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    HDAC5 depletion promotes hyper-acetylation of FOXA1 and potentiates HIF1α transcriptional activation in pancreatic cancer
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    Butyrate causes loss of specific acetylation by the histone acetyltransferase p300
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    Transcription factor BHLHE40 expression in group 3 innate lymphoid cells and RORγt⁺ antigen-presenting cells coordinates intestinal immunity
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    HPV16E7-Specific Affitoxin Induces GSDME-Mediated Pyroptosis via a Caspase-3-Independent Pathway
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    From cold to responsive: IFN-α treatment is associated with collagen VI remodeling and enhanced anti-PD-1 efficacy in Well-/dedifferentiated liposarcoma
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    Proprioceptive cortical neurons implement optimal state estimation
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    4D Single-Cell Spatial Transcriptomics Reveals Dynamic Morphogenetic Gradients and Regenerative Domains in Planarians
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    1. GigaScience 10 Jul 2026
      AbstractRegeneration relies on precise spatiotemporal gene expression and cellular responses to establish tissue identity and body patterning. Using high-resolution Stereo-seq (715 nm) on 353 sections from 16 whole animals at 8 regeneration timepoints, we constructed a 4D spatiotemporal transcriptomic map of planarian regeneration. Our analysis captured 36 refined cell types from 3,508,004 segmented cells, enabling genome-wide transcriptional imputation of gene expression dynamics across body axes at cellular, tissue, and organismal scales. We identified dynamic positional gradients and distinct spatially distributed cell types during regeneration, including an injury-induced Anterior Regenerative Zone (ARZ). The ARZ exhibited enriched positional signals in epidermal, muscle, and neural cells and was regulated by Mediator 8, which is crucial for polarity remodeling and blastema formation. This study provides a comprehensive spatial molecular and cellular map of regenerative processes, highlighting injury-induced spatial domains and key regulatory factors in planarian regeneration. We also provide an interactive web portal, offering a valuable resource for exploring and analyzing regeneration mechanisms in a spatiotemporal context.

      This work has been peer reviewed in GigaScience (see https://doi.org/10.1093/gigascience/giag064), which carries out single-anonymized peer review. These reviews are published under a CC-BY 4.0 license and were as follows:

      Reviewer 1：

      The authors employs high-resolution Stereo-seq technology combined with multi-timepoint spatial transcriptomic data to construct a 4D spatiotemporal transcriptomic map of planarian regeneration. This work significantly advances the understanding of spatial gene expression dynamics during planarian regeneration, overcoming the limitations of traditional two-dimensional and planar spatial transcriptomics. Furthermore, the authors identify a novel injury-indced Anterior Regenerative Zone (ARZ) and, through functional validation of Mediator 8 (Med8), deepen insights into the mechanisms underlying polarity remodeling in planarians. The study also provides an interactive online database, enriching spatial molecular and cellular data resources for the regenerative biology. The work is notably innovative, and the authors present convincing evidence supporting their conclusions. The manuscript overall is written well and data is presented clearly. The discussion and conclusions has done well to highlight the potential problems in this study. I have a few points that should be addressed before publishingI have a few points that should be addressed before publishing.

      1.In lines 233-236, it is reported that the positional control gene (PCG) like ndk restores its spatial expression pattern as early as 12 hpa, whereas its expression level only significantly increases at 36 hpa. Given this pronounced temporal discordance between early recovery of spatial patterning and the later peak in mRNA levels, the authors should analyze and discuss possible molecular mechanisms that could account for this discrepancy, and consider the biological implications of this phenomenon for understanding how spatial information and gene-expression regulation are coordinated during regeneration.

      2.In lines 356-360, Med8 knockdown markedly reduces the ARZ cell lineages and the expression of anterior-posterior polarity markers (e.g., sfrp-1, wnt1, wnt11-1), producing a clear effect on regeneration polarity formation.However no gross disruption of the whole-body AP axis was observed. Please further analyze and discuss the possible regulatory scope and mechanisms of Med8. Specifically, do other redundant pathways or compensatory mechanisms exist in planarians that maintain global positional information despite loss of Med8? What is the hierarchical and cell-type specificity of Med8's role in polarity regulation? Transfer Authorization Response

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    biorxiv.org/content/10.64898/2026.02.18.706529v1
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    PathoFact 2.0: An Integrative Pipeline for Predicting Antimicrobial Resistance Genes, Virulence Factors, Toxins and Biosynthetic Gene Clusters in Metagenomes
    3
    1. GigaScience 10 Jul 2026
      AbstractSummary Antimicrobial resistance genes (ARGs) and virulence factors (VFs) are central contributors to the global health crisis surrounding drug-resistant infections. PathoFact, a bioinformatics pipeline introduced in 2021, provides insights into ARGs, VFs, and bacterial toxins from metagenomic data. However, recent advancements in bioinformatics highlight the need for an updated version of PathoFact. We introduce PathoFact 2.0, an enhanced pipeline for improved ARG, VF, and toxin prediction. Key updates include an updated machine learning (ML) model for VF identification, a new ML model for toxin identification, expanded hidden Markov model profiles, and the antiSMASH 7.0 integration for predicting biosynthetic gene clusters. These upgrades make PathoFact 2.0 a more powerful, user-friendly platform for predicting microbiome-based pathogenicity and resistance, offering a crucial tool for better understanding and addressing the challenges posed by antimicrobial resistance and infectious diseases.

      This work has been peer reviewed in GigaScience (see https://doi.org/10.1093/gigascience/giag062), which carries out single-anonymized peer review. These reviews are published under a CC-BY 4.0 license and were as follows:

      Reviewer 3：

      Several methods are available to predict ARGs, VFs, Toxins, and Biosynthetic Gene Clusters. However, the authors selected only a few tools to benchmark PathoFact 2.0. I find this point lacking in the manuscript. To be useful to the scientific community, a more rigorous performance evaluation is needed. It is not fully clear how the "false" sequences were chosen. Ideally, they should be similar to known resistance genes, but should not confer resistance. Details of the parameters used to create the HMM models are not mentioned in the manuscript. The performance of the updated HMMs in comparison to the older version is not shown. It would be interesting to show how updates in DeepARG, RGI, and AMRFinderPlus have improved the performance of PathoFact 2.0 over version 1.0. *I believe the non-pathogenic dataset was constructed using sequences other than those mentioned in the section "Generalities about Machine learning training set-up and 'non-pathogenic". This means that sequences that do not contain the mentioned keyword were used as the negative dataset. These sequences include housekeeping genes, which are also too distant from the ARG, VF, etc. The real test of an ML model occurs with data from the grey zone, which has properties of both negative and positive examples. The authors can benchmark the ML model using the grey-zone data to show the efficiency of the ML model.

      Based on the above-mentioned points, I recommend for major revision of the manuscript.

    2. GigaScience 10 Jul 2026
      AbstractSummary Antimicrobial resistance genes (ARGs) and virulence factors (VFs) are central contributors to the global health crisis surrounding drug-resistant infections. PathoFact, a bioinformatics pipeline introduced in 2021, provides insights into ARGs, VFs, and bacterial toxins from metagenomic data. However, recent advancements in bioinformatics highlight the need for an updated version of PathoFact. We introduce PathoFact 2.0, an enhanced pipeline for improved ARG, VF, and toxin prediction. Key updates include an updated machine learning (ML) model for VF identification, a new ML model for toxin identification, expanded hidden Markov model profiles, and the antiSMASH 7.0 integration for predicting biosynthetic gene clusters. These upgrades make PathoFact 2.0 a more powerful, user-friendly platform for predicting microbiome-based pathogenicity and resistance, offering a crucial tool for better understanding and addressing the challenges posed by antimicrobial resistance and infectious diseases.

      This work has been peer reviewed in GigaScience (see https://doi.org/10.1093/gigascience/giag062), which carries out single-anonymized peer review. These reviews are published under a CC-BY 4.0 license and were as follows:

      Reviewer 2：

      The authors present the pipeline PathoFact2.0, which combines external modules and machine learning algorithms in order to find genes that provoke antimicrobial resistance, virulence and toxicity. They present their work, the improvement from the previous version, as a Technical Note. For what the authors say, it is the only pipeline available with those characteristics, which makes it clearly a relevant software and article. However, I believe the article requires refinement, as well as new tests that support the authors claims.

      Refinements on the article In the abstract, ARG is used as an abbreviation for Antimicrobial Resistance, not Antimicrobial Resistance Genes. Overall, the article should be more clear in what the pipeline is made for. It mentions fungi, viral, etc… sequences (which might be found on a metagenomic sample, of course), but to my understanding, all the tools and phenotypes searched for are mostly characteristic of bacteria. While the introduction offers a good resume of the genes of interests, there are some descriptions that are not particularly accurate. "Human, animal, and environmental microbiomes harbour commensal and pathogenic microorganisms, contributing to the emergence of infectious diseases" seems to say that commensal microorganisms contribute to the emergence of infectious diseases; "ARGs are genetic elements that confer bacterial resistance to antibiotics, acquired via mutations or horizontal gene transfer." seems to say that antimicrobial resistance genes are acquired via mutation (they are not, there is a difference between resistance to antimicrobials provoked by mutations and by genes). I recommend a thorough rewriting of the 6 first paragraphs. The graphs in Figures 1 and 2 have different Y axes, which are also not shown. This is, to say it lightly, very misleading. Table 1 would be much more clear as a Figure. Table 1 cited in line 211 does not exist. A short description of "dereplication" would help users lacking that knowledge. The description of the parameters of the machine learning modules are a copy-paste of the variables used by scikit-learn (lines 248 and 268). The description of the machine learning models should be clearer and more detailed, as well as not force the reader to go to the instructions of scikit-learn to check what is the meaning of those parameters. The authors analyze the performance of the models depending on the "probability" (a term that could definitely use a better introduction) using barplots. A standard to analyze the probability of a model is a ROC curve. Improvements The claims of the authors about the machine learning models for VF and toxin prediction being more accurate than similarity models is, to my understanding, not proved in the article. If it is only compared to PathoFact, which was created with a dataset made years ago, the higher performance could easily be because of more complete datasets. A fair comparison of an improved performance should be done with the same dataset (PathoFact but with the dataset collected for PathoFact2.0). Moreover, the results only show that PathoFact2 predicts more toxins and virulence factors than PathoFact. The creation of the dataset, for training and most importantly for testing, is rather unclear and described all over the article. I recommend creating a section for it, to understand better the filtering, maybe a figure (could go in the supplementary material, if necessary), and include the amount of data in each test set. The authors seem to have put a lot of effort on the testing sets (including trying to avoid testing with the same data that the models are trained with) but it gets diluted in the article and, in consequence, the test results are difficult to evaluate. The results against VirulentHunter are impressive, outperforming a fine-tuned language model. While I do not doubt that the authors are thorough in their methods, such claims require more testing. Testing using external databases (not created by the authors, maybe the same used by VirulentHunter or other models validated experimentally such as pLM4VF) would support such claims. The comparative on different bacterial strains gives more questions than answers. Are all those VF and toxins found on E. coli experimentally validated? How much overlap is there between pathogenic and non-pathogenic E. coli? Are all of the same type?

      Overall, there is a good amount of work on this project, but the article still has a lot of unanswered questions. It is a bit unclear the strengths of PathoFact2, as well as its weaknesses (any model has). Could be its speed, could be having plenty of tools contained in a pipeline. I would also appreciate a better description of the report that PathoFact2.0 produces. If its strength is the virulence and toxin prediction, more tests must be performed (as described above). This would be very beneficial for possible users of the model. Moreover, in a more technical note, I recommend the authors to add a test sample for easy testing of the model in their repository.

    3. GigaScience 10 Jul 2026
      AbstractSummary Antimicrobial resistance genes (ARGs) and virulence factors (VFs) are central contributors to the global health crisis surrounding drug-resistant infections. PathoFact, a bioinformatics pipeline introduced in 2021, provides insights into ARGs, VFs, and bacterial toxins from metagenomic data. However, recent advancements in bioinformatics highlight the need for an updated version of PathoFact. We introduce PathoFact 2.0, an enhanced pipeline for improved ARG, VF, and toxin prediction. Key updates include an updated machine learning (ML) model for VF identification, a new ML model for toxin identification, expanded hidden Markov model profiles, and the antiSMASH 7.0 integration for predicting biosynthetic gene clusters. These upgrades make PathoFact 2.0 a more powerful, user-friendly platform for predicting microbiome-based pathogenicity and resistance, offering a crucial tool for better understanding and addressing the challenges posed by antimicrobial resistance and infectious diseases.

      This work has been peer reviewed in GigaScience (see https://doi.org/10.1093/gigascience/giag062), which carries out single-anonymized peer review. These reviews are published under a CC-BY 4.0 license and were as follows:

      Reviewer 1：

      The pipeline should be very useful on shotgun metagenomics data analysis. Aside the ARGs and VFs, the features on signal peptides, toxin predictions, and BGCs in particular for specialised metabolites predictions, are welcome for detailed analysis and understanding of various transmission mechanisms. I find the approaches very appealing and I think the pipeline could be welcomed by the community.

      I only have some minor observations: - The Methods section should be placed next to the described methods. As it is, at the end of the manuscript, under Methods chapter you can only find Datasets, so a proper formatting of the Methods is required - there is a 70 blank pages buffer between References and supplementary data - could you add some future prospects in the manuscript? How well is it going to be maintained - I noticed the update are quite old. - I would also add some more details to the limitations. For instance it is clear that the pipeline is installable on Linux platforms, but did you considered making it available also for Apple silicon series? More and more researchers use this technology, and it works as good as the Linux distributions. I also tried an install on a M series Apple silicon, but unfortunately, most of the tools in the pipeline lead to multiple errors related to python versions (most of which are old), missing old dependencies versions, libraries, etc.

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    biorxiv.org/content/10.1101/2024.12.09.627531v1
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    Où manger dans le Vignoble Nantais, toutes les bonnes adresses tables ! | Destination Vignoble Nantais
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    IPython Magic Commands | Python Data Science Handbook
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    4. ngoclinh 10 Jul 2026
      %run

      chỉ chạy cái .py , không chạy cái .ipynb( thật ra cái này dịch là ipython notebook) -> nó chạy kết quả của phần <tệp>.py ra thôi

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    Congenital aphantasia reveals frontotemporal and cingulate structural alterations underlying conscious access to imagery
    5
    1. Public_Reviews 10 Jul 2026

      eLife Assessment

      This valuable study provides novel evidence that congenital aphantasia is associated with structural differences in frontotemporal and cingulate systems, with relative sparing of early visual regions and major visual pathways. The multimodal structural imaging approach is carefully implemented and will be of interest to researchers studying mental imagery and aphantasia. However, the strength of evidence is incomplete because the data cannot adjudicate between alternative cognitive interpretations, and the multiple discovery streams make the findings better viewed as key exploratory evidence, rather than as establishing a definitive structural phenotype of aphantasia.

      Summary
    2. Public_Reviews 10 Jul 2026

      Reviewer #1 (Public review):

      Summary

      In this paper, the authors provide a systematic investigation of structural brain differences associated with congenital aphantasia (self-reported lifelong absence of voluntary visual imagery). Specifically, the authors analysed a structural neuroimaging dataset involving 18 individuals with aphantasia and 18 visualizers to test two competing hypotheses: (1) that aphantasia reflects alterations in visual pathways and early visual cortex, and (2) that it instead reflects differences in higher-order frontotemporal and cingulate systems. To test these hypotheses, the authors employed multiple analysis approaches (e.g., cortical morphometry, tractometry, graph-theoretic network analysis).

      They report structural differences between the two groups in frontotemporal and cingulate systems. In contrast, they found no reliable group differences in early visual cortex or major visual tracts. On this basis, they propose that aphantasia is primarily associated with differences in higher-order systems supporting integration and conscious access to internally generated representations, rather than with deficits in sensory visual representations themselves.

      Strengths

      (1) The present work addresses an important gap in the mental imagery literature, providing a systematic investigation of structural neuroimaging differences in congenital aphantasia. By showing that structural differences between aphantasics and visualizers are mainly concentrated in frontotemporal and cingulate systems (rather than in visual cortex), it makes an important step toward a better understanding of individual differences in mental imagery and provides a set of candidate regions for future mechanistic work.

      (2) A key strength of the study is the multimodal approach employed to address the main research question, integrating tractometry, functional region-of-interest (fROI)-based tractography, graph-theoretic network analysis, and surface-based cortical morphometry, which provide a converging assessment of structural differences between aphantasics and visualizers.

      (3) The complementary use of Bayesian analyses alongside NHST to assess evidence for null results is a further strength of this work.

      Weaknesses:

      (1) A weakness of this work is related to aspects of the framing and, in particular, what can be confidently inferred from the results. The framing of existing accounts of aphantasia in the Introduction appears limited in that it reduces the views on aphantasia to two options (sensory strength account versus conscious access account) without acknowledging a third distinct position, namely that aphantasia reflects a specific deficit in the voluntary generation of imagery (Milton et al., 2021; Zeman et al., 2015, 2020; Whiteley, 2021; Cavedon-Taylor, 2022). Like the conscious access account, the view that aphantasia involves a deficit in the generation of sensory representation also speaks against the hypothesis of reduced sensory strength of internally generated representations. This third view could be acknowledged/discussed as it also maps quite well onto the presented results.

      (2) Relatedly, I think the main weakness of the paper concerns the interpretation of results being restricted to a lack of "conscious access". The paper frames its findings as mainly evidence for a conscious access failure, the view that visual representations are generated by aphantasics but cannot be consciously accessed. However, the structural findings are equally consistent with a voluntary generation failure, especially since the same higher-order regions examined can also be implicated in the top-down generation and control of imagery. The authors themselves initially define aphantasia as "lifelong absence of voluntary visual imagery". Given the nature of structural imaging data (as opposed to functional data), it is not possible with the present study to distinguish between a lack of generation versus a lack of conscious access. As such, examining this alternative interpretation appears appropriate, and it would considerably strengthen the paper. Structural MRI alone is not sufficient to dissociate imagery generation from conscious access, as these are fundamentally functional questions.

      (3) Some inconsistency and lack of clarity around the specific choice of regions/networks, which could be better motivated and explained. E.g., the "core imagery network" analysed in the white-matter connections analysis was derived from a previous 7T study (with which the sample partially overlaps) and is not necessarily the network most commonly associated with visual imagery in the literature (e.g., see Dijkstra et al., 2019; Pearson, 2019). It is, for instance, unclear why V1 was examined in the cortical thickness analysis but not in the previous one, given that both analyses are related to the visual pathway hypothesis. Related to this, in the graph-theoretic analysis, the rationale for network selection is inconsistently established in the Introduction. The attention and salience networks do have some grounding in the Introduction through the mention of specific regions such as FEF and anterior insula, though these are discussed as individual regions rather than as networks. However, the default mode network receives no motivation in the Introduction. More explicit elaboration on these choices would be appropriate.

      (3) The interpretation provided in the Discussion tends to oversimplify what is in fact a heterogeneous and rich set of structural findings into a relatively coherent mechanistic account. The observed differences are spatially and directionally variable across tracts, cortical regions, and metrics: e.g., FA is reduced in the UF and posterior interparietal corpus callosum but increased in the dorsal cingulum; cortical thickness is reduced in aPFC but increased in medial temporal regions, and so forth. The Discussion acknowledges this in part (e.g., proposing increased dorsal cingulum FA as potentially compensatory) but does not address the directional heterogeneity systematically. The authors could discuss more explicitly what the opposing directions of effects mean for their overall interpretation. Relatedly, some parts of the Discussion link specific structural findings to specific imagery processes in ways that go beyond what the current data can support. The authors could more clearly distinguish between what the structural data show and what functional interpretations are taken from prior work.

      Review 1
    3. Public_Reviews 10 Jul 2026

      Reviewer #2 (Public review):

      Summary:

      This paper addresses whether congenital aphantasia reflects an alteration of visual representations themselves, or rather of the systems that allow internally generated representations to reach conscious experience.

      Strengths:

      The study is novel and ambitious. The authors combine several complementary structural MRI approaches in a rare and well-characterised population, and the convergence of the findings toward frontotemporal and cingulate systems, with relative sparing of early visual cortex and major visual pathways, is particularly interesting because it could affect the way visual imagery is modelled and tested experimentally and clinically.

      Weaknesses:

      Overall, I found the manuscript conceptually and methodologically strong. My main concern regards the interpretation of the anatomical findings, rather than the findings per se. The authors discuss their results within a rich cognitive framework. However, the current dataset does not appear to include independent behavioural or neuropsychological measures that would allow the proposed cognitive interpretation to be tested in the same participants. As a result, the manuscript sometimes moves quite rapidly from 'these structural differences involve systems associated with higher-order control, salience, conscious access' to 'these structural differences may explain the cognitive mechanisms of aphantasia'. I agree that this is the most interesting interpretation, and probably the right one to explore. Although plausible, it remains indirect. The authors already acknowledge this point when discussing memory, affective control, and semantic processing. However, the same logic should be extended to the interpretation of the full set of findings. For example, if the salience/anterior insula findings are interpreted in relation to access to internally generated representations, it would be useful to know whether aphantasic participants also differ behaviourally on tasks tapping interoception or related aspects of internal monitoring. I appreciate that collecting additional behavioural data may not be feasible at this stage, especially given the difficulty of recruiting participants with such a specific manifestation. However, I think it should be acknowledged more explicitly in a dedicated limitation paragraph.

      Review 2
    4. Public_Reviews 10 Jul 2026

      Reviewer #3 (Public review):

      Summary:

      The authors investigate the structural brain basis of congenital aphantasia, a condition characterised by a lifelong absence of voluntary mental imagery. They test two competing accounts: one predicting structural differences in early visual pathways, the other predicting differences in higher-order frontotemporal and cingulate systems. To do this, they combine four complementary structural imaging approaches: white-matter microstructure profiling along anatomically defined tracts, tractography seeded from functional regions of interest, whole-brain structural network analysis, and cortical thickness mapping. The main finding is that white-matter differences are selective for frontotemporal and cingulate pathways and absent in early visual pathways, which the authors interpret as support for the higher-order account.

      Strengths:

      The multi-modal design is a genuine strength: running four independent analyses increases the chance of detecting real effects and of identifying false positives that appear in only one stream. The statistical choices within each analysis are appropriate. Permutation-based correction with a threshold-free method is well-suited to the tract-level comparisons. The use of Bayes factors to quantify evidence for null results, rather than simply reporting non-significant tests, is particularly valuable here, since the absence of visual pathway differences is central to the argument. The robustness checks across multiple brain parcellations for the network analysis strengthen confidence in those findings.

      Weaknesses:

      The main limitation concerns the relationship between two of the analysis streams. The measure used to weight structural connections in the network analysis is calibrated to match fiber density estimates derived from the same diffusion signal that drives the white-matter microstructure differences. If the two groups differ in tissue organisation in certain pathways (which the microstructure analysis suggests they do), that difference will feed into both measures. The authors should acknowledge this dependency when discussing convergence across analyses.

      More broadly, the imaging metrics used throughout (measures of fiber organisation and weighted connection counts) reflect what the diffusion model captures from the tissue and cannot be directly read as measures of axon number or connection strength. This is a known limitation of the field, but it is relevant to the strength of structural claims made in this paper.

      The network analysis is presented without comparison to a null network. Without this, it is hard to know whether the node-level differences reflect specific network topology or simply follow from overall differences in connectivity weight or density between groups.

      The study runs four separate discovery analyses on the same 36 participants, each corrected within itself but with no control across analysis streams. At 18 participants per group, this is exploratory work. Some of the language used in the abstract and discussion, like "first comprehensive characterization" and "selective structural phenotype", reads as more definitive than the data support at this sample size. Framing the results as hypotheses to be replicated would make the paper stronger.

      The paper frames the results as distinguishing between two competing accounts. The positive evidence for the higher-order account is clear. The absence of differences in visual pathways is a different kind of result: it means such differences were not detected in this sample, not that visual pathways are uninvolved. The discussion at times moves toward that stronger conclusion, which the data do not support.

      The cortical thickness analysis finds one cluster in the predicted direction, while the other analyses each return multiple effects. One cluster in a whole-brain search with 18 participants per group is not strong evidence and should not be presented as equivalent to the other results.

      Effect sizes are reported without confidence intervals throughout. With 18 participants per group, the uncertainty around those estimates is large, and confidence intervals would give readers a more accurate sense of what can be concluded.

      Review 3
    5. Public_Reviews 10 Jul 2026

      Author response:

      Reviewer #1 (Public review):

      Summary:

      In this paper, the authors provide a systematic investigation of structural brain differences associated with congenital aphantasia (self-reported lifelong absence of voluntary visual imagery). Specifically, the authors analysed a structural neuroimaging dataset involving 18 individuals with aphantasia and 18 visualizers to test two competing hypotheses: (1) that aphantasia reflects alterations in visual pathways and early visual cortex, and (2) that it instead reflects differences in higher-order frontotemporal and cingulate systems. To test these hypotheses, the authors employed multiple analysis approaches (e.g., cortical morphometry, tractometry, graph-theoretic network analysis).

      They report structural differences between the two groups in frontotemporal and cingulate systems. In contrast, they found no reliable group differences in early visual cortex or major visual tracts. On this basis, they propose that aphantasia is primarily associated with differences in higher-order systems supporting integration and conscious access to internally generated representations, rather than with deficits in sensory visual representations themselves.

      Strengths:

      (1) The present work addresses an important gap in the mental imagery literature, providing a systematic investigation of structural neuroimaging differences in congenital aphantasia. By showing that structural differences between aphantasics and visualizers are mainly concentrated in frontotemporal and cingulate systems (rather than in visual cortex), it makes an important step toward a better understanding of individual differences in mental imagery and provides a set of candidate regions for future mechanistic work.

      (2) A key strength of the study is the multimodal approach employed to address the main research question, integrating tractometry, functional region-of-interest (fROI)-based tractography, graph-theoretic network analysis, and surface-based cortical morphometry, which provide a converging assessment of structural differences between aphantasics and visualizers.

      (3) The complementary use of Bayesian analyses alongside NHST to assess evidence for null results is a further strength of this work.

      Weaknesses:

      (1) A weakness of this work is related to aspects of the framing and, in particular, what can be confidently inferred from the results. The framing of existing accounts of aphantasia in the Introduction appears limited in that it reduces the views on aphantasia to two options (sensory strength account versus conscious access account) without acknowledging a third distinct position, namely that aphantasia reflects a specific deficit in the voluntary generation of imagery (Milton et al., 2021; Zeman et al., 2015, 2020; Whiteley, 2021; Cavedon-Taylor, 2022). Like the conscious access account, the view that aphantasia involves a deficit in the generation of sensory representation also speaks against the hypothesis of reduced sensory strength of internally generated representations. This third view could be acknowledged/discussed as it also maps quite well onto the presented results.

      (2) Relatedly, I think the main weakness of the paper concerns the interpretation of results being restricted to a lack of "conscious access". The paper frames its findings as mainly evidence for a conscious access failure, the view that visual representations are generated by aphantasics but cannot be consciously accessed. However, the structural findings are equally consistent with a voluntary generation failure, especially since the same higher-order regions examined can also be implicated in the top-down generation and control of imagery. The authors themselves initially define aphantasia as "lifelong absence of voluntary visual imagery". Given the nature of structural imaging data (as opposed to functional data), it is not possible with the present study to distinguish between a lack of generation versus a lack of conscious access. As such, examining this alternative interpretation appears appropriate, and it would considerably strengthen the paper. Structural MRI alone is not sufficient to dissociate imagery generation from conscious access, as these are fundamentally functional questions.

      (3) Some inconsistency and lack of clarity around the specific choice of regions/networks, which could be better motivated and explained. E.g., the "core imagery network" analysed in the white-matter connections analysis was derived from a previous 7T study (with which the sample partially overlaps) and is not necessarily the network most commonly associated with visual imagery in the literature (e.g., see Dijkstra et al., 2019; Pearson, 2019). It is, for instance, unclear why V1 was examined in the cortical thickness analysis but not in the previous one, given that both analyses are related to the visual pathway hypothesis. Related to this, in the graph-theoretic analysis, the rationale for network selection is inconsistently established in the Introduction. The attention and salience networks do have some grounding in the Introduction through the mention of specific regions such as FEF and anterior insula, though these are discussed as individual regions rather than as networks. However, the default mode network receives no motivation in the Introduction. More explicit elaboration on these choices would be appropriate.

      (4) The interpretation provided in the Discussion tends to oversimplify what is in fact a heterogeneous and rich set of structural findings into a relatively coherent mechanistic account. The observed differences are spatially and directionally variable across tracts, cortical regions, and metrics: e.g., FA is reduced in the UF and posterior interparietal corpus callosum but increased in the dorsal cingulum; cortical thickness is reduced in aPFC but increased in medial temporal regions, and so forth. The Discussion acknowledges this in part (e.g., proposing increased dorsal cingulum FA as potentially compensatory) but does not address the directional heterogeneity systematically. The authors could discuss more explicitly what the opposing directions of effects mean for their overall interpretation. Relatedly, some parts of the Discussion link specific structural findings to specific imagery processes in ways that go beyond what the current data can support. The authors could more clearly distinguish between what the structural data show and what functional interpretations are taken from prior work.

      We will add two recent in-press Cortex papers to the Discussion. One provides lesion-based double-dissociation evidence against V1 as a necessary causal substrate of visual imagery. The other shows that aphantasic individuals can display visualizer-like oculomotor patterns during mental map exploration despite reporting little or no imagery vividness. Together, these studies help clarify our interpretation of our null V1 findings and structural effects in higher-order brain regions, which are consistent with aphantasia involving altered integration or access rather than a primary V1-dependent imagery deficit.

      Reviewer #2 (Public review):

      Summary:

      This paper addresses whether congenital aphantasia reflects an alteration of visual representations themselves, or rather of the systems that allow internally generated representations to reach conscious experience.

      Strengths:

      The study is novel and ambitious. The authors combine several complementary structural MRI approaches in a rare and well-characterised population, and the convergence of the findings toward frontotemporal and cingulate systems, with relative sparing of early visual cortex and major visual pathways, is particularly interesting because it could affect the way visual imagery is modelled and tested experimentally and clinically.

      Weaknesses:

      Overall, I found the manuscript conceptually and methodologically strong. My main concern regards the interpretation of the anatomical findings, rather than the findings per se. The authors discuss their results within a rich cognitive framework. However, the current dataset does not appear to include independent behavioural or neuropsychological measures that would allow the proposed cognitive interpretation to be tested in the same participants. As a result, the manuscript sometimes moves quite rapidly from 'these structural differences involve systems associated with higher-order control, salience, conscious access' to 'these structural differences may explain the cognitive mechanisms of aphantasia'. I agree that this is the most interesting interpretation, and probably the right one to explore. Although plausible, it remains indirect. The authors already acknowledge this point when discussing memory, affective control, and semantic processing. However, the same logic should be extended to the interpretation of the full set of findings. For example, if the salience/anterior insula findings are interpreted in relation to access to internally generated representations, it would be useful to know whether aphantasic participants also differ behaviourally on tasks tapping interoception or related aspects of internal monitoring. I appreciate that collecting additional behavioural data may not be feasible at this stage, especially given the difficulty of recruiting participants with such a specific manifestation. However, I think it should be acknowledged more explicitly in a dedicated limitation paragraph.

      We thank the reviewer for this thoughtful and constructive comment. Lack of introspective report of voluntary imagery is arguably the defining signature of aphantasia. This motivated us to primarily interpret our anatomical findings in a broader cognitive context of higher-order control, internal monitoring, and conscious access in aphantasia. We expect that a reliable behavioural test measuring imagery sensitivity and accessibility would allow us to direct link these findings to individual imagery ability. Nevertheless, to our best knowledge, this kind of test on imagery is still missing. Instead, our findings point to some plausible structural signature or brain regions that may be related to conscious imagery, which motivate future studies to examine their direct or causal roles. We agree with the reviewer, future studies should test the relationship between these anatomical structures and the accessibility to internal representation, together with related aspects of internal monitoring. We will therefore add a dedicated paragraph to discuss the plausible cognitive mechanisms during the revision.

      Reviewer #3 (Public review):

      Summary:

      The authors investigate the structural brain basis of congenital aphantasia, a condition characterised by a lifelong absence of voluntary mental imagery. They test two competing accounts: one predicting structural differences in early visual pathways, the other predicting differences in higher-order frontotemporal and cingulate systems. To do this, they combine four complementary structural imaging approaches: white-matter microstructure profiling along anatomically defined tracts, tractography seeded from functional regions of interest, whole-brain structural network analysis, and cortical thickness mapping. The main finding is that white-matter differences are selective for frontotemporal and cingulate pathways and absent in early visual pathways, which the authors interpret as support for the higher-order account.

      Strengths:

      The multi-modal design is a genuine strength: running four independent analyses increases the chance of detecting real effects and of identifying false positives that appear in only one stream. The statistical choices within each analysis are appropriate. Permutation-based correction with a threshold-free method is well-suited to the tract-level comparisons. The use of Bayes factors to quantify evidence for null results, rather than simply reporting non-significant tests, is particularly valuable here, since the absence of visual pathway differences is central to the argument. The robustness checks across multiple brain parcellations for the network analysis strengthen confidence in those findings.

      Weaknesses:

      The main limitation concerns the relationship between two of the analysis streams. The measure used to weight structural connections in the network analysis is calibrated to match fiber density estimates derived from the same diffusion signal that drives the white-matter microstructure differences. If the two groups differ in tissue organisation in certain pathways (which the microstructure analysis suggests they do), that difference will feed into both measures. The authors should acknowledge this dependency when discussing convergence across analyses.

      More broadly, the imaging metrics used throughout (measures of fiber organisation and weighted connection counts) reflect what the diffusion model captures from the tissue and cannot be directly read as measures of axon number or connection strength. This is a known limitation of the field, but it is relevant to the strength of structural claims made in this paper.

      The network analysis is presented without comparison to a null network. Without this, it is hard to know whether the node-level differences reflect specific network topology or simply follow from overall differences in connectivity weight or density between groups.

      The study runs four separate discovery analyses on the same 36 participants, each corrected within itself but with no control across analysis streams. At 18 participants per group, this is exploratory work. Some of the language used in the abstract and discussion, like "first comprehensive characterization" and "selective structural phenotype", reads as more definitive than the data support at this sample size. Framing the results as hypotheses to be replicated would make the paper stronger.

      The paper frames the results as distinguishing between two competing accounts. The positive evidence for the higher-order account is clear. The absence of differences in visual pathways is a different kind of result: it means such differences were not detected in this sample, not that visual pathways are uninvolved. The discussion at times moves toward that stronger conclusion, which the data do not support.

      The cortical thickness analysis finds one cluster in the predicted direction, while the other analyses each return multiple effects. One cluster in a whole-brain search with 18 participants per group is not strong evidence and should not be presented as equivalent to the other results.

      Effect sizes are reported without confidence intervals throughout. With 18 participants per group, the uncertainty around those estimates is large, and confidence intervals would give readers a more accurate sense of what can be concluded.

      We are grateful to the Reviewer for the constructive and thoughtful assessment of our manuscript. In response to the reviewer’s comments, we will revise the manuscript to clarify the dependency between diffusion-derived analysis streams, to state more explicitly the biological limits of diffusion MRI metrics, to add a null-network sensitivity analysis for the clustering coefficient findings, to include confidence intervals for reported effect sizes, and to temper the interpretation of the cortical thickness result. We will also revise the Abstract and Discussion to better reflect the exploratory nature of the study and to frame the findings as hypotheses requiring replication in larger independent samples. We believe that these revisions will make the manuscript more balanced, transparent, and appropriately cautious, while preserving the central conclusion that congenital aphantasia is associated with structural differences centered on higher-order frontotemporal and cingulate systems.

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    biorxiv.org/content/10.64898/2026.03.22.713248v2
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    Gcn5 – mTORC1 – TFEB signalling axis mediated control of autophagy regulates Drosophila blood cell homeostasis
    4
    1. Public_Reviews 10 Jul 2026

      eLife Assessment

      This valuable study identifies Gcn5 as a regulator of blood cell development in the Drosophila lymph gland, with links to autophagy and nutrient-sensing mTORC1 signalling. The evidence is solid that altering Gcn5, autophagy genes and mTORC1 activity perturbs blood cell homeostasis, and the revised manuscript adds helpful genetic and quantitative analyses. However, the evidence for a clean linear Gcn5-mTORC1-TFEB/autophagy pathway is insufficient, because several cell-type-specific phenotypes remain difficult to reconcile and the pathway logic relies on different genetic tools, cell populations and pharmacological perturbations.

      Summary
    2. Public_Reviews 10 Jul 2026

      Reviewer #1 (Public review):

      In their manuscript Arjun et al. investigate the role of the histone acetyl transferase Gcn5 in controlling drosophila blood cell homeostasis in the larval lymph gland. Using gcn5 zygotic mutants as well as targeted knock-down and over-expression of Gcn5 in various lymph gland cell populations, they show that these manipulations impact (but in a rather haphazard manner) niche cell number, blood cell progenitor maintenance, plasmatocyte differentiation, crystal cell differentiation, DNA damage accumulation. Their results suggest that Gcn5 controls autophagy and show that reducing the expression of the autophagy machinery affect blood cell differentiation. By using drugs as well as genetic approaches to modulate the mTOR pathway, they conclude that Gcn5 levels are regulated by mTOR, but that the impact of this pathway on blood cell homeostasis can override Gcn5 function.

      Overall, the main conclusions are sound but interpreting several lines of experiments and results remain complicated. Consequently, the overall picture of the role of Gcn5 in Drosophila larval lymph gland development, and its relationship to mTOR and autophagy, remains unclear.

      Review 1
    3. Public_Reviews 10 Jul 2026

      Reviewer #2 (Public review):

      Summary:

      Drosophila haematopoiesis has been shown to be governed by a number of signalling pathways such as JAK/STAT and Dpp. This important study shows a role for nutrient sensing and autophagy in determining blood cell differentiation. The authors show that General control non-derepressible 5 (Gcn5), a histone acetyltransferase affects blood cell differentiation. Gcn5 also negatively regulates autophagy through its effector TFEB which directly regulates autophagy genes. The authors also show that mTORC1 modulates Gcn5 levels and through it TFEB activity thus acting as a fine-tuning mechanism which maintains optimal levels of autophagy.

      Strengths:

      The main strength of the work lies in the interesting finding that cellular metabolic processes such as autophagy has a direct role in blood cell differentiation and has the potential to be of interest to those working on vertebrate haematopoiesis as well. The report has generated intriguing data, using promoters specific for sub sections of the lymph gland, that different cellular subsets of the lymph gland contribute differently towards haematopoiesis, but this is not followed up in detail and the final conclusions are derived from a combination of whole lymph gland perturbations as well as those from specific promoters.

      Weakness:

      (1) Gc5 seems to be expressed throughout the lymph gland but modulating it in the subsections do not have the same result. It is very striking that the knockdown of Gcn5 in the prohemocyte population does not have an effect on differentiation whereas overexpression does. And the modulations of Gcn5 in PSC also has variable effects across hemocyte subpopulations which is not explored in the manuscript. Interestingly, also the domain deletion constructs show differential effect on blood cell differentiation when altered solely in the prohemocytes which is not explained. While Gcn5 can be seen in all sections of the lymph gland in the first figure, under the HHLT-Gal4 and Hml-Gal4, Gcn5 looks cytoplasmic and almost completely excluded from the nucleus strikingly unlike Gcn5 expression under the Collier-Gal4 and Dome-Gal4. The rest of the experiments in the manuscript are done with multiple promoters, with autophagy flux measured by modulating Gcn5 with a pan hemocyte promoter, but the mTORC1-Gcn5 axis is explored using chemical modulators which affect the whole of the lymph gland (Fig7) or using two pro-hemocyte promoters (Fig8).

      (2) The knockdown of Gcn5 seems to affect the gland size (A compared to B and C). Since mTORC1 is a central regulator of cell size, it is possible that some of the effects seen in these knockdowns are potentially through mTORC1 affecting size suggesting that the signalling axis between mTORC1 and Gcn5 might not be a one-way axis as suggested in Figure 9. Also, this would mean that in experiments where absolute cell counts of crystal cells or niche cells are used to assess blood cell differentiation, further analysis to consider total cell numbers in the lymph gland would strengthen the manuscript.

      (3) A genetic manipulation of mTORC1 specifically in the pro hemocytes would strengthen the role of mTORC1 in the pathway rather than the chemical modulation which affects the whole of the lymph gland.

      Comments on the revised manuscript:

      Overall, the revisions make the narrative more coherent. The authors have also added data which substantiates their conclusions.

      However, in some instances, the authors are not clearly able to explain the discrepancies in the data (Gen-5 depletions under the Hml-Gal4 in the whole larval lysates remove p62 completely) which is not ideal.

      A query regarding the discrepancies in the immunofluorescence data: The authors have removed the IF data which suggested that there could be differences in the shuttling of Gcn5 between the nucleus and cytoplasm. The authors suggest that immunofluorescence issues are at the root of these variable results, but the reviewer wonders whether there could be further unexplored mechanisms re: shuttling that is unexplored here and would have been potentially novel.

      Review 2
    4. Public_Reviews 10 Jul 2026

      Author response:

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

      Public Reviews:

      Reviewer #1 (Public Review):

      In their manuscript, Arjun et al. investigate the role of the histone acetyltransferase Gcn5 in the control of drosophila blood cell homeostasis in the larval lymph gland. They use gcn5 zygotic mutants as well as targeted knock-down and over-expression of Gcn5 in various lymph gland populations to show that these modulations impact (in a rather haphazard manner) niche cell number, blood cell progenitor maintenance, plasmatocyte differentiation, crystal cell differentiation or DNA damage accumulation. Their results suggest that Gcn5 controls autophagy and they show that decreasing the expression of the autophagy machinery increases blood cell differentiation. Using drugs to modulate the mTOR pathway, they conclude that Gcn5 levels are regulated by mTOR but that the impact of this pathway on blood cell homeostasis can override Gcn5 function.

      While the authors did a lot of experiments and good quantifications of the blood cell phenotypes, many results do not make much sense or do not bring valuable information about Gcn5 mode of action. Several conclusions of the manuscripts are not backed by solid data (e.g. that Gcn5 action is mediated by TFEB and the autophagy machinery) and different aspects of the literature are not well taken into consideration. Some results (such as the validation of the knockdown and overexpression of Gcn5) seem flawed. There are some concerns about the results obtained with gcn5 zygotic mutants and an interpretation of the phenotypes observed upon manipulation of Gcn5 expression in different cell types is missing.

      We have now performed several experiments to address the comments raised by the reviewer and have also provided possible explanation of the phenotypes in cases where it was lacking.

      Important revisions are needed to improve the quality of the manuscript and confirm the authors' findings.

      Reviewer #2 (Public Review):

      Summary:

      Drosophila hematopoiesis has been shown to be governed by a number of signaling pathways such as JAK/STAT and Dpp. This important study shows the role of nutrient sensing and autophagy in determining blood cell differentiation. The authors show that General control non-derepressible 5 (Gcn5), a histone acetyltransferase affects blood cell differentiation. Gcn5 also negatively regulates autophagy through its effector TFEB which directly regulates autophagy genes. The authors also show that mTORC1 modulates Gcn5 levels and through it, TFEB activity thus acting as a fine-tuning mechanism that maintains optimal levels of autophagy.

      Strengths:

      The main strength of the work lies in the interesting finding that cellular metabolic processes such as autophagy have a direct role in blood cell differentiation and has the potential to be of interest to those working on vertebrate haematopoiesis as well. The report has generated intriguing data, using promoters specific for sub-sections of the lymph gland, that different cellular subsets of the lymph gland contribute differently towards haematopoiesis, but this is not followed up in detail and the final conclusions are derived from a combination of whole lymph gland perturbations as well as those from specific promoters.

      Weaknesses:

      (1) Gc5 seems to be expressed throughout the lymph gland but modulating it in the subsections does not have the same result. It is very striking that the knockdown of Gcn5 in the prohemocyte population does not have an effect on differentiation whereas overexpression does. The modulations of Gcn5 in PSC also have variable effects across hemocyte subpopulations which is not explored in the manuscript.

      We have now explained and discuss why Gcn5 modulation could be affecting the PSC size. Please check Discussion section Paragraph 1 line 10 onwards.

      Interestingly, also the domain deletion constructs show a differential effect on blood cell differentiation when altered solely in the prohemocytes which is not explained.

      Currently, with our observations all that we can comment about that data is that expression of domain deletion mutants causes aberrant hematopoiesis indicating a dominant negative phenotype since they are expressed in the wild type genetic background. Beyond this, we will be exploring mechanistically how these domains are functioning during hematopoiesis in future studies. We have already described the dominant negative effect in the text: Discussion Section Paragraph 3.

      While Gcn5 can be seen in all sections of the lymph gland in the first figure, under the HHLT-Gal4 and Hml-Gal4, Gcn5 looks cytoplasmic and almost completely excluded from the nucleus strikingly unlike Gcn5 expression under the Collier-Gal4 and Dome-Gal4.

      We have now revised Figure 1 and have only included the images with Collier-Gal4 and Dome-Gal4 which clearly shows both the niche cells, Dome-positive progenitors and Dome-negative cells of the primary LG lobe essentially showing that Gcn5 is expressed throughout the primary LG lobe. In Fig. 1C-F’, Gcn5 expression is both in the nucleus and cytoplasm as this molecule shuttles between cytoplasm and nucleus. The staining pattern with the other Gal4 could be due to problems in the immunofluorescence protocol and acquisition parameters. We have now removed those images from Figure 1. Please check revised Figure 1.

      The rest of the experiments in the manuscript are done with multiple promoters, with autophagy flux measured by modulating Gcn5 with a pan hemocyte promoter, but the mTORC1-Gcn5 axis is explored using chemical modulators which affect the whole of the lymph gland (Fig7) or using two pro-hemocyte promoters (Fig8).

      We have used a pan-hemocyte promoter for the autophagy analysis to investigate if Gcn5 regulation over autophagy is a hemocyte specific effect which we indeed see. We have removed the western blot data now in the revised manuscript where we looked at Atg8 and p62 levels in whole larval lysates when Gcn5 was perturbed using hemocyte driver as the results were puzzling and difficult to comprehend given the complete absence of a p62 band in Gcn5 knockdown conditions. Also, it’s worth noting that Hml-Gal4 is also active in the LG hemocytes. We did 2 alternate promoters for prohemocytes to cross-validate some of our results and the chemical modulators experiment was done since effects like mTOR inhibition/nutrient sensing effects are systemic and hence such modalities were employed.

      (2) The knockdown of Gcn5 seems to affect the gland size (A compared to B and C). Since mTORC1 is a central regulator of cell size, it is possible that some of the effects seen in these knockdowns are potentially through mTORC1 affecting size suggesting that the signalling axis between mTORC1 and Gcn5 might not be a one-way axis as suggested in Figure 9. Also, this would mean that in experiments where absolute cell counts of crystal cells or niche cells are used to assess blood cell differentiation, further analysis to consider total cell numbers in the lymph gland would strengthen the manuscript.

      It is a possibility that Gcn5 perturbation could be affecting lymph gland size although we have not seen any consistent trend that would point towards this phenotype either upon knockdown or over-expression. We believe Gcn5 controls blood cell differentiation phenotypes strongly via mTORC1. But in order to answer reviewer’s comment we have now re-analyzed our crystal cell differentiation data particularly and quantitated it and represented it as crystal cell differentiation index for dome-Gal4 specific Gcn5 modulation and for the data with genetic modulation of mTORC1 pathway. Please see Fig 3P and S10J for the revised analysis.

      (3) A genetic manipulation of mTORC1 specifically in the pro hemocytes would strengthen the role of mTORC1 in the pathway rather than the chemical modulation which affects the whole of the lymph gland.

      We thank the reviewer for their useful critique. We have now addressed this concern and we have genetically perturbed the mTORC1 pathway in the progenitors using both abrogation of TORC1 via depletion of Tor or Raptor or by activation using over-expression of Rheb. We have now included this data as Supplementary figures – Fig S10 and S11 and have described it in the results section. Please see results section “Chemical or genetic modulation of mTORC1 activity controls blood cell differentiation” in the revised manuscript.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      The abstract could clearly be improved. It does not make a clear presentation of what is new in the manuscript. The conclusions that Gcn5 function in the lymph gland is mediated by the autophagy machinery and the acetylation of its non-histone target TFEB are not grounded and purely circumstantial. The implication of mTOR and nutrition in drosophila larval blood cell homeostasis has already been studied but not mentioned here. Most of the time the authors do not provide any possible explanation about the phenotypes they observe and how they fit with the current literature. Several pieces of results are of serious concern.

      We would like to thank the reviewer for their feedback. We have revised the abstract and have incorporated the insights obtained from our study. We have now included relevant literature that talks about the implication of mTOR and nutrition in Drosophila larval blood cell homeostasis (Please see Introduction section Paragraph 2 in the manuscript). We have also noted the input of the reviewer on many phenotypes lacking any description of a possible explanation. We have worked on results section to provide possible explanation and speculation wherever relevant.

      In the introduction, the authors do not provide an up-to-date and accurate presentation of the field. For example, they could use much more recent and comprehensive reviews since Evans et al. 2003. (eg. MID: 30733377 or 35887113). Their choice for signaling pathways involved in Drosophila blood cell progenitors seems very much biased for lead author self-citation rather than more directly related citations. It is surprising too that the authors failed to mention a series of publications on Akt/mTOR and nutrient sensing impact on drosophila larval blood cells (PMID: 22951642 ; 22911822 ; 22407365 ; 22510984). Along the same line, there are already several reports on autophagy genes implicated in Drosophila hematopoiesis and blood cell functions (PMID: 23406899; : 33560224 ; 20498061 ; 37623416). The introduction on GCN5 is a bit of a catalogue and should be streamlined- citing a recent review would be useful (PMID: 32735945). Again, the authors fail to cite publications showing that Gcn5 levels can be modulated by nutrition (PMID 27022023; 27874008) and they do not mention that amino acids starvation or mTOR inhibition leads to a decrease in GCN5 activity / TFEB acetylation (ref 40). Taking into account all the missing information, the novelty of the present manuscript is strongly decreased.

      We would like to thank the reviewer for the detailed suggestions on including the relevant literature that are appropriate and relevant to be mentioned in the context of the observations in our manuscript. We have now included these references and have cited them as per reviewer’s suggestions. Please check Introduction section paragraph 2.

      Results

      While there is little doubt that Gcn5 is expressed in the entire primary lobes based on Fig 1C-F, the quality of the staining in G-J (especially H, J) is really poor and essentially looks like non-specific background with no clear signal in the nuclei. Better images should be presented. The conclusion of the paragraph ("all cellular populations of the LG") and title of Fig.1 are not fully accurate as the authors do not provide evidence that Gcn5 is also expressed in posterior lobes.

      As per reviewer’s suggestions, since the images in Fig 1A-D’ clearly show that Gcn5 is expressed in the entire primary LG lobe in PSC cells, MZ and CZ; we have removed panels E-H’ which lacked clear nuclear signal. Fig1A-D’ clearly show the nuclear staining pattern of Gcn5. We have also modified the conclusion of the paragraph to say that Gcn5 is expressed in cellular populations of the primary lymph gland lobe accordingly.

      Concerning, Fig S1 and Fig 2, while the analysis seems technically sound, the results are puzzling. The lack of P1 differentiation in gcn5 null heterozygotes is very surprising. The authors should check that this stock does not carry a mutation in nimC1 (for details see: PMID: 23899817) and use other plasmatocyte differentiation markers to confirm their observation (also with the different allelic combinations). I'm also concerned by the levels of plasmatocyte differentiation and crystal cell number in the control line (notably in S1H), which seem very low (and quite variable for P1 as there is a notable difference between S1H and Fig 2H). Moreover, the analysis of the allelic combinations gives rather incoherent results: PCSC cell numbers are affected only in null/hypomorph, whereas differentiation (NimC1 and Hnt), as well as DNA damage, was only increased in hypomorph homozygotes. The authors propose no hypothesis to explain these observations.

      We have now repeated these experiments with the E333st null allele by placing it on a different balancer and we observe homozygotes that are alive till late third instar/early pupal stage as shown before by Carre et al., 2005. We have now included these revised results on the plasmatocyte differentiation status of the E333st heterozygotes and homozygotes (See Fig 2 and Fig S1). We do find P1 positive cells in the E333St heterozygotes unlike earlier. Plasmatocyte and crystal cell numbers in the control line always shows some level of heterogeneity. We have included the wild type control individually with those respective mutants during the experiment hence drawing a cross comparison across two different experiments would not be appropriate. We have now explained the observations obtained on PSC cell numbers (Discussion section paragraph 1). Experiments to check all hematopoietic aspects of the gcn5 null have been done after changing the balancer line and the null mutants overall show a decrease in PSC size and a widespread increase in hemocyte differentiation which could be due to a systemic effect due to various signalling pathways being affected which needs to be investigated and is beyond the scope of this study. This has also been discussed in the Discussion section Paragraph 1.

      Although a side-by-side comparison would have been better suited, it seems that the homozygotes or trans-heterozygotes do not have stronger phenotypes than the heterozygotes as far as crystal cell and DNA damage are concerned, which is rather unexpected. Besides the authors should introduce why they look at DNA damage.

      We agree with the reviewer that for the crystal cell and DNA damage phenotype the homozygotes or trans-heterozygotes do not have a stronger phenotype as compared to the heterozygotes alone but since these are whole animal mutants there could activation/inactivation of various signalling pathways and systemic effects that would be difficult to account for and comprehend here which needs to be investigated further. The only conclusion that we draw from these observations is that Gcn5 is required for maintaining blood cell homeostasis. Regarding DNA damage, we have now included the rationale and supporting literature for why we have studied DNA damage in the context of Gcn5. Please see result section 2 paragraph 1.

      Importantly too, the authors failed to obtain gcn5 E333st/E333st (null) larvae, whereas Carre et al. originally reported that E333st/E333st individuals are viable until the late third instar larvae. I suspect that the stock they use carries additional mutations that need to be eliminated by back-crossing it to control flies for several generations. Of note too, a recent report showed that a deletion of gcn5 (generated by CRISPR) does not prevent adult emergence, challenging the conclusion that gcn5 expression is absolutely required for fly development (PMID: 37545086).

      The reviewer is right in pointing out that E333st homozygotes survive until late third instar as reported by Carre et al.,2005. We have procured the null allele again and used another balancer to obtain homozygotes and we were able to get homozygotes that survived till late third instar as reported earlier. We have now included new data from these homozygotes for all hematopoietic aspects and heterozygotes particularly for plasmatocyte differentiation Please see Fig 2 and Fig S1 and corresponding results section 2 of the manuscript.

      Concerning the validation of Gcn5 knock-down and overexpression: the results are highly dubious. In Fig S2B (hml>Gcn5 RNAi), there is virtually no Gcn5 signal in the primary lobes but hml is normally expressed only in the cortical zone. How is it possible? Similarly, the western blot (which is really too much cropped around the bands of interest) does not show any signal in the hml>Gcn5 RNAi lane (not even some background. According to the Methods section, the western was performed on whole larvae extracts; hml-mediated knock-down can not wipe out its expression in all the tissues. As for the overexpression, flag immunostaining in hml>Gcn5-flag is mostly cytoplasmic (S2E), which doesn't make sense and does not fit with S2C (Gcn5 immunostaining).

      Hml-Gal4 is a pan hemocyte driver and its expression is not limited to the CZ of the primary lymph gland lobe (Banerjee et al., 2019) and recent single cell sequencing data corroborate this that Hml domain is not limited to the cortical zone (Yarikipati and Bergmann, 2026). GFP driven by Hml-Gal4 is spread out across the primary LG lobe which could explain the phenotype of no Gcn5 signal obtained in the immunofluorescence experiment. Regarding the western blotting experiment which was performed on whole larval extracts, we were also puzzled by lack of Gcn5 bands in these lysates upon depleting Gcn5 using Hml-Gal4. We need to systematically probe further to understand expression of Gcn5 in other tissues and organs. We have now removed the western blot data as the data obtained cannot be comprehended at the moment. Regarding the FLAG staining experiment – the staining gave us a cytoplasmic pattern and since Gcn5 is known to shuttle between the cytoplasm and nucleus it is possible that the anti-FLAG staining detected the Gcn5 localizing in the cytoplasm. It is difficult to draw a direct comparison here between the images S2C and S2E as both are different antibodies.

      The initial analysis of Gcn5 level modulation in the prohemocytes, PSC or Hml+ cells is mainly descriptive and the authors do not elaborate on possible explanations based on the current literature.

      We have added a possible explanation wherever required for these respective results on Gcn5 modulation in prohemocytes, PSC and Hml positive hemocytes. Please see result section 3 where we elaborate on possible explanation for the phenotypes observed.

      The structure/function analysis of Gcn5 is based on overexpression of truncated mutants in the prohemocytes using the tep4-GAL4 driver and monitoring PSC cell, prohemocyte maintenance, plasmatocyte and crystal cell differentiation as well as DNA damage. As the overexpression of the full-length protein was made with a different driver (Dome), it is difficult to interpret the data. Nevertheless, no clear message emerges from this analysis and the authors do not reach any conclusion. Thus, the interest of these experiments remains limited.

      The structure-function analysis was largely done to understand which of the domains of Gcn5 upon over-expression results in a dominant negative like phenotype and our analysis shows that expression of some of these domain mutants results in a dominant negative phenotype in the wild type genetic background which we have now stressed upon in the text. However, further mechanistic understanding and in-depth analysis of each of these domains of Gcn5 warrants further separate investigation and is beyond the scope of this study. Please see the end of result section 4 for conclusion and possible explanation.

      The authors then analyze autophagy markers (in hml>Gcn5 LOF or GOF). Contrary to their say, hml-GAL4 is not a pan-hemocyte marker. It would have been interesting to ensure that the effects observed on Atg8 and Ref(2)P in the lymph gland are cell-autonomous- as expected for a direct role of Gcn5 on this pathway. Again, it is very surprising that p62 is not detected in the western blot on whole larval extracts when Gcn5 is knocked down in Hml+ cells only (Fig 5D). Moreover, quantifications on multiple samples will be needed to validate the increase/decrease of p62 and Atg8 as detected by western blot. As for the RT-qPCR (Fig S5), according to the Methods sections, they were made on adult blood cells but this is not explicit in the result section.

      We have corrected the text and mentioned Hml-Gal4 as a hemocyte specific Gal4 shown earlier as Gal4 marking both embryonic and larval hemocyte population (Goto et al., 2003, Yarikipati and Bergmann, 2026). Regarding the Atg8 and Ref (2)P blots – yes, it is surprising to us too that the p62 is not detected in the larval lysates when Gcn5 is depleted using Hml-Gal4. However, this result was consistent over the replicates performed and needs to be further studied. Since this phenotype of complete absence of p62 in larval lysates upon Gcn5 depletion cannot be comprehended and explained, we have removed the western blot data from the figure and have just retained the immunofluorescence data and have also quantified the Atg8 and p62 puncta per cell and included this data in Figure 5, Graphs D and E. For the qRT-PCR we have now included a description in the corresponding results section. Please see result section – result 5 under “Autophagic flux in the Drosophila blood cells is negatively regulated by Gcn5”.

      The knock-down of TFEB or several autophagy genes in the prohemocytes (tep4-GAL4) leads to a rather convincing increase in plasmatocyte and crystal cell differentiation. It would have been interesting though to quantify prohemocyte maintenance, PSC cell number, and DNA damage. Also, the authors should have performed Gcn5 GOF/LOF experiments with the same driver (they present tep>Gcn5 RNAi in Fig 8 but without the proper controls).

      We have now included data for prohemocyte index (Figure S8M) upon knockdown of TFEB and other autophagy genes along with PSC cell number, DNA damage (Supple Fig S8) in the revised manuscript. Please see corresponding results section titled “Genetic and chemical ablation of autophagy boosts blood cell differentiation in the primary lymph gland lobe” for the description of the results.

      The use of chloroquine should be better described. How long was the treatment? Did the authors observe an effect on autophagy in the lymph gland? Chrorloquine also affects lysosomal pH, so it remains to be demonstrated that the effects observed here are only autophagy-related.

      We have now written a detailed protocol for the treatment in the methods section and also mentioned the treatment time which is 16 hours in the results. We have included data to validate the effect of Chloroquine on autophagy by p62 and Atg8 staining in the LG and have quantitated the data (Refer Supple Fig S9) and the corresponding results section titled “Genetic and chemical ablation of autophagy boosts blood cell differentiation in the primary lymph gland lobe”

      Similarly, the use of drugs to activate (3BDO) or inhibit (Rapamycin) mTOR should be better controlled. More generally, given the promiscuous roles of mTOR (and autophagy) in the larvae, tissue-specific manipulations would be better suited.

      We have now perturbed mTOR pathway genetically by activation and in-activation and have studied the effect on blood cell differentiation. Please see Figure S10 and the corresponding result section titled “Chemical or genetic modulation of mTORC1 activity controls blood cell differentiation” where we discuss the results of genetic perturbation of mTOR pathway.

      Actually, as pointed out above, it has already been shown that modulation of Akt/TOR in hemocytes or amino-acid deprivation affects blood cell homeostasis (see above). The authors should definitely discuss how their results fit with the literature on this subject.

      We have added relevant literature in the introduction section and have also discussed how Gcn5 could fit into this context of nutritional sensing and control of hematopoiesis. Please check revised Introduction section paragraph 2. Also, check discussion section in last paragraph where we have discussed role of Gcn5 in nutrient sensing.

      Again, Gcn5 levels need to be quantified using multiple samples (Fig 7M, N) before concluding.

      Sorry for not including the quantitation earlier but we have now included the quantitation for the blots presented in Fig. 7 M and N.

      Finally, the authors show that 3BDO still induces an increase in blood cell differentiation when gcn5 is knocked-down in tep4+ cells and that Rapamycin still represses differentiation when Gcn5 is overexpressed in Dome+ cells. They conclude that mTORC1 overrides the effect of Gcn5. This seems a far-reaching conclusion given the available evidence.

      We have now toned down the conclusion that we make to accommodate other possibilities which we have been unable to test here currently.

      In particular, in the conditions used, the authors do not necessarily assess the activity/requirement for Gcn5 and mTORC1 in the same cell population.

      Other comments and suggestions:

      The discovery of the SAGA complex is not Grant 1999 but 1997 (PMID: 9224714).

      Ref 30 is not appropriate -nothing to do with HAT.

      GCN5 not only acetylates TFEB but also Atg7 (PMID: 28594263) to limit autophagy.

      Thank you so much for these suggestions. We have made the necessary amendments in the references.

      In the results section, the first paragraph is largely a repetition of the introduction. The same is true for most paragraphs in this section. A shorter (hypothesis-driven) introductory sentence would be more adequate.

      We have now taken the suggestion into consideration and made the necessary change in the results section throughout the manuscript.

      Fig 1: it seems that there is a higher accumulation of Gcn5 in a few cells in the cortical zone. This may correspond to crystal cells and could be easily confirmed.

      We have now checked this aspect. Please see supple fig S5 where we co-stain lozenge-GFP cells containing LG with Gcn5 to check for the accumulation. However, we do not see any accumulation in the Lozenge-positive crystal cells.

      Figure 3: the authors should also quantify the proportion of progenitors (dome>GFP+) in the different conditions.

      We have now done this and added it to the Figure. Please see panel N in Figure 3 and Figure S8M.

      Figure S3: how do the authors explain that Gcn5 knockdown in the PSC reduces plasmatocytes differentiation (but does not affect PSC cell number or crystal cell differentiation)? What could be the origin of the increase in DNA damage (essentially in CZ)? How do they explain that Gcn5 over-expression increases PSC size but does not affect (reduce?) blood cell differentiation?

      These observations need to be investigated further. We currently have no answer to these comments. The signals that are produced by the PSC could be affected due to which we observe these phenotypes like an effect on plasmatocyte differentiation and an increase in DNA damage whereas no effect on PSC cell numbers or crystal cell numbers which needs to be studied further. Also, in the case of Gcn5 over-expression in PSC we do not know how the increased size of PSC controls differentiation. This would need further experimentation and since this paper is not about the role of Gcn5 in PSC exclusively, we will look into this in our future studies. These aspects will be studied in our future follow-up studies as it is beyond the scope of the current manuscript.

      Figure S4: how do the authors explain the non-cell autonomous increase in PSC cell number upon Gcn5 KD/GOF in hml+ cells? How do they explain the increase in crystal cell number in Gcn5 GOF? Is it really cell-autonomous (i.e. all the Hnt+ cells are Hml+?)?

      We have discussed how Gcn5 depletion or over-expression in HmlΔ cells could affect PSC cell numbers. Please see discussion section, paragraph 1. Regarding the crystal cell phenotype - We have now tested if the increase in crystal cell numbers is cell autonomous by driving Gcn5 over-expression using a crystal cell specific driver and we find that the increase is cell-autonomous. Please refer to Supple Fig S5.

      The discussion is lengthy and should be reduced. It does not appropriately consider the current literature.

      We have tried to reduce the length of the discussion and have also added relevant references as per recommendations of the reviewer.

      Reviewer #2 (Recommendations For The Authors):

      (1) In general, it is not clear why in some of the experiments Tep-Gal4 is used to modulate proteins in prohemocytes while in others Dome-Gal4 is used.

      There is no particular reason. These Gal4’s have been used interchangeably as both label the hematopoietic progenitor population. Although recent single cell sequencing data has identified subsets within the progenitors namely core progenitors marked by tep4 largely and dome being a distal progenitor marker (Cho et al.,2020, Girard et al.,2021), in our study perturbations in Gcn5 using either of the Gal4’s results in a similar phenotype.

      (2) Considering alteration in lymph gland size (Figure 2), the number of positive cells should be analysed in relation to total cell numbers or s4ize.

      Although we do not find any visible differences or defects in the overall LG size in various genetic conditions discussed in this manuscript, we have done so for the plasmatocyte differentiation where we have represented it as plasmatocyte differentiation index (relative to the size of primary LG lobe) throughout the manuscript. We have now done this for crystal cell numbers too for critical genotypes in this manuscript and have represented it is as crystal cell index for example please see Figure 2O, 3P, S5G, S10J where these graphs have now been added.

      (3) Figure 1A G-I' does not look like mCD8 GFP expression, but rather cytoplasmic GFP.

      We have made the change in the figure and the corresponding text accordingly.

      (4) One of the main conclusions in the manuscript is that Gcn5 affects autophagy (Figure 5). Here, the puncta need to be quantified (relative to total cell numbers).

      Thank you for the suggestion. We have now quantitated the p62 and Atg8 positive puncta per cell and have represented it as panel D and E in Figure 5.

      (5) Figure 5 D and E show p62 and Atg8 total protein levels in the larvae when Gcn5 is modulated only in the hemocytes. It is surprising that there is a complete reduction in p62 levels across the whole larvae when Hml gal4 is used for the knockdown.

      Yes, we observe a complete absence of p62 in whole larval lysates when Gcn5 is depleted using Hml-Gal4 and we see this across replicates. This result is indeed puzzling to us and difficult to comprehend as to why a hemocyte specific driver would result in such a dramatic change hence we have decided to remove the western blot data as it is difficult to draw a solid conclusion from. We have retained the immunofluorescence data which shows a consistent alteration in autophagy upon Gcn5 perturbation using Hml-Gal4 and we have now included the quantification for the number of p62 and Atg8 positive puncta per cell for the IF data.

      (6) The beta-actin levels in the western blots in Figure 5 are highly oversaturated and do not represent loading control adequately. Also, it looks like there is substantially more total protein in 5D 3rd lane where Gcn5 is overexpressed.

      Thank you for pointing this out. We have loaded equal amount of protein in all the wells so we are unsure why the actin bands look over-saturated. We have now removed the western blot data from this figure as the data is puzzling and difficult to comprehend given a total absence of p62 in whole larval lysates in Gcn5 depletion conditions using Hml-Gal4. Hence, we are just retaining the immunofluorescence data.

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    biorxiv.org/content/10.1101/2023.07.04.547637v3
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    Behavioral Signatures of Post-Decisional Attention in Preferential Choice
    5
    1. Public_Reviews 10 Jul 2026

      eLife Assessment

      This study uses convincing modeling methods and analyses of rich behavioral datasets to investigate the role of attention in value-based decision making; for instance, as when choosing between two snacks. The results are important, as they challenge existing theories that assume that paying attention to an available option biases the eventual choice toward that option. The results suggest that the correlation between attention and decision-making is formed largely after rather than before the (internal) choice process has terminated, a finding that offers an intuitively appealing rethinking of how attention and decision-making processes interact during value-based choices.

      Summary
    2. Public_Reviews 10 Jul 2026

      Reviewer #1 (Public review):

      [Editors' note: this version has been assessed by the Reviewing Editor without further input from the original reviewers. The authors have addressed the weaknesses raised in the previous round of review.]

      Summary:

      This study examines whether gaze direction actively shapes choice during food preference decisions or whether gaze and choice evolve largely independently until the moment of commitment. The established framework in this context, the aDDM, assumes that gaze causally biases the accumulation of evidence in favour of the fixated item. The authors show convincingly that this model fails to fit key behavioural patterns across several datasets, as do other published models that make the same assumption. The authors propose an alternative model (Post-Decision-Gaze or PDG) in which gaze and decision formation are decoupled: gaze does not influence the decision process, nor is it drawn toward the ultimately chosen item, until after the decision threshold is reached. Only during the motor execution period (after commitment) is gaze directed to the chosen option. They demonstrate that this model fits several observed patterns better than the aDDM and related variants.

      Strengths:

      The work thoroughly considers multiple models and datasets. It advances an interesting alternative perspective on gaze-decision interactions and highlights meaningful shortcomings in existing models. The authors take the time to explain how modelling assumptions produce specific patterns in the data, which is certainly insightful to readers interested in the modelling of value-based decision making.

      Weaknesses:

      It is unclear to what extent the model's success relies on the way non-decision time is formalised in the model. In the proposed PDG model, non-decision time is decomposed into separate visual encoding, saccadic execution, and manual execution components. Several values (assumed or recovered) do not match known physiological or behavioural ranges. This is a common issue in the literature, and the authors may want to address it in light of broader work discussing what non-decision time consists of in both manual and saccadic actions (e.g., Bompas et al., 2024, Non decision time: the Higgs boson of decision, Psychological Review).

      Review 1
    3. Public_Reviews 10 Jul 2026

      Reviewer #2 (Public review):

      Summary:

      Zylberberg et al. reanalyze eye-tracking and behavioral data to test two predictions of the attentional Drift Diffusion Model, finding that these predictions are not met. Similarly, predictions of normative models (inspired by rational inattention) are not in line with the data, and the authors propose a post-choice model of attention. This model better accounts for the two effects but also does not account for all patterns, so the authors conclude that eye movements most likely reflect both pre- and post-decisional processes.

      Strengths:

      A clear strength is the systematic falsification-based approach of the paper, establishing (partially) new predictions and testing to what extent these are met by extant models and by a newly developed theory. The authors do a good job in providing intuitions behind the effects and the reasons why models such as the aDDM predict them. The paper is of substantial relevance for the field, as it shows that effects pertaining to the last fixation(s) should be interpreted with caution. Another strength is the paper's transparency as the authors clearly acknowledge that their new model does not do a perfect job either.

      Weaknesses:

      The paper focuses on analyzing the Krajbich 2010 data, but shows that the second effect replicates in many other datasets. A more principled approach, in which both effects are analyzed and presented for all datasets, would be more convincing. The results should then be shown together for clarity/readability.

      Similarly, it would be nice to show to what extent the models' predictions depend (not depend) on using the best-fitting parameter values (are there any parameter settings under which the two effects are not predicted?)

      Review 2
    4. Public_Reviews 10 Jul 2026

      Reviewer #3 (Public review):

      Summary:

      In this study, the authors reanalyzed choice, RT and gaze datasets collected from human subjects performing a food-choice task. They show that models that posit a causal role for attention in shaping the decision-making process fail to account for empirical observations in the data. These include the attentional drift diffusion model (aDDM) and models that derive attention-choice associations from an optimal policy. The authors show that a model that assumes that gazes are directed towards the chosen option after decision commitment captures more (but not all) empirical findings, suggesting that attention may reflect decisions once they are made instead of contributing to their formation. However, this post-decision-gaze (PDG) model failed to capture all aspects of the data, suggesting that gaze may reflect both decisional and post-decisional operations, and existing models are still missing some features of the gaze-directing process. The authors provide convincing evidence that post-decision gaze explains a number of empirical findings in this task.

      Strengths:

      (1) The analyses are generally appropriate, and the conclusions are supported by the data.

      (2) The study was rigorous, as the authors considered a number of alternative possible models for behavior, and evaluated their performance based on a wide range of qualitative predictions (as opposed to exclusively relying on model comparison).

      (3) The proposal that gaze may largely reflect post-decisional processes is interesting, and as far as I am aware, novel.

      Weaknesses:

      There was limited discussion about why one might allocate attention post-decision. I would have appreciated more discussion on the potential functional consequences or implications of post-decision gaze.

      Review 3
    5. Public_Reviews 10 Jul 2026

      Author response:

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

      Public Reviews:

      Reviewer #1 (Public review):

      It is unclear to what extent the model's success relies on the way non-decision time is formalised in the model. In the proposed PDG model, non-decision time is decomposed into separate visual encoding, saccadic execution, and manual execution components. Several values (assumed or recovered) do not match known physiological or behavioural ranges. This is a common issue in the literature, and the authors may want to address it in light of broader work discussing what non-decision time consists of in both manual and saccadic actions (e.g., Bompas et al., 2024, Non decision time: the Higgs boson of decision, Psychological Review).

      In particular, the "saccadic execution" parameter appears far too long and too variable to reflect merely execution; instead, it likely includes decisional components. This would make more sense since manual and saccadic planning essentially rely on distinct brain areas, hence it seems unrealistic that crossing a single threshold would trigger both manual and saccadic execution. Similarly, recovered manual non-decision times are substantially longer (though not more variable) than expected motor execution durations for button presses. These patterns suggest that parts of what the model treats as non-decision time are likely decisional in nature, although perhaps related to "action decision" rather than the "value-based decision" of interest to the authors. To what extent these two processes neatly follow each other or overlap could be usefully considered.

      We have added a paragraph to the Discussion explaining how our model’s estimates of sensory and motor latencies relate to corresponding values inferred from physiology or behavioral manipulations (e.g., Bompas et al., 2024). Specifically, we write:

      “The key assumption of the PDG model is that there is a delay between the moment a choice is internally committed and the moment it is externally reported with a key press. Because eye movements are typically faster than manual responses (𝜏<sub>e</sub> < 𝜏<sub>m</sub> in our simulations), this delay creates a window during which gaze can already be directed toward the covertly chosen item before the response is formally registered. We do not interpret these non-decision latencies as irreducible physiological minima for moving the eyes or pressing a button (Bompas et al., 2025). Rather, they are inferred indirectly by fitting an additive non-decision-time parameter to the behavioral data, which we decompose into a sensory delay (𝜏<sub>s</sub>) and a manual execution delay (𝜏<sub>m</sub>). Values of 𝜏<sub>e</sub> are then chosen so that the model reproduces the observed magnitude of the behavioral effects. This estimation procedure has important limitations. Some participants show relatively “flat” chronometric functions: response times vary little with value despite otherwise normal psychometric performance. Such patterns likely reflect processes not explicitly represented in the model, including procrastination, reduced motivation, task-unrelated thought, or noise in item ratings. Within a drift-diffusion framework, however, these cases are accommodated by assigning a long non-decision time together with a short evidence-accumulation period (Table S1). Consequently, some estimated non-decision times are substantially longer than would be expected if they represented only sensory and motor delays. A further limitation is conceptual. We model non-decision time as occurring either before or after evidence accumulation, whereas in reality decisional and non-decisional components are likely temporally interleaved (Graziano et al., 2011). This simplification may also inflate the recovered latency estimates. With these caveats in mind, sensory and oculomotor delays on the order of 300 ms remain broadly plausible, although they likely lie near the upper end of a realistic range. The estimated eye-movement latency is especially long. For instance, in monkeys trained to report simple perceptual decisions with a saccade, roughly 100 ms elapses between the threshold-crossing signal in parietal cortex (or the superior colliculus) and the executed eye movement (Roitman and Shadlen, 2002; Stine et al., 2023). Crucially, however, varying the assumed non-decision latencies across a reasonable range does not alter the qualitative predictions of the model (Fig. 8).”

      Further, we have added a parameter sensitivity analysis. Importantly, although the magnitude of the predicted effects depend on the non-decision latencies, the qualitative aspect of these predictions do not (new Figure 8). Specifically, (i) the increasing tendency to look at the ultimately chosen item as time elapses (new Fig. 8A), (ii) the lack of an interaction between the last-fixation bias and overall value (Fig. 8B), and (iii) the absence of an effect of choice consistency on Δdwell (Fig. 8C) are all findings that are independent of 𝜏<sub>e</sub>.

      Reviewer #2 (Public review):

      The paper focuses on analyzing the Krajbich 2010 data, but shows that the second effect replicates in many other datasets. A more principled approach, in which both effects are analyzed and presented for all datasets, would be more convincing. The results should then be shown together for clarity/readability.

      Following this suggestion (and the reviewer’s elaboration in the private comments to the authors), we have substantially restructured the manuscript. Both aDDM predictions are now presented together (new Fig. 2), and Figs. 3–4 test these predictions across multiple food-choice datasets. In doing so, we no longer treat the data from Krajbich et al. (2010) separately, and we extend the analysis of the last-fixation–choice association (MELFB) to additional datasets. We note that the same datasets could not be used in both Figs. 3 and 4, as some lack information on the final fixation required for the MELFB analysis. Nevertheless, results are highly consistent across datasets and align with findings from a recent study by Ting & Gluth (2025), which independently identified and examined one of our key predictions; this work is now cited in the revised manuscript. Finally, to reduce redundancy, we have consolidated all aDDM variants and optimal models into a single figure (new Fig. 10).

      Similarly, it would be nice to show to what extent the models' predictions depend (not depend) on using the best-fitting parameter values (are there any parameter settings under which the two effects are not predicted?)

      The key predictions of the model depend on the difference between the manual (𝜏<sub>m</sub>) and eye-movement-related (𝜏<sub>e</sub>) latencies. We have now added a parameter-sensitivity analysis to show how the model predictions depend on this difference. The new analysis shows that while the quantitative predictions do depend on the precise latency values, the results are qualitatively similar across values of 𝜏<sub>e</sub> (new Figure 8).

      Reviewer #3 (Public review):

      There was limited discussion about why one might allocate attention post-decision. I would have appreciated more discussion on the potential functional consequences or implications of post-decision gaze.

      Thank you for this suggestion. We added a new paragraph to the discussion (paragraph #2), where we argue that it is sensible for a decision maker to direct the gaze to the chosen item once a covert choice commitment has been made, as the benefits of attending to a stimulus do not end with the decision itself. Specifically we now write:

      “Instead, these observations are better explained by a post-decision account of the gaze-choice association that is, one in which gaze shifts to the selected item after a covert commitment to a choice. We argue that directing gaze to the chosen item after a covert choice commitment is sensible, as the benefits of attending to a stimulus do not end with the decision itself. In naturalistic settings, for instance, selecting a food item is typically followed by the action of reaching toward it, where visual attention supports spatial localization and motor planning for the upcoming action. Although participants in our computerized task did not physically act on their choices, these sensorimotor processes are likely highly automatized and may still be engaged by default, even when not strictly required. Beyond motor preparation, post-decisional attention may also serve additional functions, such as facilitating sensory anticipation of the reward, supporting metacognitive evaluation of the decision, and contributing to value updating for future choices. From this perspective, a degree of attentional “stickiness” whereby the chosen item remains preferentially attended after commitment could emerge as an effectively optimal policy once these post-decisional processes are taken into account. Moreover, a specific feature of the task design may further reinforce this tendency: in the snacks paradigm, the unchosen item typically disappears from the screen immediately after a response is registered. It is therefore plausible that directing gaze to the chosen item after commitment partly reflects anticipation of the imminent disappearance of the unchosen option. To disentangle these mechanisms, it would be interesting for future work to test whether this attentional bias persists when the chosen item, rather than the unchosen one, is the stimulus that disappears upon response.”

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      Major Comments:

      (1) Framing of the modelling approach

      The manuscript would benefit from acknowledging the known limitations of DDM-based frameworks, especially given that the entire study is conducted within these constraints. The introduction highlights successes of the DDM, but the manuscript does not mention any of its conceptual or empirical limitations.

      We are unsure about what specific limitations the reviewer has in mind, but we have added a paragraph to discussion mentioning some limitations, like the inflation of the non-decision times and the difficulty of interpreting the fit parameters (Paragraph #5 of Discussion: “The key assumption of the PDG model is that there is...”).

      (2) Dependence on non-decision time assumptions

      The alternative model's explanatory power appears to rely heavily on assumptions regarding the decomposition of non-decision time: fixed visual encoding (𝜏<sub>s</sub>= 0.3 s), manual non-decision time (𝜏<sub>m</sub>; two free parameters), and saccadic execution (𝜏<sub>e</sub>; fixed parameters μ<sub>e</sub> = 0.35, σ<sub>e</sub> = 0.11).

      - 𝜏<sub>e</sub> is substantially longer and more variable than typical saccadic execution times, suggesting it likely incorporates decisional components.

      - Estimated 𝜏<sub>m</sub> values are approximately twice as long as known manual execution durations.

      - σnd is more plausible, implying that variability is captured correctly but mean durations are not.

      Together, these points raise the possibility that portions of what the model treats as non-decision time are in fact part of a (action) decision process. Only then does it make sense to assume that Tm is usually larger than Te. If Tm and Te were truly execution delays, then Tm would always be larger than Te.

      You may find it helpful to consider the framework in Bompas et al. Psych Review (2024), which discusses in detail what non-decision time is likely to comprise across effectors.

      Thank you we have added (i) a sensitivity analysis showing that our results are robust to changes in the specific value used for the eye movement related latencies (new Fig. 8), and (ii) a new paragraph in Discussion addressing the issue of the mismatch between our parameter estimates and the manual and saccadic execution times (Paragraph #5 of Discussion: “The key assumption of the PDG model is that there is...”).

      (3) Code availability.

      The authors should consider sharing all relevant code and data publicly.

      We agree, we now share the code and data on GitHub and indicate so in the revised manuscript.

      Minor Comments:

      (1) Lines 74-77. These are not worded as predictions but as questions; one tests predictions, but answers questions. I feel it would be clearer to stick to predictions (like in the abstract), and the introduction could benefit from explaining these predictions in a bit more detail (I found it difficult to get my head around these predictions from the intro text only).

      We rewrote the section in the introduction where we provide a gist of the model predictions (last paragraph of Introduction). We agree with the reviewer that the previous explanation was not clear.

      (2) It is confusing that panel B appears to the left of panel A in Figure 2.

      We agree. We have restructured the manuscript (following the suggestion of another reviewer), and now Figure 2 has changed and the panels follow a more logical order.

      (3) Figure 3C - remove MATLAB toggles.

      Yes, thanks.

      (4) Figure 5A shows the proportion of left choices, but the text and legend refer to right choices.

      Good catch, thank you.

      Reviewer #2 (Recommendations for the authors):

      This may appear self-serving, but the authors seem to be unaware of some highly relevant work from our group. Most importantly, in a recent publication (Ting & Gluth, 2024, JEP General), we have already looked at the dependency of the last- (or final-) fixation bias on overall value in value-based (VB) and perceptual (P) decisions. In VB, we found a negative effect; in P we did not find a significant effect. This is largely consistent with the current results, showing a negative but not significant trend. Another relevant work is Gluth et al. (2020, Nat Hum Behav), where we extended the aDDM by assuming that the probability to fixate on an option is a function of the accumulated evidence for that option. It would be interesting to know whether this assumption changes the predictions of the aDDM. Finally, we just published a new theory on how people search for information to make efficient value-based decisions (Gluth et al., in press, Psychol Rev; https://osf.io/preprints/psyarxiv/3qzak_v2). Although this theory focuses on multi-attribute choices, it can be applied to "simple" choices, too (by assuming that there is only one attribute = value). Interestingly, while the model also mispredicts a (slight) increase of the last-fixation bias with overall value, it correctly predicts the independency of the dwell-time advantage effect on choice consistency as well as the small increase of the effect with RT (attached here is a figure to show this: [https://elife-rp.msubmit.net/elife-rp_files/2026/01/22/00149589/00/149589_0_attach_9_477122. pdf], and the match with the empirical data shown in Figure 3B and 12 is striking). In general, the model shares many features of the Callaway and Jang models, but does not need to assume a biased value prior, which the authors suggest is responsible for the misprediction of the second effect. I leave it up to the authors to discuss this new theory, but I wanted to point this out.

      Thank you for pointing this out; these are all relevant points and studies.

      We now note that the first of our predictions has recently been identified and tested by Ting and Gluth (2025).

      We also considered extending the manuscript with a variant of the model proposed by Gluth et al. (Psychological Review, 2026). In fact, we attempted to fit this model to the Krajbich et al. (2010) dataset under the assumption that the duration of each sampling epoch is a free parameter. We find this model very interesting. However, in our current implementation it appears to make the same qualitative prediction as the aDDM, namely that ΔDwell depends on choice consistency (see Author response image 1).

      Given this, we have decided not to include these results in the manuscript. It remains possible that with further development particularly with a more realistic specification of fixation durations (e.g., allowing them to depend on value) the model could account for the full set of observed effects. We think this would be best addressed in a separate study.

      That said, we do find the model promising, as it provides a better account than most of the alternative models we explored for the patterns shown in panels D, H, and I.

      Author response image 1.

      Fits of a variant of the MACS model (Gluth et al. 2026) to the data of Krajbich et al. (2010).

      The paper would benefit substantially from restructuring. The aDDM's predictions are provided first, together with the empirical data, and then the optimal models are discussed. But Figure 2 shows all of this together. Later, the new (PDG) model is elaborated, and its predictions are shown. Towards the end of the results, variations of the aDDM and combinations of aDDM and PDG are shown in a series of figures (8-11), followed by a last figure showing one of the tested effects in other datasets. All of this feels pretty much thrown together without a clear structure. For instance, the aDDM and the optimal models could be described together (or the optimal models get a separate figure). The additive variants could be described earlier. And some figures could be put into the supplement. And the empirical results of the different studies could be shown together.

      We fully agree with this suggestion. We have now restructured the manuscript along the lines proposed by the reviewer (see the more detailed explanation of the restructuring in our response to the public comments).

      I strongly suggest avoiding the term "influence" in the y-axis of Figure 2, upper row, as it implies causality. Similarly, in line 182, the term "causal influence" is used in the context of the Callaway model, but as far as I know, this is not what the model assumes.

      We replaced the y-axis label with “Association of last dwell with choice (β)”

      Reviewer #3 (Recommendations for the authors):

      (1) Figure 2 - Panel labels for A and B are reversed?

      We have restructured the manuscript (following the suggestion of another reviewer), and now Figure 2 has changed.

      (2) Does 3C include a .pdf screenshot?

      Thank you, it’s a Matlab bug on Mac. I guess they want us to switch to Python -:)

      (3) Figure 4 - It would be helpful if the green line were defined in the figure legend.

      Added

      (4) The effect size in 5B looks much more dramatic than in 2B(A?) - Is this for one example subject as opposed to all subjects? Please clarify what is different about the data.

      We are no longer showing the psychometric functions in Figure 2.

      (5) Line 252 - they say they compared the probability of choosing the right item (Fig. 5B) by the y-labels of that figure, which are all p(choose left).

      Yes, corrected now.

      (6) In general, they reference the subpanels of Figure 5 out of order, which causes the reader to jump around. They might consider reordering the panels of the figure so they follow the ordering of descriptions in the text.

      We agree, we have rearranged the figure panels to follow the ordering of the descriptions in the text.

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    Sensory adaptation and pupil-linked arousal support flexible evidence accumulation during perceptual decision making
    4
    1. Public_Reviews 10 Jul 2026

      eLife Assessment

      This important study measures single-unit activity in area MT of awake-behaving monkeys to test the idea that sensory adaptation contributes to flexible evidence accumulation during decision making. The authors provide compelling evidence that adaptation to different temporal contexts shapes both perceptual judgements and neural responses. Although the precise computational mechanisms underlying these effects remain uncertain, the results support the conclusion that recent sensory history influences the temporal dynamics of decision formation. This work will be of interest to researchers studying visual perception, sensory adaptation, and decision making.

      Summary
    2. Public_Reviews 10 Jul 2026

      Reviewer #1 (Public review):

      McGaughey and Gold ask where in the decision process the flexibility of evidence accumulation arises, proposing that it is not solely a property of downstream integrators but is also supported by stimulus-specific sensory adaptation in the middle temporal area (MT). Recording single-unit activity in rhesus macaques during a motion direction-discrimination task in which an adapting stimulus of varying temporal stability precedes an identical test stimulus, they find that more rapidly changing contexts produce weaker and less discriminable MT responses to the test stimulus, which they argue accounts in part for context-dependent changes in decision-making behavior. Through session-level correlations they further identify pupil-linked arousal as a parallel, apparently separable contributor.

      The main strength is the shift of perspective toward the encoding stage: rather than treating MT as a static input to flexible downstream integrators, the authors show that early sensory cortex can itself contribute adaptive, context-dependent signals that shape behavior. The conceptual advance is supported by a well-designed paradigm-total exposure to each motion direction is matched across conditions and the test stimulus is held identical-together with single-unit recordings and simultaneous pupillometry. The behavioral effect is consistent across three animals, and the fact that context-dependent differences emerge over repeated stimulus presentations within a trial, rather than as a sustained baseline offset across blocks, ties the effect convincingly to stimulus-specific adaptation.

      The behavioral effect constrains the temporal dynamics of decision formation but does not uniquely identify its algorithmic basis: a leak, a saturating non-linearity, or a reduction in the gain of integration are all compatible with a shallower rise of accuracy with viewing time, and the reduced MT discriminability is itself an encoding-stage efficiency effect of this kind. The manuscript appropriately treats the algorithmic basis as unresolved, noting that distinguishing these accounts would require analyses not available here, such as reverse-correlation or motion-energy kernels with lower-coherence test stimuli.

      The inference that the adaptation- and arousal-related signals operate independently rests on the absence of session-wise correlations between the neural and pupil measures and their behavioral contributions. Given the noise in the trial-wise estimates, this is best read as consistent with, rather than demonstrating, true independence, as the authors note.

      Overall, the authors largely achieve their aim of showing that sensory adaptation in MT shapes the evidence available for time-dependent perceptual decisions. The evidence for a sensory-encoding contribution is convincing, while the claim of independence between adaptation and arousal is more tentative and is framed as such.

      Review 1
    3. Public_Reviews 10 Jul 2026

      Reviewer #2 (Public review):

      McGaughey and Gold trained rhesus macaque monkeys to perform a motion-direction discrimination task in which a behaviorally irrelevant adapting stimulus with either fast or slow direction alternations preceded a variable-duration test stimulus, while simultaneously recording single-unit activity in area MT and pupil diameter. They report that adaptation to the more rapidly changing stimulus was associated with reduced behavioral sensitivity, attenuated test-evoked MT responses, and larger pupil-linked arousal signals. The authors interpret these behavioral changes as evidence for context-dependent adjustments to the temporal dynamics of decision formation and argue that these adjustments are supported by both sensory adaptation in MT and arousal-related mechanisms. More broadly, they conclude that flexible evidence accumulation in dynamic environments arises from distributed adjustments across sensory encoding and neuromodulatory systems rather than solely from changes within a downstream accumulator. If correct, this interpretation has important implications not only for our understanding of perceptual decision making, but also for broader theories concerning the functional role of sensory adaptation.

      The conclusions of the paper are generally supported by the data. Evidence for adaptation-induced changes in sensory encoding, behavior, and pupil dynamics is convincing, and the revised manuscript substantially strengthens the connection between the behavioral findings and the proposed decision-making framework.

      Comments on revised version.

      The revised manuscript provides a clearer account of how recent stimulus history influences behavioral performance. In the original version, aspects of the psychometric functions were interpreted as evidence for a more leaky evidence-accumulation process, although some of these effects could potentially have reflected alternative mechanisms, including influences of the adapting stimulus on short-duration trials. The additional analyses and discussion included in the revision clarify that information from the adapting stimulus contributes to behavior at short viewing durations and appropriately temper claims regarding the specific computational mechanism underlying the observed behavioral effects. While the data do not uniquely identify whether these effects arise from changes in leak, other nonlinearities, or related decision processes, they provide convincing evidence that recent temporal context influences the temporal dynamics of decision formation.

      My original review also noted that different sections of the manuscript relied on different behavioral metrics and analytical approaches when relating behavioral changes to neural and pupil-linked measures. The revised manuscript now provides a clearer rationale for these choices, including distinctions arising from the different trial types and time windows used in the neural and pupil analyses.

      Review 2
    4. Public_Reviews 10 Jul 2026

      Author response:

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

      Reviewer #1 (Public review):

      (1) Alternative mechanisms for performance differences.

      The authors assume that the difference in performance between the low-switch (LS) and high-switch (HS) frequency conditions is explained by a change in the "leakiness" of integration. However, several other mechanisms could potentially explain this effect:

      (1) Temporal Uncertainty: Integration might start later in the HS condition, leading to lower performance.

      (2) Reduced Efficiency: Integration could be less efficient in the HS condition (i.e., lower signal-to-noise ratio) without a change in the leak parameter itself.

      (3) Evidence Contamination: Motion information from the adapting stimulus in the HS condition may be integrated rather than ignored, which might be the case since the transition from the adapting to the test stimulus is not externally cued.

      To distinguish between these alternatives, I suggest two possible analyses. First, a formal model comparison could be performed, though I acknowledge this may be inconclusive in the absence of response-time data. Second, an analysis of motion energy kernels could be revealing; the leak hypothesis makes the specific prediction that for long test stimuli, early samples should contribute more to the choice in the LS condition than in the HS condition, relative to late samples.

      We thank the reviewer for raising these important points. We agree that we cannot definitively identify the algorithmic underpinnings of the behavioral effects we report and have made substantial revisions to the manuscript to be clearer about what is supported and what is speculative in our claims. Most importantly, we agree that we do not know if the context-dependent differences in how accuracy depends on viewing time are based on adjustments to a leak or to something else (e.g., a saturating non-linearity, as we identified in Glaze et al, 2015, that is separate from the leak itself), which we cannot resolve with this dataset, even with more formal model comparisons. We therefore:

      Changed the wording throughout the manuscript to refer to changes in leakiness as just one of several possible sources of the behavioral differences. We also added this point to the list of “limitations” (and possible future directions, including using motion-energy kernels, which would require us to use lower-coherence test stimuli) in the Discussion (L487-493).

      Added a new figure panel (Fig. 2D), a new Extended Data figure (Extended Data Fig. 3), and additional explanatory text (L168-175) that collectively describe the behavior in more detail, including quantifying a “crossover” dynamic similar to what we reported previously (Glaze et al, 2015).

      Added new explanations (L152-163) and analyses (Extended Data Fig. 9) indicating that the monkeys used some information from the end of the adapting stimulus to inform their decisions, which accounts for the patterns of choices at the shortest viewing durations.

      Indicate that the context-dependent differences in the slopes of the psychometric functions (and complementary analyses based on “raw” accuracy measures as a function of binned viewing duration) rule out the temporal uncertainty and evidence contamination explanations, but are consistent with effects on the temporal dynamics of the decision process (L175-179).

      (2) Independence of neural and pupil-linked signals.

      The authors take the lack of session-wise correlation between context-dependent contributions from neural and pupil terms as evidence that these two signals provide independent contributions to the behavioral effect. However, could this lack of correlation simply be a result of high variability or noise in these estimates? The data shown in Figure 7B suggests that measurements are very noisy, which might obscure a potential relationship.

      We agree that the lack of session-wise correlation between neural and pupil terms cannot be taken as definitive evidence of independence. We have both softened the language around the claim (L368) and added a sentence to the Discussion (L464-468) acknowledging that this lack of correlation may reflect underlying noise and/or variability rather than true independence of the underlying mechanisms.

      Reviewer #1 (Recommendations for the authors):

      (3) The neural data analyses rely fundamentally on "switch" trials (Figures 3-5). It might be informative to also examine "non-switch" trials to see if there are specific neural markers indicating the exact moment the motion stimulus becomes behaviorally relevant. Given that this may fall outside the primary focus of the paper, it is up to the authors whether to pursue this line of inquiry.

      We thank the reviewer for this suggestion. We agree and have added new analyses of data from non-switch trials (Extended Data Fig. 9), which show some effects of stimulus information from the adapting epoch on the monkeys’ choices, as we detail below in response to related comments from the other reviewers.

      Reviewer #2 (Public review):

      Aspects of the behavioral analysis would benefit from a tighter connection between theoretical claims about evidence accumulation and the empirical features of the psychometric functions. For example, the rightward shifts observed across adapting conditions are interpreted as consistent with a reset of accumulation on switch trials, but similar patterns could also arise from failures to detect the test stimulus on a subset of trials, leading responses to default to the final adaptor direction. Likewise, changes in psychometric slope and asymptote are attributed to differences in evidence accumulation without explicit modelling or consideration of alternative explanations.

      Clarifying how specific features of the psychometric functions map onto distinct components of the decision process will strengthen the link between the theoretical framework and the behavioral data.

      We agree and have made substantial revisions to address these important points. Specifically, we added a new figure panel (Fig. 2D), new Extended Data Figures (3 and 9), and several lines of explanatory text (L152-179) that collectively describe the behavior in more detail, including clarifying that: 1) for the shortest viewing durations, the monkeys’ decisions were informed by information from the adapting stimulus, which accounts for generally lower accuracy on LSF (longer exposure to the final adapting direction, thus more accumulated evidence for that direction before processing the switch) vs. HSF (shorter exposure to the final adapting direction, thus less accumulated evidence for that direction before processing the switch) switch trials; and 2) as viewing duration increased, the rate of rise of accuracy versus viewing duration was higher for LSF vs. HSF trials, implying differences in the process of evidence accumulation. As detailed in our response to a similar comment from Reviewer 1, above, we are now careful to temper our claims about the specific computational basis (e.g., a leak or other form of nonlinearity) for these differences.

      We also de-emphasized our treatment of the asymptotes of the psychometric functions. In principle, these regimes could give insights into leakiness (which can limit the total amount of information that can be accumulated) and lapses (which are measured at the asymptotes). In practice, however, the long-duration trials that constitute the asymptotes were relatively under sampled (to promote the unpredictability of the offset of the stimulus, which we believed was the more important consideration when designing the experiment), yielding unreliable estimates.

      A slight concern is the lack of a consistent analytical approach for relating behavioral changes to neural and pupil-linked measures. Different sections of the manuscript rely on different behavioral metrics-such as differences in accuracy within a selected stimulus-duration range (e.g., Figure 5C) or psychometric slope differences (Figure 6C) without clear justification for these choices. The analytical approach likewise varies between simple correlational analyses (Figure 5C, Figure 6C), pseudo-experimental group comparisons (Figures 5D, E), and the inclusion of neural or pupil terms in the behavioral psychometric regression model (Figure 7B). While each metric and approach may be defensible in isolation, adopting a more consistent framework will help convince readers that the reported effects are robust and not contingent on the selective choice of metric or analysis.

      We thank the reviewer for this thoughtful critique and agree that the rationale for our choice of behavioral metrics and analytical approaches could be stated more clearly. We have added text to the relevant sections of the Results (L247-251) clarifying these choices. In particular:

      The neural analyses (Figures 3D-E, Figure 4, Figure 5D-E) focused on preferred-motion switch trials, because: 1) low switch-frequency non-switch trials provide an additional 800 ms of exposure to the final adapting-stimulus motion direction relative to high switch-frequency non-switch trials, which confounds comparisons of context-dependent evidence encoding between conditions, and 2) MT neurons exhibit minimal responses to null motion (although note that we also included analyses based on ROC area, which is computed from both preferred- and null-motion switch trials, to account for possible contributions of null-motion responses; Figure 5A-C). Thus, to ensure a meaningful comparison between neural and behavioral measures, we used behavioral accuracy on switch trials as the relevant metric in Figure 5C-E, rather than psychometric slope, which is estimated across both switch and non-switch trials.

      The pupil analyses (Figure 6) focused on a time window preceding test-stimulus onset, representing the arousal state around when the decision process started, and included both switch and non-switch trials. Thus, for these analyses we used psychometric slope, which is estimated across both switch and non-switch trials.

      We used several different analyses to compare and contrast the neural-behavioral and pupil-behavioral relationships because they provide complementary and useful insights. The correlational analyses in Figures 5C and 6C characterize session-level relationships between neural/pupil signals and behavior. The group comparisons in Figures 5D–E provide a complementary visualization of the same relationship. The model-based approach in Figure 7 then allows direct quantification of the trial-wise contributions of each signal to behavior within a common framework. Importantly, the conclusions drawn from each approach converge on the same interpretation, which we believe speaks to the robustness of the reported effects.

      Reviewer #2 (Recommendations for the authors):

      (1) Figure 2 legend. Description of 'running average (5-trial window)' is unclear - presumably this is a running average in stimulus space rather than across trials.

      We thank the reviewer for flagging this ambiguity. We have updated the legend (L136-137) to clarify that the running average is computed across trials sorted by test-stimulus duration.

      (2) L158. Difficult to establish an asymptotic performance level for HSF conditions within the stimulus duration range tested.

      We have removed the reference to asymptotic performance and replaced it with a discussion of performance on longer-duration switch trials in the context of the newly added Figure 2D.

      (3) L515 Equation 1. While this is a standard formulation of lapse rate in psychometric functions, the construction here in terms of switch probability is not standard. Given the task and training, it seems more likely that on lapse trials, the animal will respond according to the last adapted direction (rather than randomly switch/stay with equal probability).

      We thank the reviewer for this point. We agree that it is possible that on at least some of the “lapse” trials the monkeys may respond according to the final adapting-stimulus direction rather than choosing randomly. However, we cannot distinguish those alternatives using this task design. We include a statement to this effect in Methods (L569-571).

      To explore the idea further, we refit the behavioral data using separate upper and lower asymptotes corresponding to lapse rates on switch and non-switch trials, respectively. Across monkeys, there were no significant differences between upper and lower lapse rates for either low (Wilcoxon signed-rank test for equal medians: p = 0.15, Cohen's d = -0.13) or high switchfrequency (p = 0.07, Cohen's d = -0.16) conditions. So, at the very least, there was no evidence for lapse-like errors driven by switch- (or non-switch-) specific defaults to the final adapting direction.

      (4) L256. Statistical significance of attenuation is not directly tested here.

      We have replaced "were attenuated" with "we did not identify any reliable context-stability differences" (L297) to accurately reflect what was directly tested without implying a statistical comparison between groups of sessions that was not performed.

      (5) L429. Does the increase in explanatory power warrant the increased complexity of the model here?

      We thank the reviewer for raising this important point. We used Tjur's pseudo-R<sup>2</sup> because it does not increase by default with added model complexity, making it more conservative than other R<sup>2</sup> measures in this respect. Tjur's pseudo-R<sup>2</sup> is a coefficient of discrimination, and as such its value increases only when additional terms improve the model's ability to separate predicted probabilities across response outcomes. Thus, the observed increases in explanatory power when adding neural or pupil terms reflect real improvements in discriminability rather than an artifact of model complexity. We have added a brief clarification of this point to the Methods (L662-664).

      Reviewer #3 (Public review):

      The task design may not be optimal. While the amount of time the monkey is exposed to each motion direction during the adapting stimulus is matched, it's hard to know if the reduced MT responses to the test stimulus are truly due to the greater frequency of switches during the HSF adapting stimulus or because the monkeys have been exposed to more repetitions of the stimulus. It's increased sensory adaptation in either case, but it makes it problematic to interpret this as temporal context-dependent adaptation specifically. I think this could potentially be partially addressed by an analysis that is in the paper, but could potentially be emphasized/fleshed out more, specifically the results shown in Figure 4D that seem to show that most of the reduction in neural response for adapting units occurs between the first and second stimuli.

      The reviewer raises an important point. The number of stimulus repetitions and switch frequency are confounded in the experimental design, making it difficult to attribute context-dependent differences in MT responses to the temporal pattern of switches rather than to accumulated repetitions. We also note, as the reviewer acknowledges, the observed differences reflect sensory adaptation either way. Figure 4D does offer relevant evidence, suggesting that a majority of the change in neural response occurred with just one stimulus repetition. This finding complicates an interpretation where adaptation scales with the number of stimulus repetitions. We have added several lines to the Results about these points (L231-233).

      The pupillometric analysis seems to be an indirect way of assessing whether the accumulator itself might be modulated by temporal context, but the link could be made clearer. The authors show that context-dependent behavior is related to pupil size, which is related to arousal/neuromodulation, but it would be helpful to have some idea of what neural mechanisms underlying adaptive decision-making are actually impacted by this neuromodulation. Lacking neural data to address this question (e.g., from a brain region proposed to be involved in the accumulation process), at least more discussion of this would be helpful. Essentially, I'm unsure of how to interpret the pupil results: the argument that temporal context affects instantaneous evidence encoding in MT that then drives the accumulator is very clear, but I am a bit confused about what, mechanistically, I should think about the effect of neuromodulation doing.

      We thank the reviewer for this thoughtful comment and agree that the mechanistic interpretation of the pupil results could be made clearer. We acknowledge that we cannot directly identify the neural mechanisms underlying the arousal-related contributions to adaptive evidence accumulation from pupil data alone, given that pupil size is an indirect and imperfect proxy for neural (e.g., LC-NE system) activity. However, we can offer some informed conjecture and have added to the Discussion (L469-482) in an effort to elaborate on possible mechanisms.

      Reviewer #3 (Recommendations for the authors):

      (1) Abstract could be retooled - does not emphasize the pupillometry/arousal results very much, and they are presented more as a control than an independent result.

      We agree and have revised the Abstract accordingly.

      (2) Do all neural/pupil analyses use only switch trials? Sometimes the figure captions do specify only switch trials, but not everywhere. It would be helpful to specify either in the Methods or at the beginning of each figure caption that all subplots show switch trial results. Also, if you do always use switch trials, it would be useful to see in the Supplement how the non-switch trial results differ from switch trials. It seems like they may in interesting ways based on the behavioral results (supporting a reset of evidence accumulation on switch but not non-switch trials).

      We thank the reviewer for flagging these important points. We have added a justification for switch trials (L186-190) as well as clarification about which trial types were used for which analyses (L246-249) and information about trial types to relevant figure captions. We have also added a new Extended Data figure (Extended Data Fig. 9) examining relationships between neural activity and behavior on non-switch trials. As inferred by the reviewer, behavior on non-switch trials is consistent with the use of information from the adapting stimulus.

      (3) In Figure 3C, 5B, etc, when computing firing rate for the test stimulus (50-500 ms), are differently sized windows used to compute the rate for different test stimulus durations (since some will be <500 ms)? Or are only trials where the test stimulus duration is > 500 ms used for this analysis?

      We thank the reviewer for raising this point. To clarify, the 50–500 ms window does not reflect a fixed window applicable for all trials. Rather, neural activity from 50 ms after test-stimulus onset through test-stimulus offset was included for each trial, with 500 ms serving as the upper bound for trials with longer durations (> 500 ms). We have clarified this in the Methods (L607-610) to avoid ambiguity.

      (4) I think it might be better to be consistent with the time windows used for analysis; specifically, to choose either the 50-500 ms window used in Figures 3, 4, and 5B, or the 200- 400 ms window used for the remaining analyses in Figure 5.

      We agree that using the same window for all of the analyses would improve consistency, but not doing so provides advantages that we believe take precedent and now describe in more detail. The broader 50–500 ms window used for Figures 3, 4, and 5B was chosen to characterize MT neural activity over a relatively large a time window, ensuring that every trial contributes to each estimate. Because test-stimulus durations were drawn from a truncated exponential distribution (100–1200 ms), restricting these analyses to the 200–400 ms window would have excluded the substantial proportion of trials with durations <200 ms (but would yield similar figures and conclusions). The narrower window used in subsequent analyses allows us to focus on the conditions that exhibited the biggest modulations of neural activity when comparing them to behavior.

      (5) Similarly, provide justification for using only trials ending 375-600 ms after test stimulus onset for the behavioral correlations. It seems reasonable to choose a subset of test stimulus durations where the monkeys' behavior is greater than chance but less than ceiling, but it would be good to specify this so that it doesn't seem arbitrary.

      We agree and have added text to make this important point (L249-251).

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    Decoding spine nanostructure in cultured neurons derived from mouse models of neuropsychiatric disorder reveals a schizophrenia-linked role for Ecrg4
    4
    1. Public_Reviews 10 Jul 2026

      eLife Assessment

      By investigating spine nanostructure and dynamics across multiple genetic mouse models for neurodevelopmental disorders, this important study has the potential to uncover convergent or divergent synaptic phenotypes that may be specifically associated with autism versus schizophrenia risk. The imaging and overall breadth of the methods are convincing. The purely in vitro nature of the study slightly limits the generalisability of the findings, though these limitations are acknowledged and discussed in the manuscript.

      Summary
    2. Public_Reviews 10 Jul 2026

      Reviewer #1 (Public review):

      [Editors' note: this version has been assessed by the Reviewing Editor without further input from the original reviewers. The authors have addressed the comments raised in the previous round of review.]

      Summary:

      Kashiwagi et al. undertook a population analysis of dendritic spine nanostructure applied to the objective grouping of 8 mouse models of neuropsychiatric disorders. They report that spine morphology in cultured hippocampal neurons shows a higher similarity among schizophrenia mouse models (compared with autism spectrum disorder (ASD) mouse models) and identify an effect of Ecrg4 (encoding small secretory peptides) on spine dynamics and shape in these models.

      Strengths:

      The study developed a method for objectively comparing spine properties in primary hippocampal neuron cultures from 8 mouse models of psychiatric disorders at the population level using high-resolution structured illumination microscopy (SIM) imaging. This novel technique identified two distinct groups of mouse models according to the population-level spine properties: those with ASD-related gene mutations and those with schizophrenia-related gene mutations. Functional studies, including gene knockdown and overexpression experiments, identified an effect of Ecrg4 on the spine phenotype of the schizophrenia model mice.

      Weaknesses:

      The main weakness is that the study is wholly in vitro, using cultured hippocampal neurons. The authors present this as an advantage, however, arguing that spine morphology as measured in a reduced culture system can demonstrate direct effects of gene mutations on neuronal phenotypes in the absence of indirect influences from nonneuronal cells or specific environments.

      Review 1
    3. Public_Reviews 10 Jul 2026

      Reviewer #2 (Public review):

      Okabe and colleagues build on a super-resolution-based technique they have previously developed in cultured hippocampal neurons, improving the pipeline and using it to analyze spine nanostructure differences across 8 different mouse lines with mutations in autism or schizophrenia (Sz) risk genes/pathways. It is a worthy goal to try to use multiple models to examine potential convergent (or not) phenotypes, and the authors have made a good selection of models. They identify some key differences between the autism versus the Sz risk gene models, primarily that dendritic spines are smaller in Sz models and (mostly) larger in autism risk gene models. They then focus on three models (2 Sz - 22q11.2 deletion, Setd1a; 1 ASD - Nlgn3) for timelapse imaging of spine dynamics, and together with computational modelling provide a mechanistic rationale for the smaller spines in Sz risk models. Bulk RNA sequencing of all 8 model cultures identifies several differentially expressed genes which they go on to test in cultures, finding that ecgr4 is upregulated in several Sz models and its misexpression recapitulates spine dynamics changes seen in the Sz mutants, while knockdown rescues spine dynamics changes in the Sz mutants. Overall, these have the potential to be very interesting findings and useful for the field.

      Review 2
    4. Public_Reviews 10 Jul 2026

      Author response:

      The following is the authors’ response to the previous reviews

      Public Reviews:

      Reviewer #2 (Public review):

      Okabe and colleagues build on a super-resolution-based technique they have previously developed in cultured hippocampal neurons, improving the pipeline and using it to analyze spine nanostructure differences across 8 different mouse lines with mutations in autism or schizophrenia (Sz) risk genes/pathways. It is a worthy goal to try to use multiple models to examine potential convergent (or not) phenotypes, and the authors have made a good selection of models. They identify some key differences between the autism versus the Sz risk gene models, primarily that dendritic spines are smaller in Sz models and (mostly) larger in autism risk gene models. They then focus on three models (2 Sz - 22q11.2 deletion, Setd1a; 1 ASD - Nlgn3) for time-lapse imaging of spine dynamics, and together with computational modelling provide a mechanistic rationale for the smaller spines in Sz risk models. Bulk RNA sequencing of all 8 model cultures identifies several differentially expressed genes which they go on to test in cultures, finding that ecgr4 is upregulated in several Sz models and its misexpression recapitulates spine dynamics changes seen in the Sz mutants, while knockdown rescues spine dynamics changes in the Sz mutants. Overall, these have the potential to be very interesting findings and useful for the field. My major concerns from the initial manuscript, especially regarding cherry picking and circularity have been addressed with revised analytical approaches. I have some remaining minor comments.

      (1) The comparison between two wild-type samples versus wild-type-mutant samples is helpful - I think this could be added to the manuscript.

      As suggested, we added the figure comparing two wild-type samples against wild-type mutant samples as Supplementary Figure 2. 

      (2) For results of time-lapse imaging - please spell out in the results section the direction of change (lines 270 - 277).

      As suggested, we added the direction of change (an increase in the turnover rate) to the text (page 12, lines 270-271).

      (3) Using linear mixed effect models for statistical analysis is a significant improvement. While a sample size (n) of mice = 3 is not ideal, I think given the multiple different mouse lines used and intensity of analysis, this is probably the best that can be done, although further validation in larger samples eventually is to be hoped for.

      We appreciate the reviewer for recognizing the effort required to collect data across multiple mouse lines.

      (4) The revised text is much improved, but I still think the authors should be upfront somewhere in the text that the schizophrenia-associated genes can only confer biased risk for schizophrenia (and that the clinical phenotype can also include autism). As I said before, I think this is the best we can do and I agree with their choices, but it is important not to overstate the link. The differences they see make it clear that these are still relevant distinctions.

      As suggested by the reviewer, we further modified the discussion related to the comparison between ASD- and schizophrenia-associated mouse models (pages 23-24, lines 508-522).

      “The nanoscale features of dendritic spines in mouse models of Nlgn3<sup>R451C/(y or R451C)</sup>, Syngap1<sup>+/−</sup>, POGZ<sup>Q1038R/+</sup>, and 15q11-13<sup>dup/+</sup>, which we classified as being related to ASD, are highly heterogeneous. This heterogeneity may reflect the broad clinical spectrum of ASD, which ranges from mild impairments in social skills to severe intellectual disability. Accordingly, these four mouse models may represent distinct subgroups characterized by different degrees or forms of hippocampal dysfunction. Notably, among the ASD-related models, 15q11-13<sup>dup/+</sup> showed population-level spine properties closer to those found in the 22q11.2<sup>del/+</sup> and Setd1a<sup>+/-</sup> mouse models. Although we classified 22q11.2<sup>del/+</sup> and Setd1a<sup>+/-</sup> as schizophrenia-related models, both 22q11.2 deletion syndrome and Setd1a haploinsufficiency in humans are also associated with ASD, suggesting substantial overlap in the genetic risk factors underlying ASD and schizophrenia. Further systematic analyses linking rare genetic variants to synaptic phenotypes in mouse models may provide important insights into the mechanisms underlying both shared and disorder-specific synaptic alterations in neurodevelopmental and psychiatric disorders.”

      Recommendations for the authors:

      Reviewer #2 (Recommendations for the authors):

      (1) I would suggest that it might be preferable to use the word 'neuropsychiatric' rather than 'mental' in the title.

      As suggested, we modified the manuscript title.

      (2) I think it would be clearer to say that DEGs are listed if present 'in three or more models' rather than >2 (I appreciate the latter is mathematically clear, but can easily be read as 2 or more if reading fast). This is changed in the figure legend, but I suggest it is also changed in the main text (line 352-3)

      As suggested, we changed the main text to incorporate "in three or more models" (page 16, line 352).

      (3) Please add to Methods (line 557) that 'control cultures were prepared from littermate embryos....'

      As suggested, we added the phrase "control cultures were prepared from littermate embryos" (page 26, line 559).

      (4) Sorry to add something, but please could the authors add a definition of how they calculate spine turnover (and add units to the y axis of Figure 5A-C)?

      As suggested, we modified the y-axis of Figure 5A-C (% as unit) and added the method of calculating spine turnover rate in the text (page 36, lines 808-811).

      AuthorResponse
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  18. www.biorxiv.org www.biorxiv.org
    FEDRANN: effective overlap graph construction based on dimensionality reduction and approximate nearest neighbors
    2
    1. GigaScience 10 Jul 2026
      AbstractOverlap detection is a key step in de novo genome assembly pipelines based on the Overlap-Layout-Consensus (OLC) paradigm. However, existing methods for overlap detection either rely on heuristic seed-and-extension strategies or locality-sensitive hashing (LSH), both of which struggle to handle repetitive genomic regions and the computational burden of large-scale datasets. Here, we present FEDRANN, a novel strategy for overlap graph construction that integrates feature extraction, dimensionality reduction (DR), and approximate nearest neighbor (ANN) search. We find the pipeline combining inverse document frequency (IDF) transformation, sparse random projection (SRP), and NNDescent enables accurate detection of overlaps across diverse datasets. We developed an efficient open-source implementation of this pipeline named Fedrann (https://github.com/jzhang-dev/fedrann). Through systematic benchmarking on real long-read sequencing data, we demonstrate that Fedrann produces overlap graphs comparable to or better than those generated by existing state-of-the-art tools, including MECAT2, minimap2, and wtdbg2, while maintaining competitive runtime. Despite being implemented primarily in Python, Fedrann achieves performance on par with tools written in compiled languages, owing to matrix-based representations and C-accelerated numerical libraries. Our results suggest that DR and ANN techniques offer a promising new direction for scalable and accurate overlap detection in long-read assembly and broader sequence similarity search tasks.

      This work has been peer reviewed in GigaScience (see https://doi.org/10.1093/gigascience/giag048), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

      Reviewer 2：

      The authors present METRIN-KG, a knowledge graph integrating plant metabolomic, trait, and biotic interaction datasets. The work should have substantial value for multiple plant sciences and ecology domains. The overall effort to harmonize disparate resources into an integrated, semantically coherent resource is impressive. The methodology includes a notable pipeline for ontology alignment across multiple sources. However, despite its technical strengths, several issues regarding data accessibility and manuscript structure and clarity should be addressed. The manuscript is highly technical throughout. While this level of precision is exemplary, it may alienate biologically oriented readers, and effort should be made so that the impact and manuscript is clear to a larger audience.

      Major comments

      1. The online deployment currently presents several problems that must be resolved before publication.

      2. The existence of multiple SPARQL UIs (https://kg.earthmetabolome.org/metrin which redirects to https://qlever.earthmetabolome.org/metrin-kg/ and https://sib-swiss.github.io/sparql-editor/metrin-kg) is not explained, and the redundancy is potentially confusing.

      3. At the time of review, https://qlever.earthmetabolome.org/metrin-kg/ showed expired SSL certificates, making it inaccessible for most users. Automatic certificate renewal (e.g., using certbot) should be implemented.

      4. Attempting to use the SIB UI resulted in an error due to the redirect.

      5. The certificate issue also prevented evaluation of ExpasyGPT querying against METRIN-KG.

      6. Usability represents a significant barrier. Many potential users such as biologists without semantic-web or RDF experience are unlikely to be able to interpret the current figures or formulate SPARQL queries. The manuscript briefly mentions ExpasyGPT, which has strong potential to overcome this barrier by allowing natural language querying. This tool should be emphasized more prominently, potentially with a dedicated subsection and discussion of its role in broadening the resource's accessibility.

      7. To fully demonstrate the relevance of METRIN-KG, the use-case section would benefit from quantitative summaries, visualizations (e.g., distributions, network visualizations), and biological interpretations of query results.

      8. The overall manuscript organisation would benefit from restructuring to improve readability and better expose the impact of the work done.

      9. The methodological content currently in "Mapping of TRY data" and "Mapping of GloBI data" should be moved to the Methods section, while the quantitative outputs (e.g., numbers of records) should be moved into a dedicated Results section.

      10. The current "Data re-use and case studies" section could be reorganised into Results and Discussion sections,

      11. Results include: description of outputs from the methodological steps (e.g. the ontology, the successfulness of the mapping process, the size of the final knowledge graph/number of triples, and other relevant metrics; the user interface, including being able to share and add example questions and write NL questions via ExpasyGPT; examples of SPARQL queries and case studies.

      12. Discussion includes: Reuse potential; case-study interpretations or impact; future directions including planned expansion and enhancing of ontological structure, etc.

      13. An overview and evaluation of the KG should be provided, for example the number of plant species, and the distributions of connections. Any gaps or any (potentially) biases due to the input data needs to be acknowledged. For example, it is very likely certain species may be under/overrepresented in either metabolome or interaction datasets, or possibly geographic skews could exist.

      14. The ontology is variously referred to as the "Earth Metabolome Ontology", "EMI ontology", and "EMI". Consistent naming should be adopted throughout the manuscript and associated repositories. It is also unclear whether the ontology is a result of this work. As written, the "Ontology" section under "Methods" reads more like a result/description than a methodological step. Clarifying what components are original contributions and presenting them in Results would strengthen the manuscript. Additionally, the phrases "our proposed framework" and "our approach" are ambiguous, do these refer to the ontology itself, the metadata-mapping pipeline, or the overall integration process? Finally, referring to METRIN-KG as a "tutorial to build a knowledge graph" appears to be a bit out of place, given the topic of the manuscript.

      15. Given its potential relevance beyond this project, the authors are strongly encouraged to publish the code for the metadata-mapping pipeline. In addition, the following details would strengthen the methodological rigor:

      16. Were any acceptability criteria implemented in the automated step, e.g. a minimum Cosine similarity threshold?

      17. Were the manual corrections systematically documented?

      18. In how many cases were manual corrections needed?

      19. Was any evaluation done on the embedding/model version or the source of errors?

      Minor comments

      1. The manuscript would benefit from proofreading for consistent use of Oxford commas (and an "&" instead of "and").

      2. Reference 190 contains a typo "GiHub"

      3. References should be checked (e.g. citation for [95] references both METRIN-KG Zenodo and GloBI Zenodo)

      4. The METRIN-KG Zenodo link in the article is not to the latest version (Version 5)

      5. Consider improving the figures, e.g. use of colour to better communicate the content and refining layouts.

      6. The authors should deposit a snapshot of the GitHub repository to Zenodo.

      The following are suggestions to the authors, to be followed by their own judgement:

      1. Table 1, Figure 3, Figure 4 could be moved to supplementary material to make space for figures for the case studies.

      2. The dense in-text list of ontologies in the "Metadata mapping" section could be replaced with a summarized table (e.g. by moving Supplementary Table 2, but including references to the main text).

      3. The full SPARQL queries in "Taxonomy mapping" could be moved to supplementary materials, with a high-level description left in the main text.

    2. GigaScience 10 Jul 2026
      AbstractOverlap detection is a key step in de novo genome assembly pipelines based on the Overlap-Layout-Consensus (OLC) paradigm. However, existing methods for overlap detection either rely on heuristic seed-and-extension strategies or locality-sensitive hashing (LSH), both of which struggle to handle repetitive genomic regions and the computational burden of large-scale datasets. Here, we present FEDRANN, a novel strategy for overlap graph construction that integrates feature extraction, dimensionality reduction (DR), and approximate nearest neighbor (ANN) search. We find the pipeline combining inverse document frequency (IDF) transformation, sparse random projection (SRP), and NNDescent enables accurate detection of overlaps across diverse datasets. We developed an efficient open-source implementation of this pipeline named Fedrann (https://github.com/jzhang-dev/fedrann). Through systematic benchmarking on real long-read sequencing data, we demonstrate that Fedrann produces overlap graphs comparable to or better than those generated by existing state-of-the-art tools, including MECAT2, minimap2, and wtdbg2, while maintaining competitive runtime. Despite being implemented primarily in Python, Fedrann achieves performance on par with tools written in compiled languages, owing to matrix-based representations and C-accelerated numerical libraries. Our results suggest that DR and ANN techniques offer a promising new direction for scalable and accurate overlap detection in long-read assembly and broader sequence similarity search tasks.

      This work has been peer reviewed in GigaScience (see https://doi.org/10.1093/gigascience/giag048), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

      Reviewer 1：

      Summary This paper presents FEDRANN, a novel approach to overlap detection in long-read genome assembly that combines feature extraction, dimensionality reduction (DR), and approximate nearest neighbor (ANN) search. The authors systematically evaluate a range of design choices and implement the best-performing pipeline (IDF-SRP-NNDescent) as an open-source tool, Fedrann. Benchmarking against state-of-the-art tools (minimap2, MECAT2, wtdbg2, BLEND, xRead, MHAP) shows that Fedrann achieves competitive or superior accuracy and overlap graph quality across multiple sequencing platforms (ONT, PacBio HiFi, CycloneSEQ), while maintaining reasonable runtime. The conceptual framing of overlap detection as a k-NN search problem is both innovative and potentially influential. Major Strengths 1. Novel conceptual framework: Framing overlap detection as a k-NN search problem, drawing analogies from single-cell analysis, is creative and opens new algorithmic possibilities for genome assembly. 2. Systematic evaluation: The authors carefully assess multiple feature extraction, DR, and ANN methods before converging on the optimal pipeline. 3. Strong empirical results: Fedrann demonstrates high accuracy and graph quality, often outperforming established tools while remaining runtime-efficient. Major Concerns and Recommendations 1. Memory consumption is a critical limitation o Fedrann requires >700 GB RAM for human genome datasets, which makes the tool impractical for most research labs and cost-prohibitive for cloud use. o While acknowledged, this limitation is somewhat downplayed. In its current state, Fedrann may be restricted to only very high-resource environments. o Recommendation: Either (a) demonstrate initial results of memory-reduction strategies (e.g., shared memory, memory-mapped structures, GPU acceleration), or (b) more prominently highlight this as a key limitation restricting practical adoption. 2. Incomplete evaluation at the assembly pipeline level o The paper evaluates overlap graph quality but does not show results on final genome assemblies. Major Concerns and Recommendations 1. Memory consumption is a critical limitation o Fedrann requires >700 GB RAM for human genome datasets, which makes the tool impractical for most research labs and cost-prohibitive for cloud use. o While acknowledged, this limitation is somewhat downplayed. In its current state, Fedrann may be restricted to only very high-resource environments. o Recommendation: Either (a) demonstrate initial results of memory-reduction strategies (e.g., shared memory, memory-mapped structures, GPU acceleration), or (b) more prominently highlight this as a key limitation restricting practical adoption. 2. Incomplete evaluation at the assembly pipeline level o The paper evaluates overlap graph quality but does not show results on final genome assemblies. Minor Comments • Figures S4–S6 (embedding dimension analysis) could be better explained in the main text with more intuitive interpretation. • Benchmarking fairness: tools designed for high recall (e.g., minimap2, MECAT2) may be disadvantaged by post-processing into “top k” mode. Clarify this limitation in comparisons. Minor Corrections • Figure 1: Step numbering is currently (1), (3), (4). Step (2) is missing. Overall Recommendation This paper presents a novel and promising framework for overlap detection with strong methodological rigor and empirical results. However, two major gaps — excessive memory usage and lack of assembly-level validation — must be addressed before the work can be considered fully convincing. In addition, clarity on basic parameters such as k-mer size is necessary for robustness.

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    biorxiv.org/content/10.1101/2025.05.30.656979v3
  19. www.theguardian.com www.theguardian.com
    Western Europe records hottest-ever June as heatwaves intensify
    1
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    theguardian.com/environment/2026/jul/09/western-europe-records-hottest-ever-june-as-heatwaves-intensify
  20. www.ncbi.nlm.nih.gov www.ncbi.nlm.nih.gov
    Two neurologic facets of CTLA4-related haploinsufficiency
    1
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    ncbi.nlm.nih.gov/pmc/articles/PMC7286662/
  21. www.ncbi.nlm.nih.gov www.ncbi.nlm.nih.gov
    Functional Relevance of CTLA4 Variants: an Upgraded Approach to Assess CTLA4-Dependent Transendocytosis by Flow Cytometry
    2
    1. Vibhor 10 Jul 2026
      P08FCanadianukukA86VReduced9.18Pathogenic60.8Non-pathogenic[5]NANAAlive

      Case#: P08, Female, Canadian, age: n/a, alive at the time of publication

      DiseaseAssertion: N/A

      FamilyInfo: N/A

      CasePresentingHPOs: N/A

      CaseHPOFreeText: N/A

      CaseNotHPOs:N/A

      CaseNotHPOFreeText: N/A

      CasePreviousTesting: The percentage of transendocytosis using either CD80-GFP or CD80-mScarlet CHO cells was determined in eight LRBA-deficient patients. No difference in the percentage of transendocytosis was observed between CTLA4-variant carriers (GFP median=5.4%; mScarlet median= 49.8%) and LRBA-deficient patients (GFP median=9.9%; mScarlet median, 48.6%). However, significantly lower percentages of transendocytosis were observed in LRBA-deficient patients compared to healthy donors (HD) when using CD80-mScarlet CHO cells (median, 48.6% vs. 65.5% in HD) (Fig. ​(Fig.4e),4e). This difference was not observed with CD80-GFP CHO cells (patients median of 9.9% in patients vs. 13.9% in HD). In conclusion, the CTLA4 transendocytosis method using CD80-mScarlet CHO cells enables the functional verification of LRBA deficiency, but it cannot differentiate between LRBA deficiency and CTLA4 insufficiency.

      GenotypingMethod: NGS and Sanger sequencing

      PreviouslyPublished: N/A

      Variant: NM_005214.5(CTLA4):c.257C>T (p.Ala86Val)

      ClinVar ID: 661941

      gnomAD: 0.00001859

      https://gnomad.broadinstitute.org/variant/2-203870733-C-T?dataset=gnomad_r4

      SupplementalData: Yes, all data regarding the patient was found in Table1.

      Gene:CTLA4 HGNC:2505 InheritancePattern:AutosomalDominant DiseaseEntity:CHAI AlleleOrigin:Germline Zygosity:Heterozygous “ClinVarID:661941” PreviousTesting SupplementalData Ab Deficiencies VCEP
    2. Vibhor 10 Jul 2026
      P07MGerman1824.2R75QReduced7.29Pathogenic––[5]17.65Mildly affectedAlive

      Case#: P07, Male, German, 18 years old at the time of clinical diagnosis and 24.2 years old at the time of genetic diagnosis, alive at the time of publication

      DiseaseAssertion: Mildly affected based on a CHAI score of 17.65%

      FamilyInfo: N/A

      CasePresentingHPOs: N/A

      CaseHPOFreeText: N/A

      CaseNotHPOs:N/A

      CaseNotHPOFreeText: N/A

      CasePreviousTesting: The percentage of transendocytosis using either CD80-GFP or CD80-mScarlet CHO cells was determined in eight LRBA-deficient patients. No difference in the percentage of transendocytosis was observed between CTLA4-variant carriers (GFP median=5.4%; mScarlet median= 49.8%) and LRBA-deficient patients (GFP median=9.9%; mScarlet median, 48.6%). However, significantly lower percentages of transendocytosis were observed in LRBA-deficient patients compared to healthy donors (HD) when using CD80-mScarlet CHO cells (median, 48.6% vs. 65.5% in HD) (Fig. ​(Fig.4e),4e). This difference was not observed with CD80-GFP CHO cells (patients median of 9.9% in patients vs. 13.9% in HD). In conclusion, the CTLA4 transendocytosis method using CD80-mScarlet CHO cells enables the functional verification of LRBA deficiency, but it cannot differentiate between LRBA deficiency and CTLA4 insufficiency.

      GenotypingMethod: NGS and Sanger sequencing

      PreviouslyPublished: N/A

      Variant: <br /> NM_005214.5(CTLA4):c.224G>A (p.Arg75Gln)

      ClinVar ID: 943305

      gnomAD: 0.000008673

      https://gnomad.broadinstitute.org/variant/2-203870700-G-A?dataset=gnomad_r4

      SupplementalData: Yes, all data regarding the patient was found in Table1.

      Gene:CTLA4 HGNC:2505 InheritancePattern:AutosomalDominant DiseaseEntity:CHAI AlleleOrigin:Germline Zygosity:Heterozygous “ClinVarID:943305” PreviousTesting SupplementalData Ab Deficiencies VCEP
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  22. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov
    Case Report: Aplastic anemia related to a novel CTLA4 variant
    1
    1. Vibhor 10 Jul 2026
      20-year-old male

      Case#: 20-year-old male, Race: White (ancestry unavailable) DiseaseAssertion: The patient is asserted to have "CTLA4 haploinsufficiency" manifesting as aplastic anemia. FamilyInfo: Patient's father has disease variant Case PresentingHPOs: HP:0012378 (Fatigue), HP:0001962 (Palpitations), HP:0002875 (Exertional dyspnea), HP:0001903 (Anemia), HP:0001873 (Thrombocytopenia), HP:0002608 (Celiac disease), HP:0000608 (Macular degeneration), HP:0001876 (pancytopenia), HP:0001915 (aplastic anemia), CaseHPOFreeText: ** Diagnosis at age 20 when patient presented with persistent and profound incapacitating fatigue. Bone marrow biopsy was consistent to aplastic anemia. Table 1 summarizes presenting labs and flow cytometry results. Patient was first treated with high-dose IVIG, cyclosporine, and systemic corticosteroids. He initially responded well, but 6 months into therapy he developed renal impairment and was transitioned to sirolimus. His aplastic anemia relapsed. Patient underwent haploidentical (sibling, variant negative) hematopoietic stem cell transplantation, which was curative. CaseNotHPOs: HP:4000129 (Recent blood transfusion), CaseNotHPOFreeText: N/A CasePreviousTesting: The following studies were negative: Bone marrow chromosome analysis; FISH hybridization for BCR/ABL1, monosomy 5, monosomy 7, trisomy 8, and 20q deletion; myelodysplastic syndrome mutation sequencing. GenotypingMethod: A primary immunodeficiency NGS panel was run (gene content not specified) and identified a paternally inherited heterozygous missense variant in CTLA4. Variant: The patient is heterozygous for the NM_005214.5(CTLA4):c.385T>A (p.Cys129Ser). ClinVar: 1414930 CAID: N/A gnomAD**: This variant was not found in gnomAD v.4.1.0

      Gene:CTLA4 HGNC:2505 InheritancePattern:AutosomalDominant DiseaseEntity:CHAI AlleleOrigin:Germline Zygosity:Heterozygous ClinVarID:1414930 ClinicalStatus:Symptomatic Cytopenia:Reported Transendocytosis:Defective PreviousTesting FamilyInfo Ab Deficiencies VCEP
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    pmc.ncbi.nlm.nih.gov/articles/PMC11363706/
  23. www.ncbi.nlm.nih.gov www.ncbi.nlm.nih.gov
    Novel PIK3CD mutations affecting N-terminal residues of p110δ cause APDS1 in humans
    1
    1. Vibhor 10 Jul 2026
      Patient B.1 in a second, unrelated family is a 13-year old male who presented within the first year of life

      Case#: Takeda_2017_B.1, male, 0 years (onset)

      DiseaseAssertion: APDS

      FamilyInfo: unaffected mother was tested and found not to have the variant. Father was unavailable for testing

      CasePresentingHPOs: abscess, severe diaper rash, recurrent otitis media, eczema, pneumonia, bloody stool, lymphoma, poor growth, low bone age, hypergammaglobulinema lymphocytopenia, elevated transitional B cells, sinopulmonary bacterial infection, decreased CD4+ T cell, decreased CD8+ T cells, decreased naive CD4+ T cells

      (HP:0025615, HP:0011131, HP:0000403, HP:0000964, HP:0002090, HP:0025085, HP:0002665, HP:0002716, HP:0001510, HP:0002750, HP:0010702, HP:0001888, HP:0030381, HP:0005425, HP:0032218, HP:0005415, HP:0410378)

      CaseHPOFreeText: marginal zone hyperplasia, EBV lymphadenitis, increased CD19+ B cells

      CaseNotHPOs:

      CaseNotHPOFreeText:

      CasePreviousTesting: NR

      GenotypingMethod: WES + Sanger

      PreviouslyPublished: NR

      Variant: c.241G>A (p.E81K)

      ClinVarID: NR

      CAID: CA338300169

      gnomAD: NR

      SupplementalData:

      Mutation:Missense DiseaseEntity:APDS Zygosity:Heterozygous InheritancePattern:AutosomalDominant RespiratoryFindings SevereInfection DevelopmentalDelay Lymphopenia GastroIntestinalInvolvement Gene:PIK3CD HGNC:8977 ClinicalStatus:Symptomatic CAID:CA338300169 Eczema Lymphoma Lymphoproliferation Ab Deficiencies VCEP
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    ncbi.nlm.nih.gov/pmc/articles/PMC5632585/
  24. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov
    A New Variant in CTLA4 Highlights the Heterogeneous Phenotype of CTLA4 Haploinsufficiency
    2
    1. Vibhor 10 Jul 2026
      relatives

      Case#: II.3, a 59 year old female, age of onset 58

      DiseaseAssertion: Inflammatory polyarthritis

      FamilyInfo: Table 1

      CaseHPOFreeText: presented with lymphoid proliferation, central nervous system inflammation (transverse myelitis and extensive disseminated encephalomyelitis), epilepsy, chronic kidney disease with one transplantation (benign polyclonal B-cell infiltration, interstitial fibrosis), interstitial pneumopathy, splenomegaly, hepatic abnormality with diffuse nodular hyperplasia, rectocolitis, extensive varicella zoster virus infection (VZV), viral encephalitis without documentation, Epstein–Barr virus (EBV) chronic viremia, Clostridium difficile severe colitis

      Variant: c.379T >G variant in CTLA4

      GenotypingMethod: high-throughput sequencing

      CAID: CA350138665

      Gene:CTLA4 HGNC:2505 AlleleOrigin:Germline InheritancePattern:AutosomalDominant CAID:CA350138665 FamilyInfo Ab Deficiencies VCEP
    2. Vibhor 10 Jul 2026
      18-year-old female patien

      Case#: III.7, an 18-year-old female patient

      DiseaseAssertion: immune thrombopenia, autoimmune hemolytic anemia, and Evans syndrome with infections early-onset herpes zoster and chronic Epstein-Barr virus

      FamilyInfo: Table 1

      CasePresentingHPOs: HP:0001433, HP:0002716

      CaseHPOFreeText: severe necrotic dermohypodermitis of left leg caused by Pseudomonas aeruginosa, hypogammaglobulinemia

      Variant: c.379T >G variant in CTLA4

      GenotypingMethod: high-throughput sequencing

      CAID: CA350138665

      Gene:CTLA4 HGNC:2505 InheritancePattern:AutosomalDominant AlleleOrigin:Germline Zygosity:Heterozygous CAID:CA350138665 ClinicalStatus:Symptomatic DiseaseEntity:Evanssyndrome Lymphoproliferation:Reported FamilyInfo Ab Deficiencies VCEP
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    pmc.ncbi.nlm.nih.gov/articles/PMC12605463/
  25. www.ncbi.nlm.nih.gov www.ncbi.nlm.nih.gov
    Hemophagocytic Lymphohistiocytosis in Activated PI3K Delta Syndrome: an Illustrative Case Report
    1
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    ncbi.nlm.nih.gov/pmc/articles/PMC8193594/
  26. www.ncbi.nlm.nih.gov www.ncbi.nlm.nih.gov
    Cytotoxic T Lymphocyte Antigen 4 Haploinsufficiency Presenting As Refractory Celiac-Like Disease: Case Report
    1
    1. Vibhor 10 Jul 2026
      Patient 1

      Case#: Collen_2022_Patient_1, female, infancy (onset) 23 y.o. (report), Ethnicity reported: white

      DiseaseAssertion: CTLA4 haploinsifficiency

      FamilyInfo: Mutation inherited from father, who had melanoma and was asymptomatic for autoimmunity. Paternal first cousin had type-1 diabetes. Mother and brother had no autoimmune symptoms to report.

      CasePresentingHPOs: HP:0002014, HP:0001510, HP:0002608, HP:0003261, HP:0033637, HP:0011473, HP:0002900, HP:0001903, HP:0001944, HP:0002246, HP:0001973, HP:0000872, HP:0008207, HP:0003765, HP:0004315, HP:0410240, HP:0030374 (diarrhea, low growth, celiac disease, elevated TTG IgA, endomyosial antibody, absence of duodenal villi, secondary hypokalemia, anemia, dehydration, scalloping of duodenal folds, cytopenias, hashimoto thyroiditis, Addison disease, psoriasis, low IgG, low IgA, low memory B cells)

      CaseHPOFreeText: possible lichen sclerosis, negative titers to varicella-zoster virus and mumps despite vaccination. Patient showed switched memory B cells 1.4%, unswitched memory B cells 4.9%, and intraepithelial lymphocytes. Additional diagnosis's: Celiac disease, Hashimoto thyroiditis, Addison disease, CVID

      CaseNotHPOs: HP:0002718 (recurrent bacterial infections)

      CaseNotHPOFreeText: abnormal stool culture

      CasePreviousTesting: none

      GenotypingMethod: Whole exome sequencing (research based)

      PreviouslyPublished: not reported

      Variant: NM_005214.5:c.457+2T>C

      ClinVarID: not found

      CAID: CA350138849

      gnomAD: not found

      SupplementalData: n/a

      Note: Functional information present. Immunophenotyping using flow cytometery revealed diminished expression of CTLA4 on CD4+/Foxp3+/CD45RA− memory regulatory T cells (Tregs) (Figure 3A).

      Gene:CTLA4 HGNC:2505 InheritancePattern:AutosomalDominant DiseaseEntity:CHAI AlleleOrigin:Germline Zygosity:Heterozygosity CAID:CA350138849 UnregisteredVariant ClinicalStatus:Symptomatic Cytopenia:Reported Lymphoproliferation:Reported DigestiveSystem:Affected IntegumentarySystem:Affected FamilyInfo Ab Deficiencies VCEP
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    ncbi.nlm.nih.gov/pmc/articles/PMC9352891/
  27. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov
    Transient Neonatal Diabetes Mellitus in SHORT Syndrome: A Case Report
    1
    1. Vibhor 10 Jul 2026
      patient

      Case#: patient, M, Age of Report: newborn, Ethnicity: Korean.

      CasePresentingHPOs: HP:0004322 (Short stature), HP:0004325 (Decreased body weight), HP:0040195 (Decreased head circumference), HP:0003074 (Hyperglycemia), HP:0000325 (Triangular face/Facial dysmorphim Triangular shape), HP:0011220 (Prominent forehead), HP:0000490 (Deeply set eye/Ocular depression), HP:0009125 (Lipodystrophy), HP:0000023 (Inguinal hernia), HP:0001642 (Pulmonic stenosis), HP:0001684 (Secundum atrial septal defect/ASD secundum), HP:0000684 (Delayed eruption of teeth)

      CaseHPOFreeText: To the best of our knowledge, this is the first case report of SHORT syndrome with TNDM.

      The patient was a newborn male and the only child of a healthy non-consanguineous Korean couple with a non-contributory family history. The height of his father and mother was 170 cm (−0.70 SD score) and 160 cm (−0.04 SD score), respectively. They had no dysmorphic features. The mother had regular antenatal check-up and did not have any history of medical and obstetric problems during pregnancy. He was born at 38 weeks of gestation but displayed features of IUGR during pregnancy. His birth weight was 1.8 kg (<3rd percentile), length 44 cm (<3rd percentile), and head circumference 31 cm (<3rd percentile) according to the Korean reference for birth weight based on gestational age and sex. The initial blood glucose level was 70 mg/dl. The baby was exclusively breastfed starting on day 3 and was in generally good condition. However, blood glucose level was between 218 and 263 mg/dl at 5 day of age. At the age of 20 day, his blood glucose level was still high (205–260 mg/dl), and the infant was referred to the endocrine clinic for persistent hyperglycemia assessment. On physical examination, several dysmorphic features (triangular-shaped face, prominent forehead, ocular depression, lipodystrophy at the lumbar region) and inguinal hernia were present. The systolic and diastolic blood pressure measurements were 74 and 42 mmHg, respectively. The serum c-peptide and insulin levels were 2.83 ng/ml (normal: 1.0–3.5) and 120 μU/ml (normal: 2.8–13.5), respectively. Baseline chemistry including serum blood urea nitrogen was 15.3 mg/dl (normal: 7.0–20.0), creatinine 0.9 mg/dl (normal: 0.6–1.2), aspartate aminotransferase 38 U/L (normal: 14–40), and alanine aminotransferase 16 U/L (normal: 9–45), as well as complete blood count profile were within normal range. Urinalysis showed no glucose or ketones. There was no sign of ketoacidosis and the patient had no type 1 diabetes autoantibodies (antibodies against glutamic acid decarboxylase, islet cell, islet antigen-2, and insulin). The liver and pancreas ultrasonography revealed no structural abnormality. Echocardiography at the age of 1 month confirmed mild pulmonary stenosis and ASD secundum (2 mm) which did not require surgical intervention. Neonatal diabetes mellitus (NDM) was suspected on the basis of hyperglycemia occurring within the first month of life that lasted for >2 weeks and required insulin therapy. At age of 25 day, clinical exome sequencing was performed to identify the genetic cause of NDM.

      To monitor the glycemic level, his blood glucose was measured at the beginning of each feeding session. The patient was treated with subcutaneous insulin, and blood glucose level gradually stabilized. The blood glucose levels ranged from 110–250 mg/dl during the next 10 days. An adequate glucose level was achieved at 6 weeks of age without insulin treatment. His body weight was 4.4 kg (<3rd percentile) and his length was 61.6 cm (<3rd percentile) at 10 months of age. The patient experienced no hyperglycemic episode and the glycated hemoglobin was 5.0% and insulin level 2.8 μU/ml. At 10 months of age, the patient had no teeth erupted in the oral cavity.

      CaseNotHPOs: N/A.

      CaseNotHPOFreeText: Urinalysis showed no glucose or ketones. There was no sign of ketoacidosis and the patient had no type 1 diabetes autoantibodies (antibodies against glutamic acid decarboxylase, islet cell, islet antigen-2, and insulin). The liver and pancreas ultrasonography revealed no structural abnormality.

      CasePreviousTesting: N/A.

      CaseMethod1: N/A.

      CaseMethod2: N/A.

      CaseGenotypingMethod: TruSight One sequencing panel and Sanger sequencing.

      Variant: NM_181523.3:c.1945C>T (p.Arg649Trp).

      ClinVar: 60763.

      CAID: CA344799.

      gnomAD: N/A.

      VariantEvidence: N/A.

      CaseAddInfo: Segregation analysis could not be performed due to the unavailability of parental samples.

      CasePMIDs: N/A.

      Gene:PIK3R1 HGNC:8979 PMID:34249805 InheritancePattern:AutosomalDominant DiseaseEntity:SHORTsyndrome Zygosity:NoZygosityAssertion CAID:CA344799 Ab Deficiencies VCEP
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    pmc.ncbi.nlm.nih.gov/articles/PMC8261148/
  28. www.jacionline.org www.jacionline.org
    Hematopoietic stem cell transplantation for CTLA4 deficiency
    1
    1. Vibhor 10 Jul 2026
      c.529T>G

      Case#: 2/M. 10 y.o. (onset) and 13 y.o. (at assessment), male

      DiseaseAssertion: Patient had thrombocytopenia, associated bleeding, neutropenia, and lymphoid hyperplasia in lungs, lymph nodes, and brain, refractory to immunomodulatory therapy. The diagnosis of CTLA4 haploinsufficiency was made retrospectively in 7 patients who underwent HSCT for life-threatening, treatment-resistant immune dysregulation and in 1 patient prospectively (unclear which patients were identified retrospectively and prospectively).

      FamilyInfo: None provided

      CasePresentingHPOs: HP:0001873 (Thrombocytopenia), HP:0001875 (Neutropenia), OMIM:188030 (Immune thrombocytopenic purpura/ITP), HP:0001904 (Autoimmune neutropenia)

      CaseHPOFreeText: ITP and autoimmune neutropenia, Reactive lymphoid hyperplasia—lymph nodes, lung, frontal lobe brain.

      All 8 patients received steroids and a calcineurin inhibitor before transplant

      Five patients (including this patient) had peripheral blood HSC grafts and received cyclosporine and mycophenolate mofetil (MMF) for graft versus host disease (GvHD) prophylaxis.

      Patient died 4 months post-transplant due to transplant-related mortality of severe acute gut GvHD (Acute grade IV gut).

      CaseNotHPOs: N/A

      CaseNotHPOFreeText: N/A

      CasePreviousTesting: Not found

      GenotypingMethod: Not found

      PreviouslyPublished: Yes, Schwab et al. PMID: 29729943

      Variant: NM_005214.5:c.529T>G

      ClinVarID: N/A

      CAID: CA350139018

      gnomAD: Not found

      SupplementalData: More information regarding Lymphocyte subsets and Immunoglobulins in Table I. Table II contains variant information and Table III contains further details about HSCT and a breakdown of each patient's transplant procedure.

      Note: No mention of whether or not the patient was tested using transendocytosis.

      Gene:CTLA4 HGNC:2505 ClinicalStatus:Symptomatic Cytopenia:Reported Supplemental Data Thrombocytopenia: Reported AssociatedBleeding:Reported Neutropenia:Reported LymphoidHyperplasia:Reported ImmuneThrombocytopenicPurpura:Reported ITP: Reported AutoimmuneNeutropenia: Reported ReactiveLymphoidHyperplasia:Reported PatientDeceased HSCT Acute GvHD Grade IV GvHD Lymphocyte Subsets Immunoglobulin Data Variant Information Transplant Procedure Ab Deficiencies VCEP
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    jacionline.org/article/S0091-6749(16)00380-8/fulltext
  29. www-clinicalkey-com.libproxy.lib.unc.edu www-clinicalkey-com.libproxy.lib.unc.edu
    Clinical characteristics of 30 Czech families with long QT syndrome and KCNQ1 and KCNH2 gene mutations: importance of exercise testing - ClinicalKey
    1
    1. Vibhor 10 Jul 2026
      A two-year-old girl

      Case#: 2 year old female, Albanian

      DiseaseAssertion: APDS

      FamilyInfo: The patient was the first of three children from a non-consanguineous family of Albanian origin

      CaseHPOFreeText: presented with recurrent otitis media, respiratory infections, persistent splenomegaly and nonmalignant lymphadenopathy. In the first year of life, she had recurrent episodes of wheezing associated with viral infections. In four occasions, she developed otitis media. Clinical evaluation at 17 months of age revealed splenomegaly suggesting Autoimmune lymphoproliferative disease (ALPS), but analysis of CD4 − /CD8 − /TCR alpha/beta + T cells was normal. In addition, bone marrow morphology and karyotype were normal. At the age of 21 months, the patient was hospitalized due to an additional episode of otitis caused by multidrug resistant Pseudomonas aeruginosa . Since then, she suffered of recurrent otorrhea, due to Haemophilus influenzae and Moraxella catarrhalis . Virological testing ( Table 1 ) revealed chronic low-level Epstein–Barr virus (EBV) viraemia characterized by EBV-DNA persistence and elevated anti-VCA IgM (total viral load ranging from negative to 506 copies/ml; VCA IgM ranging from 43 AU/ml to 186 AU/ml).

      CasePreviousTesting: No genotyping ot other genes

      GenotypingMethod: Genetic analysis of PIK3CD by Sanger sequencing revealed a heterozygous G > A mutation at the position c.3061 resulting in E1021K substitution

      Variant: heterozygous G > A mutation at the position c.3061 resulting in E1021K substitution

      CAID: CA145460

      gnomAD: variant is absent in gnomAD v2.1.1

      Gene:PIK3CD HGNC:8977 InheritancePattern:AutosomalDominant Zygosity:Heterozygous DiseaseEntity:APDS CAID: CA145460 ClinicalStatus:Symptomatic RecurrentInfections RespiratoryFindings DysmorphicFeatures AbnormalImmunoglobulins Ab Deficiencies VCEP
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  30. www.ncbi.nlm.nih.gov www.ncbi.nlm.nih.gov
    Phosphoinositide 3-kinase δ gene mutation predisposes to respiratory infection and airway damage
    1
    1. Vibhor 10 Jul 2026

      Case#: Angulo_2014_P5, M, 12 months old (onset), origin in England

      DiseaseAssertion: APDS

      FamilyInfo: Pedigree in figure 1. Affected sister, son, and niece

      CasePresentingHPOs: HP:0002783, HP:0410018, HP:0003496, HP:0005403, HP:0032218, HP:0005415, HP:0010976, HP:0020112, HP:0000365, HP:0002878, HP:0033537, HP:0011950, HP:0001744, HP:0025289, HP:0030387, HP:0030381, HP:0030877 (recurrent lower respiratory tract infection, recurrent ear infection, elevated IgM, decreased T cells, decreased CD4+ T cells, decreased CD8+ T cells, decreased B cells, Increased proportion of CD4+CD25+ regulatory T cells, hearing impairment, type 2 respiratory failure, mosaic attenuation, inflammatory bronchiolitis, splenomegaly, cervical lymphadenopathy, increased class switched memory B cells, increased transitional B cells, mixed obstructive/restrictive FEV1/FVC,

      CaseHPOFreeText: increased CD25+ as a percentage of CD3+, increased CD3+CD56+ as % of CD3+, increased proportion of CD4+ CD25+ CD127– CD45RA- regulatory T cells, increased proportion of CD8+ CD25+ CD127– CD45RA+ regulatory T cells, increased CD4+ CD25- CD127– CD45RA- as % of CD4+ peripherally expanded T cells, increased CD8+ CD25- CD127– CD45RA+ as % of CD8+ peripherally expanded t cells, severe necrotising pneumonia, hypoperfused right lung, recurrent salivar gland abscesses, CD

      CaseNotHPOs: HP:0410242, HP:0410240 (abnormal IgG, abnormal IgA)

      CaseNotHPOFreeText: malignancy

      CasePreviousTesting:

      GenotypingMethod: WES and Sanger

      PreviouslyPublished: not reported

      Variant: heterozygous NM_005026.5:c.3061G>A (p.E1021K)

      ClinVarID: 88675

      CAID: CA145460

      gnomAD: Not present in gnomAD

      SupplementalData: Phenotypic info in table S2

      Gene:PIK3CD HGNC:8977 InheritancePattern:AutosomalDominant DiseaseEntity:APDS AlleleOrigin:Germline Zygosity:Heterozygous ClinVarID:88675 CAID:CA145460 ClinicalStatus:Symptomatic Cytopenia:Reported RespiratorySystem:Affected Lymphoproliferation:Reported SevereInfection:Reported FamilyInfo SupplementalData Ab Deficiencies VCEP
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    ncbi.nlm.nih.gov/pmc/articles/PMC3930011/
  31. www-sciencedirect-com.libproxy.lib.unc.edu www-sciencedirect-com.libproxy.lib.unc.edu
    Paternal gonadal mosaicism as cause of a puzzling inheritance pattern of activated PI3-kinase delta syndrome
    1
    1. Vibhor 10 Jul 2026
      We

      Case#: Case 1, male, Brazilian

      DiseaseAssertion: APDS1

      FamilyInfo: We identified a kinship that included 3 half-siblings with symptoms typical for APDS1. The patient's father (I.4), a truck driver, reported that in addition to the index case, he had 4 additional children with 3 other women living in different Brazilian cities along his truck route and that 2 of these children (II.4 and II.5) had symptoms similar to the index case (Fig 1A). Of his 5 children, 1 had died at 3 years of age (II.1) with clinical symptoms similar to the index case, including hepatosplenomegaly, fever, and recurrent infections; immunologic studies were not performed. The other symptomatic half-brother (II.5) was evaluated at 5 years of age with a history of 5 episodes of pneumonia, recurrent oral candidiasis, several upper respiratory infections, and hepatosplenomegaly. The pedigree suggested that the father (I.4) carried the same autosomal-dominant PIK3CD mutation that affected 3 sons born to different mothers. However, neither he nor the mothers of the affected boys had any symptoms suggestive of APDS. Sanger sequencing demonstrated that neither the father nor the mothers of the affected boys carried the identified PIK3CD mutation in blood. This raised the possibility of germline or gonadal mosaicism in the father. To test this hypothesis, genomic DNA was extracted from his semen. As illustrated in Figure 1F, semen-derived DNA carried the heterozygous p.E1021K mutation identified in the affected sons. Based on relative peak heights, we estimated that 20% to 25% of the semen carried the mutation.

      CaseHPOFreeText: The index case (II.4 in Fig 1A) had 10 episodes of pneumonia, 2 episodes of sepsis, several upper respiratory infections, and oral moniliasis within the first year of life. He subsequently developed hepatosplenomegaly, lymphadenopathy, and an axillary abscess owing to Candida albicans. At 3 years of age, laboratory investigation showed increased immunoglobulin (Ig) M (368 mg/dL) and IgG (1,450 mg/dL) levels, normal IgA level (107 mg/dL), low CD4 (330/mm3) and increased CD8 (1,229/mm3) counts, and low CD19 B cells (17/mm3). IgG subclasses showed normal absolute levels of IgG1 (1,020 mg/dL), IgG2 (79.0 mg/dL), IgG3 (78.3 mg/dL), and IgG4 (28.1 mg/dL); however, their ratio showed a proportional decrease of IgG2.

      GenotypingMethod: Sanger sequencing. Unclear if entire PIK3CD gene was sequenced across intron/exon boundaries.

      Variant: a heterozygous PIK3CD hotspot mutation (c.3061G→A, p.E1021K) was identified by Sanger sequencing.

      CAID: CA145460

      gnomAD: absent from gnomAD v2.1.1

      Gene:PIK3CD HGNC:8977 InheritancePattern:AutosomalDominant DiseaseEntity:APDS AlleleOrigin:Germline Zygosity:Heterozygous CAID: CA145460 ClinicalStatus:Symptomatic RespiratoryFindings DysmorphicFeatures FamilyInfo Ab Deficiencies VCEP
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    www-sciencedirect-com.libproxy.lib.unc.edu/science/article/pii/S1081120617310645
  32. jmg.bmj.com jmg.bmj.com
    Genes associated with common variable immunodeficiency: one diagnosis to rule them all?
    1
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    jmg.bmj.com/content/53/9/575
  33. www.ncbi.nlm.nih.gov www.ncbi.nlm.nih.gov
    Human primary immunodeficiency caused by expression of a kinase-dead p110δ mutant
    1
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    ncbi.nlm.nih.gov/pmc/articles/PMC6387453/
  34. www.ncbi.nlm.nih.gov www.ncbi.nlm.nih.gov
    Case Report: Intestinal Nodular Lymphoid Hyperplasia as First Manifestation of Activated PI3Kδ Syndrome Due to a Novel PIK3CD Variant
    1
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    ncbi.nlm.nih.gov/pmc/articles/PMC8528001/
  35. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov
    A novel CTLA-4 deletion variant in a child with refractory autoimmune hemolytic anemia: molecular and functional characterization
    1
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    pmc.ncbi.nlm.nih.gov/articles/PMC12672875/
  36. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov
    Abatacept Induces Long-Term Reconstitution of the B-Cell Niche in a Patient With CTLA-4 Haploinsufficiency: A Case Report
    1
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    pmc.ncbi.nlm.nih.gov/articles/PMC11655169/
  37. www.ncbi.nlm.nih.gov www.ncbi.nlm.nih.gov
    Fatal CTLA‐4 heterozygosity with autoimmunity and recurrent infections: a de novo mutation
    1
    1. Vibhor 10 Jul 2026
      c.380A>G

      Case#: N/A. Patient was the only one included in this paper. Male. Age of Onset: 9 y.o. Age of evaluation: 42 y.o onwards. Age of Death: ~49 y.o. Origin in Portugal, ethnicity not specified.

      DiseaseAssertion: Evans Syndrome

      FamilyInfo: No familial segregation analysis could be performed as the patient′s first‐degree relatives (reportedly healthy) refused genetic testing, and the patient had no progeny. Additionally, when the patient was diagnosed and treated for other health conditions, it was noted that "There was no relevant family history".

      CasePresentingHPOs: ORPHA:1959 (Evan's syndrome), HP:0002014 (Diarrhea), HP:4000055 (Intestinal Inflammation), HP:0002719 (Severe/Recurrent Infections), HP:0000403 (Recurrent Otitis), HP:0002254 (Intermittent Diarrhea), HP:0001873 (Severe Thrombocytopenia), HP:0002090 (Pneumonia), HP:0004315 (low IgG), HP:0002720 (low IgA), HP:0001082 (Cholecystitis), HP:0001433 (Hepatosplenomegaly), HP:0008711 (Benign prostatic hypertrophy), HP:0012227 (urethral stricture), HP:0003508 (Proportionate Short Stature), HP:0001888 (Lymphopenia), HP:0410385 (Low levels of CD8+ T cells), HP:0410378 (Low levels of CD4+ T cells),

      CaseHPOFreeText: Lymphoproliferation, mild ileal inflammatory infiltrate on histology and hemolysis, lower limb cellulitis, IgE and IgD levels were undetectable, but IgM levels were normal, Bilateral osteonecrosis of femoral head and condyles at age 43, Facial vitiligo, Hemoglobin 12.5 g/L; leukocytes: 8700/μL; platelets 28000/μL, trabeculated bladder.

      Duodenal, ileal and bladder biopsy: inflammatory infiltrate (not characterized) Negative: direct Coombs, ANA, EBV DNA, CMV DNA, hepatitis B, C, HIV, proteinuria, urinary Ig loss Antiplatelet Ab positive.

      Normal total leukocyte count but patient had lymphopenia.

      Antidiphtheria Ab: 0.44 UI/mL (protection titer >1.0 UI/mL); peripheral blood mononuclear cell proliferation to PHA, PPD, and Candida were slightly reduced.

      Normal levels of CD3+ and CD4+ but low levels of CD8+ (T cells), Low levels of B cells, NK cells and CD4+ (CD45RA+ and CD45RO+) cells. Normal levels of CD45RA+ but high levels of CD45RO+ (CD8 + T cells).

      Born to nonconsanguineous parents.

      CaseNotHPOs: N/A

      CaseNotHPOFreeText: In 2013, the 45‐year‐old patient was admitted for sepsis. An elective total right hip replacement 6 months before had been followed by recurrent urosepsis. Postoperative diagnosis: recurrent urosepsis caused by Enteroccocus faecalis, Klebsiella pneumoniae and Pseudomonas aeruginosa.

      For the next 3 years, the patient remained free of infection on IVIG replacement (0.6–0.8 g/kg) every 3–4 weeks, with a median IgG concentration of approximately 6 g/L. In October 2016, he underwent a transurethral resection of the prostate and soon afterward developed diarrhea and significant weight loss. He was again admitted to our hospital, but after extensive investigation, no infectious or immune‐mediated cause could be found. There was an excellent response to a short course of a higher dose of oral prednisolone (30 mg/day, tapered over the next 2 months to 5 mg/day). In February 2017, he was admitted to his local hospital with left‐sided epididymo‐orchitis and rapidly died from hospital‐acquired pneumonia.

      CasePreviousTesting: Broad genetic screening using a custom panel of many immune‐related genes using an ion proton next‐generation sequencer, followed by Sanger sequencing, was performed at the Laboratory of Clinical and Infectious Diseases of the National Institute of Allergy and Infectious Diseases, Bethesda, Maryland. See Table 1.

      GenotypingMethod: CTLA‐4 sequencing was performed after amplification of the four exons. See Table 1.

      PreviouslyPublished: N/A

      Variant: NM_005214.5:c.380A>G

      ClinVarID: 949358

      CAID: CA350138668

      gnomAD: Not found

      SupplementalData: N/A

      Note: Not functionally tested using transendocytosis

      Gene:CTLA4 HGNC:2505 ClinicalStatus:Symptomatic InheritancePattern:AutosomalDominant CAID:CA350138668 ClinVarID: 949358 Zygosity:Heterozygous PatientDeceased DiseaseEntity: Evans Syndrome Diarrhea:Reported IntestinalInflammation:Reported SevereInfections:Reported RecurrentInfections:Reported RecurrentOtitis:Reported IntermittentDiarrhea:Reported Thrombocytopenia: Reported SevereThrombocytopenia:Reported Pneumonia: Reported LowIgG: Reported LowIgA:Reported Cholecystitis:Reported Hepatosplenomegaly:Reported BenignProstaticHypertrophy:Reported UrethralStricture:Reported ProportionateShortStature:Reported Lymphopenia:Reported LowCD8+TCells:Reported LowCD4+TCells:Reported Lymphoproliferation:Reported MildIlealInflammatoryInfiltrate:Reported LowerLimbCellulitis:Reported BilateralOsteonecrosis:Reported FacialVitiligo:Reported ElectiveHipReplacement:Reported Sepsis:Reported Hospital‐AcquiredPneumonia:Reported Ab Deficiencies VCEP
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    ncbi.nlm.nih.gov/pmc/articles/PMC5715409/
  38. www.biorxiv.org www.biorxiv.org
    Cooperative antibiotic response in coupled biofilm and planktonic E. faecalis communities
    5
    1. Public_Reviews 10 Jul 2026

      eLife Assessment

      This useful study combines experiments and mathematical modeling to show that antibiotic protection provided by resistant cells can extend across both surface-associated and freely growing bacterial populations. Notably, they show that treatment efficacy depends on population composition and density. The evidence supporting the main conclusions is incomplete, primarily because the biofilm context is not adequately characterized and demonstrated, raising the concern that it might represent only an aggregate of cells on the surface (rather than a biofilm) under the studied experimental conditions.

      Summary
    2. Public_Reviews 10 Jul 2026

      Reviewer #1 (Public review):

      Summary:

      This important study examines how antibiotic-resistant bacterial cells can protect neighboring sensitive cells in mixed populations that occupy both surface-associated and freely growing states. Using experiments in Enterococcus faecalis together with a mathematical model, the authors test the hypothesis that protection would be stronger in biofilm-associated populations, but instead find that resistance-mediated protection extends broadly across both population types. The work provides evidence that antibiotic efficacy depends strongly on community composition, population density, and density-dependent detoxification dynamics.

      Strengths:

      A major strength of the study is the close integration of experimental measurements with a relatively simple quantitative model that captures many of the observed population dynamics. In particular, the work highlights how interactions between antibiotic detoxification, cellular growth, and saturation at carrying capacity can generate nonintuitive behavior, including the reported population inversion effect. The agreement between the well-mixed model and the experimental observations is convincing, and the spatial analyses suggest that cells within the biofilm are sufficiently intermixed that large-scale spatial segregation is unlikely to dominate the observed behavior.

      Weaknesses:

      The mechanistic interpretation could, however, be clarified further by more explicitly emphasizing the competing timescales associated with detoxification, growth, and resource limitation. The current results suggest that when resistant cells are initially abundant, detoxification occurs rapidly relative to growth, allowing the population to approach carrying capacity after relatively few doublings, whereas slower detoxification at lower resistant fractions may permit greater expansion of sensitive cells once antibiotic concentrations decline. Additional direct measurements of antibiotic concentrations over time would also strengthen the connection between the experimental system and the modeling framework by testing whether the detoxification dynamics assumed in the model are quantitatively appropriate, although this seems very plausible.

      The study also raises interesting questions regarding the role of spatial structure and exchange between planktonic and biofilm-associated populations. It would be informative to explore whether biofilm-specific protection becomes more pronounced at lower antibiotic concentrations, where local detoxification may compete more directly with antibiotic penetration into the biofilm, and in this context, the dynamics of exchange between biofilm and planktonic populations would be interesting to understand. Overall, the evidence supporting the central conclusions is convincing, and the study will likely be of broad interest to researchers studying microbial communities, antibiotic resistance, and collective population dynamics.

      Review 1
    3. Public_Reviews 10 Jul 2026

      Reviewer #2 (Public review):

      Summary:

      In this manuscript, Martins et al. examined the cooperative response of E. faecalis cells to beta-lactams, in both planktonic culture and in biofilm. They found that the competition outcome between the susceptible and resistant strains is frequency dependent; they have also quantified how the competition curves change with inoculation OD and antibiotic concentration. To the authors' surprise, the competition dynamics are not that different in biofilm and in planktonic culture, which the author attributed to the unstructured nature of the thus-grown E. faecalis biofilms, quantified through correlation analysis. Using a well-mixed model capturing growth, death, and drug degradation by the resistant cells, the authors were able to quantitatively capture the experimental observation.

      Strengths:

      Overall, the data presented are solid. Although there is not much surprise after the understanding that the E. faecalis biofilm is unstructured, the manuscript still provides a useful "null case", so to speak, for researchers in the field when considering antibiotics in the context of biofilm. The theoretical model presented and the procedure of fitting the experimental data are useful to the research community.

      Weaknesses:

      One clarification the author should make is on the biofilm growth process. Specifically, could staining experiments be performed to demonstrate the secretion of the extracellular matrix? Just by looking at Figure 1b, it is hard to say. It remains a question whether the biofilm culture simply contains unstructured clusters rather than real biofilms (that are usually structured).

      Review 2
    4. Public_Reviews 10 Jul 2026

      Reviewer #3 (Public review):

      Summary:

      The authors studied social aspects of antibiotic resistance by co-cultivating antibiotic-resistant and sensitive Enterococcus faecalis (an important pathogen) as biofilms to assess the extent to which sensitive cells can take advantage of the protection provided by resistant cells against both a beta-lactam antibiotic and in the presence of a B-lacatamase inhibitor. By quantifying the proportion of each cell type using fluorescence microscopy, they conclude that protection is provided equally in the biofilm and planktonically, and that the biofilm is completely unstructured with regard to the locations of the two cell types. A mathematical model is then used to show that no spatial information is needed to recapitulate the results and that the protective effect can be described completely by the growth rates of the two cell types and the affinity of the β-lactamase to the antibiotic and inhibitor. The strength of evidence is difficult to assess due to unclear descriptions of some methods, and the significance of the findings is limited by the experimental setup, where antibiotics were added very close to the time of inoculation.

      Strengths:

      The co-cultivation of antibiotic-resistant and sensitive bacteria allows for exploration of the social aspects of antibiotic resistance. Fluorescently-tagged strains allow for unambiguous tracking of the two cell types. The simultaneous analysis of biofilm and planktonic cells enables insight into whether these different growth modalities are influenced by social aspects of antibiotic resistance. In analyzing the structure of the biofilm, the use of a null model with randomized cell positions allows for an accurate determination of whether the observed data are due to some effect; however, as noted below, there is a caveat to this analysis. The broad observation that biofilm and planktonic populations are linked is generally supported by the data; however, this result is closely tied to the experimental setup used. The development of a mathematical model that can recapitulate results from a second set of data with values obtained from fitting a different set of data shows robustness of the model for using it to explain the results.

      Weaknesses:

      The observed results are tied very closely to the experimental setup of adding antibiotics very close to the time of inoculation, but this connection is not discussed. The described 'population inversion' effect is better described as frequency-dependent selection for resistant cells, but frequency-dependent selection is not discussed. Confocal microscopy was used to quantify the relative proportion of antibiotic-resistant and sensitive cells in the biofilm; however, it is unclear if the entirety of the Z stacks was used to determine these proportions. This is also the case for the analysis of whether the sensitive/resistant cells are non-randomly distributed in the biofilm: it is unclear whether the vertical distance between cells was taken into account. The authors claim that biofilm and planktonic bacteria are protected equally by the presence of resistant bacteria; however, Figure 1a and b seem to clearly show that the proportion of sensitive cells is higher in the planktonic cells compared to biofilm cells when started from an equal frequency inoculum, meaning this is not always the case. The mathematical model is used to confirm the result that no spatial components are needed to describe the results; however, this is mostly linked to the initial setup of the experiment, where antibiotics are added at the time of inoculation, and no biofilm could form before the outcome of the antibiotic-cell interactions was concluded.

      Review 3
    5. Public_Reviews 10 Jul 2026

      Author response:

      We would like to thank the editors for their interest in our work and the three referees for their time and careful reading of the manuscript. The reviewers have provided a series of helpful suggestions that we discuss in this provisional reply and will seek to address in the revised version of the manuscript.

      The main concern raised is that the bacterial community we refer to as a biofilm may instead correspond to a cell aggregate. Following the passing of Prof. Kevin Wood, in whose lab the experimental work was carried out, our ability to perform additional experiments is limited. Nevertheless, we plan to wash and fluorescently stain the extracellular matrix before imaging to measure the extent to which the observed bacterial community is an attached biofilm. In the meantime, we would like to highlight the work of Wen Yu et al. [1], in which E. faecalis biofilms were grown in 96-well plates under antibiotic stress. In particular, one of the strains of E. faecalis used in this article was OG1RF, the same strain used in our study. Crystal violet staining was used to quantify biofilm biomass, providing evidence for biofilm formation under those conditions. While we recognize that the experimental setup differs from ours and that the OG1RF sample used did not contain fluorescent and resistance plasmids, these results nevertheless support the expectation that OG1RF will readily form biofilms.

      Reviewer #1 (Public review):

      The mechanistic interpretation could, however, be clarified further by more explicitly emphasizing the competing timescales associated with detoxification, growth, and resource limitation. The current results suggest that when resistant cells are initially abundant, detoxification occurs rapidly relative to growth, allowing the population to approach carrying capacity after relatively few doublings, whereas slower detoxification at lower resistant fractions may permit greater expansion of sensitive cells once antibiotic concentrations decline. Additional direct measurements of antibiotic concentrations over time would also strengthen the connection between the experimental system and the modeling framework by testing whether the detoxification dynamics assumed in the model are quantitatively appropriate, although this seems very plausible.

      The timescale of drug degradation is an important system metric. We appreciate the referee’s suggestion to quantify antibiotic concentration over time. We plan to perform experiments in which samples are collected from the culture at fixed time intervals. After removing the bacteria from the samples via centrifugation, serial dilutions of the supernatant will then be spotted on a lawn of sensitive cells to measure the antibiotic efficacy at each time point.

      Reviewer #2 (Public review):

      One clarification the author should make is on the biofilm growth process. Specifically, could staining experiments be performed to demonstrate the secretion of the extracellular matrix? Just by looking at Figure 1b, it is hard to say. It remains a question whether the biofilm culture simply contains unstructured clusters rather than real biofilms (that are usually structured).

      We agree with the referee that additional evidence would strengthen our study. As noted above, we will perform additional experiments to demonstrate the presence of an attached biofilm.

      Reviewer #3 (Public review):

      The observed results are tied very closely to the experimental setup of adding antibiotics very close to the time of inoculation, but this connection is not discussed. [...] The mathematical model is used to confirm the result that no spatial components are needed to describe the results; however, this is mostly linked to the initial setup of the experiment, where antibiotics are added at the time of inoculation, and no biofilm could form before the outcome of the antibiotic-cell interactions was concluded.

      The experiment was designed to address how coupled planktonic and biofilm populations develop in the presence of antibiotics, which we will more explicitly discuss in the revised manuscript. We do agree that investigating how mature biofilms and their planktonic populations respond to antibiotic stress is an exciting direction for future studies. However, we believe that is beyond the scope of the our study on the development of coupled populations. We will be sure to explicitly identify this limitation in our revisions.

      The described ‘population inversion’ effect is better described as frequency-dependent selection for resistant cells, but frequency-dependent selection is not discussed.

      The reviewer is correct that this ‘population inversion’ is a frequency-dependent (perhaps also density-dependent) effect, and we should have situated it within that broader ecological framework. We will use this terminology in our revisions. We do want to acknowledge that the late Dr. Kevin Wood was fond of this phrasing to describe the reversal of the dominant strain, which is not necessarily true for frequency-dependent effects. Although we do not know for certain, we suspect this was a play on the ‘population inversion’ term used in quantum physics, used to describe a system in which its excited state (high energy) population unexpectedly outnumbers its ground state (low energy) population.

      The authors claim that biofilm and planktonic bacteria are protected equally by the presence of resistant bacteria; however, Figure 1a and b seem to clearly show that the proportion of sensitive cells is higher in the planktonic cells compared to biofilm cells when started from an equal frequency inoculum, meaning this is not always the case.

      If the reviewer is indeed discussing Figures 1a and 1b, these are not comparable as the starting fractions differ. On the other hand, if the reviewer was talking about Figures 2a and 2b (which is more clearly discussed by looking at Figures 2c and 2f), we agree that it appears that planktonic communities tend to have a slightly greater frequency of sensitive cells than the biofilms. We will be sure to highlight this observation and possible explanations in our revisions. However, given the uncertainty in these observations, we do not believe the differences are sufficient to alter our overall conclusion that final resistant fractions in biofilm and planktonic populations are quantitatively similar. Furthermore, the no drug treatment shows the same trend, which suggests it’s an effect of different growth dynamics of these two strains at high density rather than driven by the protective effects of resistance cells.

      Confocal microscopy was used to quantify the relative proportion of antibiotic-resistant and sensitive cells in the biofilm; however, it is unclear if the entirety of the Z stacks was used to determine these proportions. This is also the case for the analysis of whether the sensitive/resistant cells are non-randomly distributed in the biofilm: it is unclear whether the vertical distance between cells was taken into account.

      The entirety of the Z stack was used to measure the final resistant fraction in the biofilm. On the other hand, we used only the densest slice of the Z stack to calculate the correlations. The correlations follow the same trend when calculated over less dense slices, but as density decreases, noise increases, so such plots did not bring more clarity to our conclusions and were not included in the manuscript. Additionally, only horizontal correlations (over a slice) were calculated because consecutive Z-stack slices were imaged with a 2.5 µm spacing. Given that the average cell diameter is approximately 1 µm, calculating vertical correlations may miss neighboring cells located between imaged slices, making such measurements unreliable. We will clarify the points raised by the reviewer in the results section and add more detail to the imaging methods section in the revised manuscript.

      References

      (1) Wen Yu, Kelsey M. Hallinen, and Kevin B. Wood. “Interplay between Antibiotic Efficacy and Drug-Induced Lysis Underlies Enhanced Biofilm Formation at Subinhibitory Drug Concentrations”. In: Antimicrobial Agents and Chemotherapy 62.1 (Dec. 2017), 10.1128/aac.01603–17. doi: 10.1128/aac.01603-17. url: https://journals.asm.org/doi/10.1128/aac.0160317 (visited on 01/11/2026).

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    Astrocytic Nonsense-mediated mRNA decay regulates calcium signaling to support synapse function and restrain anxiety
    5
    1. Public_Reviews 10 Jul 2026

      eLife Assessment

      This important study provides the first in vivo evidence that nonsense-mediated mRNA decay (NMD) in mature astrocytes regulates astrocyte function, synaptic plasticity, and anxiety-related behavior. Using a broad range of approaches, the authors show that conditional deletion of Upf2 alters astrocyte morphology and calcium signaling while impairing synaptic transmission and plasticity, providing solid support for the central conclusion that astrocytic NMD influences neural circuit function. Some key mechanistic claims remain incompletely supported, including whether phenotypes reflect astrocyte remodeling versus loss, the interpretation of synaptic engulfment data, the link between NMD targets and calcium signaling, and the extent to which calcium dysregulation explains the observed synaptic and behavioral effects.

      Summary
    2. Public_Reviews 10 Jul 2026

      Reviewer #1 (Public review):

      Summary:

      Lituma and colleagues investigate the role of NMD in astrocytes, an underexplored question given that prior work on NMD in the brain has focused exclusively on neurons. Using a tamoxifen-inducible, astrocyte-specific Upf2 conditional knockout (cKO) mouse, they report that loss of astrocytic NMD causes: (1) reductions in astrocyte cell volume and surface area across hippocampus, visual cortex, and prefrontal cortex; (2) decreased excitatory synapse density, reduced dendritic spine density, and impaired synaptic engulfment; (3) deficits in basal synaptic transmission and LTP, with selective impairment of mGluR-LTD; (4) elevated spontaneous calcium transients in astrocytes; and (5) anxiety-like behavior in the elevated plus maze (EPM) and contextual fear conditioning paradigms. Transcriptomic analysis of FACS-isolated astrocytes identifies 277 differentially expressed genes, ~40% of which carry canonical NMD-inducing features, implicating pathways linked to calcium signaling, phagosome formation, and glial development. A rescue experiment using the CalEx calcium extrusion pump demonstrates partial restoration of synaptic strength and anxiety behavior when astrocytic calcium is normalized.

      The study addresses an important gap in our understanding of RNA regulation in glial cells, and the overall conceptual framework is well described. The experimental design is generally appropriate, and the multi-pronged approach lends the main claims a degree of validity.

      Strengths:

      (1) Novelty: This is the first study to systematically examine NMD function in astrocytes in vivo. The identification of astrocytic NMD targets via RNA-seq combined with an NMD-inducing feature classifier is a meaningful methodological contribution.

      (2) Multi-method approach: The authors combine morphological analysis (Imaris 3D reconstruction), synaptic markers (PSD-95, LAMP2 engulfment assay), spine density measurements, acute slice electrophysiology, two-photon calcium imaging, behavioral testing, and transcriptomics. The convergence across these methods strengthens confidence in the claims.

      Weaknesses:

      (1) While the transcriptomic analysis is a valuable addition, the connection between specific NMD targets and the observed calcium phenotype remains largely correlational. The authors identify Gabbr2 and Adora1 as upregulated candidates with canonical NMD features and speculate that their elevated expression drives aberrant calcium signaling. However, no validation (e.g., qRT-PCR or protein-level confirmation) of these candidates is presented. The mechanistic pathway between NMD disruption and elevated calcium is thus inferred from pathway analysis rather than demonstrated. This is a significant gap between the transcriptomic and physiological arms of the study, and the authors should be more explicit about this limitation or, ideally, provide at least one validated target.

      (2) The reduction in astrocyte surface area in cKO mice is interpreted as contributing to reduced synapse contact and engulfment capacity. This is a reasonable hypothesis, but the study does not directly demonstrate that reduced astrocyte territory correlates with reduced synaptic coverage at the level of individual cells or brain regions. The temporal sequence of these events is unknown. Do morphological deficits precede synaptic changes? Clarification and qualification of this causal chain in the Discussion would strengthen the manuscript.

      (3) LFS-induced LTD is unaffected, while mGluR-LTD is reduced. This is intriguing and potentially informative about astrocyte contributions to distinct LTD mechanisms, but the difference receives limited discussion. Given the relevance of mGluR signaling to calcium dynamics and the identified pathway enrichments (GPCR signaling), this specificity deserves more attention.

      (4) The CTRL + CalEx condition is included in the EPM experiment but not in the electrophysiology or calcium imaging experiments, making it difficult to fully assess whether CalEx itself has off-target effects on synaptic transmission or anxiety in wild-type animals. The CTRL + CalEx EPM data (Figure 7F) appears to show a modest reduction in open arm time relative to CTRL, which, if robust, would suggest that excessive calcium reduction in astrocytes is also anxiogenic. This finding would be physiologically relevant and deserves comment.

      Review 1
    3. Public_Reviews 10 Jul 2026

      Reviewer #2 (Public review):

      Astrocytes are highly responsive to their environment and play a range of critical roles in brain function. Lituma et al. theorize that one mediator of that responsiveness is the regulation of RNA stability. They therefore undertake an assessment of astrocytes missing Upf2, a protein required for mRNA degradation via nonsense-mediated decay. This is an interesting study, approaching astrocyte biology from a novel angle. The authors take on an ambitious set of experiments, spanning morphological assessment, synaptic engulfment, electrophysiology, behavior, and calcium imaging.

      The authors show convincing data that knocking out Upf2 in astrocytes impairs synaptic plasticity, affects behavior, and changes the complement of astrocytic mRNA. These results, in and of themselves, are intriguing and suggest that NMD is an important biological process in astrocytes, warranting further study.

      My primary concern is whether the authors may be largely studying dying cells. The idea that NMD disruption has a dramatic effect on astrocyte morphology is an intriguing idea, but it is not fully established here. The nuclei in the example cKO morphology images appear small and/or fragmented. This raises concerns that the authors did not ensure that they had the full 3D morphology of the astrocyte in the section, and the cell is in part cut off, which would compromise any data on the morphology. The authors state that the tissue was sectioned at 70 um. The diameter of an astrocyte in the adult mouse brain is typically between 50 and 70 um. Unless astrocytes are perfectly positioned in the center of the slice, at this thickness, the majority of astrocytes will almost certainly be partially cut off. More detail on how cells were chosen and what quality control metrics were implemented would alleviate concerns here. An alternative possible explanation for these small/fragmented nuclei is that cKO astrocytes may be unhealthy to the point that they are actively dying. Using the transgenic ZsGreen label, the authors state that they observe a size change (Figure S4); this is not readily apparent and is not quantified in any way. It does appear from these images that there may be a loss of some astrocytes; cell death, which would also be an interesting finding, is a fundamentally different process than morphologic restructuring in living cells. The authors do attempt to count astrocytes (Figure S6B), but do so with GFAP. This is a fundamentally flawed approach. Because GFAP is not readily detectable in most healthy astrocytes in most gray matter regions, GFAP should not be used to quantify astrocyte numbers; this experiment should be repeated with a better marker, such as Aldh1l1, Sox9, etc.

      Synaptic engulfment: This is an extraordinarily high degree of engulfment in the control animals compared to many published studies, leading to concern as to the technical approach. Indeed, the overall low level of PSD-95 signal in control conditions in adult mice is concerning as to the technical accuracy of the approach. It is unclear exactly how the investigators labeled the astrocytes; presumably via the ZsGreen label, but it is never stated, and the only images shown are the highly processed Imaris renderings. The small astrocytic processes, or leaflets, that make up the vast majority of the astrocytic arbor are on the order of 100nm in diameter. The processes shown in Figure 2B are, according to the scale bar, at least 20x that size. It is difficult to have much faith in these results as currently presented.

      The signal-to-noise ratio of the GCaMP experiments is worryingly low, likely responsible for the abnormally low dF/F in all conditions and the lack of significant change between control and CalEx, when control astrocytes should show a much higher GCaMP signal than any CalEx-expressing astrocyte. That said, the higher Ca++ in Upf2 KO astrocytes is intriguing. Given the roles of elevated calcium in cell death, this may reflect cells that are unhealthy to the point that they are starting to die.

      The authors conduct a FACS-based analysis of astrocytic mRNA from control vs Upf2-KO, with intriguing results. An important caveat, though, is that a large amount of astrocytic mRNA is in the processes. If mRNA stability is being actively and rapidly regulated, it seems likely that the mRNA in the processes would be the most relevant population of regulated mRNA. FACS-based approaches to astrocyte purification will, as robustly shown elsewhere, strip off those processes. Particularly given that the authors have shown that the processes may be the most actively changing astrocytic compartment with Upf2 KO, this is a strange choice of technique vs. something like Ribotag that would preserve the mRNA in processes. At least, there should be some discussion regarding using FACS for this analysis and the consequences for profiling mRNA in astrocytic processes.

      Minor points:

      (1) The use of the Aldh1l1-CreER mouse is a strong choice and has been shown to be highly astrocyte-specific. Combining that transgenic mouse with viruses driven by different forms of the GFAP promoter is quite bizarre in several ways. First, GFAP-dependent AAVs have been shown repeatedly to have significant neuronal leak. Second, these mice are, in all cases, receiving two different viruses, driven by different forms of the GFAP promoter, and the non-Cre virus is not Cre-dependent (vs. a much more standard approach of using a Cre-dependent second virus to ensure that all analyzed cells received both viruses). The authors mention that "this experimental design ensures that phenotypes are not caused by an acute effect of tamoxifen." It is certainly true that tamoxifen is not a biologically neutral molecule. However, the mice still receive tamoxifen, both in these morphology virus experiments and in almost all other experiments. This experimental approach is not inherently bad, nor does it necessarily invalidate the data (although the near-certain neuronal contamination due to the GFAP promoter-driven viruses is a concern). It is, however, convoluted in ways that appear unnecessary. If there is a strong rationale for this approach beyond the tepid explanation already present, it should be explicitly mentioned.

      (2) The characterization of the knockout is incomplete. While the authors should be applauded for their attempts to phenotype the cells in which they observe Cre-mediated recombination, there are issues with their technical approach. Most importantly, and an issue that affects other analyses in the paper as well: the vast majority of astrocytes in the healthy cortex do not express GFAP. Therefore, using GFAP to claim high astrocyte specificity and efficiency is a fundamentally flawed approach. Second, MBP is a myelin marker, not a cytoplasmic marker, and would not successfully colocalize with a cytoplasmic marker like ZsGreen even if recombination in oligodendrocytes did occur. Third, recombination at one set of LoxP sites is not a reliable indicator of recombination at other sites. Recombination efficiency is highly dependent on the spacing between the LoxP sites and cannot be reliably extrapolated to other floxed genes without validation. Finally, the most likely culprit for off-target recombination with Aldh1l1-CreERT2 (or other astrocyte-selective Cres, and certainly the GFAP-based viral promoters) is neurons, which the investigators did not test for. Neuronal Aldh1l1-CreERT2 leak is most likely to occur in the hippocampus. With the images shown in Fig S3, it is unclear whether it is possible to convincingly colocalize Upf2 staining with a cytosolic marker of all astrocytes, such as Aldh1l1 or S100b, but such data would be more appropriate. An alternative approach to validation would be in situ hybridization.

      (3) Supplementary Table 2 should include gene IDs, not just Ensemble IDs.

      (4) It is not fully clear what the investigators are denoting as a spine in Figure 2E; the two images do not appear to have the large degree of difference that the quantification suggests. The oversaturation of the signal complicates assessment.

      (4) A more detailed discussion of the rationale behind the timeline would be helpful. What is the half-life of Upf2, and how rapidly do NMD genes build up upon Upf2 disruption? In particular, in the case of virus experiments, the timeline is quite fast: ~2.5 weeks from injection to analysis. ssAAV expression takes over a week to reach appreciable levels.

      Review 2
    4. Public_Reviews 10 Jul 2026

      Reviewer #3 (Public review):

      Summary:

      The authors investigate mRNA targets of the nonsense-mediated decay (NMD) pathway in astrocytes and link the dysfunction of NMD in astrocytes to aberrant synaptic transmission that has downstream effects on behavior. Specifically, they find a link between the aberrant synaptic transmission with elevated spontaneous calcium signaling in astrocytes, and functionally they demonstrate that manipulating astrocyte calcium signaling with CalEx modulates astrocyte calcium signaling towards wildtype levels and improves anxiety behavior. They investigate the astrocyte calcium signaling changes in Upf2 conditional knockout mice in several brain regions that have been linked to anxiety behavior, including the hippocampus and prefrontal cortex. They also observe aberrant astrocyte calcium signaling in the visual cortex, demonstrating that dysfunction of the NMD pathway in astrocytes has widespread effects on synaptic transmission in various brain regions. This work identifies, through RNA-Sequencing, potential mRNA targets of NMD in astrocytes, and shows that pathway enrichment of these targets highlights calcium signaling. Altogether, this work highlights the importance of the basic cellular process of NMD in astrocytes, which are known to have extensive local translation of proteins in their perisynaptic processes. NMD may be particularly important in astrocytes due to their intimate association of processes with neuronal synapses, and the authors suggest that alterations to NMD function in astrocytes may be an important avenue for future investigation in neurodevelopmental disorders.

      Strengths:

      Altogether, this work is a critical foundation for future research into astrocyte contributions to neurodevelopmental disorders. The authors do a thorough characterization of astrocyte conditional Upf2 knockout mice in several brain regions. They present a complete story that connects molecular events (NMD pathway regulation of mRNA degradation) to astrocyte regulation of circuit activity to organismal behavior. The electrophysiological analysis is thorough, and the manipulation of calcium activity ties astrocyte calcium activity to anxiety behavior. The RNA-sequencing dataset is useful to the scientific community and provides a resource of candidate molecules that might be dysregulated in neurodevelopmental disorders.

      Weaknesses:

      The study suffers from some overstated claims and a lack of statistical rigor in some experiments, as detailed below.

      (1) The title states that "Astrocytic Nonsense-mediated mRNA decay regulates calcium signaling to support synapse function and restrain anxiety". The term "restrain anxiety" implies that the NMD pathway has a direct effect on a molecular switch to control anxiety. Anxiety behavior is a complicated process, controlled by many biological phenomena and synaptic transmission in the circuit as a whole, and is not directly linked to a specific NMD mRNA target. This title is overstating the findings of the study.

      (2) In general, the first figures (1-2) suffer from low power (N = 3) and statistical rigor. The statistics are inflated by analyzing individual fields of view and per-cell data rather than performing the statistics on the average of biological replicates. It is preferable to show the biological replicate data so that readers can observe the natural biological variability between replicates.

      (3) The claim that astrocytes have decreased engulfment of synapses in the Upf2 conditional knockout mice is not strongly substantiated by the data. The resolution of confocal microscopy and the static nature of histological images make it difficult to measure synaptic engulfment as an active process. Additionally, the metric of quantifying the % occupancy of PSD95 puncta within the total astrocyte volume may be skewed due to overall differences in cell size (shown in Figure 1). There is not much discussion of how a decrease in astrocyte engulfment of synapses may lead to decreased synapse number. To the contrary, one might expect decreased engulfment to result in increased synapse density.

      (4) The authors use Gfap as a marker to count astrocyte cell number and assess if there are changes in cell number between genotypes (Figure S6). However, Gfap does not label all astrocytes in the cortex and, in fact, is rather an aberrantly expressed marker in conditions of inflammation, as opposed to the hippocampus, where Gfap is basally expressed in all astrocytes. In the cortex, there seems to be a trend for reduced Gfap in the conditional knockout mice, which may suggest differences in astrocyte molecular signatures rather than cell numbers. Another astrocyte marker, like Aldh1L1, will be more accurate to assess this question histologically.

      (5) The authors state that "Preventing abnormally high basal calcium activity in NMD-deficient astrocytes restores normal excitatory synapse function...". However, this claim is not substantiated by the data. CalEx manipulation certainly shifts the input-output curve but does not restore to wildtype baseline levels (Figure 6E). Additionally, synapse number does not appear to be restored to wildtype levels (Figure 6D - although the p-value for this comparison is now shown). The investigators do observe improvements in anxiety phenotypes, suggesting there is some modulation of circuit activity, but the claim that CalEx manipulation restores baseline synaptic transmission is not supported.

      Review 3
    5. Public_Reviews 10 Jul 2026

      Author response:

      We thank the reviewers for their careful reading of our manuscript and for providing positive, constructive feedback. In particular, we thank the reviewers highlighting the several strengths of our study.

      To address the reviewers’ major concerns, we will revise the presentation of our main findings (specifically data/animal vs data/ROI), provide more clarity in the Results, Methods, and Discussion sections, and modify the title to better reflect these nuances.

      Additionally, we will perform the following new experiments:

      (1) Astrocytic Marker Validation: To further confirm comparable astrocyte cell counts between the CTRL and Upf2-cKO conditions, we will perform immunostainings using Aldh1L1, Sox9, or S100b instead of GFAP.

      (2) NMD Candidate Validation: To validate top candidate NMD target transcripts, we will perform immunostainings or qRT-PCR for Gabbr2, Adora1, S100b, or Cldn9.

      (3) Sample Size Expansion: To strengthen the morphological and PSD-95 quantifications, we will increase the sample size (N) by incorporating additional animals.

      (4) Mechanistic Timeline & Phenotype Linkage: We value the reviewer’s comment regarding the timeline of morphological and Ca<sup>2+</sup> phenotypes. To gai insight into whether these phenotypes are independent or linked, we will perform 3D reconstructions in CalEx conditions to assess whether Ca<sup>2+</sup> restoration rescues astrocyte morphology in Upf2-cKO mice. This will allow us to determine if increased Ca<sup>2+</sup> activity is upstream of the morphological alterations. Taken together, we believe that incorporating these manuscript revisions will strengthen the clarity and conclusions of our work. We thank the reviewers for their time and careful evaluation of our study.

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