Reviewer #3 (Public Review):

Summary:

This manuscript sheds light on the cell cycle-dependent post-transcriptional regulation of the oncogenic kinase AURKA. AURKA mRNA is subjected to alternative polyadenylation (APA), resulting in a short and a long 3'UTR isoform. While the ratio long/short isoform is important for AURKA expression and might impact cancer development, it is not unclear how this is regulated throughout the cell cycle. Translation and decay rate of the long isoform only are targeted by let-7a miRNA and in a cell-cycle dependent manner. In contrast, the short isoform is translated highly and constantly throughout interphase. Finally, depletion of the long isoform led to an increase in proliferation and migration rates of cells. In Triple Negative Breast Cancer, where AURKA is typically overexpressed, the short isoform is predominant and its expression correlates with faster relapse times of patients, suggesting that this mechanism might play an important role in this cancer.

Originality and novelty:

The originality of this work is to show the cooperation between APA and miRNA-targeting in controlling gene expression dynamics of AURKA during cell cycle. To investigate this mechanism, the authors have developed an interesting transient single-cell and biochemical assay to rapidly study mRNA-specific gene expression in a way that measures post-transcriptional events. This manuscript puts an emphasis on the cell cycle dependent expression control of AURKA at the translation level. However, the magnitude of the changes in mRNA levels throughout the cell cycle is even greater than that of the changes in translation. Therefore, it remains unclear whether translation really is that important in controlling AURKA expression during the cell cycle. Moreover, (i) AURKA regulation by miRNA is already known (Fadaka et al., Oncotarget 2020, Zhang et al., Arch Med Sci 2020, Yuan et al., Technol Cancer Res Treat 2019, Ma et al., Oncotarget 2015), (ii) the concept of cooperation between APA and translation already is not new (Sandberg et al., Science 2008, Mayr and Bartel, Cell 2009, Masamha et al. Nature 2015), and (iii) previous transcriptome-wide studies already suggested a cell-cycle dependent control of AURKA at the translation level (Tanenbaun et al., eLife 2015, translation efficiency ratio G2/G1 = 1.59) as well as the mRNA level (Krenning et al., eLife 2022). The impact of this manuscript could be increased by investigating (i) the mechanism of cell cycle-dependent regulation by let7a expression (i.e is there changes in let7a expression or activity during the cell cycle in this model) and (ii) the origin of AURKA APA dysregulation in cancer (could it be modulated by CFIm25? (Masamha et al. Nature 2015, Tamaddon et al. Sci Rep 2020)).

Reviewer #3 (Public Review):

Moutard, Laura, et al. investigated the gene expression and functional aspects of Leydig cells in a cryopreservation/long-term culture system. The authors found that critical genetic markers for Leydig cells were diminished when compared to the in-vivo testis. The testis also showed less androgen production and androgen responsiveness. Although they did not produce normal testosterone concentrations in basal media conditions, the cultured testis still remained highly responsive to gonadotrophin exposure, exhibiting a large increase in androgen production. Even after the hCG-dependent increase in testosterone, genetic markers of Leydig cells remained low, which means there is still a missing factor in the culture media that facilitates proper Leydig cell differentiation. Optimizing this testis culture protocol to help maintain proper Leydig cell differentiation could be useful for future human testis biopsy cultures, which will help preserve fertility and child cancer patients.

Methods: In line 226, there is mention that the central necrotic area was carefully removed before RNA extraction. This is particularly problematic for the inference of these results, especially for the RT-qPCR data. Was the central necrotic area consistent between all samples and variables (16 and 30FT)? How big was the area? This makes the in-vivo testis not a proper control for all comparisons. Leydig cells are not evenly distributed throughout the testis. A lot of Leydig cells can be found toward the center of the gonad, so the results might be driven by the loss of this region of the testis.

What did the morphology of the testis look like after culturing for 16 and 30 days? These images will help confirm that the culturing method is like the Nature paper Sato et al. 2011 and also give a sense of how big the necrotic region was and how it varied with culturing time.

There are multiple comparisons being made. Bonferroni corrections on p-value should be done.

Results: In the discussion, it is mentioned that IGF1 may be a missing factor in the media that could help Leydig cell differentiation. Have the authors tried this experiment? Improving this existing culturing method will be highly valuable.

Add p-values and SEM for qPCR data. This was done for hormones, should be the same way for other results.

Regarding all RT-qPCR data-There is a switch between 3bHSD and Actb/Gapdh as housekeeping genes. There does not seem to be as some have 3bHSD and others do not. Why do Igf1 and Dhh not use 3bHSD for housekeeping? If this is the method to be used, then 3bHSD should be used as housekeeping for the protein data, instead of ACTB. Also, based on Figure 1B and Figure 2A (Hsd3b1) there does not seem to be a strong correlation between Leydig cell # and the gene expression of Hsd3b1. If Hsd3b1 is to be used as a housekeeper and a proxy for Leydig cell number a correlation between these two measurements is necessary. If there is no correlation a housekeeping gene that is stable among all samples should be used. Sorting Leydig cells and then conducting qPCR would be optimal for these experiments.

Figure 2A (CYP17a1): It is surprising that the CYP17a1 gene and protein expression is very different between D30FT and 36.5dpp, however, the immunostaining looks identical between all groups. Why is this? A lower magnification image of the testis might make it easier to see the differences in Cyp17a1 expression. Leydig cells commonly have autofluorescence and need a background quencher (TrueBlack) to visualize the true signal in Leydig cells. This might reveal the true differences in Cyp17a1.

Figure 3D: there are large differences in estradiol concentration in the testis. Could it be that the testis is becoming more female-like? Leydig and Sertoli cells with more granulosa and theca cell features? Were any female markers investigated?

Figure 3D and Figure 5A: It is hard to imagine that intratesticular estradiol is maintained for 16-30 days without sufficient CYP19 activity or substrate (testosterone). 6.5 dpp was the last day with abundant CYP19 expression, so is most of the estrogen synthesized on this first day and it sticks around? Are there differences in estradiol metabolizing enzymes? Is there an alternative mechanism for E production?

Reviewer #3 (Public Review):

Signore et al. the synthesized and functionally characterized the recombinant adult hemoglobin (Hb) proteins of extant, extinct, and ancestral sirenians to explore the putative role of Hb in helping Steller's sea cows adapt to life in extremely cold waters. The functional comparisons show that the Hb of the subarctic Steller's sea cows differs in multiple biochemical properties relative to the Hbs of the two extant sirenians in the study, the Florida manatee, and the dugong and also from the Hb inferred for the common ancestor of Steller's sea cow and dugong. Specifically, the Steller's sea cow shows reduced oxygen binding affinity, reduced sensitivity to the allosteric co-factors DPG, Cl-, and H+, increased solubility, and reduced thermal sensitivity. DPG plays an important role in regulating Hb oxygen affinity in mammals, and the lack of sensitivity to it is unique to the Hb of Steller's sea cow. Sequence comparisons show that the Hb of the Steller's sea cow differs at 11 amino acids from that of its sister group, the dugong, one of which is intriguing because it occurs in a position that is invariable among mammals at a site that is critical for DPG binding, a change from Lys to Ans in position 82 of the mature β/δ globin chain. To test the significance of this change, the authors use site directed mutagenesis to insert back a Lys in the Steller's sea cow Hb background (β/δ82Asn→Lys) and test its biochemical properties. The functional assays with the β/δ82Asn→Lys mutant indicate that reverting this position to its ancestral state drastically altered the biochemical properties of the Steller's sea cow Hb, making it functionally similar to the Hbs of manatee, dugong, and the Hb inferred for the common ancestor of Steller's sea cow and dugong.

The study's strength lies in comparing the different recombinant Hbs in an explicit evolutionary framework. The conclusions are supported by the analyses, and the results are relevant in the fields of evolutionary biology, physiology, and biochemistry because they suggest that a single amino acid substitution in a protein can have profound biochemical consequences that impact whole organism physiology.

Reviewer #3 (Public Review):

In this article, Briggs et al. used scRNAseq to get high-resolution cell cycle-regulated transcriptomes of both replicative forms of Trypanosoma brucei (PCF and BSF) without prior synchronization. Briggs et al. also demonstrated that performing the scRNAseq library immediately after thawing cryopreserved samples did not show significant differences. The authors used computational reconstruction of the cell cycle to get the dynamic expression patterns of cycling genes in both life cycle forms. They identified a core cycling transcriptome highly conserved between forms. However, some slight differences were found between them, e.g.: a switch in gene expression associated with the S-G2 transition is much more discrete in PCFs than BSFs. Moreover, as proteomics data across the cell cycle is not available for BSFs, the authors tagged the top most significant genes with transcripts peaking in G1, S, and G2/M phases with a fluorescent epitope. After comparing the transcript expression patterns with protein abundance, the authors found that the majority of genes with periodic cycling transcript and protein levels exhibited a relative delay between peak transcript and protein expression, which was expected. In summary, this work provides a valuable public tool for further investigation into gene expression dynamics throughout the cell cycle in T. brucei.

Reviewer #3 (Public Review):

Nurr1 is an orphan nuclear receptor that may be a significant target for the treatment of neurodegenerative disorders. Targeting Nurr1 with small molecule ligands has been challenging, but there has been some progress in the identification of synthetic ligands that appear to increase Nurr1 activity. Nurr1 functions as a monomer, but may also heterodimerize with RXR. Heterodimerization appears to repress Nurr1 transcriptional activation via NBRE-driven reporters. Importantly, small molecule ligands that appear to selectively activate the Nurr1/RXR heterodimer complex (and not the Nurr1 or RXR homodimers, individually) have been identified. Exactly how these ligands function in this manner is unclear.

Here, the authors demonstrate that Nurr-1/RXR agonists actually function by perturbing the heterodimer formation providing Nurr1 monomers that are much more active in driving transcription. The authors demonstrate this with a range of biochemical, biophysical, and cell-based methodologies. Cotransfection assays examining the activity of Nurr1 on an NBRE reporter illustrate that RXRalpha is a repressor of Nurr1 transcriptional activity and that this is mediated by the RXR LBD. Using this experimental model as well as RXR coactivator interaction assays (biochemical) and RXR/DR1 reporter cotransfection assays, the authors examined multiple classes of RXR ligands (RXR agonists, modulators, antagonists, and Nurr1/RXRa selective agonists) to compare their activities. The Nurr1/RXR heterodimer agonists were quite effective at inducing transcription in the Nurr1/RXR assay but relatively ineffective in the RXR - coactivator binding assay or the RXR cotransfection assay. Using the array of ligands, the authors show that the Nurr1/RXR activity does not correlate to the ability of compounds to induce RXR to recruit a coactivator or activate RXR-mediated transcription. This suggests that Nurr1/RXR heterodimer agonists may not be mediating transcriptional activation via the "standard" mechanism. One weakness here is that some compounds used in the Nurr1/RXR transcription assay are not included in the other assays and may not have been included in the correlation studies. Assessment of RXR/Nurr1 dimerization in the presence of the ligands was assessed by ITC and demonstrated a correlation between the weakening of heterodimer formation and Nurr1/transcriptional activity suggesting that modulation of dimerization may be a mechanism by which the Nurr1/RXR heterodimer specific ligands function. NMR and analytical SEC data support this hypothesis as well. With regard to the physiological significance of these observations, no studies were completed on actual Nurr1 target genes addressing this type of mechanism offering limitations on the applicability of the hypothesis. However, the mechanism proposed is strongly supported by the data and offers a novel paradigm for the development of drugs targeting this receptor and possibly other nuclear receptors as well.

Reviewer #3 (Public Review):

The authors described the one family showing autoinflammatory phenotypes with L236P variant of TNFAIP3 gene. The variant has not been reported on and they evaluated the function of this variant using in vitro and in silico methods. I think this is well-written manuscript and I agree with their interpretation about the pathogenicity of this variant, but the new finding is poor. The variant information was only a new finding.

I recommend the revision of the following points.

In Table 2, T647P seemed to be pathogenic which was evaluated with in vitro assay by Kadowaki.

Two other missense variants, V377I (Niwano, Rheumatology 2022) and T602S (Jiang W, Cellular Immunol 2022) were recently reported. These should be included in the discussion.

Reviewer #3 (Public Review):

Genome-wide association studies on asthma have been challenging, due to the accuracy of phenotyping and potential gene-environment interactions. Thus, the authors aimed to identify genetic loci associated with subtypes of childhood wheezing. One main strength of this investigation is that the data availability of wheeze from birth to adolescence among multiple birth cohorts allowed the sub-phenotyping on a large scale and high statistical power. The study is properly designed and the conclusions are well-supported. Understanding the heterogeneity in subtypes of childhood wheezing is of great clinical interest and may help inform future directions in disease prediction and prevention.

Reviewer #3 (Public Review):

Numerous experimental models are phenotyped in this manuscript including mouse neurons, iPSC-derived human neurons, knock-in mice, and knock-in iPSCs. Expression of acetylation-mimic or acetylation-null TDP-43 protein is achieved either with overexpression or CRISPR-Cas9-based knock-in. A complex phenotype is observed including loss of TDP-43 function (reduced autoregulation, increased cryptic splicing) and a gain of TDP-43 (increased insoluble TDP-43 protein). These correlate with downstream neurobehavioral changes which are most consistent with a cortical/hippocampal phenotype without a motor phenotype. Post-translational modifications of disease-associated proteins are thought to contribute to neurodegenerative disease pathogenesis, and this study succeeds in demonstrating that TDP-43 acetylation results in downstream molecular and behavioral phenotypes.

There are numerous additional strengths. TDP-43 acetylation is a post-translational modification that is known to be associated with TDP-43 inclusions that are characteristic of human diseases. An important strength is the rigorous use of multiple different experimental models (rodent cells, iPSC-derived neurons, mice, overexpression, knock-in) with overall consistent results. Moreover, multiple orthogonal endpoints are presented including histology/cytology/immunostaining, biochemistry, molecular biology, and neurobehavioral assays. As TDP-43 acetylation is known to block RNA binding, these novel cellular and mouse models represent interesting albeit complex tools to study the functional consequences of a partial loss of function. As TDP-43 regulates its own expression (i.e. autoregulation), the complexity lies at least in part due to the loss of RNA binding leading to a functional loss of TDP-43 function which includes the increased expression of the TARDBP transcript and TDP-43 protein.

Conceptually, there is a disconnect in that the mouse model exhibits primarily a cortical/hippocampal phenotype more akin to frontotemporal lobar degeneration with TDP-43 inclusions (FTLD-TDP), while TDP-43 acetylation is only seen in ALS tissues and not in FTLD-TDP tissues because most of the pathologic protein in the latter is N-terminally truncated (i.e. the acetylation site is not present). That being said, there is no mouse model which completely and faithfully recapitulates the human disease, and this mouse model avoids overt overexpression (increased TDP-43 protein expression stemming from altered autoregulation) and avoids the use of synthetic/artificial mutation (such as mutation of the TDP-43 nuclear localization signal).

In terms of the CNS phenotype, it is difficult to interpret the reduced density of NeuN-positive neurons in the mouse model as a neurodegenerative phenotype. The reduction in NeuN density but not overall cellular density is only suggestive of neurodegeneration (as opposed to, for example, a developmental phenotype) without more rigorous stereological approaches that take into account potential volumetric changes. Indeed, the absence of astrocytosis and microgliosis argues against neurodegeneration.

Some of the loss of function measures (CFTR construct splicing, shift in SORT1 protein) are subtle, although RNA sequencing clearly shows many splicing aberrations including cryptic splicing events which overall supports that a loss of TDP-43 function is observed.

Finally, there are multiple instances where multiple measurements are made on a few biological replicates. ANOVA or t-tests are not appropriate in these instances (lack of independence).

Reviewer #3 (Public Review):

The current manuscript describes the expression of multiple Natriuretic peptides and their receptor during the early embryo development in the amphibian Xenopus laevis. This signaling pathway is well known to control a broad range of physiological processes but its role in embryogenesis has not been studied before. Thus, the study presents some important novel findings. After defining the combination of ligands and receptors expressed during embryogenesis, they used loss of function experiment to test the requirement of this signaling pathway to the development of neural crest cells.

The loss of function experiments are well controlled as they use both chemical inhibitors and Morpholino that block the translation of the protein. They also rescue the phenotypes by using either mRNA from the human protein (receptor), or purified peptides (Ligands).

The results clearly show that loss of either Npr1 or Npr3 affects the development of both neural crest and placodes, while Npr2 had no visible effect. Similarly, they found that the loss of the ligands Nppa and Nppc affected neural crest and placode development while Nppb had no effect. Again, the loss of function was achieved with both Morpholino KD and inhibitors. In general, the loss of neural crest and placodal marker are associated with an expansion of neural marker (Sox2) and a corresponding decrease of epidermal marker (Keratin).

For Npr3 the author show that the loss of the protein is associated with an increase in cell death and decrease in cell proliferation which match some previous work on the role of the receptor in other cell type. It is unclear how much this can account for the striking difference in patterning observed and experiment to test this have not been performed.<br /> Overall, this work is important for the field as it shows novel genes that are critical for craniofacial and sensory development. It is likely that mutations in any genes involved in this pathway could result in birth defect which could be corrected pharmacologically.

Reviewer #3 (Public Review):

The function of a brain area is defined by its interaction with other regions. Accordingly, two areas communicate via axons and dendrites, but the language is plurimodal along the neurotransmitter-receptor dimension. Consequently, reading its neurochemical constituents is the most advanced way to characterize the brain into functional territories.

Along this theoretical line of research, Rapan et al. produced an exceptional report on the structure of the macaque frontal lobes based on cytoarchitectural division complemented with functional connectivity and neurochemical data. Results are lavishly illustrated. They report 35 cytoarchitectural areas in the prefrontal lobe with precise, different connectivity and neurotransmitter profiles together with practical anatomical landmarks. All data is openly available to the community and will constitute a cornerstone for future neuroscientific research in the macaque frontal lobes.

I congratulate the authors for this already extraordinary work.

Reviewer #3 (Public Review):

The manuscript concerns the cleavage of the Gag polyprotein lattice from the HIV virion membrane, a key stage in HIV lifecycle, and one that is required for HIV to become infectious. Since cleavage requires homodimerization between the small fraction (5%) of such Gag polyproteins that carry a protease domain, referred to as Gag-Pol, this raises questions regarding how such homodimerization can take place, and whether it can happen on the required timescales, given that Gag-Pol is typically embedded in a lattice that is observed to form one large connected component.

The authors address these questions in silico, using particle-based reaction diffusion simulations. Such simulations are rigid-body and "structure-resolved" meaning that they rigidly incorporate the geometry of the polyproteins, and their various binding interfaces, based on existing structural data. Other aspects of the simulations are also in-line with available data, including copy numbers, lattice curvature, and dissociation rates. This focused approach is a strength of the work and allows the authors to make credible claims that their simulations have relevance to HIV (as does their commitment to comparison with HALO-SNAP-based measurements of dimerization kinetics as well as iPALM experiments that characterize lattice dynamics).

A central part of the model is that it allows for the "possibility of imperfect alignment of molecules in the lattice", presumably due to the incompatibility of regular hexagonal tiling and surfaces with non-zero Gaussian curvature, such as a sphere. This is implemented via the ad-hoc imposition of a free-energy penalty when complete hexamers are formed, implying that hexamers are less stable than six ideal bonds. By varying this strain penalty, the authors can change the stability of the lattice independently of individual binding affinities, allowing its use as an effective fitting parameter when comparing to HALO-SNAP data. In the latter case, agreement between simulation and experiment can only be found at moderate levels of lattice stability.

However, such energetic penalties are present whenever the polyprotein structure must undergo deformations which, on surfaces with nonzero Gaussian curvature, should be the case for partial tilings as well as complete ones (where all six interfaces form bonds). This, therefore, appears to be a weakness of the work. An elastic implementation of polyprotein structure, for example, would permit strain to accumulate (and therefore stresses to propagate) throughout the lattice naturally, irrespective of whether complete hexamers were formed, and might reasonably be expected to impact the likelihood of different lattice structures. Whilst it is not clear how or whether this would lead to qualitatively or quantitatively different results, it is nevertheless worth remarking upon since the authors high-level claim is that lattice structure is an important determinant of the mean-first-passage times to dimerization.

Overall, I find this to be a valuable study, carried out in a solid and comprehensive manner. The primary impact of the work appears to be twofold: the unification of different experimental measurements under a single model, and the further identification of the salient parts of that model that most impact biological function. The results advance the understanding of one of the steps of the HIV lifecycle, via a better description of the mechanisms underpinning Gag-Pol dimerization. Notably, the authors stop short of drawing parallels to many related concepts and models in statistical physics, such as those concerning percolation and diffusion limited aggregation as well as the notions of dislocations and defects in crystalline matter on curved surfaces. These might reasonably have provided a basis for better understanding and quantification of the authors' simulations, as well as improving the scope for extensions and conceptual clarity.

Reviewer #3 (Public Review):

This report by Mohebi et al. provides new answers to old questions by showing that the activity of striatal cholinergic interneurons (CINs) escalates progressively during specific reward-related behaviors and that this correlates with previously observed ramps in dopamine (DA) release in the nucleus accumbens core. The report is strong and provides evidence for the authors' hypothesis that DA ramps are independent of DA neuron activity, but are instead the result of CIN activity and corresponding acetylcholine (ACh) release. The authors further demonstrate that the fidelity of CIN activation and consequent driving of DA release is even more robust in vivo than observed ex vivo slice preparations, which is fundamental for understanding the role of ACh-DA interactions in behavior. The findings complement the authors' previous evidence ventral tegmental area (VTA) DA neuron firing patterns do not show a ramping pattern; the previously reported VTA data are appropriately included here (in Fig. 3) to illustrate the absence of VTA firing during the time-locked increases in CIN activity and DA release. The present studies stop short of showing a direct link between CIN activity and DA release, however, which would require examining DA release during behavior in the presence of an antagonist of nicotinic ACh receptors. The authors also extend the understanding of the regulation of DA release by acetylcholine (ACh) by showing that optical activation of CINs in vivo promotes DA release responses that do not attenuate with repetitive stimulation. This contrasts with previous results in ex vivo striatal slices in which ACh-evoked DA release has been found to decline progressively from rundown and/or receptor desensitization. The authors propose that in vivo, AChE may be more effective in curtailing local ACh levels than in slices because of the slightly lower temperature typically used for slice studies, as well as the use of superfusion that might facilitate some AChE washout (AChE inhibitors are still effective in slices, of course). Overall, the report not only provides evidence for the cellular substrate for DA ramps but also shows the robustness of ACh-driven DA release in vivo. A few points to strengthen the report are listed below.

1) The authors give a few details about how CINs were activated at the beginning of the results, but say only that DA dynamics were monitored using fiber photometry. Given that the methods are at the end, a brief summary should be given here to indicate whether this means direct monitoring of DA or indirect via GCaMP, for example. It would be helpful to note the sensor used in the abstract, as well. In this light, as it were, RdLight1 should be described upon the first mention.

2) The authors show that infusion of DHbE in the NAc likelihood of decisions to approach the center port, as did antagonism of DA receptors. This supports the authors' argument that ramping of CIN activity and consequent ACh release underlies observed ramps in DA release. However, to show a causal interaction requires testing whether the observed DA ramps are absent after DHbE infusion in the NAc, under the same conditions that attenuated behavior.

3) In Fig. 3, the y-axis title for the upper panels should specify VTA, not simply "rate". This is stated in the legend, but should also be specified in the figure panel.

4) A recent preprint in BioRxiv by AC Krok, NX Tritsch et al. shows a related correlation between ACh and DA release in vivo in a reward task, as well as differences in other conditions. This report shows also that cortical input to CINs indeed plays a role, as suggested in the concluding sections of the present report. Consideration of the data in the preprint in the context of the present results could be valuable for the field.

Reviewer #3 (Public Review):

In this manuscript, the authors explore the mechanism of ATRIP recruitment to single-stranded DNA (ssDNA), which is important for ATR activation and the subsequent control of DNA repair and cell cycle progression. Using Xenopus egg extracts, in vitro interaction assays, and ssDNA constructs, the authors found that AP endonuclease 1 (APE1) plays a role in the recruitment of ATRIP to ssDNA independently of RPA. Moreover, APE1 domains are characterized for ssDNA, ATRIP, and RPA interaction, determining that the nuclease activities of APE1 are not required for this new mode of ATRIP recruitment. Overall, the work presented makes a compelling case for a novel role for APE1 in ATRIP recruitment that seems crucial for ATR activation (at least in the Xenopus system). The results are likely to have an important impact on our understanding of the determinants for activation of ATR signaling and cellular responses to DNA damage and replication stress. It remains unclear whether the findings will be extended to other organisms and be relevant for different types of DNA lesions. Also, there are several points of concern in the manuscript that require further clarification, especially regarding some of the quantitative analyses presented and the claimed importance of the RPA-independent mode of ATRIP recruitment for ATR activation.

Reviewer #3 (Public Review):

This work describes transcriptome profiling of dissected skin of zebrafish at post-embryonic stages, at a time when adult structures and patterns are forming. The authors have used the state-of-the-art combinatorial indexing RNA-seq approach to generate single cell (nucleus) resolution. The data appears robust and is coherent across the four different genotypes used by the authors.

The authors present the data in a logical and accessible manner, with appropriate reference to the anatomy. They include helpful images of the biology and schematics to illustrate their interpretations.

The datasets are then interrogated to define cell and signalling relationships between skin compartments in six diverse contexts. The hypotheses generated from the datasets are then tested experimentally. Overall, the experiments are appropriate and rigorously performed. They ask very interesting questions of interactions in the skin and identify novel and specific mechanisms. They validate these well.

The authors use their datasets to define lineage relationships in the dermal scales and also in the epidermis. They show that circumferential pre-scale forming cells are precursors of focal scale forming cells while there appeared a more discontinuous relationship between lineages in the epidermis.

The authors present transcriptome evidence for enamel deposition function in epidermal subdomains. This is convincingly confirmed with an ameloblastin in situ. They further demonstrate distinct expression of SCPP and collagen genes in the SFC regions.

The authors then demonstrate that Eda and TH signalling to the basal epidermal cells generates FGF and PDGF ligands to signal to surrounding mesenchyme, regulating SFC differentiation and dermal stratification respectively.

Finally they exploit RNA-seq data performed in parallel in the bnc2 mutants to identify the hypodermal cells as critical regulators of pigment patterning and define the signalling systems used.

Whilst these six interactions in the skin are disparate, the stories are unified by use of the sci-RNA-seq data to define interactions. Overall, it's an assembly of work which identifies novel and interesting cell interactions and cross-talk mechanisms. There are some aspects that require clarification:

With respect to the discontinuous relationship noted in Figure 2I in the epidermis, the authors did not make mention of the fact that there are in fact two independent sources of periderm in the zebrafish. The first periderm derives from the EVL, is segregated a gastrulation, and gradually replaced from the basal epidermis during post-embryonic stages. Could this residual EVL-derived periderm have reduced sensitivity of the trajectory mapping from basal to periderm? The authors should comment whether their transcriptome dataset likely had residual EVL-derived periderm and if this might have impacted their trajectory continuity interpretation.

The authors ask if dermal SFCs express proteins associated with cartilage formation and use Col10a1 orthologues as markers (Fig 3B, I). I wonder if these are the best transcripts to answer this question as this has also been described to label osteoblasts in certain contexts in the fish and the authors might want to refer to Li et al 2009 or Avaron et al 2005. Were other markers of cartilage formation present such as collagen2 genes? These may be more definitive. The authors might want to reinterrogate their datasets for true cartilage markers or reframe their question.

Finally, of interest, were there any clear clusters on the UMAP plots (Fig 1 Supp3A) of unassigned identity? Even comment on these and how they were dealt with would be of significant interest to the field, as it is highly unlikely all cell types in the skin have been defined. This dataset promises to be a critical reference for defining these in the future.

Minor clarification:

Fig 2E top. The authors interpret that fate-mapped SFCs at the posterior margin are progressively displaced towards the scale focus. This is confusing as the margin SFC in Fig 2E seems to show them staying largely at the margin. Please clarify if this is what you meant.

Reviewer #3 (Public Review):

This paper addresses an apparent contradiction related to the interaction between spontaneous neural activity and neural plasticity during circuit development. It is well established that spontaneous activity contains instructive information for developmental downstream circuit refinement, for instance for the formation of retinotopic projections in the visual system. These developmental cues are contained in activity correlations, and thus can be picked up by Hebbian mechanisms. However, previous work has shown that informative correlations in spontaneous activity can be found on slow time scales (100s of milliseconds). Here it is shown that in this case correlations on fast time scales arise simultaneously from the integration of unrefined inputs and that these likely interfere with developmental plasticity. The paper shows that such fast, "parasitic" correlations can appear during retinal wave activity, and provides evidence that NMDA receptors can suppress them to avoid their influence during developmental plasticity.

This work is based on detailed biophysical models of thalamic relay neurons, which were fit to data from in-vitro whole-cell recordings. A genetic algorithm was used to fit these models, which provides a family of suitable models instead of a point estimate of good parameters. This approach is a real strength as it shows that the effects generalise well to many plausible models, and do not depend on specific parameter choices. The model neurons are then placed in simulated networks (without or with anatomically informed recurrent connections) and driven by retinal wave activity recorded from the mouse. Together the simulation and analysis show under which conditions fast, undesirable correlations do and do not appear. Specifically, the key model ingredient this work identifies for the suppression of fast correlations is the presence of NMDA receptors on the recipient synapses of the relay neurons.

An open question is how the parasitic correlations are actually suppressed by NMDA receptors. Is it correct that a stronger NMDAR contribution to the transmitted activity simply low-pass filters the incoming spike trains, and that fast correlations are smoothed out as a result? So are developing circuits tuned to slower activity? This could also explain why AMPA receptors subunits with slower kinetics are expressed during development in many circuits.

Taken together, I think this paper presents a very interesting set of results. The issue with parasitic correlations is quite obvious in retrospect, and clearly, a problem developing circuits will generally face. Additionally, the presence of NMDA receptors is often linked to plasticity and is seen as less important in shaping postsynaptic integration. Although no developmental plasticity has been modelled, would it be possible to predict the possible effects of experimental manipulations?

Dual of the dual is just the original space.

It's an example of theorem 2.7-9. A linear operator has boundedness and continuity being an equivalent conditions.

Reviewer #3 (Public Review):

In their study: Comprehensive characterization of tumor microenvironment in colorectal cancer via histopathology-molecular analysis, Wu et al., aim to examine the contribution of the tumour microenvironment (TME) on biological and clinical heterogeneity in colorectal cancer (CRC).

To achieve this the authors use a vast array of publicly available datasets across a variety of biological modalities (transcriptomic, epigenetic, mutational). Using thoughtfully curated genesets the authors classify CRC into 4 holistic comprehensive characterised CRC (CCCRC) subtypes which comprise immune, stromal, and tumour features of CRC biology.

The authors investigate the association of their novel CCCRC subtypes with current "gold standard" classification schemes.

The authors' integration of deep learning methods for HE classification and subsequent association with "Tumor level" CCCRC subtypes is a refreshing addition to the study. Comment on the degree of heterogeneity observed in HE samples and correlation to the heterogeneity of CCCRC subtypes would be a welcomed addition. It is likely publicly available datasets from such platforms as 10X Genomic Visium would be available for this type of analysis.

Whilst one of the main outcomes of the study is the addition of another classification scheme to the field of colorectal cancer, the CCCRC scheme represents a holistic perspective on CRC classification.

The authors provide a welcomed graphical overview of the complex narrative of the study in Figure 7.

The authors focus on the classification of inter-patient heterogeneity and its associated predictive and prognostic utility. There appears to be a significant degree of overlap between immunosuppressive and immune excluded, and proliferative and immuno-modulatory signatures in Figure 1A. One of the major limitations of patient response to treatment is intra-patient heterogeneity, it would be nice for the authors to comment briefly on the degree of intra-patient heterogeneity of the CCCRC subtypes.

Overall the authors succeed in providing a holistic deep characterisation of CRC from the perspective of a variety of biological modalities. The authors provide a novel classification scheme for the field of CRC which demonstrates prognostic and predictive utility, which would benefit from further validation from external datasets. The authors demonstrate a pathway for integration and interpretation of complex high-dimensional data into clinically translatable currency such as the H&E.

Reviewer #3 (Public Review):

The authors report a study where, using multiple datasets with [18F]FDG PET bolus + continuous infusion ("functional PET") and BOLD fMRI data, they re-evaluate the metabolic and hemodynamic properties of the default mode network (DMN) in a task-evoked context, with a focus on posteromedial DMN due to its relevance for across-network integration.<br /> They show how posterior DMN is differently engaged depending on the chosen task: while visual and motor tasks lead to BOLD deactivations and glucose metabolic decrease, specifically in the dorsal posterior cingulate cortex (PCC) area, working memory tasks produce BOLD deactivations but metabolic increases, specifically in ventral PCC, as shown in their previous paper (Stiernman et al. 2021, https://doi.org/10.1073/pnas.2021913118). This aims to solve the controversies elicited by findings of both increased and decreased glucose consumption in the presence of BOLD deactivation in the DMN.

Additionally, they show how task-evoked glucose metabolism in posterior DMN seems to be shaped by that of the corresponding task-positive networks, with a positive link with dorsal attention and a negative link with frontoparietal network metabolism. This is explored using a type of directional connectivity analysis called "metabolic connectivity mapping", drawn from their previous work (Riedl et al. 2016, https://doi.org/10.1073/pnas.1513752113; Hahn et al. 2020, https://doi.org/10.7554/eLife.52443). They go on to speculate that concomitant BOLD deactivation and reductions in glucose expense might relate to decreased glutamatergic signaling, while BOLD deactivations accompanied by increased glucose consumption might depend on increased GABAergic neuronal activity.

This is a relevant topic because it not only shows how the DMN is flexibly engaged in different tasks but also allows us to better understand the complex relationships between BOLD fMRI and [18F]FDG PET signals, which are still not fully characterized to this day. Of course, while in resting state the situation is further complicated by the more uncertain physiological meaning of the resting BOLD signal, task-evoked states are expected to provide a more interpretable intermodal link between metabolism and hemodynamics, due to the known major changes in blood flow, blood volume, and glucose metabolism - which underlie BOLD and [18F]FDG signal changes - in response to neural activation. However, even in task states, there is not always a strong association between the two responses, as previously shown by the authors themselves (Rischka et al. 2018, https://doi.org/10.1016/j.neuroimage.2018.06.079). This is something I think the authors should stress out a little more, as they have previously done (Rischka et al. 2018, https://doi.org/10.1016/j.neuroimage.2018.06.079), both in the introduction and in reference to Figure 1, which shows clear differences between BOLD and [18F]FDG activations/deactivations (e.g., widespread negative responses in the cerebellum for [18F]FDG).

Overall, the analyses reported in the manuscript are simple and seem mostly sound, drawing from well-established methods in PET and fMRI activation studies, with additional approaches previously developed by some of the authors themselves (e.g., "metabolic connectivity mapping", Riedl et al. 2016, https://doi.org/10.1073/pnas.1513752113). Moreover, a clear strength of the paper is the high number of subjects, at least from a PET perspective, i.e., n = 50 for the Tetris task, plus group averages of previously published data for working memory (Stiernman et al. 2021, https://doi.org/10.1073/pnas.2021913118) and motor tasks (Hahn et al. 2018, https://doi.org/10.1007/s00429-017-1558-0).

The conclusions are in line with the results, and, though a little speculative, are potentially relevant for further exploration aimed at characterizing the neurotransmitter pathways underlying positive and negative BOLD and [18F]FDG responses. Moreover, the language is sufficiently clear to allow a proper understanding of the aims and the results, as well as the details of the analyses. As a side note, the title should probably be adjusted to "Task-evoked metabolic demands of the posteromedial default mode network are shaped by dorsal attention and frontoparietal control networks", to emphasize that the findings do not necessarily generalize to the resting state.

In conclusion, I am overall quite positive about this manuscript, which seems to nicely position itself within the existing literature, making some additional contributions.

Reviewer #3 (Public Review):

In this paper, the authors explore the effects of the environment, specifically temperature, on male harm to females. Male harm is the phenomenon where males reduce female fitness in polyandrous systems, where a single female may mate with multiple males. The selection of males to increase their reproductive success in male-male competition can lead to genetic conflict that increases male fitness at the expense of female fitness. Typically, male harm has been studied in single environments under optimal conditions. However, there is an increasing focus on the effect of the environment on fitness costs of male harm to females, as a way to better understand the effect of male harm on population fitness in more realistic ecological contexts. In this paper, the authors add to these studies by exploring the effect of temperature on male harm and female fitness, using the fruit fly Drosophila melanogaster, as a model system. They find that temperature affects the impact of male harm on female fitness, with male harm having the greatest effect at 24˚C relative to 20˚C and 28˚C. The authors then go on to disentangle how temperature affects the various components of male harm that impact female fitness (e.g. harassment, ejaculate toxicity). The paper demonstrates that male harm depends on ecological context, which has implications for understanding its impact on population fitness under realistic ecological scenarios, particularly with respect to climate change.

The strength of the paper is that it demonstrates that male harm (presented as differences in female life reproductive success between monogamous and polyandrous matings) changes with temperature. The authors dissect this general observation by showing that different aspects of pre-copulatory reproductive behavior, for example, male-male aggression, copulation rate, and female rejection rate, also change with temperature. Further, they demonstrate that correlates for male ejaculate quality also change with temperature, suggesting that temperature also affects post-copulatory mechanisms of male harm.

The weakness of the paper is that the method and results section are difficult to follow, which negatively impacts the interpretation of the data. The experiments are complex and need to be for what the authors are studying. Nevertheless, the paper is written in a way that makes it challenging for the reader to fully understand how precisely the experiments were conducted. Further, the authors do not explain clearly how some of the experiments relate to the phenomenon ostensibly being assayed. For example, a more detailed explanation of why mating duration and remating latency are assays for ejaculate quality in the context of sperm competition would be very helpful in interpreting the data. Further, a clearer explanation of the statistical analyses conducted

Reviewer #3 (Public Review):

Henthorn and coworkers obtain a single cell atlas of the parasite nematode Brugia malayi to search for excretory secretory products. These are involved in therapeutic responses but it is unknown what are the cell types that express them. In fact this seems as an ideal question to be addressed with single cell transcriptomics. The authors analyse their dataset, coming to the conclusion that many of these ES products are expressed broadly throughout the parasite, including secretory and non secretory types. This would be a nice conclusion if supported by the data. Then they go on to compare responses to exposure of ivermectin at the single cell level.

I must praise the attempt of using single cell transcriptomics to examine this question. These relatively novel methods have been so far used to collect information of cell types, but have immense potential for the investigation of important questions in neglected diseases like this. Fundamental knowledge about the biology of Brugia malayi and the tissue and cell types present are key to understanding their pathogenesis and advancing new therapeutic options. The authors start this research project with the right model and right technique.

My major concern is the quality of their single cell data. The authors perform no FACS or other methods to clear their suspension from cellular debris. This arises from all cell types, and then gets encapsulated with single cells in the droplet-based single cell transcriptomic process. Then, all cell barcodes receive genes from a single cell but also from a collection of cellular debris particles (arising from all other cell types). This, and only this, can in principle explain one of the major findings in the abstract: secreted antigens are expressed broadly in many cell types. This same caveat might explain their finding of pan neuronal markers broadly expressed - conceptually very similar to what the authors hold for secreted antigens, but that the authors only mention briefly and do not explain.

Reviewer #3 (Public Review):

In this manuscript, the authors (Salas et al.) have investigated the communication strategies among various strains or species of Trichomonas vaginalis. T. vaginalis is a parasite that is responsible for non-viral sexually transmitted infections, worldwide. The authors have demonstrated that highly adherent parasite uses cytoneme-like membrane structures and extracellular vesicles (EVs) to communicate with poorly adherent isolates and mount a stronger response to hosts.

The major strength of this work is the use of state-of-the microscopic techniques to analyze cytonemes and EVs. However, the weakness is the experiments shown in the manuscript are more descriptive than mechanistic. The significance of this work is high because it demonstrates the presence of a unique communication strategy in Trichomonas vaginalis. Trichomonas uses cytoneme-like elongated membrane structures and extracellular vesicles to interact with each other and induce a robust pathogenic response in host cells.

The authors have used state-of-the-art cell biology techniques to conduct the study and the data analysis is solid.

Overall, the experiments are solid and the authors were able to accomplish their objectives of demonstrating parasite communication in T. vaginalis.

Reviewer #3 (Public Review):

A current topic in the translational control field revolves around the idea that "the ribosome" is not a singular monolith machine, but rather that there are a variety of ribosomes, some with specialized functions. The presence of evolutionarily conserved ribosomal protein gene paralogs provides a platform for testing this idea. Presumably, if a paralog is required to translate a specific mRNA or class of mRNAs in a cell or organ type specific manner, it's loss should generate an observable phenotype. In this study, Xu and colleagues exploit the evolutionarily conserved eS27 and eS27L proteins to probe this hypothesis. Technically, the work is on the cutting edge of the field. Advanced genetic engineering techniques were used to generate mice lacking either paralogous gene, to create reciprocal swaps of each coding sequence into the other locus, and even to create genetically homogenous mice. The authors also use state of the art molecular biology methods, e.g. paralog-specific ribosome profiling, to search for differences in the mRNAs translated by ribosomes containing either of the two homologs.

Some phylogenetic evidence was presented suggesting that the paralogs first appeared during a gene duplication event in vertebrates: however, only and bird and one amphibian are represented. It is recommended that this analysis go deeper, parsing the amphibians and fish more finely. Although not identifying evidence for specialized ribosomes, they did find that it is essential that at least two copies of eS27 or eS27L are retained. Interestingly, the embryonic lethality of truncation alleles of either of the two paralogs result manifested at different stages of development, pointing to some kind of functional differences during development. The finding that eS27L containing ribosomes are more prevalent in lactating mammary gland and liver is an interesting observation, and that such ribosomes are preferentially associated with mRNAs involved in the cell cycle. From this the authors conclude that the data support subfunctionalization model of eukaryotic ribosomal protein S27 evolution rather than a specialized ribosome model. I also note that this is the most comprehensive and technologically advanced study of its kind in the translational control field and that it represents a significant contribution to the field of evolutionary biology.

Reviewer #3 (Public Review):

Mating changes an animal's behavior. In Drosophila, mated females have higher energy needs, suggesting that their consumption of caloric foods may be altered. While previous studies have examined post-mating changes in the consumption of specific nutrients such as salt and protein, it was not known whether the intake of sugar, their primary energy source, is also changed. This study describes a post-mating increase in sugar intake and identifies the neural circuit that mediates this change. By using precise genetic manipulations, behavioral assays, and new connectome datasets, the authors provide high-quality data to support their claims.

This study reveals several new insights into the regulation of behavior after mating: 1) Female flies increase sugar intake after mating, and this is an "anticipatory" change rather than a homeostatic change resulting from energy depletion. 2) The post-mating change in sugar intake is mediated by the sex peptide circuit, SPSNs-SAG-pC1, which is known to regulate other post-mating changes. 3) The authors identify a new downstream target of pC1, the pCd-2 neurons, which regulate feeding. pCd-2 neurons do not affect egg-laying, and neurons downstream of pC1 that regulate egg-laying or receptivity after mating do not affect sugar intake. Thus, the SPSN-SAG-pC1 circuit that regulates post-mating behaviors diverges downstream of pC1 into multiple branches regulating different behaviors. 4) The authors identify cells downstream of pCd-2, median bundle cells expressing Lgr3, the receptor for Dilp8. These cells are inhibited by pCd-2, suggesting that they are active in mated females, and promote sugar consumption. Because previous studies showed that Dilp8 and Lgr3 are expressed more highly in fed flies and suppress feeding, the present study suggests that Lgr3+ cells integrate hunger and mating signals to regulate feeding. This is an interesting circuit motif that could extend to mammals. In future studies, it will be interesting to test how hunger and mating signals are integrated within these cells (e.g. do they function redundantly, additively, etc).

Reviewer #3 (Public Review):

Seelbinder et al present local heating of the cell nucleus in live cells as a perturbation of the nucleus, which they use to interrogate mechanical properties of the nucleus. The authors use their recently developed technique of generating local heat gradients (Mittasch et al, 2018) and apply it to the cell nucleus, where they then measure the displacements/strains of chromatin as a function of distance from the heat source. They show that during the heat perturbation the nuclear area and shape remain unchanged. They measure spatially resolved strains across the nucleus and find that different parts of the nucleus exhibit different mechanical behavior. Their analysis reveals that chromatin shows both elastic and viscous properties at the timescales of seconds, with heterochromatin showing solid-like properties. In addition, they find that the nucleolus shows high resistance to the heat-induced deformation at the seconds' timescale.

Conceptually, this is an interesting and thought-provoking work allowing for new ways to perturb the cell nucleus and study its internal mechanics.

Reviewer #3 (Public Review):

In this manuscript, Li et al. describe the contribution of the ATM-E6AP-MASTL pathway in recovery from DNA damage. Different types of DNA damage trigger an increase in protein levels of mitotic kinase MASTL, also called Greatwall, caused by increased protein stability. The authors identify E3 ligase E6AP to regulate MASTL protein levels. Depletion or knockout of E6AP increases MASTL protein levels, whereas overexpression of E6AP leads to lower MASTL levels. E6AP and MASTL were suggested to interact in conditions without damage and this interaction is abrogated after DNA damage. E6AP was shown to be phosphorylated upon DNA damage on Ser218 and a phosphomimicking mutant does not interact with MASTL. Stabilization of MASTL was hypothesized to be important for recovery of the cell cycle/mitosis after DNA damage.

The identification of this novel pathway involving ATM and E6AP in MASTL regulation in the DNA damage response is interesting. However, is surprising that authors state that not a lot is known about DNA damage recovery while Greatwall and MASTL have been described to be involved in DNA damage (checkpoint) recovery. In addition, PP2A, a phosphatase downstream of MASTL is a known mediator of checkpoint recovery, in addition to other proteins like Plk1 and Claspin. Although some of the publications regarding these known mediators of DNA damage recovery are mentioned, the discussion regarding the relationship to the data in this manuscript are very limited.

The regulation of MASTL stability by E6AP is novel, although the data regarding this regulation and the interaction are not entirely convincing. In addition, several experiments presented in this paper suggest that E6AP is (additionally) involved in checkpoint signalling/activation, whereas the activation of the G2 DNA damage checkpoint was described to be independent of MASTL. Has E6AP multiple functions in the DNA damage response or is ATM-E6AP-MASTL regulation not as straightforward as presented here?

Altogether, in my opinion, not all conclusions of the manuscript are fully supported by the data.

Reviewer #3 (Public Review):

Martino et al. demonstrated that BRCA2-deficient cells (but not BRCA1-deficient cells) bear additional vulnerabilities (i.e., cytokinesis failure) outside S phase that could represent new synthetic lethality targets. Strengths of the study include the ability of the authors to recapitulate the cell death by ROCK inhibition by inhibiting another key cytokinesis enzyme, CITK. The claims are well supported by the data, and because the study indicates HR failure/replication stress is not the only possible way to achieve synthetic lethality in BRACA mutant cancers the study will be of broad interest to potential readers.

Reviewer #3 (Public Review):

In the work by Zhou, et.al., the authors pursue a mechanistic understanding of chromosome size scaling in development, a problem first noted in 1912 by Conklin and largely unstudied until very recently. Using the tools available in the Xenopus genus developmental biology system (cell extracts, related species of differing size, etc.), they nicely show that condensin I levels directly correlates with chromosome size. Further, importin levels decrease leading to axial shortening of chromosomes during development. The combined physical outcome is that Mitotic Chromatin looping changes, resulting in axial compression of chromosomes. This work represents a major step in the molecular understanding of how the genome is regulated through development and changing cell size, which also occurs in many other adult tissues and cancers. Further work to understand other contributing factors and understanding how loop structure changes the polymer dynamics of mitotic chromatin will be exciting in the future.

Reviewer #3 (Public Review):

International researchers from the International Agency for Research on Cancer and cancer screening program experts from six countries in Latina America (Argentina, Colombia), Asia (Sri Lanka, Bangladesh, Thailand), and Northern Africa (Morocco), provide detailed information on the impact of the COVID Pandemic on cancer screening and diagnostic services.

The authors examine countries that have had screening programs and a surveillance system/registry to see how the volume of screening, diagnostic procedures, and detection of precancers or cancers are impacted. The data are presented as case studies with an explanation of the program, the technologies, and then the impact. They describe no matter how low or high income the country, there was a considerable impact on the volume of screening.

Usually, the impact of the pandemic on cancer screening has been limited to Europe or North America and is usually not quantified. This information will be helpful for these countries to examine the impact on stage distribution and eventually mortality impact through modeling studies. The authors also comment on some interesting hypotheses such that the impact on recovery based on if one is detecting precancers (e.g. colon cancer/cervical cancer) vs. invasive cancers (breast cancer). Strategies that require less frequent screening,self-collection, where screening and treatment can be combined in fewer visits, or where some visits can occur via telehealth are valuable strategies or lessons learned that will allow for quicker recovery time after a pandemic.

The authors acknowledge the limitations and strengths of these case-based studies well.

It is beautiful storytelling with both qualitative and quantitative data.

Reviewer #3 (Public Review):

Ketamine has been shown to be effective at producing a rapid-antidepressant effect at low doses, but the underlying molecular mechanism of this effect is still not clear. Previous studies have suggested that the effect of low-dose ketamine may occur by promoting neuronal plasticity in the hippocampus. However, this goes against the findings that ketamine acts as a noncompetitive NMDA receptor antagonist, which should prevent NMDAR-dependent plasticity. Furthermore, a therapeutic dose of ketamine has been shown to increase neuronal Ca2+ signaling, which again does not conform to its antagonistic action on NMDA receptors. In this paper, the authors provide evidence that therapeutic low-dose ketamine increases the expression of Ca2+-permeable AMPA receptors (CP-AMPARs) by increasing phosphorylation of GluA1 subunit of AMPARs and surface expression of GluA1-containing CP-AMPARs. They further provide evidence that this is likely mediated by a decrease in calcineurin activity and that blocking CP-AMPARs prevent the antidepressant effect of ketamine in mice. One interesting finding of this study is that the authors see heightened sensitivity of ketamine in female mice, both at the level of behavioral readout and for molecular correlates. This finding is interesting in light of the different pharmacokinetics of ketamine reported in females and that ketamine metabolites can bind estrogen receptors.

Based on their data and previous findings, the authors outline a plausible molecular signaling mechanism for the antidepressant effect of ketamine. Specifically, the authors propose that reduced neuronal activity, which could be triggered by ketamine-induced NMDAR antagonism, causes homeostatic plasticity to upregulate GluA1-containing CP-AMPARs. Their data would support this idea, as phosphorylation of GluA1 as well as increased surface expression and functional incorporation of CP-AMPARs at synapses have been shown before in models of homeostatic plasticity.

Overall, the study is well-done and the data presented support the main conclusions. One main question is whether the current finding provides a conceptual advancement in our understanding of the molecular signaling involved in ketamine's antidepressant effects. There are previous studies that showed an increase in CP-AMPARs in the nucleus accumbens and an increase in the expression of GluA1 in the hippocampus with low-dose ketamine. In addition, ketamine's antidepressant effect has been shown to require GluA1 phosphorylation. The main contribution of this paper might be that it provides the potential molecular signaling within the same preparation (i.e. hippocampal neurons) and provides a causal link of CP-AMPARs in mediating the behaviorally measured antidepressant effect of ketamine.

Another question is whether the behavioral effect of ketamine is due to molecular changes in the hippocampus as outlined in this paper. A more targeted inhibition of CP-AMPAR function could resolve this issue. With the systemic application of CP-AMPAR antagonist as done in this study, it would be hard to know the role of CP-AMPAR upregulation in the hippocampus in mediating ketamine's effect. Especially, considering that low-dose ketamine has been shown to upregulate CP-AMPARs in the nucleus accumbens. While it would have been nice to know the site of action, this does not alter the conclusion that CP-AMPARs are involved in mediating the antidepressant effect of ketamine on behavioral readouts.

Reviewer #3 (Public Review):

This paper describes fundamental work which attempts to understand how universal stress proteins Rv1636/Msmeg3811 function as a sink allowing mycobacteria to use intra-bacterial cAMP. Because cAMP is a major second messenger, Rv1636 remains essential to mycobacteria. A compound that inhibits cAMP binding of Rv1636 also can effectively inhibit mycobacterial growth. The major strength of the manuscript is that the authors probed their hypothesis by different approaches. In general, the conclusions from the results are largely justified. However, I find the manuscript quite difficult to follow. Also, the results and functional analyses are inadequate as they rely on a limited set of experiments, thereby making the evidences less than compelling.

Reviewer #3 (Public Review):

In this manuscript, Villalobos-Cantor et al. have implemented the method for monitoring cellular proteome that their lab has established in cell culture models of Drosophila brains. The method uses a puromycin analog (O-propargyl-puromycin, OPP) that is locked by the addition of phenylacetyl group (PhAc-OPP) that can be unlocked by expression of Penicillin G acetylase (PGA) to tag the proteins translated in a specific cell type. When unlocked, OPP can get incorporated into the newly translating nascent peptide, and abort translation while allowing click chemistry addition of various tags, such as fluorophore-azide to visualize or biotin-azide to immunopurify polypeptides. The authors demonstrate the use of the method in adult drosophila brains expressing PGA in neurons or glia, showing that the addition of OPP is indeed PGA dependent and the proteins are only tagged in the cells that express PGA. The authors also show that when fluorophore azide is used to visualize the proteome and the samples are run on a gel, bands of various sizes can be observed to have incorporated OPP, arguing the method labels the proteome indiscriminately. The authors also optimized the protocol by titrating the amount of PhAc-OPP to use to minimize cellular stress. Also, they show that driving the expression of PGA with elav-Gal4 or repo-Gal4 is not toxic and does not cause phenotypes although Actin-Gal4 driven expression causes phenotypes. Finally the authors demonstrate the use of the technique to show that there is an age-induced decrease in total protein synthesis in the fly brain. This is a nice technique to implement in fly but the characterization of the technique is not complete in its current state. It is not clear what percentage of the nascent peptides are tagged, and whether the cells in the tissue are equally represented in the lysates for immunopurification.

Reviewer #3 (Public Review):

The myeloid bias in hematopoietic stem and progenitor cells upon genetic inactivation of Pcgf1, a component of the PRC1 complex is convincingly demonstrated by a Pcgf1 conditional allele crossed with a tamoxifen-inducible Are, combined with transplantation. The overproduction of myeloid cells may be contributed to by the derepression of two PRC target genes Cebpa and Hoxa9 at the multipotent HSPC and lineage-committed GMP levels. The involvement of these two genes is demonstrated by decreased H2AK119ub1, elevated CEBPa expression, and increased CEBPa binding motifs in KO HSCs. Functional rescue by manipulating CEBPa levels on the background of Pcgf1 KO is attempted in vitro. The derepression of Hoxa9 is shown in Pcgf1 KO GMPs, which expanded in the KO mice. Finally, constitutive inactivation of Pcgf1 results in lethal myeloproliferation. Together, this paper demonstrates another HSPC regulator, whose loss of function leads to myeloid-biased hematopoiesis, which in extreme cases could end in myeloid transformation.

Reviewer #3 (Public Review):

In this study, Cunha et al. examined the role of different oxygen tensions (21%, 5%, and 1% O2) and HIF-1α stabilisation in regulating murine and human CD8+ T cell proliferation and function. The authors find that hypoxia (1% O2) and pharmacological PHD inhibition with FG-4592, enhance murine T cell activation but impair proliferation. Furthermore, adoptive cell transfer (ACT) therapy of CD8+ T cells from both conditions reduced tumour burden in a B16-OVA melanoma model. Short hypoxic conditioning (1% O2) of human CD8+ T cells for 1 day increased HIF-1α stabilisation, with increased activation, glycolysis, and mitochondrial function still observed following 6 days of normoxic cell culture. Short hypoxic conditioning of HER2 and CD19 CAR-T cells improved their activation and cytotoxicity in vitro, while HER2 CAR-T cell counts were increased in vivo, reducing tumour burden, and increasing survival when compared to 21% O2.

Strengths:<br /> The paper convincingly demonstrates that short hypoxic conditioning in a defined window improves CAR-T cell function through in vitro cytotoxicity assays and following adoptive transfer in a preclinical HER2+-SKOV3+ positive tumour model. Thus, the major conclusion of the paper is mostly well supported by the data and could represent a novel strategy to improve CAR-T cell immunotherapy for solid tumours in the future.

Weaknesses:<br /> The extent to which hypoxic conditioning-mediated improvement in CAR-T cell function is dependent on HIF-1a-driven metabolic reprogramming is unclear and other potential mechanisms are not explored. 5FG-4592 and VHL silencing in HER2 CAR-T cells did not phenocopy each other faithfully. In addition, neither approach was as effective as short hypoxic conditioning with 1% O2 in improving CAR-T cell function in vitro or in vivo. Although the authors suggest the temporal dynamics of HIF-1α stabilisation is the key point, this is not convincingly proven, and no metabolic characterisation of these CAR-T cells was performed. It is unclear how changes elicited during short hypoxic conditioning are maintained following continued normoxic cell culture. Hypoxia is known to rapidly regulate histone methylation and chromatin structure in a HIF-independent manner (PMID: 30872525; PMID: 30872526). Are similar epigenetic changes observed in T cells, and if so, could these epigenetic changes underlie improved T cell activation?<br /> Complications may also arise when comparing different oxygen tensions given recent data that suggests standard cell culture conditions can lead to local hypoxia through a combination (https://www.biorxiv.org/content/10.1101/2022.11.29.516437v1) of cellular respiration and poor O2 diffusion. Although it is unclear how this will impact suspension T cells it does beg the question as to whether HIF-1α stability following T cell activation is (at least in part) mediated by pericellular O2 limitations in cell culture over time, even in presumed hyperoxic (21% O2) conditions? Or if T cells subsequently cultured at 21% O2 following short hypoxic conditioning (1% O2) still experience local hypoxia during the 6-day culturing protocol? It would be important to assess this in future work and at least discuss these potential weaknesses.

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Reviewer #3 (Public Review):

This is an elegant article that shows the reciprocal actions of Alkbh5 gene modulation and of a putative target, Lpin2. The work focuses on the DRG; it is important to note that the group also examines a retinal ganglion model where changes in Alkbh5 are not as prominent, and perhaps because of that the resulting effects of Alkbh5 modulation are not as pronounced. Thus, the effects of a broadly acting agent such as Alkbh5 might differ depending on the exact biology of the model. This doesn't diminish the finding at all, but the group nicely shows that the efficacy of Alkbh5 gene modulation might vary among different models.

A strength of the manuscript is the paper's use of reciprocal experimental designs to demonstrate effects. For instance, in Fig. 3 the authors show that Alkbh5 knockdown (KD) improves axonal regeneration and in Fig. 4 they show that Alkbh5 KD increases levels of methylated LPIN2, a transcript implicated in promoting axonal regeneration. Conversely, Figs. 5 and 6 also show that WT Alkbh5 over-expression inhibits axonal regeneration while an inactive Alkbh5 mutant has no effect. Alkbh5 KD is expected to increase levels of its target transcripts, and indeed, the group focuses on LPIN2 and shows that Alkbh5 KD increases LPIN2 adenosine methylation which is associated with a reciprocal decrease in total LPIN2 transcript (because m6A promotes degradation of the labeled transcripts). In Fig. 7 they go on to look at a specific target site of m6A on LPIN2. The hypothesis is that methylation of the target m6A site on LPIN2 will lead to a reduction in transcript levels; they show that mutating this target adenosine prevents the resulting effects of Alkbh5, implicating adenosine methylation in the modulation of LPIN2 levels. Finally, they show that over-expressing LPIN2 inhibits axonal regeneration while inhibiting LPIN2 increases regeneration. Thus, the experimental design includes many levels of reciprocal actions that are observed examining regeneration, adenosine methylation, and LPIN2 transcript levels. Taken together this approach is very convincing. The last figure examines the extent to which the KD approach extends to other models; the authors show that Alkbh5 is less active in a retinal ganglion model. The limited efficacy in the retinal ganglion is disappointing but serves to highlight the strength of the actions in the DRG model and provide a warning that the actions of Alkbh5 might vary significantly depending on the particular pathophysiology to which its gene modulation is being applied.

The manuscript does have some weaknesses, but the weaknesses are modest and do not change the overall interpretations of the manuscript. For instance, interpreting the quantitative efficacy in Fig. 1 and 2 depends on knowing the efficiency of uptake of the RNAi for Alkbh5 and subsequent virally transduce shAlkbh5, however, the authors do not show this efficiency. But such weaknesses are quite minor and do not change any of the conclusions.

Reviewer #3 (Public Review):

Araya-Secchi and coauthors present a very interesting study on the role of PIP2 lipids in the potential modulation of prolactin receptor signaling. The study is well-conducted and employs an integrated approach that combines NMR spectroscopy, modeling (primarily coarse-grain MD simulations), and cell biology. This combination of methods is crucial for gaining a deeper understanding of cell receptors, from their biophysical properties to their cellular functions.

The modelling work is mainly based on both coarse grain forcefield versions Martini2.2 and Martini3. These two versions of the forcefield may produce different results. Therefore, depending on the system being modeled, the results presented here should be considered in light of the limitations inherent to each version of the forcefield.

Reviewer #3 (Public Review):

In this study, the authors probe the molecular changes that occur in a neural circuit for learned behavior that depends on sensory input to maintain stereotypy. Songbirds, as the Bengalese finches used here are, are premier systems in which to ask these questions because they produce a highly stereotyped song that emerges after sensory learning becomes integrated into the function of a sensorimotor neural circuit responsible for singing. By deafening a group of birds (who show a shift in their song structure) and comparing them to hearing birds, clues as to how plasticity in motor output may emerge from genomic changes that alter the function of cells within the various components of the neural circuit.

There are multiple strengths of the paper:<br /> 1. The results may have broad implications because the type of sensorimotor neural circuit (cortico-basal ganglia-thalamic-cortical loop) used for singing is generally necessary for learned behaviors.

2. The methods and analyses are generally rigorous, including the parsing of song elements, and the type of detailed RNA sequencing and analysis that demonstrates the power of a genomic view of neural plasticity as it relates to behavioral plasticity.

3. Because the authors assayed the pallial (cortical) areas, as well as the basal ganglia component, of the sensorimotor circuit they were able to creatively compare how different facets of the network contributed to a) unmodified brain properties, b) properties perturbed after the loss of the auditory input that is required to stabilize song structure. As a result, they have added to the known molecular profiles for each of these brain areas, the accounting of how they may be specialized in comparison to the surrounding non-song brain, and what changes occur after deafening. Utilizing some existing single-cell sequencing data, the authors present for the first time some insight into what cell types may be showing the most robust changes, and therefore which may be driving the shift in song structure. The analysis further pushes in new ways to suggest how the molecular properties of a given brain area may relate to those of directly-connected areas. Together, these findings provide valuable clues as to the specific cell types and signaling properties that may be central to the production of stabilized, learned behavior.

4. One of the cortical brain areas, LMAN, was lesioned in a subset of the hearing subjects because it projects to the area that showed the greatest molecular difference between deafened and hearing birds (RA). The idea was to compare how this affected molecular properties with properties after the loss of auditory input; because RA is the output motor area for the song, its properties may be most directly tied to song structure. Using unilateral lesions was a strong choice of experimental design that allowed for rigorous analysis of this idea, and was interpretable because birds do not have a direct inter-hemispheric callosum.

The foundation of the paper is solid, though the results shown raise several questions that are not fully addressed, and limit some of the power of the implications.

The biggest questions arise from the finding that RA shows the largest number of molecular changes after deafening. The analysis and interpretations do not fully incorporate what we know of this circuit, at least from another well-studied songbird, the zebra finch, from which the authors derive other types of information. For example, it is not yet clear if RA is most changed because it is most directly involved in song output or because it receives projections from two areas within the sensorimotor circuit (LMAN and HVC). How do we consider the fact that by adulthood, LMAN and HVC cells project onto the same RA neurons? Are those the cell types being identified here? Would HVC lesions be expected to have the same effect as LMAN lesions? Are the cell types showing the greatest change those that are most involved in song output (e.g. are they projecting to nXIIts)? How do these results relate to the findings of changes in RA after HVC and LMAN lesions reported decades ago? How do these findings compare to an earlier study that also performed sequencing on areas from the sensorimotor circuit in deafened juveniles? Further, RA also receives information from the auditory processing regions of the brain, via the immediate structure RA-cup. It is not yet explicitly addressed how some effects may be from the loss of this more direct access to auditory information, rather than from information and projections originating within the sensorimotor circuit, and reinforces the question of whether or not the number of inputs to a particular brain area is a driving factor in the general pattern of changed RNAs after perturbation.

Importantly, since the LMAN lesions did not create significant changes in the song structure, it is difficult to know how to interpret the meaning of these molecular changes in RA, alone and in combination with the comparison to the RA profiles from deafened birds. Of importance is the question of whether or which molecular profiles are thus signatures of behavioral plasticity or not.

Reviewer #3 (Public Review):

This is an interesting paper but not entirely surprising. Given the known voltage dependency of intrinsic oscillations of IO neurons, the fact that a hyperpolarizing input from a GABAergic synapse or a depolarization from an excitatory input can phase shift an oscillation depending on the timing is not surprising. It could be predicted from what is already known about the underlying conductances of the oscillations in these cells. The authors, however, do provide some quantification for both the inputs and the effects they have on the oscillations. Whether or not this quantification can be extrapolated to in vivo conditions, however, remains to be seen. There are multiple technical issues that the authors need to address.

Reviewer #3 (Public Review):

In this paper the authors report a new pathway by which PTH, by activating PTH1R, increases bone mass. Specifically, they describe that PTH1R and the transcription factor Zfp467 are part of a feedback loop that promotes PTH-induced osteogenesis. By using in vivo mouse models, they indicate that deletion of Zfp467 in osteoblast progenitors increases bone mass and osteogenic differentiation whereas deletion of Zfp467 in adipocytes does not cause any bone phenotype, suggesting that deletion of Zfp467 in mesenchymal progenitors is important for bone mass. With a series of in vivo studies, they demonstrate that PTH suppresses Zfp467 expression via the cyclic AMP-PKA pathway and genetic deletion of Zfp467 causes increased PTH1R transcription by increasing nuclear translocation of p50 and activation of the P2 promoter, thus increasing the expression of PTH1R and the cellular responsiveness to PTH.

The strength of the paper are the use of genetically modified animal models, the analysis of both female and male mice and the logic flow of the in vitro data. The weaknesses of the paper are the wrong conclusion that the phenotype of the PrrxCre Zfp467 mice perfectly recapitulates one of the global Zfp467 KO mice, the lack of histomorphometric data showing increased osteoblastogenesis and the missing evidence that the forward feedback loop is relevant to the response to PTH in vivo. The analysis is therefore incomplete. Once those points are addressed, this paper would be of great interest in the bone field.

Reviewer #3 (Public Review):

The goal of this study is to identify mitochondrial pathways that would have an impact on the process of pathological cardiac hypertrophy. The paper presents a state-of-the-art analysis of the SNP variant and regulatory hot spots associated with quantitative traits, here mitochondrial protein levels and cardiac hypertrophy, using the Hybrid Mouse Diversity Panel. They identify 3 hotspots of trans-acting genetic loci that correlate with the level of proteins involved in complex I assembly, mitochondrial mRNA stability, and CoQ synthesis.

The study is overall very interesting and brings valuable information to the field. However, the impact of each of these loci on cardiac hypertrophy level, even if statistically significant, seems to be rather limited raising questions on the clinical relevance of the findings. It is an interesting study anyway and points to pathways that will deserve to be further explored in the future in clinical studies on human patients.

Reviewer #3 (Public Review):

The authors provide a timely description of new mermithid nematodes from Cretaceous amber and use it to argue an important shift in insect host exploitation. The descriptions are state-of-the-art and will become valid once the appropriate zoobank numbers are used after publication. The authors also compiled crucial and detailed new information on the host exploitation in amber nematodes in the supplementary material. This data is also depicted in pie diagrams and seems at first glance to support their interpretations of a shifts in host exploitation in fossil amber deposits when analysed appropriately and statistically but such an true analysis and depiction should be part of the main manuscript to do the compilation and interpretation justice. For the sake of reproducibility and the field, such fundamental statistical analysis as well as a statistical comparison with modern hosts would make this broad-sweeping claim of a major host shift and importance of amber deposits containing such nematode-insect interactions since the Cretaceous (even) more robust and fundamental.

Reviewer #3 (Public Review):

Li, Ruggiero-Ruff et al. examine the role of RELMα, an anti-inflammatory macrophage signature gene, in mediating sex differences in high-fat diet (HFD)-induced obesity in young mice. Specifically, the authors hypothesize that RELMα protects females against HFD-induced obesity. Comparisons between RELMα-knockout (KO) and wildtype (WT) mice of both sexes revealed sex- and RELMα-specific differences in weight gain, immune cell populations, and inflammatory signaling in response to HFD. RELMα-deficiency in females led to increased weight gain, expansion of pro-inflammatory macrophage populations, and eosinophil loss in response to HFD. Female RELMα-deficiency could be rescued by RELMα treatment or eosinophil transfer. Single-cell RNA-sequencing (scRNA-seq) of adipose stromal vascular fraction (SVF) revealed sex- and RELMα-dependent differences under HFD conditions and identified potential "pro-obesity" and "anti-obesity" genes in a cell-type-specific manner. Using trajectory analysis, the authors suggest dysregulation of macrophage-to-monocyte transition in RELMα-deficient mice.

The conclusions of this paper are mostly well supported by the data, but some aspects of the statistical and single-cell analyses will need to be corrected, clarified, and extended to enhance the report.

Strengths:<br /> The authors use several orthogonal approaches (i.e., flow cytometry, immunohistochemistry, scRNA-Seq) and models to support their hypotheses.

The authors demonstrate that phenotypes observed in HFD-fed females with RELMα-deficiency (i.e., weight gain, loss of eosinophils, a gain of M1 macrophages) can be rescued by RELMα treatment or eosinophil transfer.

The authors recognized the complexity of macrophage activation that is beyond the 'M1/M2' paradigm and informed readers in the introduction as to why this paradigm was used in this study. During the scRNA-seq analyses, the authors further sub-cluster macrophages to include more granularity.

Weaknesses:<br /> There are several instances in the text where the authors claim that there is a significant difference between the two groups, but the statistics for these comparisons are not shown in the figure.

It is unfortunate that eosinophils could not be identified in the single-cell analysis since this population of cells was shown to be important in rescuing the RELMα-deficiency in HFD-fed females. The authors should note in the discussion how future scRNA-Seq experiments could overcome this limitation (i.e., enriching immune cells prior to scRNA-Seq).

There are several issues with the scRNA-Seq analysis and interpretation. More details on the steps taken in the single-cell analyses should be included in the methods section. With regards to the 'pseudobulk' analyses presented in Figs. 5-6, several of the differentially expressed genes identified in Fig. 6 are hemoglobin genes (i.e., Hba, Hbb genes). It is not uncommon to filter these genes out of single-cell analysis since their presence usually indicates red blood cell (RBC) contamination (PMID: 31942070, PMID: 35672358). We would recommend assessing RBC contamination as well as removing Fig. 6 from the manuscript and focusing on cell-type-specific analyses. Re-analysis will likely have an impact on the overall conclusions of the study.

Within the text, there are several instances where the authors claim that a pathway is upregulated based on their Gene Ontology (GO) over-representation analysis (ORA). To come to this conclusion, the authors identify genes that are upregulated in one condition and then perform GO-ORA on these genes. However, the authors do not consider negative regulators, whose upregulation would actually decrease the pathway. Authors should either replace their GO-ORA analysis with one that considers the magnitude and direction of differentially expressed genes and provides an activation z-score (i.e., Ingenuity Pathway Analysis) or replace instances of 'upregulated' or 'downregulated' pathways with 'over-represented' pathways.

For Fig.7A, a representative tSNE plot for each group (WT Female, KO Female, WT Male, KO Male) should be shown to ensure there is proper integration of the clusters across groups. There are some instances where the scRNA-Seq data do not appear to be integrated properly (i.e., Supplemental Figure 2C). The authors should explore integration techniques (i.e., Seurat; PMID: 29608179) to correct for potential batch effects within the analysis.

LncRNA Gm47283 is identified as a gene that is differentially expressed by genotype in HFD females (Fig. 7G); however, according to Ensembl this gene is encoded on the Y-chromosome (https://uswest.ensembl.org/Mus_musculus/Gene/Summary?g=ENSMUSG00000096768;r=Y:90796007-90827734). The authors should use the RELMα genotype and sex chromosomally-encoded genes to confirm that their multiplexing was appropriate.

For Fig. 8, samples should be co-clustered and integrated across groups before performing trajectory analysis to allow for direct comparisons between groups.

Since the experiments presented in this report were from young mice using a single diet intervention, the authors should comment on how age and other obesogenic diets may impact the results found here. Also, the authors should expand their discussion as to what upstream regulators (i.e., hormones or genetics) may be driving the sex differences in RELMα expression in response to HFD.

Reviewer #3 (Public Review):

ODA-DC anchors ODA, the main force generator of ciliary beating, onto the doublet microtubules. Vertebrate ODA-DC contains 5 proteins, including Calaxin and Armc4, whose mutations are associated with defective ciliary motility in animals and human. By generating calaxin-/- and armc4-/- knockout zebrafish lines, this manuscript examined the Kupffer's vesicle cilia and spermatozoa. They showed that calaxin-/- and armc4-/- knockouts both affect ciliary motility but to different degrees. The authors conducted careful structural analyses using cryo-ET and subtomo averaging on both mutants, revealing a partial loss of ODA in calaxin-/- and a complete loss of ODA in armc4-/-. I really like the distribution analysis of calaxin-/- OADs (Figure 5), which emphasizes the strength of cryo-ET in uncovering the molecule distribution of distinct conformational states in situ. Fitting of the atomic models of ODA and ODA-DC into the cryo-ET density maps and Calaxin rescue experiments showed how Calaxin stabilizes ODA at a molecular detail. By using olfactory epithelium, the authors also presented the possible assembly mechanism of ODA-DC proteins, which is also a beautiful experiment. Finally, the authors also investigated how Ca2+ regulate the ODA-DC using cryo-ET.

The thorough structural and functional analyses of Calaxin and Armc4 in WT and gene KO animals could serve as a reference for future study of the detailed function of other ciliary proteins. The experiments are overall well designed and conducted, but some aspects need to be clarified and improved.

The authors interpret the vertebrate ODC-DC to include four linkers (line 193). However, the authors also said that loss of one linker (Calaxin) makes ODA to attach on the DMT through two linkers (line 199 and 246). These descriptions are confusing. It would make more sense to interpret the vertebrate ODC-DC as containing three linkers (CCDC151/114, Armc4/TTC25, Calaxin).

To confirm whether Calaxin directly interacts with β-tubulin (line 213), a control experiment could be needed by incubating WT axoneme with mEGFP-Calaxin followed by IF imaging.

The Immunoblotting experiment should be improved in Figure 5E. Could the authors get the same results in repeating experiments? Why is the Dnah8 signal higher in 50 mM NaCl of the (+)Calaxin group compared to that in 0 NaCl? This makes me doubt if the difference between (-)Calaxin and (+)Calaxin groups are significant.

The authors have covered several important points in the Discussion section. Now that the function of Calaxin in both mouse and zebrafish have been reported, the authors could discuss the similarity and difference of Calaxin function in different species and tissues.

Because of the limited resolution, the authors should be more careful when interpreting the small densities in the difference map, for example, in Figure 4F-G black arrows. Considering that the CCDC151/114 coiled coil is overall poorly resolved both in the WT and mutant cryo-ET maps, the different densities could be due to different map quality or data processing. This makes the following statement suspicious "This structure corresponds to the N-terminus region of CCDC151/114, suggesting that Calaxin affects the conformation of neighboring DC components".

Reviewer #3 (Public Review):

Aging impacts many cognitive functions, and how these changes affect performance in different tasks is an important question. By testing 42 older and 36 younger healthy adults with a novel learning task and MR spectroscopy, Rmus et al addressed the important question whether age-related declines in learning are driven by WM, or by deficiencies of the RL system. The task varied the role of working memory in learning by asking participants to learn about either 3 or 6 stimulus response associations from feedback (set sizes 3 and 6). The paper combines a detailed computational account of participants behaviour and striatal and prefrontal/parietal MR spectroscopy in order to assess individual glutamate and GABA levels.

The authors report an effect of set-size on learning in both are groups, and show that participant age is associated with (1) worse accuracy, (2) a larger set size performance difference, and (3) a heightened sensitivity to reward. Computational modelling showed that working memory decay differed between age groups, but that reliance on WM to perform the task at hand was similar in both age groups (similarly differing between conditions in both groups). Turning to the MRS results, the paper shows that an aggregate measure of glutamate relates to aggregate task performance, that prefrontal glutamate specifically relates to WM decay observed in the task, and that age was negatively associated with glutamate levels.

While the paper is well worth reading and offers many interesting data points, the title's suggestion that "Age-related decline in prefrontal glutamate predicts failure to efficiently deploy working memory in working memory" is, in my opinion, not fully supported by the evidence. First, the authors don't report clear evidence for any age-related differences in WM reliance in the task overall. Second, the authors find that MFG glutamate relates significantly only to WM decay, not the parameter that captures WM deployment. Third, correlations don't imply predictive relations.

Another important open question relates to the relatively large age difference in the effect of set-size on performance. The authors write that working memory will contribute less to performance in higher set size conditions. Yet, age differences are largest in the set size 6 condition, suggesting that RL-dependent learning is most severely impaired in learning (set size 6 performance), rather than WM dependent learning (set size 3 performance). Finally, a statistically significant age difference in reward sensitivity seems to be hardly integrated into the authors' overall interpretation.

The issues laid out above set aside, the paper has the potential to make an important contribution to the literature on cognitive aging.

Reviewer #3 (Public Review):

This theoretical study provides a theoretical explanation for a puzzling question arising from recent experiments: How can chromosomes behave like polymers collapsed in a poor solvent but also contain "open" active chromatin sections? The authors propose that the binding of proteins (e.g. RNAP's) to the active sections can effectively change the solvent quality for these sections and thus open them. They suggest further that chromosomes show micellar structures with inactive blocks forming the cores of the micelles. Protein binding causes swelling of the micellar shells which affects the whole chromosome structure by changing the total number of micelles. This theory fits well to live imaging data of chromatin in Drosophila larvae, like the one shown in the striking Figure 1.

The manuscript is written very clearly.

My only suggestion is that the authors, in both the theory and simulation parts, are more explicit about how the interactions between the various components are modeled. From what I could see, in the theory part, one needs to look closely at Eq. 5 to understand how the influence of the binding of proteins affects the interaction between active monomers, and in the simulation part, one needs to go to the appendix to learn that interaction strengths between monomers within the active blocks and monomers within the inactive blocks have different values. The latter is crucial to understand the micellar structure shown at the top of Fig. 5A.

Reviewer #3 (Public Review):

Although the response to stress has been extensively studied in pulmonary epithelium and mesenchyme, the post-injury proliferation and subsequent regeneration of pulmonary capillary endothelial cells remain poorly understood. Following their previous study on identifying mouse lung endothelial cell heterogeneity, Niethamer et al. reported a lung capillary subpopulation, CAP1_B with highly enriched Atf3. This capillary subpopulation expanded and increased the expression of genes involved in vascular regeneration in response to influenza-induced lung injury. Loss of Atf3 in lung endothelial cells led to abnormal alveoli structure and loss of endothelial cells through inhibiting cell proliferation and inducing apoptosis. This manuscript provided strong evidence to demonstrate the importance of Atf3 in mediating endothelial response to lung injury, which is novel to the field.

Reviewer #3 (Public Review):

The mechanistically diverse SLC26 transporters play a variety of physiological roles. The current manuscript establishes the SLC26A6 subtype as electroneutral chloride/bicarbonate exchanges and reports its high-resolution structure with chloride bound.

The claims in this manuscript are all well-supported by the data. Strengths include the comprehensive functional analysis of SLC26A6 in reconstituted liposome vesicles. The authors employ an array of assays, including chloride sensors, a newly developed fluorescent probe for bicarbonate, and assays to detect the electrogenicity of anion exchange. With this assortment of assays, the authors are able to establish the anion selectivity and stoichiometry of SLC26A6. Another strength of the manuscript is the functional comparison with SLC26A9, which permits fast, passive chloride transport, in order to benchmark the SLC26A6 activity. The structural analysis, including the assignment of the chloride binding site, is also convincing. The structural details and the chloride binding site are well-conserved among SLC26s. Finally, the authors present an interesting discussion comparing the structures of SLC26A5, SLC26A6, and SLC26A9, and how the details of the chloride binding site might influence the mechanistic distinctions between these similar transporters.

Reviewer #3 (Public Review):

In this manuscript by Douglas et al., the authors used a functional genomics approach to understand how Staphylococcus aureus survives in the bloodstream to cause bacteraemia. They identified seven novel genes that affect serum survival. The study focused on tcaA, a gene associated with resistance to the antibiotic teicoplanin and is activated when exposed to serum and plays a role in producing a critical virulence factor called wall teichoic acids (WTA) in the cell envelope. This protein affects the bacteria's sensitivity to cell wall attacking agents, human defense fatty acids, and antibiotics, as well as autolytic activity and lysostaphin sensitivity. The data in this study suggested that TcaA play a role in the ligation or retention of WTA within the cell wall. However, more work is needed to clarify that part. Interestingly, despite making the bacteria more vulnerable to serum killing, tcaA contributes to S. aureus virulence by altering the cell wall architecture, as demonstrated by the wild type strain outcompeting the tcaA mutant in a Mouse Co-infection model. The study raises an important point that TcaA in S. aureus may represent a system balancing two scenarios: it makes the bacteria more susceptible to serum killing, potentially limiting bacteraemia and providing long-term benefits between hosts; however, once established in the bloodstream, the bacteria survive and thrive, causing successful bacteraemia, as per the short-sighted evolution of virulence hypothesis. This duality highlights the complex interplay between within-host and between-host fitness in bacterial evolution. I strongly suggest creating a graphical abstract to illustrate the complex relationship between within-host and between-host fitness scenarios involving TcaA. Having this visual representation in the discussion will enhance comprehension and provide a concise summary of the complex system for the reader.

In this manuscript, the authors achieved their aims, and the results support their conclusions. This work will be fundamental for understanding this complex system and for developing novel therapeutics and vaccines for S. aureus.

Reviewer #3 (Public Review):

This contribution focuses on the zinc(II) transporter YiiP, a widely used model system of the Cation Diffusion Facilitator (CDF) superfamily. CDF proteins function as dimers and are typically involved in the maintenance of homeostasis of transition metal ions in organisms from all kingdoms of life. The system investigated here, YiiP, is a prokaryotic zinc(II)/H+ antiporter that exports zinc(II) ions from the cytosol. The authors addressed multiple crucial questions related to the functioning of YiiP, namely the specific role of the three zinc(II) binding sites present in each protomer, the zinc(II):H+ stoichiometry of antiport, and the impact of protonation on the transport process. Clarity on all these aspects is required to reach a thorough understanding of the transport cycle.

The experimental approach implemented in this work consisted of a combination of site-directed mutagenesis, high-quality 3D structural determination by cryoEM, microscale electrophoresis, thermodynamic modeling and molecular dynamics. The mutants generated in this work removed one (for the structural characterization) or two (for microscale electrophoresis) of the three zinc(II) binding sites of YiiP, allowing the authors to unravel respectively the structural role of metal binding at each site and the metal affinity of every site individually. pH-dependent measurements and constant pH molecular dynamics simulations, together with the metal affinity data, provided a detailed per-site overview of dissociation constants and Ka values of the metal-binding residues, casting light on the interplay between protonation and metal binding along the transport cycle. This thermodynamic modeling constitutes an important contribution, whose impact is however limited by the lack of an evaluation of whether the measured affinities in the various mutants differ significantly vs the affinities in the WT protein. In particular, this is true for the mutations disrupting site C, which cause a large-scale change in the quaternary structure of the protein.

Overall the authors were successful in providing a model of the transport cycle (Figure 5) that is convincing and well supported by the experimental data. The demonstration that two protomers act asymmetrically during the cycle is another nice achievement of this work, confirming previous suggestions. This novel overview of the cycle can constitute a basis for future work on other systems such as human ZnT transporters, also exploiting a methodological approach for the thermodynamic of these proteins similar to the one deployed here. The latter approach may be applicable also to other superfamilies of metal transporters.

Reviewer #3 (Public Review):

This study sought to identify relations between parameters of the diffusion decision model (DDM) and concentration of the neurotransmitters glutamate and GABA, as measured by magnetic resonance spectroscopy, and to evaluate the possibility that age moderates these relations in a developmental sample spanning middle childhood through young adulthood. The authors find a set of age-by-neurotransmitter concentration interaction effects indicating that lower levels of glutamate and greater levels of GABA in the intraparietal sulcus are related to faster non-decision times (lower values of the Ter parameter of the DDM) for "younger" participants but have the opposite relations in "older" participants (although given the way that the results are reported, the reader has little indication of what age group the terms "younger" and "older" refer to). The authors find similar interaction effects regarding relations between neurotransmitter concentration and connectivity in a visuomotor network and between neurotransmitter concentration and a fluid intelligence test. They then test moderated mediation models to determine whether functional connectivity in the visual-motor network mediates relations between neurotransmitter concentration and Ter, and whether Ter mediates the relation between neurotransmitter concentration and intelligence.

Strengths of the study include the relatively large sample size and the unique combination of brain and behavioral measures. The reported bivariate associations indicate an intriguingly consistent pattern of age-related moderation effects on the relation between neurotransmitter concentrations and several variables relevant to cognition (Ter, visuomotor connectivity, intelligence test scores) that could provide valuable insights to the field about the interplay between neurotransmitters and cognitive processes across development. However, the inferences that can be drawn from this work are seriously limited by an array of conceptual and methodological concerns.

A major conceptual issue is that the study is motivated by the premise that the nondecision time parameter (Ter) of the DDM is a major mechanistic underpinning of intelligence during child and adolescent development. There are several reasons why this premise is not well-supported. Although Ter is sometimes found to have weak correlations with scores on intelligence tests, the clearest pattern of findings across multiple studies is instead that individual differences in intelligence are primarily related to the DDM's drift rate (v) parameter (e.g., Schmiedek et al., 2007; Schulz-Zhecheva et al., 2016; Schubert & Frischkorn, 2020). The authors highlight the earlier finding that Ter mediates the effect of age on intelligence in the Krause et al. (2020) paper, but this paper is of questionable relevance to the current study. Krause et al. (2020) investigated cognitive changes in aging (ages 18-62) which are quite different from the current study's focus on development from middle childhood through young adulthood (ages ~ 7-24). Aging in older adults is known to have limited and task-specific effects on drift rate but strong effects on boundary and Ter whereas development from childhood through young adulthood coincides with the rapid maturation of drift rate (as shown in both prior research and in the current study's supplemental plots). Beyond the relatively weak evidence that Ter is a major contributor to intelligence during development, it is important to note that Ter is a nonspecific "residual" parameter (Schubert & Frischkorn, 2020) that is, theoretically, the summation of a wide array of different processes that are difficult to dissociate (perceptual encoding, visual search, motor responding). Therefore, in contrast to the drift rate and boundary parameters, it is difficult to interpret Ter as indexing a unitary mechanistic process, which is consistent with earlier findings that Ter shows limited evidence of psychometric validity as a task-general trait (Schubert et al., 2016). Finally, it is notable that bivariate tests of DDM parameters' relations with intelligence in the current study's sample (Supplemental File 9) suggest that Ter does not show a robust relation with intelligence, whereas drift rate shows relatively strong relations with intelligence in every group except for "older" individuals, who have likely fully matured and may therefore have less variance in both v and intelligence.

There are several opaque and potentially problematic features of the EZ DDM analysis. The tasks have relatively few trials spread across multiple different conditions within each task and it is unclear whether the DDM parameters were estimated separately in each condition or were estimated from trial-level data that were collapsed across conditions. This is especially concerning for the ANT, which has 96 trials distributed across 12 conditions, or apparently only 8 trials per design cell. Given the relatively low number of trials (both per design cell and overall) it is also concerning that parameter recovery studies do not appear to have been completed to ensure that this number of trials is sufficient to reliably estimate DDM parameters. In addition, accuracy rates and other behavioral summary statistics are not reported for any of the tasks. As ceiling levels of accuracy (i.e., few error RTs) can also cause prevent accurate estimation of parameters, this is another indication that assessing parameter recovery could be critical for inferences in this study.

A broader concern related to the measurement of DDM parameters is that they are each assumed to reflect the same mechanistic process across the three different tasks, but this assumption is not explicitly modeled (e.g., as a latent factor). Although the fact that Ter parameters across all three tasks have similar patterns of results is consistent with this assumption, constructing a latent factor using EFA or CFA would provide an explicit, and critical, test of the assumption. Latent factors formed from DDM parameters across the three tasks would also have several key methodological advantages over single-task measures, including separating variance in the cross-task mechanism of interest from task-specific "method variance", increasing statistical power by improving measurement of the latent mechanism, and reducing the number of multiple comparisons that need to be corrected for. The last two points are particularly critical for this study because it is possible that poor (i.e., single task) measurement and the large number of comparisons that were corrected for may have resulted in a consequential Type II error, such as a failure to detect effects involving DDM parameters other than Ter (drift rate and boundary). Related to these points, it is generally difficult for readers to judge the study's claims about the cross-task relevance of each DDM parameter or about dissociations between the different parameters because no intercorrelations between the parameters (within and between tasks) are reported or discussed.

Although the setup of the bivariate and moderation tests of relations between neurotransmitter concentrations and other variables is generally rigorous, it is concerning that only linear effects of age appear to have been considered. There appears to be clear evidence of nonlinear age-related trends in the scatterplots of parameter values displayed in Supplementary File 2.

The mediation analyses are central to the study's claims, but their results are particularly difficult to draw conclusions from due to several problematic methodological details. First, the confidence interval (CI) used to evaluate the significance of effects in these analyses was a 90% CI, essentially changing the alpha level for these tests from the conventional p<.05 to p<.10. Although this is claimed, in the Methods section, to be justified because these were "follow-up analyses based on the significant results obtained in advance", it is a highly unusual change that appears likely to have been made post hoc. Another apparent post hoc change is also mentioned in Methods: "we additionally removed cases that fell beyond three standard deviations after running the multiple regression models". It is not clear what variable or residual score this statement refers to and it is also not clear why this outlier trimming step was carried out only at the mediation stage, and not prior to the earlier bivariate analyses. The combination of an unusually high effective alpha level and potential post hoc adjustments to researcher degrees of freedom seriously undermines confidence in the mediation results. Even if the statistical hypothesis tasks are taken at face value, though, the evidence for mediation still appears very weak because the standardized effects are small and only significant in one age group (either "older" or "younger" but not both).

A broader challenge for readers is that neither the absolute ages nor the general developmental period of participants is mentioned anywhere in the main text or main plots of the paper. "Younger" and "older" mean very different things when referring to an aging sample (e.g., in the cited Klaus et al. study) versus a developing sample that spans middle childhood through young adulthood. Even if the range of the current study's sample is known, plots that split groups up by plus or minus one SD of age obscure age-related trends because the ages of the subgroups are not known. It may be easier to interpret findings if groups are instead split by neurotransmitter levels and plotted by absolute age.

Reviewer #3 (Public Review):

This work describes intracellular recordings from motor neurons of the zebrafinch. The authors use isolated brain slices allowing careful analysis of both voltage- and current-clamp recordings to document differences in action potentials in two motor cortical areas. RAPN neurons are associated with vocal commands that generate bird song, while Ald neurons are also motor neurons, but are not involved in song.

RAPN neurons are found to have much faster action potentials than Ald neurons, and pharmacological experiments provide evidence for the involvement of a particular class of voltage-gated potassium channels, Kv3, in RAPNs that presumably contributes to a faster rate of action potential repolarization and a concomitant narrowing of the action potential width. A set of experiments is included to verify that the findings obtained under normal ex vivo recording conditions (23 deg C) are retained under more physiological conditions (40 deg C). Consistent with the role of Kv3, the action potentials, and underlying potassium currents, are modified by imperfect pharmacological tools TEA, 4AP, and AUT5. Even though imperfect, together they provide support for the role of Kv3. Examination of transcripts for Kv3 family members documents that Kv3.1 is more highly expressed in RAPN than Ald neurons.

The experiments are adequately replicated and the paper is written very clearly. The authors claim that these cells are similar to Betz cells, highly specialized pyramidal neurons mainly found in the primate motor cortex and that they may play a similar role in primates and birds in generating fine motor behavior.

Some weaknesses include missing controls such as reversibility of pharmacological effects and improved statistical analysis. In addition, the linkage of RAPN neurons to Betz cells is not very strong.

Reviewer #3 (Public Review):

Here, the authors identify and characterize the role of C. elegans putative hydroxysteroid dehydrogenase gene let-767 to be essential for both lipid and endoplasmic reticulum (ER) homeostasis. They demonstrated that plays a role in lipid storage, maintaining ER morphology and that the lack of let-767 inhibits the unfolded protein response (UPR) upon proteotoxic stress, presumably by the accumulation of the predicted metabolite directly upstream of LET-767, 3-oxoacyl.

Strengths of the manuscript<br /> The complementary data in human cell line huh-7 that support the authors findings in C. elegans. The ablation of let-767 in C. elegans render the animal incapable of mounting a UPR response upon proteotoxic stress (tunicamycin). Similarly, supplementing the media of huh-7 cells with LET-767 precursor, 3-oxoacyl, attenuates the UPR activation by tunicamycin.

Overall, the experiments are well designed and in logical order throughout the manuscript.

Weakness of the manuscript<br /> The biggest weakness of this manuscript is the difficulty to appreciate the differences reported by the authors from the images provided. Providing images of higher quality or highlighting the differences to note within the figure panels will make the interpretation of data easier.

Additionally, many of the reported data are from biological duplicates. The lack of additional biological replicate might undermine the authors' findings.

Reviewer #3 (Public Review):

The work presented in the manuscript tries to identify tRNA modifications present in Mycobacterium tuberculosis (Mtb) using reverse transcription-derived error signatures with tRNA-seq. The study identified enzyme homologs and correlates them with presence of respective tRNA modifications in Mtb. The study used several chemical treatments (IAA and alkali treatment) to further enhance the reverse transcription signals and confirms the presence of modifications in the bases. tRNA modifications by two enzymes TruB and MnmA were established by doing tRNA-seq of respective deletion mutants. Ultimately, authors show that MnmA-dependent tRNA modification is important for intracellular growth of Mtb. Overall, this report identifies multiple tRNA modifications and discuss their implication in Mtb infection.

Important points to be considered:

- The presence of tRNA-based modifications is well characterised across life forms including genus Mycobacterium (Mycobacterium tuberculosis: Varshney et al, NAR, 2004; Mycobacterium bovis: Chionh et al, Nat Commun, 2016; Mycobacterium abscessus: Thomas et al, NAR, 2020). These modifications are shown to be essential for pathogenesis of multiple organisms. A comparison of tRNA modification and their respective enzymes with host organism as well as other mycobacterium strains is required. This can be discussed in detail to understand the role of common as well as specific tRNA modifications implicated in pathogenesis.

- Authors state in line 293 "Several strong signatures were detected in Mtb tRNAs but not in E. coli". Authors can elaborate more on the unique features identified and their relevance in Mtb infection in the discussion or result section.

- Deletion of MnmA is shown to be essential for E. coli growth under oxidative stress (Zhao et al, NAR, 2021). In similar lines, MnmA deleted Mtb suffers to grow in macrophage. Is oxidative stress in macrophage responsible for slow Mtb growth?

- Authors state in line 311-312 "Mtb does not contain apparent homologs of the tRNA modifying enzymes that introduce the additional modifications to s2U". This can be characterised further to rule out the possibility of other enzyme specifically employed by Mtb to introduce additional modification.

Reviewer #3 (Public Review):

In this study, Ciampa and colleagues demonstrate that HIF-1α activity is increased with gestation in humans and mice placentas and use several in vitro models to indicate that HIF activation in trophoblasts may release factors (yet to be identified) which promote myometrial contraction. Previous studies have linked placental factors to the preparation of the myometrium for labour (e.g. prostaglandins), but HIF-1α has not been implicated. Due to several issues regarding the experimental design, the results do not currently support the conclusions.

Major concerns:

1) The hypothesis states that placental aging promotes parturition via HIF-1a activation, the study does not provide any evidence of an aged placenta. Aging is considered a progressive and irreversible loss of functional capacity, inability to maintain homeostasis, and decreased ability to repair the damage. The placenta retains all these abilities throughout pregnancy [PMID: 9462184], and there's no evidence that the placenta functionally declines between 35-39 weeks, otherwise, it wouldn't be able to support fetal development. However, there is evidence of a functional decline in post-term placentas (i.e. >40 weeks in humans) but the authors compare preterm placentas with E17.5 mice placentas or 39-week human placentas, both these gestational periods are prior to the onset of parturition in most pregnancies (human = 40wkGA, mice=E18.5).

2) While the authors provide evidence that HIF-1α activity increases in both the human and mice placenta as gestation progresses, the mechanistic link between placental HIF-1α and parturition is not strongly supported. For example, most of the evidence is based on in vitro studies showing that conditioned media from trophoblasts treated with CoCl2 increased the contraction of myometrial cells. The specific factor responsible was not identified but the authors allude to pro-inflammatory factors such as cytokines. It was therefore interesting to note that the conditioned media had undergone a filtration step that removes all substances >10kDa, which includes the majority of cytokines and hormones.

3) An alternative explanation is that CoCl2 treatment-induced trophoblast differentiation and the effects on myometrial contraction may be related to differences in secreted factors produced by cytotrophoblasts versus syncytiotrophoblast. Although JAR cells do not spontaneously differentiate, they can be induced to syncytialise upon cAMP stimulation. Ref 39 the authors cite shows this. Indeed, the morphology of the cells in Fig5F that were exposed to CoCl2 indicates that they may be syncytialised. Syncytialised trophoblasts also express markers of senescence including increased SA-β-gal activity and reductions in mitochondrial activity.

4) The in vivo experiment showing reduced gestation length in pregnant mice receiving DMOG injection is interesting. However, we cannot exclude the effects of DMOG on non-placental tissues (both maternal and fetal) or the non-specific effects of DMOG (i.e. HIF-1α independent). Furthermore, previous studies using a more direct approach to alter HIF-1α activity in the placenta using trophoblast-specific overexpression of HIF-1α in mice do not lead to changes in gestation length [PMID: 30808910].

5) Lack of appropriate experimental models. E.g. JAR choriocarcinomas are not an ideal model of the human trophoblast as they are malignant. Much better models are available e.g. primary human trophoblasts from term placentas or human trophoblast stem cells from first-trimester placentas. Similarly, the mouse model is also not specific as discussed above.

6) Lack of cohesion between the different experimental models. E.g. CoCl2 was used to induce hypoxia/HIF1α in mouse TBs, but DMOG was used in vivo in mice. SA-β Gal staining was carried out in cells but not in mouse or human tissues.

7) Evidence of senescence and mitochondrial abundance could be strengthened by providing additional markers. E.g. only GLB1 mRNA expression is provided as evidence of senescence, and COX IV protein for mitochondrial abundance in mouse and human placentas.

8) Given that the main goal of this study was to investigate the role of hypoxia, hypoxia (i.e. low oxygen) was never directly induced and the results were based on chemical inducers of HIF-1α which have multiple off-target effects.

Reviewer #3 (Public Review):

In this study, Kierdorf and colleagues investigated the function of hemocytes in oxidative stress response and found that non-canonical DNA damage response (DDR) is critical for controlling JNK activity and the expression of cytokine unpaired3. Hemocyte-mediated expression of upd3 and JNK determines the susceptibility to oxidative stress and systemic energy metabolism required for animal survival, suggesting a new role for hemocytes in the direct mediation of stress response and animal survival.

Strength of the study:<br /> 1. This study demonstrates the role of hemocytes in oxidative stress response in adults and provides novel insights into hemocytes in systemic stress response and animal homeostasis.<br /> 2. The single-cell transcriptome profiling of adult hemocytes during paraquat treatment, compared to controls, would be of broad interest to scientists in the field.

Weakness of the study:<br /> 1. The authors claim that the non-canonical DNA damage response mechanism in hemocytes controls the susceptibility of animals through JNK and upd3 expression. However, the link between DDR-JNK/upd3 in oxidative stress response is incomplete and some of the descriptions do not match their data.<br /> 2. The schematic diagram does not accurately represent the authors' findings and requires further modifications.

Reviewer #3 (Public Review):

Male infertility is an important health problem. Among pathologies with multiple morphological abnormalities of the flagellum (MMAF), only 50% of the patients have no identified genetic causes. It is thus primordial to find novel genes that cause the MMAF syndrome. In the current work, the authors follow up the identification of two patients with MMAF carrying a mutation in the CCDC146 gene. To understand how mutations in CCDC146 lead to male infertility, the authors generated two mouse models: a CCDC146-knockout mouse, and a knockin mouse in which the CCDC146 locus is tagged with an HA tag. Male CCDC146-knockout mice are infertile, which proves the causative role of this gene in the observed MMAF cases. Strikingly, animals develop no other obvious pathologies, thus underpinning the specific role of CCDC146 in male fertility.

The authors have carefully characterised the subcellular roles of CCDC146 by using a combination of expansion and electron microscopy. They demonstrate that all microtubule-based organelles, such as the sperm manchette, the centrioles, as well as the sperm axonemes are defective when CCDC146 is absent. Their data show that CCDC146 is a microtubule-associated protein, and indicate, but do not prove beyond any doubt, that it could be a microtubule-inner protein (MIP).<br /> This is a solid work that defines CCDC146 as a novel cause of male infertility. The authors have performed comprehensive phenotypic analysis to define the defects in CCDC146 knockout mice. Surprisingly, the authors provide virtually no information on the penetrance of those defects - in most cases they simply show descriptive micrographs. The message of this manuscript would have been more convincing if the key phenotypes of the CCDC146 knockout mice were quantified, in particular those shown in Fig. 2E, 7A, 11B, 13.

The manuscript text is well written and easy to follow also for non-specialists. The introduction and discussion chapters contain important background information that allow putting the current work into the greater context of fertility research. The figures could have been designed more carefully, with additional information on the genotype and other details such as the antibodies used etc. directly added to the figure panels, which would improve their readability. The author might also consider pooling small figures with complementary content into one bigger figure in order to group related information together, and again facilitate the reading of the manuscript.

Overall, this manuscript provides convincing evidence for CCDC146 being essential for male fertility, and illustrates this with a large panel of phenotypic observations, which however mostly lack quantification in order to judge their penetrance. Together, the work provides important first insights into the role of a so-far unexplored proteins, CCDC146, in spermatogenesis, thereby broadening the spectrum of genes involved in male infertility.

Reviewer #3 (Public Review):

The manuscript entitled "Calcium transients trigger switch-like discharge of prostaglandin E2 (PGE2) in an ERK-dependent manner" by Watabe et al. investigates the interaction between PGE2, PKA, calcium and ERK signaling in MCDK cells and in mouse epidermis. By expressing PKA, calcium and ERK activity reporters, the authors conclude that calcium transients trigger release of PGE2 that signals through GPCR receptors EP2 and EP4 to recruit PKA in neighboring cells. Determining the dynamics of signaling molecules and their interrelationships is important to fully identify the spatiotemporal aspects of signaling mechanisms. This study addresses some aspects of the calcium-PGE2-GPCR-Erk-PKA signaling pathway in a cell line and in mouse skin ex vivo.

However, the sequential recruitment of the different signaling molecules has been described in previous studies. Hence, the novelty of the findings is limited.<br /> Additionally, the interpretation of some of the data is too speculative, more likely explanations are not considered, or not well supported by the data presented. The main conclusions the authors present are potentially artifactual (ie, cell density-dependent phenomena) and the authors need to either do further experiments to better support their conclusions or re-interpret the physiological significance of their findings.

Reviewer #3 (Public Review):

In this manuscript, Huang et al. use a variety of experimental approaches to investigate division of labor and cheater "policing" during biofilm formation in Bacillus velezensis SQR9. The authors show that SQR9 cells differentiate into two populations during biofilm development - one cell type produces extracellular matrix (ECM) and the other does not (referred to as "cheaters"). The authors go on to demonstrate that the ECM producing cells utilize a bacillunoic acid toxin system to selectively kill cheaters, keeping the cheater population in check which maintains the stability of the community. Further, the authors demonstrate that coordination of ECM production and synthesis of bacillunoic acid/immunity via acetyl-CoA carboxylase is mediated in part by Spo0A. I find the work as a whole to be compelling and thorough, and I expect it to be of broad interest to various fields of research.

Reviewer #3 (Public Review):

In this manuscript, the authors detail an exciting protocol to knock-in c-terminal tags at the endogenous locus of a gene of interest in the short-lived vertebrate, the African Turquoise Killifish. The technique is clearly explained and will be a significant advancement for the field. The method relies on the injection of a cocktail containing cas9 protein, proprietary-modified gRNA and dsDNA ordered from IDT, and a chemical enhancer of HDR.

I believe the authors demonstrate that killifish is a tractable emerging system to integrate stable fluorescent tags at desired loci. The method should be easy to reproduce since the components are all commercially available, but the proprietary nature of the modifications could make it a pricey technique for smaller labs. A protospacer adjacent motif (PAM) sequence near the desired insertion site is still a restriction using this method. That being said, I think this represents a significant advancement in knock-in methods that could be adopted in other systems. This manuscript is rather simple and straightforward, and I do not have additional criticisms or critiques.

As a side note, I think the brain sections look very professional, but since I am not a neurobiologist I will defer to the other reviewers about the accuracy and claims about the regions labeled.

For additional context, I suggest reading Wierson et al. 2020 and Seleit et al. 2022, which can both be found in the reference section.

Reviewer #3 (Public Review):

I found the results very interesting but wondered why the ERP results for the global vs. local effects are not reported. This analysis is mentioned in the methods section, but I do not find it in the results. Is this what is shown in the mid row in panel D? If yes, it should be made clearer. Is there a significant local and global deviant response in each patient group?

Additionally, eyeballing Figure 1, there are a few potential issues that may be affecting the conclusion re HER:

(1) Panel D top: it seems that the orange trace (MCS) is largely the same in both the "Local" and "global" condition. But the blue trace (UWS) shows a larger negative going deflection in the "global" case. Put differently, the UWS, but not MCS patients appear to generate a different response to the Global effect relative to the local effect. Is this the case?<br /> (2) There are some MCS subjects that appear to show a global effect that is larger than that observed in EMCS and healthy controls. How do you interpret these data?<br /> (3) How do you interpret the negative average HER data shown by many UWS patients?

Reviewer #3 (Public Review):

The authors sought to define a role for the Kazrin protein in the endosomal pathway. This effort is built on past observations of the impact of Kazrin over-expression on clathrin-mediated endocytosis. However, new Kazrin depletion experiments revealed no impact on endocytosis but a defect in the movement of early endosomes towards the nucleus. This observation that Kazrin depletion results in the dispersion of early endosomes is supported by shRNA knockdowns, CRISPR KO experiments, and the rescue of the phenotype by restoring Kazrin expression. The generalizability of the findings is supported by experiments in 2 different cell types (COS7 and MEFs). A direct role for Kazrin in linking early endosomes to dynein-dynactin is supported by observations that Kazrin is early present on endosomes and interacts with proteins of endosomes as well as with dynein-dynactin. A possible interaction with PI3P (a lipid enriched on early endosomes) is supported by a lipid binding assay. However, definitive results on this interaction would require validation by additional methods. With respect to the dynein-dynactin interactions, the authors strengthen confidence in this interaction and its putative functional relevance by identifying sequence homology between Kazrin and BICDR1 and hook3, 2 proteins with well-characterized functionally relevant roles in linking dynein-dynactin to cargos. The methods that were used to establish these functions for Kazrin were well aligned with the goals of this research and with the conclusions that were drawn. Efforts were made to quantify key observations and to provide statistical tests to establish the significance of differences that were observed. While these quantitative efforts are generally sufficient to support the major claims of the study, the data presentation would be stronger if the authors could better define the experimental sample size and the number of replicates that were performed for each experiment. Furthermore, the idea that the C-terminal region of Kazrin helps to promote the formation of "condensates" was not thoroughly supported by experimental data even if the presence of an intrinsically disordered region is supportive of this interpretation of the formation of Kazrin puncta on or near endosomes.

Reviewer #3 (Public Review):

The manuscript by Mondoloni et al characterizes two-bottle choice oral nicotine consumption and associated neurobiological phenotypes in the antiparticle nucleus (IPN) using mice. The paper shows that mice exhibit differential oral nicotine consumption and correlate this difference with nicotine-evoked inward currents in neurons of the IPN. The beta4 nAChR subunit is likely involved in these responses. The paper suggests that prolonged exposure to nicotine results in reduced nAChR functional responses in IPN neurons. Many of these results or phenotypes are reversed or reduced in mice that are null for the beta4 subunit. These results are interesting and will add a contribution to the literature. However, there are several major concerns with the nicotine exposure model and a few other items that should be addressed.

Strengths:<br /> Technical approaches are well-done. Oral nicotine, electrophysiology, and viral re-expression methods were strong and executed well.<br /> The scholarship is strong and the paper is generally well-written. The figures are high-quality.

Weaknesses:<br /> Two bottle choice (2BC) model. 2BC does not examine nicotine reinforcement, which is best shown as a volitional preference for the drug over the vehicle. Mice in this 2BC assay (and all such assays) only ever show indifference to nicotine at best - not preference. This is seen in the maximal 50% preference for the nicotine-containing bottle. 2BC assays using tastants such as saccharin are confounded. Taste responses can very likely differ from primary reinforcement and can be related to peripheral biology in the mouth/tongue rather than in the brain reward pathway. Moreover, this assay does not test free choice, as nicotine is mixed with water which the mice require to survive. Since most concentrations of nicotine are aversive, this may create a generalized conditioned aversion to drinking water - detrimental to overall health and a confounding factor. What plasma concentrations of nicotine are achieved by 2BC? When nicotine is truly reinforcing, rodents and humans titrate their plasma concentrations up to 30-50 ng/mL. The Discussion states that oral self-administration in mice mimics administration in human smokers (lines 388-389). This is unjustified and should be removed. Similarly, the paragraph in lines 409-423 is quite speculative and difficult or impossible to test. This paragraph should be removed or substantially changed to avoid speculation. Overall, the 2BC model has substantial weaknesses, and/or it is limited in the conclusions it will support.

Statistical testing on subgroups. Mice are run through an assay and assigned to subgroups based on being classified as avoiders or non-avoiders. The authors then perform statistical testing to show differences between the avoiders and non-avoiders. It is circular to do so. When the authors divided the mice into avoiders and non-avoiders, this implies that the mice are different or from different distributions in terms of nicotine intake. Conducting a statistical test within the null hypothesis framework, however, implies that the null hypothesis is being tested. The null hypothesis, by definition, is that the groups do NOT differ. Obviously, the authors will find a difference between the groups in a statistical test when they pre-sorted the mice into two groups, to begin with. Comparing effect sizes or some other comparison that does not invoke the null hypothesis would be appropriate.

Decreased nicotine-evoked currents following passive exposure to nicotine in minipumps are inconsistent with published results showing that similar nicotine exposure enhances nAChR function via several measures (Arvin et al, J Neurosci, 2019). The paper does acknowledge this previous paper and suggests that the discrepancy is explained by the fact that they used a higher concentration of nicotine (30 uM) that was able to recruit the beta4-containing receptor (whereas Arvin et al used a caged nicotine that was unable to do so). This may be true, but the citation of 30 uM nicotine undercuts the argument a bit because 30 uM nicotine is unlikely to be achieved in the brain of a person using tobacco products; nicotine levels in smokers are 100-500 nM. It should be noted in the paper that it is unclear whether the down-regulated receptors would be active at concentrations of nicotine found in the brain of a smoker. The statement in lines 440-41 ("we show that concentrations of nicotine as low as 7.5 ug/kg can engage the IPN circuitry") is misleading, as the concentration in the water is not the same as the concentration in the CSF since the latter would be expected to build up over time. The paper did not provide measurements of nicotine in plasma or CSF, so concluding that the water concentration of nicotine is related to plasma concentrations of nicotine is only speculative.

The results in Figure 2E do not appear to be from a normal distribution. For example, results cluster at low (~100 pA) responses, and a fraction of larger responses drive the similarities or differences.

10 mg/kg/day in mice or rats is likely a non-physiological exposure to nicotine. Most rats take in 1.0 to 1.5 mg/kg over a 23-hour self-administration period (O'Dell, 2007). Mice achieve similar levels during SA (Fowler, Neuropharmacology 2011). Forced exposure to 10 mg/kg/day is therefore 5 to 10-fold higher than rodents would ever expose themselves to if given the choice. This should be acknowledged in a limitations section of the Discussion.

Are the in vivo recordings in IPN enriched or specific for cells that have a spontaneous firing at rest? If so, this may or may not be the same set/type of cells that are recorded in patch experiments. The results could be biased toward a subset of neurons with spontaneous firing. There are MANY different types of neurons in IPN that are largely intermingled (see Ables et al, 2017 PNAS), so this is a potential problem.

Related to the above issue, which of the many different IPN neuron types did the group re-express beta4? Could that be controlled or did beta4 get re-expressed in an unknown set of neurons in IPN? There is insufficient information given in the methods for verification of stereotaxic injections.

Data showing that alpha3 or beta4 disruption alters MHb/IPN nAChR function and nicotine 2BC intake is not novel. In fact, some of the same authors were involved in a paper in 2011 (Frahm et al., Neuron) showing that enhanced alpha3beta4 nAChR function was associated with reduced nicotine consumption. The present paper would therefore seem to somewhat contradict prior findings from members of the research group.

Sex differences. All studies were conducted in male mice, therefore nothing was reported regarding female nicotine intake or physiology responses. Nicotine-related biology often shows sex differences, and there should be a justification provided regarding the lack of data in females. A limitations section in the Discussion section is a good place for this.

Reviewer #3 (Public Review):

The manuscript by Wang et al. investigates the mechanisms and physiological consequences of presynaptic plasticity at parallel fiber synapses of the cerebellum. Using a wide range of molecular, cellular, and genetic approaches, they show that a signaling pathway involving cAMP, EPAC, and PKCε leads to phosphorylation of RIM1α in parallel fiber terminals. Using EM and electrophysiology, they show that RIM1α (by forming a protein complex with Rab3A and Munc13) promotes docking of synaptic vesicles and increased vesicle release probability. The authors demonstrated that EPAC/PKCε are necessary for the induction of presynaptic LTP at parallel fiber synapses. The authors then extend this work to the behavioral level by showing the mice lacking EPAC or PKCε expression in cerebellar granule cells lack presynaptic LTP at parallel fiber synapses and display motor learning deficits during adaptation of the vestibular ocular reflex, a common test of cerebellum-dependent learning. The mechanisms of synaptic plasticity at parallel fiber synapses have been long investigated, but still remain unclear. This work makes a significant and convincing contribution to understanding presynaptic plasticity mechanisms. Likewise, the relative contribution of various pre- and postsynaptic forms of plasticity to cerebellar learning has long been debated but remains unsettled. This work provides novel evidence that presynaptic plasticity contributes to motor learning, possibly complimenting postsynaptic forms of plasticity. However, given the experimental conditions, it is difficult to extrapolate the slice electrophysiology findings to mechanisms of motor learning in vivo (see detailed comments below).

This manuscript provides compelling evidence for the role of EPAC and PKCε in regulating RIM1α and vesicle release. The authors use an impressive range of cellular, molecular, and genetic approaches to establish each link in the chain of the cAMP/EPAC/PKC signaling. In general, the conclusions are well supported by the data, often with multiple approaches used to address each question. In a few cases, the conclusions are overstated or not well supported by the data.

Specific comments:

1. While the data are generally very convincing, the authors overstated the conclusions in several instances. For example, the authors state that EPAC and PKCε are "required" or "essential" for vesicle docking and release. However, the author's own data show that both vesicle docking and release are clearly present (though reduced) in the absence of EPAC and PKCε, demonstrating they are not absolutely required. The language could be toned down without diminishing the impact of the excellent work.

2. The authors used analysis of cumulative EPSCs to estimate release probability (Pr) and the readily releasable pool (RRP) size. Unfortunately, this approach is likely not suited for low release probability synapses such as parallel fibers (the authors estimate Pr to be 0.04-0.06). Thanawala and Regehr (2016) extensively investigated the validity of cumulative EPSC analysis under a variety of conditions. They found that this analysis produces large errors in Pr and RRP at synapses with a Pr below ~0.2. In addition, 20 Hz EPSC stimulation (as was used here) produces much larger errors compared to the more commonly used 100 Hz stimulation. Between the low Pr at parallel fiber synapses and the low stimulus frequency used, it is likely that the cumulative EPSC analysis provides a poor estimate of Pr and RRP in this case.

3. Using a combination of genetic knockouts and pharmacology, this paper convincingly shows that presynaptic EPAC/PCKε are necessary for presynaptic LTP, but do not alter postsynaptic LTP/ LTD. However, given the experimental conditions in the slice experiments, it is difficult to extrapolate from the slice data to in vivo plasticity during motor learning. Synaptic plasticity in the cerebellar cortex is quite complex and can depend significantly on age, temperature, location, and ionic conditions. Unfortunately, these were not well matched between slice and in vivo experiments. Slice experiments used P21 mice, while in vivo experiments were performed at P60. Slice experiments were performed in the vermis, while VOR expression/adaptation generally requires the vestibulo-cerebellum/flocculus. Slice experiments were performed at room temperature, not physiological temperature. Lastly, slice experiments used 2 mM Ca2+ in the ACSF, somewhat high compared to the physiological extracellular fluid. Each of these factors can significantly affect the induction and expression of plasticity. These differences leave one wondering how well the slice data translate into understanding plasticity in the in vivo context.

4. Many experiments use synaptosomal preparation. The authors identify excitatory synapses by VGLUT labelling, but it is unclear how, or if, the authors distinguish between parallel fiber, climbing fiber, and mossy fiber synaptosomes. These synapses likely have very different properties and molecular composition, some quantification or estimation of how many synaptosomes are derived from each type of synapse would be helpful.

5. The math1-cre mouse line is used to selectively knockout EPAC or PKCε expression in cerebellar granule cells. This line also expresses Cre in unipolar brush cells (UBCs) of the cerebellum (Wang et al., 2021). This is likely not a factor in the molecular/slice studies of EPAC/PKC signaling, but UBC dysfunction could play a role in motor/learning deficits observed in vivo. This possibility is not considered in the text.

Reviewer #3 (Public Review):

Aimon and colleagues investigated brain activity in flies during spontaneous and forced walking. They used light-field microscopy to image calcium activity in the brain at high temporal resolution as the animal walked on a ball and they used the statistical inference methods PCA and ICA to tease out subregions of the brain that had distinct patterns of activity. They then sought to relate those patterns to walking. Most interesting are the experiments they performed comparing forced walking to spontaneous walking because this provides a framework to generate hypotheses about which aspects of neural activity are reporting the animal's movements versus generating those movements. The authors identify subregions and neuron types that may be involved in generating vs reporting walking. Their analysis is reasonable but could be further strengthened with a more powerful statistical framework that explicitly considered the multiple hypotheses being tested. More broadly, the work serves as a starting point to investigate the role of different regions in the brain and should spur follow-up investigations that involve more perturbative approaches in addition to the correlative approaches presented here.

Reviewer #3 (Public Review):

Bacteria sense and respond to multiple signals and cues to regulate gene expression. To define the complex network of signaling that ultimately controls transcription of many genes in cells requires an understanding of how multiple signaling systems can converge to effect gene expression and ensuing bacterial behaviors. The global transcription factor CRP has been studied for decades as a regulator of genes in response to glucose availability. It's direct and indirect effects on gene expression have been documented in E. coli and other bacteria including pathogens including Vibrio cholerae. Likewise, the master regulator of quorum sensing (QS), HapR), is a well-studied transcription factor that directly controls many genes in Vibrio cholerae and other Vibrios in response to autoinducer molecules that accumulate at high cell density. By contrast, low cell density gene expression is governed by another regulator AphA. It has not yet been described how HapR and CRP may together work to directly control transcription and what genes are under such direct dual control.

Using both in vivo methods with gene fusions to lacZ and in vitro transcription assays, the authors proceed to identify the smaller subset of genes whose transcription is directly repressed (7) and activated (2) by HapR. Prior work from this group identified the direct CRP binding sites in the V. cholerae genome as well as promoters with overlapping binding sites for AphA and CRP, thus it appears a logical extension of these prior studies is to explore here promoters for potential integration of HapR and CRP. Inclusion of this rationale was not included in the introduction of CRP protein to the in vitro experiments.

Seven genes are found to be repressed by HapR in vivo, the promoter regions of only six are repressed in vitro with purified HapR protein alone. The authors propose and then present evidence that the seventh promoter, which controls murPQ, requires CRP to be repressed by HapR both using in vivo and vitro methods. This is a critical insight that drives the rest of the manuscripts focus.

The DNase protection assay conducted supports the emerging model that both CRP and HapR bind at the same region of the murPQ promoter, but interpret is difficult due to the poor quality of the blot. There are areas of apparent protection at positions +1 to +15 that are not discussed, and the areas highlighted are difficult to observe with the blot provided.

The model proposed at the end of the manuscript proposes physiological changes in cells that occur at transitions from the low to high cell density. Experiments in the paper that could strengthen this argument are incomplete. For example, in Fig. 4e it is unclear at what cell density the experiment is conducted. The results with the wild type strain are intermediate relative to the other strains tested. Cell density should affect the result here since HapR is produced at high density but not low density. This experiment would provide important additional insights supporting their model, by measuring activity at both cell densities and also in a luxO mutant locked at the high cell density. Conducting this experiment in conditions lacking and containing glucose would also reveal whether high glucose conditions mimicking the crp results.

Throughout the paper it was challenging to account for the number of genes selected, the rationale for their selection, and how they were prioritized. For example, the authors acknowledged toward the end of the Results section that in their prior work, CRP and HapR binding sites were identified (line 321-22). It is unclear whether the loci indicated in Table 1 all from this prior study. It would be useful to denote in this table the seven genes characterized in Figure 2 and to provide the locus tag for murPQ. Of the 32 loci shown in Table 1, five were selected for further study using EMSA (line 322), but no rationale is given for studying these five and not others in the table.

Since prior work identified a consensus CRP binding motif, the authors identify the DNA sequence to which HapR binds overlaps with a sequence also predicted to bind CRP. Genome analysis identified a total of seven sites where the CRP and HapR binding sites were offset by one nucleotide as see with murPQ. Lines 327-8 describe EMSA results with several of these DNA sequences but provides no data to support this statement. Are these loci in Table 1?

Using structural models, the authors predict that HapR repression requires protein-protein interactions with CRP. Electromobility shift assays (EMSA) with purified promoter DNA, CRP and HapR (Fig 5d) and in vitro transcription using purified RNAP with these factors (Figure 5e) support this hypothesis. However, the model proports that HapR "bound tightly" and that it also had a "lower affinity" when CRP protein was used that had mutations in a putative interaction interface. These claims can be bolstered if the authors calculate the dissociation constant (Kd) value of each protein to the DNA. This provides a quantitative assessment of the binding properties of the proteins. The concentrations of each protein are not indicated in panels of the in vitro analysis, but only the geometric shapes denoting increasing protein levels. Panel 5e appears to indicate that an intermediate level of CRP was used in the presence of HapR, which presumably coincides with levels used in lane 4, but rationale is not provided. How well the levels of protein used in vitro compare to levels observed in vivo is not mentioned.

The authors are commended for seeking to connect the in vitro and vivo results obtained under lab conditions with conditions experienced by V. cholerae in niches it may occupy, such as aquatic systems. The authors briefly review the role of MurPQ in recycling of the cell wall of V. cholerae by degrading MurNAc into GlcNAc, although no references are provided (lines 146-50). Based on this physiology and results reported, the authors propose that murPQ gene expression by these two signal transduction pathways has relevance in the environment, where Vibrios, including V. cholerae, forms biofilms on exoskeleton composed of GlcNAc.

The conclusions of that work are supported by the Results presented but additional details in the text regarding the characteristics of the proteins used (Kd, concentrations) would strengthen the conclusions drawn. This work provides a roadmap for the methods and analysis required to develop the regulatory networks that converge to control gene expression in microbes. The study has the potential to inform beyond the sub-filed of Vibrios, QS and CRP regulation.

Reviewer #3 (Public Review):

The authors investigated whether reactivation of wake EEG patterns associated with left- and right-hand motor responses occurs in response to sound cues presented during REM sleep.

The question of whether reactivation occurs during REM is of substantial practical and theoretical importance. While some rodent studies have found reactivation during REM, it has generally been more difficult to observe reactivation during REM than during NREM sleep in humans (with a few notable exceptions, e.g., Schonauer et al., 2017), and the nature and function of memory reactivation in REM sleep is much less well understood than the nature and function of reactivation in NREM sleep. Finding a procedure that yields clear reactivation in REM in response to sound cues would give researchers a new tool to explore these crucial questions.

The main strength of the paper is that the core reactivation finding appears to be sound. This is an important contribution to the literature, for the reasons noted above.

The main weakness of the paper is that the ancillary claims (about the nature of reactivation) may not be supported by the data.

The claim that reactivation was mediated by high theta activity requires a significant difference in reactivation between trials with high theta power and trials with low theta, but this is not what the authors found (rather, they have a "difference of significances", where results were significant for high theta but not low theta). So, at present, the claim that theta activity is relevant is not adequately supported by the data.

The authors claim that sleep replay was sometimes temporally compressed and sometimes dilated compared to wakeful experience, but I am not sure that the data show compression and dilation. Part of the issue is that the methods are not clear. For the compression/dilation analysis, what are the features that are going into the analysis? Are the feature vectors patterns of power coefficients across electrodes (or within single electrodes?) at a single time point? or raw data from multiple electrodes at a single time point? If the feature vectors are patterns of activity at a single time point, then I don't think it's possible to conclude anything about compression/dilation in time (in this case, the observed results could simply reflect autocorrelation in the time-point-specific feature vectors - if you have a pattern that is relatively stationary in time, then compressing or dilating it in the time dimension won't change it much). If the feature vectors are spatiotemporal patterns (i.e., the patterns being fed into the classifier reflect samples from multiple frequencies/electrodes / AND time points) then it might in principle be possible to look at compression, but here I just could not figure out what is going on.

For the analyses relating to classification performance and behavior, the authors presently show that there is a significant correlation for the cued sequence but not for the other sequence. This is a "difference of significances" but not a significant difference. To justify the claim that the correlation is sequence-specific, the authors would have to run an analysis that directly compares the two sequences.

Reviewer #3 (Public Review):

Smithers et al. examine the effects of large differences in target-flanker depth on peripheral visual crowding. To investigate this, they developed a novel real-depth display and measured the perceptual errors caused by the presence of flanker objects that were presented at different distances and at either the same or at different depths from a target object that the participants had to recognize.

Their primary result is that large depth differences between flanking and target objects increase the magnitude of crowding. Interestingly, it appears to be a two-faced finding: when the target is at fixation depth, crowding is more pronounced if the flankers are behind the target as opposed to in front of it. Yet, when the flankers are at fixation depth, crowding is more pronounced if the target is behind the flankers. They explain their finding in terms of increased clutter in areas outside the limits of binocular fusion. This conclusion of the study is well supported by the data and experiments. The work provides compelling evidence that real depth may affect peripheral crowding under the specific circumstances of their experiment. Whether this finding would also apply to more natural viewing conditions, in which there is much more clutter, to begin with, remains to be determined.

Strengths:<br /> By introducing a novel multi-depth plane display authors contribute to future research on the effect of real depth differences on several visual functions and increase the potential ecological validity of their results.<br /> By using perceptual error as their dependent variable and linear mixed models to analyze their data, authors improve their ability to represent the variability in the data.<br /> The authors explain the discrepancies between their results and previous research with sufficient additional experiments and data.<br /> The inclusion of a large number of participants, which is fairly uncommon in this type of experiment.

Weaknesses:<br /> 1. At several points in the paper authors refer to the 'natural three dimensional scenes'. Indeed, the authors increase the ecological validity of their experiment by introducing actual depth differences, therefore allowing for depth cues such as accommodation, vergence and defocus blur. This is indeed a significant improvement over previous studies. However, they still use relatively impoverished visual stimuli in a tightly controlled psychophysical experiment requiring head stabilization by means of a chin rest. So, their experiment is still far removed from deploying actual, ecologically valid, conditions. Consequently, their stimuli mostly lack the complexity and associated clutter of natural stimuli as well as other potential depth cues that an observer might gain from parallax, aerial perspective, lighting, or shading. Therefore, their suggestion "that crowding has a more significant impact on our perception of natural three-dimensional environments than previously estimated with 2D displays." is stretching what can be concluded from their present work.<br /> 2. The inclusion of a large number of participants, in which none of the participants seemed to have performed all the conditions, is both a strength and a potential weakness. Their current approach of including (presumably) naive participants and having each do a portion of the experiments in itself is valid. But it also adds to the complexity of their study and presumably adds variability to their data.

Reviewer #3 (Public Review):

In this manuscript the authors use novel techniques and analytical methods on an up and coming animal model for brain evolution. The manuscript utilizes the cavefish Astyanax mexicanus, which can provide future important insights into the field of neurobiology and in evolution in general.<br /> The authors however, only argue that Astyanax is a powerful system for functionally determining basic principles of brain evolution (which clearly it will be), but fail to actually describe what brain evolution insights Astyanax gives. The data is in the paper, but the interpretation needs refinement. This would be a much more valuable paper with a thorough evolutionary context based on the already existing, extensive literature. I believe this manuscript has the potential to be extremely impactful.

Reviewer #3 (Public Review):

The authors use an impressive array of techniques to determine the role of the NBR1 autophagy receptor protein specifically in the clearing of photodamaged chloroplasts. The authors describe the mechanism(s) by which this receptor operates in this context and demonstrate that this NBR1-mediated process occurs independently of SP1 and PUB4 (whose own roles in other aspects of chloroplast autophagy have previously been shown). The authors further dissect the functional domains of NBR1 to identify which are important in this process.

The major strength of this work is the myriad techniques used to approach the problem. The data are of high quality, and on the whole, well replicated and statistically analysed. In the main, these data substantiate the findings of the authors, although some findings are quite correlative/descriptive. However, the authors show good circumspection in their conclusions and discussion. One potential weakness is that the genetic data (use of mutants) rely on single mutant alleles, therefore whilst genetic linkage to the mutations is assumed, it cannot strictly be guaranteed. The authors performed effective genetic complementation to analyse the domain structure of NBR1 shown in Figure 7. It would have been good if complementation of nbr1 and atg1 mutants and/or alternative mutant alleles had been used for experiments described in Figures 1 to 6. Without this, I think even more circumspection regarding the data obtained from these single-allele mutants would be advised.

Reviewer #3 (Public Review):

This paper examines the mechanisms that control division orientation in the basal layers of the epidermis. Previous work established LGN as a key promoter of divisions where one of the siblings populates the differentiated layers (perpendicular). This work addresses two important, related issues - the mechanisms that determine whether a particular division is planar vs perpendicular, and the function of AGS3, and LGN paralog that has been enigmatic. A central finding is that AGS3 is required for the normal distribution of planar and perpendicular divisions (roughly equal) such that in its absence the distribution is skewed towards the perpendicular. Interestingly, however, the authors find that AGS3 has no detectable effect on orientation if the orientation is measured at anaphase. This timing aspect builds upon previous work from this group demonstrating a phenomenon they term "telophase correction" in which the orientation changes at the latest phases of division (and possibly post division?). Thus AGS3 seems to exert its effect using these later mechanisms and this is supported by further analysis by the authors. Importantly, the authors show that AGS3 acts through LGN, based on localization data and an epistasis analysis. The function of AGS3 has been highly enigmatic so resolving this issue while providing a useful step towards understanding how the division orientation decision is made, makes for exciting progress towards an important problem. I found the overall narrative and presentation to be quite good and especially appreciated the thoughtful discussion section that did an excellent job of putting the results in context and speculating how unknown aspects of the mechanism might work based on current clues. With that said, I think there are some important issues that should be resolved.

Regarding the orientation measurements, the authors should specify how the midbody marker was used to mark sibling cells, especially given the midbody can move following division. For example, how can the authors be confident that the siblings in the middle panel of 1A are correct and not an adjacent cell?

Regarding quantification, it would be useful for the authors to comment on how the following would influence their measurements: 1) movements along the z-axis, and 2) movement of the nucleus within the cell.

A similar question is how much telophase correction really happens in telophase. How confident are the authors that the process actually occurs during division and not subsequent to it? What is drawn in their previous paper and in Figure 7A implies that post-division movements may be important. It would be useful for the authors to comment on whether they can make the distinction and whether or not it might be important.

Does the division angle in the AGS3 OE experiment (Figure 1D) correlate with AGS3 levels within the cell?

I found the localization data to be the weakest part of the paper and feel that some reconsideration and reanalysis are warranted.

First, the quantifications in Figures 2C, 3B, and 3F are unnecessarily vague scoring-based metrics. In 2C, "Localization pattern" should be replaced with membrane/cytoplasm ratio or an equivalent quantification. In 3B "LGN localization" should be replaced with apical/cytoplasmic and apical/basal ratios or equivalents. In 3F, "Polarized LGN frequency" should be replaced with apical/basal ratio or equivalent. It seems to me that non-AI processed data would be most appropriate for these quantifications unless such processing can be justified.

Second, it is important to note that the cytoplasmic localization of AGS3 does not allow one to conclude that AGS3 is not on the membrane. Unfortunately, high cytoplasmic signal can preclude the determination of membrane-bound signal.

Finally, I had difficulty reconciling the images of LGN shown in Figure 3 with the conclusions made by the authors.

The challenge of the localization data is troubling because an important conclusion of the paper is that AGS3 acts via LGS. The localization data provided one leg of support for this conclusion and the other is provided by an epistasis analysis. Unfortunately, this data seems to be right on the edge because it is based on the difference between the solid and dashed blue lines in Figure 5B not being significant. However, we can see how close this is by comparing the solid and dashed red lines in the adjacent 5C, which are significantly different. Between the localization data, which doesn't seem clear cut, and the epistasis experiment, which is on the razor's edge, I'm concerned that the conclusion that AGS3 acts through LGN may be going beyond what the data allows.

Reviewer #3 (Public Review):

This work by Nikolopoulos et al. expands on prior studies demonstrating the ability of a member of the Drosophila melanogaster gut microbiome, Lactiplantibacillus plantarum, to support juvenile development in nutrient-limiting conditions. Previously, the authors identified the pbpX2-dltXABCD operon of L. plantarum that when mutated eliminated the growth-promoting ability of the bacterium to flies experiencing malnutrition (protein starvation). To better understand the bacterial components that support this larval development, the authors used a combination of structural, biochemical, and mutational analysis to describe the physiological role of the DltE, a previously uncharacterized gene within the pbpX2-dltXABCD operon. Although annotated as a serine-type D-Ala-D-Ala-carboxypeptidase, this work supports its role instead as a D-ala esterase that acts upon D-alanylated lipoteichoic acids, which are directly sensed by the host to induce peptidase expression and support juvenile growth in flies.

Overall, the data is compelling, and the conclusions are well-supported. The multiple methods used to examine and support their findings - the combination of structural and biochemical analyses, and the use of both bacterial and fly mutants to substantiate and demonstrate physiological relevance was elegant in execution.

The identification of a role for this bacterial cell component is exciting as it has not previously been appreciated as a bacterial-derived signal in fly immunity and/or metabolism. This work adds to the growing evidence for the breadth and diversity of bacterial metabolites and products that underlie fly-microbiome interactions and may have implications in other animal-microbe interactions, especially L. plantarum-mediated host growth promotion in other models including mammals.

An intriguing aspect of the work is the evidence of a bifurcation of this bacterial signal on immunity and metabolism, with the pathway regulating the latter yet unknown. Likewise, determining how these cell components are sensed by the host will also be of future interest. Another unknown that may limit the implications of this study is the ubiquity of D-ala LTA production among D. melanogaster-associated L. plantarum strains and whether this is a common or rare signal/role.

Reviewer #3 (Public Review):

Bogucka-Janczi et al. have carefully dissected a role for ERK3 in the regulation of actin cytoskeleton dynamics. They identify two "nodes" of operation for ERK3 in this process, firstly, the interaction and effect of ERK3 on the small GTPases Rac1 and Cdc42, and secondly, the interaction with and effect of ERK3 on ARP3. In addition, they show a robust phosphorylation of ERK3-S189 in response to EGF stimulation. They further show that ERK3 knockdown results in a decrease of chemotaxis in response to EGF, although they have been unable to identify an important role of S189 phosphorylation in this context.

The authors have clearly carried out a large number of experiments in order to understand these complex events in a highly dynamic process. They have largely succeeded, although some aspects are rather unclear.

Reviewer #3 (Public Review):

Using a large Neuropixels dataset provided by the Allen Institute (https://allensdk.readthedocs.io/en/latest/visual_coding_neuropixels.html), Filippo & Schmitz examined propagation profiles of the hippocampal ripples along the longitudinal axis. In addition to the previously described correlation between the ripple strength and distance (Patel et al., 2013; Kumar et al., 2019), the authors revealed heterogeneous propagation patterns depending on the strength and the origin. Within the septal half of the hippocampus, 'strong' ripples (top 10% strength in a session) is more likely to propagate from the medial to the lateral while the other ripples move in the other direction. Interestingly, these strong ripples are unique in that they are generated locally and more in the medial part of the septal hippocampus. Finally, the authors found that more neurons, with higher firing rates, are engaged in the strong ripples generated in the medial part of the septal hippocampus.

The major strength of the present study is their finding of the unique propagation of the strong ripples across the longitudinal axis. Past studies examining ripple propagations did not have a particular focus on the strength of ripples and thus have not described this feature. On the other hand, however, I believe the manuscript would represent a higher significance if the authors provided more thoughts on physiological impacts and or particular roles of this unique propagation pattern. The authors propose 1) the integration of the different kinds of information and 2) the contribution of the septal hippocampus to higher memory demand (Lines 275-296). Although these views are interesting, the former only explains the longer propagation of the ripples but not the direction (i.e., the ripples could propagate from the lateral to the medial), and the latter idea is less convincing because the Neuropixels data is collected from the mice only passively receiving visual stimuli.

The propagation of the locally generated ripples across the septotemporal axis has been well described in past studies (Patel et al., 2013; Kumar & Deshmukh, 2019). The authors' findings about different directionalities of ripple propagation depending on the origin would provide a valuable view for the expert in the field of the hippocampal physiology.

Reviewer #3 (Public Review):

The authors took a comprehensive set of analyses to examine the relationship between pupil diameter / derivative and BOLD-signal during rest in the ascending arousal system nuclei in 72 young participants. Focus is on the locus coeruleus, ventral tegmental area, substantia nigra, dorsal and median raphe nuclei and the basal forebrain. Analyses were performed using various processing pipelines: canonical versus custom hemodynamic response functions, with/without smoothing, time to peak analyses and cross spectral power density analyses to define the time lag between both measurements. The authors could not replicate previous correlations between locus coeruleus BOLD and pupil measurements using standard analytic approaches, and also found no relationship between locus coeruleus BOLD and pupil measurements when using custom hemodynamic response functions. When using time to peak and cross-correlation analyses, the authors found that coupling between pupil size and AAS BOLD patterns increases with decreasing time to peak, when the two signals were close in time. The authors conclude that these findings suggest that pupil size could be used as a noninvasive readout of AAS activity under passive conditions.

These authors did a thorough assessment, and described the methods and results well and in a balanced manner.<br /> Outstanding questions:<br /> - the reliability of these observations? would we see the same findings in a different cohort or using a different sequence/field strength?<br /> - What is the independent association of each assessed nucleus with pupil dilation? That could be informative to understand their shared or unique role.

Reviewer #3 (Public Review):

The manuscript is addressing the hypothesis that KLHL40, of which mutations lead to a nemaline myopathy, leads to aberrant processing/turnover via the UPS of specific proteins. The aberrant turnover of these specific proteins then leads to the disease phenotype.

The manuscript creates two fish models knocking out orthologs of KHL40 in fish and finds that KHLH40a is necessary for maintaining fish size.

A multi-omic approach identifies potential candidates that are KHLH40 targets, specifically, Sar1a. Overexpression of Sar1a leads to some phenotypic changes ultrastructurally that resemble khl40a knockout. In vitro studies suggest some co-regulation of KHL40a with sar1a but lack the methodologic rigor at this point to be convincing. In addition, whether Sar1a dysregulation leads to more global issues seen in patients and fish remains to be established.

Reviewer #3 (Public Review):

This highly innovative study makes elegant use of single-cell RNA sequencing in a transgenic murine model of selective lung endothelial depletion to study endothelial repair and regeneration. Within 3 days after ablation of 70% of lung endothelial cells, a new stem-like endothelial population expressing markers of general capillary endothelial cells (gCap), yet also apelin, Procr, Angpt2, and CD93, yet not the gCap-typical apelin receptor emerged. This was followed at day 5 by a population of highly proliferative gCap-like endothelial cells expressing the apelin receptor along with FoxM1, which replenished all depleted endothelial populations and allowed for rapid resolution of microvascular injury. These newly identified cell states are highly reminiscent of tip and stalk cells in sprouting angiogenesis and may guide the development of new regenerative strategies.

Strengths:<br /> The present work provides important novel insights into the mechanisms of endothelial repair and reconstitution. Importantly, the authors identify a subset of gCap cells that upon endothelial depletion develops into a stem cell-like population expressing (among others) apelin, which signals via the apelin receptor to another, progenitor-like cell population that arises subsequently from the former stem cell-like population. These findings shed new light on the process of microvascular "healing" in acute lung injury and ARDS, and open up intriguing parallels to processes well known from angiogenic sprouting that may be exploited for therapeutic purposes.

Weaknesses:<br /> As with every innovative study, the emerging answers give rise to a series of new questions. Notable among those is the identity of the signal that initially drives the transition of the stem cell-like gCap population from their basal state - the recognition of such a signal may allow replicating the proposed cycle in vitro, with the opportunity to harvest cells at specific time points for both research and therapeutic purposes. Similarly, one may wonder how a lung may survive with 70% of its endothelial cells gone - do the respective vascular segments simply get excluded from perfusion (and, possibly, ventilation, as AT-II cells also decline in parallel, resulting in an emphysematous phenotype) or does fluid simply leak into the interstitium (which seems hard to reconcile with survival)? From a methodological point of view, RNA velocity analyses may be considered in follow-up studies to further substantiate the notion of a gradual transition of a subset of gCap cells from a basal to a stem cell-like to a progenitor-like and back to a basal state.

Reviewer #3 (Public Review):

This work provides a proteomic analysis of 132 early-stage (pT1) colorectal cancers (CRC) to attempt to identify proteins (or a signature pattern thereof) that might be used to predict the patient risk of lymph node metastases (LNM) and potentially stratify patients for further treatment or surveillance. The generated dataset is extensive and the methods appear solid. The work identifies a 55-protein signature that is strongly predictive of LNM in the training cohort and two validation cohorts and then generates two simplified classifiers: a 9-protein proteomic and a 5-protein immunohistochemical classifier. These also perform very well in predicting LNM. Loss of the small GTPase RHOT2 is identified as a poor prognostic factor and validated in a migration assay. The findings could allow better prognostication in CRC and, if confirmed and better validated and contextualized, might impact patient care.

Strengths:<br /> A large training cohort of resected early-stage (pT1M0) CRCs was analyzed by rigorous methods including careful quantitative analysis. The data generated are unbiased and potentially useful. A number of proteins are found to be different between CRCs with and without lymph node metastases, which are used to train a machine learning model that performs flawlessly in predicting LNM in the training cohort and very well in predicting LNM in two validation cohorts. The authors then develop two simplified classifiers that might be more readily extended into clinical care: a 9-protein proteomic assay and a 5-protein immunohistochemical assay; both of these also perform well in predicting LNM. Because LNM is a key prognostic factor, and colectomy (which includes removal of lymph nodes needed to assess LNM) carries significant risk and morbidity, particularly in rectal cancer, classifiers like these are potentially interesting. Finally, the authors identify the loss of expression of RHOT2 as a novel prognostic factor.

Weaknesses:<br /> Major points:<br /> The data are limited by a number of assumptions about metastasis, minimal contextualization of the results, and claims that are too strong given the data. Critically, the authors use the presence or absence of LNM as the study's only outcome; while LNM is a key predictor in CRC, it is uncommon in T1 CRC (generally 3-10%, 12% in this study), stochastic, inefficient, and incompletely identified by histologic evaluation. Larger resection (here, colectomy) removes both identified and occult LNM, which is probably best studied in randomized trials of lymphadenectomy in Japanese gastric cancer cohorts and should be better discussed. Critically, patient survival or disease-free survival would be more relevant outcomes. Further, absent longer-term data, many patients without identified LNM might nonetheless be high-risk and skew the cohorts. It is also not clear whether these findings would be generalizable to other early-stage colon cancers.

The data are also not correlated with the genetics of the cases, which were not discussed. The results would benefit from the inclusion of standard-of-care MSI status. The classifiers would also be much more impactful if they were generalizable beyond T1 CRCs; this could be readily tested in public datasets.

The authors explain the data as mechanistic, but, aside from one experiment modulating RHOT2 levels, they are fundamentally correlative and should be described as such.

Although they focused on areas containing >80% tumor as judged by the reading pathologist, it is unclear whether the identified proteomic changes originate from the tumor or the microenvironment.

The authors fail to properly contextualize the results or overstate the novelty of their study. A number of examples - the study is claimed as "the first proteomic study of T1 CRC" and "the first comprehensive proteomics study to focus on LNM in patients with submucosal T1 CRCs"; neither of these appears to be true, for example, Steffen et al. (Journal of Proteome Research, 2021, reference 18) may satisfy both of these, although the numbers are smaller. Many other results are reported without context, for example, proteomic characterization of mucinous carcinomas has been performed previously, a modest correlation in mucinous carcinoma is ascribed a large mechanistic role, and PDPN is discussed but is not contextualized as a protein that has been well-studied in the context of metastasis.

The data on RHOT2 are promising but very preliminary. RHOT2 is described as ubiquitous in colorectal cancer cell lines; a brief search in Human Protein Atlas shows RHOT2 RNA and proteins are ubiquitously expressed throughout the body. While its loss appears potentially prognostic, it is unclear whether this is simply a surrogate for other features, such as loss of differentiation state, and whether this is unique to CRC; multivariate analysis would be important.

Reviewer #3 (Public Review):<br /> <br /> This paper addresses the structure and mechanism of a presumed Al3+ transporter from the NRAMP superfamily from the plant Setaria italica. This protein belongs to a small clade of NRAMPs, termed NRATs that are postulated to protect plants from Al3+ which is both toxic and prevalent in soil. The NRAT clade is characterized by the substitution of key amino acids at the substrate binding site which has been shown to coordinate either Mg2+ in NRMTs or Mn2+ in classical NRAMP transporters. Evidence for Al3+ transport comes from a previous study utilizing heterologous expression in yeast; this study concluded that NRAT1 from rice (Oryza sativa) is highly specific for Al3+ over Mn2+, Fe2+, Cd2+, Mg2+ which have been shown to be transported by homologs in other clades of the NRAMP family. The current study screened the expression of five homologues of NRAT1, choosing SiNRAT for structural and functional analysis. Unlike previous work on NRAT1, SiNRAT readily transported Mn2+, and experiments with Ca2+ and Mg2+ indicate that these ions are likely also transported. Unlike classical NRAMPs, Mn2+ transport appears to be passive and not coupled to proton transport. Although technical limitations precluded direct measurement of Al3+ transport, ITC measurements provided qualitative evidence for binding in the uM range. A cryo-EM structure is presented, showing an occluded conformation similar to the recent high-resolution X-ray structure of a classical NRAMP bound to Mn2+. The structure of SiNRAT does not show bound ions, but allows comparison of the substrate binding pocket and shows the disposition of key amino acids that distinguish the NRAT clade. Finally, mutagenesis was used to evaluate the role of four of these residues, thus concluding that Ser68 plays a role in coordinating Al3+ as well as its analog Ga3+. Thus, although the transport data with Mn2+ are rigorous, interactions of the putative substrate, Al3+, are only addressed in a qualitative way. The cryo-EM structure is similarly rigorous but provides only modest insight into substrate specificity. Furthermore, the discussion of proton coupling - or the lack thereof - is very speculative. Thus, although new information on this novel clade of NRAMP transporters will be welcomed by specialists in this field, the paper is likely to have only a modest impact beyond this cohort.

Reviewer #3 (Public Review):

In the proposed manuscript, the authors use cross-sectional seroprevalence data from blood samples that were tested for evidence of antibodies against D68 for the UK. Samples were collected at 3 time points from individuals of all ages. The authors then fit a suite of serocatalytic models to explain the changing level of seropositivity by age. From each model they estimate the force of infection and assess whether there have been changes in transmissibility over the study period. D68 is an important pathogen, especially due to its links with acute flaccid myelitis, and its transmission intensity remains poorly understood. Serocatalytic models appear to be appropriate here. I have a few comments.

The biggest challenge to this project is the difficulty in assigning individuals as seronegative or seropositive. There is no clear bimodal distribution in titers that would allow obvious discrimination and apparently no good validation data with controls with known serostatus. The authors tackle this problem by presenting results to four different cut-points (1:16 to 1:128) - resulting in seropositivity ranging from around 50% to around 80%. They then run the serocatalytic models with two of these (1:16 and 1:64) - leading to a range of FoI values of 0.25-0.90 for the 1 year olds and 0.05-0.25 for older age groups (depending on model and cutpoint). This represents a substantial amount of variability. While I certainly see the benefit of attacking this uncertainty head on, it does ultimately limit the inferences that can be made about the underlying risk of infection in UK communities, except that it's very uncertain and possibly quite high.

I find the force of infection in 1 year olds very high (with a suggestion that up to 75% get infected within a year) and difficult to believe, especially as the force of infection is assumed much lower for all other ages.

The authors exclude all <1s due to maternal antibodies, which seems sensible, however, does this mean that it is impossible for <1s to become infected in the model? We know for other pathogens (e.g., dengue virus) with protection from maternal antibodies that the protection from infection is gone after a few months. Maybe allowing for infections in the first year of life too would reduce the very large, and difficult to believe, difference in risk between 1 year olds and older age groups. I suspect you wouldn't need to rely on <1 serodata - just allow for infections in this time period.

Relatedly, would it be possible to break the age data into months rather than years in these infants to help tease apart what happens in the critical early stages of life.

One of the major findings of the paper is that there is a steadily increasing R0. This again is difficult to understand. It would suggest there are either year on year increases in inherent transmissibility of the virus through fitness changes, or year on year increases in the mixing of the population. It would be useful for the authors to discuss potential explanations for an inferred gradual increase in R0.

On a similar note, I struggle to reconcile evidence of a stable or even small drop in FoI in the 1:64 models 4 and 5 from 2010/11 (Figure 3) with steadily increasing R0 in this period (Figure 4). Is this due to changes in the susceptibility proportion. It would be good to understand if there are important assumptions in the Farrington approach that may also contribute to this discrepancy.

The R0 estimates (Figure 4) should also be presented with uncertainty.

Finally, given the substantial uncertainty in the assay, it seems optimistic to attempt to fit annual force of infections in the 30 year period prior to the start of the sampling periods. I would be tempted to include a constant lambda prior to the dates of the first study across the models considered.

Reviewer #3 (Public Review):

The ability to rapidly test a large combination of drug cocktails on patient cells in culture would enhance personalized therapeutic regimens. Currently, testing 10 concentrations of 3 drugs in combination is intractable. Elgart & Loscalzo propose to take advantage of diverse drug responses within a single dish to streamline the exploration of multi-drug combinations. By sampling the population variation in uptake of multiple dyes within individual cells and delivering the dyes by a variety of modes (i.e. point injection, sequential homogenous mixing), a pipeline is developed for estimating a "response space" that arises from the complex intersections of multiple drug/dye concentration gradients.

The paper is in places very rigorous in establishing bounds in which this pipeline may have utility by defining the linearity of two-drug co-delivery, explicitly illustrating the pre-processing/binning performed on the data, reporting distributions of uptake under different environmental dye gradients, and finding a tight correlation between dye and drug response to justify the surrogate use of dye characterization for the end-goal of drug cocktail formulation. I am particularly impressed with the results depicted in Figure 6 and the associated supplemental figures as a demonstration of an application of this approach for nanocarrier-based combinatorial siRNA delivery. However, there are major weaknesses in interpretability and underlying assumptions.

A large body of work in the literature has established that the diversity in cells of identical genetic background occurs due to two components: 1) intrinsic noise - such as stochastic fluctuations in gene expression - as well as 2) extrinsic noise - variability that arises from sources that are external to the biochemical process of gene expression, such as abundances of ribosomes or stage in the cell cycle. Note that this widely-accepted definition does not separate intrinsic and extrinsic from intracellular and extracellular. The authors cite a few of these seminal papers (which focus on noise introduced to gene expression) but then define their interpretation of intrinsic noise much more broadly "... intrinsic noise as phenotype(s) fluctuations across isogenic cell populations cultured under the same conditions. Measurement noise in some cases can also be thought of as intrinsic noise. Fluctuations in cellular phenotype(s) driven by the global environment will be referred to as extrinsic noise." This misuse of widely accepted terminology creates significant confusion in the interpretation of the results.

A point of contention with redefining noise as the authors have done is that they are lumping all processes unique to the cell as intrinsic and all environmental factors as extrinsic. Thus, when statements are made such as "external factors that contribute to noise are principally manifest through convection" (line 40-41, page 2) the veracity of these assumptions must be established. For example, when a ligand binds and unbinds from a receptor due to thermal energy, that "noise" in cellular stimulation is not convection-based, yet an example of how extrinsic noise can influence cellular responses. The definition is important because the underlying premise for the pipeline presented is that "While intrinsic cell variability can be significant, we believe that it is the extrinsic factor(s) that drive sample variability in most experimental cellular systems" (lines 42-43, page 4).

Throughout, figures lack labels and sufficient explanation for interpretation, as well as the number of experiments used to generate the data that is processed through the pipeline for each condition. For a study designed to eliminate replicate culture conditions, the onus is on the authors to show that replicates are in fact fully recapitulated in the population variance after statistical binning/processing.

Ultimately, when the paper presents results such as Figure 9 as the culmination of the pipeline as applied to cell viability studies, it is unclear how useful insight is extracted from this methodology. Four drugs are applied in combination to adherent HeLa cells and time-dependent local cell density is provided as a proxy for cell viability. While it is stated that "The absolute drug concentration can be determined using the homogeneous delivery method discussed above" (line 421-422, page 19), this analysis is not performed, and I am left unsure of whether extrinsic factors are truly driving sample variability under this context. It is unclear to the reader how the point injections were administered, and no discussion of how the confounding factors of synergy or antagonism will be addressed through this methodology.

Reviewer #3 (Public Review):

The study of Nocka and colleagues examines the role of membrane scaffolding in Btk kinase activation by the Grb2 adaptor protein. The studies appear to make a case for a reinterpretation of the "Saraste dimer" of Btk as a signaling entity and assigns roles to the component domains in the Src module in Btk activation. The point of distinction from earlier studies is that this work ascribes a function to an adaptor protein as promoting the kinase activation, rather than vice versa, and also illustrates why Btk can be activated via modes distinct from its close relative, such as Itk. Importantly, these studies address these key questions through membrane tethering of Btk, which is a successful, reductionist way to mimic cellular scenarios. The writing could be improved and can absolutely be more economical in word choice and use; currently, there is a good deal of background to each section that is not always comprehensive or crucial to contextualise the findings, while key information is often omitted. The results are currently not described in a detailed manner so there is an imbalance between the findings, which should be the focus, relative to background and interpretations or models.

Reviewer #3 (Public Review):

This study describes a descending circuit that can modulate pain perception in the drosophila larvae. While descending inhibition is a major component of mammalian pain perception, it is not known if a similar circuit design exists in fruit flies. Overall the authors use clean logic to establish a role for DSK and its receptor in regulating nociception. I have made a few suggestions that I believe would strengthen the manuscript as this is an important discovery.

Major comments:

1) It's not completely clear why the authors are staining animals with an FLRFa antibody. Can the authors stain WT and DSK KO animals with a DSK antibody? Also, can the authors show in supplemental what antigen the FLRFa antibody was raised against, and what part of that peptide sequence is retained in the DSK sequence? This overall seems like a weakness in the study that could be improved on in some way by using DSK-specific tools.

2) What is the phenotype of DSK-Gal4 x UAS-TET animals? They should be hyper-reactive. If it's lethal maybe try an inducible approach.

3) Figure 9. This was not totally clear, but I think the authors were evaluating spontaneous (i.e. TRPA1-driven) rolling at 35C. The critical question is "does activating DSK-expressing neurons suppress acute heat nociception" and this hasn't really been addressed. The inclusion of PPK Gal4 + DSK Gal4 in the same animal kind of clouds the overall conclusions the reader can draw. The essential experiment is to express UAS-dTRPA1 in DSK-Gal4 or GORO-Gal4 cells, heat the animals to ~29C, and then test latency to a thermal heat probe (over a range of sub and noxious temperatures). Basically prove the model in Figure 10 showing ectopic activation or inhibition for each major step, then test heat probe responses.

4) It would also then be interesting to see how strong the descending inhibition circuit is in the context of UV burn. If this is a real descending circuit, it should presumably be able to override sensitization after injury.

Reviewer #3 (Public Review):

The author is trying to identify the mefenamic (Mef) binding site and DIDS binding site on the KCNQ1 KCNE1 complex. The authors also try to identify the mechanism of interactions using electrophysiological recording, calculating V1/2 of different mutants, and looking at the instantaneous current and the tail current. The contribution of each residue within the binding pocket was analysed using GBSA and PBSA and traditional molecular dynamics simulation. The author is trying to argue that they share the same binding pocket and their mechanism of activation.

Strengths:

1. The effect of the WT channel in the presence of 100 uM Mef is very clear, and such an effect is clearly decreased with the E1-K41C and W323A mutation. The milder effect was observed with Q147C and Y148C mutants.

2. The effect of the WT channel in the presence of 100 uM DIDS is, again, very clear, and such an effect is clearly decreased with the E1-Y46C.

3. The author has indeed achieved their aim in addressing that the binding site for both DIDS and Mef are adjacent to each other and may indeed share a pocket in the S1-E1-pore pocket. This may help the field with drug development, targeting that region in the future.

Weaknesses:

1. The computational aspect of the work is rather under-sampled - Figure 2 and Figure 4. The lack of quantitative analysis on the molecular dynamic simulation studies is striking, as only a video of a single representative replica is being shown per mutant/drug. Given that the simulations shown in the video are extremely short; some video only lasts up to 80 ns. Could the author provide longer simulations in each simulation condition (at least to 500 ns or until a stable binding pose is obtained in case the ligand does not leave the binding site), at least with three replicates per each condition? If not able to extend the length of the simulations due to resources issue, then further quantitative analysis should be conducted to prove that all simulations are converged and are sufficient. Please see the rest of the quantitative analysis in other comments.

2. Given that the protein is a tetramer, at least 12 datasets could have been curated to improve the statistic. It was also unclear how frequently the frames from the simulations were taken in order to calculate the PBSA/GBSA.

3. The lack of labels on several structures is rather unhelpful (Figure 2B, 2C, 4B). The lack of clarity of the interaction map in Figures 2D and 6A.

4. The RMSF analysis is rather unclear and unlabelled thoroughly. In fact, I still don't quite understand why n = 3, given that the protein is a tetramer. If only one out of four were docked and studied, this rationale needs to be explained and accounted for in the manuscript.

5. For the condition that the ligands suppose to leave the site (K42C for Mef and Y46A for DIDS), can you please provide simulations at a sufficient length of time to show that ligand left the site over three replicates? Given that the protein is a tetramer, I would be expecting three replicates of data to have four data points from each subunit. I would be expecting distance calculation or RMSD of the ligand position in the binding site to be calculated either as a time series or as a distribution plot to show the difference between each mutant in the ligand stability within the binding pocket. I would expect all the videos to be translatable to certain quantitative measures.

6. Given that K41 (Mef) and Y46 are very important in the coordination, could you calculate the frequency at which such residues form hydrogen bonds with the drug in the binding site? Can you also calculate the occupancy or the frequency of contact that the residues are making to the ligand (close 4-angstrom proximity etc.) and show whether those agree with the ligand interaction map obtained from ICM pro in Figure 2D?

7. Given that the author claims that both molecules share the same binding site and the mode of ligand binding seems to be very dynamic, I would expect the authors to show the distribution of the position of ligand, or space, or volume occupied by the ligand throughout multiple repeats of simulations, over sufficient sampling time that both ligand samples the same conformational space in the binding pocket. This will prove the point in the discussion - Line 463-464. "We can imagine a dynamic complex... bind/unbind from Its at a high frequency".

8. I would expect the authors to explain the significance and the importance of the PBSA/GBSA analysis as they are not reporting the same energy in several cases, especially K41 in Figure 2 - figure supplement 2. It was also questionable that Y46, which seems to have high binding energy, show no difference in the EPhys works in figure 3. These need to be commented on.

9. Can the author prove that the PBSA/GBSA analysis yielded the same average free energy throughout the MD simulation? This should be the case when the simulations are converged. The author may takes the snapshots from the first ten ns, conduct the analysis and take the average, then 50, then 100, then 250 and 500 ns. The author then hopefully expects that as the simulations get longer, the system has reached equilibrium, and the free energy obtained per residue corresponds to the ensemble average.

10. The phrase "Lowest interaction free energy fort residues in ps-KCNE1 and selected KCNQ1 domains are shown as enlarged panels (n=3 for each point)" needs further explanation. Is this from different frames? I would rather see this PBSA and GBSA calculated on every frame of the simulations, maybe at the one ns increment across 500 ns simulations, in 4 binding sites, in 3 replicas, and these are being plotted as the distribution instead of plotting the smallest number. Can you show each data point corresponding to n = 3?

11. I cannot wrap my head around what you are trying to show in Figure 2B. This could be genuinely improved with better labelling. Can you explain whether this predicted binding pose for Mef in the figure is taken from the docking or from the last frame of the simulation? Given that the binding mode seems to be quite dynamic, a single snapshot might not be very helpful. I suggest a figure describing different modes of binding. Figure 2B should be combined with figure 2C as both are not very informative.

12. Similar to the comment above, but for figure 4B. I do not understand the argument. If the author is trying to say that the pocket is closed after Mef is removed - then can you show, using MD simulation, that the pocket is openable in an apo to the state where Mef can bind? I am aware that the open pocket is generated through batches of structures through conformational sampling - but as the region is supposed to be disordered, can you show that there is a possibility of the allosteric or cryptic pocket being opened in the simulations? If not, can you show that the structure with the open pocket, when the ligand is removed, is capable of collapsing down to the structure similar to the cryo-EM structure? If none of the above work, the author might consider using PocketMiner tools to find an allosteric pocket (https://doi.org/10.1038/s41467-023-36699-3) and see a possibility that the pocket exists.

13. Figure 4C - again, can you show the RMSF analysis of all four subunits leading to 12 data points? If it is too messy to plot, can you plot a mean with a standard deviation? I would say that a 1-1.5 angstroms increase in the RMSF is not a "markedly increased", as stated on line 280. I would also encourage the authors to label whether the RMSF is calculated from the backbone, side-chain or C-alpha atoms and, ideally, compare them to see where the dynamical properties are coming from.

14. In the discussion - Lines 464-467. "Slowed deactivation of the S1/KCNE1/Pore domain/drug complex ....... By stabilising the activated complex. MD simulation suggests the latter is most likely the case." Can you point out explicitly where this has been proven? If the drug really stabilised the activated complex, can you show which intermolecular interaction within E1/S1/Pore has the drug broken and re-form to strengthen the complex formation? The authors have not disproven the point on steric hindrance either. Can this be disproved by further quantitative analysis of existing unbiased equilibrium simulations?

15. Figure 4D - Can you show this RMSF analysis for all mutants you conducted in this study, such as Y46C? Can you explain the difference in F dynamics in the KCNE3 for both Figure 4C and 4D?

16. Line 477: the author suggested that K41 and Mef may stabilise the protein-protein interface at the external region of the channel complex. Can you prove that through the change in protein-protein interaction, contact is made over time on the existing MD trajectories, whether they are broken or formed? The interface from which residues help to form and stabilise the contact? If this is just a hypothesis for future study, then this has to be stated clearly.

17. The author stated on lines 305-307 that "DIDS is stabilised by its hydrophobic and vdW contacts with KCNQ1 and KCNE1 subunits as well as by two hydrogen bonds formed between the drug and ps-KCNE1 residue L42 and KCNQ1 residue Q147" Can you show, using H-bond analysis that these two hydrogen bonds really exist stably in the simulations? Can you show, using minimum distance analysis, that L42 are in the vdW radii stably and are making close contact throughout the simulations?

18. Discussion - In line 417, the author stated that the "S1 appears to pull away from the pore" and supplemented the claim with the movie. This is insufficient. The author should demonstrate distance calculation between the S1 helix and the pore, in WT and mutants, with and without the drug. This could be shown as a time series or distribution of centre-of-mass distance over time.

19. Given that all the work were done in the open state channel with PIP2 bound (PDB entry: 6v01), could the author demonstrate, either using docking, or simulations, or alignment, or space-filling models - that the ligand, both DIDS and Mef, would not be able to fit in the binding site of a closed state channel (PDB entry: 6v00). This would help illustrate the point denoted Lines 464-467. "Slowed deactivation of the S1/KCNE1/Pore domain/drug complex... By stabilising the activated complex. MD simulation suggests the latter is most likely the case."

20. I struggle with the term "normalised response" on Line 208. What is it being normalised to? Can this be put more explicitly in the text? If normalised to WT, why is WT EQ response only 0.8?

21. The author stated that the binding pose changed in one run (lines 317 to 318). Can you comment on those changes? If the pose has changed - what has it changed to? Can you run longer simulations to see if it can reverse back to the initial confirmation? Or will it leave the site completely?

22. Binding free energy of -32 kcal/mol = -134 kJ/mol. If you try to do dG = -RTlnKd, your lnKd is -52. Your Kd is e^-52, which means it will never unbind if it exists. I am aware that this is the caveat with the methodologies. But maybe these should be highlighted throughout the manuscript.

Reviewer #3 (Public Review):

This manuscript provides new insights into an important process during cardiac development that is not well understood. The authors combined chemical inhibition experiments for PI3K as well as a genetic tool to overexpress a dominant negative PI3K specifically in cardiac progenitor cells and found that PI3K is important during cardiac fusion. By incubating embryos with the chemical inhibitor at different stages they concluded that PI3K is required between 12-20 somite stages, which corresponds to the time points that cardiac fusion occurs. They performed live imaging on cardiac progenitors during cardiac fusion and observed that inhibiting Pi3K reduces the velocity at which the cells move and affects their direction. The latter seems consistent with the observation that PI3K is not required for protrusion formation but affects the location of these protrusions. Finally, using a low dose of the PI3K inhibitor together with the previously identified Pdgf mutant suggests that both act in the same pathway to regulate the direction of migration of cardiac progenitor cels towards the midline. Overall, the manuscript is well written and experiments are well controlled providing sufficient evidence to substantiate most of their conclusions. Some open questions remain unanswered such as the mode of migration (individual or collective) that drives cardiac fusion.

I have always thought that you learn more in General Ed classes than classes dedicated to your major. Not to say learning about the innerworkings of magnets is a useless skill it is not, but you can definitely take a lot more from history or philosophy classes. But more than anything, you will not take anything from a class if you don't put in effort.

ME! I would make into a story. Assuming that I faked my death or AI is very powerful it can replicate me, I would start the museum tour in my birth and move on into my "death" . Where every year is another artifact like cloths I wore, the backpacks I had, and I would even display figures of me doing stuff similar to animals in there natural habitat.

Reviewer #3 (Public Review):

To analyze the circuit mechanisms leading to the habituation of the O-bed responses upon repeated dark flashes (DFs), the authors performed 2-photon Ca2+ imaging in larvae expressing nuclear-targeted GCaMP7f pan-neuronally panning the majority of the midbrain, hindbrain, pretectum, and thalamus. They found that while the majority of neurons across the brain depress their responsiveness during habituation, a smaller population of neurons in the dorsal regions of the brain, including the torus longitudinalis, cerebellum, and dorsal hindbrain, showed the opposite pattern, suggesting that motor-related brain regions contain non-depressed signals, and therefore likely contribute to habituation plasticity.

Further analysis using affinity propagation clustering identified 12 clusters that differed both in their adaptation to repeated DFs, as well as the shape of their response to the DF.

Next by the pharmacological screening of 1953 small molecule compounds with known targets in conjunction with the high-throughput assay, they found that 176 compounds significantly altered some aspects of measured behavior. Among them, they sought to identify the compounds that 1) have minimal effects on the naive response to DFs, but strong effects during the training and/or memory retention periods, 2) have minimal effects on other aspects of behaviors, 3) show similar behavioral effects to other compounds tested in the same molecular pathway, and identified the GABAA/C Receptor antagonists Bicuculline, Amoxapine, and Picrotoxinin (PTX). As partial antagonism of GABAAR and/or GABACR is sufficient to strongly suppress habituation but not generalized behavioral excitability, they concluded that GABA plays a very prominent role in habituation. They also identified multiple agonists of both Melatonin and Estrogen receptors, indicating that hormonal signaling may also play a prominent role in habituation response.

To integrate the results of the Ca2+ imaging experiments with the pharmacological screening results, the authors compared the Ca2+ activity patterns after treatment with vehicle, PTX, or Melatonin in the tethered larvae. The behavioral effects of PTX and Melatonin were much smaller compared with the very strong behavioral effects in freely-swimming animals, but the authors assumed that the difference was significant enough to continue further experiments. Based on the hypothesis that Melatonin and GABA cooperate during habituation, they expected PTX and Melatonin to have opposite effects. This was not the case in their results: for example, the size of the 12(Pot, M) neuron population was increased by both PTX and Melatonin, suggesting that pharmacological manipulations that affect habituation behavior manifest in complex functional alterations in the circuit, making capturing these effects by a simple difficult.

Since the 12(𝑃𝑜𝑡, 𝑀) neurons potentiate their responses and thus could act to progressively depress the responses of other neuronal classes, they examined the identity of these neurons with GABA neurons. However, GABAergic neurons in the habituating circuit are not characterized by their Adaptation Profile, suggesting that global manipulations of GABAergic signaling through PTX have complex manifestations in the functional properties of neurons.

Overall, the authors have performed an admirably large amount of work both in whole-brain neural activity imaging and pharmacological screening. However, they are not successful in integrating the results of both experiments into an acceptably consistent interpretation due to the incongruency of the results of different experiments. Although the authors present some models for interpretation, it is not easy for me to believe that this model would help the readers of this journal to deepen the understanding of the mechanisms for habituation in DF responses at the neural circuit level.

This reviewer would rather recommend the authors divide this manuscript into two and publish two papers by adding some more strengthening data for each part such as cellular manipulations, e.g. ablation to prove the critical involvement of 12(Pot, M) neurons in habituation.

Reviewer #3 (Public Review):

The authors present a multi-disciplinary structural analysis of the glideosome-associated connector (GAC), which is important for the motility of parasites within the Apicomplexa phylum. Strengths of the study include the first crystal of the GAC, revealing an elaborate pyramid structure with a protruding arch bearing a PH domain. The lipid binding analyses, featuring NMR experiments and simulations to identify key residues, provide a nice complement to the crystal structure. There are interesting differences between the structure obtained and the small-angle X-ray scattering data, which are plausibly (but not conclusively) explained by a model in which GAC uses multiple conformations. It is also puzzling that the lipid binding residues in the PH domain do not seem vital for parasite invasion, although this may be explained by the second lipid binding site in the GAC arch. The AlphaFold prediction of the interface between the GAC and a peptide from MIC2 is interesting, in that it is reminiscent of the B-catenin/E-cadherin interaction, but requires validation. The study will be useful for researchers investigating the structural mechanism of parasite motility.

Reviewer #3 (Public Review):

The authors characterized the effect of Zn2+ in potentiating OTOP1 and OTOP3 proton-activated H+ currents. They took advantage of a set of chimeras with swapped extracellular loops between OTOP3 (Zn2+-dependent potentiation) and OTOP2 (no potentiation) by neatly identifying an extracellular loop that is sufficient to confer Zn2+ potentiation. The results support the idea that within this loop resides at least part of the Zn2+ binding site, a hypothesis also confirmed by the role of a histidine residue. The authors suggested that Zn2+ potentiation of OTOP3 involves different structural elements than those required for inhibition, the conclusion that is supported by the data on the OTOP3-OTOP2 chimeras. These results shed light on a new aspect of the gating mechanism of these channels, adding an important piece to the puzzle to decipher their role in cells. This manuscript provides an important result for scientists whose research is focused on proton channels, and ion channel gating mechanisms.

Weaknesses: Although the identification of the extracellular loop represents an important result to define the structural element that confers Zn2+ potentiation to OTOP3, there are several aspects of the gating mechanism that would require a deeper analysis. The mutagenesis of the OTOP3 tm11-12 linker is very limited and does not include mutagenesis experiments in OTOP2 and OTOP1 that would further support the conclusion proposed by the authors and extend the importance of the tm11-12 linker to all the three OTOP channels (as stated in the manuscript title).<br /> Moreover, only one residue has been identified as important for Zn2+ binding. Given the three-dimensional structures of OTOP channels available to this date, particularly the chicken OTOP3 structure (PDB:6NF6), a structural analysis would certainly provide a set of putative partners for the histidine identified as the key residue for Zn2+ potentiation. Even if it is hard to understand what conformational state is represented in the structure, this analysis will provide a valid starting point to investigate the functional relevance of these residues.

Reviewer #3 (Public Review):

This work extends earlier findings from this group which showed in congenitally blind individuals preserved, presumably language-derived, representations of colour knowledge are present only in dATL. While the present study confirms the importance of language in representations in dATL, the specificity of dATL hinges on descriptive rather than inferential statistics, and future studies may be needed to demonstrate the primacy of dATL in language-based representation as well as the generalisability of effects across different flavours of conceptual knowledge.

Reviewer #3 (Public Review):<br /> <br /> In the current study, the authors present a novel and original approach (termed MINE) to analyze neuronal recordings in terms of task features. The method proposed combines the interpretability of regressor-based methods with the flexibility of convolutional neural networks and the aim is to provide an unbiased, "model-free" approach to this very important problem.

In my opinion, the authors succeed in most of these aspects. They use three datasets: an artificially-generated one that provides a ground-truth, a published dataset from wide-scale cortical mouse recordings and a novel one that studies thermosensation in larval zebrafish. MINE compares favorably in all three cases.

I believe that the paper would mostly benefit from an increased effort in clear exposition of the Taylor expansion approach, which is at the core of the method. The methods section describes the mathematics, but I wonder whether it would be possible to illustrate or schematize this in a main Figure, e.g. as an addition to Figure 1 or as a new figure. Around line 185, the manuscript reads: "We therefore perform local Taylor expansions of the network at different experimental timepoints. In other words, we differentiate the network's learned transfer function that transforms predictors into neural activity."

It would help to explicitly state with respect to what the derivative is being computed (i.e. time) and maybe a diagram (which I had to draw to understand the paper) in which a neuronal activity trace is shown and from time t onwards a prediction is computed using terms in the Taylor expansion would be very instructive (showing on an actual trace how disregarding certain terms changes the prediction and hence the conclusions about the actual dependence of the trace on the behavioral features). The formulation in terms of Jacobians and Hessians can then be restricted to the Methods section and the paper will be easier to read for a wider audience. The method is presented as a "model-free" approach (title and introduction). I think it would help to discuss this with some precision. The Taylor expansion approach does imply certain beliefs on the structure of the data (which are well founded in most cases). Do the authors agree that MINE would encapsulate any regression model where both linear and interaction terms are allowed to include an arbitrary non-linearity (in the case of the interaction terms, different non-linearities for both variables)? If this is the case, maybe an explicit statement would allow the reader to quickly identify the versatility of MINE.

I find the section relating to non-linearities interesting, but was slightly disappointed to find that the authors do not propose a single method. In Figure 3E, the authors show that a logistic regression model that combines the curvature and NLC apporaches outperforms either, but the model is not described in any sort of detail. I appreciate the attempt made by the authors to apply this to the zebrafish imaging dataset in Figure 7, but it was still unclear to me how non-linearities and complexity are related.

Reviewer #3 (Public Review):

Sarver et al., propose that TcMAC21 mice are hypermetabolic and that this is the cause of their reduced weight. Unfortunately, the developmental defects of TcMAC21 mice make this a challenging question to definitively answer. The authors claim that TcMAC21 mice are hypermetabolic due to a futile calcium cycling in skeletal muscle, which is caused by up-regulation of SLN. However, all of the data that would go into the energy balance equation (food intake, energy absorption, and energy expenditure) have been improperly analyzed. TcMAC21 pups are 8.5 g lighter than euploid littermates. The body weight data and images in Fig. 3A indicate that TcMAC21 mice runted. This difference is primarily a result of lower lean mass (FIG. 2B). This is important as it sets up many concerns that need to be addressed. Specific comments are noted below.

Specific comments:

1) It is incorrect to normalize EE to lean mass if this parameter is different between groups. Normalizing the EE data to lean mass makes it appear as though TcMAC21 mice exhibited increased EE when in fact this is a mathematical artefact. EE data should simply be plotted as ml/h (or kcal/h) per mouse. Alternatively, ANCOVA can be applied using lean mass as a covariate. Excellent reviews on this topic have been written (PMID: 20103710; PMID: 22205519).

2) It makes no sense to normalize food intake to weight, as it makes no sense to divide metabolic rate by weight as well (see above). If food intake is not normalized, this will clearly show that TcMAC21 mice eat much less than controls, and if plotted as cumulative food intake will show that TcMAC21 are smaller and gain less weight on a high-fat diet because they simply eat less. This further indicates that the major tenet of this paper is not correct.

3) The authors have tried to address the smaller weight of TcMAC21 mice by including weight-matched wild-type mice. However, they only focus on analyzing surface temperature, which is not an indicator of thermogenesis. Moreover, there is no information on whether these weight-matched wild-type mice are similar in age or body composition to the TcMAC21 mice. Nevertheless, the increased surface temperature can also indicate increased heat conservation, which is opposite to thermogenesis. It would make sense that TcMAC21 mice with massive reductions in lean mass would activate compensatory mechanisms of heat conservation to offset increased heat dissipation to the environment. This does seem to be the case, based on the data shown in Fig. 6D (see below).

4) A more optimal method of testing whether increased heat dissipation plays a role in the EE of TcMAC21 mice, is to measure EE at thermoneutrality, where energy dissipation to the environment will be minimized. Here the authors have attempted this in Fig. 6D. Unfortunately, the authors normalized EE to lean mass, artefactually elevating TcMAC21 EE. Despite this mistake, it now looks as though the large differences in EE that were seen at room temp have been attenuated, and only significantly limited to the dark phase. This indicates that in addition to the normalization artefact, higher heat dissipation from smaller TcMAC21 mice may also contribute to the elevated EE at 22C.

5) In Fig. 6D, why is the hourly plot not shown here (like 2D and 4C)? The data clearly are not as striking as the EE data at 22C?

6) GTT was similar between TcMAC21 and controls (Fig. 3I). However, the smaller insulin response could be due to the fact that glucose was normalized to body weight. It would be better to normalize to lean mass, since that is different as well, or simply give all mice the same amount of glucose that the control group receives since this is how it is done in humans.

7) The fecal energy in Fig. 4B only measures the concentration of energy per gram of feces. However, this analysis has failed to take into account total fecal excretion, which should be used to multiply the energy density of the feces. Thus, these data are incomplete and not sufficient to exclude absorption differences between the groups. And it is now curious why if all other metabolic measurements (even though wrong), such as food intake and EE are normalized to body weight, why have the authors not normalized to body weight for the feces data? Is this because if this was done this would show massive elevating in fecal energy in TcMAC21 mice and thus falsify their hypothesis?

8) I cannot find any indication of sample size in any of the EE experiments, aside from the bar graph in Fig. 6D. In any case, this experiment only an n=4 to 5 per group. This is an extremely small number for these types of experiments, so how can the authors be sure of reproducibility with such a low sample size? Are all of the other EE experiments also of similarly small sample sizes?

Reviewer #3 (Public Review):

Here, the authors aim to uncover the mechanism by which the K+ efflux channel TWIK2 contributes to activation of the canonical NLRP3 inflammasome, as a follow on from their 2018 publication identifying TWIK2 as an essential factor in ATP-induced inflammasome activation. They firstly use immunofluorescence to identify TWIK2 trafficking to the membrane following ATP challenge, and is found to colocalise with early and recycling endosomes during homeostasis. The strengths of the paper are the finding that TWIK2 localisation in cells may be altered by ATP. Biophysical investigation of membrane potential identifies extracellular Ca2+ as essential for NLRP3 activation, and the calcium-dependent small GTPase Rab11a was found to colocalise with the plasma membrane upon ATP treatment. Finally, mice harbouring Rab11a siRNA-treated macrophages were found to exhibit reduced inflammation in response to induction of sepsis, further reinforcing the potential of Rab11a targeting for novel therapeutics. However, mechanistic exploration do not provide direct evidence on TWIK2 trafficking or the involvement of Rab11a specifically with NLRP3 inflammasomes, and results with non-specific inhibitors needs to be supported by further experiments.

Reviewer #3 (Public Review):

This is a very interesting and sound work. It has been postulated that sensory neurons could optimize their information about future stimuli, but we still don't know how they can do that. This paper tackled this issue in depth with both phenomenological and mechanistic models, to understand which mechanisms could help optimize this predictive information, and show convincingly that several mechanisms can help for this.

The main limitation is that this is tested for motion at constant speed, and it would be interesting to know what happens in other cases. Also, the part about phenomenological modeling might need clarifications to understand better what really increases predictive information: it is clear the real system does it better than alternative, less realistic models, but in some cases it is not clear what is the key feature of the model.

Reviewer #3 (Public Review):

The manuscript by Hara and Kuraku addresses the question of whether some genes have a diverging gene fate (gene loss) due to underlying sequence or genomic properties. To approach this task, the authors introduce a gene loss detection pipeline that takes some previously raised technical concerns of overestimating gene loss (e.g. variations in assembly quality) into account. When applying their pipeline to >100 species, the authors report ~1,000 human genes whose orthologues were lost in multiple mammalian lineages (which they refer to as elusive genes). The study then focuses on integrating all functional evidence that can be obtained from large-scale databases for these elusive genes and test whether their genomic and evolutionary properties in the genomes of human and various other vertebrates (chimpanzee, mouse, chicken, turkey, green anole, central bearded dragon, western clawed frog, coelacanth, spotted gar, bamboo shark, whale shark) differs from the properties of the ~8,000 non-elusive genes (genes stably conserved across the compared species). In addition, the authors further analyse the human genome for the population-level variations, expression profiles and epigenetic features of elusive genes.

Overall, the study is descriptive and adds incremental evidence to an existing body of extensive gene loss literature. The topic is specialised and will be of interest to a niche audience. The text is highly redundant, repeating the same false positive issue in the introduction, methods, and discussion sections, while no clear conclusion or interpretation of their main findings are presented.

Major comments

- While some of the false discovery rate issues of gene loss detection were addressed in the presented pipeline, the authors fail to test one of the most severe cases of mis-annotating gene loss events: frameshift mutations which cause gene annotation pipelines to fail reporting these genes in the first place. Running a blastx or diamond blastx search of their elusive and non-elusive gene sets against all other genomes, should further enlighten the robustness of their gene loss detection approach

- Along this line, we noticed that when annotation files were pooled together via CD-Hit clustering, a 100% identity threshold was chosen (Methods). Since some of the pooled annotations were drawn from less high quality assemblies which yield higher likelihoods of mismatches between annotations, enforcing a 100% identity threshold will artificially remove genes due to this strict constraint. It will be paramount for this study to test the robustness of their findings when 90% and 95% identity thresholds were selected.

- While some statistical tests were applied (although we do recommend consulting a professional statistician, since some identical distributions tend to show significantly low p-values), the authors fail to discuss the fact that their elusive gene set comprises of ~5% of all human genes (assuming 21,000 genes), while their non-elusive set represents ~40% of all genes. In other words, the authors compare their sequence and genomic features against the genomic background rather than a biological signal (non-elusiveness). An analysis whereby 1,081 genes (same number as elusive set) are randomly sampled from the 21,000 gene pool is compared against the elusive and non-elusive distributions for all presented results will reveal whether the non-elusive set follows a background distribution (noise) or not.

- We also wondered whether the authors considered testing the links between recombination rate / LD and the genomic locations of their elusive genes (again compared against randomly sampled genes)?

- Given the evidence presented in Figure 6b, we do not agree with the statement (l.334-336): "These observations suggest that the elusive genes are unlikely to be regulated by distant regulatory elements". Here, a data population of ~1k genes is compared against a data population of ~8k genes and the presented difference between distributions could be a sample size artefact. We strongly recommend retesting this result with the ~1k randomly sampled genes from the total ~21,000 gene pool and then compare the distributions.

- Analogous random sampling analysis should be performed for Fig 6a,d

- We didn't see a clear pattern in Figure 7. Please quantify enrichments with statistical tests. Even if there are enriched regions, why did the authors choose a Shannon entropy cutoff configuration of <1 (low) and >1 (high)? What was the overall entropy value range? If the maximum entropy value was 10 or 100 or even more, then denoting <1 as low and >1 as high seems rather biased.

Reviewer #3 (Public Review):

In the present study by Boyle et al., the function of NPY expressing spinal neurons in pain and itch perception is studied. While the function of these neurons has been addressed previously, the difference to previous studies is the combinatorial use of AAV encoded effectors and cre transgenic mice whereas previous studies relied on cre transgenic mice and reporter mice encoding the effector or only viruses. Boyle at al. demonstrate that their strategy enabled them to restrict the analysis to only those neurons expressing NPY in the adult mouse compared to a more heterogenous population that had been studied before. By using a combination of morphology, electrophysiology and behavioral paradigms they convincingly show that NPY neurons impact pruritoception via inhibiting GRPR neurons. Furthermore, they indicate a role of NPY neurons also in nociception as activation attenuates not only responses to acute nociceptive stimuli but also blocks inflammation or nerve injury induced mechanical and heat hypersensitivity. Selectively activating NPY neurons in vivo may therefore be a promising strategy to treat neuropathic pain.

The result of this study extends and partially contrasts previous studies. The authors argue that contrasting results may be due to the different experimental strategies (e.g. only neurons expressing NPY adult in the present study versus a more heterogeneous population before).

Overall, the experiments are convincing, and the quality of the data/figures is exceptionally high.

Reviewer #3 (Public Review):

The authors study monolayers of MDCK cells on curved surfaces. These surfaces consist of hemicylindrical valleys and hills obtained through microfabrication involving glass rods and repeated molding steps. They find higher apoptotic extrusion rates in valleys compared to hills for patterns with 25 and 50 µm curvature radii, but not in valleys of 100 µm curvature radius. By using osmotic shocks and reflection interference contrast microscopy, they identify hydraulic stress to drive cell extrusion. 3D force microscopy reveals that cytoskeletal forces point towards the substrate on hills and away from the substrate in valleys. From these observations, the authors conclude that hydraulic stress-induced cell extrusion is assisted by cytoskeletal forces in the valleys and opposed on the hills. Finally, they link the hydraulic stress to the activity of focal adhesion kinase, which in turn affects cell survival through Akt signaling.

Strengths:

This work combines a new microfabrication method with state of the art 3d force microscopy that allows the authors to study curvature-dependent cell extrusion. The application of various osmotic shocks to the system clearly identifies the role of hydraulic stress in cell extrusion. The decoupling of the main driver of cell extrusion (hydraulic stress) from its curvature-dependent modulation through cytoskeletal forces, together with the mechanical activation of apoptosis is an important new finding that significantly advances our understanding of epithelial cell extrusion and could be important during developmental processes and for maintaining intact epithelia in adult organisms.

Weaknesses:

The main weakness of this work is a lack of quantification of the hydraulic stress. Furthermore, the authors do not present data on other cell types such that the phenomenon studied in this work might be specific to MDCK cells. Finally, The authors do not modify cytoskeleton contractility to check how this parameter affects the threshold curvature below which cell extrusion is no longer curvature dependent.

Reviewer #3 (Public Review):

This study shows for the first time changes in palladin expression under disease conditions and mRNA alterations in human samples. The authors have identified novel binding partners for the protein as a first step toward determining how palladin mediates its effects in the heart. Finally, through the use of mouse models to decrease palladin expression they identify a crucial role for palladin in the cardiac response to pathological stress, with some interesting findings that show the effects of palladin depend on when the protein is altered.

The novel findings of the study are supported by the data presented, but there are several instances where clarification is needed of the conclusions drawn from the data reach beyond what is presented in the Results section.

The focus on only male mice is a significant limitation of the paper, as it is well known that there are profound sex differences in the response to pathological stressors. While the ability to obtain sufficient heart samples from male and female patients may be a reasonable justification for focusing on males, the preclinical mouse model should have been examined in both sexes and the limitation of this choice should be clearly noted in the paper.

The changes in myopalladin expression were not measured in the disease model (TAC), which limits the ability to determine if myopalladin was altered in the disease state. This addition would strengthen the study.

Finally, the myofilament data are presented as evidence that changes in the contractile apparatus are contributors to the observed contractile dysfunction at the organ level. But these studies were conducted using levels of calcium that far exceed what is seen in vivo and, therefore, do not support the conclusion drawn.

Reviewer #3 (Public Review):

The ability of T cells to move through a variety of complex and disparate tissue environments is fundamental to their success in surveying and responding to infectious challenges. A better understanding of the molecular cues that regulate T cell motility in tissues is needed in order to inform therapeutic targeting of T cell migration. Contributions that are intrinsic and extrinsic to the T cells themselves have been shown to shape the pattern of T cell movement. This study uses advanced quantitative image analysis tools to dissect differences in T cell motility in different tissue locations, to better define how the tissue environment shapes the pattern of motility and scope of tissue explored. The combination of different quantitative measures of motion enables the extensive characterization of CD8 T cell motility in the lymph node, lung, and villi of the small intestine. However, there are too many variables with respect to the CD8 T cell populations used for analysis to be able to gain new insight into the impact of the tissue microenvironment itself.

The use of these advanced quantitative imaging analysis tools has the potential to significantly expand our analysis capabilities of T cell movement within and across tissues. The strength of the paper is the comprehensive analysis of multiple motility parameters designed with T cell function in mind. Specifically, with respect to the need for T cells to search a tissue area to identify antigen-bearing cells for T cell activation and identify cellular targets for the delivery of anti-microbial effector functions. The inclusion of an analysis of the "patrolled volume per time" is seen as a particularly useful advance to compare T cell behaviors across tissues.

However, with the current data sets, it is difficult to draw definitive conclusions on the impact of the tissue environment on how T cell move, given the considerable variability in the CD8 T cells themselves. Extended experimentation would be needed to fully support their key claims. In particular:

1) The authors have separated out naïve and activated CD8 T cells for their analysis, but this is a marked over-simplification. There are too many variables within these groups to be able to distinguish between differences in the T cell populations versus differences in the tissue environment. Variables include:<br /> a) T cells pre-activated in vitro before in vivo transfer (LPS-lung) versus transfer of naïve T cells for activation in vivo (Flu-lung, LCMV-villi)<br /> b) Polyclonal CD8 T cells (naïve, LPS-lung, Flu-lung) versus monoclonal (P14) CD8 T cells (LCMV-villi)<br /> c) Presence of cognate-antigen (Flu-lung, LCMV-villi) versus absence of antigen (LPS-lung)<br /> d) Cell numbers, 104 polyclonal naïve for Flu-lung versus 5 x 104 monoclonal (P14 T cells) for LCMV-villi)<br /> e) Intravital imaging (LCMV-villi) versus tissue explants (Flu-lung)

The authors do present data that suggest similarities of motility patterns within the same tissue occur despite variabilities in the CD8 T cell source, for example, the MSD is not significantly different in the two lung groups despite differences in the way the CD8 T cells were activated. However, these similarities are lost when other parameters are analyzed suggesting additional variability independent of the tissue itself.

2) Controlled experiments are needed, where the input CD8 T cell population is kept constant and the target tissue differs, to substantiate any of the current conclusions. This could be done by using a single source and/or specificity of CD8 T cells (e.g., P14 or OT-I TCR transgenics, or polyclonal in vitro activated CD8 T cells) transferred into mice where the tissue providing the antigen or inflammation source is varied (lung with pOVA-flu versus small intestine with pOVA-LCMV for example).

Alternatively, activated polyclonal CD8 T cells could be analyzed in the LPS-lung draining LN as well as in the LPS-lung to make a direct comparison between the tissues (LN versus lung) using CD8 T cells of the same activation status.

3) Differences in the micro-anatomical regions of the tissues studied may also contribute to tissue differences in movement patterns between the lung and the small intestine. The region of the small intestine imaged was specifically focused on the villi, close to the gut epithelium. Details of the location within the lung where images were taken are missing, therefore the motility differences between the lung and small intestine could reflect differences in the micro-anatomical position of the CD8 T cells within the tissue (proximal to epithelium versus parenchymal), rather than differences between the tissues themselves.

Overall, the authors have developed a quantitative multi-parameter approach to the study of T-cell motility in different tissues. Application of these analytical tools to the study of T-cell behavior in different tissue locations has the potential to reveal tissue and/or T-cell-specific patterns of movement that may help to identify molecular requirements for context-specific dynamic T-cell behavior. Their quantitative approach reveals small but statistically significant differences in particular motility parameters, the functional significance of which will require further study. The careful design of experiments to reduce as many variables as possible will be needed to increase the impact of the work and ensure new insights into this important aspect of T-cell function.

Reviewer #3 (Public Review):

In this work, the authors have built a framework for the annotation of interactions between species. The framework includes ontologies, methodologies, and an annotation tool called PHI-Canto. The framework makes use of multiple existing ontologies that are in wide use in the biocuration community. In addition, the authors have built their own project-specific controlled vocabularies and ontologies for the capture of pathogen-host interaction phenotypes (PHIPO), diseases (PHIDO), and environmental conditions (PHI-ECO). Their work builds on and extends methods that have been developed within the Gene Ontology Consortium and model organism databases. The tool PHI-Canto is an extension of the tool Canto developed by PomBase for curation. The authors used this framework to annotate pathogen-host interactions within the Pathogen-Host Interactions Database.

Strengths: The manuscript is well-written and includes significant detail regarding curation policies/methods and the use of the actual PHI-Canto tool. The appendices are very detailed and provide useful illustrations of the annotation practices and tool interface. The work has built upon and extended well-established standards and methods that have proven their utility over many years of use in the biocuration community. The authors have rigorously tested their framework with the curation of a variety of publications providing a diverse assortment of annotation challenges. The concept of a "metagenotype" is important and providing such a structured system for the capture of this information is useful. All of the materials produced by the work are completely freely available for use by the wider community.

Weaknesses: There are some areas of the manuscript and appendices which are a bit confusing and could be improved. The authors have developed their own set of disease terms (PHIDO) but do not comment on why existing disease terminologies (such as Mondo or DO) were not used or if the PHIDO terms relate to those other vocabularies. There is no discussion of the possible use of a graph representation for the capture of this complex information (which is being done in many settings including the Gene Ontology with GO Causal Activity Models (GO-CAMs)) or why such a structure was not used. Although the abstract talks about the use of the framework within the PHI database as a test case for broader use regarding interspecies interactions, there is no mention of extending the use of the tool to other species interaction communities beyond pathogen-host interactions.

Reviewer #3 (Public Review):

Eyraud and colleagues examine how fibrocytes and CD8 cells can interact with each other to promote COPD. The key findings include that CD8 cells and fibrocytes are found to exist in close proximity to each other in COPD lungs using histopathological analysis of patient samples. The authors leverage pre-existing transcriptomic data on CD8 cells to focus on chemokine release by CD8 cells as a potential pathogenic mechanism by which they could affect fibrocyte migration. In vitro studies using peripheral blood-derived CD8 cells and fibrocytes confirm increased fibrocyte migration in the presence of CD8 cells. as drivers of COPD progression. Conversely, in vitro studies show that fibrocytes exert a pro-proliferative effect on CD8 cells. The authors also use a computational model to assess how these interactions could promote the development of fibrocyte-CD8 clusters as COPD progresses over the course of 20 years.

The strengths of the study include:

1) The multi-faceted research approach that integrates histopathology from clinical COPD lung sections, in vitro co-culture studies, and computational modeling.

2) Applying computational modeling to determine how cell-cell interactions of migration and proliferation can result in distribution patterns within the lung that approximate what is found in actual clinical samples

3) Propose a feedback loop of CD8 cells and fibrocytes that could become a potential therapeutic target to interrupt a vicious cycle that promotes COPD.

However, there are also some weaknesses:

1) Specificity of the role of CD8 cells: While much of the focus is on the proximity of and interactions between CD8 cells and fibrocytes, it is not clear whether other cells similarly interact with fibrocytes. For example, CD4 cells, dendritic cells, or interstitial macrophages may similarly interact with fibrocytes as several of these also release chemokines. In the absence of a more comprehensive assessment, it becomes difficult to parse out how specific and relevant the fibrocyte-CD8 cell interactions are for COPD progression when compared to other putative interactions.

2) The transcriptomic analysis which in many ways sets the stage for the chemokine studies uses a pre-existing dataset of COPD and non-COPD samples with only n=2. The robustness of such a sample size is limited and the narrow focus on chemokines or adhesion receptors of CD8 cells in this limited sample size does not provide a more comprehensive analysis that would require larger samples sizes, studying the transcriptomes of other cell types and a broader analysis of which pathways are the most likely to be dysregulated in the cells that surround fibrocytes.

3) Specificity of the findings for COPD: The in vitro studies use circulating cells which are different from lung cells and this is appropriately acknowledged by the authors. However, it appears from the description that the cells are all from COPD patients. It is therefore not clear whether these interactions between fibrocytes and CD8 cells are unique to COPD, whether they also occur between control CD8 and fibrocytes, or only in cells obtained from patients with inflammatory/pulmonary diseases.

Reviewer #3 (Public Review):

Laure Olazcuaga et al. investigated the metabolomes of four fruit-based diets and corresponding individuals of Drosophila suzukii that reared on them using comparative metabolomics analysis. They observed that the four fruit-based diets are metabolically dissimilar. On the contrary, flies that fed on them are mostly similar in their metabolic response. From a quantitative point of view, they find that part of the fly metabolomes correlates well with that of the corresponding diet metabolomes, which is indicative of insect ingestive history. By further focusing on 71 metabolites derived from diet-specific fly ions and highly abundant fruit ions, the authors show that D. suzukii differentially accumulates diet metabolism in a compound-specific manner. The authors claim that the data support the metabolic generalism hypothesis while rejecting the multi-host metabolic specialism hypothesis. This study provides a valuable global chemical comparison of how diverse diet metabolites are processed by a generalist insect species.

Strengths:<br /> The rapid advances in high-resolution mass spectrometry have recently accelerated the discovery of many novel post-ingestive compounds through comparative metabolomics analysis of insect/frass and plant samples. Untargeted metabolomics is thus a very powerful approach for the systematic comparison of global chemical shifts when diverse plant-derived specialized metabolites are further modified or quantitatively metabolized after ingestion by insects. The technique can be readily extended to a larger micro- or macro-evolutionary context for both generalist and specialist insects to systematically investigate how plant chemical diversity contributes to dietary generalism and specialism.

Weaknesses:<br /> The authors claim that their data support the hypothesis of metabolic generalism, however, a total analysis of insect metabolism may not generate a clean dataset for direct comparison of fruit-derived metabolites with those metabolized by D. suzukii, given that much of these metabolites would be "diluted" proportionally by insect-derived metabolites. If the insect-derived metabolites predominate, then, as the authors observed, a tight clustering of D. suzukii metabolomes in the PCA plot would be expected. It is therefore very difficult to interpret these patterns.

The authors generated a qualitative dataset using the peak list produced by XCMS which contains quantitative peak areas, it is unclear how the threshold was selected to determine if a peak is present or absent in a given sample. The qualitative dataset would influence the output of their data analysis.

The authors reply on in-source fragmentation for peak annotation when authentic standards are not available. The accuracy of the annotation thus requires further validation.

(of people) dressed, or (of things) covered 穿…衣服的；覆蓋著…的

If something bad or dangerous befalls you, it happens to you. （壞事）降臨（於）

an African or Asian mammal with hoofs and large eyes that moves quickly and lightly 羚羊

The king never sleep well. How can he still make the judgement?

To change people, you need to have knowledge first.

Why does he wants the lord to imprison him?

This scene does show Joseph's defect, but it is also human defect.

This is different than the plot of the Hebrew Bible.

He does not change the law of God, too. This is somewhat like Jesus.

Quran has the same creation with that of bible.

Reviewer #3 (Public Review):

Gaze-stabilizing motor coordination and the resulting patterns of retinal image flow are computed from empirically recorded eye movement and motion capture data. These patterns are assessed in terms of the information that would be potentially useful for guiding locomotion that the retinal signals actually yield. (As opposed to the "ecological" information in the optic array, defined as independent of a particular sensor and sampling strategy).

While the question posed is fundamental, and the concept of the methodology shows promise, there are some methodological details to resolve. Also, some terminological ambiguities remain, which are the legacy of the field not having settled on a standardized meaning for several technical terms that would be consistent across laboratory setups and field experiments.

Technical limits and potential error sources should be discussed more. Additional ideas about how to extend/scale up the approach to tasks with more complex scenes, higher speed, or other additional task demands and what that might reveal beyond the present results could be discussed.

Reviewer #3 (Public Review):

In empirical data, the dependence of microbial diversity on environmental temperature can take multiple different functional forms, while the previous theory has not established a clear understanding of when the temperature-dependence of diversity should take a particular form, and why. The authors seek to understand what forms are possible, and when they will occur, via analysis of the feasibility (i.e. positivity) of Lotka-Volterra equation solutions. This is combined with an assumption for the way that species' growth rates depend on temperature, along with an assumption for the way species interaction rates depend on temperature. Together, this completely specifies the form of the Lotka-Volterra equations, and whether all species in the model can coexist indefinitely at a given temperature, or whether only a lower-diversity subset can persist.

The overall goal is valuable, and the overall approach of using this classic model of species interactions is justifiable. My main question marks relate to the way the conditions on feasibility (i.e. when all species will have positive equilibria), whether and when we need to consider the stability of these feasible solutions, and finally how general the way in which model parameters are specified to depend on temperature. I will expand on these three issues below. A more minor issue is that the authors set up this problem with extensive reference to the interaction of consumers and resources, referencing previous approaches that explicitly model these. Since resources are not explicitly present in the Lotka-Volterra formalism, it would be helpful to have a clearer justification for the authors' rationale in choosing this kind of model.

(1) Conditions on growth and interaction rates for feasibility and stability. The authors approach this using a mean field approximation, and it is important to note that there is no particular temperature dependence assumed here: as far as it goes, this analysis is completely general for arbitrary Lotka-Volterra interactions.

However, the starting point for the authors' mean field analysis is the statement that "it is not possible to meaningfully link the structure of species interactions to the exact closed-form analytical solution for [equilibria] 𝑥^*_𝑖 in the Lotka-Volterra model.

I may be misunderstanding, but I don't agree with this statement. The time-independent equilibrium solution with all species present (i.e. at non-zero abundances) takes the form

x^* = A^{-1}r

where A is the inverse of the community matrix, and r is the vector of growth rates. The exceptions to this would be when one or more species has abundance = 0, or A is not invertible. I don't think the authors intended to tackle either of these cases, but maybe I am misunderstanding that.

So to me, the difficulty here is not in writing a closed-form solution for the equilibrium x^*, it is in writing the inverse matrix as a nice function of the entries of the matrix A itself, which is where the authors want to get to. In this light, it looks to me like the condition for feasibility (i.e. that all x^* are positive, which is necessary for an ecologically-interpretable solution) is maybe an approximation for the inverse of A---perhaps valid when off-diagonal entries are small. A weakness then for me was in understanding the range of validity of this approximation, and whether it still holds when off-diagonal entries of A (i.e. inter-specific interactions) are arbitrarily large. I could not tell from the simulation runs whether this full range of off-diagonal values was tested.

As a secondary issue here, it would have been helpful to understand whether the authors' feasible solutions are always stable to small perturbations. In general, I would expect this to be an additional criterion needed to understand diversity, though as the authors point out there are certain broad classes of solutions where feasibility implies stability.

(2) I did not follow the precise rationale for selecting the temperature dependence of growth rate and interaction rates, or how the latter could be tested with empirical data, though I do think that in principle this could be a valuable way to understand the role of temperature dependence in the Lotka-Volterra equations.

First, as the authors note, "the temperature dependence of resource supply will undoubtedly be an important factor in microbial communities"

Even though resources aren't explicitly modeled here, this suggests to me that at some temperatures, resource supply will be sufficiently low for some species that their growth rates will become negative. For example, if temperature dependence is such that the limiting resource for a given species becomes too low to balance its maintenance costs (and hence mortality rate), it seems that the net growth rate will be negative. The alternative would be that temperature affects resource availability, but never such that a limiting resource leads to a negative growth rate when a taxon is rare.

On the other hand, the functional form for the distribution of growth rates (eq 3) seems to imply that growth rates are always positive. I could imagine that this is a good description of microbial populations in a setting where the resource supply rate is controlled independently of temperature, but it wasn't clear how generally this would hold.

Secondly, while I understand that the growth rate in the exponential phase for a single population can be measured to high precision in the lab as a function of temperature, the assumption for the form of the interaction rates' dependence on temperature seems very hard to test using empirical data. In the section starting L193, the authors seem to fit the model parameters using growth rate dependence on temperature, but then assume that it is reasonable to "use the same thermal response for growth rates and interactions". I did not follow this, and I think a weakness here is in not providing clear evidence that the functional form assumed in Equation (4) actually holds.

Reviewer #3 (Public Review):

The manuscript entitled "Osteoblast-intrinsic defect in glucose metabolism impairs bone formation in type II diabetic mice" by Song et al. showed that osteoblast activity was compromised due to impaired glucose metabolism using a youth-onset T2D mouse model. The investigators induced youth-onset T2D in 22-week-old C57BL/6J male mice by a high-fat diet (HFD) starting at 6 weeks of age and injection of low-dose streptozotocin three times at 12-week-old. Then they demonstrated that metformin promoted glycolysis and osteoblast differentiation in vitro and increased bone mass in the diabetic mice. It was also demonstrated that targeted overexpression of Hif1a or Pfkfb3, but not Glut1, in osteoblasts reduced bone loss in T2D mice. Overall, the investigators made a great effort to characterize the changes in metabolism in the bone of the B6/C57 mice by HFD and metformin with microCT, dynamic histomorphometry, C13 isotype labeling in vivo, scRNA-seq and metabolic assays with bone marrow mesenchymal cells in vitro.

Reviewer #3 (Public Review):

WDR62 is a spindle pole-associated scaffold protein. Recessive mutations in WDR62 account for the second most common cause of autosomal recessive primary microcephaly (MCPH). This paper investigates how a C-terminal truncating mutation D955AfsX112 in WDR62 causes MCPH using iPSCs from a patient. The authors generated neuroepithelial (NES) cells, cortical progenitors, and neurons from the patient-derived and isogenic retro-mutated iPSC lines. They found that: (1) the mutant WDR62 fails to localize to the spindle poles during mitosis; (2) patient-derived iPS-NES cells exhibit shorter primary cilia and significantly smaller spindle angles; (3) the mutation leads to differentiation defects in iPSC-derived cortical neurons; (4) during the interphase-to-mitosis transition, WDR62 translocates from the Golgi apparatus to the spindle poles in a microtubule-dependent manner; and (5) the mutation prevents WDR62 shuttling from the Golgi to the spindle poles. Using the isogenic retro-mutated iPSC lines as the control increased the rigor of the current study. In general, this is a very carefully designed study, the data support the authors' conclusions, and confirm previous findings of WDR62 functions.