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:

The authiors show that SVZ derived astrocytes respond to a middle carotid artery occlusion (MCAO) hypoxia lesion by secreting and modulating hyaluronan at the edge of the lesion (penumbra) and that hyaluronin is a chemoattractant to SVZ astrocytes. They use lineage tracing of SVZ cells to determine their origin. They also find that SVZ derived astrocytes express Thbs-4 but astrocytes at the MCAO-induced scar do not. Also, they demonstrate that decreased HA in the SVZ is correlated with gliogenesis. While much of the paper is descriptive/correlative they do overexpress Hyaluronan synthase 2 via viral vectors and show this is sufficient to recruit astrocytes to the injury. Interestingly, astrocytes preferred to migrate to the MCAO than to the region of overexpressed HAS2.

Strengths:

The field has largely ignored the gliogenic response of the SVZ, especially with regards to astrocytic function. These cells and especially newborn cells may provide support for regeneration. Emigrated cells from the SVZ have been shown to be neuroprotective via creating pro-survival environments, but their expression and deposition of beneficial extracellular matrix molecules is poorly understood. Therefore, this study is timely and important. The paper is very well written and the flow of results logical.

Comments on revised version:

The authors have addressed my points and the paper is much improved. Here are the salient remaining issues that I suggest be addressed.

The authors have still not shown, using loss of function studies, that Hyaluronan is necessary for SVZ astrogenesis and or migration to MCAO lesions.

(1) The co-expression of EGFr with Thbs4 and the literature examination is useful.

(2) Too bad they cannot explain the lack of effect of the MCAO on type C cells. The comparison with kainate-induced epilepsy in the hippocampus may or may not be relevant.

(3) Thanks for including the orthogonal confocal views in Fig S6D.

(4) The statement that "BrdU+/Thbs4+ cells mostly in the dorsal area" and therefore they mostly focused on that region is strange. Figure 8 clearly shows Thbs4 staining all along the striatal SVZ. Do they mean the dorsal segment of the striatal SVZ or the subcallosal SVZ? Fig. 4b and Fig 4f clearly show the "subcallosal" area as the one analysed but other figures show the dorsal striatal region (Fig. 2a). This is important because of the well-known embryological and neurogenic differences between the regions.

(5) It is good to know that the harsh MCAO's had already been excluded.

(6) Sorry for the lack of clarity - in addition to Thbs4, I was referring to mouse versus rat Hyaluronan degradation genes (Hyal1, Hyal2 and Hyal3) and hyaluronan synthase genes (HAS1 and HAS2) in order to address the overall species differences in hyaluronan biology thus justifying the "shift" from mouse to rat. You examine these in the (weirdly positioned) Fig. 8h,i. Please add a few sentences on mouse vs rat Thbs4 and Hyaluronan relevant genes.

(7) Thank you for the better justification of using the naked mole rat HA synthase.

Reviewer #3 (Public review):

Summary:

The authors aimed to study the activation of gliogenesis and the role of newborn astrocytes in a post-ischemic scenario. Combining immunofluorescence, BrdU-tracing and genetic cellular labelling, they tracked the migration of newborn astrocytes (expressing Thbs4) and found that Thbs4-positive astrocytes modulate the extracellular matrix at the lesion border by synthesis but also degradation of hyaluronan. Their results point to a relevant function of SVZ newborn astrocytes in the modulation of the glial scar after brain ischemia. This work's major strength is the fact that it is tackling the function of SVZ newborn astrocytes, whose role is undisclosed so far.

Strengths:

The article is innovative, of good quality, and clearly written, with properly described Materials and Methods, data analysis and presentation. In general, the methods are designed properly to answer the main question of the authors, being a major strength. Interpretation of the data is also in general well done, with results supporting the main conclusions of this article.

In this revised version, the points raised/weaknesses were clarified and discussed in the article.

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.

Reviewer #2 (Public review):

Summary:

The overall goal of Eleni et al. is to determine if the suppression of LH pulses during lactation is mediated by prolactin signaling at kisspeptin neurons. To address this, the authors used GCaMP fiber photometry and serial blood sampling to reveal that in vivo episodic arcuate kisspeptin neuron activity and LH pulses are suppressed throughout pregnancy and lactation. The authors further utilized knockout models to demonstrate that the loss of prolactin receptor signaling at kisspeptin cells prevents the suppression of kisspeptin cell activity and results in the early reestablishment of fertility during lactation. The work demonstrates exemplary design and technique, and the outcomes of these experiments are sophistically discussed.

Strengths:

This manuscript demonstrates exceptional skill with powerful techniques and reveals a key role for arcuate kisspeptin neurons in maintaining lactation-induced infertility in mice. In a difficult feat, the authors used fiber photometry to map the activity of arcuate kisspeptin cells into lactation and weaning without disrupting parturition, lactation, or maternal behavior. The authors used a knockout approach to identify if the inhibition of fertility by prolactin is mediated via direct signaling at arcuate kisspeptin cells. Although the model does not perfectly eliminate prolactin receptor expression in all kisspeptin neurons, results from the achieved knockdown support the conclusion that prolactin signaling at kisspeptin neurons is required to maintain lactational infertility. The methods are advanced and appropriate for the aims, the study is rigorously conducted, and the conclusions are thoughtfully discussed.

Comments on the latest version:

All comments and suggestions have been addressed by the authors in this revision.

Reviewer #1 (Public review):

Summary:

Wang and colleagues presented an investigation of pig-origin bacteria Bacillus velezensis HBXN2020, for its released genome sequence, in vivo safety issue, probiotic effects in vitro, and protection against Salmonella infection in a murine model. Various techniques and assays are performed; the main results are all descriptive, without new insight advancing the field or a mechanistic understanding of the observed protection.

Strengths:

An extensive study on the probiotic properties of the Bacillus velezensis strain HBXN2020

Weaknesses:

The main results are descriptive without mechanistic insight. Additionally, most of the results and analysis parts are separated without a link or a story-telling way to deliver a concise message.

Now the manuscript has made appropriate and considerable improvements.

Joint Public Review:

When the left-right asymmetry of an animal body is established, a barrier that prevents the mixing of signals or cells across the midline is essential. Such midline barrier preventing the spreading of asymmetric Nodal signaling during early left-right patterning has been identified. However, midline barriers during later asymmetric organogenesis have remained largely unknown, except in the brain. In this study, the authors discovered an unexpected structure in the midline of the developing midgut in the chick. Using immunofluorescence, they convincingly show the chemical composition of this midline structure as a double basement membrane and its transient existence during the left-right patterning of the dorsal mesentery, that authors showed previously to be essential for forming the gut loop and guiding local vasculogenesis. Labelling experiments demonstrate a physical and chemical barrier function, to cell mixing and signal diffusion in the dorsal mesentery. Cell labelling and graft experiments rule out a cellular composition of the midline from dorsal mesenchyme or endoderm origin and rule out an inducing role by the notochord. Based on laminin expression pattern and Ntn4 resistance, the authors propose a model, whereby the midline basement membrane is progressively deposited by the descending endoderm. Observations of a transient midline basement membrane in the veiled chameleon suggest a conserved mechanism in birds and reptiles.

Laterality defects encompass severe malformations of visceral organs, with a heterogenous spectrum that remains poorly understood, by lack of knowledge of the different players of left-right asymmetry. This fundamental work significantly advances our understanding of left-right asymmetric organogenesis, by identifying an organ-specific and stage-specific midline barrier. The complexities of basement membrane assembly, maintenance and function are of importance in several other contexts, as for example in the kidney and brain. Thus, this original work is of broad interest.

Overall, reviewers refer to a strong and elegant paper discovering a novel midline structure, combining classic but challenging techniques, and well thought tools, to show the dynamics, chemical and physical properties of the midline. Reviewers also indicate that further work will be necessary to conclude on the origin and impact of the midline for asymmetric organogenesis. They acknowledge that this is currently technically challenging and that authors have made several attempts to answer these questions by different means. The article includes an interesting discussion about these points and the mechanism of midline breakdown.

Reviewer #2 (Public review):

The fledgling field of epitranscriptomics has encountered various technical roadblocks with implications as to the validity of early epitranscriptomics mapping data. As a prime example, the low specificity of (supposedly) modification-specific antibodies for the enrichment of modified RNAs, has been ignored for quite some time and is only now recognized for its dismal reproducibility (between different labs), which necessitates the development of alternative methods for modification detection.

Furthermore, early attempts to map individual epitranscriptomes using sequencing-based techniques are largely characterized by the deliberate avoidance of orthogonal approaches aimed at confirming the existence of RNA modifications that have been originally identified.

Improved methodology, the inclusion of various controls, and better mapping algorithms as well as the application of robust statistics for the identification of false-positive RNA modification calls have allowed revisiting original (seminal) publications whose early mapping data allowed making hyperbolic claims about the number, localization and importance of RNA modifications, especially in mRNA. Besides the existence of m6A in mRNA, the detectable incidence of RNA modifications in mRNAs has drastically dropped.

As for m5C, the subject of the manuscript submitted by Zhou et al., its identification in mRNA goes back to Squires et al., 2012 reporting on >10.000 sites in mRNA of a human cancer cell line, followed by intermittent findings reporting on pretty much every number between 0 to > 100.000 m5C sites in different human cell-derived mRNA transcriptomes. The reason for such discrepancy is most likely of a technical nature. Importantly, all studies reporting on actual transcript numbers that were m5C-modified relied on RNA bisulfite sequencing, an NGS-based method, that can discriminate between methylated and non-methylated Cs after chemical deamination of C but not m5C. RNA bisulfite sequencing has a notoriously high background due to deamination artifacts, which occur largely due to incomplete denaturation of double-stranded regions (denaturing-resistant) of RNA molecules. Furthermore, m5C sites in mRNAs have now been mapped to regions that have not only sequence identity but also structural features of tRNAs. Various studies revealed that the highly conserved m5C RNA methyltransferases NSUN2 and NSUN6 do not only accept tRNAs but also other RNAs (including mRNAs) as methylation substrates, which in combination account for most of the RNA bisulfite-mapped m5C sites in human mRNA transcriptomes. Is m5C in mRNA only a result of the Star activity of tRNA or rRNA modification enzymes, or is their low stoichiometry biologically relevant?<br /> In light of the short-comings of existing tools to robustly determine m5C in transcriptomes, other methods, like DRAM-seq, allowing to map m5C independently of ex situ RNA treatment with chemicals, are needed to arrive at a more solid "ground state", from which it will be possible to state and test various hypotheses as to the biological function of m5C, especially in lowly abundant RNAs such as mRNA.

Importantly, the identification of >10.000 sites containing m5C increases through DRAM-Seq, increases the number of potential m5C marks in human cancer cells from a couple of 100 (after rigorous post-hoc analysis of RNA bisulfite sequencing data) by orders of magnitude. This begs the question, whether or not the application of these editing tools results in editing artefacts overstating the number of actual m5C sites in the human cancer transcriptome.

Remaining comments after resubmission:

(1) The use of two m5C reader proteins is likely a reason for the high number of edits introduced by the DRAM-Seq method. Both ALYREF and YBX1 are ubiquitous proteins with multiple roles in RNA metabolism including splicing and mRNA export. It is reasonable to assume that both ALYREF and YBX1 bind to many mRNAs that do not contain m5C.<br /> To substantiate the author's claim that ALYREF or YBX1 binds m5C-modified RNAs to an extent that would allow distinguishing its binding to non-modified RNAs from binding to m5C-modified RNAs, it would be recommendable to provide data on the affinity of these, supposedly proven, m5C readers to non-modified versus m5C-modified RNAs. To do so, this reviewer suggests performing experiments as described in Slama et al., 2020 (doi: 10.1016/j.ymeth.2018.10.020). Mind you that using dot blots like in so many published studies to show modification-specific antibody or protein binding, is insufficient as an argument because no antibody, nor protein encounters nanograms to micrograms of a specific RNA identity in a cell. This issue remains a major caveat in all studies using so-called RNA modification reader proteins as bait for detecting RNA modifications in epitranscriptomics research and becomes a pertinent problem, if used as a platform for base-editing similar to the work presented in this manuscript.

(2) Using sodium arsenite treatment of cells as a means to change the m5C status of transcripts through the downregulation of the two major m5C writer proteins NSUN2 and NSUN6 is problematic and the conclusions from these experiments are not warranted. Sodium arsenite is a chemical that poisons every protein containing thiol groups. Not only do NSUN proteins contain cysteines but also the base editor fusion proteins. Arsenite will inactivate these proteins, hence the editing frequency will drop, as observed in the experiments shown in Figure 5, which the authors explain with fewer m5C sites to be detected by the fusion proteins.

(3) The authors should move high-confidence editing site data contained in Supplementary Tables 2 and 3 into one of the main Figures to substantiate what is discussed in Figure 4A. However, the data needs to be visualized in another way then excel format. Furthermore, Supplementary Table 2 does not contain a description of the columns, while Supplementary Table 3 contains a single row with letters and numbers.

Reviewer #1 (Public review):

Summary:

The authors use analysis of existing data, mathematical modelling, and new experiments, to explore the relationship between protein expression noise, translation efficiency, and transcriptional bursting.

Strengths:

The analysis of the old data and the new data presented is interesting and mostly convincing.

Weaknesses:

(1) My main concern is the analysis presented in Figure 4. This is the core of mechanistic analysis that suggests ribosomal demand can explain the observed phenomenon. I am both confused by the assumptions used here and the details of the mathematical modelling used in this section. Firstly, the authors' assumption that the fluctuations of a single gene mRNA levels will significantly affect ribosome demand is puzzling. On average the total level of mRNA across all genes would stay very constant and therefore there are no big fluctuations in the ribosome demand due to the burstiness of transcription of individual genes. Secondly, the analysis uses 19 mathematical functions that are in Table S1, but there are not really enough details for me to understand how this is used, are these included in a TASEP simulation? In what way are mRNA-prev and mRNA-curr used? What is the mechanistic meaning of different terms and exponents? As the authors use this analysis to argue ribosomal demand is at play, I would like this section to be very much clarified.

(2) Overall, the paper is very long and as there are analytical expressions for protein noise (e.g. see Paulsson Nature 2004), some of these results do not need to rely on Gillespie simulations. Protein CV (noise) can be written as three terms representing protein noise contribution, mRNA expression contribution, and bursty transcription contribution. For example, the results in panel 1 are fully consistent with the parameter regime, protein noise is negligible compared to transcriptional noise.

Reviewer #2 (Public review):

This work by Pal et al. studied the relationship between protein expression noise and translational efficiency. They proposed a model based on ribosome demand to explain the positive correlation between them, which is new as far as I realize. Nevertheless, I found the evidence of the main idea that it is the ribosome demand generating this correlation is weak. Below are my major and minor comments.

Major comments:

(1) Besides a hypothetical numerical model, I did not find any direct experimental evidence supporting the ribosome demand model. Therefore, I think the main conclusions of this work are a bit overstated.

(2) I found that the enhancement of protein noise due to high translational efficiency is quite mild, as shown in Figure 6A-B, which makes the biological significance of this effect unclear.

(3) The captions for most of the figures are short and do not provide much explanation, making the figures difficult to read.

(4) It would be helpful if the authors could define the meanings of noise (e.g., coefficient of variation?) and translational efficiency in the very beginning to avoid any confusion. It is also unclear to me whether the noise from the experimental data is defined according to protein numbers or concentrations, which is presumably important since budding yeasts are growing cells.

(5) The conclusions from Figures 1D and 1E are not new. For example, the constant protein noise as a function of mean protein expression is a known result of the two-state model of gene expression, e.g., see Equation (4) in Paulsson, Physics of Life Reviews 2005.

(6) In Figure 4C-D, it is unclear to me how the authors changed the mean protein expression if the translation initiation rate is a function of variation in mRNA number and other random variables.

(7) If I understand correctly, the authors somehow changed the translation initiation rate to change the mean protein expression in Figures 4C-D. However, the authors changed the protein sequences in the experimental data of Figure 6. I am not sure if the comparison between simulations and experimental data is appropriate.

Reviewer #1 (Public review):

Summary:

Numerous mechanism and structural studies reported the cooperative role of Oct4 and Sox2 during the establishment of pluripotency during reprogramming. Due to the difficulty in sample collection and RNA-seq with low-number cells, the precise mechanisms remain in early embryos. This manuscript reported the role of OCT4 and SOX2 in mouse early embryos using knockout models with low-input ATAC-seq and RNA-seq. Compared to the control, chromatin accessibility and transcriptome were affected when Oct4 and Sox2 were deleted in early ICM. Specifically, decreased ATAC-seq peaks showed enrichment of Motifs of TF such as OCT, SOX, and OCT-SOX, indicating their importance during early development. Moreover, by deep analysis of ATAC-seq and RNA-seq data, they found Oct4 and Sox2 target enhancer to activate their downstream genes. In addition, they also uncovered the role of OS during development from the morula to ICM, which provided the scientific community with a more comprehensive understanding.

Strengths:

On the whole, the manuscript is innovative, and the conclusions of this paper are mostly well supported by data, however, there are some issues that need to be addressed.

Weaknesses:

Major Points:

(1) In Figure 1, a more detailed description of the knockout strategy should be provided to clarify itself. The knockout strategy in Fig1 is somewhat obscure, such as how is OCT4 inactivated in Oct4mKO2 heterozygotes. As shown in Figure 1, the exon of OCT4 is not deleted, and its promoter is not destroyed. Therefore, how does OCT4 inactivate to form heterozygotes?

(2) Is ZP 3-Cre expressed in the zygotes? Is there any residual protein?

(3) What motifs are enriched in the rising ATAC-seq peaks after knocking out of OCT4 and SOX2?

(4) The ordinate of Fig4c is lost.

(5) Signals of H3K4me1, H3K27ac, and so on are usually used to define enhancers, and the loci of enhancers vary greatly in different cells. In the manuscript, the authors defined ATAC-seq peaks far from the TSS as enhancers. The definition in this manuscript is not strictly an enhancer.

(6) If Oct4 and Sox2 truly activate sap 30 and Uhrf 1, what effect does interfering with both genes have on gene expression and chromatin accessibility？

Reviewer #2 (Public review):

In this manuscript, Hou et al. investigate the interplay between OCT4 and SOX2 in driving the pluripotent state during early embryonic lineage development. Using knockout (KO) embryos, the authors specifically analyze the transcriptome and chromatin state within the ICM-to-EPI developmental trajectory. They emphasize the critical role of OCT4 and the supportive function of SOX2, along with other factors, in promoting embryonic fate. Although the paper presents high-quality data, several key claims are not well-supported, and direct evidence is generally lacking.

Major Points:

(1) Although the authors claim that both maternal KO and maternal KO/zygotic hetero KO mice develop normally, the molecular changes in these groups appear overestimated. A wildtype control is recommended for a more robust comparison.

(2) The authors assert that OCT4 and SOX2 activate the pluripotent network via the OCT-SOX enhancer. However, the definition of this enhancer is based solely on proximity to TSSs, which is a rough approximation. Canonical enhancers are typically located in intronic and intergenic regions and marked by H3K4me1 or H3K27ac. Re-analyzing enhancer regions with these standards could be beneficial. Additionally, the definitions of "close to" or "near" in lines 183-184 are unclear and not defined in the legends or methods.

(3) There is no evidence that the decreased peaks/enhancers could be the direct targets of Oct4 and Sox2 throughout this manuscript. Figures 2 and 4 show only minimal peak annotations related to OCT and SOX motifs, and there is a lack of chromatin IP data. Therefore, claims about direct targets are not substantiated and should be appropriately revised.

(4) Lines 143-146 lack direct data to support the claim. Actually, the main difference in cluster I, 11 and 3, 8, 14 is whether the peak contains OCT-SOX motif. However, the reviewer cannot get any information of peaks activated by OCT4 rather than SOX2 in cluster I, 11.

Minor Points:

(1) Lines 153-159: The figure panel does not show obvious enrichment of SOX2 signals or significant differences in H3K27ac signals across clusters, thus not supporting the claim.

(2) Lines 189-190: The term "identify" is overstated for the integrative analysis of RNA-seq and ATAC-seq, which typically helps infer TF targets rather than definitively identifying them.

(3) The Discussion is lengthy and should be condensed.

Reviewer #1 (Public review):

Summary:

This study makes use of the EM reconstruction of the fly brain to investigate the morphology and topography of the synapses between retinotopic, loom-sensitive visual projection neurons (VPNs) and downstream descending neurons (DNs). The authors analyzed the distribution of synapses on the dendritic trees of DNs and performed multi-compartmental modelling to study the implications of the synaptic arrangements for neuronal integration of input signals.

Until recently, it has been unclear how spatial information is passed from retinotopic loom-sensitive neurons to descending neurons because the axons of the VPNs terminate in small optic glomeruli with no apparent topographic organization. It has recently been shown that synaptic weight gradients of VPNs connecting to DNs are the main mechanisms that allow for directed behavioral output (Dombrovski et al.). This study now goes one step further to determine if precise synapse location on the dendritic tree contributes further to the information processing. The study suggests that (1) none of the VPNs investigated show a retinotopic organization of synapses on DN dendrites. (2) Synapses of single VPNs are locally clustered. (3) Initial EPSPs at the synaptic location have, as expected, varying amplitudes but the amplitudes are passively normalized and only cover a small range when measured at the SIZ. (4) A near random distribution of synapses allows for linear integration of synaptic inputs when only a few VPNs are activated.

Strengths:

This study provides a detailed picture of the synapse distribution for a set of VPN and DN pairs, in combination with multi-compartmental modelling fitted to electrophysiological data. The data and methods are clear. The findings are overall interesting. The computational pipeline, which should ideally be made publicly available, will allow the community to make similar analyses on different neuronal classes, which will facilitate the detection of more general mechanisms of dendritic computation.

Weaknesses:

- In my opinion, we need more detail on the electrophysiological data and the fitting of the multi-compartmental model, which is the foundation of large parts of the study.<br /> - The study shows that the synapses of an individual VPN are locally clustered and suggests this as evidence for clustering of synapses of similar tuning (as has been shown previously in other systems). I am not fully convinced by the arguments here, since synapses of a single neuron are by necessity not randomly distributed in space.<br /> - As written, it was in parts unclear to me what the main hypotheses and conclusions were - e.g., how would a retinotopic distribution of synapses on dendritic trees contribute to information processing? Are the model predictions in line with the presumed behavioural role of these neurons?

Reviewer #2 (Public review):

Summary:

This article investigates the distribution of synapses on the dendritic arbors of descending neurons in the looming circuit of the fly visual system. The authors use publicly available EM reconstruction data of the adult fly brain to identify the positions of synapses from several types of visual projection neuron (VPN) to descending neuron (DN) connections. VPN dendrites are retinotopically organized, and axons from different VPN populations innervate distinct optic glomeruli. Yet the authors did not find any retinotopic organization of the synapses in the VPN-DN pairs they analyzed. They then constructed passive electrical models of the DNs with their structures extracted from the EM reconstructions. They focused on two specific DNs and parameterized their models by conducting whole-cell recordings within a voltage range below spiking threshold. Simulation of these passive models showed that irrespective of the location of a synapse, EPSPs became very similar at the spike initiation zone. This is consistent with the idea of synaptic democracy where EPSPs at far away synapses have higher amplitude compared to those nearer to the spike initiation zone so that they all attenuate to similar amplitudes while reaching there. The authors found that activating synapses from individual VPNs have the same effect as activating a random set of synapses. They conclude that despite some clustering of VPN synapses at small scale, they are distributed randomly over the dendritic arbor of DNs so that their EPSP amplitude encode the number of activated synapses, avoiding sublinearity from shunting effect.

Strengths:

- Experimental confirmation of the location of the spike initiation zone in the DN arbors is interesting and may provide better understanding of signal processing in these neurons.<br /> - Passive parameters obtained through electrophysiological recordings are useful.<br /> - These morphologically detailed single neuron models, if made available publicly, will be beneficial for building more complete models to understand the fly visual circuit.<br /> - The authors have complemented the work of Dombrovski et al by analyzing the distribution of synapses in more detail from EM data for a different set of neurons.

Weaknesses:

DNs are upstream of motorneurons, and one would expect, as demonstrated by Dombrovski et al, that specific DNs being activated by input from specific regions of the visual field will activate motoneurons so that the fly moves away from a looming object.

The current work analyzed the synapse distribution on two DNs that do not seem to have such role, and emphasize the lack of retinotopy. However, it is not clear why one would expect retinotopy in synapse location on the dendritic arbor. The comparison with mammalian visual circuits is not appropriate because those layers are extracting more and more complex visual features, whereas Drosophila DNs are supposed to drive motoneurons to generate suitable escape behavior.

- The authors do not suggest the functional roles of these DNs in controlling the movement of the fly. They argue that the synapse distribution and the passive electrotonic structure of these neurons are optimized to make the composite EPSP encode the number of activated synapses, but do not explain why this is important.

- Although DNs are spiking neurons, the authors limit their work to the subthreshold passive domain. If the EPSP at the spike initiation zone crosses spiking threshold, will encoding the number of synapses in EPSP amplitude still matter? Will it matter either if the composite EPSP remains subthreshold?

- The temporal aspect of the input has been ignored by the authors in their simulations. First, it is not clear all the synapses from a single VPN should get activated together. One would expect a spike in a VPN to arrive at different synapses with different time delays depending on their electrotonic distance from the spike initiation zone and the signal propagation speed in the neurites.

A looming stimulus should be expanding with time, but from the description of the simulations it does not seem that the authors have tried to incorporate this aspect in their design of the synaptic activation.

- The suggestion in the abstract that linear encoding of synapse number is default strategy which is then tuned by active properties and plasticity seems strange. Developmentally active properties do not get inserted into passive neurons.

- Much of the analysis (Figures 4, 5, 12) show relationships with physical distance along dendrite. In studying passive neurons it is more informative to use electrotonic distance which provides better insight.

Reviewer #1 (Public review):

In the manuscript entitled "A VgrG2b fragment cleaved by caspase-11/4 promotes Pseudomonas aeruginosa infection through suppressing the NLRP3 inflammasome", Qian et al. found an activation of the non-canonical inflammasome, but not the downstream NLRP3 inflammasome, during the infection of macrophage by P. aeruginosa, which is in sharp contrast to that by E. coli (Figure 1). In realizing that the suppression of the NLRP3 inflammasome is Caspase-11 dependent, the authors performed a screening among P. aeruginosa proteins and identified VgrG2b being a major substrate of Caspase-11 (Figure 2). Next, the authors mapped the cleavage site on VgrG2b to D883, and demonstrated that cleavage of VgrG2b by Caspase-11 is essential for the suppression of the NLRP3 inflammasome (Figure 3). Furthermore, they found that a binding between the C-terminal fragment of the cleaved VgrG2b and NLRP3 existed (Figure 4), which was then proved to block the association of NLRP3 with NEK7 (Figure 5). Finally, the authors demonstrated that blocking of VgrG2b cleavage, by either mutation of the D883 or administration of a designed peptide, effectively improved the survival rate of the P. aeruginosa-infected mice (Figure 6). This is a well-designed and executed study, with the results clearly presented and stated.

Reviewer #2 (Public review):

Summary:

In their manuscript, Quian and colleagues identified a novel mechanism by which Pseudomonas control inflammatory responses upon inflammasome activation. They identified a caspase-11 substrate (VgrG2b) which, upon cleavage, binds and inhibits the NLRP3 to reduce the production of pro-inflammatory cytokines. This is a unique mechanism that allows for the tailoring of the innate immune response upon bacterial recognition.

Strengths:

The authors are presenting here a novel conceptual framework in host-pathogen interactions. Their work is supported by a range of approaches (biochemical, cellular immunology, microbiology, animal models), and their conclusions are supported by multiple independent evidences. The work is likely to have an important impact on the innate immunity field and host-pathogen interactions field and may guide the development of novel inhibitors.

Weaknesses:

Although quite exhaustive, a few of the authors' conclusions are not fully supported (e.g., caspase-11 directly cleaving VgrG2b, the unique affinity of VgrG2b-C for NLRP3) and would require complementary approaches to validate their findings fully. This is minimal.

Reviewer #1 (Public review):

Summary:

Wang, Po-Kai, et al., utilized the de novo polarization of MDCK cells cultured in Matrigel to assess the interdependence between polarity protein localization, centrosome positioning, and apical membrane formation. They show that the inhibition of Plk4 with Centrinone does not prevent apical membrane formation, but does result in its delay, a phenotype the authors attribute to the loss of centrosomes due to the inhibition of centriole duplication. However, the targeted mutagenesis of specific centrosome proteins implicated in the positioning of centrosomes in other cell types (CEP164, ODF2, PCNT, and CEP120) did not affect centrosome positioning in 3D cultured MDCK cells. A screen of proteins previously implicated in MDCK polarization revealed that the polarity protein Par-3 was upstream of centrosome positioning, similar to other cell types.

Strengths:

The investigation into the temporal requirement and interdependence of previously proposed regulators of cell polarization and lumen formation is valuable to the community. Wang et al., have provided a detailed analysis of many of these components at defined stages of polarity establishment. Furthermore, the generation of PCNT, p53, ODF2, Cep120, and Cep164 knockout MDCK cell lines is likely valuable to the community.

Weaknesses:

Additional quantifications would highly improve this manuscript, for example it is unclear whether the centrosome perturbation affects gamma tubulin levels and therefore microtubule nucleation, it is also not clear how they affect the localization of the trafficking machinery/polarity proteins. For example, in Figure 4, the authors measure the intensity of Gp134 at the apical membrane initiation site following cytokinesis, but there is no measure of Gp134 at the centrosome prior to this.

Reviewer #2 (Public review):

Summary:

The authors decoupled several players that are thought to contribute to the establishment of epithelial polarity and determined their causal relationship. This provides a new picture of the respective roles of junctional proteins (Par3), the centrosome, and endomembrane compartments (Cdc42, Rab11, Gp135) from upstream to downstream.<br /> Their conclusions are based on live imaging of all players during the early steps of polarity establishment and on the knock-down of their expression in the simplest ever model of epithelial polarity: a cell doublet surrounded by ECM.

The position of the centrosome is often taken as a readout for the orientation of the cell polarity axis. There is a long-standing debate about the actual role of the centrosome in the establishment of this polarity axis. Here, using a minimal model of epithelial polarization, a doublet of daugthers MDCK cultured in Matrigel, the authors made several key observations that bring new light to our understanding of a mechanism that has been studied for many years without being fully explained:

(1) They showed that centriole can reach their polarized position without most of their microtubule-anchoring structures. These observations challenge the standard model according to which centrosomes are moved by the production and transmission of forces along microtubules.

(2) (However) they showed that epithelial polarity can be established in the absence of centriole.

(3) (Somehow more expectedly) they also showed that epithelial polarity can't be established in the absence of Par3.

(4) They found that most other polarity players that are transported through the cytoplasm in lipid vesicles, and finally fused to the basal or apical pole of epithelial cells, are moved along an axis which is defined by the position of centrosome and orientation of microtubules.

(5) Surprisingly, two non-daughters cells that were brought in contact (for 6h) could partially polarize by recruiting a few Par3 molecules but not the other polarity markers.

(6) Even more surprisingly, in the absence of ECM, Par 3 and centrosomes could move to their proper position close to the intercellular junction after cytokinesis but other polarity markers (at least GP135) localized to the opposite, non-adhesive, side. So the polarity of the centrosome-microtubule network could be dissociated from the localisation of GP135 (which was believed to be transported along this network).

Strengths:

(1) The simplicity and reproducibility of the system allow a very quantitative description of cell polarity and protein localisation.

(2) The experiments are quite straightforward, well-executed, and properly analyzed.

(3) The writing is clear and conclusions are convincing.

Weaknesses:

(1) The simplicity of the system may not capture some of the mechanisms involved in the establishment of cell polarity in more physiological conditions (fluid flow, electrical potential, ion gradients,...).

(2) The absence of centriole in centrinone-treated cells might not prevent the coalescence of centrosomal protein in a kind of MTOC which might still orient microtubules and intracellular traffic. How are microtubules organized in the absence of centriole? If they still form a radial array, the absence of a centriole at the center of it somehow does not conflict with classical views in the field.

(3) The mechanism is still far from clear and this study shines some light on our lack of understanding. Basic and key questions remain:<br /> a) How is the centrosome moved toward the Par3-rich pole? This is particularly difficult to answer if the mechanism does not imply the anchoring of MTs to the centriole or PCM.<br /> b) What happens during cytokinesis that organises Par3 and intercellular junction in a way that can't be achieved by simply bringing two cells together? In larger epithelia cells have neighbours that are not daughters, still, they can form tight junctions with Par3 which participates in the establishment of cell polarity as much as those that are closer to the cytokinetic bridge (as judged by the overall cell symmetry). Is the protocol of cell aggregation fully capturing the interaction mechanism of non-daughter cells?

Reviewer #3 (Public review):

Here, Wang et al. aim to clarify the role of the centrosome and conserved polarity regulators in apical membrane formation during the polarization of MDCK cells cultured in 3D. Through well-presented and rigorous studies, the authors focused on the emergence of polarity as a single MDCK cell divided in 3D culture to form a two-cell cyst with a nascent lumen. Focusing on these very initial stages, rather than in later large cyst formation as in most studies, is a real strength of this study. The authors found that conserved polarity regulators Gp135/podocalyxin, Crb3, Cdc42, and the recycling endosome component Rab11a all localize to the centrosome before localizing to the apical membrane initiation site (AMIS) following cytokinesis. This protein relocalization was concomitant with a repositioning of centrosomes towards the AMIS. In contrast, Par3, aPKC, and the junctional components E-cadherin and ZO1 localize directly to the AMIS without first localizing to the centrosome. Based on the timing of the localization of these proteins, these observational studies suggested that Par3 is upstream of centrosome repositioning towards the AMIS and that the centrosome might be required for delivery of apical/luminal proteins to the AMIS.

To test this hypothesis, the authors generated numerous new cell lines and/or employed pharmacological inhibitors to determine the hierarchy of localization among these components. They found that removal of the centrosome via centrinone treatment severely delayed and weakened the delivery of Gp135 to the AMIS and single lumen formation, although normal lumenogenesis was apparently rescued with time. This effect was not due to the presence of CEP164, ODF2, CEP120, or Pericentrin. Par3 depletion perturbed the repositioning of the centrosome towards the AMIS and the relocalization of the Gp135 and Rab11 to the AMIS, causing these proteins to get stuck at the centrosome. Finally, the authors culture the MDCK cells in several ways (forced aggregation and ECM depleted) to try and further uncouple localization of the pertinent components, finding that Par3 can localize to the cell-cell interface in the absence of cell division. Par3 localized to the edge of the cell-cell contacts in the absence of ECM and this localization was not sufficient to orient the centrosomes to this site, indicating the importance of other factors in centrosome recruitment.

Together, these data suggest a model where Par3 positions the centrosome at the AMIS and is required for the efficient transfer of more downstream polarity determinants (Gp135 and Rab11) to the apical membrane from the centrosome. The authors present solid and compelling data and are well-positioned to directly test this model with their existing system and tools. In particular, one obvious mechanism here is that centrosome-based microtubules help to efficiently direct the transport of molecules required to reinforce polarity and/or promote lumenogenesis. This model is not really explored by the authors except by Pericentrin and subdistal appendage depletion and the authors do not test whether these perturbations affect centrosomal microtubules. Exploring the role of microtubules in this process could considerably add to the mechanisms presented here. In its current state, this paper is a careful observation of the events of MCDK polarization and will fill a knowledge gap in this field. However, the mechanism could be significantly bolstered with existing tools, thereby elevating our understanding of how polarity emerges in this system.

Reviewer #1 (Public review):

Previous studies have highlighted some of these paracrine activities of Toxoplasma - and Rasogi et al (mBio, 2020) used a single cell sequencing approach of cells infected in vitro with the WT or MYR KO parasites - and one of their conclusions was that MYR-1 dependent paracrine activities counteract ROP-dependent processes. Similarly, Chen et al (JEM 2020) highlighted that a particular rhoptry protein (ROP16) could be injected into uninfected macrophages and move them to an anti-inflammatory state that might benefit the parasite.

There are caveats around immunity and as yet no insight into how this works. In Figure 2 there is a marked defect in the ability of the parasites to expand at day 2 and day 5. Together, these data sets suggest that this paracrine effect mediated by MYR-1 works early - well before the development of adaptive responses.

Reviewer #2 (Public review):

Summary:

In this manuscript by Torelli et al., the authors propose that the major function of MYR1 and MYR1-dependent secreted proteins is to contribute to parasite survival in a paracrine manner rather than to protect parasites from cell-autonomous immune response. The authors conclude that these paracrine effects rescue ∆MYR1 or knockouts of MYR1-dependent effectors within pooled in vivo CRISPR screens.

Strengths:

The authors raised a more general concern that pooled CRISPR screens (not only in Toxoplasma but also other microbes or cancers) would miss important genes by "paracrine masking effect". Although there is no doubt that pooled CRISPR screens (especially in vivo CRISPR screens) are powerful techniques, I think this topic could be of interest to those fields and researchers.

Weaknesses:

In this version, the reviewer is not entirely convinced of the 'paracrine masking effect' because the in vivo experiments should include appropriate controls (see major point 2).

(1) It is convincing that co-infection of WT and ∆MYR1 parasites could rescue the growth of ∆MYR1 in mice shown by in vivo luciferase imaging. Also, this is consistent with ∆MYR1 parasites showing no in vivo fitness defect in the in vivo CRISPR screens conducted by several groups. Meanwhile, it has been reported previously and shown in this manuscript that ∆MYR1 parasites have an in vitro growth defect; however, ∆MYR1 parasites show no in vitro fitness defect the in vitro pooled CRISPR screen. The authors show that the competition defect of ∆MYR1 parasites cannot be rescued by co-infection with WT parasites in Figure 1c, which might indicate that no paracrine rescue occurred in an in vitro environment. The authors seem not to mention these discrepancies between in vitro CRISPR screens and in vitro competition assays. Why do ∆MYR1 parasites possess neutral in vitro fitness scores in in vitro CRISPR screens? Could the authors describe a reasonable hypothesis?

(2) The authors developed a mixed infection assay with an inoculum containing a 20:80 ratio of ΔMYR1-Luc parasites with either WT parasites or ΔMYR1 mutants not expressing luciferase, showing that the in vivo growth defect of ∆MYR1 parasites is rescued by the presence of WT parasites. Since this experiment lacks appropriate controls, interpretation could be difficult. Is this phenomenon specific to MYR1? If a co-inoculum of ∆GRA12-Luc with either WT parasites or GRA12 parasites not expressing luciferase is included, the data could be appropriately interpreted.

(3) In the Discussion part, the authors argue that the rescue phenotype of mixed infection is not due to co-infection of host cells (lines 307-310). This data is important to support the authors' paracrine hypothesis and should be shown in the main figure.

(4) In the Discussion part, the authors assume that the rescue phenotype is the result of multiple MYR1-dependent effectors. I admit that this hypothesis could be possible since a recently published paper described the concerted action of numerous MYR1-dependent or independent effectors contributing to the hypermigration of infected cells (Ten Hoeve et al., mBio, 2024). I think this paragraph would be kind of overstated since the authors did not test any of the candidate effectors. Since the authors possess ∆IST parasites, they can test whether IST is involved in the "paracrine masking effect" or not to support their claim.

Reviewer #1 (Public review):

Hotinger et al. explore the population dynamics of Salmonella enterica serovar Typhimurium in mice using genetically tagged bacteria. In addition to physiological observations, pathology assessments, and CFU measurements, the study emphasizes quantifying host bottleneck sizes that limit Salmonella colonization and dissemination. The authors also investigate the genetic distances between bacterial populations at various infection sites within the host.

Initially, the study confirms that pretreatment with the antibiotic streptomycin before inoculation via orogastric gavage increases the bacterial burden in the gastrointestinal (GI) tract, leading to more severe symptoms and heightened fecal shedding of bacteria. This pretreatment also significantly reduces between-animal variation in bacterial burden and fecal shedding. The authors then calculate founding population sizes across different organs, discovering a severe bottleneck in the intestine, with founding populations reduced by approximately 10^6-fold compared to the inoculum size. Streptomycin pretreatment increases the founding population size and bacterial replication in the GI tract. Moreover, by calculating genetic distances between populations, the authors demonstrate that, in untreated mice, Salmonella populations within the GI tract are genetically dissimilar, suggesting limited exchange between colonization sites. In contrast, streptomycin pretreatment reduces genetic distances, indicating increased exchange.

In extraintestinal organs, the bacterial burden is generally not substantially increased by streptomycin pretreatment, with significant differences observed only in the mesenteric lymph nodes and bile. However, the founding population sizes in these organs are increased. By comparing genetic distances between organs, the authors provide evidence that subpopulations colonizing extraintestinal organs diverge early after infection from those in the GI tract. This hypothesis is further tested by measuring bacterial burden and founding population sizes in the liver and GI tract at 5 and 120 hours post-infection. Additionally, they compare orogastric gavage infection with the less injurious method of infection via drinking, finding similar results for CFUs, founding populations, and genetic distances. These results argue against injuries during gavage as a route of direct infection.

To bypass bottlenecks associated with the GI tract, the authors compare intravenous (IV) and intraperitoneal (IP) routes of infection. They find approximately a 10-fold increase in bacterial burden and founding population size in immune-rich organs with IV/IP routes compared to orogastric gavage in streptomycin-pretreated animals. This difference is interpreted as a result of "extra steps required to reach systemic organs."

While IP and IV routes yield similar results in immune-rich organs, IP infections lead to higher bacterial burdens in nearby sites, such as the pancreas, adipose tissue, and intraperitoneal wash, as well as somewhat increased founding population sizes. The authors correlate these findings with the presence of white lesions in adipose tissue. Genetic distance comparisons reveal that, apart from the spleen and liver, IP infections lead to genetically distinct populations in infected organs, whereas IV infections generally result in higher genetic similarity.

Finally, the authors investigate GI tract reseeding, identifying two distinct routes. They observe that the GI tracts of IP/IV-infected mice are colonized either by a clonal or a diversely tagged bacterial population. In clonally reseeded animals, the genetic distance within the GI tract is very low (often zero) compared to the bile population, which is predominantly clonal or pauciclonal. These animals also display pathological signs, such as cloudy/hardened bile and increased bacterial burden, leading the authors to conclude that the GI tract was reseeded by bacteria from the gallbladder bile. In contrast, animals reseeded by more complex bacterial populations show that bile contributes only a minor fraction of the tags. Given the large founding population size in these animals' GI tracts, which is larger than in orogastrically infected animals, the authors suggest a highly permissive second reseeding route, largely independent of bile. They speculate that this route may involve a reversal of known mechanisms that the pathogen uses to escape from the intestine.

The manuscript presents a substantial body of work that offers a meticulously detailed understanding of the population dynamics of S. Typhimurium in mice. It quantifies the processes shaping the within-host dynamics of this pathogen and provides new insights into its spread, including previously unrecognized dissemination routes. The methodology is appropriate and carefully executed, and the manuscript is well-written, clearly presented, and concise. The authors' conclusions are well-supported by experimental results and thoroughly discussed. This work underscores the power of using highly diverse barcoded pathogens to uncover the within-host population dynamics of infections and will likely inspire further investigations into the molecular mechanisms underlying the bottlenecks and dissemination routes described here.

Major point:

Substantial conclusions in the manuscript rely on genetic distance measurements using the Cavalli-Sforza chord distance. However, it is unclear whether these genetic distance measurements are independent of the founding population size. I would anticipate that in populations with larger founding population sizes, where the relative tag frequencies are closer to those in the inoculum, the genetic distances would appear smaller compared to populations with smaller founding sizes independent of their actual relatedness. This potential dependency could have implications for the interpretation of findings, such as those in Figures 2B and 2D, where antibiotic-pretreated animals consistently exhibit higher founding population sizes and smaller genetic distances compared to untreated animals.

Reviewer #2 (Public review):

In this paper, Hotinger et. al. propose an improved barcoded library system, called STAMPR, to study Salmonella population dynamics during infection. Using this system, the authors demonstrate significant diversity in the colonization of different Salmonella clones (defined by the presence of different barcodes) not only across different organs (liver, spleen, adipose tissues, pancreas, and gall bladder) but also within different compartments of the same gastrointestinal tissue. Additionally, this system revealed that microbiota competition is the major bottleneck in Salmonella intestinal colonization, which can be mitigated by streptomycin treatment. However, this has been demonstrated previously in numerous publications. They also show that there was minimal sharing between populations found in the intestine and those in the other organs. Upon IV and IP infection to bypass the intestinal bottleneck, they were able to demonstrate, using this library, that Salmonella can renter the intestine through two possible routes. One route is essentially the reverse path used to escape the gut, leading to a diverse intestinal population; while the other, through the bile, typically results in a clonal population. Although the authors showed that the STAMPR pipeline improved the ability to identify founder populations and their diversity within the same animal during infections, some of the conclusions appear speculative and not fully supported.

(1) It's particularly interesting how the authors, using this system, demonstrate the dominant role of the microbiota bottleneck in Salmonella colonization and how it is widened by antibiotic treatment (Figure 1). Additionally, the ability to track Salmonella reseeding of the gut from other organs starting with IV and IP injections of the pathogen provides a new tool to study population dynamics (Figure 5). However, I don't think it is possible to argue that the proximal and distal small intestine, Peyer's patches (PPs), cecum, colon, and feces have different founder populations for reasons other than stochastic variations. All the barcoded Salmonella clones have the same fitness and the fact that some are found or expanded in one region of the gastrointestinal tract rather than another likely results from random chance - such as being forced in a specific region of the gut for physical or spatial reasons-and subsequent expansion, rather than any inherent biological cause. For example, some bacteria may randomly adhere to the mucus, some may swim toward the epithelial layer, while others remain in the lumen; all will proliferate in those respective sites. In this way, different founder populations arise based on random localization during movement through the gastrointestinal tract, which is an observation, but it doesn't significantly contribute to understanding pathogen colonization dynamics or pathogenesis. Therefore, I would suggest placing less emphasis on describing these differences or better discussing this aspect, especially in the context of the gastrointestinal tract.

(2) I do think that STAMPR is useful for studying the dynamics of pathogen spread to organs where Salmonella likely resides intracellularly (Figure 3). The observation that the liver is colonized by an early intestinal population, which continues to proliferate at a steady rate throughout the infection, is very interesting and may be due to the unique nature of the organ compared to the mucosal environment. What is the biological relevance during infection? Do the authors observe the same pattern (Figures 3C and G) when normalizing the population data for the spleen and mesenteric lymph nodes (mLN)? If not, what do the authors think is driving this different distribution?

(3) Figure 6: Could the bile pathology be due to increased general bacterial translocation rather than Salmonella colonization specifically? Did the authors check for the presence of other bacteria (potentially also proliferating) in the bile? Do the authors know whether Salmonella's metabolic activity in the bile could be responsible for gallbladder pathology?

Reviewer #1 (Public review):

Summary:

The investigators in this study analyzed the dataset assembly from 540 Salmonella isolates, and those from 45 recent isolates from Zhejiang University of China. The analysis and comparison of the resistome and mobilome of these isolates identified a significantly higher rate of cross-region dissemination compared to localized propagation. This study highlights the key role of the resistome in driving the transition and evolutionary history of S. Gallinarum.

Strengths:

The isolates included in this study were from 16 countries in the past century (1920 to 2023). While the study uses S. Gallinarun as the prototype, the conclusion from this work will likely apply to other Salmonella serotypes and other pathogens.

Weaknesses:

While the isolates came from 16 countries, most strains in this study were originally from China.

Reviewer #2 (Public review):

Summary:

The authors sequence 45 new samples of S. Gallinarum, a commensal Salmonella found in chickens, which can sometimes cause disease. They combine these sequences with around 500 from public databases, determine the population structure of the pathogen, and coarse relationships of lineages with geography. The authors further investigate known anti-microbial genes found in these genomes, how they associate with each other, whether they have been horizontally transferred, and date the emergence of clades.

Strengths:

(1) It doesn't seem that much is known about this serovar, so publicly available new sequences from a high-burden region are a valuable addition to the literature.

(2) Combining these sequences with publicly available sequences is a good way to better contextualise any findings.

Weaknesses:

There are many issues with the genomic analysis that undermine the conclusions, the major ones I identified being:

(1) Recombination removal using gubbins was not presented fully anywhere. In this diversity of species, it is usually impossible to remove recombination in this way. A phylogeny with genetic scale and the gubbins results is needed. Critically, results on timing the emergence (fig2) depend on this, and cannot be trusted given the data presented.

(2) The use of BEAST was also only briefly presented, but is the basis of a major conclusion of the paper. Plot S3 (root-to-tip regression) is unconvincing as a basis of this data fitting a molecular clock model. We would need more information on this analysis, including convergence and credible intervals.

(3) Using a distance of 100 SNPs for a transmission is completely arbitrary. This would at least need to be justified in terms of the evolutionary rate and serial interval.

(4) The HGT definition is non-standard, and phylogeny (vertical inheritance) is not controlled for.<br /> The cited method:<br /> 'In this study, potentially recently transferred ARGs were defined as those with perfect identity (more than 99% nucleotide identity and 100% coverage) in distinct plasmids in distinct host bacteria using BLASTn (E-value {less than or equal to}10−5)'<br /> This clearly does not apply here, as the application of distinct hosts and plasmids cannot be used. Subsequent analysis using this method is likely invalid, and some of it (e.g. Figure 6c) is statistically very poor.

(5) Associations between lineages, resistome, mobilome, etc do not control for the effect of genetic background/phylogeny. So e.g. the claim 'the resistome also demonstrated a lineage-preferential distribution' is not well-supported.

(6) The invasiveness index is not well described, and the difference in means is not biologically convincing as although it appears significant, it is very small.

(7) 'In more detail, both the resistome and mobilome exhibited a steady decline until the 1980s, followed by a consistent increase from the 1980s to the 2010s. However, after the 2010s, a subsequent decrease was identified.'<br /> Where is the data/plot to support this? Is it a significant change? Is this due to sampling or phylogenetics?

(8) It is not clear what the burden of disease this pathogen causes in the population, or how significant it is to agricultural policy. The article claims to 'provide valuable insights for targeted policy interventions.', but no such interventions are described.

(9) The abstract mentions stepwise evolution as a main aim, but no results refer to this.

(10) The authors attribute changes in population dynamics to normalisation in China-EU relations and hen fever. However, even if the date is correct, this is not a strongly supported causal claim, as many other reasons are also possible (for example other industrial processes which may have changed during this period).

(11) No acknowledgment of potential undersampling outside of China is made, for example, 'Notably, all bvSP isolates from Asia were exclusively found in China, which can be manually divided into three distinct regions (southern, eastern, and northern).'. Perhaps we just haven't looked in other places?

(12) Many of the conclusions are highly speculative and not supported by the data.

(13) The figures are not always the best presentation of the data:<br /> a. Stacked bar plots in Figure 1 are hard to interpret, the total numbers need to be shown. Panel C conveys little information.<br /> b. Figure 4B: stacked bars are hard to read and do not show totals.<br /> c. Figure 5 has no obvious interpretation or significance.

In summary, the quality of analysis is poor and likely flawed (although there is not always enough information on methods present to confidently assess this or provide recommendations for how it might be improved). So, the stated conclusions are not supported.

Reviewer #1 (Public review):

Summary:

In the manuscript "Heat Shock Factor Regulation of Antimicrobial Peptides Expression Suggests a Conserved Defense Mechanism Induced by Febrile Temperature in Arthropods," Xiao and colleagues examine the role of the shrimp Litopenaeus vannamei HSF1 ortholog (LvHSF1) in the response to viral infection. The authors provide compelling support for their conclusions that the activation of LvHSF1 limits viral load at high temperatures. Specifically, the authors convincingly show that (i) LvHSF1 mRNA and protein are induced in response to viral infection at high temperatures, (ii) increased LvHSF1 levels can directly induce the expression of the nSWD (and directly or indirectly other antibacterial peptides, AMPs), (ii) nSWD's antimicrobial activities can limit viral load, and, (iv) LvHSF1 protects survival at high temperatures following virus infection. These data thus provide a model by which an increase in HSF1 levels limits viral load through the transcription of antimicrobial peptides and provides a rationale for the febrile response as a conserved response to viral infection.

Strengths:

The large body of careful time series experiments, tissue profiling, and validation of RNA-seq data is convincing. Several experimental methodologies are used to support the authors' conclusions that nSWD is an LvHSf1 target and increased LvHSF1 alone can explain increased levels of nSWD. Similar carefully conducted experiments also conclusively implicate nSWD protein in limiting WSSV viral loads.

Weaknesses:

Despite this compelling data regarding the protective role of HSF1 in the febrile response, what remains unexplained and complicates the authors' model is the observation that losing LvHSF1 at 'normal' temperatures of 25C is not detrimental to survival, even though viral loads increase and nSWD is likely still subject to LvHSF1 regulation. These observations suggest that WSSV infection may have other detrimental effects on the cell not reflected by viral load and that LvHSF1 may play additional roles in protecting the organism from these effects of WSSV infection, such as perhaps, perturbations to protein homeostasis. This is worth discussing, especially in light of the rather complicated roles of hormesis in protection from infection, the role of HSF1 in hormesis responses, and the findings from other groups that the authors discuss.

Reviewer #2 (Public review):

Temperature is a critical factor affecting the progression of viral diseases in vertebrates and invertebrates. In the current study, the authors investigate mechanisms by which high temperatures promote anti-viral resistance in shrimp. They show that high temperatures induce HSF1 expression, which in turn upregulates AMPs. The AMPs target viral envelope proteins and inhibit viral infection/replication. The authors confirm this process in drosophila and suggest that there may be a conserved mechanism of high-temperature mediated anti-viral response in arthropods. These findings will enhance our understanding of how high temperature improves resistance to viral infection in animals.

The conclusions of this paper are mostly well supported by data, but some aspects of data analysis need to be clarified and extended. Further investigation on how WSSV infection is affected by AMP would have strengthened the study.

Reviewer #3 (Public review):

In the manuscript titled "Heat Shock Factor Regulation of Antimicrobial Peptides Expression Suggests a Conserved Defense Mechanism Induced by Febrile Temperature in Arthropods", the authors investigate the role of heat shock factor 1 (HSF1) in regulating antimicrobial peptides (AMPs) in response to viral infections, particularly focusing on febrile temperatures. Using shrimp (Litopenaeus vannamei) and Drosophila S2 cells as models, this study shows that HSF1 induces the expression of AMPs, which in turn inhibit viral replication, offering insights into how febrile temperatures enhance immune responses. The study demonstrates that HSF1 binds to heat shock elements (HSE) in AMPs, suggesting a conserved antiviral defense mechanism in arthropods. The findings are informative for understanding innate immunity against viral infections, particularly in aquaculture. However the logical flow of the paper can be improved.

Reviewer #1 (Public review):

Summary:

Dr. Santamaria's group previously utilized antigen-specific nanomedicines to induce immune tolerance in treating autoimmune diseases. The success of this therapeutic strategy has been linked to expanded regulatory mechanisms, particularly the role of T-regulatory type-1 (TR1) cells. However, the differentiation program of TR1 cells remained largely unclear. Previous work from the authors suggested that TR1 cells originate from T follicular helper (TFH) cells. In the current study, the authors aimed to investigate the epigenetic mechanisms underlying the transdifferentiation of TFH cells into IL-10-producing TR1 cells. Specifically, they sought to determine whether this process involves extensive chromatin remodeling or is driven by pre-existing epigenetic modifications. Their goal was to understand the transcriptional and epigenetic changes facilitating this transition and to explore the potential therapeutic implications of manipulating this pathway.

The authors successfully demonstrated that the TFH-to-TR1 transdifferentiation process is driven by pre-existing epigenetic modifications rather than extensive new chromatin remodeling. The comprehensive transcriptional and epigenetic analyses provide robust evidence supporting their conclusions.

Strengths:

(1) The study employs a broad range of bulk and single-cell transcriptional and epigenetic tools, including RNA-seq, ATAC-seq, ChIP-seq, and DNA methylation analysis. This comprehensive approach provides a detailed examination of the epigenetic landscape during the TFH-to-TR1 transition.

(2) The use of high-throughput sequencing technologies and sophisticated bioinformatics analyses strengthens the foundation for the conclusions drawn.

(3) The data generated can serve as a valuable resource for the scientific community, offering insights into the epigenetic regulation of T cell plasticity.

(4) The findings have significant implications for developing new therapeutic strategies for autoimmune diseases, making the research highly relevant and impactful.

Weaknesses:

(1) While the study focuses on transcriptional and epigenetic analyses, the authors are currently undertaking efforts to validate these findings functionally. Ongoing research aims to further explore the roles of key transcription factors in the TFH-to-TR1 transition, reflecting the authors' commitment to building on the insights gained from this study.

(2) The identification of key transcription factors and epigenetic marks is a strong foundation for future work. The authors are actively investigating how these factors drive chromatin remodeling, which will enhance the mechanistic understanding of the TFH-to-TR1 process in future studies.

(3) Although the study provides a valuable snapshot of the epigenetic landscape, the authors are pursuing additional research to assess the dynamics of these changes over time. These ongoing efforts will contribute to a deeper understanding of the stability and progression of the observed epigenetic modifications.

Comments on revised version:

The authors have effectively discussed and addressed all previously raised questions. There are no further concerns.

Reviewer #2 (Public review):

Summary:

This study, based on their previous findings that TFH cells can be converted into TR1 cells, conducted a highly detailed and comprehensive epigenetic investigation to answer whether TR1 differentiation from TFH is driven by epigenetic changes. Their evidence indicated that the downregulation of TFH-related genes during the TFH to TR1 transition depends on chromatin closure, while the upregulation of TR1-related genes does not depend on epigenetic changes.

Strengths:

A significant advantage of their approach lies in its detailed and comprehensive assessment of epigenetics. Their analysis of epigenetics covers chromatin open regions, histone modifications, DNA methylation, and using both single-cell and bulk techniques to validate their findings. As for their results, observations from different epigenetic perspectives mutually supported each other, lending greater credibility to their conclusions. This study effectively demonstrates that 1. the TFH-to-TR1 differentiation process is associated with massive closure of OCRs, and 2. the TR1-poised epigenome of TFH cells is a key enabler of this transdifferentiation process. Considering the extensive changes in epigenetic patterns involved in other CD4+ T lineage commitment processes, the similarity between TFH and TR1 in their epigenetics is intriguing.

They performed correlation analysis to answer the association between "pMHC-NP-induced epigenetic change" and "gene expression change in TR1". Also, they have made their raw data publicly available, providing a comprehensive epigenomic database of pMHC-NP induced TR1 cells. This will serve as a valuable reference for future research.

Weaknesses:

A major limitation is that this study heavily relies on a premise from the previous studies performed by the same group on pMHC-NP-induced T cell responses. This significantly limits the relevance of their conclusion to a broader perspective. Specifically, differential OCRs between Tet+ and naïve T cells were limited to only 821, as compared to 10,919 differential OCRs between KLH-TFH and naïve T cells (Fig. 2A), indicating that the precursors and T cell clonotypes that responded to pMHC-NP were extremely limited. I acknowledge that this limitation has been added and discussed in the Discussion section of the revised manuscript.

Reviewer #1 (Public review):

Summary:

The authors have developed a valuable method based on a fully cell-free system to express a channel protein and integrated it into a membrane vesicle in order to characterize it biophysically. The study presents a useful alternative to study channels that are not amenable to be studied by more traditional methods.

Strengths:

The evidence supporting the claims of the authors is solid and convincing. The method will be of interest to researchers working on ionic channels, allowing to study a wide range of ion channel functions such as those involved in transport, interaction with lipids or pharmacology.

Weaknesses:

The inclusion of a mechanistic interpretation how the channel protein folds into a protomer or a tetramer to become functional into the membrane, would strengthen the study.

Comments on revised version:

In the revised version, the authors did not experimentally addressed how tetrameric or protomeric proteins are actually produced. However, they performed new experiments to assess the amount of tetramers that are being actually formed. They used a size-exclusion chromatography to conclude that the protomers and tetramers species of complexes are formed and assembled.

The authors have addressed most of my minor concerns and have modified or updated the manuscript following my recommendations, so I have no further comments.

Reviewer #2 (Public review):

It is challenging to study the biophysical properties of organelle channels using conventional electrophysiology. The conventional reconstitution methods requires multiple steps and can be contaminated by endogenous ionophores from the host cell lines after purification. To overcome this challenge, in this manuscript, Larmore et al. described a fully synthetic method to assay the functional properties of the TRPP channel family. The TRPP channels are an important organelle ion channel family that natively traffic to primary cilia and ER organelles. The authors utilized cell-free protein expression and reconstitution of the synthetic channel protein into giant unilamellar vesicles (GUV), the single channel properties can be measured using voltage-clamp electrophysiology. Using this innovative method, the authors characterized their membrane integration, orientation, and conductance, comparing the results to those of endogenous channels. The manuscript is well-written and may present broad interest to the ion channel community studying organelle ion channels. Particularly because of the challenges of patching native cilia cells, the functional characterization is highly concentrated in very few labs. This method may provide an alternative approach to investigate other channels resistant to biophysical analysis and pharmacological characterization.

Comments on revised version:

The authors have addressed my concerns. This excellent method manuscript would benefit the study of organelle channels.

Reviewer #1 (Public review):

In this paper, the authors show that disruption of calcineurin, which is encoded by tax-6 in C. elegans, results in increased susceptibility to P. aeruginosa but extends lifespan. In exploring the mechanisms involved, the authors show that disruption of tax-6 decreases the rate of defecation leading to intestinal accumulation of bacteria and distension of the intestinal lumen. The authors further show that the lifespan extension is dependent on hlh-30, which may be involved in breaking down lipids following deficits in defecation, and nhr-8, whose levels are increased by deficits in defecation. The authors propose a model in which disruption of the defecation motor program is responsible for the effect of calcineurin on pathogen susceptibility and lifespan, but do not exclude the possibility that calcineurin affects these phenotypes independently of defecation.

Reviewer #2 (Public review):

The relationships between genes and phenotypes are complex and the impact of deleting or a gene can often have multifaceted and unforeseen consequences. This paper dissected the role of calcineurin, encoded by tax-6, in various phenotypes in C. elegans, including lifespan, pathogen susceptibility, the defecation motor program, and nutrient absorption or calorie restriction, through a series of genetic and behavioral analyses. Many genes in these pathways were tested yielding robust results. Classic epistasis analyses were used to distinguish between genes operating in the same or separate pathways. Researchers in the related fields will be very interested in looking through the data presented in this paper in great detail and benefit from it.

Overall, this paper supports a model in which the increased lifespan and heightened pathogen susceptibility observed following calcineurin inhibition result from the disruptions in the defecation motor program but through distinct pathways. A defective defecation motor program leads to intestine bloating and compromised nutrient absorption. Calorie restriction resulting from poor nutrient absorption affects lifespan, whereas increased colonization in the bloated intestine heightens pathogen susceptibility. The observation that knockdown of several other DMP-related genes also results in increased lifespan and pathogen susceptibility further reinforces the proposed model.

Reviewer #1 (Public review):

The authors present the cryo-EM structure of PSI-fucoxanthin chlorophyll a/c-binding proteins (FCPs) supercomplex from the diatom Thalassiosira pseudonana CCMP1335 at a global resolution of 2.3 Å. This exceptional resolution allows the authors to construct a near-atomic model of the entire supercomplex and elucidate the molecular details of FCPs arrangement. The high-resolution structure reveals subunits not previously identified in earlier reconstructions and models, as well as sequence analysis of PSI-FCPIs from other diatoms and red algae. Additionally, the authors use their model in conjunction with a phylogenetic analysis to compare and contrast the structural features of the T. pseudonana supercomplex with those of Chaetoceros gracilis, uncovering key structural features that contribute to the efficiency of light energy conversion in diatoms.

The study employs the advanced technique of single particle cryo-electron microscopy to visualize the complex architecture of the PSI supercomplex at near-atomic resolution and analyze the specific roles of FCPs in enhancing photosynthetic performance in diatoms.

Overall, the approach and data are both compelling and of high quality. The paper is well written and will be of wide interest for comprehending the molecular mechanisms of photosynthesis in diatoms. This work provides valuable insights for applications in bioenergy, environmental conservation, plant physiology, and membrane protein structural biology.

Reviewer #1 (Public review):

Summary:

The authors aim at dissecting the relationship between hair-cell directional mechanosensation and orientation-linked synaptic selectivity, using mice and the zebrafish. They find that Gpr156 mutant animals homogenize the orientation of hair cells without affecting the selectivity of afferent neurons, suggesting that hair-cell orientation is not the feature that determines synaptic selectivity. Therefore, the process of Emx2-dependent synaptic selectivity bifurcates downstream of Gpr156.

Strengths:

This is an interesting and solid paper. It solves an interesting problem and establishes a framework for the following studies. That is, to ask what are the putative targets of Emx2 that affect synaptic selectivity.<br /> The quality of the data is generally excellent.

Weaknesses:

The feeling is that the advance derived from the results is very limited.

Comments on revised version:

I am happy with the authors' reply and do not wish to modify my initial assessment.

Reviewer #2 (Public Review):

Summary:

The authors inquire in particular whether the receptor Gpr156, which is necessary for hair cells to reverse their polarities in the zebrafish lateral line and mammalian otolith organs downstream of the differential expression of the transcription factor Emx2, also controls the mechanosensitive properties of hair cells and ultimately an animal's behavior. This study thoroughly addresses the issue by analyzing the morphology, electrophysiological responses, and afferent connections of hair cells found in different regions of the mammalian utricle and the Ca2+ responses of lateral line neuromasts in both wild-type animals and gpr156 mutants. Although many features of hair cell function are preserved in the mutants-such as development of the mechanosensory organs and the Emx2-dependent, polarity-specific afferent wiring and synaptic pairing-there are a few key changes. In the zebrafish neuromast, the magnitude of responses of all hair cells to water flow resembles that of the wild-type hair cells that respond to flow arriving from the tail. These responses are larger than those observed in hair cells that are sensitive to flow arriving from the head and resemble effects previously observed in Emx2 mutants. The authors note that this behavior suggests that the Emx2-GPR156 signaling axis also impinges on hair cell mechanotransduction. Although mutant mice exhibit normal posture and balance, they display defects in swimming behavior. Moreover, their vestibulo-ocular reflexes are perturbed. The authors note that the gpr156 mutant is a good model to study the role of opposing hair cell polarity in the vestibular system, for the wiring patterns follow the expression patterns of Emx2, even though hair cells are all of the same polarity. This paper excels at describing the effects of gpr156 perturbation in mouse and zebrafish models and will be of interest to those studying the vestibular system, hair cell polarity, and the role of inner-ear organs in animal behavior.

The study is exceptional in including, not only morphological and immunohistochemical indices of cellular identity but also electrophysiological properties. The mutant hair cells of murine maculæ display essentially normal mechanoelectrical transduction and adaptation-with two or even three kinetic components-as well as normal voltage-activated ionic currents.

Reviewer #1 (Public review):

Summary:

This work proposes a new method, DyNetCP, for inferring dynamic functional connectivity between neurons from spike data. DyNetCP is based on a neural network model with a two-stage model architecture of static and dynamic functional connectivity.<br /> This work evaluates the accuracy of the synaptic connectivity inference and shows that DyNetCP can infer the excitatory synaptic connectivity more accurately than a state-of-the-art model (GLMCC) by analyzing the simulated spike trains. Furthermore, it is shown that the inference results obtained by DyNetCP from large-scale in-vivo recordings are similar to the results obtained by the existing methods (jitter-corrected CCG and JPSTH). Finally, this work investigates the dynamic connectivity in the primary visual area VISp and in the visual areas using DyNetCP.

Strengths:

The strength of the paper is that it proposes a method to extract the dynamics of functional connectivity from spike trains of multiple neurons. The method is potentially useful for analyzing parallel spike trains in general, as there are only a few methods (e.g. Aertsen et al., J. Neurophysiol., 1989, Shimazaki et al., PLoS Comput Biol 2012) that infer the dynamic connectivity from spikes. Furthermore, the approach of DyNetCP is different from the existing methods: while the proposed method is based on the neural network, the previous methods are based on either the descriptive statistics (JSPH) or the Ising model.

Weaknesses:

Although the paper proposes a new method, DyNetCP, for inferring the dynamic functional connectivity, its strengths are neither clear nor directly demonstrated in this paper. That is, insufficient analyses are performed to support the usefulness of DyNetCP.<br /> First, this paper attempts to show the superiority of DyNetCP by comparing the performance of synaptic connectivity inference with GLMCC (Fig. 2). However, the improvement in the synaptic connectivity inference does not seem to be convincing. While this paper compares the performance of DyNetCP with a state-of-the-art method (GLMCC), there are several problems with the comparison. For example,

(1) It is unclear how accurately the proposed method can infer the dynamic connectivity.<br /> (2) This paper does not compare with existing approach (e.g., classical JPSTH: Aertsen et al., J. Neurophysiol., 1989, and other methods : Stevenson and Koerding, NIPS, 2011; Linderman et al., NIPS, 2014; Song et al., J. Neurosci. Methods, 2015), and<br /> (3) only a population of neurons generated from the Hodgkin-Huxley model was evaluated.

Thus, the results in this paper are not sufficient to conclude the superiority of DyNetCP in the estimation of synaptic connections. In addition, this paper compares the proposed method with the standard statistical methods Jitter-corrected CCG (Fig. 3) and JPSTH (Fig. 4). Unfortunately, these results do not show the superiority of the proposed method. It only shows that the results obtained by the proposed method are consistent with those obtained by the existing methods (CCG or JPSTH). This paper also compares the proposed method with the standard statistical methods, such as jitter-corrected CCG (Fig. 3) and JPSTH (Fig. 4). It only shows that the results obtained by the proposed method are consistent with those obtained by the existing methods (CCG or JPSTH), which does not show the superiority of the proposed method.

In summary, although DyNetCP has the potential to infer the dynamic (time-dependent) correlation more accurately than existing methods, the paper does not provide sufficient analysis to make this claim. It is also unclear whether the proposed method is superior to the existing methods for estimating functional connectivity, such as JPSTH and statistical approach (Stevenson and Koerding, NIPS, 2011; Linderman et al., NIPS, 2014). Thus, the strength of DyNetCP is unclear.

Reviewer #2 (Public review):

Summary:

Here the authors describe a model for tracking time-varying coupling between neurons from multi-electrode spike recordings. Their approach extends a GLM with static coupling between neurons to include dynamic weights, learned by a long-short-term-memory (LSTM) model. Each connection has a corresponding LSTM embedding and is read-out by a multi-layer perceptron to predict the time-varying weight.

Strengths:

This is an interesting approach to an open problem in neural data analysis. I think, in general, the method would be interesting to computational neuroscientists.

Weaknesses:

It is somewhat difficult to interpret what the model is doing. I think it would be worthwhile to add some additional results that make it more clear what types of patterns are being described and how.

Major Issues:

Simulation for dynamic connectivity. It certainly seems doable to simulate a recurrent spiking network whose weights change over time, and I think this would be a worthwhile validation for this DyNetCP model. In particular, I think it would be valuable to understand how much the model overfits, and how accurately it can track known changes in coupling strength. If the only goal is "smoothing" time-varying CCGs, there are much easier statistical methods to do this (c.f. McKenzie et al. Neuron, 2021. Ren, Wei, Ghanbari, Stevenson. J Neurosci, 2022), and simulations could be useful to illustrate what the model adds beyond smoothing.

Stimulus vs noise correlations. For studying correlations between neurons in sensory systems that are strongly driven by stimuli, it's common to use shuffling over trials to distinguish between stimulus correlations and "noise" correlations or putative synaptic connections. This would be a valuable comparison for Fig 5 to show if these are dynamic stimulus correlations or noise correlations. I would also suggest just plotting the CCGs calculated with a moving window to better illustrate how (and if) the dynamic weights differ from the data.

Minor Issues:

Introduction - it may be useful to mention that there have been some previous attempts to describe time-varying connectivity from spikes both with probabilistic models: Stevenson and Kording, Neurips (2011), Linderman, Stock, and Adams, Neurips (2014), Robinson, Berger, and Song, Neural Computation (2016), Wei and Stevenson, Neural Comp (2021) ... and with descriptive statistics: Fujisawa et al. Nat Neuroscience (2008), English et al. Neuron (2017), McKenzie et al. Neuron (2021).

In the sections "Static DyNetCP ...reproduce". It may be useful to have some additional context to interpret the CCG-DyNetCP correlations and CCG-GLMCC correlations (for simulation). If I understand right, these are on training data (not cross-validated) and the DyNetCP model is using NM+1 parameters to predict ~100 data points (It would also be good to say what N and M are for the results here). The GLMCC model has 2 or 3 parameters (if I remember right?).

In the section "Static connectivity inferred by the DyNetCP from in-vivo recordings is biologically interpretable"... I may have missed it, but how is the "functional delay" calculated? And am I understanding right that for the DyNetCP you are just using [w_i\toj, w_j\toi] in place of the CCG?

Reviewer #1 (Public review):

Summary:

In this work, authors utilize recurrent neural networks (RNNs) to explore the question of when and how neural dynamics and the network's output are related from a geometrical point of view. The authors found that RNNs operate between two extremes: an 'aligned' regime in which the weights and the largest PCs are strongly correlated and an 'oblique' regime where the output weights and the largest PCs are poorly correlated. Large output weights led to oblique dynamics, and small output weights to aligned dynamics. This feature impacts whether networks are robust to perturbation along output directions. Results were linked to experimental data by showing that these different regimes can be identified in neural recordings from several experiments.

Strengths:

Diverse set of relevant tasks<br /> Similarity measure well chosen<br /> Explored various hyperparameter settings

Weaknesses:

One of the major connections to found BCI data with neural variance aligned to the outputs. Maybe I was confused about something, but doesn't this have to be the case based on the design of the experiment? The outputs of the BCI are chosen to align with the largest principal components of the data.

Proposed experiments maybe have already been done (New neural activity patterns emerge with long-term learning, Oby et al. 2019). My understanding of these results is that activity moved to be aligned as the manifold changed, but more analyses could be done to more fully understand the relationship between those experiments and this work.

Analysis of networks was thorough, but connections to neural data were weak. I am thoroughly convinced of the reported effect of large or small output weights in networks. I also think this framing could aid in future studies of interactions between brain regions.

This is an interesting framing to consider the relationship between upstream activity and downstream outputs. As more labs record from several brain regions simultaneously, this work will provide an important theoretical framework for thinking about the relative geometries of neural representations between brain regions.

It will be interesting to compare the relationship between geometries of representations and neural dynamics across connected different brain areas that are closer to the periphery vs. more central.

Exciting to think about the versatility of the oblique regime for shared representations and network dynamics across different computations.

Versatility of oblique regime could lead to differences between subjects in neural data.

Reviewer #2 (Public review):

Summary:

This paper tackles the problem of understanding when the dynamics of neural population activity do and do not align with some target output, such as an arm movement. The authors develop a theoretical framework based on RNNs showing that an alignment of neural dynamics to an output can be simply controlled by the magnitude of the read-out weight vector while the RNN is being trained: small magnitude vectors result in aligned dynamics, where low-dimensional neural activity recapitulates the target; large magnitude vectors result in "oblique" dynamics, where encoding is spread across many dimensions. The paper further explores how the aligned and oblique regimes differ, in particular that the oblique regime allows degenerate solutions for the same target output.

Strengths:

- A really interesting new idea that different dynamics of neural circuits can arise simply from the initial magnitude of the output weight vector: once written out (Eq 3) it becomes obvious, which I take as the mark of a genuinely insightful idea

- The offered framework potentially unifies a collection of separate experimental results and ideas, largely from studies of motor cortex in primate: the idea that much of the ongoing dynamics do not encode movement parameters; the existence of the "null space" of preparatory activity; and that ongoing dynamics of motor cortex can rotate in the same direction even when the arm movement is rotating in opposite directions.

- The main text is well written, with a wide-ranging set of key results synthesised and illustrated well and concisely

- Shows the occurrence of the aligned and oblique regimes generalises across a range of simulated behavioural tasks

- A deep analytical investigation of when the regimes occur and how they evolve over training

- Shows where the oblique regime may be advantageous: allows multiple solutions to the same problem; and differs in sensitivity to perturbation and noise

- An insightful corollary result that noise in training is needed to obtain the oblique regime

- Tests whether the aligned and oblique regimes can be seen in neural recordings from primate cortex in a range of motor control tasks

- The revised text offers greater clarity and precision about when the aligned and oblique regimes occur and in the interpretation of the analyses of neural data

Weaknesses:

- The depth of analytical treatment in the Methods is impressive; however, the paper and the Methods analyses are largely independent, with the numerous results in the latter not being mentioned in the Results or Discussion. It in effect operates as two papers.

Reviewer #1 (Public review):

Koesters and colleagues investigated the role of the small GTPase Rab3A in homeostatic scaling of miniature synaptic transmission in primary mouse cortical cultures using electrophysiology and immunohistochemistry. The major finding is that TTX incubation for 48 hours does not induce an increase in the amplitude of excitatory synaptic miniature events in neuronal cultures derived from Rab3A KO and Rab3A Earlybird mutant mice. NASPM application had comparable effects on mEPSC amplitude in control and after TTX, implying that Ca2+-permeable glutamate receptors are unlikely modulated during synaptic scaling. Immunohistochemical analysis revealed no significant changes in GluA2 puncta size, intensity, and integral in control and Rab3A KO cultures. Finally, they provide evidence that loss of Rab3A in neurons, but not astrocytes, blocks homeostatic scaling. Based on these data, the authors propose a model in which neuronal Rab3A is required for homeostatic scaling of synaptic transmission through GluA2-dependent and independent mechanisms.

While the title of the manuscript is mostly supported by data of solid quality, many conclusions, as well as the final model, cannot be derived from the results presented. Importantly, the data do not support that GluA2 levels change upon TTX treatment in control cultures, rendering conclusions regarding Rab3A's role in TTX-dependent GluA2 modulation spurious. Other aspects of the model, such as a Rab3A-dependent release of a tropic factor, cannot be derived from the data.

The following points should be addressed:

(1) There is no (significant) increase in GluA2 levels (intensity, area, or integral) upon TTX treatment in controls (Fig. 5). Conclusions regarding Rab3As role in TTX-dependent GluA2 modulation should be revised accordingly. Hence, the data shown in Fig. 4 - 7 do not allow drawing conclusions in the context of Rab3A-dependent GluA2 modulation and scaling.

(2) The effects of Rab3A on TTX-induced mini frequency modulation remains unclear, because TTX does not induce a change in mini frequency in the Rab3A+/Ebd control (Fig. 2). The respective conclusions should be revised accordingly (l. 427).

(3) The model is still not supported by the data. In particular, data supporting a negative regulation of Rab3A by APs, Rab3A-dependent release of a tropic factor, or a Rab3A-dependent increase in GluA2 abundance are not presented.

(4) Data points are not overlapping and appear "quantal" in most box plots. How were the data rounded?

Reviewer #2 (Public review):

In the revised manuscript, the authors investigated the role of a presynaptic protein, Rab3A, in the homeostatic synaptic plasticity in cultured cortical neurons. The study was motivated by their previous findings that Rab3A is required for expression of similar homeostatic mechanisms at the neuromuscular junction. The authors first show that untreated WT neurons express homeostatic synaptic plasticity in response to 48h of TTX treatment (upregulation of both mEPSC amplitude and frequency), whereas neurons lacking Rab3A or carrying a dominant negative mutated Rab3A (earlybird) do not. They also demonstrate that only neuronal, but not glial Rab3A is responsible for this defect. Furthermore, they confirm the increased mEPSC amplitudes in WT neurons reflect the addition of GluA2-containing AMPA receptors rather than calcium-permeable ones, as previously reported by multiple labs. However, the increase in mEPSC amplitude is not accompanied by a corresponding upregulation of GluA2 synaptic clusters according to their IHC data (cluster size and intensity trend slightly upwards but not reaching significance). They further show that this modest upward trend is absent in Rab3A KO neurons, and conclude that Rab3A is involved in postsynaptic GluA2 upregulation during homeostatic synaptic plasticity.

When compared to the original version, the authors have done an admirable job in switching to more established ways to assess homeostatic synaptic plasticity by comparing the mean mEPSC amplitude and frequency, which has greatly improved the legibility of the manuscript to the public. Their data in Figures 1,2, and 8 clearly demonstrate that functional Rab3A in cortical neurons is required for the homeostatic regulation of mEPSCs.

However, the authors still have not provided further investigation of the mechanisms behind the role of Rab3A in this form of plasticity, and the revision therefore has added little to the significance of the study. Moreover, the experimental design for the investigation of the mismatch between mEPSC amplitude and GluA2 cluster fluorescence remains questionable, making it difficult to draw any credible conclusions from groups of data that not only look similar to the eye but also show no significance statistically.

A major claim the authors want to make is that Rab3A, although a presynaptic protein, regulates postsynaptic GluA2, and they do this by showing in Figure 5 that the upward trend of GluA2 cluster size and intensity is absent in Rab3A KO neurons. First, it is difficult to convince readers that this upward trend is real in Figures 5B-D without getting more samples. Second, the authors pick GluA2 clusters on the primary dendrites, whereas mEPSC events come from a much larger synapse population (e.g., more distal), therefore it makes sense that these two forms of measurement do not show matching changes, and this caveat could be addressed by sampling more diverse dendritic locations. Without a convincing phenotype in WT neurons, the support for this claim is weak.

Another claim of the authors is that this mismatch between mEPSC amplitude and GluA2 cluster sizes with the same culture suggests there are other factors contributing to the mEPSC amplitude. They do this by comparing results from individual culture dissociations, which greatly suffer from undersampling (Figure 6). In particular, they point out that 2 out of 3 dissociations show "matching" upward trends in mEPSC and GluA2 cluster (figure 6A and 6B) while the third one shows opposite trends, and use this to support their claim. Anyone who has done culture preparation would know the high variability between dissociations, which is why culture data are always pooled for assessment of any population trend. Anything could have happened to this particular dissociation (culture #3, figure 6C), and drawing conclusion from this single incident does little to support this claim. At least, they should double the dissociation numbers, and their claim would be much more convincing if a similar phenomenon occurs again. Besides, as mentioned above, all these mismatching trends could just be due to sampling differences.

In summary, this study establishes that neuronal Rab3A plays a role in homeostatic synaptic plasticity, but so do a number of other molecules that have been implicated in homeostatic synaptic plasticity in the past two decades (only will grow with the new techniques such as RNAseq). Without going beyond this finding and demonstrating how exactly Rab3A participates in the induction and/or expression of this form of plasticity, or maybe the potential Rab3A-mediated functional and behavioral defects in vivo, the contribution of the current study to the field is limited. However, given the presynaptic location of Rab3A, this finding could serve as a starting point for researchers interested in pre-postsynaptic cross-talk during homeostatic plasticity in general.

Reviewer #3 (Public review):

This manuscript presents a number of interesting findings that have the potential to increase our understanding of the mechanism underlying homeostatic synaptic plasticity (HSP). The data broadly support that Rab3A plays a role in HSP, although the site and mechanism of action remain uncertain.

The authors clearly demonstrate the Rab3A plays a role in HSP at excitatory synapses, with substantially less plasticity occurring in the Rab3A KO neurons. There is also no apparent HSP in the Earlybird Rab3A mutation, although baseline synaptic strength seems already elevated. In this context, it is unclear if the plasticity is absent or just occluded by a ceiling effect due the synapses already being strengthened. Occlusion may also occur in the mixed cultures, with Rab3A missing from neurons but not astrocytes. The authors do appropriately discuss both options. There are also differences in genetic background between the Rab3A KO and Earlybird mutants that could also impact the results, which are also noted. The authors have solid data showing that Rab3A is unlikely to be active in astrocytes, Finally, they attempt to study the linkage between synaptic strength during HSP and AMPA receptor trafficking and conclude that trafficking may not be solely responsible for the changes in synaptic strength.

Strengths:

This work adds another player into the mechanisms underlying an important form of synaptic plasticity. The plasticity is likely only reduced, suggesting Rab3A is only partially required and perhaps multiple mechanisms contribute. The authors speculate about some possible novel mechanisms.

However, the conclusions on the partial dissociation of AMPAR trafficking and synaptic response are made from somewhat weaker data. On average, across 3 culture sets, they saw similar magnitude of change in mEPSC amplitude and GluA2 cluster area and integral, but the GluA2 data was not significant. This is likely due to the nature of the datasets. Their imaging method involves only assessing puncta pairs (GluA2/VGlut1) clearly associated with a MAP2 labeled dendrite. This is a small subset of synapses, with usually less than 20 synapses per neuron analyzed (as stated by the authors). The mEPSC recordings will be averaging across several hundred events, which likely represent a hundred or more synapses given reasonable expectations on release probability. It has been reported, in work from this lab as well as by direct monitoring of tagged AMPARs during HSP (Wang, et al., 2019), that individual synapses are quite variable in their response. So there will almost necessarily be higher variability in the imaging data due to the smaller number of synapses sampled. The overall trends, though, are in alignment with previous data implicating receptor trafficking as the mechanism for HSP. However, the authors go on to evaluate each of the individual cultures, where 2 show similar changes between the mEPSC data and GluA2 clusters, and 1 culture showing little/no change in GluA2 clusters. The n's are very low here, and none of the datasets are significant. They want to conclude for this culture, there was a change in mEPSC amplitude that was not accompanied by a change in GluA2 at synaptic sites. But these data are collected from different coverslips, and due to the low n's, the potential under-sampling of the GluA2 clusters, and neuron-to-neuron variability, it is very hard to distinguish if this apparent difference is a methodological issue rather than a biological one. Much stronger data would be necessary to conclude that additional factors beyond receptor trafficking are required for HSP.

Other questions arise from the NASPM experiments, used to justify looking at GluA2 (and not GluA1) in the immunostaining. First, there is a frequency effect that is unclear in origin. One would expect NASPM to merely block some fraction of the post-synaptic current, and not affect pre-synaptic release or block whole synapses. However the change in frequency seems to argue (as the authors do) that some synapses only have CP-AMPARs, while the rest of the synapses have few or none. Another possibility is that there are pre-synaptic NASPM-sensitive receptors that influence release probability. Further, the amplitude data show a strong trend towards smaller amplitude following NASPM treatment (Fig 3B). The p value for both control and TTX neurons was 0.08 - it is very difficult to argue that there is no effect. The decrease on average is larger in the TTX neurons, and some cells show a strong effect. It is possible there is some heterogeneity between neurons on whether GluA1/A2 heteromers or GluA1 homomers are added during HSP. This would impact the weakly supported conclusions about the GluA2 imaging vs mEPSC amplitude data.

Unaddressed issues that would greatly increase the impact of the paper:

(1) Is Rab3A acting pre-synaptically, post-synaptically or both? The authors provide good evidence that Rab3A is acting within neurons and not astrocytes. But where it is acting (pre or post) would aid substantially in understanding its role. They could use sparse knock-down of Rab3A, or simply mix cultures from KO and WT mice (with appropriate tags/labels). The general view in the field has been that HSP is regulated post-synaptically via regulation of AMPAR trafficking, and considerable evidence supports this view. The more support for their suggestion of a pre-synaptic site of control, the better.

(2) Rab3A is also found at inhibitory synapses. It would be very informative to know if HSP at inhibitory synapses is similarly affected. This is particularly relevant as at inhibitory synapses, one expects a removal of GABARs (ie the opposite of whatever is happening at excitatory synapses). If both processes are regulated by Rab3A, this might suggest a role for this protein more upstream in the signaling; an effect only at excitatory synapses would argue for a more specific role just at these synapses.

Reviewer #1 (Public review):

Summary:

The authors conducted a study on one of the fundamental research topics in neuroscience: neural mechanisms of credit assignment. Building on the original studies of Walton and his colleagues and subsequent studies on the same topic, the authors extended the research into the delayed credit assignment problem with clever task design, which compared the non-delayed (direct) and delayed (indirect) credit assignment processes. Their primary goal was to elucidate the neural basis of these processes in humans, advancing our understanding beyond previous studies.

Strengths:

(1) Innovative task design distinguishing between direct and indirect credit assignment.

(2) Use of sophisticated multivariate pattern analysis to identify neural correlates of pending representations.

(3) Well-executed study with clear presentation of results.

(4) Extension of previous research to human subjects, providing valuable comparative insights.

Considerations for Future Research:

(1) The task design, while clear and effective, might be further developed to capture more real-world complexity in credit assignment.

(2) There's potential for deeper exploration of the role of task structure understanding in credit assignment processes.

(3) The interpretation of lateral orbitofrontal cortex (lOFC) involvement could be expanded to consider its role in both credit assignment and task structure representation.

Achievement of Aims and Support of Conclusions:

The authors successfully achieved their aim of investigating direct and indirect credit assignment processes in humans. Their results provide valuable insights into the neural representations involved in these processes. The study's conclusions are generally well-supported by the data, particularly in identifying neural correlates of pending representations crucial for delayed credit assignment.

Impact on the Field and Utility of Methods:

This study makes a significant contribution to the field of credit assignment research by bridging animal and human studies. The methods, particularly the multivariate pattern analysis approach, provide a robust template for future investigations in this area. The data generated offers valuable insights for researchers comparing human and animal models of credit assignment, as well as those studying the neural basis of decision-making and learning.

The study's focus on the lOFC and its role in credit assignment adds to our understanding of this brain region's function.

Additional Context and Future Directions:

(1) Temporal ambiguity in credit assignment: While the current design provides clear task conditions, future studies could explore more ambiguous scenarios to further reflect real-world complexity.

(2) Role of task structure understanding: The difference in task comprehension between human subjects in this study and animal subjects in previous studies offers an interesting point of comparison.

(3) The authors used a sophisticated method of multivariate pattern analysis to find the neural correlate of the pending representation of the previous choice, which will be used for the credit assignment process in the later trials. The authors tend to use expressions that these representations are maintained throughout this intervening period. However, the analysis period is specifically at the feedback period, which is irrelevant to the credit assignment of the immediately preceding choice. This task period can interfere with the ongoing credit assignment process. Thus, rather than the passive process of maintaining the information of the previous choice, the activity of this specific period can mean the active process of protecting the information from interfering and irrelevant information. It would be great if the authors could comment on this important interpretational issue.

(4) Broader neural involvement: While the focus on specific regions of interest (ROIs) provided clear results, future studies could benefit from a whole-brain analysis approach to provide a more comprehensive understanding of the neural networks involved in credit assignment.

Reviewer #2 (Public review):

Summary:

The present manuscript addresses a longstanding challenge in neuroscience: how the brain assigns credit for delayed outcomes, especially in real-world learning scenarios where decisions and outcomes are separated by time. The authors focus on the lateral orbitofrontal cortex and hippocampus, key regions involved in contingent learning. By integrating fMRI data and behavioral tasks, the authors examined how neural circuits maintain a causal link between past decisions and delayed outcomes. Their findings offer insights into mechanisms that could have critical implications for understanding human decision-making.

Strengths:

(1) The experimental designs were extremely well thought-out. The authors successfully coupled behavioral data and neural measures (through fMRI) to explore the neural mechanisms of contingent learning. This integration adds robustness to the findings and strengthens their relevance.

(2) The emphasis on the interaction between the lateral orbitofrontal cortex (lOFC) and hippocampus (HC) in this study is very well-targeted. The reported findings regarding their dynamic interactions provide valuable insights into contingent learning in humans.

(3) The use of an advanced modeling framework and analytical techniques allowed the authors to uncover new mechanistic insights regarding a complex case of the decision-making process. The methods developed will also benefit analyses of future neuroimaging data on a range of decision-making tasks as well.

Weaknesses:

Given the limited temporal resolution of fMRI and that the measured signal is an indirect measure of neural activity, it is unclear the extent to which the reported causality reflects the true relationship/interactions between neurons in different regions.

Reviewer #3 (Public review):

The authors apply multivoxel decoding analyses from fMRI during reward feedback about the cues previously chosen that led to that feedback. They compare two versions of the task - one in which the feedback is provided about the current trial, and one in which the feedback is provided about the previous trial. Reward probability changes slowly over time, so subjects need to identify which cues are leading to reward at a given time. They find that evidence for recall of the cue in the lateral orbitofrontal cortex (lOFC) and hippocampus (HC). They also find that in the second condition, where feedback is for the one-back trial, this representation is mediated by the lateral frontal pole (FPl).

Overall, the analyses are clean and elegant and seem to be complete. I have only a few comments.

(1) They do find (not surprisingly) that the one-back task is harder. It would be good to ensure that the reason that they had more trouble detecting direct HC & lOFC effects on the harder task was not because the task is harder and thus that there are more learning failures on the harder one-back task. (I suspect their explanation that it is mediated by FPl is likely to be correct. But it would be nice to do some subsampling of the zero-back task [matched to the success rate of the one-back task] to ensure that they still see the direct HC and lOFC there).

(2) The evidence that they present in the main text (Figure 3) that the HC and lOFC are mediated by FPl is a correlation. I found the evidence presented in Supplemental Figure 7 to be much more convincing. As I understand it, what they are showing in SF7 is that when FPl decodes the cue, then (and only then) HC and lOFC decode the cue. If my understanding is correct, then this is a much cleaner explanation for what is going on than the secondary correlation analysis. If my understanding here is incorrect, then they should provide a better explanation of what is going on so as to not confuse the reader.

(3) I like the idea of "credit spreading" across trials (Figure 1E). I think that credit spreading in each direction (into the past [lower left] and into the future [upper right]) is not equivalent. This can be seen in Figure 1D, where the two tasks show credit spreading differently. I think a lot more could be studied here. Does credit spreading in each of these directions decode in interesting ways in different places in the brain?

Reviewer #1 (Public review):

In this study, Deshmukh et al. provide an elegant illustration of Haldane's sieve, the population genetics concept stating that novel advantageous alleles are more likely to fix if dominant because dominant alleles are more readily exposed to selection. To achieve this, the authors rely on a uniquely suited study system, the female-polymorphic butterfly Papilio polytes.

Deshmukh et al. first reconstruct the chronology of allele evolution in the P. polytes species group, clearly establishing the non-mimetic cyrus allele as ancestral, followed by the origin of the mimetic allele polytes/theseus, via a previously characterized inversion of the dsx locus, and most recently, the origin of the romulus allele in the P. polytes lineage, after its split from P. javanus. The authors then examine the two crucial predictions of Haldane's sieve, using the three alleles of P. polytes (cyrus, polytes, and romulus). First, they report with compelling evidence that these alleles are sequentially dominant, or put in other words, novel adaptive alleles either are or quickly become dominant upon their origin. Second, the authors find a robust signature of positive selection at the dsx locus, across all five species that share the polytes allele.

In addition to exquisitely exemplifying Haldane's sieve, this study characterizes the genetic differences (or lack thereof) between mimetic alleles at the dsx locus. Remarkably, the polytes and romulus alleles are profoundly differentiated, despite their short divergence time (< 0.5 my), whereas the polytes and theseus alleles are indistinguishable across both coding and intronic sequences of dsx. Finally, the study reports incidental evidence of exon swaps between the polytes and romulus alleles. These exon swaps caused intermediate colour patterns and suggest that (rare) recombination might be a mechanism by which novel morphs evolve.

This study advances our understanding of the evolution of the mimicry polymorphism in Papilio butterflies. This is an important contribution to a system already at the forefront of research on the genetic and developmental basis of sex-specific phenotypic morphs, which are common in insects. More generally, the findings of this study have important implications for how we think about the molecular dynamics of adaptation. In particular, I found that finding extensive genetic divergence between the polytes and romulus alleles is striking, and it challenges the way I used to think about the evolution of this and other otherwise conserved developmental genes. I think that this study is also a great resource for teaching evolution. By linking classic population genetic theory to modern genomic methods, while using visually appealing traits (colour patterns), this study provides a simple yet compelling example to bring to a classroom.

In general, I think that the conclusions of the study, in terms of the evolutionary history of the locus, the dominance relationships between P. polytes alleles, and the inference of a selective sweep in spite of contemporary balancing selection, are strongly supported; the data set is impressive and the analyses are all rigorous. I nonetheless think that there are a few ways in which the current presentation of these data could lead to confusion, and should be clarified and potentially also expanded.

(1) The study is presented as addressing a paradox related to the evolution of phenotypic novelty in "highly constrained genetic architectures". If I understand correctly, these constraints are assumed to arise because the dsx inversion acts as a barrier to recombination. I agree that recombination in the mimicry locus is reduced and that recombination can be a source of phenotypic novelty. However, I'm not convinced that the presence of a structural variant necessarily constrains the potential evolution of novel discrete phenotypes. Instead, I'm having a hard time coming up with examples of discrete phenotypic polymorphisms that do not involve structural variants. If there is a paradox here, I think it should be more clearly justified, including an explanation of what a constrained genetic architecture means. I also think that the Discussion would be the place to return to this supposed paradox, and tell us exactly how the observations of exon swaps and the genetic characterization of the different mimicry alleles help resolve it.

(2) While Haldane's sieve is clearly demonstrated in the P. polytes lineage (with cyrus, polytes, and romulus alleles), there is another allele trio (cyrus, polytes, and theseus) for which Haldane's sieve could also be expected. However, the chronological order in which polytes and theseus evolved remains unresolved, precluding a similar investigation of sequential dominance. Likewise, the locus that differentiates polytes from theseus is unknown, so it's not currently feasible to identify a signature of positive selection shared by P. javanus and P. alphenor at this locus. I, therefore, think that it is premature to conclude that the evolution of these mimicry polymorphisms generally follows Haldane's sieve; of two allele trios, only one currently shows the expected pattern.

Reviewer #2 (Public review):

Summary:

Deshmukh and colleagues studied the evolution of mimetic morphs in the Papilio polytes species group. They investigate the timing of origin of haplotypes associated with different morphs, their dominance relationships, associations with different isoform expressions, and evidence for selection and recombination in the sequence data. P. polytes is a textbook example of a Batesian mimic, and this study provides important nuanced insights into its evolution, and will therefore be relevant to many evolutionary biologists. I find the results regarding dominance and the sequence of events generally convincing, but I have some concerns about the motivation and interpretation of some other analyses, particularly the tests for selection.

Strengths:

This study uses widespread sampling, large sample sizes from crossing experiments, and a wide range of data sources.

Weaknesses:

(1) Purpose and premise of selective sweep analysis