Reviewer #1 (Public review):

Summary:

The Notch signaling pathway plays important roles in many developmental and disease processes. Although well-studied there remain many puzzling aspects. One is the fact that as well as activating the receptor through a trans-activation, the transmembrane ligands can interact with receptors present in the same cell. These cis-interactions are usually inhibitory, but in some cases, as in the assays used here, they may also be activating. With a total of 6 ligands and 4 receptor there are potentially a wide array of possible outcomes when different combinations are co-expressed in vivo. Here the authors set out to make a systematic analysis of the qualitative and quantitative differences in the signaling output from different receptor ligand combinations, generating sets of "signaling" (ligand expressing) and "receiving" (receptor +/- ligand expressing cells).

The readout of pathway activity is transcriptional, relying on the fusion of GAL4 in the intracellular part of the receptor. Positive ligand interactions result in proteolytic release of Gal4 that turns on expression of H2B-citrine. As an indicator of ligand and receptor expression levels, they are linked via TA to H2B mCherry and H2B mTurq expression respectively. The authors also manipulate expression of the glycosyltransferase Lunatic-Fringe (LFng) that modifies the EGF repeats in the extracellular domains impacting on their interactions. The testing of multiple ligand receptor combinations at varying expression levels is a tour de force, with over 50 stable cell lines generated, and yields valuable insights although as a whole, the results are quite complex.

Strengths:

Taking a reductionist approach to test systematically differences in the signaling strength, binding strength and cis-interactions from the different ligands in the context of the Notch1 and Notch 2 receptors (they justify well they choice of players to test via this approach) produces a baseline understanding of the different properties and leads to some unexpected and interesting findings. Notably:<br /> - Jag1 ligand expressing cells failed to activate Notch1 receptor although were capable of activating Notch2. Conversely, Jag2 cells elicited the strongest activation of both receptors. The results with Jag1 are surprising also because it exhibits some of the strongest binding to plate bound ligands. The failure to activate Notch1 has major functional significance and it will be important in future to understanding the mechanistic basis.<br /> - Jagged ligands have the strongest ciis-inhibitory effects and the receptors differ in their sensitivity to cis-inhibition by Dll ligands. These observations are in keeping with earlier in vivo and cell culture studies. More referencing of those would better place the work in context but it nicely supports and extends previous studies that were conducted in different ways.<br /> - Responses to most trans-activating ligands showed a degree of ultrasensitivity but this was not the case for cis-interactions where effects were more linear. This has implications for the way the two mechanisms operate and for how the signaling levels will be impacted by ligand expression levels.<br /> - Qualitatively similar results are obtained in a second cell line, suggesting they reflect fundamental properties of the ligands/receptors.

Weaknesses:

One weakness is that the methods used to quantify the expression of ligands and receptors rely on co-translation of tagged nuclear H2B proteins. These may not accurately capture surface levels/correctly modified transmembrane proteins. In general, the multiple conditions tested partly compensate for the concerns - for example as Jag1 cells do activate Notch2 even if they do not activate Notch1 some Jag1 must be getting to the surface. But even with Notch2, Jag1 activities are on the lower side, making it important to clarify, especially given the different outcomes with the plated ligands. Similarly, is the fact that all ligands "signalled strongest to Notch2" an inherent property or due to differences in surface levels Notch 2 compared to Notch1?.. The results would be considerably strengthened by calibration of the ligand/receptor levels (and ideally their sub-cellular localizations). Assessing the membrane protein levels would be relatively straightforward to perform on som eof the basic conditions because their ligand constructs contain Flag tags, making it plausible to relate surface protein to H2B, and there are antibodies available for Notch1 and Notch2

In the revised version this has been addressed to some extent. A figure showing the relationship between co-translated mTurquiose and surface receptor expression for some clones (Figure 1-figure supplement 1B) goes some way to address the concerns that differences in Notch1 and Notch 2 could be due to the receptor levels. The data analyzing surface ligand levels is more equivocal, (a Western blot for biotinylated surface proteins), as the levels detected vary substantially between Dll1 and Dll4 (the latter barely detectable). But as a signal for surface expression of Jag1 was obtained this rules-out one concern that this ligand was failing to reach the surface. A discussion of the caveats of the approach is warranted, to make clear the limitations.

Cis-activation as a mode of signaling has only emerged from these synthetic cell culture assays raising questions about its physiological relevance. Cis-activation is only seen at the higher ligand (Dll1, Dll4) levels, how physiological are the expression levels of the ligands/receptors in these assays? Is it likely that this would make a major contribution in vivo? Is it possible that the cells convert themselves into "signaling" and "receiving" sub-populations within the culture by post-translational mechanism. Again some analysis of the ligand/receptors in the cultures would be a valuable addition to show whether or not there are major heterogeneities.

It is hard to appreciate how much cell to cell variability in the "output" there is. For example, low "outputs" could arise from fewer cells becoming activated or from all cells being activated less. As presented, only the latter is considered. That maybe already evident in their data, but not easy for the reader to distinguish from the way they are presented. For example, in many of the graphs, data have been processed through multiple steps of normalization. Some discussion/consideration this point is needed.

Impact:<br /> Overall, cataloguing of the outcomes from the different ligand-receptor combinations, both in cis and trans, yields a valuable baseline for those investigating their functional roles in different contexts. There is still a long way to go before it will be possible to make a predictive model for outcomes based on expression levels, but this work gives an idea about the landscape and the complexities. This is especially important now that signaling relationships are frequently hypothesised based on single cell transcriptomic data. The results presented here demonstrate that the relationships are not straightforward when multiple players are involved.

Reviewer #2 (Public review):

Summary:

In this manuscript the authors extend their previous studies on trans-activation, cis-inhibition (PMID: 25255098) and cis-activation (PMID: 30628888) of the Notch pathway. Here they create a large number of cell lines using CHO-K1 and C2C12 cells expressing either Notch1-Gal4 or Notch2-Gal4 receptors which express a fluorescent protein upon receptor activation (receiver cells). For cis-inhibition and cis-activation assays, these cells were engineered to express one of the four canonical Notch ligands (Dll1, Dll4, Jag1, Jag2) under tetracycline control. Some of the receiver cells were also transfected with a Lunatic fringe (Lfng) plasmid to produce cells with a range of Lfng expression levels. Sender cells expressing all of the canonical ligands were also produced. Cells were mixed in a variety of co-culture assays to highlight trans-activation, cis-activation, and cis-inhibition. All four ligands were able to trans-activate Notch1 and Notch 2, although Jag1 transactivated Notch1 weakly. Lfng enhanced trans-activation of both Notch receptors by Dll1 and Dll4, and inhibited both receptors by Jag 1 and Jag2. Cis-expression of all four ligands were predominantly inhibitory, but Dll1 and Dll4 showed strong cis-activation of Notch2. Interestingly, cis-ligands preferentially inhibited trans-activation by the same ligand, with varying effects on other trans-ligands.

Strengths:

This represents the most comprehensive and rigorous analysis of the effects of canonical ligands on cis- and trans-activation, and cis-inhibition, of Notch1 and Notch2 in the presence or absence of Lfng so far. Studying cis-inhibition and cis-activation is difficult in vivo due to the presence of multiple Notch ligands and receptors (and Fringes) that often occur in single cells. The methods described here are a step towards generating cells expressing more complex arrays of ligands, receptors and Fringes to better mimic in vivo effects on Notch function.

In addition, the fact that their transactivation results with most ligands on Notch1 and 2 in the presence or absence of Lfng were largely consistent with previous publications provides confidence that the author's assays are working properly.

Weaknesses:

In the original version, there was a major concern about quantifying the amount of Notch receptors and ligands on the cell surface (especially Jag1) based on total fluorescence. The authors have added data to demonstrate that most of the receptors and ligands are on the cell surface, allaying most of these concerns.

Reviewer #1 (Public Review):

Colomb et al have further explored the mechanisms of action of a family of three immunodulatory proteins produced by the murine gastrointestinal nematode parasite Heligmosomoides polygyrus bakeri. The family of HpARI proteins binds to the alarmin interleukin 33 and depending on family members, exhibits differential activities, either suppressive or enhancing. The present work extends previous studies by this group showing the binding of DNA by members of this family through a complement control protein (CCP1) domain. Moreover, they identify two members of the family that bind via this domain in a non-specific manner to the extracellular matrix molecule heparan sulphate through a basic charged patch in CCP1. The authors thus propose that binding to DNA or heparan sulphate extends the suppressive action of these two parasite molecules, whereas the third family member does not bind and consequently has a shorter half-life and may function via diffusion.

Reviewer #2 (Public Review):

Colomb et al. investigated here the heparin-binding activity of the HpARI family proteins from H. polygyrus. HpARIs bind to IL-33, a pleiotropic cytokine, and modulate its activities. HpARI1/2 has suppressive functions, while HpARI3 can enhance the interaction between IL-33 and its receptor. This study builds upon their previous observation that HpARI2 binds DNA via its CCP1 domain. Here, the authors tested the CCP1 domain of HpARIs in binding heparan sulfate, an important component of the extracellular matrix, and found that 1/2 bind heparan, but 3 cannot, which is related to their half-lives in vivo.

Reviewer #1 (Public review):

Summary:

In their manuscript, Zhou et al. analyze the factors controlling the activation and maintenance of a sustained cell cycle block in response to persistent DNA DSBs. By conditionally depleting components of the DDC using auxin-inducible degrons, the authors verified that some of them are only required for the activation (e.g., Dun1) or the maintenance (e.g., Chk1) of the DSB-dependent cell cycle arrest, while others such as Ddc2, Rad24, Rad9 or Rad53 are required for both processes. Notably, they further show that after a prolonged arrest (>24 h) in a strain carrying two DSBs, the DDC becomes dispensable and the mitotic block is then maintained by SAC proteins such as Mad1, Mad2 or the mitotic exit network (MEN) component Bub2.

Strengths:

The manuscript dissects the specific role of different components of the DDC and the SAC during the induction of a cell cycle arrest induced by DNA damage, as well as their contribution for the short-term and long-term maintenance of a DNA DSB-induced mitotic block. Overall, the experiments are well described and properly executed, and the data in the manuscript are clearly presented. The conclusions drawn are generally well supported by the experimental data. Their observations contribute to drawing a clearer picture of the relative contribution of these factors to the maintenance of genome stability in cells exposed to permanent DNA damage.

Weaknesses:

The main weakness of the study is that it is fundamentally based on the use of the auxin-inducible degron (AID) strategy to deplete proteins. This widely used method allows an efficient depletion of proteins in the cell. However, the drawback is that a tag is added to the protein, which can affect the functionality of the targeted protein or modify its capacity to interact with others. In fact, three of the proteins that are depleted using the AID systems are shown to be clearly hypomorphic, and hence their capacity to induce a strong checkpoint response might be compromised. A corroboration of at least some of the results using an alternative manner to eliminate the proteins would help to strengthen the conclusions of the manuscript.

Reviewer #2 (Public review):

Summary:

The manuscript analyzes and attempts to discriminate genetic requirements for DNA damage-induced cell cycle checkpoint induction, maintenance, and adaptation in budding yeast bearing one or two unrepairable DNA double strand breaks using auxin-induced degradation (AID) of key DNA damage response (DDR) factors. The study paid particular attention to solving a puzzle regarding how yeasts bearing two unrepaired DNA breaks fail to engage in "adaptation" whereas those with a single unrepairable break eventually resume cell cycling after a prolonged (up to 12 h) G2 arrest.

The key findings are: 1. Genetic requirements for the entry and the maintenance of DDC are separable. For instance, Dun1 is partially required for the entry but not the DDC maintenance whereas Chk1 is only required for maintenance. 2. Cells with two unrepairable breaks respond to DDR only up to a certain time (~12-15 h post damage) and beyond this point, depend on spindle assembly checkpoint (SAC) and mitotic exit network (MEN) to halt cell cycling. 3. The authors also propose an interesting concept that the location of DNA breaks and their distance to centromeres are important factors dictating the effect of SAC/MEN on the duration of cell cycle arrest after prolonged arrest (and cells become "deaf" to persistent arrest signals) and yeast's adaptability following DNA damage. The results provide most compelling evidence to date on the role of SAC/MEN in DNA damage response and cell cycle arrest albeit its impact might be limited to the handful of model systems due to the vastly different centromeric elements and far larger chromosome sizes in metazoan cells. The study albeit briefly discussed the basis of transitions from entry, maintenance, and adaptation ( ex. changes in centromeric architectures), it does not offer detailed explanations or a testable hypothesis to this topic.

Overall, the conclusion of the study is well supported by the elegant set of genetic experimental data and employed multiple readouts on DDC factor depletion on checkpoint integrity and cell cycle status. Although the study simply measures Rad53 phosphorylation as the primary metric to assess checkpoint status, it successfully demonstrated how the signaling is modified through the different stages and that eventually cells become recalcitrant to DDC signaling after a prolonged arrest. The results are clear, and rigorously tested and carefully interpreted with good discussion on the possible limitations. The revision provided detailed responses to the reviewers' comments and addressed a few key concerns, one of which is universally raised by the reviewers on the full functionality of AID tagged DDC factors, by simply expressing excess Rad9-AID to restore more normal looking checkpoint response. It will be interesting if the excess expression of other DDC factors could overcome suboptimal checkpoints in cells after 24 h post damage.

Reviewer #3 (Public review):

Summary:

The DNA damage checkpoint (DDC) inhibits the metaphase-anaphase transition to repair various types of DNA damage, including DNA double strand breaks (DSBs). One irreparable DSB can maintain the DDC for 12-15 hours in yeast, after which the cells resume the cell cycle. If there are two DSBs, the DDC is maintained for at least 24 hours. In this study, the authors take advantage of this tighter DDC to investigate whether the best-known proteins involved in establishing the DDC are also responsible for its long-term maintenance during irreparable DSBs. They do this by cleverly degrading such proteins after DSB formation. They show that most, but not all, DDC proteins maintain the cell cycle block. Interestingly, DDC proteins become dispensable after 15 hours and the block is then maintained by spindle assembly checkpoint (SAC) proteins.

Strengths:

The authors have engineered a tight yeast system to study DDC shutdown after irreparable DSBs and used it to address whether checkpoint proteins (DDC and SAC) contribute to the long-term maintenance of DSB-mediated G2/M block. The different roles of Ddc2, Chk1 and Dun1 are interesting, while the fact that SAC overtakes DDC after 15 hours is intriguing and highlights how DSBs near and far from centromeres can have a profound impact on cell adaptation to DSBs. In their revision, the authors have now improved the Rad9-AID methodology to place Rad9 in the context of DDC adaptation, as well as widening the association between adaptation and proximity to centromeres.

Weaknesses:

Some of the results they present essentially confirm their own previous findings, albeit with a tighter strain design for long-term arrest. Conclusions about the maintenance of G2/M in several mutant combinations could have been strengthened by adding simple microscopy experiments with DAPI staining. No clear mechanism for how depletion of Bub2, but not Bfa1, can relieve the G2/M (metaphase) block is given.

Reviewer #1 (Public review):

Summary:

The authors investigated the function of Microrchidia (MORC) proteins in the human malaria parasite Plasmodium falciparum. Recognizing MORC's implication in DNA compaction and gene silencing across diverse species, the study aimed to explore the influence of PfMORC on transcriptional regulation, life cycle progression and survival of the malaria parasite. Depletion of PfMORC leads to the collapse of heterochromatin and thus to the killing of the parasite. The potential regulatory role of PfMORC in the survival of the parasite suggests that it may be central to the development of new antimalarial strategies.

Strengths:

The application of the cutting-edge CRISPR/Cas9 genome editing tool, combined with other molecular and genomic approaches, provides a robust methodology. Comprehensive ChIP-seq experiments indicate PfMORC's interaction with sub-telomeric areas and genes tied to antigenic variation, suggesting its pivotal role in stage transition. The incorporation of Hi-C studies is noteworthy, enabling the visualization of changes in chromatin conformation in response to PfMORC knockdown.

Weaknesses:

Although disruption of PfMORC affects chromatin architecture and stage-specific gene expression, determining a direct cause-effect relationship requires further investigation. Furthermore, while numerous interacting partners have been identified, their validation is critical and understanding their role in directing MORC to its targets or in influencing the chromatin compaction activities of MORC is essential for further clarification. In addition, the authors should adjust their conclusions in the manuscript to more accurately represent the multifaceted functions of MORC in the parasite.

Reviewer #1 (Public review):

The authors previously showed in cell culture that Su(H), the transcription factor mediating Notch pathway activity in Drosophila, was phosphorylated on S269 and they found that a phospho-deficient Su(H) allele behaves as a moderate gain of Notch activity in flies, notably during blood cell development. Since downregulation of Notch signaling is important for the production of specialized blood cell types (lamellocytes) in response to wasp parasitism, the authors hypothesized that Su(H) phosphorylation might be involved in this cellular immune response.<br /> Consistent with their hypothesis, the authors now show that Su(H)S269A knock-in flies display a reduced response to wasp parasitism and that Su(H) is phosphorylated upon infestation. Using in vitro kinase assays and a genetic screen, they identify the PKCa family member Pkc53E as the putative kinase involved in Su(H) phosphorylation and they show that Pkc53E can bind Su(H). They further show that Pkc53E deficit or its knock-down in larval blood cells results in similar blood cell phenotypes as Su(H)S269A and their epistatic analyses indicate that Pkc53E acts upstream of Su(H). Finally, they show that Pkc53E mutants aslo display a compromised immune response to wasp parasitism.

Strengths

The manuscript is well presented and the experiments are sound, with a good combination of genetic and biochemical approaches and several clear phenotypes backing the main conclusions. Notably Su(H)S269A mutation strongly reduces lamellocyte production. Moreover, the epistatic data are convincing, notably concerning the relationship between Notch/Su(H) and Pkc53E for crystal cell production.<br /> Even though it is not fully established, the overall model is credible and interesting. In addition, it opens further avenues of research to study the activation of Pkc in response to an immune challenge.

Weaknesses

Apparently, the hypothesis that Pkc53E is required for Su(H) phosphorylation in vivo could not be directly tested due to the lack of an appropriate tool (the specificity and sensitivity of the current anti-pS269 antibody was insufficient).<br /> Also, the poor immune response of Pkc53E mutant might rather be linked to their constitutively reduced circulating blood cell number than to a deficit in Notch/Su(H) down-regulation following wasp infestation.

Reviewer #2 (Public review):

The current draft by Deischel et.al., describes the role of Pkc53E in the phosphorylation of Su(H) to down regulate its transcriptional activity to mount a successful immune response upon parasitic wasp-infection. Overall, I find the study interesting and relevant especially the identification of Pkc53E in phosphorylation of Su(H) is very nice. The authors have proved the central idea linking phosphorylation of Su(H) via Pkc53E to implying its modulation of Notch activity to mount a robust immune response is now well addressed in its entirety and I find the paper indeed very interesting.

Comments on revised version:

The authors have addressed all pending concerns and I have no further comments. I indeed complement the authors for their wonderful piece of work.

Reviewer #3 (Public review):

Diechsel et al. provide important and valuable insights into how Notch signaling is shut down in response to parasitic wasp infestation in order to suppress crystal cell fate and favor lamellocyte production. The study shows that CSL transcription factor Su(H) is phosphorylated at S269A in response to parasitic wasp infestation and this inhibitory phosphorylation is critical for shutting down Notch. The authors go on to perform a screen for kinases responsible for this phosphorylation and have identified Pkc53E as the specific kinase acting on Su(H) at S269A. Using analysis of mutants, RNAi and biochemistry-based approaches the authors convincingly show how Pkc53E-Su(H) interaction is critical for remodeling hematopoiesis upon wasp challenge. I find the study interesting, and the data presented supports the overall conclusions made by the authors. The authors have addressed all my comments satisfactorily in the revised submission.

Strengths:

The manuscript is well presented, and the conclusions made are backed by genetic, biochemical and molecular biology-based approaches. Overall, the authors convincingly demonstrate how Pkc53E mediated phosphorylated of Su(H) shuts down Notch signaling during wasp infestation in Drosophila.

Weaknesses:

The exact molecular trigger for activation of Pkc53E is still uncharacterized and it would be interesting to know how Pkc53E gets activated during wasp infestation and whether Pkc53E gets activated turning down Notch in other stress induced scenarios.

The authors have addressed comments satisfactorily. Overall, I think the findings are interesting and would be useful to the field of developmental biology and immunology and address an important gap in the field. The most significant conclusion from the work is how Notch acts as a molecular switch during parasitic wasp infestation.

Reviewer #1 (Public review):

In this work, the authors study the dynamics of fast-adapting pathogens under immune pressure in a host population with prior immunity. In an immunologically diverse population, an antigenically escaping variant can perform a partial sweep, as opposed to a sweep in a homogeneous population. In a certain parameter regime, the frequency dynamics can be mapped onto a random walk with zero mean, which is reminiscent of neutral dynamics, albeit with differences in higher order moments. Next, they develop a simplified effective model of time dependent selection with expiring fitness advantage, and posit that the resulting partial sweep dynamics could explain the behaviour of influenza trajectories empirically found in earlier work (Barrat-Charlaix et al. Molecular Biology and Evolution, 2021). Finally, the authors put forward an interesting hypothesis: the mode of evolution is connected to the age of a lineage since ingression into the human population. A mode of meandering frequency trajectories and delayed fixation has indeed been observed in one of the long-established subtypes of human influenza, albeit so far only over a limited period from 2013 to 2020. The paper is overall interesting and well-written.

In the revised version, the authors have addressed questions on the role of clonal interference by new simulations in the SI, clarified the connection between the SIR model and vanishing-fitness models, and placed their analysis into the broader context of consumer resource dynamics.

However, the general conclusion, as stated in the abstract, that variant trajectories become unpredictable as a consequence of the SIR dynamics remains somewhat misleading. Two aspects contribute to this problem. (1) The empirical observation of ``quasi-neutrality', i.e. the absence of a net frequency increase inferred as an average of many trajectories at intermediate frequencies, does not imply that individual trajectories are neutral (i.e., fully stochastic and unpredictable) over the time span of observation. Rather, it just says that some have a positive and some have a negative selection coefficient over that time span. (2) As stated by the authors, the observation of average quasi-neutrality is indeed incompatible with the travelling wave model, where initially successful new variants are assumed to retain a fixed, positive selection coefficient from origination to fixation. This observation also limits predictions by extrapolation, where a positive selection coefficient inferred at small frequency is assumed to remain the same at later times and higher frequencies. However, predictions derived from Gog and Grenfell's multi-strain SIR model, as used by several authors, do not make the assumption of fixed selection coefficients and incorporate trajectory-specific, time-dependent expiration effects into their model predictions. This distinction remains blurred throughout the text of the paper.

Reviewer #3 (Public review):

In this work the authors present a multi-strain SIR model in which viruses circulate in a heterogeneous population with different groups characterized by different cross-immunity structures. They reformulate the qualitative features of these SIR dynamics as a random walk characterized by new variants saturating at intermediate frequencies. Then they recast their microscopic description to an effective formalism in which viral strains lose fitness independently from one another. They study several features of this process numerically and analytically, such as the average variants frequency, the probability of fixation, and the coalescent time. They compare qualitatively the dynamics of this model to variants dynamics in RNA viruses such as flu and SARS-CoV-2

The idea that vanishing fitness mechanisms that produce partial sweeps may explain important features of flu evolution is very interesting. Its simplicity and potential generality make it a powerful framework. As noted by the authors, this may have important implications for predictability of virus evolution and such a framework may be beneficial when trying to build predictive models for vaccine design. The vanishing fitness model is well analyzed and produces interesting structures in the strains coalescent. Even though the comparison with data is largely qualitative, this formalism would be helpful when developing more accurate microscopic ingredients that could reproduce viral dynamics quantitatively.<br /> This general framework has the potential to be more universal than human RNA viruses, in situations where invading mutants would saturate at intermediate frequencies.

The qualitative connection between the coarse-grained features of these vanishing fitness dynamics and structured SIR processes offers additional intuition relevant to host-pathogens interactions, although as noted by the authors other ecological processes could drive similar evolutionary patterns. The additions in the revised manuscript, substantiating more thoroughly the connection between the SIR and the vanishing fitness description, are important to better appreciate the scope of the work.

Reviewer #1 (Public review):

Summary:

The authors used a novel multi-dimensional experience sampling (mDES) approach to identify data-driven patterns of experience samples that they use to interrogate fMRI data collected during naturalistic movie-watching data. They identify a set of multi-sensory features of a set of movies that delineate low-dimensional gradients of BOLD fMRI signal patterns that have previously been linked to fundamental axes of cortical organization.

Strengths:

* The novel solution to challenges associated with experience sampling offer potential access to aspects of experience that have been challenging to assess.

Weaknesses:

* The lack of direct interrogation of individual differences/reliability of the mDES scores warrants some pause.

Reviewer #2 (Public review):

Summary:

The present study explores how thoughts map onto brain activity, a notoriously challenging question because of the dynamic, subjective, and abstract nature of thoughts. To tackle this question, the authors collected continuous thought ratings from participants watching a movie, and additionally made use of an open-source fMRI dataset recorded during movie watching as well as five established gradients of brain variation as identified in resting state data. Using a voxel-space approach, the results show that episodic knowledge, verbal detail, and sensory engagement of thoughts commonly modulate visual and auditory cortex, while intrusive distraction modulates the frontoparietal network. Additionally, sensory engagement mapped onto a gradient from primary to association cortex, while episodic knowledge mapped onto a gradient from the dorsal attention network to visual cortex. Building on the association between behavioral performance and neural activation, the authors conclude that sensory coupling to external input and frontoparietal executive control are key to comprehension in naturalistic settings.

The manuscript stands out for its methodological advancements in quantifying thoughts over time and its aim to study the implementation of thoughts in the brain during naturalistic movie watching. However, the conceptualization of thoughts remains vague, limiting the study's insights into brain function.

Strengths:

(1) The study raises a question that has been difficult to study in naturalistic settings so far but is key to understanding human cognition, namely how thoughts map onto brain activation.<br /> (2) The thought ratings introduce a novel method for continuously tracking thoughts, promising utility beyond this study.<br /> (3) The authors used diverse data types, metrics, and analyses to substantiate the effects of thinking from multiple perspectives.

Weaknesses:

(1) The distinction between thinking and stimulus processing (in the sense of detecting and assigning meaning to features, modulated by factors such as attention) remains unclear. Is "thinking" a form of conscious access or a reportable read-out from sensory and higher-level stimulus processing? Or does it simply refer to the method used here to identify different processing states?<br /> (2) The dimensions of thought appear to be directly linked to brain areas traditionally associated with core faculties of perception and cognition. For example, superior temporal cortex codes for speech information, which is also where thought reports on verbal detail localize in this study. This raises the question of whether the present study truly captures mechanisms specific to thinking and distinct from processing, especially given that individual variations in reports were not considered and movie-specific features were not controlled for.

Reviewer #3 (Public review):

This study attempted to investigate the relations between processing in the human brain during movie watching and corresponding thought processes. This is a highly interesting question, as movie watching presents a semi-constrained task, combining naturally occurring thoughts and common processing of sensory inputs across participants. This task is inherently difficult because in order to know what participants are thinking at any given moment, one has to interrupt the same thought process which is the object of study.

This study attempts to deal with this issue by aggregating staggered experience sampling data across participants in one behavioral study and using the population level thought patterns to model brain activity in different participants in an open access fMRI dataset.

The behavioral data consist of 120 participants who watched 3 11-minute movie clips. Participants responded to the mDES questionnaire: 16 visual scales characterizing ongoing thought 5 times, two minutes apart, in each clip. The 16 items are first reduced to 4 factors using PCA, and their levels are compared across the different movies. The factors are "episodic knowledge", "intrusive distraction", "verbal detail", and "sensory engagement". The factors differ between the clips, and distraction is negatively correlated with movie comprehension and sensory engagement is positively correlated with comprehension.

The components are aggregated across participants (transforming single subject mDES answers into PCA space and concatenating responses of different participants) and are used as regressors in a GLM analysis. This analysis identifies brain regions corresponding to the components. The resulting brain maps reveal activations that are consistent with the proposed mental processes (e.g. negative loading for intrusion in frontoparietal network, positive loadings for visual and auditory cortices for sensory engagement).

Then, the coordinates for brain regions which were significant for more than one component are entered into a paper search in neurosynth. It is not clear what this analysis demonstrates beyond the fact that sensory engagement contained both visual and auditory components.

The next analysis projected group-averaged brain activation onto gradients (based on previous work) and used gradient timecourses to predict the behavioral report timecourses. This revealed that high activations in gradient 1 (sensory→association) predicted high sensory engagement, and that "episodic knowledge" thought patterns were predicted by increased visual cortex activations. Then, permutation tests were performed to see whether these thought pattern related activations corresponded to well defined regions on a given cluster.

This paper is framed as presenting a new paradigm but it does little to discuss what this paradigm serves, what are its limitations and how it should have been tested. The novelty appears to be in using experience sampling from 1 sample to model the responses of a second sample.

What are the considerations for treating high-order thought patterns that occur during film viewing as stable enough to use across participants? What would be the limitations of this method? (Do all people reading this paper think comparable thoughts reading through the sections?) This is briefly discussed in the revised manuscript and generally treated as an opportunity rather than as a limitation.

In conclusion, this study tackles a highly interesting subject and does it creatively and expertly. It fails to discuss and establish the utility and appropriateness of its proposed method.

Reviewer #1 (Public Review):

In this manuscript, the authors study the effects of synaptic activity on the process of eye-specific segregation, focusing on the role of caspase 3, classically associated with apoptosis. The method for synaptic silencing is elegant and requires intrauterine injection of a tetanus toxin light chain into the eye. The authors report that this silencing leads to increased caspase 3 in the contralateral eye (Figure 1) and demonstrate evidence of punctate caspase 3 that does not overlap neuronal markers like map2. However, the quantifications showing increased caspase 3 in the silenced eye (done at P5) are complicated by overlap with the signal from entire dying cells in the thalamus. The authors also show that global caspase 3 deficiency impairs the process of eye-specific segregation and circuit refinement (Figures 3-4).

The authors also report that "synapse weakening-induced caspase-3 activation determines the specificity of synapse elimination mediated by microglia but not astrocytes" (abstract). They report that microglia engulf fewer RGC axon terminals in caspase 3 deficient animals (Figure 5), and that this preferentially occurs in silenced terminals, but this preferential effect is lost in caspase 3 knockouts. Based on this, the authors conclude that caspase 3 directs microglia to eliminate weaker synapses. However, a much simpler and critical experiment that the authors did not perform is to eliminate microglia and show that the caspase 3 dependent effects go away. Without this experiment, there is no reason to assume that microglia are directing synaptic elimination.

Finally, the authors also report that caspase 3 deficiency alters synapse loss in 6-month-old female APP/PS1 mice, but this is not really related to the rest of the paper.

Reviewer #2 (Public Review):

Summary:

This manuscript by Yu et al. demonstrates that activation of caspase-3 is essential for synapse elimination by microglia, but not by astrocytes. This study also reveals that caspase 3 activation-mediated synapse elimination is required for retinogeniculate circuit refinement and eye-specific territories segregation in dLGN in an activity-dependent manner. Inhibition of synaptic activity increases caspase-3 activation and microglial phagocytosis, while caspase-3 deficiency blocks microglia-mediated synapse elimination and circuit refinement in the dLGN. The authors further demonstrate that caspase-3 activation mediates synapse loss in AD, loss of caspase-3 prevented synapse loss in AD mice. Overall, this study reveals that caspase-3 activation is an important mechanism underlying the selectivity of microglia-mediated synapse elimination during brain development and in neurodegenerative diseases.

Strengths:

A previous study (Gyorffy B. et al., PNSA 2018) has shown that caspase-3 signal correlates with C1q tagging of synapses (mostly using in vitro approaches), which suggests that caspase-3 would be an underlying mechanism of microglial selection of synapses for removal. The current study provides direct in vivo evidence demonstrating that caspase-3 activation is essential for microglial elimination of synapses in both brain development and neurodegeneration.

The paper is well-organized and easy to read. The schematic drawings are helpful for understanding the experimental designs and purposes.

Weaknesses:

It seems that astrocytes contain large amounts of engulfed materials from ipsilateral and contralateral axon terminals (Figure S11B) and that caspase-3 deficiency also decreased the volume of engulfed materials by astrocytes (Figures S11C, D). So the possibility that astrocyte-mediated synapse elimination contributes to circuit refinement in dLGN cannot be excluded.

Does blocking single or dual inactivation of synapse activity (using TeTxLC) increase microglial or astrocytic engulfment of synaptic materials (of one or both sides) in dLGN?

Reviewer #1 (Public review):

Summary:

This paper is an incremental follow-up to the authors' recent paper which showed that Purkinje cells make inhibitory synapses onto brainstem neurons in the parabrachial nucleus which project directly to the forebrain. In that precedent paper, the authors used a mouse line that expresses the presynaptic marker synaptophysin in Purkinje cells to identify Purkinje cell terminals in the brainstem and they observed labeled puncta not only in the vestibular and parabrachial nuclei, as expected, but also in neighboring dorsal brainstem nuclei, prominently the central pontine grey. The present study, motivated by the lack of thorough characterization of PC projections to the brainstem, uses the same mouse line to anatomically map the density and a PC-specific channelrhodopsin mouse line to electrophysiologically assess the strength of Purkinje cell synapses in dorsal brainstem nuclei. The main findings are (1) the density of Purkinje cell synapses is highest in vestibular and parabrachial nuclei and correlates with the magnitude of evoked inhibitory synaptic currents, and (2) Purkinje cells also synapse in the central pontine grey nucleus but not in the locus coeruleus or mesencephalic nucleus.

Strengths:

The complementary use of anatomical and electrophysiological methods to survey the distribution and efficacy of Purkinje cell synapses on brainstem neurons in mouse lines that express markers and light-sensitive opsins specifically in Purkinje cells is the major strength of this study. By systematically mapping presynaptic terminals and light-evoked inhibitory postsynaptic currents in the dorsal brainstem, the authors provide convincing evidence that Purkinje cells do synapse directly onto pontine central grey and nearby neurons but do not synapse onto trigeminal motor or locus coeruleus neurons. Their results also confirm previously documented heterogeneity of Purkinje cell inputs to the vestibular nucleus and parabrachial neurons.

Weaknesses:

Although the study provides strong evidence that Purkinje cells do not make extensive synapses onto LC neurons, which is a helpful caveat given previous reports to the contrary, it falls short of providing the comprehensive characterization of Purkinje cell brainstem synapses which seemed to be the primary motivation of the study. The main information provided is a regional assessment of PC density and efficacy, which seems of limited utility given that we are not informed about the different sources of PC inputs, variations in the sizes of PC terminals, the subcellular location of synaptic terminals, or the anatomical and physiological heterogeneity of postsynaptic cell types. The title of this paper would be more accurate if "characterization" were replaced by "survey".

Several of the study's conclusions are quite general and have already been made for vestibular nuclei, including the suggestions in the Abstract, Results, and Discussion that PCs selectively influence brainstem subregions and that PCs target cell types with specific behavioral roles.

Reviewer #2 (Public review):

Summary:

While it is often assumed that the cerebellar cortex connects, via its sole output neuron, the Purkinje cell, exclusively to the cerebellar nuclei, axonal projections of the Purkinje cells to dorsal brainstem regions have been well documented. This paper provides comprehensive mapping and quantification of such extracerebellar projections of the Purkinje cells, most of which are confirmed with electrophysiology in slice preparation. A notable methodological strength of this work is the use of highly Purkinje cell-specific transgenic strategies, enabling selective and unbiased visualization of Purkinje terminals in the brainstem. By utilizing these selective mouse lines, the study offers compelling evidence challenging the general assumption that Purkinje cell targets are limited to the cerebellar nuclei. While the individual connections presented are not entirely novel, this paper provides a thorough and unambiguous demonstration of their collective significance. Regarding another major claim of this paper, "characterization of direct Purkinje cell outputs (Title)", however, the depth of electrophysiological analysis is limited to the presence/absence of physiological Purkinje input to postsynaptic brainstem neurons whose known cell types are mostly blinded. Overall, conceptual advance is largely limited to confirmatory or incremental, although it would be useful for the field to have the comprehensive landscape presented.

Strengths:

(1) Unsupervised comprehensive mapping and quantification of the Purkinje terminals in the dorsal brainstem are enabled, for the first time, by using the current state-of-the-art mouse lines, BAC-Pcp2-Cre and synaptophysin-tdTomato reporter (Ai34).

(2) Combinatorial quantification with vGAT puncta and synaptophysin-tdTomato labeled Purkinje terminals clarifies the anatomical significance of the Purkinje terminals as an inhibitory source in each dorsal brainstem region.

(3) Electrophysiological confirmation of the presence of physiological Purkinje synaptic input to 7 out of 9 dorsal brainstem regions identified.

(4) Pan-Purkinje ChR2 reporter provides solid electrophysiological evidence to help understand the possible influence of the Purkinje cells onto LC.

Weaknesses:

(1) The present paper is largely confirmatory of what is presented in a previous paper published by the author's group (Chen et al., 2023, Nat Neurosci). In this preceding paper, the author's group used AAV1-mediated anterograde transsynaptic strategy to identify postsynaptic neurons of the Purkinje cells. The experiments performed in the present paper are, by nature, complementary to the AAV1 tracing which can also infect retrogradely and thus is not able to demonstrate the direction of synaptic connections between reciprocally connected regions. Anatomical findings are all consistent with the preceding paper. The likely absence of robust physiological connections from the Purkinje to LC has also been evidenced in the preceding paper by examining c-Fos response to Purkinje terminal photoinhibition at the PBN/LC region.

(2) Although the authors appear to assume uniform cell type and postsynaptic response in each of the dorsal brainstem nuclei (as noted in the Discussion, "PCs likely function similarly to their inputs to the cerebellar nuclei, where a very brief pause in firing can lead to large and rapid elevations in target cell firing"), we know that the responses to the Purkinje cell input are cell type dependent, which vary in neurotransmitter, output targets, somata size, and distribution, in the cerebellar and vestibular nuclei (Shin et al., 2011, J Neurosci; Najac and Raman, 2015, J Neurosci; Özcan et al., 2020, J Neurosci). This consideration impacts the interpretation of two key findings: (a) "Large ... PC-IPSCs are preferentially observed in subregions with the highest densities of PC synapses (Abstract)". For example, we know that the terminal sparse regions reported in the present paper do contain Floccular Targeted Neurons that are sparse yet have dense somatic terminals with profound postinhibitory rebound (Shin et al.). Despite their sparsity, these postsynaptic neurons play a distinct and critical role in proper vestibuloocular reflex. Therefore, associating broad synaptic density with "PC preferential" targets, as written in the Abstract, may not fully capture the behavioral significance of Purkinje extracerebellar projections. (b) "We conclude ... only a small fraction of cell. This suggests that PCs target cell types with specific behavioral roles (Abstract, the last sentence)". Prior research has already established that "PCs target cell types with specific behavioral roles in brainstem regions". Also, whether 23 % (for PCG), for example, is "a small fraction" would be subjective: it might represent a numerically small but functionally important cell type population. The physiological characterization provided in the present cell type-blind analysis could, from a functional perspective, even be decremental when compared to existing cell type-specific analyses of the Purkinje cell inputs in the literature.

(3) The quantification analyses used to draw conclusions about<br /> (a) the significance of PC terminals among all GABAergic terminals and<br /> (b) the fractions of electrophysiologically responsive postsynaptic brainstem neurons may have potential sampling considerations:.<br /> (b.i) this study appears to have selected subregions from each brainstem nucleus for quantification (Figure 2). However, the criteria for selecting these subregions are not explicitly detailed, which could affect the interpretation of the results.<br /> (b.ii) the mapping of recorded cells (Figure 3) seems to show a higher concentration in terminal-rich regions of the vestibular nuclei.

Reviewer #3 (Public review):

Summary:

The manuscript by Chen and colleagues explores the connections from cerebellar Purkinje cells to various brainstem nuclei. They combine two methods - presynaptic puncta labeling as putative presynaptic markers, and optogenetics, to test the anatomical projections and functional connectivity from Purkinje cells onto a variety of brainstem nuclei. Overall, their study provides an atlas of sorts of Purkinje cell connectivity to the brainstem, which includes a critical analysis of some of their own data from another publication. Overall, the value of this work is to both provide neural substrates by which Purkinje cells may influence the brainstem and subsequent brain regions independent of the deep cerebellar nuclei and also, to provide a critical analysis of viral-based methods to explore neuronal connectivity.

Strengths:

The strengths lie in the simplicity of the study, the number of cells patched, and the relationship between the presence of putative presynaptic puncta and electrophysiological results. This type of study is important and should provide a foundation for future work exploring cerebellar inputs and outputs. Overall, I think that the critique of viral-based methods to define connectivity, and a more holistic assessment of what connectivity is and how it should be defined is timely and warranted, as I think this is under-appreciated by many groups and overall, there is a good deal of research being published that do not properly consider the issues that this manuscript raises about what viral-based connectivity maps do and do not tell us.

Weaknesses:

While I overall liked the manuscript, I do have a few concerns that relate to interpretation of results, and discussion of technological limitations. The main concerns I have relate to the techniques that the authors use, and an insufficient discussion of their limitations. The authors use a Cre-dependent mouse line that expresses a synaptophysin-tomato marker, which the authors confidently state is a marker of synapses. This is misleading. Synaptophysin is a vesicle marker, and as such, labels axons, where vesicles are present in transit, and likely cell bodies where the protein is being produced. As such, the presence of tdtomato should not be interpreted definitively as the presence of a synapse. The use of vGAT as a marker, while this helps to constrain the selection of putative pre-synaptic sites, is also a vesicle marker and will likely suffer the same limitations (though in this case, the expression is endogenous and not driven by the ROSA locus). A more conservative interpretation of the data would be that the authors are assessing putative pre-synaptic sites with their analysis. This interpretation is wholly consistent with their findings showing the presence of tdtomato in some regions but only sparse connectivity - this would be expected in the event that axons are passing through. If the authors wish to strongly assert that they are specifically assessing synapses, a marker better restricted to synapses and not vesicles may be more appropriate.

Similarly, while optogenetics/slice electrophysiology remains the state of the art for assessing connectivity between cell populations, it is not without limitations. For example, connections that are not contained within the thickness of the slice (here, 200 um, which is not particularly thick for slice ephys preps) will not be detected. As such, the absence of connections is harder to interpret than the presence of connections. Slices were only made in the coronal plane, which means that if there is a particular topology to certain connections that is orthogonal to that plane, those connections may be under-represented. As such, all connectivity analyses likely are under-representations of the actual connectivity that exists in the intact brain. Therefore, perhaps the authors should consider revising their assessments of connections, or lack thereof, of Purkinje cells to e.g., LC cells. While their data do make a compelling case that the connections between Purkinje cells and LC cells are not particularly strong or numerous, especially compared to other nearby brainstem nuclei, their analyses do indicate that at least some such connections do exist. Thus, rather than saying that the viral methods such as rabies virus are not accurate reflections of connectivity - perhaps a more circumspect argument would be that the quantitative connectivity maps reported by other groups using rabies virus do not always reflect connectivity defined by other means e.g., functional connections with optogenetics. In some cases, the authors do suggest this (e.g."Together, these findings indicate that reliance on anatomical tracing experiments alone is insufficient to establish the presence and importance of a synaptic connection"), but in other cases, they are more dismissive of viral tracing results (e.g. "it further suggests that these neurons project to the cerebellum and were not retrogradely labeled"). Furthermore, some statements are a bit misleading e.g., mentioning that rabies methods are critically dependent on starter cell identity immediately following the citation of studies mapping inputs onto LC cells. While in general, this claim has merit, the studies cited (19-21) use Dbh-Cre to define LC-NE cells which does have good fidelity to the cells of interest in the LC. Therefore, rewording this section in order to raise these issues generally without proximity to the citations in the previous sentence may maintain the authors' intention without suggesting that perhaps the rabies studies from LC-NE cells that identified inputs from Purkinje cells were inaccurate due to poor fidelity of the Cre line. Overall, this manuscript would certainly not be the first report indicating that the rabies virus does not provide a quantitative map of input connections. In my opinion, this is still under-appreciated by the broad community and should be explicitly discussed. Thus, an acknowledgment of previous literature on this topic and how their work contributes to that argument is warranted.

Reviewer #1 (Public Review):

Bursicon is a key hormone regulating cuticle tanning in insects. While the molecular mechanisms of its function are rather well studied--especially in the model insect Drosophila melanogaster, its effects and functions in different tissues are less well understood. Here, the authors show that bursicon and its receptor play a role in regulating aspects of the seasonal polyphenism of Cacopsylla chinensis. They found that low temperature treatment activated the bursicon signaling pathway during the transition from summer form to winter form and affect cuticle pigment and chitin content, and cuticle thickness. In addition, the authors show that miR-6012 targets the bursicon receptor, CcBurs-R, thereby modulating the function of bursicon signaling pathway in the seasonal polyphenism of C. chinensis. This discovery expands our knowledge of the roles of neuropeptide bursicon action in arthropod biology.

Reviewer comments on revised version

(a) Major concerns<br /> (1) The revision did not respond to the major concern regarding the threshold response that defines polyphenism. Therefore, it still falls short of the claims made, since the claims were not revised either. Specifically, the authors now include a time series of tanning at two different temperatures, demonstrating the time points at which the induced tanning proceeds (Fig. S1). However, the appropriate response to that comment would have temperatures on the x-axis, not time. Intermediate temperatures are needed to test whether the induction is a threshold response or simply a continuous norm of reaction.<br /> (2) The authors also did not respond to the major comment regarding environmental induction of miR-6012 expression. Rather, Fig. 5E shows a time series under two temperatures, similar to the tanning time series. To test whether its induction is a threshold response (again, what defines polyphenism), a series of induction conditions is needed. Fig. 5E simply shows changes in expression over time under one induction temperature (25 ºC).<br /> (3) Although the manuscript title has been changed, little to nothing else in the revised text addresses the concern that this study is about tanning in psyllids, not seasonal polyphenism. The other traits making up the polyphenism, as well as their threshold response, were not measured.

In summary, this revision failed to address most of the chief concerns of the review summary. This manuscript should be reframed as a study of tanning in a species other than Drosophila, and any claims about polyphenism (that is, an environmentally induced threshold trait) still need to be tested.

Regarding the other concerns raised by the reviewers:

(4) Issues related to the assignment of the receptor used as a bursicon receptor were satisfactorily addressed.<br /> (5) Experiments regarding the timing of cuticle production presented in Supplementary Figure 1 are valuable, albeit, there are still some inaccuracies: i) the layering of the cuticle is not given accurately as there are more than the 3 layers indicated in the manuscript; ii), the reduced endocuticle in all relevant dsRNA cases suggests a massive molting defect that may underline the involvement of bursicon in molting in general, potentially masking its effect on morph transition. In other words, the phenotype is too strong to allow for the interpretation of its function with respect to morph transition. It would have been necessary to apply different concentrations of dsRNA in order to address this point. iii) The developmental timing at 10oC vs. 25oC seem to be similar, although duration would be expected to be longer at 10oC; iv) It would have been nice to see the days of development also for dsRNA injected animals.<br /> (6) Another unresolved point regards the source and target tissue of bursicon signaling. Admittedly, this problem is difficult to solve in a small insect species.

Reviewer #1 (Public review):

Over the last decade, numerous studies have identified adaptation signals in modern humans driven by genomic variants introgressed from archaic hominins such as Neanderthals and Denisovans. One of the most classic signals comes from a beneficial haplotype in the EPAS1 gene in Tibetans that is evidently of Denisovan origin and facilitated high altitude adaptation (HAA). Given that HAA is a complex trait with numerous underlying genetic contributions, in this paper Ferraretti et al. asked whether Denisovan introgression facilitated HAA in other ways by contributing to additional HAA-related genetic variants. Specifically, the authors considered that if such signature exists, they most likely are only mild signals from polygenic selection, or soft sweeps on standing archaic variation, in contrast to a strong and nearly complete selection signal like the EPAS1. They leveraged a few recently developed methods, including a composite likelihood method for detecting adaptive introgression and a biological network-based method for detecting polygenic selection, and identified compelling evidence of additional genes that exhibit Denisovan-like adaptive introgression signature and contributed to the polygenic adaptation at high altitude in Tibetans.

Strength:

The study is well motivated by an important question, which is, whether archaic introgression can drive polygenic adaptation via multiple small effect contributions in genes underlying different biological pathways regulating a complex trait (such as HAA). This is a valid question and the influence of archaic introgression on polygenic adaptation has not been thoroughly explored by previous studies

The authors reexamined previously published high-altitude Tibetan whole genome data and detected new evidence of adaptive introgression and polygenic selection. Specifically, by applying VolcanoFinder, they confirmed previously identified adaptive introgression alleles such as EPAS1 and PPARA. By applying signet, they identified subsets of biological pathways enriched for archaic variants that contributed to HAA polygenic selection. They also leveraged additional methods such as LASSI and haplotype plotting to help confirm the signature of natural selection on their newly discovered adaptive introgression candidate genes.

Weakness:

The manuscript also improved substantially since the initial review, and the new candidate genes presented here now harbor compelling and convincing evidence of both adaptive introgression and HAA polygenic selection. There are no notable weaknesses in the revised manuscript and updated results.

Reviewer #2 (Public review):

Summary:

In Ferrareti et al. they identify adaptively introgressed genes using VolcanoFinder and then identify pathways enriched for adaptively introgressed genes. They use signet to identify pathways that are enriched for Denisovan alleles. The authors find that angiogenesis is one of the biological functions enriched for introgression.

Strengths:

Most papers that have studied the genetic basis of high altitude (HA) adaptation in Tibet have highly emphasized the role of a few genes (e.g. EPAS1, EGLN1), and in this paper the authors look for more subtle signals of selection in other genes to investigate how archaic introgression may be enriched at the pathway level. A couple of methods are used to confirm the consistency of the results.

Looking into the biological functions enriched for Denisovan introgression in Tibetans is important for characterizing the impact of Denisovan introgression in facilitating high altitude adaptations.

Weaknesses:

I thank the authors for providing an improved version of their manuscript.

Reviewer #1 (Public review):

After revisions:

My concerns have been addressed.

Prior to revisions:

Summary:<br /> The authors introduce a denoising-style model that incorporates both structure and primary-sequence embeddings to generate richer embeddings of peptides. My understanding is that the authors use ESM for the primary sequence embeddings, take resolved structures (or use structural predictions from AlphaFold when they're not available), then develop an architecture to combine these two with a loss that seems reminiscent of diffusion models or masked language model approaches. The embeddings can be viewed as ensemble-style embedding of the two levels of sequence information, or with AlphaFold, an ensemble of two methods (ESM+AlphaFold). The authors also gather external datasets to evaluate their approach and compare it to previous approaches. The approach seems promising, and appears to out-compete previous methods at several tasks. Nonetheless, I have strong concerns about a lack of verbosity as well as exclusion of relevant methods and references.

Advances:<br /> I appreciate the breadth of the analysis and comparisons to other methods. The authors separate tasks, models, and sizes of models in an intuitive, easy-to-read fashion that I find valuable for selecting a method for embedding peptides. Moreover, the authors gather two datasets for evaluating embeddings' utility for predicting thermostability. Overall, the work should be helpful for the field as more groups choose methods/pretraining strategies amenable to their goals, and can do so in an evidence-guided manner.

Considerations:<br /> Primarily, a majority of the results and conclusions (e.g., Table 3) are reached using data and methods from ProteinGym, yet the best-performing methods on ProteinGym are excluded from the paper (e.g., EVE-based models and GEMME). In the ProteinGym database, these methods outperform ProtSSN models. Moreover, these models were published over a year---or even 4 years in the case of GEMME---before ProtSSN, and I do not see justification for their exclusion in the text.

Secondly, related to comparison of other models, there is no section in the methods about how other models were used, or how their scores were computed. When comparing these models, I think it's crucial that there are explicit derivations or explanations for the exact task used for scoring each method. In other words, if the pre-training is indeed the important advance of the paper, the paper needs to show this more explicitly by explaining exactly which components of the model (and previous models) are used for evaluation. Are the authors extracting the final hidden layer representations of the model, treating these as features, then using these features in a regression task to predict fitness/thermostability/DDG etc.? How are the model embeddings of other methods being used, since, for example, many of these methods output a k-dimensional embedding of a given sequence, rather than one single score that can be correlated with some fitness/functional metric. Summarily, I think the text is lacking an explicit mention of how these embeddings are being summarized or used, as well as how this compares to the model presented.

I think the above issues can mainly be addressed by considering and incorporating points from Li et al. 2024[1] and potentially Tang & Koo 2024[2]. Li et al.[1] make extremely explicit the use of pretraining for downstream prediction tasks. Moreover, they benchmark pretraining strategies explicitly on thermostability (one of the main considerations in the submitted manuscript), yet there is no mention of this work nor the dataset used (FLIP (Dallago et al., 2021)) in this current work. I think a reference and discussion of [1] is critical, and I would also like to see comparisons in line with [1], as [1] is very clear about what features from pretraining are used, and how. If the comparisons with previous methods were done in this fashion, this level of detail needs to be included in the text.

To conclude, I think the manuscript would benefit substantially from a more thorough comparison of previous methods. Maybe one way of doing this is following [1] or [2], and using the final embeddings of each method for a variety of regression tasks---to really make clear where these methods are performing relative to one another. I think a more thorough methods section detailing how previous methods did their scoring is also important. Lastly, TranceptEVE (or a model comparable to it) and GEMME should also be mentioned in these results, or at the bare minimum, be given justification for their absence.

[1] Feature Reuse and Scaling: Understanding Transfer Learning with Protein Language Models Francesca-Zhoufan Li, Ava P. Amini, Yisong Yue, Kevin K. Yang, Alex X. Lu bioRxiv 2024.02.05.578959; doi: https://doi.org/10.1101/2024.02.05.578959<br /> [2] Evaluating the representational power of pre-trained DNA language models for regulatory genomics Ziqi Tang, Peter K Koo bioRxiv 2024.02.29.582810; doi: https://doi.org/10.1101/2024.02.29.582810

Reviewer #2 (Public review):

Summary:

To design proteins and predict disease, we want to predict the effects of mutations on the function of a protein. To make these predictions, biologists have long turned to statistical models that learn patterns that are conserved across evolution. There is potential to improve our predictions however by incorporating structure. In this paper the authors build a denoising auto-encoder model that incorporates sequence and structure to predict mutation effects. The model is trained to predict the sequence of a protein given its perturbed sequence and structure. The authors demonstrate that this model is able to predict the effects of mutations better than sequence-only models.

As well, the authors curate a set of assays measuring the effect of mutations on thermostability. They demonstrate their model also predicts the effects of these mutations better than previous models and make this benchmark available for the community.

Strengths:

The authors describe a method that makes accurate mutation effect predictions by informing its predictions with structure.

Weaknesses:

In the review period, the authors included a previous method, SaProt, that similarly uses protein structure to predict the effects of mutations, in their evaluations.<br /> They see that SaProt performs similarly to their method.

Readers should note that methods labelled as "few-shot" in comparisons do not make use of experimental labels, but rather use sequences inferred as homologous; these sequences are also often available even if the protein has never been experimentally tested.

ProteinGym is largely made of deep mutational scans, which measure the effect of every mutation on a protein. These new benchmarks contain on average measurements of less than a percent of all possible point mutations of their respective proteins. It is unclear what sorts of protein regions these mutations are more likely to lie in; therefore it is challenging to make conclusions about what a model has necessarily learned based on its score on this benchmark. For example, several assays in this new benchmark seem to be similar to each other, such as four assays on ubiquitin performed in pH 2.25 to pH 3.0.

The authors state that their new benchmarks are potentially more useful than those of ProteinGym, citing Frazer 2021; readers should be aware that the mutations from the later source are actually mutations whose impact on human health has been determined through multiple sources, including population genetics, clinical evidence and some experiment.

Reviewer #1 (Public review):

Summary:

The manuscript by Yao S. and colleagues aims to monitor the potential autosomal regulatory role of the master regulator of X chromosome inactivation, the Xist long non-coding RNA. It has recently become apparent that in the human system, Xist RNA can not only spread in cis on the future inactive X chromosome but also reach some autosomal regions where it recruits transcriptional repression and Polycomb marking. Previous work has also reported that Xist RNA can show a diffused signal in some biological contexts in FISH experiments.

In this study, the authors investigate whether Xist represses autosomal loci in differentiating female mouse embryonic stem cells (ESCs) and somatic mouse embryonic fibroblasts (MEFs). They perform a time course of ESC differentiation followed by Capture Hybridization of Associated RNA Targets (CHART) on both female and male ESCs, as well as pulldowns with sense oligos for Xist. The authors also examine transcriptional activity through RNA-seq and integrate this data with prior ChIP-seq experiments. Additional experiments were conducted in MEFs and Xist-ΔB repeat mutants, the latter fails to recruit Polycomb repressors.

Based on this experimental design, the authors make several bold claims:

(1) Xist binds to about a hundred specific autosomal regions.<br /> (2) This binding is specific to promoter regions rather than broad spreading.<br /> (3) Xist autosomal signal is inversely correlated with PRC1/2 marks but positively correlated with transcription.<br /> (4) Xist targeting results in the attenuation of transcription at autosomal regions.<br /> (5) The B-repeat region is important for autosomal Xist binding and gene repression.<br /> (6) Xist binding to autosomal regions also occurs in somatic cells but does not lead to gene repression.

Together, these claims suggest that Xist might play a role in modulating the expression of autosomal genes in specific developmental and cellular contexts in mice.

Strengths:

This paper deals with an interesting hypothesis that Xist ncRNA can also function at autosomal loci.

Weaknesses:

The claims reported in this paper are largely unsubstantiated by the data, with multiple misinterpretations, lacking controls, and inadequate statistics. Fundamental flaws in the experimental design/analysis preclude the validity of the findings. Major concerns are listed below:

(1) The entire paper is based on the CHART observation that Xist is specifically targeted to autosomal promoters. Overall, the data analysis is flawed and does not support such conclusions. Importantly the sense WT and the 0h controls are not used, nor are the biological replicates. Data is typically visualized without quantification, and when quantified, control loci/gene sets are erroneously selected. Firstly, CHART validation on the X in FigS1 is misleading and not based on any quantifications (e.g., see the scale on Kdm6a (0-190) compared to Cdkl5 (0-40)). If scaled appropriately, there is Xist signal on the escapee. All X-linked loci should have been quantified and classified based on escape status; sense control should also be quantified, and biological replicates should be shown separately. Secondly, and most importantly, Figure 1 does not convincingly show specific Xist autosomal binding. Panel A quantification is on extremely variable y-scales and actually shows that Xist is recruited globally to nearly all autosomal genes, likely indicating an unspecific signal. Again, the sense and 0h controls should have been quantified along with biological replicates. Upon inspecting genome browser tracks of all regions reported in the manuscript (Rbm14, Srp9, Brf1, Cand2, Thra, Kmt2c, Kmt2e, Stau2, and Bcl7b), the signal is unspecific on all sites with the possible exception of Kmt2e. On all other loci, there is either a strong signal in the 0h ESC controls or more signal in some of the sense controls. This implies that peak calling is picking up false positive regions. How many peaks would have been picked up if the sense or the 0h controls were used for peak calling? It is likely that there would be a lot since there are also possible "peaks" (e.g., Fzd9) in control tracks. Further inspection of the data was not possible as the authors did not provide access to the raw fastq files. When inspecting results from past published experiments {Engreitz, 2013 #1839} reported regions were not bound by Xist. Thirdly, contrary to the authors' claim, deleting the B repeat does not lead to a loss of autosomal signal. Indeed, comparing Fig1A and Fig2B side by side clearly shows no difference in the autosomal signal, likely because the autosomal signal is CHART background. Properly quantifying the signal with separate replicates as well as the sense and 0h controls is vital. Overall current data together with published results indicate that CHART peak calling on autosomes is due to technical noise or artefacts.

(2) The RNA-seq analysis is also flawed and precludes strong statements. Firstly, the analysis frequently lacks statistical analysis (Fig3B, FigS2B-C) and is often based on visualizations (Fig 3D-G) without quantifications. Day 4 B-repeat deletion does not lead to a significant change in the expression of genes close to Xist signal (Fig3H, d14 does not fully show). Secondly, for all transcriptional analysis, it is important to show autosomal non-target genes, which is not always done. Indeed, both males and B repeat deletion will lead to transcriptional changes on autosomes as a secondary effect from different X inactivation status. The control set, if used, is inappropriate as it compares one randomly selected set of ~100 genes. This introduces sampling error and compares different classes of genes. Since Xist signal targets more active genes, it is important to always compare autosomal target genes to all other autosomal genes with similar basal expression patterns.

(3) The ChIP-seq analysis also has some problems. The authors claim that there is no positive correlation between genes close to Xist autosomal binding (10kb) compared to those 50kb away (Fig 3C, S2D); however, this analysis is based entirely on metagene visualization. Signal within the Xist binding sites should be quantified (not genes close by) and compared to other types of genomic loci and promoters. Focusing on the 50kb group only as controls is misleading. Secondly, the authors only look at PRC mark signal upon differentiation; what about the 0h timepoint, i.e., is there pre-marking? Most worryingly, the data analysis is not consistent between figures (see Fig3C vs 5H-I). In Fig5, the group of Xist targets was chosen as those within 100kb of Xist binding, which would encompass all the control regions from Fig3C. In this analysis, the authors report that there is Xist-dependent H3K27me3 deposition, and in fact, here the Xist autosomal targets have more of it than the controls. Overall, all of this analysis is misleading, and clear conclusions cannot be made.

All in all, because the fundamental observation is not robust (see point 1), all subsequent analyses are also affected. There are also multiple other inconsistencies within the analysis; however, they have not been included here for brevity.

Reviewer #2 (Public review):

Summary:

To follow-up on recent reports of Xist-autosome interaction the authors examine female (and male transgenic) mESCs and MEFs by CHARTseq. Upon finding that only 10% of reads map to X, they sought to identify reproducible alternative sites of Xist-binding, and identify ~100 autosomal Xist-binding sites and show a transient impact on expression.

Strengths:

The authors address a topical and interesting question with a series of models including developmental timepoints and utilize unbiased approaches (CHARTseq, RNAseq). For the CHARTseq they have controls of both sense probes and male cells; and indeed do detect considerable background with their controls. The use of deletions emphasizes that intact functional Xist is involved. The use of 'metagene' plots provides a visual summation of genic impact.

Weaknesses:

Overall, the result presentation has many 'sample' gene presentations (in contrast to the stronger 'metagene' summation of all genes). The manuscript often relies on discussion of prior X chromosomal studies, while the data generated would allow assessment of the X within this study to confirm concordance with prior results using the current methodology/cell lines. Many of the 'follow-up' analyses are in fact reprocessing and comparison of published datasets. The figure legends are limited, and sample size and/or source of control is not always clear. While similar numbers of autosomal Xist-binding sites were often observed, the presented data did not clarify how many were consistent across time-points/cell types. While there were multiple time points/lines assessed, only 2 replicates were generally done.

Aim achievement:

The authors do identify autosomal sites with enrichment of chromatin marks and evidence of silencing. More details regarding sample size and controls (both treatment, and most importantly choice of 'non-targets' - discussed in comments to authors) are required to determine if the results support the conclusions.

Specific scenarios for which I am concerned about the strength of evidence underlying the conclusion:

I found the conclusion "Thus, RepB is required not only for Xist to localize to the X- chromosome but also for its localization to the ~100 autosomal genes " (p5) in constrast to the statement 2 lines prior: "A similar number of Xist peaks across autosomes in ΔRepB cells was observed and the autosomal targets remained similar". Some quantitative statistics would assist in determining impact, both on autosomes and also X; perhaps similar to the quintile analysis done for expression.

It is stated that there is a significant suppression of X-linked genes with the autosomal transgenes; however, only an example is shown in Figure 4B. To support this statement, a full X chromosomal geneset should be shown in panels F and G, which should also list the number of replicates. As these are hybrid cells, perhaps allelic suppression could be monitored? Is Med14 usually subject to X inactivation in the Ctrl cells, and is the expression reduced from both X chromosomes or preferentially the active (or inactive) X chromosome?

The expression change for autosomes after transgene induction is barely significant; and it was not clear what was used as the Ctrl? This is a critical comparator as doxycycline alone can change expression patterns.

In the discussion there is the statement. "Genetic analysis coupled to transcriptomic analysis showed that Xist down-regulates the target autosomal genes without silencing them. This effect leads to clear sex difference - where female cells express the ~100 or so autosomal genes at a lower level than male cells (Figure 7H)." This sweeping statement fails to include that in MEFs there is no significant expression difference, in transgenics only borderline significance, and at d14 no significant expression difference. The down-regulation overall seems to be transient during development while targeting is ongoing?

Finally, I would have liked to see discussion of the consistency of the identified genes to support the conclusion that the autosomal sites are not merely the results of Xist diffusion.

The impact of Xist on autosomes is important for consideration of impact of changes in Xist expression with disease (notably cancers). Knowing the targets (if consistent) would enable assessment of such impact.

Reviewer #3 (Public review):

Summary:

Yao et al use CHART to identify chromatin associated with Xist in female mouse ESCs, and, as control, male ESCs at various timepoints of differentiation. Besides binding of Xist to X chromosome regions they found significant binding to autosomes, concentrating mostly on promoter regions of around 100 autosomal genes, as elucidated by MACS. The authors went on to show that the RepB repeat is mostly responsible for these autosomal interactions using a female ESC line in which RepB is deleted. Evidence is provided that Xist interacts with active autosomal genes containing lower coverage of repressive marks H3K27me3 and H2AK119ub and that RepB dependent Xist binding leads to dampening of expression, but not silencing of autosomal genes. These results were confirmed by overexpression studies using transgenic ESCs with doxycycline-inducible Xist as well as via a small molecule inhibitor of Xist (X1), inducing/inhibiting the dampening of autosomal genes, respectively. Finally, using MEFs and Xist mutants RepB or RepE the authors provide evidence that Xist is bound to autosomal genes in cells after the XCI process but appears not to affect gene expression. The data presented appear generally clear and consistent and indicate some differences between human and mouse autosomal regulation by Xist.

Strengths:

Regulation of autosomal gene expression by Xist is a "big deal" as misregulation of this lncRNA causes developmental defects and human disease. Moreover, this finding may explain sex-specific developmental differences between the sexes. The results in this manuscript identify specific mouse autosomal genes bound by Xist and decipher critical Xist regions that mediate this binding and gene dampening. The methods used in this study are appropriate, and the overall data presented appear convincing and are consistent, indicating some differences between human and mouse autosomal regulation by Xist.

Weaknesses:

(1) The figure legends and/or descriptions of data are often very short lacking detail, and this unnecessarily impedes the reading of the manuscript, in particular the figures would benefit not only from more detailed descriptions/explanations of what has been done but also what is shown. This will facilitate the reading and overall comprehension by the reader. One out of many examples: In Fig S1B in the CHART data at d4 and d7 there is not only signal in female WT Xist antisense but also in female sense control. For a reader that is not an expert in XCI it would be helpful to point out in the legend that this signal corresponds to the lncRNA Tsix (I suppose), that is transcribed on the other strand.

(2) Different scales are used in the lower panels of Figures 1A and 2A, which makes it difficult to directly compare signals between the different differentiation stages.

(3) In this study some of the findings on mouse cells contrast previously published results in human ESCs: 1) Xist binding occurs preferentially to promoters in mice, not in human. 2) Binding of Xist is mostly detected in polycomb-depleted regions in mice but there is a positive correlation between Xist RNA and PRC2 marks in human ESCs. These differences are surprising but may be very interesting and relevant. While I am aware that this might be a difficult task, it would be helpful to experimentally address this issue in order to distinguish whether species specific and/or methodological differences between the studies are responsible for these differences.

Joint Public Review:

Summary:

The authors present an intriguing investigation into the pathogenesis of Pol III variants associated with neurodegeneration. They established an inducible mouse model to overcome developmental lethality, administering 5 doses of tamoxifen to initiate the knock-in of the mutant allele. Subsequent behavioral assessments and histological analyses revealed potential neurological deficits. Robust analyses of the tRNA transcriptome, conducted via northern blotting and RNA sequencing, suggested a selective deleterious effect of the variant on the cerebrum, in contrast to the cerebellum and non-cerebral tissues. Through this work, the authors identified molecular changes caused by Pol III mutations, particularly in the tRNA transcriptome, and demonstrated its relative progression and selectivity in brain tissue. Overall, this study provides valuable insights into the neurological manifestations of certain genetic disorders and sheds light on transcripts/products that are constitutively expressed in various tissues.

Strengths:

The authors utilize an innovative mouse model to constitutively knock in the gene, enhancing the study's robustness. Behavioral data collection using a spectrometer reduces experimenter bias and effectively complements the neurological disorder manifestations. Transcriptome analyses are extensive and informative, covering various tissue types and identifying stress response elements and mitochondrial transcriptome patterns. Additionally, metabolic studies involving pancreatic activity and glucose consumption were conducted to eliminate potential glucose dysfunction, strengthening the histological analyses.

Comments on revised version from expert Editor #1:

The authors in the revised manuscript have effectively responded to all of the comments and suggestions raised by both reviewers. Overall, I find the revised version to be an important contribution to the field and the strength of evidence supporting the work's claims to be compelling.

Comments on revised version from expert Editor #2:

The authors have responded constructively to all the comments in the first round of reviews and clarified many issues in the manuscript. The current report represents a significant advance.

Comments on revised version from Reviewer #2:

The authors should include their clarifications of all concern raised by reviewer #2 (mentioned in the previous weaknesses) in the main text. They should consider including point #2 to point #10 in the main text (discussion section). The should highlight limitations of this study in discussion.

Also, they should clearly state that deciphering brain area specific behavioural deficits is beyond the scope of the manuscript with appropriate justification mentioned in the rebuttal letter.

I still do not agree with the author to state that "brain region-specific sensitivities to a defect in Pol III transcription". The changes are global and also not restricted to brain. Authors may consider restating this sentence. It is obvious that transcription defects related to tRNA production will lead to alteration in whole body physiology.

Reviewer #1 (Public review):

Summary:

The authors explored how the presence of interspecific introgressions in the genome affects the recombination landscape. This research aims to shed light on the genetic phenomena influencing the evolution of introgressed regions. However, it is important to note that the study is based on examining only one generation, which limits the scope for making broad evolutionary conclusions. In this study, yeast hybrids with large introgressions (ranging from several to several dozen percent of the chromosome length) from another yeast species were crossed. The products of meiosis were then isolated and sequenced to examine the genome-wide distribution of both crossovers (COs) and noncrossovers (NCOs). The authors found a significant reduction in the frequency of COs within the introgressed regions, which is a phenomenon well-documented in various systems. They also report that introgressed regions exhibit an increased frequency of NCOs. Unfortunately, this conclusion seems flawed, as there is no accurate method for correcting the detection level of NCOs when the compared regions (introgressed and non-introgressed) differ drastically in SNP density. The authors further confirmed that introgressions significantly limit the local shuffling of genetic information, and while NCOs contribute slightly to this shuffling, they do not compensate for the loss of CO recombination. This is widely known fact.

In summary, the study makes a limited contribution to the understanding of how polymorphism impacts meiotic recombination. The conclusion regarding the increase in NCO frequency in polymorphic regions is likely incorrect.

Reviewer #3 (Public review):

When members of two related but diverged species mate, the resulting hybrids can produce offspring where parts of one species' genome replace those of the other. These "introgressions" often create regions with a much greater density of sequence differences than are normally found between members of the same species. Previous studies have shown that increased sequence differences, when heterozygous, can reduce recombination during meiosis specifically in the region of increased difference. However, most of these studies have focused on crossover recombination, and have not measured noncrossovers. The current study uses a pair of Saccharomyces uvarum crosses: one between two natural isolates that, while exhibiting some divergence, do not contain introgressions; the other is between two fermentation strains that,<br /> when combined, are heterozygous for 9 large regions of introgression that have much greater divergence than the rest of the genome. The authors wished to determine if introgressions differently affected crossovers and noncrossovers, and, if so, what impact that would have on the gene shuffling that occurs during<br /> meiosis.

While both crossovers and noncrossovers were measured, assessing the true impact of increased heterology (inherent in heterozygous introgressions) is complicated by the fact that the increased marker density in heterozygous introgressions also increases the ability to detect noncrossovers. The authors now use a revised correction aimed at compensating for this difference, and based on that correction, conclude that, while as expected crossovers are decreased by increased sequence heterology, noncrossovers neither increase nor decrease substantially. They then show that genetic shuffling overall is substantially reduced in regions of heterozygous introgression, which is not surprising given that one type of event is reduced and the other remains at similar levels. However, the correction currently used remains poorly justified, tests of its validity are not presented. Thus, the only possibly novel conclusion, that noncrossovers are less affected by heterology than crossovers, remains to be adequately tested.

In conclusion, of the three main conclusions as stated in the abstract, one (that crossovers go down) has been shown in many systems, one (that noncrossovers increase) is wrong, and the third (that allele shuffling is reduced) is obvious. Given this, the impact of this work on the field will be minimal at best, and negative to the extent that readers are led astray.

I am not sure how much detail we want to give here. Not sure how frequently this feature is being used. If we want to give more info, we could list the steps for each edit action. They differ a lot and have some slightly tricky steps. Especially the last step differs (sometimes it's enough to click Apply, sometimes it's necessary to click Save Edits

Reviewer #1 (Public review):

Assessment:

This fundamental work advances our understanding of navigation and path integration in mammals by using a clever behavioral paradigm. The paper provides compelling evidence that mice are able to create and use a cognitive map to find "short cuts" in an environment, using only the location of rewards relative to the point of entry to the environment and path integration, and need not rely on visual landmarks.

Summary:

The authors have designed a novel experimental apparatus called the 'Hidden Food Maze (HFM)' and a beautiful suite of behavioral experiments using this apparatus to investigate the interplay between allothetic and idiothetic cues in navigation. The results presented provide a clear demonstration of the central claim of the paper, namely that mice only need a fixed start location and path integration to develop a cognitive map. The experiments and analyses conducted to test the main claim of the paper -- that the animals have formed a cognitive map -- are conclusive and include many thoughtfully designed control experiments to eliminate alternatives.

Strengths:

The 90 degree rotationally symmetric design and use of 4 distal landmarks and 4 quadrants with their corresponding rotationally equivalent locations (REL) lends itself to teasing apart the influence of path integration and landmark-based navigation in a clever way. The authors use a complete set of experiments and associated controls to show that mice can use a start location and path integration to develop a cognitive map and generate shortcut routes to new locations.

Weaknesses:

There were no major weaknesses identified that were not addressed during revisions.

Reviewer #3 (Public review):

Summary:

How is it that animals find learned food locations in their daily life? Do they use landmarks to home in on these learned locations or do they learn a path based on self-motion (turn left, take ten steps forward, turn right, etc.). This study carefully examines this question in a well-designed behavioral apparatus. A key finding is that to support the observed behavior in the hidden food arena, mice appear to not use the distal cues that are present in the environment for performing this task. Removal of such cues did not change the learning rate, for example. In a clever analysis of whether the resulting cognitive map based on self-motion cues could allow a mouse to take a shortcut, it was found that indeed they are. The work nicely shows the evolution of the rodent's learning of the task, and the role of active sensing in the targeted reduction of uncertainty of food location proximal to its expected location.

Strengths:

A convincing demonstration that mice can synthesize a cognitive map for the finding of a static reward using body frame-based cues. Showing that uncertainty of final target location is resolved by an active sensing process of probing holes proximal to the expected location. Showing that changing the position of entry into the arena rotates the anticipated location of the reward in a manner consistent with failure to use distal cues.

Weaknesses:

Weaknesses: The Reviewing Editor felt that previously identified weaknesses from Reviewer #3 were adequately addressed in the final manuscript.

Reviewer #1 (Public review):

Summary:

This study investigated the role of plectin, a cytoskeletal crosslinker protein, in liver cancer formation and progression. Using the liver-specific Plectin knockout mouse model, the authors convincingly showed that PLECTIN is critical for hepatocarcinogenesis, as functional inhibition of plectin suppressed tumor formation in several models. They also provided evidence to show that inhibition of plectin inhibited HCC cell invasion and reduced metastatic outgrowth in the lung. Mechanistically, they suggested that plectin inhibition attenuated FAK, MAPK/ERK, and PI3K/AKT signaling.

Strengths:

The authors generated a liver-specific plectin knockout mouse model. By using DEN and sgP53/MYC models, the authors convincingly demonstrated an oncogenic role of PLECTIN in HCC development. plecstatin-1 (PST), as a plectin inhibitor, showed promising efficacy in inhibiting HCC growth, which provides a basis for potentially treating HCC using PST.

The MIR images for tracking tumor growth in animal models were compelling. The high-quality confocal images and related qualifications convincingly showed the impact of plectin functional inhibition on contractility and adhesions in HCC cells.

Weaknesses:

The conclusions of this paper are primarily well supported by data. However, some claims were not fully supported by the data presented.

The authors suggest that plectin controls oncogenic FAK, MAPK/Erk, and PI3K/Akt signaling in HCC cells, representing the mechanisms by which plectin promotes HCC formation and progression. However, the effect of plectin inactivation on these signaling was inconsistent in Huh7 and SNU-475 cells (Figure 3D), despite similar cell growth inhibition in both cell lines (Figure 2G). For example, pAKT and pERK were only reduced by plectin inhibition in SNU-475 cells but not in Huh7 cells. In addition, pFAK was not changed by plectin inhibition in both cells, and the ratio of pFAK/FAK was increased in both cells. Thus, it is hard to convince me that plectin promotes HCC formation and progression by regulating these signalings. Overall, the mechanistic studies in this manuscript lack sufficient depth.

The authors claimed that plectin inactivation inhibits HCC invasion and metastasis using in vitro and in vivo models. However, the results from in vivo models were not as compelling as the in vitro data. The lung colonization assay is not an ideal in vivo model for studying HCC metastasis and invasion, especially when plectin inhibition suppresses HCC cell growth and survival. Using an orthotopic model that can metastasize into the lung or spleen could be much more convincing for an essential claim. Also, in Figure 6H, histology images of lungs from this experiment need to be shown to understand plectin's effect on metastasis better. Figure 6G, it is unclear how many mice were used for this experiment. Did these mice die due to the tumor burdens in the lungs?

The whole paper used inhibition strategies to understand the function of plectin. However, the expression of plectin in Huh7 cells is low (Figure 1D). It might be more appropriate to overexpress plectin in this cell line or others with low plectin expression to examine the effect on HCC cell growth and migration.

Reviewer #2 (Public review):

Summary:

Plectin is a cytolinker that associates with all three main components of the cytoskeleton and intercellular junctions and is essential for epithelial tissue integrity. Previous reports showed that PLEC regulates tumor growth and metastasis in different cancers. In this manuscript, the authors described PLEC as a target in the initiation and growth of HCC. They showed that inhibiting PLEC reduced tumorigenesis in different in vitro and in vivo HCC models, including in a xenograft model, DEN model, oncogene-induced HCC model, and a lung metastasis model. Mechanistically, the authors showed that inhibiting PLEC results in a disorganized cytoskeleton, deficiency in cell migration, and changes in relevant signaling pathways.

Strengths:

In general, the data are shown in multiple ways and support the main conclusion of the manuscript. The results add to the field by highlighting the importance of cellular mechanics in cancer progression.

Weaknesses:

(1) The annotation of mouse numbers is confusing. In Figures 2A B D E F, it should be the same experiment, but the N numbers in A are 6 and 5. In E and F they are 8 and 3. Similarly, in Figure 2H, in the tumor size curve, the N values are 4,4,5,6. In the table, N values are 8,8,10,11 (the authors showed 8,7,8,7 tumors that formed in the picture).

(2) In Figure 3D and Figure S3C, the changes in most of the proteins/phosphorylation sites are not convincing/consistent. These data are not essential for the conclusion of the paper and WB is semi-quantitative. Maybe including more plots of the proteins from proteomic data could strengthen their detailed conclusions about the link between Plectin and the FAK, MAPK/Erk, PI3K/Akt pathways as shown in 3E.

(3) Figure S7A and B, The pictures do not show any tumor, which is different from Figure 7A and B (and from the quantification in S7A lower right). Is it just because male mice were used in Figure 7 and female mice were used in Figure S7? Is there literature supporting the sex difference for the Myc-sgP53 model?

(4) Figure 2F, S2A, PleΔAlb mice more frequently formed larger tumors, as reflected by overall tumor size increase. The interpretation of the authors is "possibly implying reduced migration or increased cohesion of plectin-depleted cells". It is quite arbitrary to make this suggestion in the absence of substantial data or literature to support this theory.

(5) Mutation or KO PLEC has been shown to cause severe diseases in humans and mice, including skin blistering, muscular dystrophy, and progressive familial intrahepatic cholestasis. Please elaborate on the potential side effects of targeting plectin to treat HCC.

Reviewer #3 (Public review):

Summary:

In this manuscript, Outla Z et al described the analysis of plectin in HCC pathogenesis. Specifically, it was found that elevated plectin levels in liver tumors, correlated with poor prognosis for HCC patients. Mechanistically, it showed that plectin-dependent disruption of cytoskeletal networks leads to the attenuation of oncogenic FAK, MAPK/Erk, and PI3K/AKT signals. Finally, the authors showed that plectin inhibitor plecstatin-1 (PST) is well-tolerated and capable of overcoming therapy resistance in HCC.

Strengths:

The studies of plectin are not entirely novel (Pubmed: 36613521). Nevertheless, the current manuscript provides a much more detailed mechanistic study and the results have translational implications. Additional strengths include convincing cell biology data, such as plectin regulates cytoskeletal networks, and HCC migration/invasion.

Weaknesses:

Multiple major issues are noted, and the conclusion is not well supported by the data presented.

(1) The rationale for using Huh7 cells in the manuscript is not well explained as it has the lowest plectin expression levels.

(2) The KO cell experiments should be supplemented with overexpression experiments.

(3) There is significant concern that while ablation of Ple led to reduced tumor number, these mice had larger tumors. The data indicate that plectin may have distinct roles in HCC initiation versus progression. The data are not well explained and do not fully support that plectin promotes hepatocarcinogenesis.

(4) Figure 3 showed that plectin does not regulate p-FAK/FAK expression. Therefore, the statement that plectin regulates the FAK pathway is not valid. Furthermore, there are too many variables in turns of p-AKT and p-ERK expression, making the conclusion not well supported.

(5) The studies of plecstatin-1 in HCC should be expanded to a panel of human HCC cells with various plectin expression levels in turns of cell growth and cell migration. The IC50 values should be determined and correlate with plectin expression.

(6) One of the major issues is the mechanistic studies focusing on plectin regulating HCC migration/metastasis, whereas the in vivo mouse studies focus on HCC formation (Figures 3 and 7). These are distinct processes and should not be mixed.

(7) Figure 7B showed that Ple KO mice were treated with PST, but the data are not presented in the manuscript. Tumor cell proliferation and apoptosis rates should be analyzed as well.

(8) The status of FAK, AKT, and ERK pathway activation was not analyzed in mouse liver samples. In Figure 7D, most of the adjusted p-values are not significant.

(9) There is no evidence to support that PST is capable of overcoming therapy resistance in HCC. For example, no comparison with the current standard care was provided in the preclinical studies.

Reviewer #1 (Public review):

In this revised manuscript, the authors aim to elucidate the cytological mechanisms by which conjugated linoleic acids (CLAs) influence intramuscular fat deposition and muscle fiber transformation in pig models. They have utilized single-nucleus RNA sequencing (snRNA-seq) to explore the effects of CLA supplementation on cell populations, muscle fiber types, and adipocyte differentiation pathways in pig skeletal muscles. Notably, the authors have made significant efforts in addressing the previous concerns raised by the reviewers, clarifying key aspects of their methodology and data analysis.

Strengths:

(1) Thorough validation of key findings: The authors have addressed the need for further validation by including qPCR, immunofluorescence staining, and western blotting to verify changes in muscle fiber types and adipocyte populations, which strengthens their conclusions.

(2) Improved figure presentation: The authors have enhanced figure quality, particularly for the Oil Red O and Nile Red staining images, which now better depict the organization of lipid droplets (Figure 7A). Statistical significance markers have also been clarified (Figure 7I and 7K).

Weaknesses:

(1) Cross-species analysis and generalizability of the results: Although the authors could not perform a comparative analysis across species due to data limitations, they acknowledged this gap and focused on analyzing regulatory mechanisms specific to pigs. Their explanation is reasonable given the current availability of snRNA-seq datasets on muscle fat deposition in other human and mouse.

(2) Mechanistic depth in JNK signaling pathway: While the inclusion of additional experiments is a positive step, the exploration of the JNK signaling pathway could still benefit from deeper analysis of downstream transcriptional regulators. The current discussion acknowledges this limitation, but future studies should aim to address this gap fully.

(3) Limited exploration of other muscle groups: The authors did not expand their analysis to additional muscle groups, leaving some uncertainty regarding whether other muscle groups might respond differently to CLA supplementation. Further studies in this direction could enhance the understanding of muscle fiber dynamics across the organism.

Reviewer #2 (Public review):

Summary:

This study comprehensively presents data from single nuclei sequencing of Heigai pig skeletal muscle in response to conjugated linoleic acid supplementation. The authors identify changes in myofiber type and adipocyte subpopulations induced by linoleic acid at depth previously unobserved. The authors show that linoleic acid supplementation decreased the total myofiber count, specifically reducing type II muscle fiber types (IIB), myotendinous junctions, and neuromuscular junctions, whereas type I muscle fibers are increased. Moreover, the authors identify changes in adipocyte pools, specifically in a population marked by SCD1/DGAT2. To validate the skeletal muscle remodeling in response to linoleic acid supplementation, the authors compare transcriptomics data from Laiwu pigs, a model of high intramuscular fat, to Heigai pigs. The results verify changes in adipocyte subpopulations when pigs have higher intramuscular fat, either genetically or diet-induced. Targeted examination using cell-cell communication network analysis revealed associations with high intramuscular fat with fibro-adipogenic progenitors (FAPs).  The authors then conclude that conjugated linoleic acid induces FAPs towards adipogenic commitment. Specifically, they show that linoleic acid stimulates FAPs to become SCD1/DGAT2+ adipocytes via JNK signaling. The authors conclude that their findings demonstrate the effects of conjugated linoleic acid on skeletal muscle fat formation in pigs, which could serve as a model for studying human skeletal muscle diseases.

Strengths:

The comprehensive data analysis provides information on conjugated linoleic acid effects on pig skeletal muscle and organ function. The notion that linoleic acid induces skeletal muscle composition and fat accumulation is considered a strength and demonstrates the effect of dietary interactions on organ remodeling. This could have implications for the pig farming industry to promote muscle marbling. Additionally, these data may inform the remodeling of human skeletal muscle under dietary behaviors, such as elimination and supplementation diets and chronic overnutrition of nutrient-poor diets. However, the biggest strength resides in thorough data collection at the single nuclei level, which was extrapolated to other types of Chinese pigs.

Weaknesses:

Although the authors compiled a substantial and comprehensive dataset, the scope of cellular and molecular-level validation still needs to be expanded. For instance, the single nuclei data suggest changes in myofiber type after linoleic acid supplementation, but these findings need more thorough validation. Further histological and physiological assessments are necessary to address fiber types and oxidative potential. Similarly, the authors propose that linoleic acid alters adipocyte populations, FAPs, and preadipocytes; however, there are limited cellular and molecular analyses to confirm these findings. The identified JNK signaling pathways require additional follow-ups on the molecular mechanism or transcriptional regulation. However, these issues are discussed as potential areas for future exploration. While various individual studies have been conducted on mouse/human skeletal muscle and adipose tissues, these have only been briefly discussed, and further investigation is warranted. Additionally, the authors incorporate two pig models into their results, but they only examine one muscle group. Exploring whether other muscle groups respond similarly or differently to linoleic acid supplementation would be valuable. Furthermore, the authors should discuss how their results translate to human and pig nutrition, such as the desirability and cost-effectiveness for pig farmers and human diets high in linoleic acid. Notably, while the single nuclei data is comprehensive, there needs to be a statement on data deposition and code availability, allowing others access to these datasets.

Reviewer #1 (Public review):

Summary:

In this work, the authors continue their investigations on the key role of glycosylation to modulate the function of a therapeutic antibody. As follow up of their previous demonstration on how ADCC was heavily affected by the glycans at the Fc gamma receptor (FcγR)IIIa, they now dissect the contributions of the different glycans that decorate the diverse glycosylation sites. Using a well designed mutation strategy, accompanied by exhaustive biophysical measurements, with extensive use of NMR, using both standard and newly developed methodologies, they demonstrate that there is one specific locus, N162, which is heavily involved in the stabilization of (FcγR)IIIa and that the concomitant NK function is regulated by the glycan at this site.

Strengths:

The methodological aspects are carried out at the maximum level.

Weaknesses:

The exact (or the best possible assessment) of the glycan composition at the N162 site.

Reviewer #2 (Public review):

Summary:

The authors set out to demonstrate a mechanistic link between Fcgamma receptor (IIIA) glycosylation and IgG binding affinity and signaling - resulting in antibody-dependent cellular cytotoxicity - ADCC. The work builds off prior findings from this group about the general impact of glycosylation on FcR (Fc receptor)-IgG binding.

Strengths:

The structural data (NMR) is highly compelling and very significant to the field. A demonstration of how IgG interacts with FcgRIIIA in a manner sensitive to glycosylation of both the IgG and the FcR fills a critical knowledge gap. The approach to demonstrate the selective impact of glycosylation at N162 is also excellent and convincing. The manuscript/study is, overall, very strong.

Weaknesses:

After revision, which I feel addressed the minor concerns well, the last comment about significance in the long-term is all that remains. Essentially, it will be important in downstream research to determine whether changes in N162 glycan composition ever occur naturally as a result of some factor(s) that include various disease states, inflammation, age, and so on. The answer (either way) does not diminish the importance of understanding molecular details governing antibody-receptor interactions, but it would be very interesting to know if those glycans are regulated in a way that modulates ADCC activity.

Reviewer #1 (Public review):

The manuscript by Wang et al. investigates the role of Rnf220 in hindbrain development and Hox expression. The authors suggest that Rnf220 controls Hox expression in the hindbrain through regulating WDR5 levels. The authors combine in vivo experiments with experiments in P19 cells to demonstrate this mechanism. However, the in vivo data does not provide strong support for the claims the authors make and the role of Rnf in Hox maintenance and pons development is unclear.

While the authors partially addressed some of the issues raised in the first round of reviews, and the in vitro data showing a relationship between Rnf220 and WDR5 is convincing, some issues still remain about the experimental evidence supporting their claims and the relationship of this work with previous studies demonstrating the role of Hox proteins in pontine nuclei in vivo.

The authors say they were unable to detect Hox levels via in situ hybridization at late embryonic stages, stating that the levels are likely too low to be detected-yet they are presumably high enough to cause ectopic targeting of pontine neurons. Work from the Rijli group, which the authors cite, shows that Hox3-5 paralogs can be clearly detected both by in situ and by staining with commercially available antibodies. Since a major claim of this paper is the upregulation of Hox genes in Rnf220+/- mice through WDR5 regulation, the authors need to show this more convincingly. The inability to detect Hox upregulation, and subsequent rescue, by means other than qPCR in vivo remains a major weakness of the paper. The authors also do not discuss how broad upregulation of all Hox paralogs leads to the changes in PN targeting in the context of previous work.

The links between Wdr5 expression, epigenetic modifications, Hox expression and axon mistargeting in vivo remains somewhat tenuous. For example, the authors show epigenetic modification changes in some Hox genes, but not Hox5 paralogs, and only show the rescue by Wdr5 KO in vitro. Similarly, they do not attempt to show rescue of axon targeting in vivo after presumably restoring Hox levels by Wdr5 inhibition or knockdown.

Reviewer #1 (Public review):

Summary:

The authors investigate the compaction of HIV DNA by the viral enzyme integrase (IN) in vitro.

Strengths:

The authors employ robust techniques, including single-molecule force microscopy and spectroscopy, to investigate the impact of IN-DNA interactions on DNA conformation. Additionally, they interpret their experimental findings using coarse-grained Monte Carlo simulations.

Weaknesses:

The authors could provide a more in-depth discussion of the biophysical reasons behind their experimental observations. Currently, there is insufficient analysis to explain why certain behaviors are observed experimentally.

Reviewer #2 (Public review):

Summary:

This is a high-quality biophysical study providing valuable new in vitro information on the modes of HIV-1 integrase protein (IN) interaction with the double stranded (ds)DNA.

Strengths:

Both main experimental approaches used in this study: magnetic tweezers (MT) and atomic force microscopy (AFM) are used at the state-of-the-art level.

Weaknesses:

(1) The findings of Fig.1 suggest modest preference of IN oligomers for the processed DNA ends typical of the viral dsDNA in the intasome and the DNA with blunt ends relative to the IN-oligomer binding to the random internal sites on DNA. This is an impressive result. Is it completely new? What was known about it? Can IN oligomer bind and unbind on the time of experiment? Is it an equilibrium preference? Was the effect of Mg2+ in that binding known?

(2) Regarding the AFM-observed IN-induced DNA bending and looping. How defined is the DNA crossover angle in the looped state? How many IN molecules typically hold it together? What density of IN per DNA length is needed to observe formation of IN oligomers, and their induced DNA beds and loops? It looks like more information on the two dsDNA crossover points held together by IN oligomers can be obtained from the AFM images, similar to the ones in Fig. S22. In particular, the preferred crossover angle (similar to bending angel of one DNA) and the total number of IN proteins within the oligomer holding this crossover point together can be extracted from the AFM data at higher resolution.

(3) Similarly, questions for Fig.3. What is the typical binding density (i.e. IN per DNA unit length) required for the IN-induced rosette formation? For the IN-induced 3D condensation? I understand that the AFM is not the good method to estimate the protein:DNA stoichiometry, as the mica surface and its treatment affect the protein/DNA interactions compared to the bulk solution. But still, in combination with the MT data there should be at least approximate estimate of the degree of DNA saturation. With IN oligomers that cause these sharp cooperative structural transitions of the complex. The fact that higher salt increases critical concentration of IN for these transitions is consistent with the critical levels of DNA saturation with IN required for each transition. Also, the fact that the rosette formation is not observed on shorter 3Kbp DNA but is observed on longer 4.8Kbp and 9Kbp comes from the lower probability of looping in the shorter DNA and can be discussed/interpreted. Maybe the persistence length of the DNA/IN complex at this level of its saturation can be estimated from these data. This persistence length should be shorter than for the bare DNA, as the IN binding induces DNA bending.

(4) In the section describing the simulations of the IN-induced dsDNA compaction the authors introduce a very simple model in which IN tetramer is presented as a bead of the size of ~12 bp similar to the binding site size of the singe IN on DNA with the four binding sites for DNA. It would be useful to discuss the published experimental structural data on the IN-DNA complexes available to better rationalize this choice of the model. In general, more overview of the available information on IN-DNA complexes and discussion of how present results fit into the general story and add to it would be useful. The authors fit their modeling results to their experimental data to obtain the individual monomeric IN-DNA interaction strength of 5 kBT. What is the geometry of these for DNA binding sites on the IN tetramer? Is it important for the complex structure? Also, the authors mention that the additional IN-IN interactions are required to reproduce their AFM results. What is the geometry and the strength of these interactions? It should matter for the structure of the IN-DNA aggregate. For example, if the IN molecules or DNA-bound oligomers were only interacting head-to-tail on the DNA that they bind to, it would lead to the filament formation, rather than the 3D condensate. What was the density of the IN oligomers on DNA to lead to each of the two AFM-observed transitions: (i) the "rosette formation" and (ii) the denser 3D aggregate formation? It may be possible to answer these important questions based on the AFM images. Is the higher resolution AFM measuring the oligomer sizes and their densities on the DNA possible?

(5) Regarding the elastic and viscoelastic properties of the IN-DNA complexes studied in Fig. 4. These are very interesting observations that could take more interpretation. For example, why is the rosette center in Fig.4C has lower stiffness that the loop area? Is it because in the loops the stiffness is more of the background and bare DNA is felt? Does the stiffness of the fully compacted complex in Fig.4D follow the density of the globule?

(6) Also, more interpretation of the observed dwell times and velocity distributions of the complex unfolding vs force can be provided, and what it tells us about the interactions that hold this complex together.

(7) The effect of ALINIs on the structure of rosette and denser condensate is interesting. Based on the published notion on where ALINIS bind to IN and what kind of interactions they prevent can these results be better interpreted? Maybe the IN-IN interactions that hold the rosette together are the same as the ones that hold the dense aggregate together, but just at higher [IN]? And because the fewer IN interactions have to hold large DNA loops in the rosette, they are weaker interactions that are easier to disrupt via the same ALINI-IN interactions?

(8) Finally, in the discussion it would be quite valuable if the authors could comment on the conclusions based on their findings for the in vivo IN-DNA interactions inside the mature capsid. As there are 100-150 IN molecules per capsid within the very small capsid volume, do all of these IN bunch up together on the dsDNA being synthesized? By the end of the reverse transcription when the vDNA ends are synthesized and processed, can this IN oligomer be re-bound to form the synapse of the vDNA ends?

Reviewer #3 (Public review):

Summary:

In this work, the authors aims and efforts point towards evaluating the interaction mechanisms between viral protein integrase (IN) and viral DNA. They develop a multifaceted approach to probe the effect that IN has on the formation and structure of IN-DNA complexes under different environmental conditions to determine the role of IN in early stages of infection. HIV infection is considered a global pandemic with huge challenges in both treatment and prevention. This work presents a step towards understanding the mechanisms in early infection and thus prevention.

The experimental work is carried out using single molecule imaging and force spectroscopy, alongside computational verification using Monte-Carlo simulations. The authors use a range of well-established methods to quantitatively evaluate this, pushing forward the current state of the art.

The paper shows that in the presence of IN, DNA is compacted into a condensate in a biphasic manner, first forming a 'semi-compact' rosette condensate followed by a fully compacted condensate. As HIV DNA must be fully compacted to enter the cell nucleus for infection, this work describes the importance of the role of IN and the conditions required for it to reach a full condensate, and hence provides a new understanding on the early role of IN in infection. Furthermore, the authors show that the semi-compact rosette condensate (i.e. the first phase) is susceptible to IN inhibitors whereas the second compaction phase is insusceptible. This work provides us with information that using inhibitors in the early stages of IN-DNA interaction, infection may be prevented.

Strengths:

The authors present a strong piece of work, using current experimental and computational methods to investigate IN-DNA interactions and to convincingly describe their experimental observations. Firstly the data and analysis shown from AFM and MT experiments convincingly show a two-phase compaction of DNA upon interaction with IN. The authors use Monte-Carlo simulations to model DNA-IN interactions, specifically showing that their experimental results of a two-phase compaction can only be observed via simulations if IN-IN attraction is included.

The authors aim of showing the effect of IN on the compaction of DNA was achieved successfully using AFM and MT. Furthermore, the works show clearly the susceptibility of the partially compacted DNA-IN core to inhibitors. Overall the conclusions in this paper are supported well by their experimental data and it is likely that this paper will not only be used as a model for future experimental work to explore other retroviral nucleoprotein condensation but also to develop a deeper understanding of the role of IN-inhibitors infection prevention.

Finally, the article is written very coherently and is well supported by critical analysis of their findings and appropriate referencing to supplementary figures.

Overall, this article is very worthy and through extensive and detailed work the authors probe difficult questions regarding HIV infection, which currently poses a huge global risk. The work completed by the authors substantially advances our understanding of HIV infection and can be used by those in the future to probe this question further.

Weaknesses:

Important aspects of the methodologies in this paper are not described in detail. For example, force volume curves have been used to evaluate the mechanical properties of the DNA-IN complex. Force-volume measurements are prone to a number of errors, particularly relating to data acquisition and analysis. The methodology presented is not clear on how the data is acquired, whether statically or in amplitude modulation, which affects analysis and interpretation. Although the authors do recognise some of the difficulties with force curve analysis, a more rigorous study could have been provided with citations to additional relevant literature (particularly taking note of the methods).

A minor point is that it is not clear that the AFM imaging is performed in air, in contrast to AFM force spectroscopy in liquid, which could affect the interpretation of the data and therefore comparisons which are drawn between the two. This is made more challenging as the methodology for the compaction measurements is not described in the methods, and the code is not provided. The source code should be made open-access and available to enable the work to be better understood and reproduced.

Reviewer #1 (Public review):

DiPeso et al. develop two tools to (i) classify micronucleated (MN) cells, which they call VCS MN, and (ii) segment micronuclei and nuclei with MMFinder. They then use these tools to identify transcriptional changes in MN cells.

The strengths of this study are:

(1) Developing highly specialized tools to speed up the analysis of specific cellular phenomena such as MN formation and rupture is likely valuable to the community and neglected by developers of more generalist methods.

(2) A lot of work and ideas have gone into this manuscript. It is clearly a valuable contribution.

(3) Combining automated analysis, single-cell labeling, and cell sorting is an exciting approach to enrich phenotypes of interest, which the authors demonstrate here.

Weaknesses:

(1) Images and ground truth labels are not shared for others to develop potentially better analysis methods.

(2) Evaluations of the methods are often not fully explained in the text.

(3) To my mind, the various metrics used to evaluate VCS MN reveal it not to be terribly reliable. Recall and PPV hover in the 70-80% range except for the PPV for MN+. It is what it is - but do the authors think one has to spend time manually correcting the output or do they suggest one uses it as is?

Reviewer #2 (Public review):

Summary:

Micronuclei are aberrant nuclear structures frequently seen following the missegregation of chromosomes. The authors present two image analysis methods, one robust and another rapid, to identify micronuclei (MN) bearing cells. The authors induce chromosome missegregation using an MPS1 inhibitor to check their software outcomes. In missegregation-induced cells, the authors do not distinguish cells that have MN from those that have MN with additional segregation defects. The authors use RNAseq to assess the outcomes of their MN-identifying methods: they do not observe a transcriptomic signature specific to MN but find changes that correlate with aneuploidy status. Overall, this work offers new tools to identify MN-presenting cells, and it sets the stage with clear benchmarks for further software development.

Strengths:

Currently, there are no robust MN classifiers with a clear quantification of their efficiency across cell lines (mIoU score). The software presented here tries to address this gap. GitHub material (tools, protocols, etc) provided is a great asset to naive and experienced computational biologists. The method has been tested in more than one cell line. This method can help integrate cell biology and 'omics' studies.

Weaknesses:

Although the classifier outperforms available tools for MN segmentation by providing mIOU, it's not yet at a point where it can be reliably applied to functional genomics assays where we expect a range of phenotypic penetrance.

Spindle checkpoint loss (e.g., MPS1 inhibition) is expected to cause a variety of nuclear atypia: misshapen, multinucleated, and micronucleated cells. It may be difficult to obtain a pure MN population following MPS1 inhibitor treatment, as many cells are likely to present MN among multinucleated or misshapen nuclear compartments. Given this situation, the transcriptomic impact of MN is unlikely to be retrieved using this experimental design, but this does not negate the significance of the work. The discussion will have to consider the nature, origin, and proportion of MN/rupture-only states - for example, lagging chromatids and unaligned chromosomes can result in different states of micronuclei and also distinct cell fates.

Reviewer #3 (Public review):

Summary:

The authors develop a method to visually analyze micronuclei using automated methods. The authors then use these methods to isolate MN post-photoactivation and analyze transcriptional changes in cells with and without micronuclei of RPE-1 cells. The authors observe in RPE-1 cells that MN-containing cells show similar transcriptomic changes as aneuploidy, and that MN rupture does not lead to vast changes in the transcriptome.

Strengths:

The authors develop a method that allows for automating measurements and analysis of micronuclei. This has been something that the field has been missing for a long time. Using such a method has the potential to advance micronuclei biology. The authors also develop a method to identify cells with micronuclei in real time and mark them using photoconversion and then isolate them via FACS. The authors use this method to study the transcriptome. This method is very powerful as it allows for the sorting of a heterogenous population and subsequent analysis with a much higher sample number than could be previously done.

Weaknesses:

The major weakness of this paper is that the results from the RNA-seq analysis are difficult to interpret as very few changes are found to begin with between cells with MN and cells without. The authors have to use a 1.5-fold cut-off to detect any changes in general. This is most likely due to the sequencing read depth used by the authors. Moreover, there are large variances between replicates in experiments looking at cells with ruptured versus intact micronuclei. This limits our ability to assess if the lack of changes is due to truly not having changes between these populations or experimental limitations. Moreover, the authors use RPE-1 cells which lack cGAS, which may contribute to the lack of changes observed. Thus, it is possible that these results are not consistent with what would occur in primary tissues or just in general in cells with a proficient cGAS/STING pathway.

Reviewer #1 (Public review):

Summary:

The manuscript entitled "Phosphodiesterase 1A Physically Interacts with YTHDF2 and Reinforces the Progression of Non-Small Cell Lung Cancer" explores the role of PDE1A in promoting NSCLC progression by binding to the m6A reader YTHDF2 and regulating the mRNA stability of several novel target genes, consequently activating the STAT3 pathway and leading to metastasis and drug resistance.

Strengths:

The study addresses a novel mechanism involving PDE1A and YTHDF2 interaction in NSCLC, contributing to our understanding of cancer progression.

Weaknesses:

The following issues should be addressed:

(1) The body weight changes and/or survival times of each group in the in vivo metastasis studies should be provided.

(2) In Figure 7, the direct binding between YTHDF2 and the potential target genes should be further validated by silencing YTHDF2 to observe the half-life of the mRNA levels of target genes, in addition to silencing PDE1A.

(3) In Figure 7, the potential methylation sites of "A" on the target genes such as SOCS2 should be verified by mutation analysis, followed by m6A IP or reporter assays.

(4) In Figure 6G, the correlation between the mRNA levels of STAT3 and YTHDF2 needs clarification. According to the authors' mechanism, the STAT3 pathway is activated, rather than upregulation of mRNA levels (or protein levels, as shown in Figure 6F). Figure 7 does not provide evidence that STAT3 is a bona fide target gene regulated by YTHDF2.

(5) The final figure, which discusses sensitization to cisplatin by PDE1A suppression, does not appear to be closely related to the interaction or regulation of PDE1A/YTHDF2. If the authors claim this is an m6A-associated event, additional evidence is needed. Otherwise, this part could be removed from the manuscript.

Reviewer #2 (Public review):

This manuscript aims to investigate the biological impact and mechanisms of phosphodiesterase 1A (PDE1A) in promoting non-small cell lung cancer (NSCLC) progression. They first analyzed several databases and used three established NSCLC cell lines and a normal cell line to demonstrate that PDE1A is overexpressed in lung cancer and its expression negatively correlated with the outcomes of patients. Based on this data, they suggested PDE1A could be considered as a novel prognostic predictor in lung cancer treatment and progression. To study the biological function of PDE1A in NSCLC, they focused on testing the effect of inhibition of PDE1A genetically and pharmacologically on cell proliferation, migration, and invasion in vitro. They also used an experimental metastasis model via tail vein injection of H1299 cells to test if PDE1A promoted metastasis. By database analysis, they also decided to investigate if PDE1A promoted angiogenesis by co-culturing NSCLC cells with HUVECs as well as assessing the tumors from the subcutaneous xenograft model. However, in this model, whether PDE1A modulation impacted tumor metastasis was not examined. To address the mechanism of how PDE1A promotes metastasis, the authors again performed a bioinformatic and GSEA enrichment analysis and confirmed PDE1A indeed activated STAT3 signaling to promote migration. In combination with IP followed by Mass spectrometry, they found PDE1A is a partner of YTHDF2, the cooperation of PDE1A and YTHDF2 negatively regulated SOCS2 mRNA as demonstrated by RIP assay, and ultimately activated STAT3 signaling. Finally, the authors shifted the direction from metastasis to chemoresistance, specifically, they found that PDEA1 inhibitions sensitized NSCLC cells to cisplatin through MET and NRF2 signaling.

Strength:

Overall, the manuscript was well-written and the majority of the data supported the conclusions. The authors used a series of methods including cell lines, animal models, and database analysis to demonstrate the novel roles and mechanism of how PDE1 promotes NSCLC invasion and metastasis as well as cisplatin sensitivity. Given that PDE1A inhibitors have been perused to use in clinic, this study provided valuable findings that have the translational potential for NSCLC treatment.

Weaknesses:

The role of YTHDF2 in PDE1A-promoted tumor metastasis was not investigated. To make the findings more clinical and physiologically relevant, it would be interesting to test if inhibition of PDE1A impacts metastasis using lung cancer orthotopic and patient-derived xenograft models. It is also important to use a cisplatin-resistant NSCLC cell line to test if a PDE1A inhibitor has the potential to sensitize cisplatin in vitro and in vivo. Furthermore, this study relied heavily on different database analyses, although providing novel and compelling data that was followed up and confirmed in the paper, it is critical to have detailed statistical description section on data acquisition throughout the manuscript.

Reviewer #1 (Public review):

Summary:

This study aims to uncover molecular and structural details underlying the broad substrate specificity of glycosaminoglycan lyases belonging to a specific family (PL35). They determined the crystal structures of two such enzymes, conducted in vitro enzyme activity assays, and a thorough structure-guided mutagenesis campaign to interrogate the role of specific residues. They made progress towards achieving their aims but I see significant holes in data that need to be determined and in the authors' analyses.

Impact on the field:

I expect this work will have a limited impact on the field, although, with additional experimental work and better analysis, this paper will be able to stand on its own as a solid piece of structure-function analysis.

Strengths:

The major strengths of the study were the combination of structure and enzyme activity assays, comprehensive structural analysis, as well as a thorough structure-guided mutagenesis campaign.

Weaknesses:

There were several weaknesses, particularly:

(1) The authors claim to have done an ICP-MS experiment to show Mn2+ binds to their enzyme but did not present the data. The authors could have used the anomalous scattering properties of Mn2+ at the synchrotron to determine the presence and location of this cation (i.e. fluorescence spectra, and/or anomalous data collection at the Mn2+ absorption peak).

(2) The authors have an over-reliance on molecular docking for understanding the position of substrates bound to the enzyme. The docking analysis performed was cursory at best; Autodock Vina is a fine program but more rigorous software could have been chosen, as well we molecular dynamics simulations. As well the authors do not use any substrate/product-bound structures from the broader PL enzyme family to guide the placement of the substrates in the GAGases, and interpret the molecular docking models.

(3) The conclusion that the structures of GAGase II and VII are most similar to the structures of alginate lyases (Table 2 data), and the authors' reliance on DALI, are both questioned. DALI uses a global alignment algorithm, which when used for multi-domain enzymes such as these tends to result in sub-optimal alignment of active site residues, particularly if the active site is formed between the two domains as is the case here. The authors should evaluate local alignment methods focused on the optimization of the superposition of a single domain; these methods may result in a more appropriate alignment of the active site residues and different alignment statistics. This may influence the overall conclusion of the evolutionary history of these PL35 enzymes.

(4) The data on the GAGase III residue His188 is not well interpreted; substitution of this residue clearly impacts HA and HS hydrolysis as well. The data on the impact on alginate hydrolysis is weak, which could be due to the fact that the WT enzyme has poor activity against alginate to start with.

(5) The authors did not use the words "homology", "homologous", or "homolog" correctly (these terms mean the subjects have a known evolutionary relationship, which may or may not be known in the contexts the authors used these targets); the words "similarity" and "similar" are recommended to be used instead.

(6) The authors discuss a "shorter" cavity in GAGases, which does not make sense and is not supported by any figure or analysis. I recommend a figure with a surface representation of the various enzymes of interest, with dimensions of the cavity labeled (as a supplemental figure). The authors also do not specifically define what subsites are in the context of this family of enzymes, nor do they specifically label or indicate the location of the subsites on the figures of the GAGase II and IV enzyme structures.

Reviewer #2 (Public review):

Summary:

Wei et al. present the X-ray crystallographic structures of two PL35 family glycosaminoglycan (GAG) lyases that display a broad substrate specificity. The structural data show that there is a high degree of structural homology between these enzymes and GAGases that have previously been structurally characterized. Central to this are the N-terminal (α/α)7 toroid domain and the C-terminal two-layered β-sheet domain. Structural alignment of these novel PL35 lyases with previously deposited structures shows a highly conserved triplet of residues at the heart of the active sites. Docking studies identified potentially important residues for substrate binding and turnover, and subsequent site-directed mutagenesis paired with enzymatic assays confirmed the importance of many of these residues. A third PL35 GAGase that is able to turn over alginate was not crystallized, but a predicted model showed a conserved active site Asn was mutated to a His, which could potentially explain its ability to act on alginate. Mutation of the His into either Ala or Asn abrogated its activity on alginate, providing supporting evidence for the importance of the His. Finally, a catalytic mechanism is proposed for the activity of the PL35 lyases. Overall, the authors used an appropriate set of methods to investigate their claims, and the data largely support their conclusions. These results will likely provide a platform for further studies into the broad substrate specificity of PL35 lyases, as well as for studies into the evolutionary origins of these unique enzymes

Strengths:

The crystallographic data are of very high quality, and the use of modern structural prediction tools to allow for comparison of GAGase III to GAGase II/GAGase VII was nice to see. The authors were comprehensive in their comparison of the PL35 lyases to those in other families. The use of molecular docking to identify key residues and the use of site-directed mutagenesis to investigate substrate specificity was good, especially going the extra distance to mutate the conserved Asn to His in GAGase II and GAGase VII.

Weaknesses:

The structural models simply are not complete. A cursory look at the electron density and the models show that there are many positive density peaks that have not had anything modelled into them. The electron density also does not support the placement of a Mn2+ in the model. The authors indicate that ICP-MS was done to identify the metal, but no ICP-MS data is presented in the main text or supplementary. I believe the authors put too much emphasis on the possibility of GAGase III representing an evolutionary intermediate between GAG lyases and alginate lyases based on a single Asn to His mutation in the active site, and I don't believe that enough time was spent discussing how this "more open and shorter" catalytic cavity would necessarily mean that the enzyme could accommodate a broader set of substrates. Finally, the proposed mechanism does not bring the enzyme back to its starting state.

Reviewer #1 (Public review):

This paper by Poverlein et al reports the substantial membrane deformation around the oxidative phosphorylation super complex, proposing that this deformation is a key part of super complex formation. I found the paper interesting and well-written but identified a number of technical issues that I suggest should be addressed:

(1) Neither the acyl chain chemical makeup nor the protonation state of CDL are specified. The acyl chain is likely 18:2/18:2/18:2/18:2, but the choice of the protonation state is not straightforward.

(2) The analysis of the bilayer deformation lacks membrane mechanical expertise. Here I am not ridiculing the authors - the presentation is very conservative: they find a deformed bilayer, do not say what the energy is, but rather try a range of energies in their Monte Carlo model - a good strategy for a group that focuses on protein simulations. The bending modulus and area compressibility modulus are part of the standard model for quantifying the energy of a deformed membrane. I suppose in theory these might be computed by looking at the per-lipid distribution in thickness fluctuations, but this route is extremely perilous on a per-molecule basis. Instead, the fluctuation in the projected area of a lipid patch is used to imply the modulus [see Venable et al "Mechanical properties of lipid bilayers from molecular dynamics simulation" 2015 and citations within]. Variations in the local thickness of the membrane imply local variations of the leaflet normal vector (the vector perpendicular to the leaflet surface), which is curvature. With curvature and thickness, the deformation energy is analyzed.

See:<br /> Two papers: "Gramicidin A Channel Formation Induces Local Lipid Redistribution" by Olaf Andersen and colleagues. Here the formation of a short peptide dimer is experimentally linked to hydrophobic mismatch. The presence of a short lipid reduces the influence of the mismatch. See below regarding their model cardiolipin, which they claim is shorter than the surrounding lipid matrix.

Also, see:<br /> Faraldo-Gomez lab "Membrane transporter dimerization driven by differential lipid solvation energetics of dissociated and associated states", 2021. Mondal et al "Membrane Driven Spatial Organization of GPCRs" 2013 and many citations within these papers.

While I strongly recommend putting the membrane deformation into standard model terms, I believe the authors should retain the basic conservative approach that the membrane is strongly deformed around the proteins and that making the SC reduces the deformation, then exploring the consequences with their discrete model.

(1) If CDL matches the hydrophobic thickness of the protein it would disrupt SC formation, not favor it. The authors' hypothesis is that the SC stabilizes the deformed membrane around the separated elements. Lipids that are compatible with the monomer deformed region stabilize the monomer, similarly to a surfactant. That is, if CDL prefers the interface because the interface is thin and their CDL is thin, CDL should prevent SC formation. A simpler hypothesis is that CDL's unique electrostatics are part of the glue.

(2) Error bars for lipid and Q* enrichments should be computed averaging over multi-lipid regions of the protein interface, e.g., dividing the protein-lipid interface into six to ten domains, in particular functionally relevant regions. Anionic lipids may have long, >500 ns residence times, which makes lipid enrichment large and characterization of error bars challenging in short simulations. Smaller regions will be noisy. The plots depicted in, for example, Figure S2 are noisy.

(3) The membrane deformation is repeatedly referred to as "entropic" without justification. The bilayer has significant entropic and enthalpic terms just like any biomolecule, why are the authors singling out entropy? The standard "Helfrich" energetic Hamiltonian is a free energy model in that it implicitly integrates over many lipid degrees of freedom.

(4) Figure S7 shows the surface area per lipid and leaflet height. This appears to show a result that is central to the interpretation of SC formation but which makes very little sense. One simply does not increase both the height and area of a lipid. This is a change in the lipid volume! The bulk compressibility of most anything is much higher than its Young's modulus [similar to area compressibility]. Instead, something else has happened. My guess is that there is *bilayer* curvature around these proteins and that it has been misinterpreted as area/thickness changes with opposite signs of the two leaflets. If a leaflet gets thin, its area expands. If the manuscript had more details regarding how they computed thickness I could help more. Perhaps they measured the height of a specific atom of the lipid above the average mid-plane normal? The mid-plane of a highly curved membrane would deflect from zero locally and could be misinterpreted as a thickness change.

(5) The authors write expertly about how conformational changes are interpreted in terms of function but the language is repeatedly suggestive. Can they put their findings into a more quantitative form with statistical analysis? "The EDA thus suggests that the dynamics of CI and CIII2 are allosterically coupled."

(6) The authors write "We find that an increase in the lipid tail length decreases the relative stability of the SC (Figure S5C)" This is a critical point but I could not interpret Figure S5C consistently with this sentence. Can the authors explain this?

(7) The authors use a 6x6 and 15x15 lattice to analyze SC formation. The SC assembly has 6 units of E_strain favoring its assembly, which they take up to 4 kT. At 3 kT, the SC should be favored by 18 kT, or a Boltzmann factor of 10^8. With only 225 sites, specific and non-specific complex formation should be robust. Can the authors please check their numbers or provide a qualitative guide to the data that would make clear what I'm missing?

In summary, the qualitative data presented are interesting (especially the combination of molecular modeling with simpler Monte Carlo modeling aiding broader interpretation of the results) ... but confusing in terms of the non-standard presentation of membrane mechanics and the difficulty of this reviewer to interpret some of the underlying figures: especially, the thickness of the leaflets around the protein and the relative thickness of cardiolipin. Resolving the quantitative interpretation of the bilayer deformation would greatly enhance the significance of their Monte Carlo model of SC formation.

Reviewer #2 (Public review):

Summary:

The authors have used large-scale atomistic and coarse-grained molecular dynamics simulations on the respiratory chain complex and investigated the effect of the complex on the inner mitochondrial membrane. They have also used a simple phenomenological model to establish that the super complex (SC) assembly and stabilisation are driven by the interplay between the "entropic" forces due to strain energy and the enthalpies forces (specific and non-specific) between lipid and protein domains. The authors also show that the SC in the membrane leads to thinning and there is preferential localisation of certain lipids (Cardiolipin) in the annular region of the complex. The data reports that the SC assembly has an effect on the conformational dynamics of individual proteins making up the assembled complex and they undergo "allosteric crosstalk" to maintain the stable functional complex. From their conformational analyses of the proteins (individual and while in the complex) and membrane "structural" properties (such as thinning/lateral organization etc) as well from the out of their phenomenological lattice model, the authors have provided possible implications and molecular origin about the function of the complex in terms of aspects such as charge currents in internal mitochondrion membrane, proton transport activity and ATP synthesis.

Strengths:

The work is bold in terms of undertaking modelling and simulation of such a large complex that requires simulations of about a million atoms for long time scales. This requires technical acumen and resources. Also, the effort to make connections to experimental readouts has to be appreciated (though it is difficult to connect functional pathways with limited (additive forcefield) simulations.

Weakness:

There are several weaknesses in the paper (please see the list below). Claims such as "entropic effect", "membrane strain energy" and "allosteric cross talks" are not properly supported by evidence and seem far-fetched at times. There are other weaknesses as well. Please see the list below.

(i) Membrane "strain energy" has been loosely used and no effort is made to explain what the authors mean by the term and how they would quantify it. If the membrane is simulated in stress-free conditions, where are strains building up from?

(ii) In result #1 (Protein membrane interaction modulates the lipid dynamics ....), I strongly feel that the readouts from simulations are overinterpreted. Membrane lateral organization in terms of lipids having preferential localisation is not new (see doi: 10.1021/acscentsci.8b00143) nor membrane thinning and implications to function (https://doi.org/10.1091/mbc.E20-12-0794). The distortions that are visible could be due to a mismatch in the number of lipids that need to be there between the upper and lower leaflets after the protein complex is incorporated. Also, the physiological membrane will have several chemically different lipids that will minimise such distortions as well as would be asymmetric across the leaflets - none of which has been considered. Connecting chain length to strain energy is also not well supported - are the authors trying to correlate membrane order (Lo vs Ld) with strain energy?

(iii) Entropic effect: What is the evidence towards the entropic effect? If strain energy is entropic, the authors first need to establish that. They discuss enthalpy-entropy compensation but there is no clear data or evidence to support that argument. The lipids will rearrange themselves or have a preference to be close to certain regions of the protein and that generally arises because of enthalpies reasons (see the body of work done by Carol Robinson with Mass Spec where certain lipids prefer proteins in the GAS phase, certainly there is no entropy at play there). I find the claims of entropic effects very unconvincing.

(iv) The changes in conformations dynamics are subtle as reported by the authors and the allosteric arguments are made based on normal mode analyses. In the complex, there are large overlapping regions between the CI, CIII2, and SCI/III2. I am not sure how the allosteric crosstalk claim is established in this work - some more analyses and data would be useful. Normal mode analyses (EDA) suggest that the motions are coupled and correlated - I am not convinced that it suggests that there is allosteric cross-talk.

(v) The lattice model should be described better and the rationale for choosing the equation needs to be established. Specific interactions look unfavourable in the equation as compared to non-specific interactions.

Reviewer #3 (Public review):

Summary:

In this contribution, the authors report atomistic, coarse-grained, and lattice simulations to analyze the mechanism of supercomplex (SC) formation in mitochondria. The results highlight the importance of membrane deformation as one of the major driving forces for SC formation, which is not entirely surprising given prior work on membrane protein assembly, but certainly of major mechanistic significance for the specific systems of interest.

Strengths:

The combination of complementary approaches, including an interesting (re)analysis of cryo-EM data, is particularly powerful and might be applicable to the analysis of related systems. The calculations also revealed that SC formation has interesting impacts on the structural and dynamical (motional correlation) properties of the individual protein components, suggesting further functional relevance of SC formation. Overall, the study is rather thorough and highly creative, and the impact on the field is expected to be significant.

Weaknesses:

In general, I don't think the work contains any obvious weaknesses, although I was left with some questions.

Reviewer #1 (Public review):

Summary:

Tamoxifen resistance is a common problem in partially ER-positive patients undergoing endocrine therapy, and this manuscript has important research significance as it is based on clinical practical issues. The manuscript discovered that the absence of FRMD8 in breast epithelial cells can promote the progression of breast cancer, thus proposing the hypothesis that FRMD8 affects tamoxifen resistance and validating this hypothesis through a series of experiments. The manuscript has a certain theoretical reference value.

Strengths:

At present, research on the role of FRMD8 in breast cancer is very limited. This manuscript leverages the MMTV-Cre+;Frmd8fl/fl;PyMT mouse model to study the role of FRMD8 in tamoxifen resistance, and single-cell sequencing technology discovered the interaction between FRMD8 and ESR1. At the mechanistic level, this manuscript has demonstrated two ways in which FRMD8 affects ERα, providing some new insights into the development of ER-positive breast cancer in patients who are resistant to tamoxifen.

Weaknesses:

This manuscript repeatedly emphasizes the role of FRMD8/FOXO3A in tamoxifen resistance in ER-positive breast cancer, but the specific mechanisms have not yet been fully elucidated. Whether FRMD8 can become a biomarker should be verified in large clinical samples or clinical data.

Reviewer #2 (Public review):

Summary:

The manuscript presents a valuable finding on the impact of FRMD8 loss on tumor progression and the resistance to tamoxifen therapy. The author conducted systematic experiments to explore the role of FRMD8 in breast cancer and its potential regulatory mechanisms, confirming that FRMD8 could serve as a potential target to revere tamoxifen resistance.

Strengths:

The majority of the research is logically clear, smooth, and persuasive.

Weaknesses:

Some research in the article lacks depth and some sentences are poorly organized.

Reviewer #1 (Public review):

Summary:

In this manuscript, the authors propose that LAPTM4B plays a role in suppressing the TGF-β/SMAD signaling pathway and suggest that enhancing LAPTM4B function could be a potential therapeutic strategy for alleviating BLM-induced lung fibrosis. Their data show that LAPTM4B knockdown exacerbates fibrosis phenotypes, both in vivo and in vitro, while LAPTM4B overexpression mitigates these effects by recruiting NEDD4L to destabilize SMAD proteins.

Strengths:

The findings are significant for the lung disease field, and the data presented support the authors' conclusions. This work would be of even higher interest after sufficiently addressing the weaknesses listed below.

Weaknesses:

Several issues need to be addressed. First, it is unclear why the authors chose to focus on LAPTM4B specifically, rather than other members of the LAPTM family, such as LAPTM4A or LAPTM5. Additionally, the manuscript does not address whether lysosomes are involved in the degradation of ubiquitinated LAPTM4B.

Reviewer #2 (Public review):

Summary:

It was previously documented that lysosomal localization of the Lysosomal transmembrane proteins LAPTM4 or 5 (including LAPTM4b) is regulated by Nedd4 family ubiquitin ligases, and independently, that Nedd4l regulates IPF (Idiopathic Pulmonary Fibrosis) in mouse lungs via regulation of the TGFb pathway (ie, Nedd4l lung-specific KO mice develop IPF due to reduced ability to suppress the TGFb pathway -PMID: 32332792 ). Here, Xu et al investigated the role of LAPTM4b in IPF and suggested that the suppression of IPF by LAPTM4b, which they discovered here, is mediated via its interaction with Nedd4L, which normally suppresses TGFb signaling.

Strengths:

Overall, this is an interesting paper that identified for the first time a suppressive role of LAPTM4b in IPF, using both in vivo mouse models and cell culture studies.

Weaknesses:

(1) The most obvious shortcoming of this study is the lack of experimental evidence that the suppressive effect of LAPTM4b on IPF is mediated by Nedd4l.

(2) Along the same lines, despite the authors' claim, overexpression of Nedd4L in cells does not increase SMAD3 ubiquitination (Fig 6D), which is a marker of TGFbR activation. Likewise, in Fig 5E, SMAD2 seems to be ubiquitinated similarly in the presence or absence of LAPTM4b (despite claims that LAPTM4b promotes ubiquitination of SMAD2). Same for K48 ubiquitination of TGFbR (Figure 5H).

(3) How does LAPTM4b interact with SMAD2 or 3, or TGFbR?

(4) All immunofluorescence (IF) studies depict 1 or 2 cells, with no quantification or statistics.

(5) Some of the Western blots (WB) are also not quantified, so any claims of an effect cannot be evaluated without such quantification and statistics.

(6) In the IF studies showing lung tissue (eg Figure 1B), why is LAPTM4b (wildtype) localized to the plasma membrane instead of lysosomes/endosomes?

Reviewer #1 (Public review):

Summary:

The manuscript performs a comprehensive biochemical, structural, and bioinformatic analysis of TseP, a type 6 secretion system effector from Aeromonas dhakensis that includes the identification of a domain required for secretion and residues conferring target organism specificity. Through targeted mutations, they have expanded the target range of a T6SS effector to include a gram-positive species, which is not typically susceptible to T6SS attack.

Strengths:

All of the experiments presented in the study are well-motivated and the conclusions are generally sound.

Weaknesses:

There are some issues with the clarity of figures. For example, the microscopy figures could have been more clearly presented as cell counts/quantification rather than representative images. Similarly, loading controls for the secreted proteins for the westerns probably should be shown.

Also, some of the minor/secondary conclusions reached regarding the "independence" of the N and C term domains of the TseP are a bit overreaching.

Reviewer #2 (Public review):

Summary:

Wang et al. investigate the role of TseP, a Type VI secretion system (T6SS) effector molecule, revealing its dual enzymatic activities as both an amidase and a lysozyme. This discovery significantly enhances the understanding of T6SS effectors, which are known for their roles in interbacterial competition and survival in polymicrobial environments. TseP's dual function is proposed to play a crucial role in bacterial survival strategies, particularly in hostile environments where competition between bacterial species is prevalent.

Strengths:

(1) The dual enzymatic function of TseP is a significant contribution, expanding the understanding of T6SS effectors.

(2) The study provides important insights into bacterial survival strategies, particularly in interbacterial competition.

(3) The findings have implications for antimicrobial research and understanding bacterial interactions in complex environments.

Weaknesses:

(1) The manuscript assumes familiarity with previous work, making it difficult to follow. Mutants and strains need clearer definitions and references.

(2) Figures lack proper controls, quantification, and clarity in some areas, notably in Figures 1A and 1C.

(3) The Materials and Methods section is poorly organized, hindering reproducibility. Biophysical validation of Zn²⁺ interaction and structural integrity of proteins need to be addressed.

(4) Discrepancies in protein degradation patterns and activities across different figures raise concerns about data reliability.

Reviewer #3 (Public review):

Summary:

Type VI secretion systems (T6SS) are employed by bacteria to inject competitor cells with numerous effector proteins. These effectors can kill injected cells via an array of enzymatic activities. A common class of T6SS effector are peptidoglycan (PG) lysing enzymes. In this manuscript, the authors characterize a PG-lysing effector-TseP-from the pathogen Aeromonas dhakensis. While the C-terminal domain of TseP was known to have lysozyme activity, the N-terminal domain was uncharacterized. Here, the authors functionally characterize TsePN as a zinc-dependent amidase. This discovery is somewhat novel because it is rare for PG-lysing effectors to have amidase and lysozyme activity.

In the second half of the manuscript, the authors utilize a crystal structure of the lysozyme TsePC domain to inform the engineering of this domain to lyse gram-positive peptidoglycan.

Strengths:

The two halves of the manuscript considered together provide a nice characterization of a unique T6SS effector and reveal potentially general principles for lysozyme engineering.

Weaknesses:

The advantage of fusing amidase and lysozyme domains in a single effector is not discussed but would appear to be a pertinent question. Labeling of the figures could be improved to help readers understand the data.

Reviewer #1 (Public review):

Summary:

This manuscript seeks to estimate the causal effect of genes on disease. To do so, they introduce a novel algorithm, termed the Root Causal Strength using Perturbations (RCSP) algorithm. RCSP uses perturb-seq to first estimate the gene regulatory network structure among genes, and then uses bulk RNA-seq with phenotype data on the samples to estimate causal effects of genes on the phenotype conditional on the learned network structure. The authors assess the performance of RCSP in comparison to other methods via simulation. Next, they apply RCSP to two real human datasets: 513 individuals age-related macular degeneration and 137 individuals with multiple sclerosis.

Strengths:

The authors tackle an important and ambitious problem - the identification of causal contributors to disease in the context of a causal inference framework. As the authors point out, observational RNA-seq data is insufficient for this kind of causal discovery, since it is very challenging to recover the true underlying graph from observational data; interventional data are needed. However, little perturb-seq data has been generated with annotated phenotype data, and much bulk RNA-seq data has already been generated, so it is useful to propose an algorithm to integrate the two as the authors have done.

The authors also offer substantial theoretical exposition for their work, bringing to bear both the literature on causal discovery as well as literature on the genetic architecture of complex traits.

Weaknesses:

The notion of a "root" causal gene - which the authors define based on a graph theoretic notion of topologically sorting graphs - requires a graph that is directed and acyclic. It is the latter that constitutes an important weakness here - it simply is a large simplification of human biology to draw out a DAG including hundreds of genes and a phenotype Y and to claim that the true graph contains no cycles. This is briefly touched upon the discussion, but given the fundamental nature of this choice - the manuscript should devote at least some of the main results to exploring the consequence of mischaracterizing true cyclic graphs as DAGs in this framework. For example - consider the authors' analysis of T cell infiltration in multiple sclerosis (MS). CD4+ effector T cells have the interesting property that they are stimulated by IL2 as a growth factor; yet IL2 also stimulates the activation of (suppressive) regulatory T cells. What does it mean to analyze CD4+ regulation in disease with a graph that does not consider IL2 (or other cytokine) mediated feedback loops/cycles?

I also encourage the authors to consider more carefully when graph structure learned from perturb-seq can be ported over to bulk RNA-seq. Consider again the MS CD4+ example - the authors first start with a large perturb-seq experiment (Replogle et al., 2022) performed in K562 cells. To what extent are K562 cells, which are derived from a leukemia cell line, suitable for learning the regulatory structure of CD4+ cells from individuals with an MS diagnosis? Presumably this structure is not exactly correct - to what extent is the RCSP algorithm sensitive to false edges in this graph? This leap - from cell line to primary human cells - is also not modeled in the simulation. Although challenging - it would be ideal for the RCSP to model or reflect the challenges in correctly identifying the regulatory structure.

It should also be noted that in most perturb-seq experiments, the entire genome is not perturbed, and frequently important TFs (that presumably are very far "upstream" and thus candidate "root" causal genes) are not expressed highly enough to be detected with scRNA-seq. In that context - perhaps slightly modifying the language regarding RCSP's capabilities might be helpful for the manuscript - perhaps it would be better to describe it has an algorithm for causal discovery among a set of genes that were perturbed and measured, rather than a truly complete search for causal factors. Perhaps more broadly - it would also benefit the manuscript to devote slightly more text to describing the kinds of scenarios where RCSP (and similar ideas) would be most appropriately applied - perhaps a well-powered, phenotype annotated perturb-seq dataset performed in a disease relevant primary cell.

Reviewer #2 (Public review):

Summary:

This paper presents a very interesting use of a causal graph framework to identify the "root genes" of a disease phenotype. Root genes are the genes that cause a cascade of events that ultimately leads to the disease phenotype, assuming the disease progression is linear.

Strengths:

- The methodology has a solid theoretical background.<br /> - This is a novel use of the causal graph framework to infer root causes in a graph

Weaknesses:

(1) General Comments<br /> First, I have some general comments. I would argue that the main premise of the study might be inaccurate or incomplete. There are three major attributes of real biological systems, which are not considered in this work.

One is that the process from health-to-disease is not linear most of the time with many checks along the way that aim to prevent the disease phenotype. This leads to a non-deterministic nature of the path from health-to-disease. In other words, with the same root gene perturbations, and depending on other factors outside of gene expression, someone may develop a phenotype in a year, another in 10 years and someone else never. Claiming that this information is included in the error terms might not be sufficient to address this issue. The authors should discuss this limitation.

Two, the paper assumes that the network connectivity will remain the same after perturbation. This is not always true due to backup mechanisms in the cells. For example, suppose that a cell wants to create product P and it can do it through two alternative paths:<br /> Path #1: A -> B -> P Path #2: A -> C -> P<br /> Now suppose that path #1 is more efficient, so when B can be produced, path #2 is inactive. Once the perturbation blocks element B from being produced, the graph connectivity changes by activation of path #2. I did not see the authors taking this into consideration, which seems to be a major limitation in using perturb-seq results to infer connectivities.

Three, there is substantial system heterogeneity that may cause the same phenotype. This goes beyond the authors claim that although the initial gene causes of a disease may differ from person to person, at some point they will all converge to changes in the same set of "root genes". This is not true for many diseases, which are defined based on symptoms and lab tests at the patient level. You may have two completely different molecular pathologies that lead to the development of the same symptoms and test results. Breast cancer with its subtypes is a prime example of that. In theory, this issue could be addressed if there is infinite sample size. However, this assumption is largely violated in all existing biological datasets.

All the above limit the usefulness of this method for most chronic diseases, although it might still lead to interesting discoveries in cancer (in which the association between genes' dysregulation and development of cancer is more direct and occurs in less amount of time).

With these in mind, the theoretical and algorithmic advances this paper offers are interesting. And the theoretical proofs are solid.

(2) Specific comments.<br /> I am curious how the simulated data were generated and processed. Specifically, were the values of the synthetic variables Z-scored? If not, then I would expect that the variance of every variable will increase from the roots of the graph to the leaves. That will give an advantage in any algorithm aiming to identify causal relations based on error terms. For fairness and completeness, the authors should Z-score the values in the synthetic data and compare the results.

The algorithm seems to require both RNA-seq and Perturb-seq data (Algorithm 1, page 14). Can it function with RNA-seq data only? What will be different in this case?

(3) Additional comments:<br /> Although the manuscript is generally written clearly, some parts are not clear and others have missing details that make the narrative difficult to follow up. Some specific examples:<br /> - Synthetic data generation: how many different graphs (SEMs) did they start from? (30?) How many samples per graph? Did they test different sample sizes?<br /> - The presentation of comparative results (Suppl fig 4 and 7) is not clear. No details are given on how these results were generated. (what does it mean "The first column denotes the standard deviation of the outputs for each algorithm"?) Why all other methods have higher SD differences than RCSP? Is it a matter of scaling? Shouldn't they have at least some values near zero since the authors "added the minimum value so that all histograms begin at zero"? also, why RCSP results are more like a negative binomial distribution and every other is kind of normal?<br /> - What is the significance of genes changing expression "from left to right" in a UMAP plot? (eg Fig. 3h and 3g)

The authors somewhat overstate the novelty of their algorithm. Representation of GRNs as causal graphs dates back in 2000 with the work of Nir Friedman in yeast. Other methods were developed more recently that look on regulatory network changes at the single sample level which the authors do not seem to be aware (e.g., Ellington et al, NeurIPS 2023 workshop GenBio and Bushur et al, 2019, Bioinformatics are two such examples). The methods they mention are for single cell data and they are not designed to connect single sample-level changes to a person's phenotype. The RCS method needs to be put in the right background context in order to bring up what is really novel about it.

Reviewer #3 (Public review):

Summary:

The authors provide an interesting and novel approach, RCSP, to determining what they call the "root causal genes" for a disease, i.e. the most upstream, initial causes of disease. RCSP leverages perturbation (e.g. Perturb-seq) and observational RNA-seq data, the latter from patients. They show using both theory and simulations that if their assumptions hold then the method performs remarkably well, compared to both simple and available state-of-the-art baselines. Whether the required assumptions hold for real diseases is questionable. They show superficially reasonable results on AMD and MS.

Strengths:

The idea of integrating perturbation and observational RNA-seq dataset to better understand the causal basis of disease is powerful and timely. We are just beginning to see genome-wide perturbation assay, albeit in limited cell-types currently. For many diseases, research cohorts have at least bulk observational RNA-seq from a/the disease-relevant tissue(s). Given this, RCSP's strategy of learning the required causal structure from perturbations and applying this knowledge in the observational context is pragmatic and will likely become widely applicable as Perturb-seq data in more cell-types/contexts becomes available.

The causal inference reasoning is another strength. A more obvious approach would be to attempt to learn the causal network structure from the perturbation data and leverage this in the observational data. However, structure learning in high-dimensions is notoriously difficult, despite recent innovations such as differentiable approaches. The authors notice that to estimate the root causal effect for a gene X, one only needs access to a (superset of) the causal ancestors of X: much easier relationships to detect than the full network.

The applications are also reasonably well chosen, being some of the few cases where genome-scale perturb-seq is available in a roughly appropriate (see below) cell-type, and observational RNA-seq is available at a reasonable sample size.

Weaknesses:

Several assumptions of the method are problematic. The most concerning is that the observational expression changes are all causally upstream of disease. There is work using Mendelian randomization (MR) showing that the _opposite_ is more likely to be true: most differential expression in disease cohorts is a consequence rather than a cause of disease (https://www.nature.com/articles/s41467-021-25805-y). Indeed, the oxidative stress of AMD has known cellular responses including the upregulation of p53. The authors need to think carefully about how this impacts their framework. Can the theory say anything in this light? Simulations could also be designed to address robustness.

A closely related issue is the DAG assumption of no cycles. This assumption is brought to bear because it required for much classical causal machinery, but is unrealistic in biology where feedback is pervasive. How robust is RCSP to (mild) violations of this assumption? Simulations would be a straightforward way to address this.

The authors spend considerable effort arguing that technical sampling noise in X can effectively be ignored (at least in bulk). While the mathematical arguments here are reasonable, they miss the bigger picture point that the measured gene expression X can only ever be a noisy/biased proxy for the expression changes that caused disease: 1) Those events happened before the disease manifested, possibly early in development for some conditions like neurodevelopmental disorders. 2) bulk RNA-seq gives only an average across cell-types, whereas specific cell-types are likely "causal". 3) only a small sample, at a single time point, is typically available. Expression in other parts of the tissue and at different times will be variable.

My remaining concerns are more minor.

While there are connections to the omnigenic model, the latter is somewhat misrepresented. 1) The authors refer to the "core genes" of the omnigenic model as being at the end (longitudinally) of pathogenesis. The omnigenic model makes no statements about temporally ordering: in causal inference terminology the core genes are simply the direct cause of disease. 2) "Complex diseases often have an overwhelming number of causes, but the root causal genes may only represent a small subset implicating a more omnigenic than polygenic model" A key observation underlying the omnigenic model is that genetic heritability is spread throughout the genome (and somewhat concentrated near genes expressed in disease relevant cell types). This implies that (almost) all expressed genes, or their associated (e)SNPs, are "root causes".

The claim that root causal genes would be good therapeutic targets feels unfounded. If these are highly variable across individuals then the choice of treatment becomes challenging. By contrast the causal effects may converge on core genes before impacting disease, so that intervening on the core genes might be preferable. The jury is still out on these questions, so the claim should at least be made hypothetical.

The closest thing to a gold standard I believe we have for "root causal genes" is integration of molecular QTLs and GWAS, specifically coloc/MR. Here the "E" of RCSP are explicitly represented as SNPs. I don't know if there is good data for AMD but there certainly is for MS. The authors should assess the overlap with their results. Another orthogonal avenue would be to check whether the root causal genes change early in disease progression.

The available perturb-seq datasets have limitations beyond on the control of the authors. 1) The set of genes that are perturbed. The authors address this by simply sub-setting their analysis to the intersection of genes represented in the perturbation and observational data. However, this may mean that a true ancestor of X is not modeled/perturbed, limiting the formal claims that can be made. Additionally, some proportion of genes that are nominally perturbed show little to no actual perturbation effect (for example, due to poor guide RNA choice) which will also lead to missing ancestors.

The authors provide no mechanism for statistical inference/significance for their results at either the individual or aggregated level. While I am a proponent of using effect sizes more than p-values, there is still value in understanding how much signal is present relative to a reasonable null.

I agree with the authors that age coming out of a "root cause" is potentially encouraging. However, it is also quite different in nature to expression, including being "measured" exactly. Will RCSP be biased towards variables that have lower measurement error?

Finally, it's a stretch to call K562 cells "lymphoblasts". They are more myeloid than lymphoid.

Reviewer #1 (Public review):

Summary:

Pham and colleagues provide an illuminating investigation of aquaporin-4 water flux in the brain utilizing ex vivo and in vivo techniques. The authors first show in acute brain slices, and in vivo with fiber photometry, SRB loaded astrocytes swell after inhibition of AQP4 with TGN-020, indicative of tonic water efflux from astrocytes in physiological conditions. Excitingly, they find that TGN-020 increased the ADC in DW-MRI in a region-specific manner, potentially due to AQP4 density. The resolution of the DW-MRI cannot distinguish between intracellular or extracellular compartments, but the data point to an overall accumulation of water in the brain with AQP4 inhibition. These results provide further clarity on water movement through AQP4 in health and disease.

Overall, the data support the main conclusions of the article, with some room for more detailed treatment of the data to extend the findings.

Strengths:

The authors have a thorough investigation of AQP4 inhibition in acute brain slices. The demonstration of tonic water efflux through AQP4 at baseline is novel and important in and of itself. Their further testing of TGN-020 in hyper- and hypo-osmotic solutions shows the expected reduction of swelling/shrinking with AQP4 blockade.

Their experiment with cortical spreading depression further highlights the importance of water efflux from astrocytes via AQP4 and transient water fluxes as a result of osmotic gradients. Inhibition of AQP4 increases the speed of tissue swelling, pointing to a role in efflux of water from the brain.

The use of DW-MRI provides a non-invasive measure of water flux after TGN-020 treatment.

Weaknesses:

The authors specifically use GCaMP6 and light sheet microscopy to image their brain sections in order to identify astrocytic microdomains. However, their presentation of the data neglects a more detailed treatment of the calcium signaling. It would be quite interesting to see whether these calcium events are differentially affected by AQP4 inhibition based on their cellular localization (ie. processes vs. soma vs. vascular endfeet which all have different AQP4 expression).

The authors show the inhibition of AQP4 with TGN-020 shortens the onset time of the swelling associated with cortical spreading depression in brain slices. However, they do not show quantification for much of the other features of the CSD swelling, (ie. the duration of swelling, speed of swelling, recovery from swelling)

Comments on revised version:

The authors have addressed these suggestions as additional supplementary figures. Notably they find increased calcium signaling and stronger inhibition of calcium signaling by TGN-020 in astrocytic endfeet, where AQP4 is enriched.

Significance:

AQP4 is a bidirectional water channel that is constitutively open, thus water flux through it is always regulated by local osmotic gradients. Still, characterizing this water flux has been challenging, as the AQP4 channel is incredibly water selective. The authors here present important data showing that application of TGN-020 alone causes astrocytic swelling, indicating that there is constant efflux of water from astrocytes via AQP4 in basal conditions. This has been suggested before, as the authors rightfully highlight in their discussion, but the evidence had previously come from electron microscopy data from genetic knockout mice.

AQP4 expression has been linked with glymphatic circulation of cerebrospinal fluid through perivascular spaces since its rediscovery in 2012 [1]. Further studies of aging[2], genetic models[3], and physiological circadian variation[4], have revealed it is not simply AQP4 expression but AQP4 polarization to astrocytic vascular endfeet that is imperative for facilitating glymphatic flow. Still a lingering question in the field is how AQP4 facilitates fluid circulation. This study represents an important step in our understanding of AQP4's function, as basal efflux of water via AQP4 might promote clearance of interstitial fluid to allow influx of cerebrospinal fluid into the brain. Beyond glymphatic fluid circulation, clearly AQP4 dependent volume changes will differentially alter astrocytic calcium signaling and, in turn, neuronal activity.

(1) Iliff, J.J., et al., A Paravascular Pathway Facilitates CSF Flow Through the Brain Parenchyma and the Clearance of Interstitial Solutes, Including Amyloid β. Sci Transl Med, 2012. 4(147): p. 147ra111.<br /> (2) Kress, B.T., et al., Impairment of paravascular clearance pathways in the aging brain. Ann Neurol, 2014. 76(6): p. 845-61.<br /> (3) Mestre, H., et al., Aquaporin-4-dependent Glymphatic Solute Transport in the Rodent Brain. eLife, 2018. 7.<br /> (4) Hablitz, L., et al., Circadian control of brain glymphatic and lymphatic fluid flow. Nature communications, 2020. 11(1).

Reviewer #3 (Public review):

Summary:

In this manuscript, the Authors propose that astrocytic water channel AQP4 represents the dominant pathway for tonic water efflux without which astrocytes undergo cell swelling. The authors measure changes in astrocytic sulforhodamine B fluorescence as the proxy for cell volume dynamics. Using this approach, they have performed a technically elegant series of ex vivo and in vivo experiments exploring changes in astrocytic volume "signal" in response to the AQP4 inhibitor TGN-020 and/or neuronal stimulation. The key findings are that TGN-020 produces an apparent swelling of astrocytes and modifies astrocytic cell volume dynamics after spreading depolarizations. This study is perceived as potentially highly significant. However, several technical caveats could be considered better and perhaps addressed through additional experiments.

Strengths:

(1) This is a technically sound study, in which the Authors employed a number of complementary ex vivo and in vivo techniques. The presented results are of interest to the field and potentially highly significant.

(2) The innovative use of sulforhodamine B for in situ measurements of astrocyte cell volume dynamics is thoroughly validated in brain slices by quantifying changes in sulforhodamine fluorescence in response to hypoosmotic and hyperosmotic media.

(3) The combination of cell volume measurements with registering functional outcomes in both astrocytes and neurons (cell-specific GCaMP6 signaling) is appropriate and adds to the significance of the work.

(4) The use of ChR2 optogenetics for producing spreading depolarization allows to avoid many complications of chemical manipulations and much appreciated.

Remaining limitations:

(1) In the opinion of this reviewer, the effects of TGN-020 are not entirely consistent with the current knowledge on water permeability in astrocytes and the relative contribution of AQP4 to this process.

Specifically, genetic deletion of AQP4 reduces plasmalemmal water permeability in astrocytes by ~two-three-fold (when measured at 37oC, E. Solenov et al., AJP-Cell, 2004). This difference is significant but thought to have limited impact on steady-state water distribution. To the best of this reviewer's knowledge, cultured AQP4-null astrocytes do not show changes in degree of hypoosmotic swelling or hyperosmotic shrinkage. Thus, the findings of Solenov et al. are not (entirely) congruent with the conclusions of the current manuscript.

Also, as noted by the Authors, the AQP4 knockout does not modify astrocytes swelling induced by hypoosmotic solution in brain slices (T.R. Murphy et al., Front Neurosci., 2017), further suggesting that AQP4 is not a significant rate-limiting factor for water movement across astrocyte membranes.

The Authors do discuss the above-mentioned discrepancies and explain them by the context-dependent changes in water fluxes. Nevertheless, with these caveats in mind, it would be highly desirable to utilize an independent method measuring astrocytic volume and extracellular volume fraction.

(2) As noted by this reviewer and now discussed by the Authors, changes in ADC signal (presented in in Fig. 5) may be confounded by the previously reported TGN-020-induced hyphemia (e.g., H. Igarashi et al., NeuroReport, 2013) and/or changes water fluxes across pia matter which is highly enriched in AQP4. If this is the case, the proposed brain water accumulation may be explained by factors other than astrocytic water homeostasis. This caveat certainly deserves further experimental exploration.

Reviewer #1 (Public review):

Summary:

The authors study the variability of patient response of NSCLC patients on immune checkpoint inhibitors using single-cell RNA sequencing in a cohort of 26 patients and 33 samples (primary and metastatic sites), mainly focusing on 11 patients and 14 samples for association analyses, to understand the variability of patient response based on immune cell fractions and tumor cell expression patterns. The authors find immune cell fraction and clonal expansion differences, as well as tumor expression differences between responders and non-responders, partly validating previous hypotheses, and partly suggesting new markers for ICI response. Integrating immune and tumor sources of signal the authors claim to improve prediction of response markedly, albeit in a small cohort and using in-sample metrics.

Strengths:

- The problem of studying the tumor microenvironment, as well as the interplay between tumor and immune features is important and interesting and needed to explain heterogeneity of patient response and be able to predict it.<br /> - Extensive analysis of the scRNAseq data with respect to immune and tumor features on different axes of hypothesis relating to immune response and tumor immune evasion using state of the art methods.<br /> - The authors provide an interesting scRNAseq data set with well-curated cell types linked to outcomes data, which is valuable<br /> - High-quality immune cell type annotation including annotations based on additional ADT data<br /> - Integration of TCRseq to confirm subtype of T-cell annotation and clonality analysis<br /> - Interesting analysis of cell programs/states of the (predicted) tumor cells and characterization thereof

Weaknesses:

- Generally a very heterogeneous and small cohort where adjustments for confounding is hard. Additionally, there are many tests for association with outcome, where necessary multiple testing adjustments negate signal and confirmation bias likely, so biological take-aways have to be questioned.<br /> - The authors claim a very high "accuracy" performance, however given the small cohort and possible overfitting due to in-sample ROC the generalization of this to other cohorts is questionable.<br /> - Due to the small cohort with a lot of variability, more external validation is needed to be convincingly reproducible, especially when talking about AUC/accuracy of a predictor.

Reviewer #2 (Public review):

Summary:

The authors have utilised deep profiling methods to generate deeper insights into the features of the TME that drive responsiveness to PD-1 therapy in NSCLC.

Strengths:

The main strengths of this work lie in the methodology of integrating single cell sequencing, genetic data and TCRseq data to generate hypotheses regarding determinants of IO responsiveness.

Some of the findings in this study are not surprising and well precedented eg. association of Treg, STAT3 and NFkB with ICI resistance and CD8+ activation in ICI responders and thus act as an additional dataset to add weight to this prior body of evidence. Whilst the role of Th17 in PD-1 resistance has been previously reported (eg. Cancer Immunol Immunother 2023 Apr;72(4):1047-1058, Cancer Immunol Immunother 2024 Feb 13;73(3):47, Nat Commun. 2021; 12: 2606 ) these studies have used non-clinical models or peripheral blood readouts. Here the authors have supplemented current knowledge by characterization of the TME of the tumor itself.

Weaknesses:

Unfortunately, the study is hampered by the small sample size and heterogeneous population and whilst the authors have attempted to bring in an additional dataset to demonstrate robustness of their approach, the small sample size has limited their ability to draw statistically supported conclusions. There is also limited validation of signatures/methods in independent cohorts and no functional characterisation of the findings. Because of these factors, this work (as it stands) does have value to the field but will likely have a relatively low overall impact.

Reviewer #1 (Public review):

Summary:

The authors have presented data showing that there is a greater amount of spontaneous differentiation in human pluripotent cells cultured in suspension vs static and have used PKCβ and Wnt signaling pathway inhibitors to decrease the amount of differentiation in suspension culture.

Strengths:

This is a very comprehensive study that uses a number of different rector designs and scales in addition to a number of unbiased outcomes to determine how suspension impacts the behaviour of the cells and in turn how the addition of inhibitors counteracts this effect. Furthermore, the authors were also able to derive new hiPSC lines in suspension with this adapted protocol.

Weaknesses:

The main weakness of this study is the lack of optimization with each bioreactor change. It has been shown multiple times in the literature that the expansion and behaviour of pluripotent cells can be dramatically impacted by impeller shape, RPM, reactor design and multiple other factors. It remains unclear to me how much of the results the authors observed (e.g. increased spontaneous differentiation) was due to not having an optimized bioreactor protocol in place (per bioreactor vessel type). For instance - was the starting seeding density, RPM, impeller shape, feeding schedule, and/or anything other aspect optimized for any of the reactors used in the study and if not, how were the values used in the study determined?

Post-revision:

The authors did a commendable job in responding and addressing my comments and concerns in addition to those of the other reviewers. I think this study will be of interest to the field and will add to our collective knowledge on how PSCs react to being cultured in suspension conditions.

Reviewer #2 (Public review):

This study by Matsuo-Takasaki et al. reported the development of a novel suspension culture system for hiPSC maintenance using Wnt/PKC inhibitors. The authors showed elegantly that inhibition of the Wnt and PKC signaling pathways would repress spontaneous differentiation into neuroectoderm and mesendoderm in hiPSCs, thereby maintaining cell pluripotency in suspension culture. This is a solid study with substantial data to demonstrate the quality of the hiPSC maintained in the suspension culture system, including long-term maintenance in >10 passages, robust effect in multiple hiPSC lines, and a panel of conventional hiPSC QC assays. Notably, large-scale expansion of a clinical grade hiPSC using a bioreactor was also demonstrated, which highlighted the translational value of the findings here. In addition, the author demonstrated a wide range of applications for the IWR1+LY suspension culture system, including support for freezing/thawing and PBMC-iPSC generation in suspension culture format. The novel suspension culture system reported here is exciting, with significant implications in simplifying the current culture method of iPSC and upscaling iPSC manufacturing.

Review for second submission:

In this revised manuscript, the authors provided new data to further support that suspension culture with Wnt/PKC inhibitors can be used for long-term hiPSC maintenance across multiple cell lines, as well as comparison with current benchmark culture system. New discussion sections were also added to put the findings into perspective of current development and the need for hiPSC maintenance culture system, and the figures were updated to improve readability. Overall, the authors have addressed all my concerns in this revised manuscript. Congratulations to the authors on this very interesting study.

Reviewer #3 (Public review):

In the current manuscript, Matsuo-Takasaki et al. demonstrate that the addition of PKCβ and WNT signaling pathway inhibitors to suspension cultures of iPSCs effectively suppresses spontaneous differentiation. These conditions are well-suited for the large-scale expansion of iPSCs. The authors have shown that, under these conditions, they can successfully perform single-cell cloning, direct cryopreservation, and iPSC derivation from PBMCs. Furthermore, they provide a comprehensive characterization of iPSCs grown in these conditions, including assessments of undifferentiated stem cell markers and genetic stability.

They have elegantly demonstrated that iPSCs cultured in these conditions can differentiate into derivatives of all three germ layers. By differentiating iPSCs into dopaminergic neural progenitors, cardiomyocytes, and hepatocytes, the authors show that differentiation is comparable to that of adherent cultures. This new method of expanding iPSCs has significant potential for clinical applications. The authors also tested these conditions in multiple cell lines and observed consistent results.

Although the authors have elaborated on the mechanism to some extent-suggesting that PKCβ and WNT signaling pathway inhibition suppresses differentiation and shifts cells toward a naïve pluripotency state in suspension cultures-further research is needed to fully understand this process. Nevertheless, their findings are promising and will be beneficial for producing scalable amounts of iPSCs in controlled conditions.

Reviewer #1 (Public review):

Summary:

This is a large cohort of ischemic stroke patients from a single centre. The author successfully set up predictive models for PTS.

Strengths:

The design and implementation of the trial are acceptable, with the credibility of the results. It may provide evidence of seizure prevention in the field of stroke treatment.

Weaknesses:

My concerns are well responded to.

Reviewer #2 (Public review):

Summary

The authors present multiple machine-learning methodologies to predict post-stroke epilepsy (PSE) from admission clinical data.

Strengths

The Statistical Approach section is very well written. The approaches used in this section are very sensible for the data in question.

Typos have now been addressed and improved interpretability has been added to the paper, which is appreciated.

Weaknesses

The authors have clarified that the first features available for each patient have been used. However, they have not shown that these features did not occur before the time of post-stroke epilepsy. Explicit clarification of this should be performed.

The likely impact of the work on the field

If this model works as claimed, it will be useful for predicting PSE. This has some direct clinical utility.

Analysis of features contributing to PSE may provide clinical researchers with ideas for further research on the underlying aetiology of PSE.

Reviewer #3 (Public review):

Summary:

The authors report the performance of a series of machine learning models inferred from a large-scale dataset and externally validated with an independent cohort of patients, to predict the risk of post-stroke epilepsy. Some of the reported models have very good explicative performance, and seem to have very good predictive ability.

Strengths:

The models have been derived from real-world large-scale data.

Performances of the best-performing models seem to be very good according to the external validation results.

Early prediction of risk of post-stroke epilepsy would be of high interest to implement early therapeutic interventions that could improve prognosis.

Code is publicly available. The authors also stated that the datasets used are available on request.

Weaknesses:

The writing of the article may be significantly improved.

Although the external validation is appreciated, cross-validation to check robustness of the models would also be welcome.

External validation results may be biased/overoptimistic, since the authors informed that "The external validation cohort focused more on collecting positive cases 80 to examine the model's ability to identify positive samples", which may result in overoptimistic PPV and Sensitivity estimations. The specificity for the external validation set has not been disclosed.

Reviewer #1 (Public review):

Summary:

The authors have nicely demonstrated the efficiency of the HCR v.3.0 using hr38 mRNA expression as a marker of neuronal activity. This is very important in the Drosophila neuroscience field as in situ hybridization in adult Drosophila brains have been so far very challenging to do and replicate. The HCR v.3.0 has been described before [Choi et al., (2018)] and is now the property of the non-profit organization Molecular Technologies, who are the ones responsible for designing the probes. Here, taking advantage of this new FISH method, the authors have demonstrated the use of the FISH to identify neurons activated by a specific behavioral task using hr38 mRNA as a marker of neuronal activation. They named their method HI-FISH.<br /> In addition, based on the catFISH method [Guzowski et al., 1999], the authors were able to distinguish between newly activated neurons (nascent nuclear mRNA) and mature hr38 mRNA showing an earlier activation. They describe this method as HI-catFISH.<br /> Finally, to test what are the neurons activated downstream of their neuronal group of interest, the authors combined the HI-FISH method with optogenetic using chrimson. They named this method opto-HI-FISH.

Using these three new methods, the authors have addressed the following biological question: are love and aggressiveness neuronally the same in Drosophila?<br /> Here, the authors focused on the male specific P1a neurons which are activated by both an aggressive context (male-male encounter) and sexual context (male female encounter).

Strengths:

The demonstration of the efficiency of the method is very convincing and well-performed. It gives the will for the reader to apply the method to their own subject.

Weaknesses:

The more neurons are present, the more difficult it is to identify neurons. This is something to take into account when applying these methods.

Reviewer #2 (Public review):

Summary:

Watanabe et al. introduce a novel approach for activity-dependent labeling of neural circuits in Drosophila at single-cell resolution, based on detecting the expression of the immediate early gene Hr38 using in situ hybridization. While activity mapping of neurons during specific behaviors is well-established in rodent models, its application in Drosophila has been limited, primarily due to technical constraints. By overcoming these challenges, this study tackles an important and timely issue, providing a foundational tool that will serve as a key reference in the field of circuit neuroscience.

Strengths:

The principal strength of this method lies in its versatility and high sensitivity. It can be applied to a broad range of biological questions and enables the investigation of dynamic transcriptional regulation across an unlimited number of genes with a strong signal-to-noise ratio. As such, it holds great potential for widespread use across research labs.

Weaknesses:

No major weaknesses; all concerns have been adequately addressed.

Reviewer #1 (Public Review):

Summary:

Li et al investigated how adjuvants such as MPLA and CpG influence antigen presentation at the level of the Antigen presenting cell and MHCII : peptide interaction. They found that use of MPLA or CpG influences the exogenous peptide repertoire presented by MHC II molecules. Additionally, their observations included the finding that peptides with low-stability peptide:MHC interactions yielded more robust CD4+ T cell responses in mice. These phenomena were illustrated specifically for 2 pattern recognition receptor activating adjuvants. This work represents a step forward for how adjuvants program CD4+ Th responses and provide further evidence regarding expected mechanisms of PRR adjuvants in enhancing CD4+ T cell responses in the setting of vaccination.

Strengths:

The authors use a variety of systems to analyze this question. Initial observations were collected in an H pylori model of vaccination with a demonstration of immunodominance differences simply by adjuvant type, followed by analysis of MHC:peptide as well as proteomic analysis with comparison by adjuvant group. Their analysis returns to peptide immunization and analysis of strength of relative CD4+ T cell responses, through calculation of IC:50 values and strength of binding. This is a comprehensive work. The logical sequence of experiments makes sense and follows an unexpected observation through to trying to understand that process further with peptide immunization and its impact on Th responses. This work will premise further studies into the mechanisms of adjuvants on T cells

Weaknesses:

While MDP has a different manner of interaction as an adjuvant compared to CpG and MPLA, it is unclear why MDP has a different impact on peptide presentation and it should be further investigated, or at minimum highlighted in the discussion as an area that requires further investigation.

It is alluded by the authors that TLR activating adjuvants mediate selective, low affinity, exogenous peptide binding onto MHC class II molecules. However, this was not demonstrated to be related specifically to TLR binding. Wonder if some work with TLR deficient mice (TLR 4KO for example) could evaluate this phenomenon more specifically

Lastly, it is unclear if the peptide immunization experiment reveals a clear pattern related to high and low stability peptides among the peptides analyzed.

Reviewer #2 (Public Review):

Adjuvants boost antigen-specific immune responses to vaccines. However, whether adjuvants modulate the epitope immunodominance and the mechanisms involved in adjuvant's effect on antigen processing and presentation are not fully characterized. In this manuscript, Li et al report that immunodominant epitopes recognized by antigen-specific T cells are altered by adjuvants.

Using MPLA, CpG, and MDP adjuvants and H. pylori antigens, the authors screened the dominant epitopes of Th1 responses in mice post-vaccination with different adjuvants and found that adjuvants altered antigen-specific CD4+ T cell immunodominant epitope hierarchy. They show that adjuvants, MPLA and CpG especially, modulate the peptide repertoires presented on the surface of APCs. Surprisingly, adjuvant favored the presentation of low-stability peptides rather than high-stability peptides by APCs. As a result, the low stability peptide presented in adjuvant groups elicits T cell response effectively.

Reviewer #1 (Public review):

This is a very important paper, using a large dataset to definitively understand a phenomenon so far addressed using a range of diverging definitions and methods, typically with insufficient statistical power.

Reviewer #2 (Public review):

Summary:

This important study uses convincing evidence to compare how different operationalizations of adverse childhood experience exposure related to patterns of skin conductance response during a fear conditioning task in a large sample of adults. Specifically, the authors compared the following operationalizations: dichotomization of the sample into "exposed" and "non-exposed" categories, cumulative adversity exposure, specificity of adversity exposure, and dimensional (threat versus deprivation) adversity exposure. The paper is thoughtfully framed and provides clear descriptions and rationale for procedures, as well as package version information and code. The authors' overall aim of translating theoretical models of adversity into statistical models, and comparing the explanatory power of each model, respectively, is an important and helpful addition to the literature.

Several outstanding strengths of this paper are the large sample size and its primary aim of statistically comparing leading theoretical models of adversity exposure in the context of skin conductance response. This paper also helpfully reports Cohen's d effect sizes, which aid in interpreting the magnitude of the findings. The methods and results are thorough and well-described.

Reviewer #1 (Public review):

The manuscript entitled "A septo-hypothalamic-medullary circuit directs stress-induced analgesia" by Shah et al., showed that the dLS-to-LHA circuit is sufficient and necessary for stress-induced analgesia (SIA), which is mediated by the rostral ventromedial medulla (RVM) in a opioid-dependent manner. This study is interesting and important and the conclusions are largely supported by the data. I have a few concerns as follows:

(1) The present data show that activation of dLS neurons produces SIA, however, this manipulation is non-specific. It may be better to see the effect of specific manipulation of stress-activated c-Fos positive neurons in the dLS using combination of the Tet-Off system and chemogenetic/optogenetic tools.<br /> (2) Depending on its duration, and intensity, stress can exert potent and bidirectional modulatory effects on pain, either reducing pain (SIA) or exacerbating it (stress-induced hyperalgesia,SIH). Whether this circuit in the manuscript is involved in SIH.<br /> (3) It are well-accepted that opioid and cannabinoid receptors participate in the SIA, especially, a critical role of the RVM endocannabinoid system in the SIA, why author focus their study on opioid system?<br /> (4) Whether silencing of the dLS neurons affects stress-induced anxiety-like behaviors? Or， what is the relationship between of SIA and level of stress-induced anxiety?<br /> (5) Please provide the direct electrophysiological evidence for confirming the efficacy of the MP-CNO.<br /> (6) Whether LHA is a specific downstream target for SIA, whether LHA is involved in stress-induced anxiety-like behaviors?<br /> (7) Whether LHA neurons have direct projections to the RVM? If yes, what is its role in the SIA?

Reviewer #2 (Public review):

Shah et al. investigate the role of an understudied neural circuitry, specifically the dLS -> LHA -> RVM pathway, in mediating stress-induced analgesia. The authors use a combination of advanced techniques to provide convincing evidence for the involvement of this circuit in modulating pain under stress.

The study begins by mapping the neural circuitry through a series of intersectional tracings. Following this, the authors use behavioral tests along with optogenetic and chemogenetic manipulations to confirm the pathway's role in promoting analgesia. Additionally, fiber photometry is employed to monitor the activity of each brain region in response to stress and pain.

While the study is comprehensive and the findings are convincing, a key concern arises regarding the overarching hypothesis that restraint-induced stress promotes analgesia. A more straightforward interpretation could be that intense struggling, rather than stress itself, might drive the observed analgesic responses.

Reviewer #1 (Public review):

The manuscript by Engelfriet et.al. addresses an interesting question in animal physiology - how do animals adapt to cold. Using polysome profiling and puromycin labeling, the authors confirm that in C. elegans exposed to a cooling regimen, protein synthesis is decreased globally. They then use RNAseq and ribosome profiling to propose that this decrease is driven mainly by decreased transcription, while translation of most mRNAs continues in the cold at a slower rate. They also find many transcripts whose expression is increased in the cold, and suggest that transcription of some of the cold-induced genes reflects activation of the IRE-1/XBP-1 UPR pathway. The authors further suggest that activation of the UPR by cold is due to cold-induced protein misfolding and perturbations in lipids in the ER, and that UPR activation is beneficial for cold survival.

The finding that a decrease in protein synthesis that is characteristic of cold exposure and hibernation is driven primarily by changes in transcription rather than translation is quite interesting and different from findings in other studies. It would be important to understand the reason for this difference. The findings that some of the cold-induced transcription in worms reflects XBP-1-dependent activity of IRE-1 is also new, while UPR activation by lipid perturbations both agrees with previous observations but also exposes differences. The differences highlight the need for better understanding of how different temperature exposures affect different lipids, as cold adaptation is widespread in nature, and cooling is often used in the clinical settings.

However, some concerns with interpretations and technical issues make several major conclusions in this manuscript less rigorous, as explained in detail in comments below. In particular, the two major concerns I have: 1) the contradiction between the strong reduction of global translation, with puromycin incorporation gel showing no detectable protein synthesis in cold, and an apparently large fraction of transcripts whose abundance and translation in Fig. 2A are both strongly increased. 2) The fact that no transcripts were examined for dependance on IRE-1/XBP-1 for their induction by cold, except for one transcriptional reporter, and some weaknesses (see below) in data showing activation of IRE-1/XBP-1 pathway. The conclusion for induction of UPR by cold via specific activation of IRE-1/XBP-1 pathway, in my opinion, requires additional experiments.

Major concerns:

(1) Fig. 1B shows polysomes still present on day 1 of 4{degree sign}C exposure, but the gel in Fig. 1C suggests a complete lack of protein synthesis. Why? What is then the evidence that ribosomal footprints used in much of the paper as evidence of ongoing active translation are from actual translating rather than still bound to transcripts but stationary ribosomes, considering that cooling to 4{degree sign}C is often used to 'freeze' protein complexes and prevent separation of their subunits? The authors should explain whether ribosome profiling as a measure of active translation has been evaluated specifically at 4{degree sign}C, or test this experimentally. They should also provide some evidence (like Western blots) of increases in protein levels for at least some of the strongly cold-upregulated transcripts, like lips-11.

As puromycin incorporation seems to be the one direct measure of global protein synthesis here, it conflicts with much of the translation data, especially considering that quite a large fraction of transcripts have increased both mRNA levels and ribosome footprints, and thus presumably increased translation at 4{degree sign}C, in Fig. 2A.

Also, it is not clear how quantitation in Fig. 1C relates to the gel shown, the quantitation seems to indicate about 50-60% reduction of the signal, while the gel shows no discernable signal.

(2) It is striking that plips-11::GFP reporter is induced in day 1 of 4{degree sign}C exposure, apparently to the extent that is similar to its induction by a large dose of tunicamycin (Fig. 3 supplement), but the three IRE-1 dependent UPR transcripts from Shen 2005 list were not induced at all on day 1(Fig. 4 supplement). Moreover, the accumulation of the misfolded CPL-1 reporter, that was interpreted as evidence that misfolding may be triggering UPR at 4{degree sign}C, was only observed on day 1, when the induction of the three IRE-1 targets is absent, but not on day 3, when it is stronger. How does this agree with the conclusion of UPR activation by cold via IRE-1/XBP-1 pathway? It is true that the authors do note very little overlap between IRE-1/XBP-1-dependent genes induced by different stress conditions, but for most of this paper, they draw parallels between tunicamycin-induced and cold-induced IRE-1/XBP-1 activation.

The conclusion that "the transcription of some cold-induced genes reflects the activation of unfolded protein response (UPR)..." is based on analysis of only one gene, lips-11. No other genes were examined for IRE-1 dependence of their induction by cold, neither the other 8 genes that are common between the cold-induced genes here and the ER stress/IRE-1-induced in Shen 2005 (Venn diagram in Figure 7 supplement), nor the hsp-4 reporter. What is the evidence that lips-11 is not the only gene whose induction by cold in this paper's dataset depends on IRE-1? This is a major weakness and needs to be addressed.

Furthermore, whether induction by cold of lips-11 itself is due to IRE1 activation was not tested, only a partial decrease of reporter fluorescence by ire-1 RNAi is shown. A quantitative measure of the change of lips-11 transcript in ire-1 and xbp-1 mutants is needed to establish if it depends on IRE-1/XBP-1 pathway.

The authors could provide more information and the additional data for the transcripts upregulated by both ER stress and cold, including the endogenous lips-11 and hsp-4 transcripts: their identity, fold induction by both cold and ER stress, how their induction is ranked in the corresponding datasets (all of these are from existing data), and do they depend on IRE-1/XBP-1 for induction by cold? Without these additional data, and considering that the authors did not directly measure the splicing of xbp-1 transcript (see comment for Fig. 3 below), the conclusion that cold induces UPR by specific activation of IRE-1/XBP-1 pathway is premature.

There are also technical issues that are making it difficult to interpret some of the results, and missing controls that decrease the rigor of conclusions:

(1) For RNAseq and ribosome occupancy, were the 20{degree sign}C day 1 adult animals collected at the same time as the other set was moved to 4{degree sign}C, or were they additionally grown at 20{degree sign}C for the same length of time as the 4{degree sign}C incubations, which would make them day 2 adults or older at the time of analysis? This information is only given for SUnSET: "animals were cultivated for 1 or 3 additional days at 4{degree sign}C or 20{degree sign}C". This could be a major concern in interpreting translation data: First, the inducibility of both UPR and HSR in worms is lost at exactly this transition, from day 1 to day 2 or 3 adults, depending on the reporting lab (for example Taylor and Dillin 2013, Labbadia and Morimoto, 2015, De-Souza et al 2022). How do authors account for this? Would results with reporter induction, or induction of IRE-1 target genes in Fig. 4, change if day 1 adults were used for 20{degree sign}C?

Second, if animals at the time of shift to 4{degree sign}C were only beginning their reproduction, they will presumably not develop further during hibernation, while an additional day at 20{degree sign}C will bring them to the full reproductive capacity. Did 4{degree sign}C and 20{degree sign}C animals used for RNAseq and ribosome occupancy have similar numbers of embryos, and were the embryos at similar stages? If embryos were retained in one condition vs the other, how much would they contribute in terms of transcripts, and do the authors expect the same adaptive programs operating in embryos and in the adults?

(2) Second, no population density is given for most of the experiments, despite the known strong effects of crowding (high pheromone) on C. elegans growth. From the only two specifics that are given, it seems that very different population sizes were used: for example, 150 L1s were used in survival assay, while 12,000 L1s in SUnSET. Have the authors compared results they got at high population densities with what would happen when animals are grown in uncrowded plates? At least a baseline comparison in the beginning should have been done.

(3) Fig. 3: it is unclear why the accepted and well characterized quantitative measure of IRE1 activation, the splicing of xbp-1transcript, is not determined directly by RT-PCR. The fluorescent XBP-1spliced reporter, to my knowledge, has not been tested for its quantitative nature and thus its use here is insufficient.

Furthermore, the image of this fluorescent reporter in Fig. 3b shows only one anterior-most row of cells of intestine, and quantitation was done with 2 to 5 nuclei per animal, while lips-11 is induced in entire intestine. Was there spliced XBP-1 in the rest of the intestinal nuclei? Could the authors show/quantify the entire animal (20 intestinal cells) rather than one or two rows of cells?

(4) The differences in the outcomes from this study and the previous one (Dudkevich 2022) that used 15{degree sign}C to 2{degree sign}C cooling approach are puzzling, as they would suggest two quite different IRE-1 dependent programs of cold tolerance. It would be good if authors commented on overlapping/non-overlapping genes, and provided their thoughts on the origin of these differences considering the small difference in temperatures. Second, have the authors performed a control where they reproduced the rescue by FA supplementation of poor survival of ire-1 mutants after the 15{degree sign}C to 2{degree sign}C shift?

Without this or another positive control, and without measuring change in lipid composition in their own experiments, it is unclear whether the different outcomes with respect to FAs are due to a real difference in adaptive programs at these temperatures, or to failure in supplementation?

(5) Have the authors tested whether and by how much ire-1(ok799) mutation shortens the lifespan at 20{degree sign}C? This needs to be done before the defect in survival of ire-1 mutants in Fig. 7a can be interpreted.

Reviewer #2 (Public review):

Summary:

This study investigates cold induced states in C. elegans, using polysome profiling and RNA seq to identify genes that are differentially regulated and concluding that cold-specific gene regulation occurs at the transcriptional level. This study also includes analysis of one gene from the differentially regulated set, lips-11 (a lipase), and finds that it is regulated in response to a specific set of ER stress factors.

Strengths:

(1) Understanding how environmental conditions are linked to stress pathways is generally interesting.

(2) The study used well-established genetic tools to analyze ER stress pathways.

Weaknesses:

(1) The conclusions regarding a general transcriptional response are based on one gene, lips-11, which does not affect survival in response to cold. We would suggest altering the title, to replace "Reprograming gene expression: with" Regulation of the lipase lips-11".

(2) There is no gene ontology with the gene expression data.

(3) Definitive conclusions regarding transcription vs translational effects would require use of blockers such as alpha amanatin or cyclohexamide.

(4) Conclusions regarding the role of lipids are based on supplementation with oleic acid or choline, yet there is no lipid analysis of the cold animals, or after lips-1 knockdown. Although choline is important for PC production, adding choline in normal PC could have many other metabolic impacts and doesn't necessarily implicate PC with out lipidomic or genetic evidence.

Reviewer #3 (Public review):

Summary:

The authors sought to understand the molecular mechanisms that cells use to survive cold temperatures by studying gene expression regulation in response to cold in C. elegans. They determined whether gene expression changes during cold adaptation occur primarily at the transcriptional level and identified specific pathways, such as the unfolded protein response pathway, that are activated to possibly promote survival under cold conditions.

Strengths:

Effective use of bulk RNA sequencing (RNA-seq) to measure transcript abundance and ribosome profiling (ribo-seq) to assess translation rates, providing a comprehensive view of gene expression regulation during cold adaptation. This combined approach allows for correlation between mRNA levels and their translation, thereby offering evidence for the authors' conclusion that transcriptional regulation is the primary mechanism of cold-specific gene expression changes.

Weaknesses:

The study has several weaknesses: it provides limited novel insights into pathways mediating transcriptional regulation of cold-inducible genes, as IRE-1 and XBP-1 are already well-known responders to endoplasmic reticulum stress, including that induced by cold. Additionally, the weak cold sensitivity phenotype observed in ire-1 mutants casts doubt on the pathway's key role in cold adaptation. The study also overlooks previous research (e.g. PMID: 27540856) that links IRE-1 to SKN-1, another major stress-responsive pathway, potentially missing important interactions and mechanisms involved in cold adaptation.

Reviewer #1 (Public review):

Summary

In this manuscript, Day et al. present a high-throughput version of expansion microscopy to increase the throughput of this well-established super-resolution imaging technique. Through technical innovations in liquid handling with custom-fabricated tools and modifications to how the expandable hydrogels are polymerized, the authors show robust ~4-fold expansion of cultured cells in 96-well plates. They go on to show that HiExM can be used for applications such as drug screens by testing the effect of doxorubicin on human cardiomyocytes. Interestingly, the effects of this drug on changing DNA organization were only detectable by ExM, demonstrating the utility of HiExM for such studies.

Overall, this is a very well-written manuscript presenting an important technical advance that overcomes a major limitation of ExM - throughput. As a method, HiExM appears extremely useful and the data generally support the conclusions.

Strengths

Hi-ExM overcomes a major limitation of ExM by increasing the throughput and reducing the need for manual handling of gels. The authors do an excellent job of explaining each variation introduced to HiExM to make this work and thoroughly characterize the impressive expansion isotropy. The dox experiments are generally well-controlled and the comparison to an alternative stressor (H2O2) significantly strengthens the conclusions.

Weaknesses

(1) It is still unclear to me whether or not cells that do not expand remain in the well given the response to point 1. The authors say the cells are digested and washed away but then say that there is a remaining signal from the unexpanded DNA in some cases. I believe this is still a concern that potential users of the protocol should be aware of.

Editor note: this comment has been addressed in the latest version.

(2) Regarding the response to point 9, I think this information should be included in the manuscript, possibly in the methods. It is important for others to have a sense of how long imaging may take if they were to adopt this method.

Editor note: this comment has been addressed in the latest version.

Reviewer #2 (Public review):

Summary:

In the present work, the authors present an engineering solution to sample preparation in 96-well plates for high-throughput super resolution microscopy via Expansion Microscopy. This is not a trivial problem, as the well cannot be filled with the gel, which would prohibit expansion of the gel. They thus engineered a device that can spot a small droplet of hydrogel solution and keep it in place as it polymerises. It occupies only a small portion space at the center of each well, the gel can expand into all directions and imaging and staining can proceed by liquid handling robots and an automated microscope.

Strengths:

In contrast to Reference 8, the authors system is compatible with standard 96 well imaging plates for high-throughput automated microscopy and automated liquid handling for most parts of the protocol. They thus provide a clear path towards high throughput exM and high throughout super resolution microscopy, which is a timely and important goal.

Addition upon revision:

The authors addressed this reviewer's suggestions.

Reviewer #3 (Public review):

Summary:

Day et al. introduced high-throughput expansion microscopy (HiExM), a method facilitating the simultaneous adaptation of expansion microscopy for cells cultured in a 96-well plate format. The distinctive features of this method include: 1) the use of a specialized device for delivering a minimal amount (~230 nL) of gel solution to each well of a conventional 96-well plate, and 2) the application of the photochemical initiator, Irgacure 2959, to successfully form and expand toroidal gel within each well.

Addition upon revision:

Overall, the authors have adequately addressed most of the concerns raised. There are a few minor issues that require attention.

Minor comments:

Figure S10: There appears to be a discrepancy in the panel labeling. The current labels are E-H, but it is unclear whether panels A-D exist. Also, this reviewer thought that panels G and H would benefit from statistical testing to strengthen the conclusions. As a general rule for scientific graph presentation, the y-axis of all graphs should start at zero unless there is a compelling reason not to do so.

Editor note: this comment has been addressed in the latest version.

Reviewer #1 (Public review):

By examining the prevalence of interactions with ancient amino acids of coenzymes in ancient versus recent folds, the authors noticed an increased interaction propensity for ancient interactions. They infer from this that coenzymes might have played an important role in prebiotic proteins. By only focusing on coenzymes, the authors may have overestimated their importance. What about other small molecules that existed in the prebiotic soup? Do they also prefer such ancient amino acids? if so, this might reflect the interaction propensity of specific amino acids rather than some possible role in very ancient proteins. Or it might diminish the conjectured importance of coenzymes. The analysis, which is very straightforward, is technically correct. However, the conclusions might not be as strong as presented. This paper presents an excellent summary of contemporary thought on what might have constituted prebiotic proteins and their properties.

Reviewer #2 (Public review):

This study advances the model that the first canonical amino acids to emerge in life bound the earliest cofactors and led to the first proteins. The focus is on organic/organometallic cofactors, building on previous work on metals - ie. those in the groups of Bromberg, Dupont and others as well cited in the manuscript. Studies of this type are limited both by data availability and confounding chemical effects that are exacerbated by the timescale of evolutionary inference tackled here. However, the analysis provides a solid addition to the field and complements existing metal-focused studies as well as those Longo, Russell and others (also well cited).

Reviewer #2 (Public review):

The manuscript points out that TMB cut-offs are not strong predictors of response to immunotherapy or overall survival. By randomly shuffling TMB values within cohorts to simulate a null distribution of log-rank test p-values, they show that under correction, the statistical significance of previously reported TMB cut-offs for predicting outcomes is questionable. There is a clinical need for a better prediction of treatment response than TMB alone can provide. However, the analysis does not convincingly refute the validity of the well-known pan-cancer correlation between TMB and immunotherapy response. (In a supplemental analysis, the authors attempt to demonstrate a lack of correlation by specifically removing the most supportive cancer types from a pan-cancer correlation test.) The failure to detect significant TMB cut-offs may be due to insufficient power, as the examined cohorts have relatively low sample sizes. A power analysis would be informative of what cohort sizes are needed to detect small to modest effects of TMB on immune response.

The manuscript provides a simple model of immunogenicity that is tailored to be consistent with a claimed lack of relationship between TMB and response to immunotherapy. Under the model, if each mutation that a tumor has acquired has a relatively high probability of being immunogenic (~10%, they suggest), and if 1-2 immunogenic mutations is enough to induce an immune response, then most tumors produce an immune response, and TMB and response should be uncorrelated except in very low-TMB tumors. The question then becomes whether the response is sufficient to wipe out tumor cells in conjunction with immunotherapy, which is essentially the same question of predicting response that motivated the original analysis. While TMB alone is not an excellent predictor of treatment response, the pan-cancer correlation between TMB and response/survival is highly significant, so the model's only independent prediction is wrong. Additionally, experiments to predict and validate neoepitopes suggest that a much smaller fraction of nonsynonymous mutations produce immune responses (1,2).

A key idea that is overlooked in this manuscript is that of survivorship bias: self-evidently, none of the mutations found at the time of sequencing have been immunogenic enough to provoke a response capable of eliminating the tumor. While the authors suggest that immunoediting "is inefficient, allowing tumors to accumulate a high TMB," the alternative explanation fits the neoepitope literature better: most mutations that reach high allele frequency in tumor cells are not immunogenic in typical (or patient-specific) tumor environments. Of course, immunotherapies sometimes succeed in overcoming the evolved immune evasion of tumors. Higher-TMB tumors are likely to continue to have higher mutation rates after sequencing; increased generation of new immunogenic mutations may partially explain their modestly improved responses to therapy.

References:<br /> (1) Wells, D. K. et al. Key Parameters of Tumor Epitope Immunogenicity Revealed Through a Consortium Approach Improve Neoantigen Prediction. Cell 183, 818-834.e13 (2020).<br /> (2) Yadav, M. et al. Predicting immunogenic tumour mutations by combining mass spectrometry and exome sequencing. Nature 515, 572-576 (2014).

Reviewer #1 (Public Review):

This work makes several contributions: (1) a method for the self-supervised segmentation of cells in 3D microscopy images, (2) an cell-segmented dataset comprising six volumes from a mesoSPIM sample of a mouse brain, and (3) a napari plugin to apply and train the proposed method.

(1) Method

This work presents itself as a generalizable method contribution with a wide scope: self-supervised 3D cell segmentation in microscopy images. My main critique is that there is almost no evidence for the proposed method to have that wide of a scope. Instead, the paper is more akin to a case report that shows that a particular self-supervised method is good enough to segment cells in two datasets with specific properties.

To support the claim that their method "address[es] the inherent complexity of quantifying cells in 3D volumes", the method should be evaluated in a comprehensive study including different kinds of light and electron microscopy images, different markers, and resolutions to cover the diversity of microscopy images that both title and abstract are alluding to.

The main dataset used here (a mesoSPIM dataset of a whole mouse brain) features well-isolated cells that are easily distinguishable from the background. Otsu thresholding followed by a connected component analysis already segments most of those cells correctly. The proposed method relies on an intensity-based segmentation method (a soft version of a normalized cut) and has at least five free parameters (radius, intensity, and spatial sigma for SoftNCut, as well as a morphological closing radius, and a merge threshold for touching cells in the post-processing). Given the benefit of tweaking parameters (like thresholds, morphological operation radii, and expected object sizes), it would be illuminating to know how other non-learning-based methods will compare on this dataset, especially if given the same treatment of segmentation post-processing that the proposed method receives. After inspecting the WNet3D predictions (using the napari plugin) on the used datasets I find them almost identical to the raw intensity values, casting doubt as to whether the high segmentation accuracy is really due to the self-supervised learning or instead a function of the post-processing pipeline after thresholding.

I suggest the following baselines be included to better understand how much of the segmentation accuracy is due to parameter tweaking on the considered datasets versus a novel method contribution:<br /> * comparison to thresholding (with the same post-processing as the proposed method)<br /> * comparison to a normalized cut segmentation (with the same post-processing as the proposed method)<br /> * comparison to references 8 and 9.

I further strongly encourage the authors to discuss the limitations of their method. From what I understand, the proposed method works only on well-separated objects (due to the semantic segmentation bottleneck), is based on contrastive FG/BG intensity values (due to the SoftNCut loss), and requires tuning of a few parameters (which might be challenging if no ground-truth is available).

(2) Dataset

I commend the authors for providing ground-truth labels for more than 2500 cells. I would appreciate it if the Methods section could mention how exactly the cells were labelled. I found a good overlap between the ground truth and Otsu thresholding of the intensity images. Was the ground truth generated by proofreading an initial automatic segmentation, or entirely done by hand? If the former, which method was used to generate the initial segmentation, and are there any concerns that the ground truth might be biased towards a given segmentation method?

(3) Napari plugin

The plugin is well-documented and works by following the installation instructions. However, I was not able to recreate the segmentations reported in the paper with the default settings for the pre-trained WNet3D: segments are generally too large and there are a lot of false positives. Both the prediction and the final instance segmentation also show substantial border artifacts, possibly due to a block-wise processing scheme.

Reviewer #2 (Public Review):

Summary:

The authors propose a new method for self-supervised learning of 3d semantic segmentation for fluorescence microscopy. It is based on a WNet architecture (Encoder / Decoder using a UNet for each of these components) that reconstructs the image data after binarization in the bottleneck with a soft n-cuts clustering. They annotate a new dataset for nucleus segmentation in mesoSPIM imaging and train their model on this dataset. They create a napari plugin that provides access to this model and provides additional functionality for training of own models (both supervised and self-supervised), data labeling, and instance segmentation via post-processing of the semantic model predictions. This plugin also provides access to models trained on the contributed dataset in a supervised fashion.

Strengths:

(1) The idea behind the self-supervised learning loss is interesting.

(2) The paper addresses an important challenge. Data annotation is very time-consuming for 3d microscopy data, so a self-supervised method that yields similar results to supervised segmentation would provide massive benefits.

Weaknesses:

The experiments presented by the authors do not adequately support the claims made in the paper. There are several shortcomings in the design of the experiment and presentation of the results. Further, it is unclear if results of similar quality as reported can be achieved within the GUI by non-expert users.

Major weaknesses:

(1) The main experiments are conducted on the new mesoSPIM dataset, which contains quite small and well separated nuclei. It is unclear if the good performance of the novel self-supervised learning method compared to CellPose and StarDist would hold for dataset with other characteristics, such as larger nuclei with a more complex morphology or crowded nuclei. Further, additional preprocessing of the mesoSPIM images may improve results for StarDist and CellPose (see the first point in minor weaknesses). Note: having a method that works better for small nuclei would be an important contribution. But I am uncertain the claims hold for larger and/or more crowded nuclei as the current version of the paper implies. The contribution of the paper would be stronger if a comparison with StarDist / CellPose was also done on the additional datasets from Figure 2.

(2) The experimental setup for the additional datasets seems to be unrealistic. In general, the description of these experiments is quite short and so the exact strategy is unclear from the text. However, you write the following: "The channel containing the foreground was then thresholded and the Voronoi-Otsu algorithm used to generate instance labels (for Platynereis data), with hyperparameters based on the Dice metric with the ground truth." I.e., the hyperparameters for the post-processing are found based on the ground truth. From the description it is unclear whether this is done a) on the part of the data that is then also used to compute metrics or b) on a separate validation split that is not used to compute metrics. If a): this is not a valid experimental setup and amounts to training on your test set. If b): this is ok from an experimental point of view, but likely still significantly overestimates the quality of predictions that can be achieved by manual tuning of these hyperparameters by a user that is not themselves a developer of this plugin or an absolute expert in classical image analysis, see also 3. Note that the paper provides notebooks to reproduce the experimental results. This is very laudable, but I believe that a more extended description of the experiments in the text would still be very helpful to understand the set-up for the reader. Further, from inspection of these notebooks it becomes clear that hyper-parameters where indeed found on the testset (a), so the results are not valid in the current form.

(3) I cannot obtain similar results to the ones reported in the manuscript using the plugin. I tried to obtain some of the results from the paper qualitatively: First I downloaded one of the volumes from the mesoSPIM dataset (c5image) and applied the WNet3D to it. The prediction looks ok, however the value range is quite narrow (Average BG intensity ~0.4, FG intensity 0.6-0.7). I try to apply the instance segmentation using "Convert to instance labels" from "Utilities". Using "Voronoi-Otsu" does not work due to an error in pyClesperanto ("clGetPlatformIDs failed: PLATFORM_NOT_FOUND_KHR"). Segmentation via "Connected Components" and "Watershed" requires extensive manual tuning to get a somewhat decent result, which is still far from perfect.

Then I tried to obtain the results for the Mouse Skull Nuclei Dataset from EmbedSeg. The results look like a denoised version of the input image, not a semantic segmentation. I was skeptical from the beginning that the method would transfer without retraining, due to the very different morphology of nuclei (much larger and elongated). None of the available segmentation methods yield a good result, the best I can achieve is a strong over-segmentation with watersheds.

Minor weaknesses:

(1) CellPose can work better if images are resized so that the median object size in new images matches the training data. For CellPose the cyto2 model should do this automatically. It would be important to report if this was done, and if not would be advisable to check if this can improve results.

(2) It is a bit confusing that F1-Score and Dice Score are used interchangeably to evaluate results. The dice score only evaluates semantic predictions, whereas F1-Score evaluates the actual instance segmentation results. I would advise to only use F1-Score, which is the more appropriate metric. For Figure 1f either the mean F1 score over thresholds or F1 @ 0.5 could be reported. Furthermore, I would advise adopting the recommendations on metric reporting from https://www.nature.com/articles/s41592-023-01942-8.

(3) A more conceptual limitation is that the (self-supervised) method is limited to intensity-based segmentation, and so will not be able to work for cases where structures cannot be distinguished based on intensity only. It is further unclear how well it can separate crowded nuclei. While some object separation can be achieved by morphological operations this is generally limited for crowded segmentation tasks and the main motivation behind the segmentation objective used in StarDist, CellPose, and other instance segmentation methods. This limitation is only superficially acknowledged in "Note that WNet3D uses brightness to detect objects [...]" but should be discussed in more depth.

Note: this limitation does not mean at all that the underlying contribution is not significant, but I think it is important to address this in more detail so that potential users know where the method is applicable and where it isn't.

Reviewer #1 (Public review):

Summary:

This study uses a cell-based computational model to simulate and study T cell development in the thymus. They initially applied this model to assess the effect of the thymic epithelial cells (TECs) network on thymocyte proliferation and demonstrated that increasing TEC size, density, or protrusions increased the number of thymocytes. They postulated and confirmed that this was due to changes in IL7 signalling and then expanded this work to encompass various environmental and cell-based parameters, including Notch signalling, cell cycle duration, and cell motility. Critical outcomes from the computational model were tested in vivo using medaka fish, such as the role of IL-7 signalling and minimal effect of Notch signalling.

Strengths:

The strength of the paper is the use of computational modelling to obtain unique insights into the niche parameters that control T cell development, such as the role of TEC architecture, while anchoring those findings with in vivo experiments. I can't comment on the model itself, as I am not an expert in modelling, however, the conclusions of the paper seem to be well-supported by the model.

Weaknesses:

One potential issue is that many of the conclusions are drawn from the number of thymocytes, or related parameters such as the thymic size or proliferation of the thymocytes. The study only touches briefly on the influence of the thymic niche on other aspects of thymocyte behaviour, such as their differentiation and death.

Reviewer #2 (Public review):

Summary:

The authors have worked up a ``virtual thymus' using EPISIM, which has already been published. Attractive features of the computational model are stochasticity, cell-to-cell variability, and spatial heterogeneiety. They seek to explore the role of TECs, that release IL-7 which is important in the process of thymocyte division.

In the model, ordinary clones have IL7R levels chosen from a distribution, while `lesioned' clones have an IL7R value set to the maximum. The observation is that the lesioned clones are larger families, but the difference is not dramatic. This might be called a cell-intrinsic mechanism. One promising cell-extrinsic mechanism is mentioned: if a lesioned clone happens to be near a source of IL-7 and begins to proliferate, the progeny can crowd out cells of other clones and monopolise the IL-7 source. The effect will be more noticeable if sources are rare, so is seen when the TEC network is sparse.

Strengths:

Thymic disfunctions are of interest, not least because of T-ALL. New cells are added, one at a time, to simulate the conveyor belt of thymocytes on a background of stationary cells. They are thus able to follow cell lineages, which is interesting because one progenitor can give rise to many progeny.

There are some experimental results in Figures 4,5 and 6. For example, il7 crispant embryos have fewer thymocytes and smaller thymii; but increasing IL-7 availability produces large thymii.

Weaknesses:

On the negative side, like most agent-based models, there are dozens of parameters and assumptions whose values and validity are hard to ascertain.

The stated aim is to mimic a 2.5-to-11 day-old medaka thymus, but the constructed model is a geometrical subset that holds about 100 cells at a time in a steady state. The manuscript contains very many figures and lengthy descriptions of simulations run with different parameters values and assumptions. The abstract and conclusion did not help me understand what exactly has been done and learned. No attempt to synthesise observations in any mathematical formula is made.

Reviewer #3 (Public review):

Summary:

Tsingos et al. seek to advance beyond the current paradigm that proliferation of malignant cells in T-cell acute lymphoblastic leukemia occurs in a cell-autonomous fashion. Using a computational agent-based model and experimental validation, they show instead that cell proliferation also depends on interaction with thymic epithelial cells (TEC) in the thymic niche. One key finding is that a dense TEC network inhibits the proliferation of malignant cells and favors the proliferation of normal cells, whereas a sparse TEC network leads to rapid expansion of malignant thymocytes.

Strengths:

A key strength of this study is that it combines computational modeling using an agent-based model with experimental work. The original modeling and novel experimental work strengthen each other well. In the agent-based model, the authors also tested the effects of varying a few key parameters of cell proliferation.

Weaknesses:

A minor weakness is that the authors did not conduct a global sensitivity analysis of all parameters in their agent-based model to show that the model is robust to variation, which would demonstrate that their results would still hold under a reasonable level of variation in the model and model parameters. This is a minor point, and such a supporting study would end in an appendix or supplement.

Reviewer #1 (Public review):

Summary:

In this manuscript, authors intended to prove that gut GLP-1 expression and secretion can be regulated by Piezo1, and hence by mechanistic/stretching regulation. For this purpose, they have assessed Piezo1 expression in STC-1 cell line (a mouse GLP-1 producing cell line) and mouse gut, showing the correlation between Piezo1 level and Gcg levels (Fig. S1). They then aimed to generate gut L cell-specific Piezo1 KO mice and claimed the mice show impaired glucose tolerance and GLP-1 production, which can be mitigated by Ex-4 treatment (Fig. 1-2). Pharmacological agents (Yoda1 and GsMTx4) and mechanic activation (intestinal bead implantation) were then utilized to prove the existence of ileal Piezo1-regulated GLP-1 synthesis (Fig. 3). This was followed by testing such mechanism in a limited amount of primary L cells and mainly in the STC-1 cell line (Fig. 4-7).

While the novelty of the study is somehow appreciable, the bio-medical significance is not well demonstrated in the manuscript. The authors stated (in lines between lines 78-83) a number of potential side effects of GLP-1 analogs, how can the mechanistic study of GLP-1 production on its own be essential for the development of new drug targets for the treatment of diabetes. Furthermore, the study does not provide a clear mechanistic insight how the claimed CaMKKbeta/CaMKIV-mTORC1 signaling pathway upregulated both GLP-1 production and secretion. This reviewer also has concerns about the experimental design and data presented in the current manuscript, including the issue of how can proglucagon expression can be assessed by Western blotting.

Strengths:

Novelty of the concept.

Weaknesses:

Experimental design and key experiment information.

Reviewer #2 (Public review):

Summary:

The study by Huang and colleagues focuses on GLP-1 producing enteroendocrine (EEC) L-cells and their regulation of GLP-1 production by a mechanogated ion channel Piezo1. The study describes Piezo1 expression by L-cells and using an exciting intersectional mouse model (villin to target epithelium and Gcg to target GLP-1 producing cells and others like glucagon producing pancreatic endocrine cells), which allows L-cell specific Piezo1 knockout. Using this model, they find an impairment of glucose tolerance, increased body weight, reduced GLP-1 content, and changes to the CaMKKbeta-CaMKIV-mTORC1 signaling pathway using normal diet and then high fat diet. Piezo1 chemical agonist and intestinal bead implantation reversed these changes and improved the disrupted phenotype. Using primary sorted L-cells and cell model STC-1, they found that stretch and Piezo1 activation increased GLP-1 and altered the molecular changes described above.

Strengths:

This is an interesting study testing a novel hypothesis that may have important mechanistic and translational implications. The authors generated an important intersectional genetics mouse model that allowed them to target Piezo1 L-cells specifically, and the surprising result of impaired metabolism is intriguing.

Weaknesses:

However, there are several critical limitations that require resolution before making the conclusions that the authors make. (1) A potential explanation for the data, and one that is consistent with existing literature [see for example, PMC5334365, PMC4593481], is that epithelial Piezo1, which is broadly expressed by the GI epithelium, impacts epithelial cell density and survival, and as such, if Piezo1 is involved in L-cell physiology, it may be through regulation of cell density. Thus, it is critical to determine L-cell densities and epithelial integrity in controls and Piezo1 knockouts systematically across the length of the gut, since the authors do not make it clear which gut region contributes to the phenotype they see. Current immunohistochemistry data are not convincing. (2) Calcium signaling in L-cells is implicated in their typical role of being gut chemosensors, and Piezo1 is a calcium channel, so it is not clear whether any calcium-related signaling mechanism would phenocopy these results. (3) Intestinal bead implantation, while intriguing, does not have clear mechanisms - and is likely to provide a point of intestinal obstruction and dysmotility. (4) previous studies, some that are very important, but not cited, contradict the presented results (e.g., epithelial Piezo1 role in insulin secretion) and require reconciliation.<br /> Overall, this study makes an interesting observation but the data are not currently strong enough to support the conclusions.

- There needs to be data localizing Piezo1 to L-cells and importantly, this needs to be quantified - are all L-cells (small bowel and colon) Piezo1 positive? This is because several studies show Piezo1 affecting epithelial cell densities. If there are changes in L-cell or other EEC densities in Piezo1 knockout, that shift can potentially explain the changes that the authors see in glucose metabolism and weight.<br /> - The intersectional model for L-cell transduction needs a deeper validation. Images in Fig 1e are not convincing for transduction of GFP in L-cells. The co-localization studies are not convincing, especially because Piezo1 labeling is very broad. There needs to be stronger validation of the intersectional Gcg-Villin-Piezo1 KO model. It is important to determine whether L-cell Piezo1 localization epithelium in small bowel and colon is present (above) and affected specifically in the knockout.<br /> - The authors state that "Villin-1 (encoded by Vill1 gene) is expressed in the gastrointestinal epithelium, including L cells, but not in pancreatic α cells" (line 378-379). However, Villin is highly expressed in whole mouse islets (https://doi.org/10.1016/j.molmet.2016.05.015, Figure 1A).<br /> - There needs to be quantification of L-cells in Piezo1 knockout. This is because several studies show Piezo1 affecting epithelial cell densities. If there are changes in L-cell or other EEC densities in Piezo1 knockout, that shift can potentially explain the changes that the authors see in glucose metabolism and weight.<br /> - L-cells are classically considered to be chemosensors. Do nutritive signals, which presumably also increase calcium compete or complement or dominate L-cell GLP1 synthesis regulation?<br /> - The mechanism of Glp1 synthesis vs release downstream of Piezo1 is not clear. The authors hypothesize that "Piezo1 might regulate GLP-1 synthesis through the CaMKKβ/CaMKIV-mTOR signaling pathway". However, references cited suggest that Ca2+ or cAMP lead to GLP-1-release, while mTOR primarily acts on the regulation of gene expression by promoting Gcg gene expression. These pathways do not clearly link to Piezo1  GLP-1 production. These mechanisms need to be reconciled.<br /> - Previous study PMID 32640190 (not cited here) found that Villin-driven Piezo1 knockout, which knocks out Piezo1 from all epithelial intestinal cells (including L-cells), showed no significant alterations in blood glucose or body weight. This is opposite of the presented findings and therefore the current results require reconciliation.

Comments on revised version:

The authors have addressed several comments that were common to the reviewers - specificity and validity of the intersectional model, mechanism of signaling downstream of Piezo1 and reconciliation of the results with previous studies. The authors have provided extensive experiments and revisions which have made the manuscript stronger. However, many important questions remain, and unfortunately, the intersectional mouse model and mechanisms remain unclear.

- I appreciate the authors quantifying the density of L cells in the intersectional Piezo knockout. There is a very clear >50% drop-off in GLP-1+ cells with the Piezo1 knockout (Supp fig 7c, d). Interestingly, there was not a decrease in PYY+ cells, which is curious because GLP1 and PYY are co-expressed in L cells. The mechanism of regulation of one hormone but not the other in the same cell requires clarification and would be relevant for this work. To begin with, co-labeling PYY and GLP1 and showing that one hormone can be found without the other would be useful.<br /> - Piezo1 immunofluorescence has very high background and overall poor specificity (Fig supp 5 and Fig supp 6B are good examples of poor Piezo1 immunofluorescence). Another method for labeling Piezo1 (e.g. via RNAscope) is required - and where tried (e.g., Fig 1L), the results are not convincing.<br /> - The intersectional mouse model requires further validation. The data presented in Fig 1E do not help - the GFP positive cells do not look like L-cells and there appear to be GFP positive cells in the muscle and submucosa.<br /> - Since Piezo1 is known to affect epithelial cell life span, barrier function maybe compromised. While I appreciate that the authors have obtain some images and measured zonular and occluded, this is unfortunately a suboptimal evaluation of barrier function.<br /> - The mechanisms of calcium signaling that will presumably lead to GLP1 release due to Piezo1 activation and mTOR which authors link to GLP1 synthesis remain unreconciled.<br /> - Intestinal bead implantation may provide an important area of obstruction, in addition to potential mechanical stimulation. Unfortunately whole gut transit time and fecal weight do not assay these functions well.<br /> - I believe that the explanation regarding lack of previous findings connecting Piezo1 in the epithelium and glucose tolerance remain poorly reconciled with the current findings.

Reviewer #3 (Public review):

Summary:

In this work, the authors proposed that the mechano-gated ion channel Piezo1 enhances GLP-1 production and secretion possibly through stimulating Ca2+-CaMKKbeta-CaMKIV-mTORC1 signaling pathway. By using intestinal L cell-specific piezo1 knock-out mice, intestinal bead implantation mice model, and the chemical agonist Yoda1, the authors claimed that piezo1 promotes pro-glucagon expression, GLP-1 production and secretion. In sorted primary intestinal L cells and STC-1 cells, the authors validated that CaMKKbeta-CaMKIV-mTORC1 signaling pathway positively regulated GLP-1 production and secretion. This study provides new evidence about the specific role of piezo1 in intestinal L cells, broadening the understanding of metabolic functions of piezo1.

Strengths:

The new concept and innovative in vivo and in vitro models.

Weaknesses:

Although the authors have addressed most of the issues in the revised manuscript, there are still some questions that need to be clarified.

(1) This study claimed that piezo1 enhances proglucagon expression, GLP-1 production and secretion through Ca2+-CaMKKbeta-CaMKIV-mTORC1 signaling pathway, which is a highly time-consuming process. However, as a mechano-gated ion channel, it should exert functions promptly. Is it possibly that piezo1 directly stimulates GLP-1 release by influx of Ca2+? if so, have authors measured intracellular Ca2+ concentration?<br /> (2) The authors proposed that the CaMKKbeta-CaMKIV-mTORC1 signaling pathway mediated the effects of piezo1. However, the data is not convincing. At least, chemical inhibitors of CaMKKbeta/CaMKIV/mTORC1 should be used in intL-piezo1 KO mice or STC-1 cells to see if piezo1-induced GLP-1 secretion was abrogated by these chemical inhibitors.<br /> (3) According to previous studies of the team, piezo1 could enhance insulin, ghrelin and GLP-1 secretion while inhibit glucagon production in pancreatic α-cells. In a recent work, the authors found that piezo1 in enterocytes suppresses nutrient absorption. Why an ion channel has these various effects in different cells? What is the fundamental and common mechanism underlying its metabolic functions? Its value as a drug target? These questions need to be discussed in more details.

Reviewer #1 (Public review):

Summary:

This study investigates the role of Hox genes in determining the position of the forelimb bud through experimental loss- and gain-of-function approaches in chicken embryos. The loss-of-function experiments involved expressing dominant-negative versions of specific Hox genes in the limb bud to assess their necessity for limb formation. Gain-of-function experiments entailed expressing full-length Hox genes anterior to the limb field in the lateral mesoderm. The results were evaluated by analyzing the expression of genes involved in limb development, such as Fgf8, Fgf10, Shh, and Tbx5, the latter specifically marking the forelimb.

The findings indicate that introducing dominant-negative forms of Hoxa4, Hoxa5, Hoxa6, and Hoxa7 into the forelimb field reduces bud size and downregulates certain limb markers. Conversely, introducing active versions of these genes rostral to the normal forelimb position shows that Hox4 and Hox5 have no effect, whereas Hox6 and Hox7 extend the forelimb anteriorly or create a small bulge rostral to the forelimb. The authors conclude that Hox4 and Hox5 provide permissive cues for forelimb formation throughout the neck region, with the final forelimb position determined by the instructive cues of Hox6/7 in the lateral plate mesoderm.

Strengths:

The authors endeavor to address the longstanding question of what determines limb position, particularly that of the forelimb, in the vertebrate embryo.

Weaknesses:

In my opinion, the study is preliminary and requires additional controls and explanations for conflicting results observed in mice:

(1) The activity of the dominant negatives lacks appropriate controls. This is crucial given that mouse mutants for PG5, PG6, PG7, and three of the four PG4 genes show no major effects on limb induction or growth. Understanding these discrepancies is essential.

(2) The authors mention redundancies in Hox activity, consistent with numerous previous reports. However, they only use single dominant-negative versions of each Hox paralog gene individually. If Hox4 and Hox5 functions are redundant, experiments should include simultaneous dominant negatives for both groups.

(3) The main conclusion that Hox4 and Hox5 provide permissive cues on which Hox6/7 induce the forelimb is not sufficiently supported by the data. An experiment expressing simultaneous dnHox4/5 and Hox6/7 is needed. If the hypothesis is correct, this should block Hox6/7's capacity to expand the limb bud or generate an extra bulge.

(4) The identity of the extra bulge or extended limb bud is unclear. The only marker supporting its identity as a forelimb is Tbx5, while other typical limb development markers are absent. Tbx5 is also expressed in other regions besides the forelimb, and its presence does not guarantee forelimb identity. For instance, snakes express Tbx5 in the lateral mesoderm along much of their body axis.

(5) It is important to analyze the skeletons of all embryos to assess the effect of reduced limb buds upon dnHox expression and determine whether extra skeletal elements develop from the extended bud or ectopic bulge.

Reviewer #2 (Public review):

Summary:

This manuscript investigates the role of Hox genes in the specification of forelimb position. The central conclusions are that Hox paralogy group (PG) 6/7 genes are both necessary and sufficient to induce forelimb buds. In addition, the authors argue that HoxPG4/5 genes are necessary, but, by contrast to Hox PG6/7 genes, Hox PG4/5 genes are not sufficient to induce forelimb budding. To test the roles of Hox4-7 genes in limb development, the authors use both gain-of-function (GOF) and loss-of-function (LOF) approaches in chick embryos.

In LOF experiments, they produced dominant negative forms of Hoxa4, Hoxa5, Hoxa6, and Hoxa7, which lack the DNA-binding domain, and they electroporated these constructs into the prospective wing field of the lateral plate mesoderm (LPM) in pre-limb bud stage (HH12) chick embryos. All 4 constructs resulted in down-regulation of Tbx5 (an early marker of forelimb development), and of its target gene, Fgf10, which is required for the initiation of limb budding, in the lateral plate mesoderm. The dominant negative experiments also caused down-regulation of Fgf8 in the overlying limb ectoderm and a marked reduction in the size of the early wing bud. Based on the LOF results, the authors conclude that each of the Hoxa4-7 genes is required for the specification of the forelimb field and for the establishment of the Fgf10-Fgf8 feedback loop in wing bud mesenchyme and overlying epithelium.

The authors then use a GOF strategy to investigate whether the same genes are sufficient to induce forelimb budding. They test this hypothesis using the neck, a region that is known to be incompetent to form limbs in response to Fgf signaling. Overexpression of full-length Hoxa6 and Hoxa7 in the neck region caused ectopic expression of Tbx5 in the neck region, which fits with "posteriorization" of cells at neck level, as Tbx5 typically marks the forelimb and flank (interlimb) region of the lateral plate mesoderm. Consistent with a posterior transformation of positional identity (neck to forelimb), overexpression of Hoxa6 or Hoxa7 leads to activation of Fgf10 expression and development of an ectopic forelimb bud from (or extension of the normal forelimb bud into) the neck region). By contrast, overexpression of either Hoxa4 or Hoxa5 in the neck region is not sufficient to induce ectopic forelimb budding. Curiously, the ectopic forelimb buds do not express Fgf8 in the overlying ectoderm or develop beyond the bud stage. The latter finding is consistent with previous work showing that neck ectoderm is not competent to support outgrowth of transplanted limb bud mesenchyme. The authors investigate the mechanistic basis of this early arrest of outgrowth by comparing the transcriptomes of ectopic limb buds, normal forelimb buds, and normal neck cells.

The RNA sequencing analysis shows that while some limb development genes (e.g., Lmx1b, Hoxa9, Hoxd9, Hoxa10, Hoxd10) are activated in the ectopic limb bud, other key components of the circuit (e.g., Shh, Fgf8, Hox12/13 paralogs) are not established, leading them to conclude that failure of neck ectoderm to form an AER underlies the arrested outgrowth of ectopic limb buds.

Strengths:

This study provides the first evidence that altering the Hox code in neck lateral plate mesoderm (LPM) is sufficient to induce ectopic development of forelimb buds at the neck level. For more than 30 years, developmental biologists have speculated and provided indirect evidence that Hox genes are involved in the specification of forelimb position, but to my knowledge, no study has shown that altering Hox gene expression alone can induce limb development outside of the normal limb field. The finding that Hox6/7 paralogs are sufficient for forelimb bud development, whereas Hox4/5 paralogs are not, suggests that specification of forelimb identity requires instructive signaling that is a specific property of Hox6/7 paralogs. The GOF experiments significantly extend the knowledge of limb specification beyond that which has come from Hox gene manipulations in mice.

Weaknesses:

(1) By contrast to the GOF experiments that induce ectopic limb budding, the LOF experiments, which use dominant negative forms of Hoxa4, Hoxa5, Hoxa6, and Hoxa7, are more challenging to interpret due to the absence of data on the specificity of the dominant negative constructs. Absent such controls, one cannot be certain that effects on limb development are due to disruption of the specific Hox proteins that are being targeted.

(2) A test of their central hypothesis regarding the necessity and sufficiency of the Hox genes under investigation would be to co-transfect the neck with full-length Hoxa6/a7 AND the dnHoxA4/a5. If their hypothesis is correct, then the dn constructs should block the limb-inducing ability of Hoxa6/a7 overexpression (again, validation of specificity of the DN constructs is important here).

(3) The paper could be strengthened by providing some additional data, which should already exist in their RNA-Seq dataset, such as supplementary material that shows the actual gene expression data that are represented in the Venn diagram, heatmap, and GO analysis in Figure 3.

(4) The results of these experiments in chick embryos are rather unexpected based on previous knockout experiments in mice, and this needs to be discussed.

Reviewer #1 (Public review):

Summary:

Fernandez et al. investigate the influence of maternal behavior on bat pup vocal development in Saccopteryx bilineata, a species known to exhibit vocal production learning. The authors performed detailed longitudinal observations of wild mother-pup interactions to ask whether non-vocal maternal displays during juvenile vocal practice or 'babbling', affect vocal production. Specifically, the study examines the durations of pup babbling events and the developmental babbling phase, in relation to the amount of female display behavior, as well as pup age and the number of nearby singing adult males. Furthermore, the authors examine pup vocal repertoire size and maturation in relation to the number of maternal displays encountered during babbling. Statistical models identify female display behavior as a predictor of i) babbling bout duration, ii) the length of the babbling phase, iii) song composition, and iv) syllable maturation. Notably, these outcomes were not influenced by the number of nearby adult males (the pups' source of song models) and were largely independent of general maturation (pup age). These findings highlight the impact of non-vocal aspects of social interactions in guiding mammalian vocal development.

Strengths:

Historically, work on developmental vocal learning has focused on how juvenile vocalizations are influenced by the sounds produced by nearby adults (often males). In contrast, this study takes the novel approach of examining juvenile vocal ontogeny in relation to non-vocal maternal behavior, in one of the few mammals known to exhibit vocal production learning. The authors collected an impressive dataset from multiple wild bat colonies in two Central American countries. This includes longitudinal acoustic recordings and behavioral monitoring of individual mother-pup pairs, across development.

The identified relationships between maternal behavior and bat pup vocalizations have intriguing implications for understanding the mechanisms that enable vocal production learning in mammals, including human speech acquisition. As such, these findings are likely to be relevant to a broad audience interested in the evolution and development of social behavior as well as sensory-motor learning.

Weaknesses:

The authors qualitatively describe specific patterns of female displays during pup babbling, however, subsequent quantitative analyses are based on two aggregate measures of female behavior that pool across display types. Consequently, it remains unclear how certain maternal behaviors might differentially influence pup vocalizations (e.g. through specific feedback contingencies or more general modulation of pup behavioral states).

In analyzing the effects of maternal behavior on song maturation, the authors focus on the most common syllable type produced across pups. This approach is justified based on the syllable variability within and across individuals, however, additional quantification and visual presentation of categorized syllable data would improve clarity and potentially strengthen resulting claims.

Reviewer #2 (Public review):

Summary:

This study explores how maternal behaviors influence vocal learning in the greater sac-winged bat (Saccopteryx bilineata). Over two field seasons, researchers tracked 19 bat pups from six wild colonies, examining vocal development aspects such as vocal practice duration, syllable repertoire size, and song syllable acquisition. The findings show that maternal behaviors significantly impact the length of daily babbling sessions and the overall babbling phase, while the presence of adult male tutors does not.

The researchers conducted detailed acoustic analyses, categorizing syllables and evaluating the variety and presence of learned song syllables. They discovered that maternal interactions enhance both the number and diversity of learned syllables and the production of mature syllables in the pups' vocalizations. A notable correlation was found between the extent of acoustic changes in the most common learned syllable type and maternal activity, highlighting the key role of maternal feedback in shaping pups' vocal development.

In summary, this study emphasizes the crucial role of maternal social feedback in the vocal development of S. bilineata. Maternal behaviors not only increase vocal practice but also aid in acquiring and refining a complex vocal repertoire. These insights enhance our understanding of social interactions in mammalian vocal learning and draw interesting parallels between bat and human vocal development.

Strengths:

This paper makes significant contributions to the field of vocal learning by looking at the role of maternal behaviors in shaping the vocal learning phenotype of Saccopteryx bilineata. The paper uses a longitudinal approach, tracking the vocal ontogeny of bat pups from birth to weaning across six colonies and two field seasons, allowing the authors to assess how maternal interactions influence various aspects of vocal practice and learning, providing strong empirical evidence for the critical role of social feedback in non-human mammalian vocal learners. This kind of evidence highlights the complexity of the vocal learning phenotype and shows that it goes beyond the right auditory experience and having the right circuitry.

The paper offers a nuanced understanding of how specific maternal behaviors impact the acquisition and refinement of the vocal repertoire, while showing the number of male tutors - the source of adult song - did not have much of an effect. The correlation between maternal activity and acoustic changes in learned syllable types is a novel finding that underscores the importance of non-vocal social interactions in vocal learning. In vocal learning research, with some notable exceptions, experience is often understood as auditory experience. This paper highlights how, even though that is one important piece of the puzzle, other kinds of experience directly affect the development of vocal behavior. This is of particular importance in the case of a mammalian species such as Saccopteryx bilineata, as this kind of result is perhaps more often associated with avian species.

Moreover, the study's findings have broader implications for our understanding of vocal learning across species. By drawing parallels between bat and human vocal development (and in some ways to bird vocal development), the paper highlights common mechanisms that may underlie vocal practice and learning in both humans and other mammals. This interdisciplinary perspective enriches the field and encourages further comparative studies, ultimately advancing our knowledge of the evolutionary and developmental processes that shape vocal productive learning in all its dimensions.

Weaknesses:

Some weaknesses can be pointed out, but in fairness, the authors acknowledge them in one way or another. As such, these are not flaws per se, but gaps that can be filled with further research.

Experimental manipulations, such as controlled playback experiments or controlled environments, could strengthen the causal claims by directly testing the effects of specific maternal behaviors on vocal development. Certainly, the strengths of the paper will be consolidated after such work is performed.

The reliance on the number of singing males as a proxy for social acoustic input. This measure does not account for the variability in the quality, frequency, or duration of the male songs to which the pups are exposed. A more detailed analysis of the acoustic environment, including direct measurements of song exposure and its impact on vocal learning, would provide a clearer understanding of the role of male tutors.

Finally, and although it would be unlikely that these results are unique to Saccopteryx bilineata, the study's focus on a single species limits at present the generalizability of some of its findings to other vocal learning mammals. While the parallels drawn between bat and human vocal development are intriguing, the conclusions will be more robust when supported by comparative studies involving multiple species of vocal learners. This will help to identify whether the observed maternal influences on vocal development reported here are unique to Saccopteryx bilineata or represent a broader phenomenon in chiropteran, mammalian, or general vocal learning. Expanding the scope of research to include a wider range of species and incorporating cross-species comparisons will significantly enhance the contribution of this study to the field of vocal learning.

Reviewer #1 (Public review):

Summary:

This study aimed at replicating two previous findings that showed (1) a link between prediction tendencies and neural speech tracking, and (2) that eye movements track speech. The main findings were replicated which supports the robustness of these results. The authors also investigated interactions between prediction tendencies and ocular speech tracking, but the data did not reveal clear relationships. The authors propose a framework that integrates the findings of the study and proposes how eye movements and prediction tendencies shape perception.

Strengths:

This is a well-written paper that addresses interesting research questions, bringing together two subfields that are usually studied in separation: auditory speech and eye movements. The authors aimed at replicating findings from two of their previous studies, which was overall successful and speaks for the robustness of the findings. The overall approach is convincing, methods and analyses appear to be thorough, and results are compelling.

Weaknesses:

Linking the new to the previous studies could have been done in more detail, and the extent to which results were replicated could have been discussed more thoroughly.

Eye movement behavior could have been presented in more detail and the authors could have attempted to understand whether there is a particular component in eye movement behavior (e.g., microsaccades) that drives the observed effects.

Reviewer #2 (Public review):

Summary

Schubert et al. recorded MEG and eye-tracking activity while participants were listening to stories in single-speaker or multi-speaker speech. In a separate task, MEG was recorded while the same participants were listening to four types of pure tones in either structured (75% predictable) or random (25%) sequences. The MEG data from this task was used to quantify individual 'prediction tendency': the amount by which the neural signal is modulated by whether or not a repeated tone was (un)predictable, given the context. In a replication of earlier work, this prediction tendency was found to correlate with 'neural speech tracking' during the main task. Neural speech tracking is quantified as the multivariate relationship between MEG activity and speech amplitude envelope. Prediction tendency did not correlate with 'ocular speech tracking' during the main task. Neural speech tracking was further modulated by local semantic violations in the speech material, and by whether or not a distracting speaker was present. The authors suggest that part of the neural speech tracking is mediated by ocular speech tracking. Story comprehension was negatively related to ocular speech tracking.

Strengths

This is an ambitious study, and the authors' attempt to integrate the many reported findings related to prediction and attention in one framework is laudable. The data acquisition and analyses appear to be done with great attention to methodological detail (perhaps even with too much focus on detail-see below). Furthermore, the experimental paradigm used is more naturalistic than was previously done in similar setups (i.e. stories instead of sentences).

Weaknesses

For many of the key variables and analysis choices (e.g. neural/ocular speech tracking, prediction tendency, mediation) it is not directly clear how these relate to the theoretical entities under study, and why they were quantified in this particular way. Relatedly, while the analysis pipeline is outlined in much detail, an overarching rationale and important intermediate results are often missing, which makes it difficult to judge the strength of the evidence presented. Furthermore, some analysis choices appear rather ad-hoc and should be made uniform and/or better motivated.

Reviewer #3 (Public review):

Summary:

In this paper, the authors measured neural activity (using MEG) and eye gaze while individuals listened to speech from either one or two speakers, which sometimes contained semantic incongruencies.

The stated aim is to replicate two previous findings by this group: (1) that there is "ocular speech tracking" (that eye-movements track the audio of the speech), (2) that individual differences in neural response to tones that are predictable vs. not-predictable in their pitch is linked to neural response to speech. In addition, here they try to link the above two effects to each other, and to link "attention, prediction, and active sensing".

Strengths:

This is an ambitious project, that tackles an important issue and combines different sources of data (neural data, eye-movements, individual differences in another task) in order to obtain a comprehensive "model" of the involvement of eye-movements in sensory processing.

The authors use many adequate methods and sophisticated data-analysis tools (including MEG source analysis and multivariate statistical models) in order to achieve this.

Weaknesses:

Although I sympathize with the goal of the paper and agree that this is an interesting and important theoretical avenue to pursue, I am unfortunately not convinced by the results and find that many of the claims are very weakly substantiated in the actual data.

Since most of the analyses presented here are derivations of statistical models and very little actual data is presented, I found it very difficult to assess the reliability and validity of the results, as they currently stand. I would be happy to see a thoroughly revised version, where much more of the data is presented, as well as control analyses and rigorous and well-documented statistical testing (including addressing multiple comparisons).

These are the main points of concern that I have regarding the paper, in its current format.

(1) Prediction tendencies - assessed by listening to sequences of rhythmic tones, where the pitch was either "predictable" (i.e., followed a fixed pattern, with 25% repetition) or "unpredictable" (no particular order to the sounds). This is a very specific type of prediction, which is a general term that can operate along many different dimensions. Why was this specific design selected? Is there theoretical reason to believe that this type of prediction is also relevant to "semantic" predictions or other predictive aspects of speech processing?

(2) On the same point - I was disappointed that the results of "prediction tendencies" were not reported in full, but only used later on to assess correlations with other metrics. Even though this is a "replication" of previous work, one would like to fully understand the results from this independent study. On that note, I would also appreciate a more detailed explanation of the method used to derive the "prediction tendency" metric (e.g, what portion of the MEG signal is used? Why use a pre-stimulus and not a post-stimulus time window? How is the response affected by the 3Hz steady-state response that it is riding on? How are signals integrated across channels? Can we get a sense of what this "tendency" looks like in the actual neural signal, rather than just a single number derived per participant (an illustration is provided in Figure 1, but it would be nice to see the actual data)? How is this measure verified statistically? What is its distribution across the sample? Ideally, we would want enough information for others to be able to replicate this finding).

(3) Semantic violations - half the nouns ending sentences were replaced to create incongruent endings. Can you provide more detail about this - e.g., how were the words selected? How were the recordings matched (e.g., could they be detected due to audio editing?)? What are the "lexically identical controls that are mentioned"? Also, is there any behavioral data to know how this affected listeners? Having so many incongruent sentences might be annoying/change the nature of listening. Were they told in advance about these?

(4) TRF in multi-speaker condition: was a univariate or multivariate model used? Since the single-speaker condition only contains one speech stimulus - can we know if univariate and multivariate models are directly comparable (in terms of variance explained)? Was any comparison to permutations done for this analysis to assess noise/chance levels?

(5) TRF analysis at the word level: from my experience, 2-second segments are insufficient for deriving meaningful TRFs (see for example the recent work by Mesik & Wojtczak). Can you please give further details about how the analysis of the response to semantic violations was conducted? What was the model trained on (the full speech or just the 2-second long segments?) Is there a particular advantage to TRFs here, relative - say - to ERPs (one would expect a relatively nice N400 response, not)? In general, it would be nice to see the TRF results on their own (and not just the modulation effects).

(6) Another related point that I did not quite understand - is the dependent measure used for the regression model "neural speech envelope tracking" the r-value derived just from the 2sec-long epochs? Or from the entire speech stimulus? The text mentions the "effect of neural speech tracking" - but it's not clear if this refers to the single-speaker vs. two-speaker conditions or to the prediction manipulation. Or is it different in the different analyses? Please spell out exactly what metric was used in each analysis.

Reviewer #1 (Public review):

Summary:

This is an interesting follow-up to a paper published in Human Molecular Genetics reporting novel roles in corticogenesis of the Kif7 motor protein that can regulate the activator as well as the repressor functions of the Gli transcription factors in Shh signalling. This new work investigates how a null mutation in the Kif7 gene affects the formation of corticofugal and thalamocortical axon tracts and the migration of cortical interneurons. It demonstrates that the Kif7 null mutant embryos present with ventriculomegaly and heterotopias as observed in patients carrying KIF7 mutations. The Kif7 mutation also disrupts the connectivity between the cortex and thalamus and leads to an abnormal projection of thalamocortical axons. Moreover, cortical interneurons show migratory defects that are mirrored in cortical slices treated with the Shh inhibitor cyclopamine suggesting that the Kif7 mutation results in a down-regulation of Shh signalling. Interestingly, these defects are much less severe at later stages of corticogenesis.

Strengths/weaknesses:

The findings of this manuscript are clearly presented and are based on detailed analyses. Using a compelling set of experiments, especially the live imaging to monitor interneuron migration, the authors convincingly investigate Kif7's roles and their results support their major claims. The migratory defects in interneurons and the potential role of Shh signalling present novel findings and provide some mechanistic insights but rescue experiments would further support Kif7's role in interneuron migration. Similarly, the mechanism underlying the misprojection which has previously been reported in other cilia mutants remains unexplored. Taken together, this manuscript makes novel contributions to our understanding of the role of primary cilia in forebrain development and to the aetiology of neural symptoms in ciliopathy patients.

Reviewer #2 (Public review):

Summary:

This study investigates the role of KIF7, a ciliary kinesin involved in the Sonic Hedgehog (SHH) signaling pathway, in cortical development using Kif7 knockout mice. The researchers examined embryonic cortex development (mainly at E14.5), focusing on structural changes and neuronal migration abnormalities.

Strengths:

(1) The phenotype observed is interesting, and the findings provide neurodevelopmental insight into some of the symptoms and malformations seen in patients with KIF7 mutations.

(2) The authors assess several features of cortical development, including structural changes in layers of the developing cortex, connectivity of the cortex with the thalamus, as well as migration of cINs from CGE and MGE to the cortex.

Weaknesses:

(1) The Kif7 null does have phenotype differences from individual mutations seen in patients. It would be interesting to add more thoughts about how the null differs from these mutants in ciliary structure and SHH signaling via the cilium.

(2) The description of altered cortex development at E14.5 is perhaps rather descriptive. It would be useful to assess more closely the changes occurring in different cell types and stages. For this it seems very important to have a time course of cortical development and how the structural organization changes over time. This would be easy to assess with the addition of serial sections from the same mice. It might also be interesting to see how SHH signaling is altered in different cortical cell types over time with a SHH signaling reporter mouse.

(3) Abnormal neurodevelopmental phenotypes have been widely reported in the absence of other key genes affecting primary cilia function (Willaredt et al., J Neurosci 2008; Guo et al., Nat Commun 2015). It would be interesting to have more discussion of how the Kif7 null phenotype compares to some of these other mutants.

(4) The authors see alterations in cIN migration to the cortex and observe distinct differences in the pattern of expression of Cxcl12 as well as suggest cell-intrinsic differences within cIN in their ability to migrate. The slice culture experiments though make it a little difficult to interpret the cell intrinsic effects on cIN of loss of Kif7, as the differences in Cxcl12 patterns still exist presumably in the slice cultures. It would be useful to assess their motility in an assay where they were isolated, as well as assess transcriptional changes in cINs in vivo lacking KIF7 for expression patterns that may affect motility or other aspects of migration.

Reviewer #1 (Public review):

This study offers a valuable investigation into the role of cholecystokinin (CCK) in thalamocortical plasticity during early development and adulthood, employing a range of experimental techniques. The authors demonstrate that tetanic stimulation of the auditory thalamus induces cortical long-term potentiation (LTP), which can be evoked through either electrical or optical stimulation of the thalamus or by noise bursts. They further show that thalamocortical LTP is abolished when thalamic CCK is knocked down or when cortical CCK receptors are blocked. Interestingly, in 18-month-old mice, thalamocortical LTP was largely absent but could be restored through the cortical application of CCK. The authors conclude that CCK contributes to thalamocortical plasticity and may enhance thalamocortical plasticity in aged subjects.

While the study presents compelling evidence, I would like to offer several suggestions for the authors' consideration:

(1) Thalamocortical LTP and NMDA-Dependence:<br /> It is well established that thalamocortical LTP is NMDA receptor-dependent, and blocking cortical NMDA receptors can abolish LTP. This raises the question of why thalamocortical LTP is eliminated when thalamic CCK is knocked down or when cortical CCK receptors are blocked. If I correctly understand the authors' hypothesis - that CCK promotes LTP through CCKR-intracellular Ca2+-AMPAR. This pathway should not directly interfere with the NMDA-dependent mechanism. A clearer explanation of this interaction would be beneficial.

(2) Complexity of the Thalamocortical System:<br /> The thalamocortical system is intricate, with different cortical and thalamic subdivisions serving distinct functions. In this study, it is not fully clear which subdivisions were targeted for stimulation and recording, which could significantly influence the interpretation of the findings. Clarifying this aspect would enhance the study's robustness.

(3) Statistical Variability:<br /> Biological data, including field excitatory postsynaptic potentials (fEPSPs) and LTP, often exhibit significant variability between samples, sometimes resulting in a standard deviation that exceeds 50% of the mean value. The reported standard deviation of LTP in this study, however, appears unusually small, particularly given the relatively limited sample size. Further discussion of this observation might be warranted.

(4) EYFP Expression and Virus Targeting:<br /> The authors indicate that AAV9-EFIa-ChETA-EYFP was injected into the medial geniculate body (MGB) and subsequently expressed in both the MGB and cortex. If I understand correctly, the authors assume that cortical expression represents thalamocortical terminals rather than cortical neurons. However, co-expression of CCK receptors does not necessarily imply that the virus selectively infected thalamocortical terminals. The physiological data regarding cortical activation of thalamocortical terminals could be questioned if the cortical expression represents cortical neurons or both cortical neurons and thalamocortical terminals.

(5) Consideration of Previous Literature:<br /> A number of studies have thoroughly characterized auditory thalamocortical LTP during early development and adulthood. It may be beneficial for the authors to integrate insights from this body of work, as reliance on data from the somatosensory thalamocortical system might not fully capture the nuances of the auditory pathway. A more comprehensive discussion of the relevant literature could enhance the study's context and impact.

(6) Therapeutic Implications:<br /> While the authors suggest potential therapeutic applications of their findings, it may be somewhat premature to draw such conclusions based on the current evidence. Although speculative discussion is not harmful, it may not significantly add to the study's conclusions at this stage.

Reviewer #2 (Public review):

Summary:

This work used multiple approaches to show that CCK is critical for long-term potentiation (LTP) in the auditory thalamocortical pathway. They also showed that the CCK mediation of LTP is age-dependent and supports frequency discrimination. This work is important because it opens up a new avenue of investigation of the roles of neuropeptides in sensory plasticity.

Strengths:

The main strength is the multiple approaches used to comprehensively examine the role of CCK in auditory thalamocortical LTP. Thus, the authors do provide a compelling set of data that CCK mediates thalamocortical LTP in an age-dependent manner.

Weaknesses:

The behavioral assessment is relatively limited but may be fleshed out in future work.

Reviewer #3 (Public review):

Summary:

Cholecystokinin (CCK) is highly expressed in auditory thalamocortical (MGB) neurons and CCK has been found to shape cortical plasticity dynamics. In order to understand how CCK shapes synaptic plasticity in the auditory thalamocortical pathway, they assessed the role of CCK signaling across multiple mechanisms of LTP induction with the auditory thalamocortical (MGB - layer IV Auditory Cortex) circuit in mice. In these physiology experiments that leverage multiple mechanisms of LTP induction and a rigorous manipulation of CCK and CCK-dependent signaling, they establish an essential role of auditory thalamocortical LTP on the co-release of CCK from auditory thalamic neurons. By carefully assessing the development of this plasticity over time and CCK expression, they go on to identify a window of time that CCK is produced throughout early and middle adulthood in auditory thalamocortical neurons to establish a window for plasticity from 3 weeks to 1.5 years in mice, with limited LTP occurring outside of this window. The authors go on to show that CCK signaling and its effect on LTP in the auditory cortex is also capable of modifying frequency discrimination accuracy in an auditory PPI task. In evaluating the impact of CCK on modulating PPI task performance, it also seems that in mice <1.5 years old CCK-dependent effects on cortical plasticity are almost saturated. While exogenous CCK can modestly improve discrimination of only very similar tones, exogenous focal delivery of CCK in older mice can significantly improve learning in a PPI task to bring their discrimination ability in line with those from young adult mice.

Strengths:

(1) The clarity of the results along with the rigor multi-angled approach provide significant support for the claim that CCK is essential for auditory thalamocortical synaptic LTP. This approach uses a combination of electrical, acoustic, and optogenetic pathway stimulation alongside conditional expression approaches, germline knockout, viral RNA downregulation, and pharmacological blockade. Through the combination of these experimental configures the authors demonstrate that high-frequency stimulation-induced LTP is reliant on co-release of CCK from glutamatergic MGB terminals projecting to the auditory cortex.

(2) The careful analysis of the CCK, CCKB receptor, and LTP expression is also a strength that puts the finding into the context of mechanistic causes and potential therapies for age-dependent sensory/auditory processing changes. Similarly, not only do these data identify a fundamental biological mechanism, but they also provide support for the idea that exogenous asynchronous stimulation of the CCKBR is capable of restoring an age-dependent loss in plasticity.

(3) Although experiments to simultaneously relate LTP and behavioral change or identify a causal relationship between LTP and frequency discrimination are not made, there is still convincing evidence that CCK signaling in the auditory cortex (known to determine synaptic LTP) is important for auditory processing/frequency discrimination. These experiments are key for establishing the relevance of this mechanism.

Weaknesses:

(1) Given the magnitude of the evoked responses, one expects that pyramidal neurons in layer IV are primarily those that undergo CCK-dependent plasticity, but the degree to which PV-interneurons and pyramidal neurons participate in this process differently is unclear.

(2) While these data support an important role for CCK in synaptic LTP in the auditory thalamocortical pathway, perhaps temporal processing of acoustic stimuli is as or more important than frequency discrimination. Given the enhanced responsivity of the system, it is unclear whether this mechanism would improve or reduce the fidelity of temporal processing in this circuit. Understanding this dynamic may also require consideration of cell type as raised in weakness #1.

(3) In Figure 1, an example of increased spontaneous and evoked firing activity of single neurons after HFS is provided. Yet it is surprising that the group data are analyzed only for the fEPSP. It seems that single-neuron data would also be useful at this point to provide insight into how CCK and HFS affect temporal processing and spontaneous activity/excitability, especially given the example in 1F.

(4) The authors mention that CCK mRNA was absent in CCK-KO mice, but the data are not provided.

(5) The circuitry that determines PPI requires multiple brain areas, including the auditory cortex. Given the complicated dynamics of this process, it may be helpful to consider what, if anything, is known specifically about how layer IV synaptic plasticity in the auditory cortex may shape this behavior.

Reviewer #1 (Public review):

Lu et. al. proposed here a direct role of LPS in inducing hepatic fat accumulation and that the metabolism of LPS therefore can mitigate fatty liver injury. With an Acyloxyacyl hydrolase whole-body KO mice, they demonstrated that Acyloxyacyl hydrolase deletion resulted in higher hepatic fat accumulation over 8 months of high glucose/high fructose diet. Previous literature has found that hepatocyte TLR4 (which is a main receptor for binding LPS) KO reduced fatty liver in the MAFLD model, and this paper complements this by showing that degradation/metabolism of LPS can also reduce fatty liver. This result proposed a very interesting mechanism and the translational implications of utilizing Acyloxyacyl hydrolase to decrease LPS exposure are intriguing.

The strengths of the present study include that they raised a very simplistic mechanism with LPS that is of interest in many diseases. The phenotype shown in the study is strong. The mechanism proposed by the findings is generally well supported.

There are also several shortcomings in the findings of this study. As AOAH is a whole-body KO, the source production of AOAH in MAFLD is unclear. Although the authors used published single-cell RNA-seq data and flow-isolated liver cells, physiologically LPS degradation could occur in the blood or the liver. The authors linked LPS to hepatocyte fatty acid oxidation via SREBP1. The mechanism is not explored in great depth. Is this signaling TLR4? In this model, LPS could activate macrophages and mediate the worsening of hepatocyte fatty liver injury via the paracrine effect instead of directly signaling to hepatocytes, thus it is not clear that this is a strictly hepatocyte LPS effect. It would also be very interesting to see if the administration of the AOAH enzyme orally could mitigate MAFLD injury. Overall, this work adds to the current understanding of the gut-liver axis and development of MAFLD and will be of interest to many readers.

Reviewer #2 (Public review):

The authors of this article investigated the impact of the host enzyme AOAH on the progression of MASLD in mice. To achieve this, they utilized whole-body Aoah-/- mice. The authors demonstrated that AOAH reduced LPS-induced lipid accumulation in the liver, probably by decreasing the expression and activation of SREBP1. In addition, AOAH reduced hepatic inflammation and minimized tissue damage.

However, this paper is descriptive without a clear mechanistic study. Another major limitation is the use of who-body KO mice so the cellular source of the enzyme remains undefined. Moreover, since LPS-mediated SREBP1 regulation or LPS-mediated MASLD progression is already documented, the role of AOAH in SREBP1-dependent lipid accumulation and MASLD progression is largely expected.

Specific comments:

(1) The overall human relevance of the current study remains unclear.

(2) Is AOAH secreted from macrophages or other immune cells? Are there any other functions of AOAH within the cells?

(3) Due to using whole-body KO mice, the role of AOAH in specific cell types was unclear in this study, which is one of the major limitations of this study. The authors should at least conduct in vitro experiments using a co-culture system of hepatocytes and Kupffer cells (or other immune cells) isolated from WT or Aoah-/- mice.

(4) It has been well-known that intestinal tight junction permeability is increased by LPS or inflammatory cytokines. However, in Figure 3E, intestinal permeability is comparable between the groups in both diet groups. The authors should discuss more about this result. In addition, intestinal junctional protein should be determined by Western blot and IHC (or IF) to further confirm this finding.

(5) In Figure 6, LPS i.g. Aoah-/- group is missing. This group should be included to better interpret the results.

(6) The term NAFLD has been suggested to be changed to MASLD as the novel nomenclature according to the guidelines of AASLD and EASL.

Reviewer #1 (Public review):

Summary:

This is by far the phylogenetic analysis with the most comprehensive coverage for the Nemacheilidae family in Cobitoidea. It is a much-lauded effort. The conclusions derived using phylogenetic tools coincide with geological events, though not without difficulties (Africa pathway).

Strengths:

Comprehensive use of genetic tools

Weaknesses:

Lack of more fossil records.

Reviewer #2 (Public review):

Summary:

The authors present the results of molecular phylogenetic analysis with very comprehensive samplings including 471 specimens belonging to 250 species, trying to give a holistic reconstruction of the evolutionary history of freshwater fishes (Nemacheilidae) across Eurasia since the early Eocene. This is of great interest to general readers.

Strengths:

They provide very vast data and conduct comprehensive analyses. They suggested that Nemacheilidae contain 6 major clades, and the earliest differentiation can be dated to the early Eocene.

Weaknesses:

The analysis is incomplete, and the manuscript discussion is not well organized. The authors did not discuss the systematic problems that widely exist. They also did not use the conventional way to discuss the evolutionary process of branches or clades, but just chronologically described the overall history.

Reviewer #1 (Public review):

Summary:

The article provides valuable information on the role of CCR4 in an inflammatory condition, namely, the arteriosclerosis plaque. The data demonstrated that in the absence of CCR4, the Th1 cells infiltrated the plaque and Tregs lost its functions. The data are clear and well-presented. Mostly importantly, the data on CCR4-specific deficiency in Regulatory T cells is more impressive.

Strengths:

The data are clear, well performed, and interesting in focusing on the plaque and compared to peripheral organs. The disease is relevant and the data could be used to understand the risk of patients under immunomodulator use.

Weaknesses:

Still, we don't know the mechanism, besides migration.

Reviewer #2 (Public review):

Summary:

Tanaka et al. investigated the role of CCR4 in early atherosclerosis, focusing on the immune modulation elicited by this chemokine receptor under hypercholesterolemia. The study found that Ccr4 deficiency led to qualitative changes in atherosclerotic plaques, characterized by an increased inflammatory phenotype. The authors further analyzed the CD4 T cell immune response in para-aortic lymph nodes and atherosclerotic aorta, showing an increase mainly in Th1 cells and the Th1/Treg ratio in Ccr4-/-Apoe-/- mice compared to Apoe-/- mice. They then focused on Tregs, demonstrating that Ccr4 deficiency impaired their immunosuppressive function in in-vitro assays and elegantly showed that Ccr4-deficient Tregs had, as expected, impaired migration to the atherosclerotic aorta. Adoptive cell transfer of Ccr4-/- Tregs to Apoe-/- mice mimicked early atherosclerosis development in Ccr4-/-Apoe-/- mice. Therefore, this work shows that CCR4 plays an important role in early atherosclerosis but not in advanced stages.

Strengths:

Several in vivo and in vitro approaches were used to address the role of CCR4 in early atherosclerosis. Particularly, through the adoptive cell transfer of CCR4+ or CCR4- Tregs, the authors aimed to directly demonstrate the role of CCR4 in Tregs' protection against early atherosclerosis.

Weaknesses:

The isolation of Tregs was inadequately controlled; they were isolated based solely on CD4 and CD25 expression. CD25 is also expressed by activated effector T cells, meaning the analyzed cells could be a pool of mainly Tregs but also include effector T cells.

The study primarily focused on Th1 and Tregs without thoroughly investigating other CD4 T cell subsets. Th17 cells are known to play an important role in atherosclerosis; non-pathogenic Th17 cells express CCR4, while pathogenic Th17 cells do not. Considering that Figure 3 shows an increased frequency of IL17-expressing CD4 T cells compared to Apoe-/- mice, and given the imprecise Treg isolation, differences in non-pathogenic Th17 cells could be contributing to the observed effects.

Furthermore, the clinical relevance of these findings is not discussed. As an initial approach, the authors could analyze public datasets to determine if certain Ccr4 single nucleotide polymorphisms correlate with a higher incidence of atherosclerosis.

Reviewer #3 (Public review):

Summary:

In this paper, Tanaka and colleagues address the role played by the C-C chemokine receptor 4 (CCR4) in developing early atherosclerotic plaques using ApoE-deficient mice fed with a standard chow diet as a model. Since CCR4 is expressed in several T CD4+ lymphocyte subsets, the authors examined the consequences of CCR4 deficiency on the differentiation profile and traffic of T CD4+ lymphocytes. By histological analysis of aortic lesions, they demonstrated that the absence of CCR4 promoted the development of early atherosclerosis, characterized by an inflammatory reaction with increased levels of macrophages and T CD4+ inflammatory lymphocytes while decreased collagen content. Using flow cytometry together with mRNA expression analysis for identifying T CD4+ cell subsets, the authors found that the accelerated aortic inflammation induced by CCR4 deficiency correlated with higher proliferation of T CD4+ cells in lymphoid tissues, favouring the expansion of the pro-inflammatory effector Th1 cell subset, typically found in atherosclerotic lesions. Interestingly, the increased T CD4+ cell response occurred despite the expansion of T CD4+ Foxp3+ regulatory cells (Treg), which were in higher numbers in the lymphoid tissues of CCR4-deficient mice, suggesting the absence of CCR4 interfered with the regulatory actions of Treg cells. Using in vitro and or in vivo approaches, the authors found evidence of CCR4 requirement for Treg suppressive activity and migratory capacity to inflamed aortic areas, contributing to why CCR4 deficiency induced an augmented Th1/Treg ratio in the aortic lesions. These findings might not be surprising considering the demonstrated involvement of CCR4 in driving Treg migration to inflamed tissues in immune-related pathological models and Treg-dendritic cell contact for imprinting suppressive signals. However, in previous studies using a murine model of advanced atherosclerosis, neither hematopoietic nor systemic CCR4 deficiency altered the development of the aortic lesions. The authors included a thoughtful discussion about hypothetical mechanisms explaining these contrasting results, highlighting putative differences in the role played by the CCL17/CCL22-CCR4 axis along the stages of atherosclerosis development in this murine model.

Major strengths and weaknesses:

The main effects of CCR4 deficiency on early atherosclerosis development and Treg functional loss are valuable and supported by collected data. In vivo studies for comparing Treg-tissue accumulation or atherosclerotic lesions in Apoe-/- mice that received Treg derived from Apoe-/- or Apoe-/-Ccr4-/- mice, strengthening results. However, an incomplete description of methods (particularly flow cytometry) and data analysis weakens some conclusions of this study. Readers should note some inconsistencies in the T CD4+ response analysis in different tissues. In aortic lesions, but not in lymphoid tissues (peripheral, para-aortic, and spleen), the ratio Th1/Treg was used for evaluating the effect of CCR4 deficiency on the profile of Th cell subsets. In lymphoid tissues, increments in the frequency of both effector Th1 and Treg were observed in CCR4-deficient Apoe-/- mice compared to CCR4-sufficient Apoe-/- mice. Therefore, it is not convincing that CCR4-deficiency shifts Th1 cell/Treg balance toward Th1 cell responses in all lymphoid tissues; this claim needs to be revised by the authors. The Treg dysfunction, caused by CCR4 deficiency, enhanced T CD4+ activation and might have amplified rather than shifted, the typical biased Th1-mediated inflammatory response observed in the lymphoid tissues of hypercholesterolemic mice. A different scenario emerged in aortic lesions, where recruitment of effector Th1 cells, but not of additional effector T CD4+ cell subsets expanded in lymphoid tissues, leading to a higher Th1/Treg balance. Also, effector Th17 cells seem to predominate among effector TCD45+CD3+CD4+ cells in the aorta of Apoe-/- mice, and the Th1/Th17 balance appears to have increased as a consequence of CCR4 deficiency as well. Modulation of Th1/Th17 balance might be responsible for changes in the type and functional properties of recruited inflammatory cells in the aorta.

Study limitations:

This investigation has some limitations. Current tools for single-cell characterization have revealed the phenotypic heterogeneity and dynamics of aortic leukocytes, including T cells, which are among the principal aortic leukocytes found in mouse and human atherosclerotic lesions (doi:10.1161/CIRCRESAHA.117.312513). The flow cytometry analysis applied in this study cannot distinguish the generation of particular phenotypes within T CD4+ subsets, including putative phenotypes of no-suppressive T cells expressing low levels of Foxp3, as seems could occur in other chronic inflammatory disorders (doi: 10.1038/nm.3432; doi: 10.1172/JCI79014). Limitations due to the use of a complete CCR4 knockout mouse and putative differences in CCR4-mediated mechanisms along atherosclerosis stages and in human atherosclerosis were commented on by the authors in the discussion.

Global Impact

This work opens the way for a deeper analysis of the contribution of CCR4 and its ligands to the activation and differentiation of T CD4+ lymphocytes during atherosclerosis development, with these lymphocytes being fundamental players in the generation of pro-atherogenic and anti-atherogenic immune responses. Differences in the mechanisms mediated by the CCL17/CCL22-CCR4 axis among early and advanced atherosclerosis highlight the complex landscape to examine and validate in human samples and the need to achieve a deep knowledge for identifying genuine and safe targets capable of promoting protective anti-atherogenic immune responses.

Reviewer #1 (Public review):

Summary:

In this important paper the authors investigate the temporal dynamics of expectation of pain using a combined fMRI-EEG approach. More specifically, by modifying the expectations of higher or lower pain on a trial-to- trial basis they report that expectations largely share the same set of activations before the administration of the painful stimulus and that the coding of the valence of the stimulus is observed only after the nociceptive input has been presented. fMRI informed EEG analysis suggested that the temporal sequence of information processing involved the Dorsolateral prefrontal cortex (DLPFC), the anterior insula and the anterior cingulate cortex. The strength of evidence is convincing, the methods are solid, but a few alternative interpretations about the findings related to the control group, as well as a more in depth discussion on the correlations between the BOLD and EEG signals would strengthen the manuscript.

Strengths:

In line with open science principles, the article presents the data and the results in a complete and transparent fashion.<br /> On the theoretical standpoint, the authors make a step forward in our understanding of how expectations modulate pain by introducing a combination of spatial and temporal investigation. It is becoming increasingly clear that our appraisal of the world is dynamic, guided by previous experiences and mapped on a combination of what we expect and what we get. New research methods, questions and analyses are needed to capture this evolving process.

Weaknesses:

The authors have addressed my concerns about the control condition and made some adjustments, namely acknowledging that participants cannot be "expectations" free and investigating whether scores in the control condition are simply due to a "regression to the mean".

General considerations and reflections

Inducing expectations in the desired direction is not a straightforward task, and results might depend on the exact experimental conditions and the comparison group. In this sense, the authors choice of having 3 groups of positive, negative and "neutral" expectations is to be praised. On the other hand, also control groups form their expectations, and this can constitute a confounder in every experiment using expectation manipulation, if not appropriately investigated. The authors have addressed this element in their revised submission.

In addition, although fMRI is still (probably) the best available tool we have to understand the spatial representation of cortical processing, limitations about not only the temporal but even the spatial resolution should be acknowledged. This has been done. Given the anatomical and physiological complexity of the cortical connections, as we know from the animal world, it is still well possible that sub circuits are activated also for positive and negative expectations, but cannot be observed due to the limitation of our techniques. Indeed, on an empirical/evolutionary bases, it would remain unclear why we should have a system that waits for the valence of a stimulus to show differential responses.<br /> Also, moving in a dimension of network and graph theory, one would not expect single areas to be responsible for distinct processes, but rather that they would more integrate information in a shared way, potentially with different feedback and feedforward communications. As such, it becomes more difficult to assume the insula as a center for coding potential pain, perhaps more of a node in a system that signals potential dangers for the integrity of the body.<br /> The rationale for the choice of their EEG band has been outlined.

Reviewer #2 (Public review):

I appreciate the authors' thorough revision of the manuscript, which has significantly improved its quality. I have no additional comments or requests for further changes.

However, I remain in slight disagreement regarding the characterization of the neutral condition. My perspective is that it resembles more of a "medium" condition, making it challenging to understand what would be common to "high-medium" and "low-medium" contrasts. I suspect that the neutral condition might represent a state of high uncertainty since participants are informed that the algorithm cannot provide a prediction. From this viewpoint, the observed similarities in effects for both positive and negative expectations may actually reflect differences between certainty and uncertainty rather than the specific expectations themselves.

Nevertheless, the authors have addressed alternative interpretations of their discussion section, and I have no further requests. The paper is well-executed and demonstrates several strengths: the procedure effectively induced varying levels of expectations with clear impacts on pain ratings. Additionally, the integration of fMRI with EEG is commendable for tracking the transition from anticipatory to pain periods. Overall, the manuscript is strong and contributes valuable insights to the field.

Reviewer #1 (Public review):

Summary:

In this paper, Misic et al showed that white matter properties can be used to classify subacute back pain patients that will develop persisting pain.

Strengths:

Compared to most previous papers studying associations between white matter properties and chronic pain, the strength of the method is to perform a prediction in unseen data. Another strength of the paper is the use of three different cohorts. This is an interesting paper that provides a valuable contribution to the field.

Weaknesses:

The main weakness of this study is the sample size. It remains small despite having 3 cohorts. This is problematic because results are often overfitted in such a small sample size brain imaging study, especially when all the data are available to the authors at the time of training the model (Poldrack et al., Scanning the horizon: towards transparent and reproducible neuroimaging research, Nature Reviews in Neuroscience 2017). Thus, having access to all the data, the authors have a high degree of flexibility in data analysis, as they can retrain their model any number of time until it generalizes across all three cohorts. In this case, the testing set could easily become part of the training making it difficult to assess the real performance, especially for small sample size studies.

Even if the performance was properly assessed their models show AUCs between 0.65-0.70, which is usually considered as poor, and most likely without potential clinical use. Despite this, their conclusion was: "This biomarker is easy to obtain (~10 min 18 of scanning time) and opens the door for translation into clinical practice." One may ask who is really willing to use an MRI signature with a relatively poor performance that can be outperformed by self-report questionnaires?

Overall, these criticisms are more about the wording sometimes use and the inference they made. I still think this is a very relevant contribution to the field. Showing predictive performance through cross validation and testing in multiple cohorts is not an easy task and this is a strong effort by the team. I strongly believe this approach is the right one and I believe the authors did a good job.

Reviewer #2 (Public review):

The present study aims to investigate brain white matter predictors of back pain chronicity. To this end, a discovery cohort of 28 patients with subacute back pain (SBP) was studied using white matter diffusion imaging. The cohort was investigated at baseline and one-year follow-up when 16 patients had recovered (SBPr) and 12 had persistent back pain (SBPp). A comparison of baseline scans revealed that SBPr patients had higher fractional anisotropy values in the right superior longitudinal fasciculus SLF) than SBPp patients and that FA values predicted changes in pain severity. Moreover, the FA values of SBPr patients were larger than those of healthy participants, suggesting a role of FA of the SLF in resilience to chronic pain. These findings were replicated in two other independent datasets. The authors conclude that the right SLF might be a robust predictive biomarker of CBP development with the potential for clinical translation.<br /> Developing predictive biomarkers for pain chronicity is an interesting, timely, and potentially clinically relevant topic. The paradigm and the analysis are sound, the results are convincing, and the interpretation is adequate. A particular strength of the study is the discovery-replication approach with replications of the findings in two independent datasets.

Reviewer #3 (Public review):

Summary:

The authors suggest a new biomarker of chronic back pain with an option to predict a result of treatment.

Strengths:

The results were reproduced in three studies.

Weaknesses:

The number of participants is still low, an explanation of microstructure changes was not given, and some technical drawbacks are presented.

Reviewer #1 (Public review):

Summary:

The authors intended to investigate the earliest mechanisms enabling self-prioritization, especially in the attention. Combining a temporal order judgement task with computational modelling based on the Theory of Visual Attention (TVA), the authors suggested that the shapes associated with the self can fundamentally alter the attentional selection of sensory information into awareness. This self-prioritization in attentional selection occurs automatically at early perceptual stages. Furthermore, the processing benefits obtained from attentional selection via self-relatedness and physical salience were separated from each other.

Strengths:

The manuscript is written in a way that is easy to follow. The methods of the paper are very clear and appropriate.

Weaknesses:

There are two main concerns:

(1) The authors had a too strong pre-hypothesis that self-prioritization was associated with attention. They used the prior entry to consciousness (awareness) as an index of attention, which is not appropriate. There may be other processing that makes the stimulus prior to entry to consciousness (e.g. high arousal, high sensitivity), but not attention. The self-related/associated stimulus may be involved in such processing but not attention to make the stimulus easily caught. Perhaps the authors could include other methods such as EEG or MEG to answer this question.

(2) The authors suggested that there are two independent attention processes. I suspect that the brain needs two attention systems. Is there a probability that the social and perceptual (physical properties of the stimulus) salience fired the same attention processing through different processing?

Reviewer #2 (Public review):

Summary:

The main aim of this research was to explore whether and how self-associations (as opposed to other associations) bias early attentional selection, and whether this can explain well-known self-prioritization phenomena, such as the self-advantage in perceptual matching tasks. The authors adopted the Visual Attention Theory (VAT) by estimating VAT parameters using a hierarchical Bayesian model from the field of attention and applied it to investigate the mechanisms underlying self-prioritization. They also discussed the constraints on the self-prioritization effect in attentional selection. The key conclusions reported were:

(1) Self-association enhances both attentional weights and processing capacity

(2) Self-prioritization in attentional selection occurs automatically but diminishes when active social decoding is required, and

(3) Social and perceptual salience capture attention through distinct mechanisms.

Strengths:

Transferring the Theory of Visual Attention parameters estimated by a hierarchical Bayesian model to investigate self-prioritization in attentional selection was a smart approach. This method provides a valuable tool for accessing the very early stages of self-processing, i.e., attention selection. The authors conclude that self-associations can bias visual attention by enhancing both attentional weights and processing capacity and that this process occurs automatically. These findings offer new insights into self-prioritization from the perspective of the early stage of attentional selection.

Weaknesses:

(1) The results are not convincing enough to definitively support their conclusions. This is due to inconsistent findings (e.g., the model selection suggested condition-specific c parameters, but the increase in processing capacity was only slight; the correlations between attentional selection bias and SPE were inconsistent across experiments), unexpected results (e.g., when examining the impact of social association on processing rates, the other-associated stimuli were processed faster after social association, while the self-associated stimuli were processed more slowly), and weak correlations between attentional bias and behavioral SPE, which were reported without any p-value corrections. Additionally, the reasons why the attentional bias of self-association occurs automatically but disappears during active social decoding remain difficult to explain. It is also possible that the self-association with shapes was not strong enough to demonstrate attention bias, rather than the automatic processes as the authors suggest. Although these inconsistencies and unexpected results were discussed, all were post hoc explanations. To convince readers, empirical evidence is needed to support these unexpected findings.

(2) The generalization of the findings needs further examination. The current results seem to rely heavily on the perceptual matching task. Whether this attentional selection mechanism of self-prioritization can be generalized to other stimuli, such as self-name, self-face, or other domains of self-association advantages, remains to be tested. In other words, more converging evidence is needed.

(3) The comparison between the "social" and "perceptual" tasks remains debatable, as it is challenging to equate the levels of social salience and perceptual salience. In addition, these two tasks differ not only in terms of social decoding processes but also in other aspects such as task difficulty. Whether the observed differences between the tasks can definitively suggest the specificity of social decoding, as the authors claim, needs further confirmation.

Reviewer #1 (Public review):

Summary:

Liu et al., present an immersion objective adapter design called RIM-Deep, which can be utilized for enhancing axial resolution and reducing spherical aberrations during inverted confocal microscopy of thick cleared tissue.

Strengths:

RI mismatches present a significant challenge to deep tissue imaging, and developing a robust immersion method is valuable in preventing losses in resolution. Liu et al., present data showing that RIM-Deep is suitable for tissue cleared with two different clearing techniques, demonstrating the adaptability and versatility of the approach.

Weaknesses:

Liu et al., claim to have developed a useful technique for deep tissue imaging, but in its current form, the paper does not provide sufficient evidence that their technique performs better than existing ones.

Reviewer #2 (Public review):

Summary:

Liu et al investigated the performance of a novel imaging technique called RIM-Deep to enhance the imaging depth for cleared samples. Usually, the imaging depth using the classical confocal microscopy sample chamber is limited due to optical aberrations, resulting in loss of resolution and image quality. To overcome this limitation and increase depth, they generated a special imaging chamber, that is affixed to the objective and filled with a solution matching the refractive indices to reduce aberrations. Importantly, the study was conducted using a standard confocal microscope, that has not been modified apart from exchanging the standard sample chamber with the RIM-Deep sample holder. Upon analysing the imaging depth, the authors claim that the RIM-Deep method increased the depth from 2 mm to 5 mm. In summary, RIM-Deep has the potential to significantly enhance imaging quality of thick samples on a low budget, making in-depth measurements possible for a wide range of researchers that have access to an inverted confocal microscope.

Strengths:

The authors used different clearing methods to demonstrate the suitability of RIM-Deep for various sample preparation protocols with clearing solutions of different refractive indices. They clearly demonstrate that the RIM-Deep chamber is compatible with all 3 methods. Brain samples are characterized by complex networks of cells and are often hard to visualize. Despite the dense, complex structure of brain tissue, the RIM-Deep method generated high quality images of all 3 samples given. As the authors already stated, increasing imaging depth often goes hand in hand with purchasing expensive new equipment, exchanging several microscopy parts or purchasing a new microscopy set-up. Innovations, such as the RIM-Deep chamber, hence, might pave the way for cost-effective imaging and expand the applicability of an inverted confocal microscope.

Weaknesses:

(1) However, since this study introduces a novel imaging technique, and therefore, aims to revolutionize the way of imaging large samples, additional control experiments would strengthen the data. From the 3 clearing protocol used (CUBIC, MACS and iDISCO), only the brain section from Macaca fascicularis cleared with iDISCO was imaged with the standard chamber and the RIM-Deep method. This comparison indeed shows that the imaging depth thereby increases more than 2-fold, which is a significant enhancement in terms of microscopy. However, it would have been important to evaluate and show the difference of the imaging depth also on the other two samples, since they were cleared with different protocols and, thus, treated with clearing solutions of different refractive indices compared to iDCISCO.

(2) The description of the figures and figure panels should be improved for a better understanding of the experiments performed and the thus resulting images/data.

(3) While the authors used a Nikon AX inverted laser scanning confocal microscope, the study would highly benefit from evaluating the performance of the RIM-Deep method using other inverted confocal microscopes or even wide-field microscopes.

Reviewer #1 (Public review):

The authors have successfully addressed most of the issues raised in the first review. Nevertheless, some of the mentioned problems require further attention, mostly regarding the formal derivation of the learning rules, as well as connections to previous research.

Regarding the derivations of learning rules: The authors have provided Goal functions for each of the plastic neural connections to give some insight into what these connections do. However, as I understand, this does not address the main concern raised in the previous review: Why do these rules lead to overall network dynamics that sample from the input distribution? Virtually all other work on neural sampling that I am aware of (e.g., from Maass Lab, Lengyel Lab, etc.) start from a single goal function for all connections that somehow quantifies the difference of network dynamics from the target distribution. In the presented work the authors specify different goal functions for the different weights, which does not make clear how the desired network dynamics are ultimately achieved.

This becomes especially evident looking at the two different recurrent connections (M and G). M minimizes the difference between network activity f and recurrent prediction DKL[f|phi(My)], but why is this alone not enough to ensure a good sampling? G minimizes the squared error [f-phi(Gy)]^2, but what does that mean? The problem is that the goal functions are self-consistent in the sense that both f and phi(Gy) depend on G, which makes an interpretation very difficult. Ultimately it's easier to interpret this by looking at the plasticity rule and see that it leads to a balance. For G the authors furthermore actually ignore the derived plasticity rule and switch to a rule similar to the one for M, meaning that the actual goal function for G is also something like DKL[f|phi(Gy)]. Overall, an overarching optimization goal for the entire network is missing, which makes the interpretation very difficult. I understand that this might be very difficult to provide at this stage, but the authors should at least point out this shortcoming as an open question for the proposed framework.

Regarding the relation to previous work the authors have provided a lot more detailed discussion, which very much clears up the contributions and novel ideas in their work. Still, there are some claims that are not consistent with the literature. Especially, in lines 767 ff. the authors state that Kappel et al "assumed plasticity only at recurrent synapses projecting onto the excitatory neurons. In addition, unlike our model, the cell assembly memberships need to be preconfigured in the [...] model." This is not correct, as Kappel et al learn both the feed-forward and recurrent connections, hence the main difference is that in Kappel et al sampling is sequential and not random. This is why I mentioned this work in the first review, as it speaks against the authors claims of novelty (719 ff.), which should be adjusted accordingly.

Reviewer #2 (Public review):

Summary:

The paper reconsiders the formation of Hebbian-type assemblies, with their spontaneous reactivation representing the statistics of the sensory inputs, in the light of predictive synaptic plasticity. It convincingly shows that not all plasticity rules can be predictive in the narrow sense. While plasticity for the excitatory synapses (the forward projecting and recurrent ones) are predictive, two types of plasticity in the recurrent inhibition is required: a homeostatic and competitive one.

Details:

Besides the excitatory forward and recurrent connections that are learned based on predictive synaptic plasticity, two types of inhibitory plasticity are considered. A first type of inhibition is homeostatic and roughly balances excitation within the cell assemblies. Plasticity in this type 1 inhibition is also predictive, analogous to the plasticity of the excitatory synapses. However, plasticity in type 2 inhibition is competitive and has a switched sign. Both types of inhibitory plasticity, the predictive (homeostatic) and the anti-predictive (competitive) one, work together with the predictive excitatory plasticity to form cell assemblies representing sensory stimuli. Only if the two types of homeostatic and competitive inhibitory plasticity are present, will the spontaneous replay of the assemblies reflect the statistics of the stimulus presentation.

Critical review:

The simulations include Dale's law, making them more biologically realistic. The paper emphasizes predictive plasticity and introduces type 1 inhibitory plasticity that, by construction, tries to fully explain away the excitatory input. In the absence of external inputs, however, due to the symmetry between the excitatory and inhibitory-type-1 plasticity rules, excitation and inhibition tend to fully cancel each other. Multiple options may solve the dilemma:

(1) As other predictive dendritic plasticity models assume, the presynaptic source for recurrent inhibition is typically less informative than the presynaptic source of excitation, so that inhibition is not able to fully explain away excitation.

(2) Beside the inhibitory predictive plasticity that mirrors the analogous excitatory predictive plasticity, and additional competitive plasticity can be introduced.

The paper chooses solution (2) and suggests and additional inhibitory recurrent pathway that is not predictive, but instead anti-predictive with a reversed sign. The combination of the two types of inhibitory plasticities lead to a stable formation of cell assemblies. The stable target activity of the plasticity rules in a memory recall is not anymore 0, as it would be with only type-1-inhibitory plasticity.<br /> Instead, the target activity of plasticity is now enhanced within a winning assembly, and also positive but reduced in the loosing assemblies.

Reviewer #3 (Public review):

Summary:

The work shows how learned assembly structure and its influence on replay during spontaneous activity can reflect the statistics of stimulus input. In particular, stimuli that are more frequent during training elicit stronger wiring and more frequent activation during replay. Past works (Litwin-Kumar and Doiron, 2014; Zenke et al., 2015) have not addressed this specific question, as classic homeostatic mechanisms forced activity to be similar across all assemblies. Here, the authors use a dynamic gain and threshold mechanism to circumnavigate this issue and link this mechanism to a cellular monitoring of membrane potential history.

Strengths:

(1) This is an interesting advance, and the authors link this to experimental work in sensory learning in environments with non-uniform stimulus probabilities.

(2) The authors consider their mechanism in a variety of models of increasing complexity (simple stimuli, complex stimuli; ignoring Dale's law, incorporating Dale's law).

(3) Links a cellular mechanism of internal gain control (their variable h) to assembly formation and the non-uniformity of spontaneous replay activity. Offers a promise of relating cellular and synaptic plasticity mechanisms under a common goal of assembly formation.

Weaknesses:

(1) However, while the manuscript does show that assembly wiring does follow stimulus likelihood, it is not clear how the assembly specific statistics of h reflect these likelihoods. I find this to be a key issue.

(2) The authors model does take advantage of the sigmoidal transfer function, and after learning an assembly is either fully active or near fully silent (Fig. 2a). This somewhat artificial saturation may be the reason that classic homeostasis is not required, since runaway activity is not as damaging to network activity.

(3) Classic mechanisms of homeostatic regulation (synaptic scaling, inhibitory plasticity) try to ensure that firing rates match a target rate (on average). If the target rate is the same for all neurons then having elevated firing rates for one assembly compared to others during spontaneous activity would be difficult. If these homeostatic mechanisms were incorporated, how would they permit the elevated firing rates for assemblies that represent more likely stimuli?

Reviewer #1 (Public review):

Summary:

The authors of this article have presented a timely and well-written study exploring the impact of group identification on collective behaviors and performance. The breadth of analyses is impressive and contributes significantly to our understanding of the collective performance. However, there are several areas where further clarification and revision would strengthen the study.

Strengths:

(1) Timeliness and Relevance:<br /> The topic is highly relevant, particularly in today's interconnected and team-oriented work environments. Triadic hyperscanning is important to understand group dynamics, but most previous work has been limited to dyadic work.

(2) Comprehensive Analysis:<br /> The authors have conducted extensive analyses, offering valuable insights into how group identification affects collective behaviors.

(3) Clear Writing:<br /> The manuscript is well-written and easy to follow, making complex concepts accessible.

Weaknesses (clarifications needed):

(1) Experimental Design:<br /> The study does not mention whether the authors examined sex differences or any measures of attractiveness or hierarchy among participants (e.g., students vs. teachers). Including these variables could provide a more nuanced understanding of group dynamics.

(2) fNIRS Data Acquisition:<br /> The authors' approach to addressing individual differences in anatomy is lacking in detail. Understanding how they identified the optimal channels for synchrony between participants would be beneficial. Was this done by averaging to find the location with the highest coherence?

(3) Behavioral Analysis:<br /> For group identification, the analysis currently uses a dichotomous approach. Introducing a regression model to capture the degree of identification could offer more granular insights into how varying levels of group identification affect collective behavior and performance.

(4) Single Brain Activation Analysis:<br /> The application of the General Linear Model (GLM) is unclear, particularly given the long block durations and absence of multiple trials. Further explanation is needed on how the GLM was implemented under these conditions.

(5) Within-group neural Synchrony (GNS) Calculation:<br /> The method for calculating GNS could be improved by using mutual information instead of pairwise summation, as suggested by Xie et al. (2020) in their study on fMRI triadic hyperscanning. Additionally, the explanation of GNS calculation is inconsistent. At one point, it is mentioned that GNS was averaged across time and channels, while elsewhere, it is stated that channels with the highest GNS were selected. Clarification on this point is essential.

(6) Placement of fNIRS Probes:<br /> The probes were only placed in the frontal regions, despite literature suggesting that the superior temporal sulcus (STS) and temporoparietal junction (TPJ) regions are crucial for triadic team performance. A justification for this choice or inclusion of these regions in future studies would be beneficial.

(7) Interpretation of fNIRS Data:<br /> Given that fNIRS signals are slow, similar to BOLD signals in fMRI, the interpretation of Figure 6 raises concerns. It suggests that it takes several minutes (on the order of 4-5 minutes) for people to collaborate, which seems implausible. More context or re-evaluation of this interpretation is needed.

Reviewer #2 (Public review):

Summary:

This study primarily aims to examine the relationship between collective performance and group identification. Additionally, the authors propose that inter-brain synchronization (IBS) underlies collective performance and that changes in intra-brain functional connectivity or single-brain activation may, in turn, underlie IBS. The topic addressed in this paper is of great importance in the field using hyperscanning. However, the details of the experiments and analysis described in the paper are unclear, and the hypothesis as to why IBS is thought to underlie collective performance is not clearly presented. In addition, some of the analysis seems to be inappropriate.

Strengths:

I find the model presented in Figure 7 to be intriguing. Understanding why inter-brain synchronization occurs and how it is supported by specific single-brain activations or intra-brain functional connectivity is indeed a critical area for researchers conducting hyperscanning studies to explore.

Understanding triadic-interaction is really important, while almost all hyperscanning neuroimaging focuses on the dyadic interaction. The exploring neural/behavioral/psychological basis behind triadic interaction is a promising method for understanding collective behavior and decision-making.

Weaknesses:

The authors need to clearly articulate their hypothesis regarding why neural synchronization occurs during social interaction. For example, in line 284, it is stated that "It is plausible that neural synchronization is closely associated with group identification and collective performance...", but this is far from self-evident. Neural synchronization can occur even when people are merely watching a movie (Hasson et al., 2004), and movie-watchers are not engaged in collective behavior. There is no direct link between the IBS and collective behavior. The authors should explain why they believe inter-brain synchronization occurs in interactive settings and why they think it is related to collective behavior/performance.

The authors state that "GNS in the OFC was a reliable neuromarker, indicating the influence of group identification on collective performance," but this claim is too strong. Please refer to Figure 4B. Do the authors really believe that collective performance can be predicted given the correlation with the large variance shown? There is a significant discrepancy between observing a correlation between two variables and asserting that one variable is a predictive biomarker for the other.

Why are the individual answers being analyzed as collective performance (See, L-184)? Although these are performances that emerge after the group discussion, they seem to be individual performances rather than collective ones. Typically, wouldn't the result of a consensus be considered a collective performance? The authors should clarify why the individual's answer is being treated as the measure of collective performance.

Performing SPM-based mapping followed by conducting a t-test on the channels within statistically significant regions constitutes double dipping, which is not an acceptable method (Kriegeskorte et al., 2011). This issue is evident in, for example, Figures 3A and 4A.

Please refer to the following source:<br /> https://www.nature.com/articles/nn.2303

In several key analyses within this study (e.g., single-brain activation in the paragraph starting from L398, neural synchronization in the paragraph starting from L393), the TPJ is mentioned alongside the DLPFC. However, in subsequent detailed analyses, the TPJ is entirely ignored.

The method for analyzing single-brain activation is unclear. Although it is mentioned that GLM (generalized linear model) was used, it is not specified what regressors were prepared, nor which regressor's β-values are reported as brain activity. Without this information, it is difficult to assess the validity of the reported results.

While the model illustrated in Figure 7 seems to be interesting, for me, it seems not to be based on the results of this study. This is because the study did not investigate the causal relationships among the three metrics. I guess, Figure 5D might be intended to explain this, but the details of the analysis are not provided, making it unclear what is being presented.

The details of the experiment are not described at all. While I can somewhat grasp what was done abstractly, the lack of specific information makes it impossible to replicate the study.

Reviewer #4 (Public review):

Summary:

This is an important study that underscores that reproduction-survival trade-offs are not manifested (contrary to what generally accepted theory predicts) across a range of studies on birds. This has been studied by a meta-analytical approach, gathering data from a set of 46 papers (30 bird species). The overall conclusion is that there are no trade-offs apparent unless experimental manipulations push the natural variability to extreme values. In the wild, the general pattern for within-species variation is that birds with (naturally) larger clutches survive better.

Strengths:

I agree this study highlights important issues and provides good evidence of what it claims, using appropriate methods.

Weaknesses:

I also think, however, that it would benefit from broadening its horizon beyond bird studies. The conclusions can be reinforced through insights from other taxa. General reasoning is that there is positive pleiotropy (i.e. individuals vary in quality and therefore some are more fit (perform better) than others. Of course, this is within their current environment (biotic, abiotic, social. ...), with consequences of maintaining genetic variation across generations - outlined in Maklakov et al. 2015 (https://doi.org/10.1002/bies.201500025). This explains the outcomes of this study very well and would come to less controversy and surprise for a more general audience.

I have two fish examples in my mind where this trade-off is also discounted. Of course, given that it is beyond brood-caring birds, the wording in those studies is slightly different, but the evolutionary insight is the same. First, within species but across populations, Reznick et al. (2004, DOI: 10.1038/nature02936) demonstrated a positive correlation between reproduction and parental survival in guppies. Second, an annual killifish study (2021, DOI: 10.1111/1365-2656.13382) showed, within a population, a positive association between reproduction and (reproductive) aging.

In fruit flies, there is also a strong experimental study demonstrating the absence of reproduction-lifespan trade-offs (DOI: 10.1016/j.cub.2013.09.049).

I suggest that incorporating insights from those studies would broaden the scope and reach of the current manuscript.

Likely impact:

I think this is an important contribution to a slow shift in how we perceive the importance of trade-offs in ecology and evolution in general. While the current view still is that one individual excelling in one measure of its life history (i.e. receiving benefits) must struggle (i.e. pay costs) in another part. However, a positive correlation between all aspects of life history traits is possible within an individual (such as due to developmental conditions or fitting to a particular environment). Simply, some individuals can perform generally better (be of good quality than others).

Reviewer #1 (Public review):

Summary

In this study, Nishi et al. claim that the ratio of long-term hematopoietic stem cell (LT-HSC) versus short-term HSC (ST-HSC) determines the lineage output of HSCs and reduced ratio of ST-HSC in aged mice causes myeloid-biased hematopoiesis. Authors used Hoxb5 reporter mice to isolated LT-HSC and ST-HSC and performed molecular analyses and transplantation assays to support their arguments. How hematopoietic system becomes myeloid-biased upon aging is an important question with many implications in disease context as well. However, this study needs more definitive data.

(1) Authors' experimental designs have some caveats to definitely support their claims. Authors claimed that aged LT-HSCs have no myeloid-biased clone expansion using transplantation assays. In these experiments, authors used 10 HSCs and young mice as recipients. Given the huge expansion of old HSC by number and known heterogeneity in immunophenotypically defined HSC populations, it is questionable how 10 out of so many old HSCs (an average of 300,000 up to 500,000 cells per mouse; Mitchell et al., Nature Cell Biology, 2023) can faithfully represent old HSC population. The Hoxb5+ old HSC primary and secondary recipient mice data (Fig. 2C and D) support this concern. In addition, they only used young recipients. Considering the importance of inflammatory aged niche in the myeloid-biased lineage output, transplanting young vs old LT-HSCs into aged mice will complete the whole picture.

(2) Authors' molecular data analyses need more rigor with unbiased approaches. They claimed that neither aged LT-HSCs nor aged ST-HSCs exhibited myeloid or lymphoid gene set enrichment but aged bulk HSCs, which are just a sum of LT-HSCs and ST-HSCs by their gating scheme (Fig. 4A), showed the "tendency" of enrichment of myeloid-related genes based on the selected gene set (Fig. 4D). Although the proportion of ST-HSCs is reduced in bulk HSCs upon aging, since ST-HSCs do not exhibit lymphoid gene set enrichment based on their data, it is hard to understand how aged bulk HSCs have more myeloid gene set enrichment compared to young bulk HSCs. This bulk HSC data rather suggest that there could be a trend toward certain lineage bias (although not significant) in aged LT-HSCs or ST-HSCs. Authors need to verify the molecular lineage priming of LT-HSCs and ST-HSCs using another comprehensive dataset.

(3) Although authors could not find any molecular evidence for myeloid-biased hematopoiesis from old HSCs (either LT or ST), they argued that the ratio between LT-HSC and ST-HSC causes myeloid-biased hematopoiesis upon aging based on young HSC experiments (Fig. 6). However, old ST-HSC functional data showed that they barely contribute to blood production unlike young Hoxb5- HSCs (ST-HSC) in the transplantation setting (Fig. 2). Is there any evidence that in unperturbed native old hematopoiesis, old Hoxb5- HSCs (ST-HSC) still contribute to blood production? If so, what are their lineage potential/output? Without this information, it is hard to argue that the different ratio causes myeloid-biased hematopoiesis in aging context.

Reviewer #2 (Public review):

Summary:

Nishi et al, investigate the well-known and previously described phenomenon of age-associated myeloid-biased hematopoiesis. Using a previously established HoxB5mCherry mouse model, they used HoxB5+ and HoxB5- HSCs to discriminate cells with long-term (LT-HSCs) and short-term (ST-HSCs) reconstitution potential and compared these populations to immunophenotypically defined 'bulk HSCs' that consists of a mixture of LT-HSC and ST-HSCs. They then isolated these HSC populations from young and aged mice to test their function and myeloid bias in non-competitive and competitive transplants into young and aged recipients. Based on quantification of hematopoietic cell frequencies in the bone marrow, peripheral blood, and in some experiments the spleen and thymus, the authors argue against the currently held belief that myeloid-biased HSCs expand with age.

While aspects of their work are fascinating and might have merit, several issues weaken the overall strength of the arguments and interpretation. Multiple experiments were done with a very low number of recipient mice, showed very large standard deviations, and had no statistically detectable difference between experimental groups. While the authors conclude that these experimental groups are not different, the displayed results seem too variable to conclude anything with certainty. The sensitivity of the performed experiments (e.g. Fig 3; Fig 6C, D) is too low to detect even reasonably strong differences between experimental groups and is thus inadequate to support the author's claims. This weakness of the study is not acknowledged in the text and is also not discussed. To support their conclusions the authors need to provide higher n-numbers and provide a detailed power analysis of the transplants in the methods section.

As the authors attempt to challenge the current model of the age-associated expansion of myeloid-biased HSCs (which has been observed and reproduced by many different groups), ideally additional strong evidence in the form of single-cell transplants is provided.

It is also unclear why the authors believe that the observed reduction of ST-HSCs relative to LT-HSCs explains the myeloid-biased phenotype observed in the peripheral blood. This point seems counterintuitive and requires further explanation.

Based on my understanding of the presented data, the authors argue that myeloid-biased HSCs do not exist, as<br /> a) they detect no difference between young/aged HSCs after transplant (mind low n-numbers and large std!!!); b) myeloid progenitors downstream of HSCs only show minor or no changes in frequency and c) aged LT-HSCs do not outperform young LT-HSC in myeloid output LT-HScs in competitive transplants (mind low n-numbers and large std!!!).<br /> However, given the low n-numbers and high variance of the results, the argument seems weak and the presented data does not support the claims sufficiently. That the number of downstream progenitors does not change could be explained by other mechanisms, for instance, the frequently reported differentiation short-cuts of HSCs and/or changes in the microenvironment.

Strengths:

The authors present an interesting observation and offer an alternative explanation of the origins of aged-associated myeloid-biased hematopoiesis. Their data regarding the role of the microenvironment in the spleen and thymus appears to be convincing.

Weaknesses:

"Then, we found that the myeloid lineage proportions from young and aged LT-HSCs were nearly comparable during the observation period after transplantation (Fig. 3, B and C)."<br /> [Comment to the authors]: Given the large standard deviation and low n-numbers, the power of the analysis to detect differences between experimental groups is very low. Experimental groups with too large standard deviations (as displayed here) are difficult to interpret and might be inconclusive. The absence of clearly detectable differences between young and aged transplanted HSCs could thus simply be a false-negative result. The shown experimental results hence do not provide strong evidence for the author's interpretation of the data. The authors should add additional transplants and include a detailed power analysis to be able to detect differences between experimental groups with reasonable sensitivity.

Line 293: "Based on these findings, we concluded that myeloid-biased hematopoiesis observed following transplantation of aged HSCs was caused by a relative decrease in ST-HSC in the bulk-HSC compartment in aged mice rather than the selective expansion of myeloid-biased HSC clones."<br /> Couldn't that also be explained by an increase in myeloid-biased HSCs, as repeatedly reported and seen in the expansion of CD150+ HSCs? It is not intuitively clear why a reduction of ST-HSCs clones would lead to a myeloid bias. The author should try to explain more clearly where they believe the increased number of myeloid cells comes from. What is the source of myeloid cells if the authors believe they are not derived from the expanded population of myeloid-biased HSCs?

Reviewer #3 (Public review):

In this manuscript, Nishi et al. propose a new model to explain the previously reported myeloid-biased hematopoiesis associated with aging. Traditionally, this phenotype has been explained by the expansion of myeloid-biased hematopoietic stem cell (HSC) clones during aging. Here, the authors question this idea and show how their Hoxb5 reporter model can discriminate long-term (LT) and short-term (ST) HSC and characterized their lineage output after transplant. From these analyses, the authors conclude that changes during aging in the LT/ST HSC proportion explain the myeloid bias observed.

Although the topic is appropriate and the new model provides a new way to think about lineage-biased output observed in multiple hematopoietic contexts, some of the experimental design choices, as well as some of the conclusions drawn from the results could be substantially improved. Also, they do not propose any potential mechanism to explain this process, which reduces the potential impact and novelty of the study.

The authors have satisfactorily replied to some of my comments. However, there are multiple key aspects that still remain unresolved.

Reviewer #1 (Public review):

Summary:

The study identifies two types of activation: one that is cue-triggered and non-specific to motion directions, and another that is specific to the exposed motion directions but occurs in a reversed manner. The finding that activity in the medial temporal lobe (MTL) preceded that in the visual cortex suggests that the visual cortex may serve as a platform for the manifestation of replay events, which potentially enhance visual sequence learning.

Strengths:

Identifying the two types of activation after exposure to a sequence of motion directions is very interesting. The experimental design, procedures, and analyses are solid. The findings are interesting and novel.

Weaknesses:

It was not immediately clear to me why the second type of activation was suggested to occur spontaneously. The procedural differences in the analyses that distinguished between the two types of activation need to be a little better clarified.

Reviewer #2 (Public review):

This paper shows and analyzes an interesting phenomenon. It shows that when people are exposed to sequences of moving dots (that is moving dots in one direction, followed by another direction, etc.), showing either the starting movement direction or ending movement direction causes a coarse-grained brain response that is similar to that elicited by the complete sequence of 4 directions. However, they show by decoding the sensor responses that this brain activity actually does not carry information about the actual sequence and the motion directions, at least not on the time scale of the initial sequence. They also show a reverse reply on a highly compressed time scale, which is elicited during the period of elevated activity, and activated by the first and last elements of the sequence, but not others. Additionally, these replays seem to occur during periods of cortical ripples, similar to what is found in animal studies.

These results are intriguing. They are based on MEG recordings in humans, and finding such replays in humans is novel. Also, this is based on what seems to be sophisticated statistical analysis. However, this is the main problem with this paper. The statistical analysis is not explained well at all, and therefore its validity is hard to evaluate. I am not at all saying it is incorrect; what I am saying is that given how it is explained, it cannot be evaluated.

Reviewer #1 (Public review):

These experiments are some of the first to assess the role of dopamine release and the activity of D1 and D2 MSNs in pair bond formation in Mandarin voles. This is a novel and comprehensive study that presents exciting data about how the dopamine system is involved in pair bonding. The authors provide very detailed methods and clearly presented results. Here they show dopamine release in the NAc shell is enhanced when male voles encounter their pair bonded partner 7 days after co-habitation. In addition, D2 MSN activity decreases whereas D1 MSN activity increases when sniffing the pair-bonded partner.

The authors do not provide justification for why they only use males in the current study, without discussing sex as a biological variable these data can only inform readers about one sex (which in pair-bonded animals by definition have 2 sexes). In addition, the authors do not use an isosbestic control wavelength in photometry experiments, although they do use EGFP control mice which show no effects of these interventions, a within-subject control such as an isosbestic excitation wavelength could give more confidence in these data and rule out motion artefacts within subjects.

There is an existing literature (cited in this manuscript) from Aragona et al., (particularly Aragona et al., 2006) which has highlighted key differences in the roles of rostral versus caudal NAc shell dopamine in pair bond formation and maintenance. Specifically, they report that dopamine transmission promoting pair bonding only occurs in the rostral shell and not the caudal shell or core regions. Given that the authors have targeted more caudally a discussion of how these results fit with previous work and why there may be differences in these areas is warranted.

The authors could discuss the differences between pair bond formation and pair bond maintenance more deeply.

The authors have successfully characterised the involvement of dopamine release, changes in D1 and D2 MSNs, and projections to the VP in pair bonding voles. Their conclusions are supported by their data and they make a number of very reasonable discussion points acknowledging various limitations.

Reviewer #2 (Public review):

Summary:

Using in vivo fiber-photometry the authors first establish that DA release when contacting their partner mouse increases with days of cohabitation while this increase is not observed when contacting a stranger mouse. Similar effects are found in D1-MSNs and D2-MSNs with the D1-MSN responses increasing and D2-MSN responses decreasing with days of cohabitation. They then use slice physiology to identify underlying plasticity/adaptation mechanisms that could contribute to the changes in D1/D2-MSN responses. Last, to address causality the authors use chemogenetic tools to selectively inhibit or activate NAc shell D1 or D2 neurons that project to the ventral pallidum. They found that D2 inhibition facilitates bond formation while D2 excitation inhibits bond formation. In contrast, both D1-MSN activation and inhibition inhibit bond formation.

Strengths:

The strength of the manuscript lies in combining in vivo physiology to demonstrate circuit engagement and chemogenetic manipulation studies to address circuit involvement in pair bond formation in a monogamous vole.

Weaknesses:

Weaknesses include that a large set of experiments within the manuscript are dependent on using short promoters for D1 and D2 receptors in viral vectors. As the authors acknowledge this approach can lead to ectopic expression and the presented immunohistochemistry supports this notion. It seems to me that the presented quantification underestimates the degree of ectopic expression that is observed by eye when looking at the presented immunohistochemistry. However, given that Cre transgenic animals are not available for Microtus mandarinus and given the distinct physiological and behavioral outcomes when imaging and manipulating both viral-targeted populations this concern is minor.

The slice physiology experiments provide some interesting outcomes but it is unclear how they can be linked to the in vivo physiological outcomes and some of the outcomes don't match intuitively (e.g. cohabitation enhances excitatory/inhibitory balance in D2-MSNs but the degree of contact-induced inhibition is enhanced in D2-MSN).

One interesting finding is that the relationship between D2-MSN and pair bond formation is quite clear (inhibition facilitates while excitation inhibits pair bond formation). In contrast, the role of D1-MSNs is more complicated since both excitation and inhibition disrupt pair bond formation. This is not convincingly discussed.

It seemed a missed opportunity that physiological readout is limited to males. I understand though that adding females may be beyond the scope of this manuscript.

Reviewer #3 (Public review):

Summary:

The manuscript is evaluating changes in dopamine signaling in the nucleus accumbens following pair bonding and exposure to various stimuli in mandarin voles. In addition, the authors present chemogenetic data that demonstrate excitation and inhibition of D1 and D2 MSN affect pair bond formation.

Strengths:

The experimental designs are strong. The approaches are innovative and use cutting-edge methods. The manuscript is well written.

Weaknesses:

The statistical results are not presented, and not all statistical analyses are appropriate. Additionally, some details of methods are absent.

Reviewer #1 (Public review):

The present manuscript by Zhou and colleagues investigates the impact of a new combination of compounds termed CHIR99021 and A-485 on stimulating cardiac cell regeneration. This manuscript fits the journal and addresses an important contribution to scientific knowledge.

Comments on latest version:

The authors have addressed all of our comments.

Reviewer #2 (Public review):

Summary:

This manuscript reports that a combination of two small molecules, 2C (CHIR99027 and A-485) enabled to induce the dedifferentiation of hESC-derived cardiomyocytes (CMs) into regenerative cardiac cells (RCC). These RCCs had disassembled sarcomeric structures and elevated expression of embryonic cardiogenic genes such as ISL1, which exhibited proliferative potential and were able to differentiate into cardiomyocytes, endothelial cells, and smooth muscle cells. Lineage tracing further suggested that RCCs originated from TNNT2+ cells, not pre-existing ISL1+ cells. Furthermore, 2C treatment increased the numbers of RCC cells in neonatal rat and adult mouse hearts, and improves cardiac function post-MI in adult mice. Mechanistically, bulk RNA-seq analysis revealed that 2C led to elevated expression of embryonic cardiogenic genes while down-regulation of CM-specific genes. Single-cell RNA-seq data showed that 2C promoted cardiomyocyte transition into an intermediate state that are marked with ACTA2 and COL1A1, which subsequently transform into RCCs. Finally, ChIP-seq analysis demonstrated that CHIR99027 enhanced H3K9Ac and H3K27Ac modifications in embryonic cardiac genes, while A-485 inhibited these modifications in cardiac-specific genes. These combined alterations effectively induced the dedifferentiation of cardiomyocytes into RCCs. Overall, this is an important work, presenting a putative cardiac regenerative cell types that may represent endogenous cardiac regeneration in regenerative animals. With that said, here are suggestions for the authors:

Strengths:

Overall, this work is quite comprehensive and is logically and rigorously designed. The phenotypic and functional data on 2C are strong.

Weaknesses or suggestions:

(1) In Figure 4, the authors should perform additional experiments on analyzing 2C effect on cardiomyocytes, endothelial cells, and fibroblasts in adult mouse hearts after myocardial infarction.<br /> (2) In Figures 5-7, the mechanistic insights of 2C are primarily derived from transcriptomic and genomic datasets without experimental verification.<br /> (3) The authors should compare transcriptomic profiling of the RCCs with other putative cardiac progenitors from public databases.

Reviewer #3 (Public review):

Summary:

The ability of cardiac cells to regenerate has been the object of intense (and sometimes controversial) research in biology. While lower organisms can robustly undergo cardiac regeneration by reactivation of embryonic cardiogenic pathway, this ability is strongly reduced in mice, both temporally and qualitatively. Finding a way to derive precursor cells with regenerative ability from differentiated cells in mammals has been challenging.

Zhou, He and colleagues hypothesized that ISL-1-positive cells would show regenerative capacity and developed a small molecules screen to dedifferentiate cardiomyocytes (CM) to ISL1-positive precursor cells. Using hESC-derived CM, authors found that the combination of both, WNT activation (CHIR99021) and p300 acetyltransferase inhibition (A-485) (named 2C protocol) induces CM dedifferentiation to regenerative cardiac cells (RCCs). RCCs are proliferative and re-express embryonic cardiogenic genes while decreasing expression of more mature cardiac genes, bringing them towards a more precursor-like state. RCCs were able to differentiate to CM, smooth muscle cells and endothelial cells, highlighting their multipotent property. In vivo administration of 2C in rats and mice had protective effects upon myocardial infarction.

Mechanistically, authors report that 2C protocol drives CM-specific transcriptional and epigenetic changes.

Strengths:

The authors made a great effort to validate their data using orthogonal ways, and several hESC lines. The use of lineage tracing convincingly showed a dedifferentiation from CM. They translate their findings into an in vivo model of myocardial injury, and show functional cardiac regeneration post injury. They also showed that 2C could surprisingly be used as preventive treatment. Together their data may suggest a regenerative effect of 2C both in vitro and in vivo settings. If confirmed, this study might unlock therapeutic strategy for cardiac regeneration.

Weaknesses:

Updated General comments:

Experimental design & Interpretation

(1) The titration provided by the author following the first round of revision is puzzling to me. Based on the authors explanation, the initial screen was performed using 10uM of A-485, allowing the authors to choose CHIR + A-485 as a combination of drugs increasing Isl1-positive cells. However, in the titration provided, the combination of CHIR + 10uM of A-485 (used during the screen) shows *no* increase of the percentage of Isl-1-positive cells compared to DMSO control. How is that possible? Can the authors provide a transparent explanation of the experimental design for their screen. How was A-485 isolated from the 4000+ compounds tested if it does not show any effect on the titration? This titration raises significant concerns about the rational of following up with the combination of compounds.

(2) The authors have not really addressed the concern raised earlier. If only ~1% of the cells de-differentiate and become Isl-positive, how can anybody quantify a nuclear/cytosolic ratio at the global population and show statistical significant when only 1% of the cells should be different?

(3) Authors now provide a quantification of the effect of I-BET-762 (Supp 1H). While the authors state " [the combination of CHIR + I-BET-762] was less effective than A-485 in combination with CHIR99021", the figure provided does not test that. A side-by-side comparaison of the effect of A485 and I-BET should have been performed on the same graph. I-BET increases by 4 fold, while A-485 increases by 5-fold, which, based on the variation of their data, will unlikely be statistically different. The rational for disregarding the effect of I-BET-762 is therefore weakened.

(4) Why NR2F2 is statistically significant in one set of experiments (Fig 2 - Fig. supplement 1) and then non-significant in another set (Fig. 1G) using the exact same experiment design (NC vs 2C for 60h) and similar statistical test applied?

Statistics & Data Acquisition

(1) Authors should refrain from deriving statistics from 2 biological repeats (Figure 3G).<br /> (2) Authors still do not state whether the normality of their data was tested.<br /> (3) What is the rational for using a two-way ANOVA for Fig 3G? Authors are only comparing the effect of their treatment for each marker. Same question for most panels from Figure 1, Fig 2C, 2F, and throughout the manuscript. This needs clarification/justification especially because in other experiments, they used multiple unpaired t-test (Fig 2 - Fig. supplement 1).

Others

(1) Authors should try to make their manuscript colorblind-friendly: No modification added following this comment.

Reviewer #1 (Public review):

Summary:

The study investigates the impact of Clonal Hematopoiesis of Indeterminate Potential (CHIP) on Immune Checkpoint Inhibitor (ICI) therapy outcomes in NSCLC patients, analyzing blood samples from 100 patients pre- and post-ICI therapy for CHIP, and conducting single-cell RNA sequencing (scRNA-seq) of PBMCs in 63 samples, with validation in 180 more patients through whole exome sequencing. Findings show no significant CHIP influence on ICI response, but a higher CHIP prevalence in NSCLC compared to controls and a notable CHIP burden in squamous cell carcinoma. Severely affected CHIP groups showed NF-kB pathway gene enrichment in myeloid clusters.

Strengths:

The study is commendable for analyzing a significant cohort of 100 patients for CHIP and utilizing scRNA-seq on 63 samples, showcasing the use of cutting-edge technology.

The study tackles the vital clinical question of predicting ICI therapy outcomes in NSCLC.

Weaknesses:

The study groups, comprising NSCLC patients and healthy controls, exhibit notable differences in sex distribution and smoking status. Given that smoking is a well-established factor influencing CHIP status, this introduces potential confounding variables that may impact the study's conclusions. The authors have appropriately acknowledged these disparities and provided a transparent discussion of their implications.

Comments on revised submission:

The authors thoroughly addressed all my concerns. Thank you very much for your additional work.

Reviewer #1 (Public review):

Summary:

The present work from Velloso and collaborators investigated the transcription profiles of resident and recruited hypothalamic microglia. They found sex-dependent differences between males and females and identified the protective role of chemokine receptor CXCR3 against diet-induced obesity.

Strengths:

(1) Novelty<br /> (2) Relevance, since this work provides evidence about a subset of recruited microglia that has a protective effect against DIO. This provides a new concept in hypothalamic inflammation and obesity.

Comments on revised version:

All my comments have been addressed.

Reviewer #2 (Public review):

Summary:

This study by Mendes et al provides novel key insights in the role of chemotaxis and immune cell recruitment into the hypothalamus in the development of diet-induced obesity. Specifically, the authors first revealed that although transcriptional changes in hypothalamic resident microglia following exposure to high-fat feeding are minor, there are compelling transcriptomic differences between resident microglia and microglia recruited to the hypothalamus, and these are sexually dimorphic. Using independent loss-of-function studies, the authors also demonstrate an important role of CXCR3 and hypothalamic CXCL10 in the hypothalamic recruitment of CCR2+ positive cells on metabolism following exposure to high-fat diet-feeding in mice. This manuscript puts forth conceptually novel evidence that inhibition of chemotaxis-mediated immune cell recruitment accelerates body weight gain in high-fat diet-feeding, suggesting that a subset of microglia which express CXCR3 may confer protective, anti-obesogenic effects.

Strengths:

The work is exciting and relevant given the prevalence of obesity and the consequences of inflammation in the brain on perturbations of energy metabolism and ensuant metabolic diseases. Hypothalamic inflammation is associated with disrupted energy balance, and activated microglia within the hypothalamus resulting from excessive caloric intake and saturated fatty acids are often thought to be mediators of impairment of hypothalamic regulation of metabolism. The present work reports a novel notion in which immune cells recruited into the hypothalamus which express chemokine receptor CXCR3 may have a protective role against diet-induced obesity. In vivo studies reported herein demonstrate that inhibition of CXCR3 exacerbates high-fat diet-induced body weight gain, increases circulating triglycerides and fasting glucose levels, worsens glucose tolerance, and increases the expression of orexigenic neuropeptides, at least in female mice.

This work provides a highly interesting and needed overview of preclinical and clinical brain inflammation, which is relevant to readers with an interest in metabolism and immunometabolism in the context of obesity.

Using flow cytometry, cell sorting, and transcriptomics including RNA-sequencing, the manuscript provides novel insights on transcriptional landscapes of resident and recruited microglia in the hypothalamus. Importantly, sex differences are investigated.

Overall, the manuscript is perceived to be highly interesting, relevant, and timely. The discussion is thoughtful, well-articulated, and a pleasure to read and felt to be of interest to a broad audience.

Weaknesses:

There were no major weaknesses perceived. Some comments for potential textual additions to the results/discussion are provided below.

Could the authors comment on the choice of peripheral administration of CXCR3 antagonist as opposed to central (e.g. icv) administration? Indeed, systemic inhibition of CXCR3 produced significant alterations in body weight gain and glucose tolerance in female mice given high-fat diet and reduced CCR2 and CXCR3 immunostaining in the hypothalamus. Could changes to peripheral (e.g. WAT, liver) immune responses to the diet underlie the metabolic changes observed?

Besides hypothalamic mRNA levels of chemokines and chemokine receptors, does systemic CXCR3 antagonism affect other aspects linked to diet-induced impairments of hypothalamic regulation of energy homeostasis, like inflammation, ER stress and/or mitochondrial dynamics/function? It would be interesting to reveal the consequence of reduced CCR2+ microglial migration to the hypothalamus with chronic high-fat diet exposure.

Reviewer #1 (Public review):

Summary:

The manuscript by Zhou et al offers new high resolution Cryo-EM structures of two human biotin-dependent enzymes: propionyl-CoA carboxylase (PCC) and methycrotonyl-CoA carboxylase (MCC). While X-ray crystal structures and Cryo-EM structures have previously been reported for bacterial and trypanosomal versions of MCC and for bacterial versions of PCC, this marks one of the first high resolution Cryo-EM structures of the human version of these enzymes. Using the biotin cofactor as an affinity tag, this team purified a group of four different human biotin-dependent carboxylases from cultured human Expi 293F (kidney) cells (PCC, MCC, acetyl-CoA carboxylase (ACC), and pyruvate carboxylase). Following further enrichment by size-exclusion chromatography, they were able to vitrify the sample and pick enough particles of MCC and PCC to separately refine the structures of both enzymes to relatively high average resolutions (the Cryo-EM structure of ACC also appears to have been determined from these same micrographs, though this is the subject of a separate publication). To determine the impact of substrate binding on the structure of these enzymes and to gain insights into substrate selectivity, they also separately incubated with propionyl-CoA and acetyl-CoA and vitrified the samples under active turnover conditions, yielding a set of cryo-EM structures for both MCC and PCC in the presence and absence of substrates and substrate analogues.

Strengths:

The manuscript has several strengths. It is clearly written, the figures are clear and the sample preparation methods appear to be well described. This study demonstrates that Cryo-EM is an ideal structural method to investigate the structure of these heterogeneous samples of large biotin-dependent enzymes. As a consequence, many new Cryo-EM structures of biotin-dependent enzymes are emerging, thanks to the natural inclusion of a built-in biotin affinity tag. While the authors report no major differences between the human and bacterial forms of these enzymes, it remains an important finding that they demonstrate how/if the structure of the human enzymes are or are not distinct from the bacterial enzymes. The MCC structures also provide evidence for a transition for BCCP-biotin from an exo-binding site to an endo-binding site in response to acetyl-CoA binding. This contributes to a growing number of biotin-dependent carboxylase structures that reveal BCCP-biotin binding at locations both inside (endo-) and outside (exo-) of the active site.

Weaknesses:

There are some minor weaknesses. Notably, there are not a lot of new insights coming from this paper. The structural comparisons between MCC and PCC have already been described in the literature and there were not a lot of significant changes (outside of the exo- to endo- transition) in the presence vs. absence of substrate analogues. There are sections of this manuscript that do not sufficiently clarify what represents a new insight from the current set of structures (there are few of them), vs. what is largely recapitulating what has been seen in previous structures.

There is not a great deal of depth of analysis in the discussion. For example, no new insights were gained with respect to the factors contributing to substrate selectivity (the factors contributing to selectivity for propionyl-CoA vs. acetyl-CoA in PCC). The authors acknowledge that they are limited in their interpretations as a consequence of the acyl groups being unresolved in all of the structures. They offer a simple, overarching and not particularly insightful explanation that the longer acyl group in propionyl-CoA may mediate stronger hydrophobic interactions that stabilize the alpha carbon of the acyl group at the proper position. The authors did not take the opportunity to describe the specific interactions that may be responsible for the stronger hydrophobic interaction nor do they offer any plausible explanation for how these might account for an astounding difference in the selectivity for propionyl-CoA vs. acetyl-CoA. Essentially, the authors concede that these cryo-EM structures offer no new insights into the structural basis for substrate selectivity in PCC, confirming that these structures do not yet fully capture the proper conformational states.

Some of these minor deficiencies aside, the overall aim of contributing new cryo-EM structures of the human MCC and PCC has been achieved. While I am not a cryo-EM expert, I see no flaws in the methodology or approach. While the contributions from these structures are somewhat incremental, it is nevertheless important to have these representative examples of the human enzymes and it is noteworthy to see a new example of the exo-binding site in a biotin-dependent enzyme.

Reviewer #2 (Public review):

Summary:

This paper reports the structures of two human biotin-dependent carboxylases. The authors used endogenously purified proteins and solved the structures in high resolutions. Based on the structures, they defined the binding site for acyl-CoA and biotin and reported the potential conformational changes in biotin position.

Strengths:

The authors effectively utilized the biotin of the two proteins and obtained homogeneous proteins from human cells. They determined the high-resolution structures of the two enzymes in apo and substrate-bound states.

Comments and questions to the manuscripts:

(1) I'm quite impressed with the protein purification and structure determination, but I think some functional characterization of the purified proteins should be included in the manuscript. The activity of enzymes should be the foundation of all structures and other speculations based on structures.

(2) In Figure 1B, the structure of MCC is shown as two layers of beta units and two layers of alpha units, while there is only one layer of alpha units resolved in the density maps. I suggest the authors show the structures resolved based on the density maps and show the complete structure with the docked layer in the supplementary figure.

(3) In the introduction, I suggest the author provide more information about the previous studies about the structure and reaction mechanisms of BDCs, what is the knowledge gap, and what problem you will resolve with a higher resolution structure. For example, you mentioned in line 52 that G437 and A438 are catalytic residues, are these residues reported as catalytic residues or this is based on your structures? Has the catalytic mechanism been reported before? Has the role of biotin in catalytic reactions revealed in previous studies?

(4) In the discussion, the authors indicate that the movement of biotin could be related to the recognition of acyl-CoA in BDCs, however, they didn't observe a change in the propionyl-CoA bound MCC structure, which is contradictory to their speculation. What could be the explanation for the exception in the MCC structure?

(5) In the discussion, the authors indicate that the selectivity of PCC to different acyl-CoA is determined by the recognition of the acyl chain. However, there are no figures or descriptions about the recognition of the acyl chain by PCC and MCC. It will be more informative if they can show more details about substrate recognition in Figures 3 and 4.

(6) How are the solved structures compared with the latest Alphafold3 prediction?

Reviewer #1 (Public review):

Summary:

DMS-MaP is a sequencing-based method for assessing RNA folding by detecting methyl adducts on unpaired A and C residues created by treatment with dimethylsulfate (DMS). DMS also creates methyl adducts on the N7 position of G, which could be sensitive to tertiary interactions with that atom, but N7-methyl adducts cannot be detected directly by sequencing. In this work, the authors adopt a previously developed method for converting N7-methyl-G to an abasic site to make it detectable by sequencing and then show that the ability of DMS to form an N7-methyl-G adduct is sensitive to RNA structural context. In particular, they look at the G-quadruplex structure motif, which is dense with N7-G interactions, is biologically important, and lacks conclusive methods for in-cell structural analysis.

Strengths:

- The authors clearly show that established methods for detecting N7-methyl-G adducts can be used to detect those adducts from DMS and that the formation of those adducts is sensitive to structural context, particularly G-quadruplexes.

- The authors assess the N7-methyl-G signal through a wide range of useful probing analyses, including standard folding, adduct correlations, mutate-and-map, and single-read clustering.

- The authors show encouraging preliminary results toward the detection of G-quadruplexes in cells using their method. Reliable detection of RNA G-quadruplexes in cells is a major limitation for the field and this result could lead to a significant advance.

- Overall, the work shows convincingly that N7-methyl-G adducts from DMS provide valuable structural information and that established data analyses can be adapted to incorporate the information.

Weaknesses:

- Most of the validation work is done on the spinach aptamer and it and polyUG RNA are the only RNAs tested that have a known 3D structure. Although it is a useful model for validating this method, it does not provide a comprehensive view of what results to expect across varied RNA structures.

- It's not clear from this work what the predictive power of BASH-MaP would be when trying to identify G-quadruplexes in RNA sequences of unknown structure. Although clusters of G's with low reactivity and correlated mutations seem to be a strong signal for G-quadruplexes, no effort was made to test a range of G-rich sequences that are known to form G-quadruplexes or not. Having this information would be critical for assessing the ability of BASH-MaP to identify G-quadruplexes in cells.

- Although the authors present interesting results from various types of analysis, the code currently available on Github lacks the documentation and examples necessary to be useful to the broader community.

- There are aspects of the DAGGER analysis that could limit its robustness or utility for different RNAs:

(1) Folding of the RNA based on individual reads does not represent single-molecule folding since each read contains only a small fraction of the possible adducts that could have formed on that molecule. As a result, each fold will largely be driven by the naive folding algorithm. The DANCE-MaP algorithm that was also used by the authors addresses this concern.<br /> (2) G residues in a loop will have a different impact on RNA folding than those in a G-quadruplex. This difference could reduce the accuracy of CONTRAfold predictions when forcing G-quadruplex residues to be unpaired. That said, predicting secondary structure around G-quadruplexes is a challenge for folding algorithms.<br /> (3) Incorporation of the G mutations requires prior knowledge of the RNA 3D structure, limiting the utility of the method to predicting alternative conformations in structures that are already well characterized.

Reviewer #3 (Public review):

Summary:

In this study the authors aim to develop an experimental/computational pipeline to assess the modification status of an RNA following treatment with dimethylsulfate (DMS). Building upon the more common DMS Map method, which predominantly assesses the modification status of the Watson-Crick-Franklin face of A's and C's, the authors insert a chemical processing step in the workflow prior to deep sequencing that enables detection of methylation at the N7 position of guanosine residues. This approach, termed BASH MaP, provides a more complete assessment of the true modification status of an RNA following DMS treatment, and this new information provides a powerful set of constraints for assessing the secondary structure and conformational state of an RNA. In developing this work, the authors use Spinach as a model RNA. Spinach is a fluorogenic RNA that binds and activates the fluorescence of a small molecule ligand. Crystal structures of this RNA with ligand bound show that it contains a G-quadruplex motif. In applying BASH MaP to Spinach, the authors also perform the more standard DMS MaP for comparison. They show that the BASH MaP workflow appears to retain the information yielded by DMS MaP while providing new information about guanosine modifications. In Spinach, the G-quadruplex G's have the least reactive N7 positions, consistent with the engagement of N7 in hydrogen bonding interactions at G's involved in quadruplex formation. Moreover, because the inclusion of data corresponding to G increases the number of misincorporations per transcript, BASH MaP is more amenable to analysis of co-occurring misincorporations through statistical analysis, especially in combination with site-specific mutations. These co-occurring misincorporations provide information regarding what nucleotides are structurally coupled within an RNA conformation. By deploying a likelihood-ratio statistical test on BASH MaP data, the authors can identify Gs in G-quadruplexes, deconvolute G-G correlation networks, base-triple interactions and even stacking interactions. Further, the authors develop a pipeline to use the BASH MaP-derived G-modification data to assist in the prediction of RNA secondary structure and identify alternative conformations adopted by a particular RNA. This seems to help with the prediction of secondary structure for Spinach RNA.

Strengths:

The BASH Map procedure and downstream data analysis pipeline more fully identifies the complement of methylations to be identified from DMS treatment of RNA, thereby enriching the information content. This in turn allows for more robust computational/statistical analysis, which likely will lead to more accurate structure predictions. This seems to be the case for the Spinach RNA.

Weaknesses:

The authors demonstrate that their method can detect G-quadruplexes in Spinach and some other RNAs both in vitro and in cells. While application to other RNAs is beyond the scope of the current manuscript, the performance of BASH MaP and associated computational analysis in the context of other RNAs remains to be determined.

Reviewer #1 (Public review):

Using a knock-out mutant strain, the authors tried to decipher the role of the last gene in the mycofactocin operon, mftG. They found that MftG was essential for growth in the presence of ethanol as the sole carbon source, but not for the metabolism of ethanol, evidenced by the equal production of acetaldehyde in the mutant and wild type strains when grown with ethanol (Fig 3). The phenotypic characterization of ΔmftG cells revealed a growth-arrest phenotype in ethanol, reminiscent of starvation conditions (Fig 4). Investigation of cofactor metabolism revealed that MftG was not required to maintain redox balance via NADH/NAD+, but was important for energy production (ATP) in ethanol. Since mycobacteria cannot grow via substrate-level phosphorylation alone, this pointed to a role of MftG in respiration during ethanol metabolism. The accumulation of reduced mycofactocin points to impaired cofactor cycling in the absence of MftG, which would impact the availability of reducing equivalents to feed into the electron transport chain for respiration (Fig 5). This was confirmed when looking at oxygen consumption in membrane preparations from the mutant and would type strains with reduced mycofactocin electron donors (Fig 7). The transcriptional analysis supported the starvation phenotype, as well as perturbations in energy metabolism, and may be beneficial if described prior to respiratory activity data.<br /> The data and conclusions support the role of MftG in ethanol metabolism.

Reviewer #3 (Public review):

Summary:

The work by Graca et al. describes a GMC flavoprotein dehydrogenase (MftG) in the ethanol metabolism of mycobacteria and provides evidence that it shuttles electrons from the mycofactocin redox cofactor to the electron transport chain.

Strengths:

Overall, this study is compelling, exceptionally well designed and thoroughly conducted. An impressively diverse set of different experimental approaches is combined to pin down the role of this enzyme and scrutinize the effects of its presence or absence in mycobacteria cells growing on ethanol and other substrates. Other strengths of this work are the clear writing style and stellar data presentation in the figures, which makes it easy also for non-experts to follow the logic of the paper. Overall, this work therefore closes an important gap in our understanding of ethanol oxidation in mycobacteria, with possible implications for the future treatment of bacterial infections.

Weaknesses:

I see no major weaknesses of this work, which in my opinion leaves no doubt about the role of MftG.

Reviewer #4 (Public review):

Summary:

The manuscript by Graça et al. explores the role of MftG in the ethanol metabolism of mycobacteria. The authors hypothesise that MftG functions as a mycofactocin dehydrogenase, regenerating mycofactocin by shuttling electrons to the respiratory chain of mycobacteria. Although the study primarily uses M. smegmatis as a model microorganism, the findings have more general implications for understanding mycobacterial metabolism. Identifying the specific partner to which MftG transfers its electrons within the respiratory chain of mycobacteria would be an important next step, as pointed out by the authors.

Strengths:

The authors have used a wide range of tools to support their hypothesis, including co-occurrence analyses, gene knockout and complementation experiments, as well as biochemical assays and transcriptomics studies.<br /> An interesting observation that the mftG deletion mutant grown on ethanol as the sole carbon source exhibited a growth defect resembling a starvation phenotype.<br /> MftG was shown to catalyse the electron transfer from mycofactocinol to components of the respiratory chain, highlighting the flexibility and complexity of mycobacterial redox metabolism.

Weaknesses:

Could the authors elaborate more on the differences between the WT strains in Fig. 3C and 3E? in Fig. 3C, the ethanol concentration for the WT strain is similar to that of WT-mftG and ∆mftG-mftG, whereas the acetate concentration in thw WT strain differs significantly from the other two strains. How this observation relates to ethanol oxidation, as indicated on page 12.<br /> The authors conclude from their functional assays that MftG catalyses single-turnover reactions, likely using FAD present in the active site as an electron acceptor. While this is plausible, the current experimental set up doesn't fully support this conclusions, and the language around this claim should be softened.<br /> The authors suggest in the manuscript that the quinone pool (page 24) may act as the electron acceptor from mycofactocinol, but later in in the discussion section (page 30) they propose cytochromes as the potential recipients. If the authors consider both possibilities valid, I suggest discussing both options in the manuscript.

Reviewer #1 (Public review):

The authors sought to determine the impact of early antiretroviral treatment on the size, composition, and decay of the HIV latent reservoir. This reservoir represents the source of viral rebound upon treatment interruption and therefore constitutes the greatest challenge to achieving an HIV cure. A particular strength of this study is that it reports on reservoir characteristics in African women, a significantly understudied population, of whom some have initiated treatment within days of acute HIV diagnosis. With the use of highly sensitive and current technologies, including digital droplet PCR and near full-length genome next-generation sequencing, the authors generated a valuable dataset for investigation of proviral dynamics in women initiating early treatment compared to those initiating treatment in chronic infection. The authors confirm previous reports that early antiretroviral treatment restricts reservoir size, but further show that this restriction extends to defective viral genomes, where late treatment initiation was associated with a greater frequency of defective genomes. Furthermore, an additional strength of this study is the longitudinal comparison of viral dynamics post-treatment, wherein early treatment was shown to be associated with a more rapid rate of decay in proviral genomes, regardless of intactness, over a period of one year post-treatment. While it is indicated that intact genomes were not detected after one year following early treatment initiation, sampling depth is noted as a limitation of the study by the authors, and caution should thus be taken with interpretation where sequence numbers are low. Defective genomes are more abundant than intact genomes and are therefore more likely to be sampled. Early treatment was also associated with reduced proviral diversity and fewer instances of polymorphisms associated with cytotoxic T-lymphocyte immune selection. This is expected given that rapid evolution and extensive immune selection are synonymous with HIV infection in the absence of treatment, yet points to an additional benefit of early treatment in the context of immune therapies to restrict the reservoir.

This is one of the first studies to report the mapping of longitudinal intactness of proviral genomes in the globally dominant subtype C. The data and findings from this study therefore represent a much-needed resource in furthering our understanding of HIV persistence and informing broadly impactful cure strategies. The analysis on clonal expansion of proviral genomes may be limited by higher sequence homogeneity in hyperacute infection i.e., cells with different proviral integration sites may have a higher likelihood of containing identical genomes compared to chronic infection.

Overall, these data demonstrate the distinct benefits of early treatment initiation at reducing the barrier to a functional cure for HIV, not only by restricting viral abundance and diversity but also potentially through the preservation of immune function and limiting immune escape. It therefore provides clues to curative strategies even in settings where early diagnosis and treatment may be unlikely.

Reviewer #2 (Public review):

HIV infection is characterized by viral integration into permissive host cells - an event that occurs very early in viral-host encounter. This constitutes the HIV proviral reservoir and is a feature of HIV infection that provides the greatest challenge for eradicating HIV-1 infection once an individual is infected.

This study looks at how starting HIV treatment very early after infection, which substantially reduces the peak viral load detectable (compared to untreated infection), affects the amount and characteristics of the viral reservoir. The authors studied 35 women in South Africa who were at high risk of getting HIV. Some of these women started HIV treatment very soon after getting infected, while others started later. This study is well designed and has as its focus a very well characterized cohort. Comparison groups are appropriately selected to address proviral DNA characterization and dynamics in the context of acute and chronic treated HIV-1. The amount of HIV and various characteristics of the genetic makeup of the virus (intact/defective proviral genome) was evaluated over one year of treatment. Methods employed for proviral DNA characterization are state of the art and provide in-depth insights into the reservoir in peripheral blood.

While starting treatment early didn't reduce the amount of HIV DNA at the outset, it did lead to a gradual decrease in total HIV DNA quantity over time. In contrast, those who started treatment later didn't see much change in this parameter. Starting treatment early led to a faster decrease in intact provirus (a measure of replication-competence), compared to starting treatment later. Additionally, early treatment reduced genetic diversity of the viral DNA and resulted in fewer immune escape variants within intact genomes. This suggests that collectively having a smaller intact replication-competent reservoir, less viral variability, and less opportunity for virus to evade the immune system - are all features that are likely to facilitate more effective clearance of viral reservoir, especially when combined with other intervention strategies.

Major strengths of the study include the cohort of very early treated persons with HIV and the depth of study. These are important findings, particularly as the study was conducted in HIV-1 subtype C infected women (more cure studies have focussed on men and with subtype B infection)- and in populations most affected by HIV and in need of HIV cure interventions. This is highly relevant because it cannot be assumed that any interventions employed for reducing/clearing the HIV reservoir would perform similarly in men and women or across different populations. Other factors also deserve consideration and include age, and environment (e.g. other comorbidities and coinfections).

Reviewer #1 (Public review):

Summary:

The present study's main aim is to investigate the mechanism of how VirR controls the magnitude of MEV release in Mtb. The authors used various techniques, including genetics, transcriptomics, proteomics, and ultrastructural and biochemical methods. Several observations were made to link VirR-mediated vesiculogenesis with PG metabolism, lipid metabolism, and cell wall permeability. Finally, the authors presented evidence of a direct physical interaction of VirR with the LCP proteins involved in linking PG with AG, providing clues that VirR might act as a scaffold for LCP proteins and remodel the cell wall of Mtb. Since the Mtb cell wall provides a formidable anatomical barrier for the entry of antibiotics, targeting VirR might weaken the permeability of the pathogen along with the stimulation of the immune system due to enhanced vesiculogenesis. Therefore, VirR could be an excellent drug target. Overall, the study is an essential area of TB biology.

Strengths:

The authors have done a commendable job of comprehensively examining the phenotypes associated with the VirR mutant using various techniques. Application of Cryo-EM technology confirmed increased thickness and altered arrangement of CM-L1 layer. The authors also confirmed that increased vesicle release in the mutant was not due to cell lysis, which contrasts with studies in other bacterial species.

Another strength of the manuscript is that biochemical experiments show altered permeability and PG turnover in the mutant, which fits with later experiments where authors provide evidence of a direct physical interaction of VirR with LCP proteins.

Transcriptomics and proteomics data were helpful in making connections with lipid metabolism, which the authors confirmed by analyzing the lipids and metabolites of the mutant.

Lastly, using three approaches, the authors confirm that VirR interacts with LCP proteins in Mtb via the LytR_C terminal domain.

Altogether, the work is comprehensive, experiments are designed well, and conclusions were made based on the data generated after verification using multiple complementary approaches.

Weaknesses:

The major weakness is that the mechanism of VirR-mediated EV release remains enigmatic. Most of the findings are observational and only associate enhanced vesiculogenesis observed in the VirR mutant with cell wall permeability and PG metabolism. Authors suggest that EV release occurs during cell division when PG is most fragile. However, this has yet to be tested in the manuscript - the AFM of the VirR mutant, which produces thicker PG with more pore density, displays enhanced vesiculogenesis. No evidence was presented to show that the PG of the mutant is fragile, and there are differences in cell division to explain increased vesiculogenesis. These observations, counterintuitive to the authors' hypothesis, need detailed experimental verification.

Transcriptomic data only adds a little substantial. Transcriptomic data do not correlate with the proteomics data. It remains unclear how VirR deregulates transcription. TLCs of lipids are not quantitative. For example, the TLC image of PDIM is poor; quantitative estimation needs metabolic labeling of lipids with radioactive precursors. Further, change in PDIMs is likely to affect other lipids (SL-1, PAT/DAT) that share a common precursor (propionyl- CoA).

The connection of cholesterol with cell wall permeability is tenuous. Cholesterol will serve as a carbon source and contribute to the biosynthesis of methyl-branched lipids such as PDIM, SL-1, and PAD/DAT. Carbon sources also affect other aspects of physiology (redox, respiration, ATP), which can directly affect permeability and import/export of drugs. Authors should investigate whether restoration of the normal level of permeability and EV release is not due to the maintenance of cell wall lipid balance upon cholesterol exposure of the VirR mutant.

Finally, protein interaction data is based on experiments done once without statistical analysis. If the interaction between VirR and LCP protein is expected on the mycobacterial membrane, how SPLIT_GFP system expressed in the cytoplasm is physiologically relevant. No explanation was provided as to why VirR interacts with the truncated version of LCP proteins and not with the full-length proteins.

Reviewer #2 (Public review):

Summary:

In this work, Vivian Salgueiro et al. have comprehensively investigated the role of VirR in the vesicle production process in Mtb using state-of-the-art omics, imaging, and several biochemical assays. From the present study, authors have drawn a positive correlation between cell membrane permeability and vasculogenesis and implicated VirR in affecting membrane permeability, thereby impacting vasculogenesis.

Strengths:

The authors have discovered a critical factor (i.e. membrane permeability) that affects vesicle production and release in Mycobacteria, which can broadly be applied to other bacteria and may be of significant interest to other scientists in the field. Through omics and multiple targeted assays such as targeted metabolomics, PG isolation, analysis of Diaminopimelic acid and glycosyl composition of the cell wall, and, importantly, molecular interactions with PG-AG ligating canonical LCP proteins, the authors have established that VirR is a central scaffold at the cell envelope remodelling process which is critical for MEV production.

Weaknesses:

Throughout the study, the authors have utilized a CRISPR knockout of VirR. VirR is a non-essential gene for the growth of Mtb; a null mutant of VirR would have been a better choice for the study.

Comments on the revised version:

Concerns flagged about using CRISPR -guide RNA mediated knockdown of viral has yet to be addressed entirely. I understand that the authors could not get knock out despite attempts and hence they have guide RNA mediated knockdown strategy. However, I wondered if the authors looked at the levels of the downstream genes in this knockdown.

Authors have used the virmut-Comp strain for some of the experiments. However, the materials and methods must describe how this strain was generated. Given the mutant is a CRISPR-guide RNA mediated knockdown. The CRISPR construct may have taken up the L5 loci. Did authors use episomal construct for complementation? If so, what is the expression level of virR in the complementation construct? What are the expression levels of downstream genes in mutant and complementation strains? This is important because the transcriptome analysis was redone by considering complementation strain. The complemented strain is written as virmut-C or virmut-Comp. This has to be consistent.

Reviewer #1 (Public review):

Summary:

In this paper, Hackwell and colleagues performed technically impressive, long-term, GCaMP fiber photometry recordings from Kiss1 neurons in the arcuate nucleus of mice during multiple reproductive states. The data show an immediate suppression of activity of arc Kiss1 neuronal activity during pregnancy that is maintained during lactation. In the absence of any apparent change in suckling stimulus or milk production, mice lacking prolactin receptors in arcuate Kiss1 neurons regained Kiss1 episodic activity and estrous cyclicity faster than control mice, demonstrating that direct prolactin action on Kiss1 neurons is at least partially responsible for suppressing fertility in this species. The effect of loss of prolactin receptors from CamK2a expressing neurons was even greater, indicating either that prolactin sensitivity in Kiss1 neurons of the RP3V contributes to lactational infertility or that other prolactin-sensitive neurons are involved. These data demonstrate the important role of prolactin in suppressing Kiss1 neuron activity and thereby fertility during the lactational period in the mouse.

Strengths:

This is the first study to monitor activity of the GnRH pulse-generating system across different reproductive states in the same animal. Another strength in the study design is that it isolated the effects of prolactin by maintaining normal lactation and suckling (assessed indirectly using pup growth curves). The study also offers insight into the phenomenon of postpartum ovulation in mice. The results showed a brief reactivation of arcuate Kiss1 activity immediately prior to parturition, attributed to falling progesterone levels at the end of pregnancy. This hypothesis will be of interest to the field and is likely to inspire testing in future studies. With the exceptions mentioned below, the conclusions of the paper are well supported by the data and the aims of the study were achieved. This paper is likely to raise the standard for technical expectations in the field and spark new interest in the direct impact of prolactin on Kiss1 neurons during lactation in other species.

Weaknesses:

A weakness in the approach is the use of genetic models that do not offer complete deletion of the prolactin receptor from targeted neuronal populations. A substantial proportion of Kiss1 neurons in both models retains the receptor. As a result, it is not clear whether the partial maintenance of cyclicity during lactation in the genetic models is due to incomplete deletion or to the involvement of other factors. In addition, results showing no impact of progesterone on LH secretion during lactation are surprising, given the effectiveness of progesterone-containing birth control in lactating women. While the authors assert their findings may reflect an important role for prolactin in lactational infertility in other mammalian species, that remains to be seen. Hyperprolactinemia is known to suppress GnRH release, but its importance in the suppression of cyclicity during the lactation is controversial. Indeed, in several species, the stimulus of suckling is considered to be the main driver of lactational fertility suppression. Data from rats shows that exogenous prolactin was unable to suppress LH release in dams deprived of their pups shortly after birth; both suckling and prolactin were necessary to suppress a post-ovariectomy rise in LH levels. The duration of amenorrhea does not correlate with average prolactin levels in humans, and suckling but not prolactin was required to suppress the postpartum rise in LH in the rhesus monkey. The protocol of this or other studies might result in discordant results; alternatively, mice may be an outlier in their mechanism of cycle suppression.

Comments on revised version:

I remain enthusiastic about this article, which has been substantially improved in this revision. However, I didn't feel the authors responded to any of the points I raised previously in my public review (see Weaknesses), for example by adding to the manuscript's discussion section. These are the larger, conceptual issues that speak to the value of the paper in the context of the existing literature. The authors could also state they feel they have addressed the issues raised sufficiently in the text.