosogane, M., Schiffers, S., & Oberdoerffer, S. (2022). Direct epitranscriptomic regulation of mammalian translation initiation through N4-acetylcytidine. Molecular cell, 82(15), 2797-2814. e2711.

Tsien, J. Z., Chen, D. F., Gerber, D., Tom, C., Mercer, E. H., Anderson, D. J., Mayford, M., Kandel, E. R., & Tonegawa, S. (1996). Subregion-and cell type–restricted gene knockout in mouse brain. Cell, 87(7), 1317-1326.

what if these aren't models

but are in morphic resonance with something that is outside the brain driven by the shift of attention

This is confusing: AttributeRuler is a pipe like all the others listed under "Attribute Rulers", and the plural "Attribute Rulers" does not make any sense here. Correct: "Below is a complete list of the standard pipes and matchers from spaCy (a matcher "just" finds patterns and does not tag or manipulate data in the same way as pipes)." 1.3.1.1 Standard Pipes - AttributeRuler - DependencyParser - etc.

eLife Assessment

This is a valuable study that investigates peptidoglycan (PG) recycling in Caulobacter crescentus, demonstrating its importance for β-lactam resistance, cell morphology, and cell division. The findings are compelling, although limited complementation somewhat constrains the interpretation of specific gene functions.

Reviewer #1 (Public review):

Summary:

In their manuscript, Richter and colleagues comprehensively investigate the cell wall recycling pathway in the model alphaproteobacterium Caulobacter crescentus using biochemical, imaging, and genetic approaches. They clearly demonstrate that this organism encodes a functional peptidoglycan recycling pathway and demonstrate the activities of many enzymes and transporters within this pathway. They leverage imaging and growth assays to demonstrate that mutants in peptidoglycan recycling have varying degrees of beta-lactam sensitivity as well as morphological and cell division defects. They propose that, rather than impacting the levels or activity of the major beta-lactamase, BlaA, defects in PG recycling lead to beta-lactam sensitivity by limiting the availability of new cell wall precursors. The findings will be of interest to those in the field of bacterial cell wall biochemistry, antibiotics and antibiotic resistance, and bacterial morphogenesis.

Strengths:

Overall, the manuscript is laid out logically, and the data are comprehensive, quantitative, and rigorous. The mutants and their phenotypes will be a valuable resource for Caulobacter researchers.

Weaknesses:

The only major missing piece is the complementation of mutants to demonstrate that loss of the targeted gene is responsible for the observed phenotypes.

Reviewer #2 (Public review):

Summary:

Pia Richter et al. investigated the peptidoglycan (PG) recycling metabolism in the alpha-proteobacterium Caulobacter crescentus. The authors first identified a functional recycling pathway in this organism, which is similar to the Pseudomonas route, and they characterized two key enzymes (NagZ, AmiR) of this pathway, showing that AmiR differs in specificity from the AmpD counterpart of E. coli. Further, they studied the effects of deletions within the PG recycling pathway (ampG, amiR, nagZ, sdpA, blaA, nagA1, nagA2, amgK, nagK mutants), showing filamentation and cell widening, thereby revealing a link between PG recycling and cell division. Finally, they provide a link between PG recycling and beta-lactam sensitivity in C. crescents that is not caused by activation of a beta-lactamase, but rather is a result of reduced supply of PG building blocks increasing the sensitivity of penicillin-binding proteins.

Strengths:

This work adds to the understanding of the role of PG recycling in alpha-proteobacteria, which significantly differ in their mode of cell wall growth from the better studied gamma-proteobacteria.

Weaknesses:

The findings are not entirely novel as recent studies by Modi et al. 2025 mBio (studying C. crescentus) and Gilmore & Cava 2022 Nat. Commun. (studying Agrobacterium tumefaciens) came to similar conclusions.

Use the same "contact banner" as on the home page: "Nejste si čímkoliv jistí…"

the same as on home page

https://web.archive.org/web/20251202103136/https://www.marketplace.org/story/2024/10/24/private-equity-buying-up-businesses-in-the-skilled-trades-hvac-plumbing-electrician

#2024/10 article describing how private equity firms are buying up skilled trades companies. Plumbers, solar panel / heat pump installers etc. A weak signal imo. Would you want private equity in real world entities? It's one thing for 'new fields' or scale-ups etc. Another when extraction hits trades we rely on (although easy enough for other entrants to help circumvent)

(via [[Chris Aldrich]])

• Although these methods and practices have been introduced to help further develop ones ability to read and write, some often don't use them.
• Many still resort to their natural language and writing, which can be beneficial but also lacks the chance to develop. These students and children need to take full advantage of the extensive opportunities given to them.
• With many gaps and conflicts still in the way, researchers are very confident on working towards a goal to help improve and develop children's reading and writing.

• Phonology, Orthography, and Morphology are all 3 important components that each writer must take into account and master these methods as a writer.
• All of these not only improve your understanding as a writer, these help you see improvement and compose better writing pieces.
• Phonology studies how sounds are put into words, orthography is the standard spelling system of a written language, and Morphology is the study of different words and word form structures.
• All these work hand in hand with developing ones ability to be a better writer.

• The Table 1 model on the article demonstrates and shows the statistical data of fixations and reading levels by school grade.
• This is a further example that shows the difference in young children and adults with their eye movements as they grow older and progress.

• Many practice methods to learn basic reading and writing varied throughout the world.
• Different countries and regions had different standards but many were very similar, just in different handwriting and understanding.
• It's important for an individual to be fluent and understand their own true native language to further develop into a better writer.

• With shifts and changes showing promising steps, there still seemed to be questions on if these changes will stay in place or will they benefit
• Granted, the focus and attention on these issues brought a lot of attention to the situation which made it easier to push for the change
• Many still question what will the main focus and priority be? will they continue to focus towards writing and reading studies or will they branch out to different issues?

• For most of the beginning, the main focus of composition studies was mainly aimed towards post modernity but ironically failed and declined as if shifted to relative disciplinary independence.
• Slowly schools began to implement alien and outside texts and teachings into educational fields
• The 1990s brought much interest towards understanding student writing through different perspectives such as gender, race, sexuality, and class

• Composition studies have been around for quite some time, which was heavily committed to the study of writing.
• The studies date back all the way to the late 1960s and into the early 1970s.
• Throughout its early introduction, they mainly were postmodernism and challenged diverse languages and other tones.
• As time eventually went on, there began to be a shift towards cultural acceptance and independence, and postmodernism would decline.

• I wonder what triggered the shift from postmodernism in English studies to a more free and independent approach to Composition and English studies
• I feel like it had a lot to do with postmodernism English studies and standard English not being accomplished nor making any further advancements.
• This in return pushed for a more open and inviting way for Composition studies for many varieties of students.

• After years and years of studying and investigating, many ways of communicating and writing can be taught effectively and efficiently to many students.
• This is also a very slow process but it's one that'll be well worth it in the long run. It's important to understand the basics in composition studies as it is very common and much needed in society and not just as a writer.
• Developing basic composition skills helps individuals better understand how to communicate effectively and more properly.

Postmodernism declined after composition studies shifted.

The differences of how different eras focused on composition and writing.

how far back composition studies started back from.

the process of embracing interdisciplinary over the modern method of English

The main criticism towards writing and writing studies.

This method of linking writing courses with student placements has become more popular,

After years of research, written communication can be effectively taught.

what composition studies focuses towards

I found this study insightful because it shows that high textbook costs affect student engagement and performance. Using open textbooks not only saves money but also allows students to access materials instantly.

LiDA103

Major lack of data about the number of landfill in general and from each member state

This section stands out because it shows how the meaning of “social” shifts depending on context. Social media can isolate people as individuals, but it can also create collective action, like #BlackLivesMatter or #MeToo. The author shows how the word reflects both personal interaction and large political movements. It demonstrates how “society” is not a fixed thing but a dynamic process shaped through technology and participation.

Emerson’s view of society as a force that suppresses individual freedom is interesting but also very reductive. This annotation makes me think about how easy it is to imagine “society” as something separate from people, when in reality individuals help create and shape it. The passage shows how the conflict between the individual and the collective is more complicated than a simple opposition, which fits the author’s argument about the word’s ambiguity.

The author points out that “society” is often used without clear meaning, and I notice that I also tend to use the word in a broad and imprecise way. This section reminds me that academic writing requires specificity. When people blame “society,” they usually refer to certain institutions or groups, not every person. The author’s criticism pushes me to be more intentional about who or what I actually mean when I use the term.

UDA (Unified Data Architecture) at Netflix - Summary

Problem Statement

• Netflix faces growing complexity as offerings expand across films, series, games, live events, and ads

• Core business concepts (actor, movie) are modeled independently across multiple systems with no coordination

  "Each system models these concepts differently and in isolation, with little coordination or shared understanding."

Key Challenges Addressed

• Duplicated and Inconsistent Models — Teams re-model same entities in different systems with conflicting definitions
• Inconsistent Terminology — Different terms for same concept, or same term for different concepts

• Data Quality Issues — Discrepancies and broken references hard to detect across microservices

  "While identifiers and foreign keys exist, they are inconsistently modeled and poorly documented"

• Limited Connectivity — Cross-system relationships effectively non-existent

What is UDA?

• Foundation for connected data in Content Engineering

  "UDA enables teams to model domains once and represent them consistently across systems — powering automation, discoverability, and semantic interoperability."

Core Capabilities

1. Register and connect domain models — Formal conceptualizations of federated business domains
2. Catalog and map domain models to data containers — GraphQL resolvers, Data Mesh sources, Iceberg tables
3. Transpile domain models into schema languages — GraphQL, Avro, SQL, RDF, Java while preserving semantics
4. Move data faithfully between containers — Automatic handling of data transformation between systems
5. Discover and explore domain concepts — Via search and graph traversal
6. Programmatically introspect the knowledge graph — Using Java, GraphQL, or SPARQL

Technical Foundation

• Knowledge Graph — Built on RDF and SHACL

  "We chose RDF and SHACL as the foundation for UDA's knowledge graph"

• Named-graph-first information model — Each named graph conforms to a governing model

Upper Metamodel

• Language for formally describing domains and their concepts

  "Upper is the metamodel for Connected Data in UDA — the model for all models"

• Key properties:

  • Self-referencing — Models itself as a domain model
  • Self-describing — Defines the concept of a domain model
  • Self-validating — Conforms to its own model
• Domain models expressed as conceptual RDF, organized into named graphs
• Enables projections to GraphQL, Avro, Iceberg, Java

Mappings

• Data connecting domain models to data containers

  "A Mapping connects nodes in a subgraph of the domain model to nodes in a subgraph of a container representation"

• Enable discovery by walking knowledge graph to find concept materializations

• Support intent-based automation for data movement

Projections

• Produce concrete data containers (GraphQL schemas, Data Mesh sources)

  "Each projection is a concrete realization of Upper's denotational semantics, ensuring semantic interoperability across all containers"

• Transpilation targets: GraphQL, Avro (Data Mesh flavor)

• Some containers auto-populated (Iceberg Tables) via Data Mesh platform

Early Adopters

Primary Data Management (PDM)

• Single place for business users to manage controlled vocabularies

• Uses SKOS (Simple Knowledge Organization System) W3C standard

  "PDM uses Domain Models to integrate SKOS into the rest of Content Engineering's ecosystem"

• Auto-generates: UI, Domain Graph Service, GraphQL APIs, Data Mesh pipelines, warehouse data products

Sphere

• Self-service operational reporting tool

  "Instead of specifying exact tables and join keys, users simply can search for familiar business concepts such as 'actors' or 'movies'"

• Uses UDA knowledge graph for query generation via graph traversal

• Identifies join strategies, boundaries, and islands in data landscape

Future Directions

• Protobuf/gRPC projections
• Materializing knowledge graph of instance data
• Solving Graph Search challenges

Key References

• Netflix Enterprise GraphQL Gateway
• Data Mesh Platform
• Data Movement via Data Mesh
• Domain Graph Service Framework
• UDA GitHub Repository

isn't this good? thinking about how people will respond to what you put in the public before you do it?

yeah but nielsen prototyped all this reactive surveillance

eh? its the profit model and the availability to be remote controled by wealthy interests

Butters, Roland W. 1921. “The Science of the Filing Engineer.” Filing & Office Management 6(7): 193–94. https://www.google.com/books/edition/Filing_Office_Management/o1rnAAAAMAAJ?hl=en&gbpv=1&pg=PA193&printsec=frontcover&dq=duplex.

Alphabetic filing accounts for 95% of filing systems according to Butters.

This is likely the reason why most commonplacers using index card systems use alphabetic set ups by subject rather than Niklas Luhmann's duplex numeric variation.

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The use of the cross reference sheet

A portion of the reason why Luhmann chose a duplex numeric filing system for his zettelkasten.

The numeric arrangement elim-inates the decimal feature and is used when the number of main divisions is likely to exceed nine, a primary and

This is exactly Niklas Luhamann's zettelkasten numbering system.

Dillard, J. L. 1919. “Subject and Classification Filing: Classification of Files in the U.S. Explosive Plant at Nitro, W. Va.” Filing: A Magazine on Indexing & Filing 3(2): 401–3. https://www.google.com/books/edition/Filing/nxFLAAAAYAAJ?hl=en&gbpv=1&pg=PA401&printsec=frontcover&dq=duplex (December 2, 2025).

drawbacks of alphabetic and numeric systems with respect to cross referencing

Luhmann actively used red colored pencils in his cross referencing system, a practice that was suggested by many indexing and filing textbooks of his era.

In the Numeric system of subject filing, consec-

advantages of duplex numeric systems over decimal systems

Lennig, Margaret Antoinette. 1920. Filing Methods: A Text Book on the Filing & Indexing of Commercial & Government Records. https://www.google.com/books/edition/Filing_Methods/vVagv_GyENwC?hl=en&gbpv=0.

Luhmann would likely have been aware of duplex numeric systems of filing from legal and governmental filing work. It's not a difficult jump to go from client to subject matter to keep ideas bundled "in one place."

expanding numeric systems is quite easy

Numeric systems can be expanded to duplex num.eric systems

Notice the one-to-one correspondences between these systems.

There is a sort of artificial lack of extension, but numerically it really doesn't exist mathematically.

Hudders, Eugene Russell. 1919. Indexing and Filing: A Manual of Standard Practice. 5th ed. New York: The Ronald Press Company. https://www.google.com/books/edition/Indexing_and_Filing/p_MRAAAAYAAJ?hl=en&gbpv=0.

need a community to support us and keep us in check to prevent us from becoming lonely and falling into the tempatations of satan

goal of this is to become humble and be dependent on God rather than our material pleasures

one must abstain from lowly matters that concern the material world so one could be attentive to the greater askings of God

to achieve true freedom one must have a personal conversation with the Lord to share ones experiences

a pefect man is one who turns their eyes to the Lord and remembers that they can be more by the power of God and those around him

News values of conflict and unusualness of the matter

Crafts a narrative that Pope Leo is a reform-minded leader

Sources used in this article in particular include clergy and Vatican officials. Missing voices are critics, non-Catholics, and marginalized groups. Many of the same things I have seen in past articles.

NCB Chicago uses the news values we talked in class, timeliness and symbolism to mark the moment.

Fact-based reporting, reputable news source. Their source is neutral, but the only problem with that is they might not provide much analysis to the story...

The headline from NBC 4 Chicago positions the story as a quick explainer. It talks about both his U.S. roots and Peruvian ties. It strategically balances national pride with global identity.

I would describe social media as functional and dysfunctional

Local TV coverage uses vivid imagery and metaphors to dramatize the moment.

Shows the news values of prominence for the matter. It's not every day you have a new pope picked, and the pope is a huge job as he's responsible for 1.4 billion Catholics. Additionally, conflict is portrayed, linking his papacy to civic pride and political symbolism.

Sources include clergy, analysts, politicians, and parishioners. Missing voices are critics or marginalized groups.

Shows timeliness of the matter with the recent passing of Pope Francis, and impact. It marks the transition from mourning Francis to celebrating Leo XIV.

Puts a spotlight on news values such as proximity and human interest, connecting his papacy to everyday Chicago identity.

The headline puts an emphasis both on local pride and global significance. It frames the story as a mix of biography and breaking news.

What I find interesting is that anxiety about new technologies has almost recurred throughout history. When Thamus in Plato’s story said that writing would make people “look wise but actually know nothing,” is strikingly similar to the current assessment of AI that it makes us appear knowledgeable, but in reality know nothing. Each generation believes that technology is disrupting our attention and our life, the pattern is almost always the same.

The duplex numeric method of filing is designed for use when the number of primary or main subjects exceeds 10 and the second sub-

Specifically note the "is not advisable" portion, particularly with respect to N. Luhmann's practice.

overguiding: the activity of using more card index dividers than might be absolutely necessary

subject indexes are considered more useful than the duplex name indexes.

west anno

what is ts twin

This quote shows that a strike isn’t just people not showing up to work — it only becomes a “strike” when the workers themselves understand their actions as a deliberate protest. The author is saying that events can’t be described accurately unless we include the perspective and intentions of the people involved. Their subjectivity (their thoughts, motivations, and awareness) is part of the event itself. Without that, the description is incomplete.

Here, the author is criticizing America for only practicing democracy on paper, not in real life. She’s saying that giving people the right to vote is not enough if they are still treated unfairly, ignored, or left in poverty. This suggests that social conditions like where people live, who they interact with, and what opportunities they have matter just as much as political rights. The evidence highlights how democracy is incomplete when it doesn’t reach people’s daily lives.

my annotation

wait kinda like how freud talked about the pros and cons of western religion, being that the restriction and servitude to a higher power and the guilt that comes with that is the price you pay for the guiding principles and community

uml wiki annotation

second annotation

first annotation 1

This is a concise, memorable statement that provides a definitive link between the material (food/wealth) and the spiritual/intellectual (thinking). Explain the powerful, slightly humorous imagery of "The lamp in the spine" as the internal light of inspiration or vitality, and how the plain, poor meal (the "beef and prunes") at the women's college fails to fuel this "lamp."

i found most of this one to be not as good as the others but i do like this ending part.