Reviewer #1 (Public Review):

Zhang et al. have submitted a manuscript demonstrating that STAT3 regulates RNA polymerase III transcription in human tumor cell lines. They present several lines of evidence for this proposal. They show that short hairpin (sh)RNAs that repress STAT3 inhibit Pol III transcription and limit proliferation in HepG2, HuH-7, and 293T cells. Accordingly, overexpression of STAT3 enhances Pol III transcription and increases proliferation in the same cell lines. STAT3-dependent EdU incorporation into synthesized DNA confirmed the proliferation results. The Pol-III transcription inhibitor ML-60218 reversed the positive proliferation effects of STAT3 overexpression. In a mouse xenograft model, overexpression of STAT3 enhanced tumor growth of HepG2 cells, whereas suppression of STAT3 inhibited its growth. Consistent with these results, overexpression of STAT3 enhanced colony formation of HepG2 cells in soft agar, whereas STAT3 suppression inhibited it. ChIP data suggest that STAT3 shRNAs reduce the presence of TBP, BRF1, TFIIIC subunits, and POLR3A at Pol III genes regulated by gene-internal promoters. However, STAT3 does not bind to 5S, 7SL, U6, and tRNA Met genes. In addition, STAT3 does not affect the expression of various Pol III transcription factors. RNA-seq in STAT3-shRNA-expressing HepG2 cells and in shRNA-expressing control cells revealed upregulation of 356 and downregulation of 590 Pol II-transcribed genes. None of the Pol III transcription factors were affected. Among the genes whose expression was enhanced by silencing STAT3 was TP73. Accordingly, overexpression of STAT3 decreased mRNA expression of TP73. To show that TP73 acts downstream of STAT3, the authors demonstrated that HepG2 cells expressing both STAT3 shRNAs and TP73 shRNAs did not exhibit decreased Pol III transcription or proliferation. Consistent with these results, TP73 shRNAs enhance Pol III transcription in HepG2 cells, and overexpression of endogenous TP73 represses Pol III transcription. This inhibition of TP73 is caused by the disruption of TFIIIB assembly. Consequently, TP73shRNAs increase the presence of Pol III factors at Pol III genes without affecting their expression. However, co-IP with alphaTP73 antibodies detected TBP, TFIIIC2, and TFIIICC3 but not BRF1, and vice versa. Moreover, shTP73 enhanced the co-IP of TBP with antiBRF1 antibodies. To discover molecular mechanisms explaining how TP73 expression is indirectly regulated by STAT3, the authors identified miR-106a-5p as a potential regulator. In agreement with a regulatory role, miR-106a-5p mimics reduce TP73 expression and enhance Pol III transcription. Finally, Zhang et al. show that STAT3 binds to the miR-106a-5p promoter and activates miR-106a-5p promoter transcription in a luciferase assay.

Overall, the data presented in this manuscript is clean and convincing and clearly supports the proposed model.

Reviewer #1 (Public Review):

LIS1 is a key dynein regulator and mutations in LIS1 cause the human brain developmental disease lissencephaly. The authors have previously reported a 3.1Å structure of yeast dynein bound to Pac1 (budding yeast LIS1) (Gillies et al., 2022, Elife). However, mutations they designed using the yeast dynein-PAC1 structure had mild effects on human dynein activation in vitro. Here they reported cryo-EM structures of human dynein-LIS1 complexes. While LIS1 and Pac1 bind to roughly the same sites (ring and stalk) of the dynein motor domains at the level of the 2D class averages, their current 3D cryo-EM structures of human dynein bound to one and two human LIS1 beta-propeller domains (4.0 Å and 4.1 Å resolution respectively) have revealed interesting similarities and differences in the interaction sites. In addition, they have provided the locations of missense mutations of LIS1 and dynein that cause lissencephaly and other human brain developmental or neurodegenerative disorders in the context of the human dynein-LIS1 structure. Overall, this first detailed structural analysis on human dynein-LIS1 interaction is well presented and will be important to the dynein field as well as people interested in lissencephaly and/or other neurodevelopmental disorders.

Methods are convincing. I do think it is important to point out that the dynein motor domain rather than full length dynein was used in this study. A relative weakness is the lack of functional analyses on the involved amino acids in the dynein-LIS1 and LIS1-LIS1 interaction interfaces. This is in contrast to the Gillies et al., 2022 paper, in which multiple functional assays were presented. However, knowing that functional assays are much more difficult to perform in human cells than in budding yeast, functional tests can be done in the future after this structural work is published.

Reviewer #1 (Public Review):

This paper provides de novo assembly of full-length 18S and 28S rRNA sequences from 33 mosquito species for whom no genome sequence exists. This is a very useful approach and dataset and provides a new tool by which wild-caught mosquitoes can be species-identified. Additionally, the existence of rRNA reference sequences will allow more effective depletion of these hyperabundant species of RNA prior to investing in RNA-seq of other cellular RNAs from a given sample. It is interesting how phylogenetic trees constructed using 28s rRNA compare to the more standard mitochondrial cytochrome c oxidase I gene. The availability of these data will be very useful for field entomologists and the method by which the rRNAs were obtained may be broadly useful for scientists contemplating a similar approach in less-studied species of medical or biological importance.

Reviewer #1 (Public Review):

The author's findings are as follows:<br /> (A) S.pombe Rlc1 is highly phosphorylated at Ser35 during the mitotic phase under respiratory metabolism.<br /> (B) This phosphorylation promotes the assembly and contraction of the contractile actomyosin ring (CAR).<br /> (C) This mechanism sustains CAR assembly and contraction under the respiratory metabolism, generating ROS. The ROS activates SAPK, which inhibits F-actin formation by reducing For3 expression.

They are important findings explaining the robustness of proliferative and regenerative activity of the eukaryotic cells.<br /> The data presented in the paper support the author's model.

Although there are controversial reports on Rlc1 phosphorylation, whether it activates or inactivates type II myosin in S.pombe, this paper does not terminate the debate. Inhibitory phosphorylation on Rlc1 was reported by Mohan Balasubramanian laboratory and Susan Lowey laboratory before, as the authors referred to in this paper. In contrast, the author's model showed that Rlc1 is phosphorylated to facilitate cell division. Since the molecular mechanism of the CAR assembly and contraction is still not defined well yet, the research field should welcome this study to facilitate the discussion in the future.

Reviewer #1 (Public Review):

Starrett, Gabriel et al. investigated 43 bladder cancers (primary tumors), 5 metastases and 14 normal tissues from 43 solid organ transplant recipients of 5 Transplant Cancer Match Study participating registries (US) for the presence of viral genetic signatures, their host genome integration and possible contribution in carcinogenesis. They isolated DNA and RNA from FFPE tissues to perform state of the art whole genome and transcriptome sequencing. They find that 20 of the primary tumors, 3 of the metastases and 7 of the normal tissues harbor viral signatures with BKPyV and JCPyV being the most prevalent viruses detected. The bulk of the experiments focuses on the 9 BKPyV-positive primary tumors. They report that several of the BKPyV-positive tumors show host genome integration of BKPyV with associated focal amplifications of adjacent host chromosome regions, with chromosome 1 being the most prevalent. Furthermore, BKPyV-positive tumors show a distinct transcriptomic signature with gene expression changes related to DNA damage responses, cell cycle progression, angiogenesis, chromatin organization, mitotic spindle assembly, chromosome condensation/separation and neuronal differentiation. The authors only touch the features of other virus-positive tumors, e.g. those with JCPyV and HPV signals, without offering further detail or thought. The overall mutation signature analysis reveals no clear correlation between presence of viral sequences and tumor mutation burden suggesting that many different, virus-unrelated, factors possibly contribute to bladder cancer genesis and progression. Most striking are cases potentially linked to aristolochic acid, ABOBUCK3 and SBS5. Thus, while the approach is state-of-the-art, the causality of viral signatures and oncogenesis and vice versa remains unsolved.

Strengths:<br /> 1) The study assesses 43 primary tumors, 5 metastases and 14 normal tissues from 43 solid organ transplants of different kinds (24x kidney, 4x liver, 14x heart and/or lung, 1x pancreas) rather than just focusing on a particular organ.

2) The study makes use of whole genome sequencing and transcriptomics and the assayed material is extracted from FFPE tissue, which shows a high level of practical, technical and computational skills and expertise.

Weaknesses:<br /> 1) There have been multiple inconsistencies in sample number and figure references throughout the publication. Is it 19 or 20 cases that have viral sequences detected? A comprehensive checker board table showing all cases, the available tissue samples and respective analyses would be in order.

2) The overall low coverage of the whole genome sequencing, which the authors mention, and the relatively big variation in coverage in both datasets (WGS, transcriptomics) are major limitations of the study. Possibly, this was done to increase specificity, but sorting out and discarding reads may also be problematic. Please comment.

Reviewer #1 (Public Review):

In the manuscript Malagon et al. investigate the nano-organization of asynchronous release at glutamatergic synapses. The authors conduct near-TIRF imaging to probe the localization of synchronous and asynchronous release sites at a single synapses using vGlut-pHluorin. Recent work in the field of synaptic neurobiology has focused on investigating how different modes of neurotransmission are organized in the presynaptic bouton, however, discrepancy remains on the sub-synaptic localization of asynchronous release sites and whether these are independent from synchronous release locations. While a variety of techniques including flash-freeze EM and super resolution microscopy have been employed, the use of live imaging by Malagon et al. provides further insight at the single synapse level.

With an impressive resolution of 27nm in live synapses the authors are able delineate synchronous and asynchronous release events within the same active zone. Furthermore, beyond the pure localization of release sites, how the vGlut-pHluorin fluorescent signal decays following fusion provides insight into distinct endocytic mechanisms. The authors delineate two populations of asynchronous events - one located within the active zone center and one ectopically outside this map (as defined by synchronous release sites). Synchronous and asynchronous demonstrate similar kinetics for the ultra-fast component of endocytosis with major differences in the fast component, which is calcium dependent for synchronous release. The authors demonstrate a consistent pattern in the localization and kinetics of release across multiple types of experiments with both EGTA and Sr2+ manipulation.

Inclusion of further analyses on already acquired data would greatly strengthen the paper, such as if single synapses preference one type of release over another. While this paper reconciles differences in the field major questions still remain; what is the mechanism for calcium independent and calcium dependent endocytosis and how does this differ between synchronous and asynchronous release. This paper sets the stage for further work probing what presynaptic machinery drives the segregation of release, what proteins mediate the differences in exocytosis-endocytosis coupling, and how the nano-organization of asynchronous release sites imparts autonomous roles for asynchronous release.

Reviewer #1 (Public Review):

In this manuscript, May et al use H2B overexpression driven by Keratin14 Cre-mediated excision of a loxP-stop cassette to quantify bulk chromatin dynamics in the live epidermis. They observe heterogeneity of H2B distribution within the basal stem cell layer and a change in distribution when the stem cells delaminate into the suprabasal layers. They further show that these chromatin rearrangements precede cell fate commitment, as detected by adding another Cre-mediated transgene on top (tetO-Cre mediated Keratin10 reporter). Finally, they generate an MST stem-loop transgene for the keratin 10 transcript and observe transcriptional bursting.

The manuscript uses elegant in vivo imaging approaches to describe a set of observations that are logically based on a panel of studies that have used genetic approaches to dissect the role of heterochromatin and histone/DNA modifications in epidermal state transitions (Aarenstrup et al., 2008; Driskell et al., 2012; Eckert et al., 2011; Ezhkova et al., 2011; Ezhkova et al., 2009; Fessing et al., 2011; Indra et al., 2005; Kashiwagi et al., 2007; Lien et al., 2011; Luis et al., 2011; Mejetta et al., 2011; Sen et al., 2010). In addition, the MST stem-loop analysis is a nice technical advance, confirming transcriptional bursting as a general phenomenon of how transcription is regulated in cells (see work from Daniel Larsson, Jonathan Chubb, Arjun Raj, and others). The value of the study in my view is recapitulating these known phenomena in a live tissue setting with high-quality imaging and careful quantification. Overall the analyses appear thorough, although the overall changes appear relatively minor, which is perhaps to be expected from imaging bulk H2B distribution as a proxy for chromatin states.

There is one major technical concern that might impact the interpretation of the data. The authors combine Cre lines for their key conclusions (Krt10 reporter and SRF KO) and analyze single cells that thus express very high levels of Cre. Knowing that Cre will target non-loxP sites and is genotoxic, it is possible that the effect of chromatin is due to high levels of Cre expression in single cells rather than specific effects due to cell state transitions. I would encourage the authors to carefully quantify the dose-dependent effects of the Cre protein (independent of the LoxP sites) on chromatin organization. Along these lines, is the phenotype of the SRF KO similar in the presence of two Cre alleles versus just one?

The second issue is the conclusion of "chromatin spinning". Concluding that chromatin is spinning would in my view require that the authors demonstrate that the nuclear envelope is not moving or is moving less than the chromatin. To support this conclusion the authors should do double imaging for example with LINC complex proteins, an ER/outer nuclear membrane marker, or equivalent.

Reviewer #1 (Public Review):

The shift from outcrossing to selfing is one of the most prevalent evolutionary events in flowering plants. The ecological and genetic backgrounds of these transitions have been of major interest for decades, and one of the key questions was the dating of this transition. Timing of pseudogenization of the self-incompatibility (SI) genes has been used as a proxy for this transition because loss-of-function mutations of SI genes are often responsible for the evolution of predominant selfing. However, SI genes are identified only in a limited number of taxa, and in some cases, the evolution of selfing is not necessarily associated with loss of SI. Therefore, an independent time estimate of the evolution of selfing by genome-wide polymorphism data has been considered important in this field.<br /> <br /> This study provides two statistical methods: SMC-based and ABC-based methods. Both methods intend to detect the genome-wide signatures of the outcrossing-to-selfing transition that alters the ratio of population recombination rate and mutation rate. Authors validated these methods by using the simulated data, confirming that both methods can generally infer the timing of the outcrossing-to-selfing transition jointly with population size changes, although its precision depends on several population history settings.  <br /> <br /> This study would be an important contribution to the field of mating system evolution. By applying the proposed methods to many other selfing organisms, we may be able to see a general picture of the timescale of the outcrossing-to-selfing transition combined with population size dynamics. At the same time, this is one of the extensions of the SMC method, which has already been well utilized for various inferences, including population size and recombination rate heterogeneity.  <br /> <br /> I do not find a major weakness in the methodologies of this study, but I have a few comments on their applications to the data of Arabidopsis thaliana. It is important that these estimates largely depend on what input data is used, especially the mutation rate and recombination rate. While the authors claim that their estimate is older than Bechsgaard's estimate (<413 kyrs), these two studies used different mutation rates: the authors used Ossowski's mutation rate, and Bechsgaard used Koch's mutation rate (Koch et al. MBE 2010). To compare these two estimates, it is important to use the same mutation rate. Shimizu & Tsuchimatsu (2015; Ann Rev Eco Evo Syst) in detail discussed this point and showed that Bechsgaard's estimate becomes <1.48 myrs when Ossowski's mutation rate was used (see Figure 4). Then it happens to overlap with the estimate of this study.<br /> <br /> I am also concerned about the genomic regions of Arabidopsis thaliana used for this study. Authors chose specific five regions based on homogeneity of recombination rates and diversity, but how does the estimated change when randomly chosen genomic regions are used? If it is important to choose "preferable" regions according to the homogeneity of recombination rates and diversity, it may be useful to describe how these regions should be chosen for future applications of this method to other organisms.

Reviewer #1 (Public Review):

Landshammer et al. characterized the role of LNCSOX17, a previously not annotated lncRNA, in the regulation of human endoderm differentiation, further reinforcing the importance of lncRNAs in the regulation of human stem cells differentiation and embryonic development. LNCSOX17 is a unique lncRNA as it does not regulate neighboring SOX17 gene within the TAD.

Employing different loss-of-function methods (i.e. CRISPR-Cas9 MECP2, CRISPR-pAS), the authors manage to untangle the complexity of the LNCSOX17 locus, showing that it contains a distal enhancer of SOX17, a transcription factor crucial for the determination of endodermal cell fate, and, on the other hand, it operates as RNA transcript to guarantee endodermal cell differentiation.<br /> Although lncRNA LNCSOX17 does not regulate SOX17 levels and chromatin occupancy, the authors show that its loss leads to the impairment of definitive endodermal differentiation, in line with the downregulation of endoderm-related genes and markers (eg CXCR4). These data fit well with the LNCSOX17 expression profile, which indeed appears to be restricted to early human definitive endoderm. The combination of multiple genomic techniques to manipulate the LNCSOX17 locus, together with the evidence of a clear phenotype upon loss of this lncRNA, constitutes the strength of the paper.

The mechanism of how LNCSOX17 regulates endoderm differentiation is not clear and should be strengthened. The reader had a feeling that the identification of the LNCSOX17 molecular mechanism in definitive endoderm differentiation was not the focus of the work, but at the same time, it was also clear that the authors put a lot of effort to address this biological question by employing several-omics approaches (i.e. RNA pulldown, RNA-seq, CRISPR, HI-C).

Overall the conclusions are supported by the data but some methods used in this manuscript (eg RIP, Pulldown) should be strengthen with alternative tools. The manuscript is easy to read and the figures are nicely represented.

Reviewer #1 (Public Review):

The manuscript by Shi et al reports a crystal structure of partial Rad6 from K lactis in complex with Bre1 RBD domain. The structure provides detailed interactions between these two proteins, which are validated by mutagenesis and functional studies. Overall, this is a well-executed study with information useful for the histone ubiquitin field.

Reviewer #1 (Public Review):

Sun et al. investigated the circuit mechanism of a novel type of synaptic plasticity in the projection from the visual cortex to the auditory cortex (VC-AC), which is thought to play an important role in visuo-auditory associative learning. The key question behind this paper is what is the role of CCK positive projection from the entorhinal cortex in the plasticity of VC-AC projections? They discover that the strength of VC-AC projections does not change when pairing the stimulation of this pathway with the acoustic stimulation of the auditory cortex (AC) unless CCK is applied to the AC or CCK positive projection from the entorhinal cortex to auditory cortex (EC-AC) is optogenetically stimulated. In contrast, optogenetically stimulating VC-AC projections, which express a lower level of CCK than the EC-AC projection, do not induce such synaptic plasticity. Interestingly, the data also indicates that even if the EC-AC pathway is stimulated 500ms ahead of the pairing of stimulating VC-AC pathway and the AC, the VC-AC synaptic strength can still be potentiated, consistent with the long-lasting nature of CCK as a neuropeptide. By performing a fear conditioning assay, the authors demonstrate that the CCK signaling is indeed required for the association of visual and auditory cues.

The proposed mechanism is interesting because it not only helps explain the heterosynaptic plasticity of the visual-auditory projection but also will provide insight into how the entorhinal cortex as an association area contributes to the association of visual and auditory cues. Nevertheless, this study suffers from the lack of a few key experiments, which prevents drawing a conclusion on the contribution of CCK release from the EC-AC projection to the plasticity of the VC→AC projection.

1. One main conclusion from figures 1-3 is that CCK released from the EC-AC projection is required for the plasticity of VC-AC projection in addition to pairing VALS with noise/electrical stimulation. But the data in those figures cannot exclude alternative explanations that CCK alone or the pairing CCK with either VALS or noise are sufficient to make the VC-AC synaptic connection more potent. It concerns the mechanism underlying the effect of CCK: CCK may function simply as a neuromodulator to regulate the excitatory synaptic transmission, but not to promote long term synaptic plasticity.

2. Similar issue exists in Fig. 2H and 3J. Without proper controls, it is impossible to tell whether all three conditions (HFLSEA, VALA, noise/electrical stimulation) are necessary for potentiated AC responses to acoustic/electrical stimulation.

3. Fig. 2E and 3G show that the stimulation of CCK-positive EC-AC projection is required for the plasticity of VC-AC projection. Considering most EC-AC projection neurons co-release glutamate and CCK, however, we cannot tell if CCK or glutamate or both matter to this type of plasticity. Even though the long delay in Fig 5B is consistent with the neuropeptide nature of CCK, direct experimental evidence is needed, since it is where the novelty of the paper is.

4. In Fig. 6, the authors examined the necessity of CCK for the generation of the visuo-auditory association. The experimental approach of injection CCK receptor blocker or CCK-4 is not specific to the EC-AC pathway. There is neither a link between VC-AC plasticity nor this behavioral result. Thus, the explanatory power of this experiment is limited in the context set up by the first 5 figures.

5. In page 16, line 322-326, the authors concluded that to induce the plasticity of VC→AC projection, Delay 1 should be longer than 10 ms and Delay 2 should be longer than 0 ms. This conclusion was not fully supported by the data from Figure 5B-D, because there is no data point between -65 ms and 10 ms for Delay 1 (for example 0 ms), and no negative values for Delay 2.

Reviewer #1 (Public Review):

Wang, Y. et al. investigated the role of TPL2 signaling in acute and chronic neuroinflammatory conditions using small molecule inhibitors and a TPL2 kinase-dead mutant mouse line. They find that TPL2 is upregulated by various brain-resident cells, including microglia, astrocytes, and endothelial cells, during neurodegenerative disease progression and following peripheral LPS injection. They show that upon pharmacological and genetic inhibition during acute LPS stimulation, pro-inflammatory cytokine concentration, microgliosis, and neuronal loss can be reversed. In chronic neuroinflammation, as seen in a tauopathy mouse model, the loss of TPL2 rescues reactive gliosis, immune cell infiltration, neurodegeneration, and cognitive health. Interestingly, TPL2 loss of function was not significantly beneficial in models of nerve injury and stroke. By analyzing their multiple sequencing datasets and those of other research teams, the authors find that TPL2 aids to upregulate transcripts for the DAM signature, immediate early genes, and astrocyte reactivity. These data build together to further emphasize the intricacy and importance of the immune component in neurodegeneration and other neuroinflammatory conditions.

The conclusions of this paper are mostly well supported by their data, but further confirmation of sequencing results and microglia intrinsic mechanisms need to be expanded.

1. In the discussion section, it will be important to highlight that TPL2 could also be directly contributing to tauopathy disease progression through its actions in brain-resident endothelial cells. They spend a lot of time characterizing the effects of TPL2 on in vitro microglial responses and do not adequately discuss the potential that their disease phenotypes in the tauopathy model have more to do with TPL2's ability to regulate BBB permeability or facets of endothelial biology. It will be important to highlight that there are various discrete cellular mechanisms (e.g. functions for TPL2 in microglia, endothelial cells, astrocytes, peripheral immune cells, etc.) that could be underlying the disease readouts seen in their global TPL2 kinase-dead mice. They should discuss this in the context of previous literature demonstrating roles for TPL2 in other non-microglial cell types (e.g. Nanou et al PMID: 34038728).<br /> 2. Hippocampal single-cell RNA sequencing led the authors to report that TLP2KD in the PS19 model of tauopathy reduced the number of T-cell and dendritic cell (DC) infiltrates into the brain. The authors should corroborate this finding with immunohistochemistry or flow cytometry to confirm the presence of changing CD4+, CD8+, and DC populations. Most notably, it is critical for them to enumerate the cell numbers in an effort to validate that there are indeed empirical, and not just proportional, reductions in these cell populations.<br /> 3. The authors concluded from Figure 3 that TPL2 plays a key role in in vivo microglia and astrocyte activation. Adding in an in vitro study, like those done in Figures 1, 2, and S4, that looks at a cell-autonomous role for TPL2 in astrocyte reactivity would strengthen this claim and rule out a microglial-independent pathway of TPL2 inflammation.<br /> 4. Although the TPL2KD mouse line is a valuable tool to impair TPL2's function while retaining its expression, the researchers failed to comment on the potential effects a global mutation in TPL2 could have in their model systems. Peripheral immunological challenges, like their IP injections of LPS, could behave differently and affect the nervous system in a microglia-independent pathway if monocyte/macrophage signaling is also impaired.<br /> 5. Oligodendrocytes and OPCs have comparable numbers of DEGs to astrocytes (Figure S11a). What is changing within their transcriptional profile?

Reviewer #1 (Public Review):

This study aimed at identifying genes that contribute to the neurological manifestations underlying Rett syndrome and MECP2 duplication syndrome, caused respectively by loss- and gain-of-function of the MECP2 gene. By interrogating murine and human transcriptomics datasets, the authors identified the growth differentiation factor 11 (Gdf11) as a gene whose expression is positively correlated with Mecp2. Through CUT&RUN approaches, the authors also provide initial evidence that Mecp2 regulates Gdf11 expression through epigenetic mechanisms.

By crossing Mecp2 duplication mice (MECP2-TG1) with mice with monoallelic loss of Gdf11 (Gdf11tm2b/+), the authors succeeded to ameliorate part of the behavioral phenotypes of the MECP2-TG1 mice. The authors also provided compelling evidence that Gdf11 haploinsufficiency is deleterious per se, in keeping with the neurological manifestations documented in individuals with GDF11 loss-of-function variants. The authors also tried to tie the behavioral deficits resulting from Gdf11 haploinsufficiency to deficits in adult hippocampal neurogenesis but observed no differences in neural progenitor pools in the dentate gyrus of Gdf11tm2b/+ mice compared to controls.

Strengths

• The identification of Gdf11 as a downstream Mecp2 target derives from an unbiased approach combining multiple transcriptomic datasets. The authors started with the analyses of a dataset from a recent study rectifying Mecp2 expression with antisense oligonucleotide, and then extended to another 20 datasets from human postmortem studies or mouse models.<br /> • The correlation between Gdf11 and Mecp2 expression has been validated with rigorous mouse genetics approaches, using both Mecp2 null and Mecp2 duplication models.<br /> • The behavioral batteries used to characterize the neurological phenotypes of the Gdf11tm2b/+ and MECP2-TG1;Gdf11tm2b/+ lines are comprehensive and robust.<br /> • Sex is properly accounted for, as the tests have been conducted on both males and females and the data for animals of each sex are displayed.<br /> • The study advances the field in that it identified a potential disease modifier of MECP2-related disorders. Given that rectifying Gdf11 expression alleviates part of the behavioral anomalies in the Mecp2 duplication mouse, this study has implications for therapeutic developments in MECP2-related disorders, especially MECP2 duplication syndrome.<br /> • Beyond the repercussion for understanding the mechanisms of MECP2-related disorders, the study also provides face validity for the Gdf11tm2b/+ mouse as a model for GDF11 heterozygous loss-of-function variants associated with neurological phenotypes.

Weaknesses

• Gdf11 is critical for skeletal development, and this important information is not considered as a potential confounder or discussed in the manuscript. McPherron et al (1999) have shown that Gdf11-/- mice show skeletal abnormalities, in line with the skeletal phenotypes detected in individuals with monoallelic loss of GDF11. The observation of a truncated tail in Gdf11tm2b/tm2b neonates (Figure S3C) suggests that a skeletal phenotype might be also present in the Gdf11tm2b line. McPherron et al (1999) have also reported milder skeletal anomalies in Gdf11+/- mice, for example the presence of an additional thoracic segment with an associated pair of ribs. This information is missing in the manuscript. The authors did not investigate potential skeletal phenotypes in Gdf11tm2b/+ mice and how they might contribute to some of the behavioral outcomes, for example reduced latency to fall in rotarod.<br /> • One caveat not discussed in the frame of beneficial effects of Gdf11 reduction in MECP2-TG1 mice is the impact of Gdf11 loss on survival. The authors have shown that Gdf11tm2b/+ have reduced survival, and 30% MECP2-TG1 mice have shown to die between 20 weeks and 1 year of age (Collins et al., Human Molecular Genetics, 2004). Whether MECP2-TG1;Gdf11tm2b/+ mice have a further decrease in longevity compared to MECP2-TG1 mice has not been investigated or discussed. This is important to correctly interpret the health status of the MECP2-TG1;Gdf11tm2b/+ mice undergoing behavioral testing at 12 weeks of age (and the resulting behavioral outcomes). It also has ramifications related to therapeutic development.<br /> • The manuscript is missing a discussion about the potential cell-specific effects of the Mecp2-mediated regulation of Gdf11. Figure 1B shows that Mecp2 and Gdf11 expression is correlated in all datasets but in inhibitory neurons isolated from postmortem brains of individuals with Rett syndrome. Given the evidence of MECP2-related pathology in both excitatory and inhibitory neurons, this is an important area that remains unaddressed.<br /> • More caution should be taken when interpreting mouse behavior in relationship to complex human behavioral traits. Expressions like "anxious mice" should be avoided.<br /> • In open field test, MECP2-TG1 show no differences in distance in the center of the arena over the total distance traveled (Collins et al., Human Molecular Genetics, 2004). MECP2-TG1 mice in this study display reduced number of entries in the center of the arena, and this anomaly is rescued in MECP2-TG1;Gdf11tm2b/+ mice. The relationship between the two measures and how they relate to thigmotaxis is not explained.<br /> • The fear conditioning data should be interpreted with greater caution. First, during learning training, the percentage of time spent freezing in the second post-tone phase is expected to be higher compared to the time of administration of second tone or the first post-tone phase, unlike what observed in Figures S2B and S3I. Second, both MECP2-TG1 and Gdf11tm2b/+ mice have changes in freezing behavior during the learning phases (Figure S2B, S3I), which affect interpretation of changes in contextual and cue-dependent testing. This integration of data interpretation across the learning and testing phases is missing. Third, the cumulative plots showing the percentage of time spent freezing in testing phases (Figure 2C, 3E, S2B) are not informative with respect with the temporal dynamic of the behavior (over 5 min for the contextual testing and 6 minutes for the cued testing). Fourth, the general hypoactivity of MECP2-TG1 and general hyperactivity in Gdf11tm2b/+ are not considered as potential confounders of the freezing behaviors observed in the fear conditioning paradigms.<br /> • The statistical considerations are missing information on how data normality was assessed and outliers investigated and treated.

Reviewer #1 (Public Review):

Malaria parasites contain a relict plastid organelle, called apicoplast, which harbors essential metabolic pathways such as iron-sulfur cluster and isoprenoid precursor biosynthetic pathways. In this paper, the authors investigated the apicoplast iron-sulfur (FeS) pathway in P. falciparum. Using an elegant chemical bypass genetic method, they deleted four nuclear genes encoding apicoplast FeS pathway proteins involved in sulfur acquisition or FeS cluster assembly (SufS, SufE, SufC and SufD), and demonstrated that all four are essential for parasite survival. Interestingly, an additional phenotype characterized by disruption of the apicoplast was observed with sufS (but not other mutants). The authors hypothesized that the loss of the apicoplast in the absence of SufS could be due to an additional function of SufS in tRNA thiolation, a pathway that relies on sulfur transfer. Based on sequence homology they identified a putative apicoplast tRNA thiolation enzyme, PfMnmA, and confirmed by genetic tagging that PfMnmA localizes to the parasite apicoplast. Using the chemical bypass system, they further show through knockdown or knockout strategies that PfMnmA is required for parasite survival and for apicoplast maintenance, similar to SufS.

The authors then used a series of genetic complementation with bacterial enzymes, and show that SufS and MnmA can be replaced by two enzymes from Bacillus subtilis, the cysteine desulfurase BsYrvO and the tRNA thio-uridylase BsMnmA, respectively. In B. subtilis, YrvO mediates the direct transfer of sulfur to MnmA, which mediates tRNA thiolation. Based on the genetic complementation results, the authors infer that SufS has a dual function in P. falciparum, in FeS biosynthesis (together with other Suf proteins), and in apicoplast maintenance via tRNA thiolation. The work is very well performed and the manuscript is well written. The evidence for a dual role of SufS is compelling. However, the claimed role of PfSufS/PfMnmA in tRNA modification is not directly addressed, which would make this exciting story more complete. The identification of new essential metabolic pathways is of great interest as the apicoplast is a potential target for antimalarial therapies.

Reviewer #1 (Public Review):

This manuscript demonstrates that the activation of several oncogenic pathways including WNT, PI3K, and PKA in mesenchymal stem cells (MSC) paradoxically induces the expression and secretion of osteosarcoma-suppressing proteins in MSC conditional mediums. The authors provide the in vivo evidence showing that the PKA-induced MSC conditional medium as well as the recombinant calreticulin and procollagen C-endopeptidase enhancer (PCOLCE), the expression of which increases in PKA-induced MSC conditional medium, inhibit osteosarcoma tumor growth and tumor-associated bone destruction in an osteosarcoma xenograft mouse model. The in vitro mechanistic studies further unveil that PKA-induced MSC conditional medium, calreticulin, and PCOLCE suppress cell proliferation, survival, and migration of human osteosarcoma cell lines. These inhibitory effects are additive with the canonical Cisplatin chemotherapy in vivo and in vitro. The actions of calreticulin and PCOLCE on osteosarcoma cells are mediated by their interactions with CD47 and APP (amyloid precursor protein), respectively. The strengths of this report are that (a) the data presented are of high quality and convincing. (b) The results largely support the conclusions of this study. (c) The findings are novel and have translational potential to develop more efficient targeted therapies for the treatment of this most malignant primary bone cancer in conjunction with canonical chemotherapies. The weaknesses include (a) the lack of in vivo evidence that the PKA-stimulated MSC conditional medium and calreticulin inhibit osteosarcoma tumor cell proliferation and survival in vivo and (b) the potential effects of these two treatments on osteoblast differentiation and bone formation which may contribute to the higher trabecular and cortical bone mass observed in treated mice have not been examined.

Reviewer #1 (Public Review):

Smela and colleagues used in silico predictions as well as reports from the literature to identify candidate transcription factors that were likely to promote granulosa-like differentiation of hiPSCs. After careful evaluation and validation using granulosa marker expression and estradiol production as read-outs, the authors identify combinations of NR5A1 with RUNX1 or RUNX2 that are necessary and sufficient to derive granulosa-like cells from hiPSCs. This section of the study is well-controlled and carefully explained, and the authors' conclusions are supported by the data. The authors then use their granulosa-like cells in concert with previously developed human primordial germ cell-like cells (hPGCLCs) to generate human ovaroids. They show that while their TF-induced granulosa-like cells initially and rapidly support the maturation of hPGCLCs into DAZL+ gonadal germ cells, DAXL+ cells are eventually lost to cell death or off-target differentiation. The authors candidly report the need for troubleshooting this aspect of the study, but this is an encouraging and important first step toward a fully human TF-induced organoid model of human ovary development. I am slightly less convinced by the data presented in the ovaroid section of the manuscript, as the immunostaining and gene expression data do not seem to fully align with in vivo conditions, and the authors do not address this discrepancy to my satisfaction in the current version of the text.

Weaknesses: The manuscript would benefit from a diagram illustrating the experimental approach from the selection of transcription factors to the generation of granulosa-like cells to the assembly of ovaroids. This would increase the accessibility of the data to an audience unfamiliar with iPSC and organoid strategies. In its current form, the data presented does not convince me that follicle-like structures form in the human ovaroid model.

Strengths: The authors address a critical gap in resources by providing a model for human hiPSC-derived granulosa-like cells. This resource will undoubtedly advance our molecular understanding of human ovary development and allow critical functional studies on the establishment and preservation of human female fertility. The manuscript is very didactic and easy to follow. The conclusions are well supported by the data, and the discussion candidly raises caveats and further directions of the work.

Reviewer #1 (Public Review):

In this manuscript, Cover et al. examine the role of thalamic neurons of the rostral intralaminar nuclei (rILN) that project to the dorsal striatum (DS) in mice performing a reinforced action sequence task. Using patch-clamp electrophysiology, they find that neurons from the three rILN (CM, PC, and CL) have similar electrophysiological properties. Using fiber photometry recordings of calcium activity from rILN neurons that project to DS, they show that these neurons increase in activity at the first lever press and reward acquisition in mice performing a lever pressing operant task. They additionally demonstrate that this action initiation and reward-related activity exists more generally in mice performing other movements or rewarded tasks. Building on their lab's previous work, the authors further find that by optogenetically activating or inhibiting these rILN-DS neurons, mice will increase or decrease task performance, respectively. Lastly, the authors show that a variety of cortical and subcortical areas have input to rILN-DS neurons suggesting that these neurons might act as an integrator of signals from such areas during task performance.

• The authors beautifully show that the electrophysiological properties of CM, PC, and CL neurons are similar and go on to treat the rILN as one homogenous nucleus for functional fiber photometry recordings and optogenetic stimulations. It seems that these recordings and stimulations were only performed in CL, as indicated in the images (Fig. 2A, 4A). Is this the case, or were CM, PC, and CL neurons sampled? It would be helpful to clarify if DS projecting neurons from all rILN nuclei show the reported action initiation and reward acquisition activity or only CL neurons.

• Along similar lines, to what extent of rILN was targeted for optogenetic activation and inhibition? It seems that the authors implanted a total of 4 optic fibers, two on each side (please clarify in methods). What was the reasoning behind this? Please show that only rILN and not PF was activated/inhibited.

• While AAV1 is becoming a popular tool for transsynaptic labeling, performing confirmatory patch-clamp recordings with optogenetic activation of inputs, would provide better evidence for the synaptic connection between upstream regions, such as ACC and OFC, and rILN neurons.

• In addition, the transsynaptic tracing experiments would benefit from showing the cell count quantifications in CM, PC, and CL. It seems that the authors have already performed this quantification for constructing their diagrams on the right. To make any point about the relative strength of afferent innervation to rILN-DS neurons showing such quantification would be necessary.

• Why is the injection site for the retrograde cre-dependent tdTomato AAV (Fig. 5 middle left panels) showing expression? Is the cre coming through transsynaptic AAV1 from direct projections of each AAV1 injection site (AAV1 is not supposed to spread across a second synapse)? The diagrams suggest that not all regions (e.g. SUM or SC) have direct projections to DS.

Reviewer #1 (Public Review):

Like other sensory organs, the inner ear has a rich population of pericytes, essential for sensory hair cell heath and normal hearing. In this study, using an inducible and conditional pericyte depletion mouse (PdgfrbCreERT2/iDTR) model, the authors demonstrate that the pericytes play critical roles in maintaining vascular volume and integrity of spiral ganglion neurons (SGNs) in the cochlea. Moreover, using the co-culture models, they show vigorous vascular and neuronal growth in neonatal SGN explants in the presence of exogenous pericytes. Mechanistically, this study demonstrates that these roles are achieved mainly through the interactions between pericyte-released exosomes containing VEGF-A and VEGFR2-expressing the vessels and SGNs.

Overall, the data are analyzed thoroughly, and the conclusions are novel and convincing. It is mechanistically solid. The study is somewhat translationally limited. Nevertheless, understanding the roles of organ-specific pericytes is paramount, making this study timely and significant.

Reviewer #1 (Public Review):

This paper describes the accrual of RSV mutations in a severely immunocompromised child with persistent infection and demonstrates that ribavirin increases the observed mutation rate with base pair changes (C to U and G to A) compatible with its known mechanism. The paper utilizes a mathematical model to explain the counterintuitive finding that viral load does not decrease despite loss of viral fitness and clinical improvement. Positive selection is observed but does not keep pace with deleterious mutations induced by ribavirin. Overall, though the data is restricted and limited to a single person, the analysis is rigorous and supports the paper's interesting conclusions.

The paper is fascinating, but its generalizability is somewhat limited by the single study participant. Nevertheless, comparisons of therapy-induced deleterious mutations versus adaptive mutations over time is potentially important for multiple viruses.

Reviewer #1 (Public Review):

This paper reports an analysis of the inhibition of the serotonin transporter, SERT, by a novel compound, ECSI#6. The authors perform a comprehensive analysis of SERT transport inhibition for the new agent and compare its properties to those of other well-characterized agents: cocaine and noribogaine, with the data pointing to an unusual noncompetitive mechanism of inhibition, a model also supported by electrophysiological recordings of transport currents. Based on the results of these experiments the authors conclude that ESCI#6 binds essentially exclusively to the inward-facing state of the transporter. The authors further present experiments suggesting that ESCI#6 can stabilize the folded form of an ER-arrested SERT mutant and recover its trafficking to the plasma membrane, with some in-vivo drosophila experiments perhaps also supporting this conclusion. Finally, kinetic simulations using a transport model with rate constants from previous experiments support the basic conclusions of the first sections of the paper.

Strengths:<br /> The transport experiments and simulations here are thorough, carefully performed, and reasonably interpreted. The authors' arguments for noncompetitive inhibition seem well-thought-out and reasonable, as is the conclusion that ESCI#6 binds to the inward-facing state of the transporter. The simulations are also thorough and support the conclusions. In the discussion, the comparison of enzyme noncompetitive inhibition to the process studied here was thoughtful and interesting. Also, the care and analysis of the uptake data are a strength of the paper, with well-presented evidence of reproducibility and statistics. The electrophysiology data is more limited but does communicate the essential conclusion.

Weaknesses:<br /> The most important concern about the work is the interpretation of the in-vivo drosophila data. Though the SERT fluorescence with WT protein is strong, I cannot see any fluorescence in either drug-treated image from the PG mutant. In this context, shouldn't there be additional intracellular staining for ER-resident SERT? If the cell bodies of these cells are elsewhere this should be clearly pointed out.

Similarly, the single Western blot demonstrating enhanced glycosylation in the presence of Noribogaine or ECSI#6 could be strengthened. I can see the increased band at a high molecular weight that the authors attribute to the fully glycosylated form, but this smear, and the band below, look quite different from those in the blot shown in the El-Kasaby et al reference, raising concerns that the band could be aggregated or dimerized protein rather than a glycosylated form. This concern could easily be addressed by control experiments with appropriate glycosidases, as shown in the reference.

The overall interest in the work is reduced given the quite low affinity of ECSI#6 for the transporter.

Reviewer #1 (Public Review):

Protein oligomerization is essential to their in vivo function, and it is generally challenging to determine the distribution of oligomeric states and the corresponding conformational ensembles. By combining coarse-grained molecular dynamics simulations and experimental small-angle X-ray scattering profiles at different protein concentrations, the authors have established a robust approach to self-consistently determine the oligomeric state(s) and the conformational ensemble. The approach has been applied specifically to the speckle-type POZ protein (SPOP) and generated new insights into the conformational ensemble and structural features that determine the ensemble. The model was further tested by the analysis of several relevant mutants as well as models with different types of structural restraints. The results also support the isodesmic self-association model, with KD values comparable to those measured from independent experiments in the literature. The approach is potentially applicable to a broad set of systems.

Reviewer #1 (Public Review):

Hyphal fusion is a common process in filamentous fungi that requires a tightly regulated, oscillatory cell-to-cell dialogue between the two fusion partners. While several signaling components functioning in this process have previously been identified, the actual signal(s) exchanged during the molecular dialogue between two genetically identical cells have remained a mistery. In this study, the authors show that even when growing in the absence of a fusion partner, hyphae of a nematode pathogenic fungus already undergo signal oscillations that are in phase with their growth oscillations. After detecting the presence of a fusion partner, a slowdown of the oscillation frequencies occurs (entrainment), followed by a transition to an anti-phasic synchronization of the oscillations between the two partners. Based on a mathematical model the authors postulate a mechanism involving the oscillatory secretion/uptake of a signaling compound from a shared extracellular space. To experimentally validate the model, they visualize anti-phasic oscillations of intracellular Ca2+ concentrations in two approaching hyphae and find that they are anti-phasic with the recruitment of chitin synthase B. Moreover, addition of a calcium-chelating agent to the medium abolishes molecular oscillations and anti-phasic synchronization in the two hyphae. Based on these results, the authors conclude that extracellular Ca2+ is essential for the signaling mechanism during the cell-to-cell dialogue.

This is a very solid and well-performed microscopical study that provides new insights into the signaling mechanisms during hyphal fusion. Novel findings include: 1) the occurrence of signal oscillations at the tip of individual growing hyphae (monologue) that are in phase with the growth oscillations; 2) the presence of an entrainment phase involving a slowdown of the oscillation frequency upon detection of a potential fusion partner (entrainment) followed by a transition to an anti-phasic synchronization; 3) the detection of anti-phasic intracellular calcium oscillations during the molcular dialogue; 4) the establishment of a model predicting the secretion/uptake of a signaling compound (possibly calcium).

In general, the results are clearly presented and most of the conclusions are well justified by the data. I had some problems in interpreting the model based on the accompanying text, likely because of a confusion between the two different concepts of signaling component and signaling compound. Furthermore, the fluctuations of the fluorescent calcium probe R-GECO in Fig. 3d are difficult to detect for the untrained eye. Finally, the conclusion that intracellular Ca2+ oscillations are caused by uptake of extracellular Ca2+ is not fully supported by the data. These points can all be addressed by minor changes in the text and Figures.

Reviewer #1 (Public Review):

This paper provides the first comprehensive analysis since the doubling of the NIH budget, on how the institute is able to keep up with inflationary pressures and fully support investigators. Through a series of descriptive graphs and regression analyses as well as modeling and transformations, the authors demonstrate the relative similarities between inflation trends and NIH support over time. Interestingly larger, more solicited projects, including greater number of clinical studies, are now driving a greater proportion of the costs for NIH. The modeling is relevant but the limitations need to be recognized and these include: the issue of personnel costs, not well captured by their approach, and productivity; i.e. is the increase in spending matched by an increase in traditional metrics (manuscripts, other grants, policy change, etc. Nevertheless, the bottom line, of interest to funders, investigators, and institutions, is that NIH has been able to maintain support at a level commensurate with inflation.

Reviewer #1 (Public Review):

In mammals, limb-innervating motor neurons are found at brachial and lumbar levels of the spinal cord. While it has been known for a long time that a combination of transcription factors (e.g., Hox, FoxP1) is necessary for the development of these motor neurons, it remains unclear whether similar or distinct transcriptional programs operate in brachial and lumbar motor neurons. This study advances our understanding of how motor pools are specified in the lumbar region. The authors found, in hindlimb-innervation motor neurons, that the LIM homeodomain transcription factor Isl2 is selectively required for motor pool organization, neuromuscular connectivity, and hindlimb locomotion.

Major conclusions include:

1. Settling position of motor neurons is impaired in Isl2 mutant mice; MMC neurons at all levels and LMC neurons at the lumbar level.<br /> 2. Isl2 controls Pea3 expression in lumbar motor pools.<br /> 3. A transcriptomic analysis uncovered multiple Isl2 downstream target genes.<br /> 4. The connectivity and function of hindlimb motor pools are disrupted in Isl2 mutant mice.

The conclusions are supported by experimental evidence.

Strengths:

The study fills an important knowledge gap by uncovering a developmental role for the LIM homeodomain transcription factor Isl2 in hindlimb motor pools.

The authors employ an impressive array of genetic, molecular, behavioral, and electrophysiological methods to comprehensively characterize the function of Isl2 in spinal motor neurons.

Weaknesses:

Most experiments have been conducted in Isl2 global KO mice, raising the issue of cell autonomy. However, the key conclusion of Isl2 controlling Pea3 expression has been independently confirmed in animals lacking Isl2 activity selectively in motor neurons (Olig2Cre line).

The mechanistic details downstream of Isl2 remain elusive.

Reviewer #1 (Public Review):

Obesity is a risk factor for OA development and progression and its molecular mechanisms remain unknown. In this study, the authors demonstrated that obese OA patients and ApoE KO mice showed a pronounced synovitis and enhanced macrophage infiltration in synovial tissues. In addition, obese OA mice had severe cartilage degradation and increased apoptotic cells in synovial tissues than OA mice without obesity. GAS6 is a secreted glycoprotein and during M1 macrophage polarization, GAS6 secretion is decreased, leading to impaired macrophage efferocytosis in synovial apoptotic cells. Intra-articular injection of GAS6 restored the phagocytic capacity of macrophages and decreased the levels of TUNEL-positive cells, preserving cartilage thickness and preventing OA progression in obese OA mice. Overall speaking, this study is well-designed and carefully executed. The data presented are supportive of the conclusion that the authors made.

Reviewer #1 (Public Review):

In the article "MHC class I and MHC class II reporter mice enable analysis of immune oligodendroglia in mouse models of multiple sclerosis", Em P Harrington and colleagues describe two new mouse reporter models, that allow tracing cell lineages that activate the expression of CD74 and B2m genes, involved in MCHI and MHCII pathways, respectively. The authors then use these models to confirm the emergence of oligodendroglia with immune properties in the context of the EAE mouse model of MS. These mice models will be an excellent tool for the scientific community to investigate the contribution of MHCI and MHCII populations to the development of neuroimmunological disorders.

Reviewer #1 (Public Review):

The authors succeeded in fitting their Jansen-Rit model parameters to accurately reproduce individual TEPs. This is a major success already and the first study of this kind to the best of my knowledge. Then the authors make use of this fitted model to introduce virtual lesions in specific time windows after stimulation to analyze which of the response waveforms are local and which come from recurrent circles inside the network. The methodological steps are nicely explained. The authors use a novel parameter fitting method that proves very successful. They use completely openly available data sets and publish their code in a manner that makes reproduction easy. I really enjoyed reading this paper and suspect its methodology to set a new landmark in the field of brain stimulation simulation. The conclusions of the authors are well supported by their results, however, some analysis steps should be clarified, which are specified in the essential revisions.

Reviewer #1 (Public Review):

Ciliary length control is a basic question in cell biology and is fascinating. Regulation of IFT via calcium is a simple model that can explain length control. In this model, ciliary elongation associates with an increase in intraciliary calcium level that leads to calcium increase at the ciliary base. Calcium increase acts to reduce IFT injection and thus ciliary assembly rate. The longer the cilia, the more increase of calcium level and the more reduction of IFT injection and thus the ciliary assembly rate. When the cilia approach the genetic defined length, the gradual reducing assembly rate eventually balances the constitutive disassembly activity. Cilia then stop elongation and a final length is achieved. This work tested this model by manipulating the calcium level in cilia by using an ion channel mutant and treatment of the cells with EGTA. In addition, IFT injection was measured before and after calcium ciliary influx. Based on the outcome of these and other experiments, it was concluded that there is no correlation between changes in calcium level and IFT injection, thus challenging the previous model. This work is well written and the experiments appear to be properly executed. It nicely showed an increase of intraciliary calcium during cilia elongation, and beautifully showed that ciliary calcium influx depends on extracellular calcium. However, I felt the current data are inadequate to support the author's conclusion.

The authors showed that ciliary calcium increases along with ciliary elongation, which correlates with reduction of IFT injection. Thus, this result would support that calcium increase reduces IFT injection. To test whether reducing calcium influx would alter the IFT injection, the authors used an ion channel mutant cav2. Indeed, ciliary calcium level in the mutant cilia appears to be lower compared to the control in average. After measuring ciliary calcium level and IFT injection during ciliary elongation with mathematical analysis, it was concluded that reducing ciliary calcium level did not lead to increased IFT injection, which is distinct from the control cells. Thus, the authors concluded that calcium does not act as a negative regulator of IFT injection. However, if one examines the calcium flux in Figure 3B and IFT injection in Figure 4B of cilia less than 6 micron, one may draw a different conclusion. For the mutant cilia, the calcium influx is higher than that in control cilia and IFT injection is reduced compared to the control. Thus, this analysis is the opposite of the authors' conclusion, and is supporting the previous model. There is a rapid change in ciliary assembly rate at the early stages of ciliary assembly (see Figure 1C), thus, the changes in calcium influx and IFT injection in the earlier assembly stage would be more appropriate to assess the relationship between intraciliary calcium level and IFT injection.

The authors used EGTA treatment to support their conclusion. However, EGTA treatment may induce a global calcium change of the cell, the outcome may not reflect actual regulation of IFT injection by ciliary calcium influx. For example, as reported elsewhere, the change of cAMP level in the cell body and cilia has a different impact on ciliary length and hedgehog regulation. The slower assembly of cilia in EGTA treated cells may be caused by many other factors instead of sole regulation by IFT.

The authors only examined the impact of reducing ciliary calcium influx. To further support the authors' conclusion, it is recommended that the authors should examine IFT injection in a condition where ciliary calcium level is increased. Using calcium ionophore may not be a good choice as it may change the global calcium level. One approach to consider is using mutants of a calcium pump present in cilia.

The conclusion on line 272-273 may need more evidence. The authors showed that addition of 1 mM CaCl2 does not change ciliary assembly, and used this as one of the evidences to argue against the ion-current model. The addition of calcium extracellularly may not alter intracellular/intraciliary calcium level given that cells have robust systems to control calcium homeostasis. To support the authors' conclusion, one should measure the changes of calcium level in the cell/cilia or revise their conclusion.

The authors showed nicely the changes in IFT properties before, during and after ciliary calcium influx and found that the intensity and frequency of IFT do not have a correlation with calcium influx though calcium influx restarts paused IFT trains for retrograde transport as previously reported (Collingride 2013). The authors again concluded that this is supporting their conclusions in that there is no correlation between IFT injection and calcium influx. However, I am not sure whether the short pulses of calcium influx at one time point would change the calcium level in the whole cilia in a significant way that would alter IFT injection at the ciliary base.

Reviewer #1 (Public Review)

This paper focuses on the hydrodynamic interactions between in-line swimming fish by observing how real fish swim behind a robotic mechanism (a rigid NACA airfoil). After ensuring that the airfoil can generate a real-fish-like wake (reverse Von Karman Vortices), the authors found, compared to swimming alone, real fish swimming behind the airfoil will reduce tail moving frequency, synchronize tail movement with the airfoil, and experience lower pressure around the anterior of the fish. The results indicate fish do save energy and improve efficiency by swimming directly behind the thrust type of vortices. The experimental design is good and the collected data generally support the conclusions drawn. The article could, however, be improved by providing more quantitative comparisons in addition to the qualitative visualizations.

Reviewer #1 (Public Review):

Agip et al. have resolved the first cryoEM structure of the mitochondrial Complex I from Drosophila melanogaster, an important model organism in biology. The structure revealed a 43-subunit enzyme complex that closely resembles the mammalian Complex I. The authors resolved Complex I in three different conformational states at 3.3-4.0 Å global resolution, with an overall resemblance to the active form of the mammalian Complex I, but also with some characteristic conformational changes near the quinone substrate pocket and surrounding subunits that resemble, at least in part, the deactive form of the mammalian enzyme. The third resolved class was considered 'damaged/broken', and a possible artifact arising from the sample preparation. Biochemical assays showed that the Drosophila Complex I does not undergo an active/deactive transition (as characterized by the N-ethylmaleimide sensitivity), although the structures revealed an exposed ND3 loop that has been linked to transition. The authors could also show that conformational change between an alpha and pi form of transmembrane helix (TM3-ND6) is likely to be involved in catalysis, and distinct from the deactivation mechanism of the mammalian isoform. Due to the 3.3 Å global resolution, water molecules could not be experimentally resolved, and how the observed conformational changes link to the proton pumping activity therefore remains an open question and basis for future studies. Overall I find that this work provides an important basis for understanding mechanistic principles of the mitochondrial Complex I and more specifically a starting point for detailed genetic studies on the fruit fly Complex I.

Reviewer #1 (Public Review):

Neuronal tissues are very complex and are composed of a large number of neuronal types. With the advent of single-cell sequencing, many researchers have used this technology to generate atlases of neuronal structures that would describe in detail the transcriptome profiles of the different cell types. Along these lines, in this manuscript, the authors present single-cell transcriptomic data of the fruitless-expressing neurons in the Drosophila male and female central nervous systems. The authors initially compare cell cluster composition between male and female flies. They then use the expression of known markers (such as Hox genes and KC neuronal markers) to annotate several of their clusters. Then, they look in detail at the expression of different terminal neuronal genes in their transcriptomic data: first, they look into neurotransmitter-related genes and how they are expressed in the fruitless-expressing neurons; they describe in detail these populations that they then verify the expression patterns by looking into genetic intersections of Fru with different neurotransmitter-related genes. Then, they look at Fru-neurons that express circadian clock genes, different neuropeptides and neuropeptide receptors, and different subunits of acetylcholine receptors. Finally, they look into genes that are differentially expressed between male and female neurons that belong to the same clusters. They find a large number of genes; through GO term enrichment analysis, they conclude that many IgSF proteins are differentially expressed, so they look into their expression in Fru-neurons in more detail. Finally, they compare transcription factor expression between male and female neurons of the same cluster and they identify 69 TFs with cluster-specific sex-differential expression.

In general, the authors achieved their goal of generating and presenting a large and very useful dataset that will definitely open a large number of research avenues and has already raised a number of interesting hypotheses. The data seem to be of good quality and the authors present a different aspect of their atlas.

The main drawback is that many of the analyses are very superficial, resulting in the manuscript being handwavy and unsupported. The manuscript would benefit by reducing the number of "analyses" to the ones that are also in vivo validated and by discussing some of the drawbacks that are inherent to their experimental procedure.<br /> 1) The authors treat their male, female, and full datasets as three different samples. At the end of the day, these are, for the most part, equivalent neuronal types. The authors should decide to a) either only use the full dataset and present all analyses in this, or b) give a clear correspondence of male and female clusters onto the full ones.<br /> 2) Most of their sections are heavily reliant on marker genes. In fact, in almost every section they mention how many of their genes of interest are marker genes. This depends heavily on specific cutoffs, making the conclusions fragile. Similarly, GO terms are used selectively and are, in many cases, vague (such as "signaling", "neurogenesis", "translation").<br /> 3) A few of the results are not confirmed in vivo. The authors should add a Discussion section where they discuss the inherent issues of their analyses. Are there clusters of low quality? Are there many doublets?<br /> On the same note, their clusters are obviously non-homogeneous (i.e. they house more than one cell types. This could obviously affect the authors' cluster-specific sex-differential expression, as differences could also be attributed to the differential composition of the male and female subclusters.<br /> 4) Immunostainings are often unannotated and, in some cases especially in the Supplement, they are blurry. The authors should annotate their images and provide better images whenever possible.<br /> 5) I believe that the manuscript would benefit significantly by being heavily reduced in size and being focused on in vivo rigorously confirmed observations.

Reviewer #1 (Public Review):

The author has generated a specific version of alpha-fold deep neural network-based protein folding prediction programme for TCR-pMHC docking. The alpha-fold multimer programme doesn't perform well for TCR-pMHC docking as the TCR uses random amino acids in the CDRs and the docking geometry is flexible. A version of the alpha-fold was developed that provides templates for TCR alpha-beta pairing and docking with class I pMHC. This enables structural predictions that can be used to rank TCR for docking with a set of peptides to identify the best peptide based on the quality of the structural prediction - with the best binders having the smallest residuals. This approach provides a step toward more general prediction and may immediately solve a class of practical problems in which one wants to determine what pMHC a given TCR recognizes from a limited set of possible peptides.

Reviewer #1 (Public Review):

The ABC transporter ABCG2 exports xenobiotics, including chemotherapy reagents, from a number of different organs. Understanding the mechanism of ATP-dependent transport is of fundamental importance, yet current models have been larger derived from structures and protein dynamics have been carried out in artificial environments. Here the authors have used a fluorescent-labeled antibody specific to the inward-facing conformation and monitored this state in a cell by confocal microscopy and fluorescence-correlation spectroscopy (FSC). They conclude that ATP binding drives substrate efflux and the resetting to an inward-facing conformation requires ATP hydrolysis and the subsequent dissociation of the hydrolysis products. Both the mechanistic insights and methodology employed will be of interest to the biochemistry and transport biology fields.

Strengths: The paper exploits a fluorescent labelled antibody to probe some interesting mechanistic questions in a close-to-native environment. The use of different inhibitors and nucleotides to trap different states is beautifully done and the mechanistic interpretation is convincing. The use of FSC to probe the differences in transporter dynamics in the presence of substrates seems novel and is likely to be of general interest.

Weaknesses. The main weakness is that the probe is only able to detect a signal for the inward state and so a change in conformational state has to be derived from a diminished response, I.e., no probe to specifically monitor the outward-facing state.

Reviewer #1 (Public Review):

Hafez and collaborators describe the construction and analysis of a computational model of a mushroom body neuron. The anatomy derives from a combination of electron microscopy reconstructions of MBON-α3 and also from light microscopy. The physiological parameters derive from publications that measured them, in addition to the author's own electrophysiological recordings with patch-clamp.

There are two main findings. First, the dendritic arbor of MBON-α3 is electrotonically compact, meaning, individual connections from Kenyon cells will similarly elicit action potentials independently as to where, spatially, the synapses lay on the arbor. Second, in simulation, exploration of changes in the strength of Kenyon cell inputs illustrate two possible ways to alter the strength of the KC-MBON physiological connection, showing that either could account for the observed synaptic depression in the establishment of associative memories. The properties of each approach differ.

Overall, the manuscript clearly describes the journey from connectomics and electrophysiology to computational modeling and exploration of the physiological properties of a circuit in simulation.

Reviewer #1 (Public Review):

The authors present a retrospective study of COVID-19 mortality within 30 days from a positive SARS-CoV-2 PCR in 1115 patients with cancer and 2851 patients without cancer. Patients were recruited from 16 different centres from 8 countries across 5 continents. Patients were recruited between January and November 2020. All patients with a positive SARS-CoV-2 PCR were included. Demographic and clinical data were collected from electronic patient records. The primary outcome was 30-day mortality. Data were retrieved from patient records and there is a significant proportion of missing data.

The authors found that age and the presence of cancer were independent risk factors of 30-day mortality. Remdesivir was associated with reduced mortality. Within cancer patients, those with haematological malignancies and lung cancer had the highest risk. Overall, the findings of this study are in line with previously published results and don't provide major new insights.

Strength:

This is a multicentric study across several countries including over 3000 patients.

Limitations<br /> 1) This is not the first cohort study in cancer patients, several large studies have addressed risk factors of mortality before (for example Kuderer et al., The Lancet, 2020 and Chaves-McGregor et al. JAMA Oncology, 2021).

2) The authors identify Remdesivir to reduce mortality in cancer patients and those without cancer. The efficacy of Remdesivir has been addressed in large prospective trials, albeit not in cancer patients.

3) Treatment of patients with COVID-19 likely varied by country but the authors haven't addressed the impact of this.

4) Given that the recruited patients were all unvaccinated, the results are likely not completely transferable to the current situation. Vaccination and current antivirals and monoclonal antibodies have reduced the risk of severe disease and death. The current omicron variant has different properties compared to earlier strains. In fact, studies have shown that mortality in cancer patients has improved since 2020 (OnCovid Study Group, JAMA Oncology, 2021).

In conclusion, the authors largely confirm findings from other studies that patients with cancer were at an increased risk of death after COVID-19 infection, especially early on in the pandemic.

first half is type 1, first half is type 2

thow dean uses deer and how poe uses cat ... not how chris uses deer

Reviewer #1 (Public Review):

The authors aim was to determine the role of initial procalcitonin (PCT) measurements in cancer patients admitted with COVID-19 infection in reducing the intensity and duration of empiric antibiotic therapy. This was a retrospective study of all patients admitted to a single cancer center with COVID-19 infection and at least one PCT test within 72 hours of admission. The cut off PCT value to divide patients into two groups was 0.25 ng/ml (those with >= 0.25 ng/ml having a higher suspicion of bacterial infection). The study found that compared to patients with low PCT levels had shorter hospital stays, lower rate of mortality, and received less antibiotic therapy. The paper is well written, the study methods and statistics are sound, the population well characterized and large enough for valid comparison, and the results support the authors conclusions. The study provides support that PCT can be used in this special at risk population as it has been used in other COVID-19 patient populations that have been better studied. The study has limitations that the authors report: retrospective, single center study, bacterial infections may have been missed (no uniformity of cultures collected), and empiric antimicrobial therapy was at the discretion of the treating team (no standardized empiric therapy). The findings of this study may not be generalizable to other cancer patient populations and there may be other confounding variables not identified.

Reviewer #1 (Public Review):

Diehl & Redish set out to capture how cognitive and behavioral linked activity varies along the medial wall of the rodent prefrontal cortex during a complex decision-making task. They found four clusters of cells along the dorsal-ventral axis that were firing more similarly to other cells in the same cluster than cells in other clusters, suggesting there are 4 distinct subdivisions in rodent mPFC. Their detailed analysis of decision-making, reward, and evaluation showed that though some cells in each area responded to these different cognitive aspects, there was a difference in how widespread these signals were in the different subdivisions. They found more decision-related activity in the ACC, more post-decision evaluative activity in the dorsal parts of the prelimbic, and more ventral areas involved with motivational factors. They argue that the prelimbic area is actually 2 distinct areas that should be considered separately. This paper is very well analyzed and the methodological aspects regarding histological confirmation and neuronal spiking are exceptionally thorough. The task is well-studied and conclusively provides insights into multiple facets of high-level cognition. The main weakness is the unequal distribution of cells recorded in each area. Mainly, this is a problem for the ACC where substantially fewer units were recorded. This takes away some from the interpretation of ACC activity, however, most of the findings about ACC are consistent with previous reports from this lab and others. This does not take away from the success the authors achieved in characterizing the differences and similarities in functional correlates along the medial wall. The identification of two distinct subdivisions in the prelimbic area is novel and is likely to have a substantial impact on the field. At the least, the specific location within prelimbic that future studies purport to either record from, sample from, or manipulate will need to be reported so that these future findings can be correctly interpreted. This is a major shift in the field's conceptualization of this oft-studied part of the brain.

Public Review:

In this article, the authors have taken up the substantial task of combing through thousands of published meta-analyses and systematic reviews, with the goal of identifying the subset that specifically seeks to measure the association between elapsed time ("lag-time") in various milestones of cancer diagnosis or treatment (e.g. time elapse from symptom onset to first seen by primary care physician) and cancer outcomes. Within this subset, they have identified and summarized the findings on how these lag times are related to certain cancer outcomes. For example, how much does a delay in the start of adjuvant chemotherapy after surgery for breast cancer increase the mortality rate for these patients? The overarching goal of this work is to characterize the pre-Covid-19 landscape of these relationships and thereby provide a basis for studying what impact the pandemic had on worsened outcomes for cancer patients due to treatment delays. The authors have done an excellent job in their review of systematic reviews and meta-analyses, both describing their methodology well and interpreting their findings. The immediate connection to the Covid-19 pandemic is somewhat tenuous and primarily left to the reader to determine.

Reviewer #1 (Public Review):

This paper shows how evolutionary dynamics, together with high variance species-species interactions in a generalized Lotka-Volterra framework, can stabilize the population and delay extinctions. Moreover, the stable regime is shown to correspond to the clonal interference regime from population dynamics. Thus, this work extends Robert May's seminal work on the stability of a complex system by considering the stabilizing effect of evolution.

Strengths:

- The paper is well written, the questions well-motivated and the ideas presented in a coherent and easy to understand manner. Prior literature was referenced to a sufficient degree (though of course a lot was left out). Importantly, the author is honest about the limitations of the modeling choices, not attempting to over-sell the work or to hide inconvenient details. In this sense, this paper is a good contribution to the literature since it gives the reader a clear perspective on an interesting question.

- Kudos for sharing the code in github. The code looks organized and easy to reuse.

Weaknesses:

- Interactions are assumed to be drawn from a log-normal distribution. Clearly, this does not capture true ecological interactions. It is unclear how applicable the results are to real ecosystems.

- The paper assumes saturating nutrients and states that they "do not expect that the addition of a reasonable carrying capacity will change our qualitative results". However, competition for resources can lead to loss of diversity. Moreover, ecological systems are known to respond to large changes in the carrying capacity. Therefore, it should be further elucidated if indeed the addition of a carrying capacity will destabilize the results. Especially since there appears a significant increase in the population size in the stable conditions: an increase that is not clear if it could be supported when the carrying capacity was already limiting population sizes before the increase.

- It appears in the text that "there are key differences between the model and actual bacteria-phage systems, and the model should not be interpreted as one that will directly map onto a biological scenario". I agree with this statement. However, by distancing the model from biological scenarios it makes its predictions hard to validate in a real system, leaving us with no obvious way to infer how to apply its conclusions. Indeed, both explicit examples given in lines 125-130: phase-bacteria and T-cell-antigen are not quite captured by modeling choices. I would have much preferred a specific biological system fixed in mind, then minimally modeled in a way that there is hope to directly link the modeling results to experiments. Especially since there is a wealth of available microbial population data, as well as much being generated.

- As stated, "the population fitness distribution is never able to 'settle'..." is indicative of the driven nature (driven by strong noise) of the quasi steady state as opposed to a stability that arises from the system dynamics.

Justification of claims and conclusions:

The paper is honest in reflecting the weaknesses (stated above) in the modeling generality and applicability on actual systems. This is commendable, and the claims as stated are justified but the applicability of these claims remains unclear. There are some conjectures raised in the discussion but they remain unsupported and allocated to "future work".

Reviewer #1 (Public Review):

This is a well-written report on one of the biggest killer diseases. The report is based on a large longitudinal cohort and uses solid analytical methodologies. Three main valuable findings are reported: association between coronary heart disease (CHD) and a polygenic risk score (PRS), a combination of multiple traditional risk factors (SCORE2), and history of Fusobacterium nucleatum infection. While the first 2 associations are not novel, they are welcome independent replications of previous findings in a novel design. A putative role of F. nucleatum and other infections in increasing CHD risk has also been reported before but remains more elusive with some suggestion that they may increase CHD risk by promoting arterial inflammation. The strength of this study is to demonstrate an independent role of this bacterium after controlling inflammation markers as well as other risk factors in a prospective study. If this finding can be confirmed, the prevalence of the bacterium (15% in the cohort) means it should be considered as another serious CHD risk factor. The authors should discuss the implications of multiple testing.

Reviewer #1 (Public Review):

In 2007 it was observed that, although the central elements of galactose utilization are similar in both S. cerevisiae and C. albicans (clustered metabolic genes, transcriptional induction in the presence of galactose) the induction mechanisms were different. Until now, however, although the way the presence of galactose was sensed and this information transmitted to the induction of gene expression was well understood in S. cerevisiae, it was quite mysterious in C. albicans. This work proposes that in C. albicans, the general transcription regulator Rep1 serves as a direct galactose binding protein and that the binding of galactose to Rep1 allows it to serve as a scaffold to collect the transcriptional machinery necessary to induce the elements of the Gal regulon.

The first line of evidence for the Rep1 scaffold model is the observation that Rep1 is needed for C. albicans to both grow on galactose and to induce the genes encoding the galactose processing proteins Gal1 and Gal10. Previous candidate regulators Rtg1 and Rtg3 only blocked growth on galactose in the presence of Antimycin A, so Rep1 represents a first element specifically required for galactose growth. Further analysis of Rep1 function involved the observation that Rep1 was a member of the family of transcription factors including Ntd80, a TF that has been implicated in a variety of cellular controls. The authors investigated a specific unique domain of Rep1 by moving it to Ndt80 - the fusion protein did not allow complementation of the galactose growth defect, suggesting this domain was not critical to the Rep1 involvement in galactose growth. Further analysis of Rep1 domains by deletions showed that removal of the putative transcriptional activation domain of the protein also did not block either growth on galactose medium or galactose-mediated induction of GAL1 and GAL10 expression. The Rep1 protein was found to be constitutively bound to the promoters of GAL1 and GAL10, and not really influenced in this binding by carbon source.

To attempt to determine the connection between the apparent constitutive binding and the galactose-mediated induction of gene expression the authors investigated the relationship between sugars and the Rep1 protein. Modelling suggested a possible galactose binding pocket, binding was shown biochemically, and mutations within the presumed binding site disrupted galactose binding and protein function.

The authors next assess how the binding of galactose to Rep1 leads to gene induction because the binding to the regulated promoters seems constitutive, and the activation domain seems unimportant for protein function, and in fact, doesn't act as an activation domain in a 1 hybrid assay. They speculate protein binding and search for interacting proteins by mass spec after IP with a tagged Rep1 protein in the presence of galactose. Orf19.4959 is identified and tested. The binding data is presented as a supplementary table and includes many hits that do not appear promising candidates. Inactivation of the TF Orf19.4959 blocks growth on galactose and induction of the GAL1 and GAL10 genes, and the protein, called Cga1, does have transactivating ability in a 1 hybrid assay. The authors thus propose that galactose binding to Rep1 facilitates the binding of Cga1 and leads to the activation of gene expression for galactose metabolism.

This model is tested by immunoprecipitation assays that showed Cga1-Rep1 interaction only in the presence of galactose, and that DNA association of Cga1 to GAL promoters was galactose and Rep1 dependent. Further experiments provide a framework for Rep1 function in other pathways and suggest a candidate polyA binding motif for the Rep1 protein. The generalization of the model is proposed by noting a pattern of Rep1/Cga1 presence in other fungal species.

Reviewer #1 (Public Review):

Oxidation of some KCNQ7 channels enhances channel activity. The manuscript by Nuñez and coauthors concluded that oxidation in the S2S3 linker of these channels disrupted the interaction between S2S3 and CaM EF-hand 3 (EF3). This mechanism is Ca2+-dependent. The apo EF3 no longer interacted with S2S3, and H2O2 no longer activated the channel. Electrophysiological recordings and fluorescence and NMR measurements of CaM with isolated helices A and B (CRD) and S2S3 of the channel were performed. While the results were in general clear with good quality, how the results support the conclusion was not clearly described. The approach using isolated molecular components in the study needs further validation since some of the results seem to show major conflicts with the results and mechanisms proposed in previous studies.

1) Previous studies showed differential responses of Kv7 channels to oxidation; Kv7.2, 4, and 5 are sensitive to oxidation regulation but Kv7.1 and 3 do not change upon H2O2 treatment. These differences were attributed at least partially to the sequence differences in S2S3 among Kv7 channels (ref 10 of this manuscript). The results in this manuscript show some major differences from the previous study. First, in all experiments, no difference was observed among Kv7 channels. Second, in Fig 3-6, S2S3 from KCNQ1 was used. The rationale for using KCNQ1 S2S3 and the interpretation of results is not justified considering that KCNQ1 S2S3 has fewer Cys residues and was least affected by oxidation in the previous study.

2) In Fig 6, oxidation of S2S3 leads to a reduction of S2S3-CaM interaction, which leads to an increase of currents (Fig 1C). In Fig 4, Ca2+ loading leads to a reduced S2S3-CaM (EF3) interaction, which should also lead to an increase of currents based on Fig 6 conclusions. However, it is the EF3 mutation (destroying Ca2+ binding) that leads to the current increase (Fig 1B), contradictory to what Fig 6 data suggested.

Reviewer #1 (Public Review):

Observations made on histological patterns of SCC tumor invasion prompt the authors to investigate the seemingly broad distribution of invasion strategies employed by SCC tumor cells in tissue. Using computational modelling and testing the arising predictions in two experimental models of SCC invasion, the authors conclude matrix proteolysis and cell-cell junctions to play key roles in determining invasion strand width and cell adhesion strength to be a minor contributor.

Strengths of the study:<br /> - The authors acknowledge the complexity of invasion patterns employed by SCC tumor cells in tissue and provide new insight into the underlying complex cellular processes.<br /> - The approach of combining computational simulations and testing their predictions experimentally with two models is powerful.

Weaknesses of the study:<br /> - Cell proliferation (affected by proteolysis and cell-cell junctions) is indicated as a key contributor to the generation of broad strand invasion. However, proliferation is not investigated using the same experimental models used to investigate invasion and is not included as a parameter in the computational models.<br /> - The outcomes of their KO strategies on the cell-matrix and cell-cell adhesion are not fully demonstrated.

Reviewer #1 (Public Review):

This manuscript provides an in-depth analysis of the advantages and potential pitfalls of the application of Granger Causality (GC) to calcium imaging data, especially regarding various types of pre-processing. The key strength of the manuscript is the rigor and thoroughness of the authors' approach, and it is very clear how one would go about replicating their work. On the other hand, it is not from the results how well one should trust the results of GC for an unknown system, as many results rely on having some specialized knowledge about the measurements beforehand.

Strengths:

- Understanding how to measure causality is a key problem in modern science, and with the increasing abundance of wide-field calcium imaging, understanding how to assess information flow between neurons from these data is of wide interest and importance.

- I was impressed by the rigor and explicitness of the authors' approach. In papers like this, there is the temptation to sweep problems under the rug and highlight the successes. Here, the authors present, in a clearly organized format, the effects of various methods and analysis decisions. Moreover, the methods are described in a manner such that they could be (relatively) easily implemented by the reader.

- In general, the approach of using the GC value of the F-statistics and then normalizing by a null model is an appealing method that has a lot of intuitive and quantitative value.

Weaknesses:

- It's not clear to me what lessons are specific to the system they are studying and which ones are to be taken as more general lessons. Certainly, dealing with slow calcium dynamics, motion artifacts, and smoothing, are general problems in calcium imaging, but I found myself puzzled a bit about how to decide which neurons are "strange" without a lot of system-specific knowledge. This seems to be a rather important effect, and having a bit more guidance in the discussion would be useful.

- Somewhat related, I'm not entirely sure what results I should take home from the hindbrain analysis. It is clear that there is a more-or-less global signal modulating all neural activity, but this is a common occurrence in population recordings (often, one subtracts this off via PCA or another means before proceeding). Is the general lack of causal links (via the MVGC at least) a generic phenomenon in recurrent networks, or is there something more system-specific here? Accordingly, it might be interesting to run a recurrent neural network simulation with similar properties to the hindbrain (and perhaps with correlated driving) to see what GC/MVGC would predict. Is there any hope of these methods finding information flow in recurrent networks, or should we restrict the method to networks where we expect the primary mode of information transmission to be feedforward?

Reviewer #1 (Public Review):

For membrane transporters, the factors that define transport cycle state equilibria and kinetics remains a major question. In contrast to ion channels where electrophysiological single-channel recordings reveal transitions between states, this has not been possible for slower transport proteins and so this information must be extracted from bulk transport behavior. However, recent single-molecule microscopy studies, such as FRET, have provided a new way of identifying transitions between conformational ensembles and connecting this to transport behaviors. However, the resolution of FRET can be limiting in that it requires multiple labeling with large fluorophores that have their own freedom to move, thus reducing the ability to detect small conformational changes. In the present study, Zhou et al. address this by using a different single-molecule approach of polarization microscopy, and investigate the small conformational changes associated with the AdiC arginine/agmatine antiporter from the APC super-family of transport proteins. Here, they anchor bis-TMR-maleimide onto helix 6, a part of the protein that has been identified to change orientation in the different crystal structures of AdiC and other APC homologues in inward, outward and occluded states. By "fixing" the protein onto microscopy slides, they are able to detect the change in polarization angles of the emitted fluorescence and map that onto relative changes in helix 6 orientation. Analyzing these data, they propose a model of four states that exchange in equilibrium, with and without the substrate, setting the stage for quantifying equilibrium constants and kinetics for a detailed mapping of the transport cycle, presented in an accompanying article.

This is certainly a cutting-edge approach that offers the potential to resolve the equilibrium reactions between small conformational changes and thus has the potential to push forward the mechanistic and quantitative investigation of membrane transport. However, at this point the studies require further validation on several levels. This includes an independent investigation of whether the protein being studied (i.e. with all tags, mutations, labeling, nanodisc solubilzation) confers the same substrate binding and transport behavior that has been reported previously, and is being used as comparison data here. In addition, there is some concern that the anchoring of the protein may bias conformational equilibria in some way and so it would be worthwhile to map out if this effect is limiting by changing linker lengths, within a range where it is still possible to resolve changes in polarization angles. Finally, the results are very dependent on the post-processing of the single-molecule trajectories that include changepoint analysis, averaging and clustering algorithms, yet there is little data provided to examine the robustness of each of these steps in the ultimate determination of the four-state model. While the observation that some of the states identified show a linkage to the arginine substrate, further validation along the lines mentioned above are required before a full analysis of the transport cycle is rationalized.

Reviewer #1 (Public Review):

This interesting manuscript from the Perozo and Faraldo-Gomez labs investigates the molecular mechanisms underlying the activation of the mechanosensitive ion channel MscS. The authors use a clever combination of cryoEM, coarse-grained (CG) and all-atom (AA) molecular dynamics simulations to determine the first (putatively) open conformation of the WT MscS channel and to show that this channel induces profound deformations of the membrane in the closed but not in the open state. Strikingly, MD simulations reveal that, contrary to what was previously assumed, lipids occupying cavities near the closed pore (hook lipids) come from the outer rather than inner leaflets. On pore opening, the membrane adopts a more relaxed conformation where the lipids contacting the protein are in less strained and tilted conformations. The authors thus propose a mechanism for sensing tension where the equilibrium between the open and closed conformations of the channel is dictated by differences in the membrane morphology in the two states rather than by the association and dissociation of individual lipids with the protein.

Major<br /> The observations on the hook lipids are critical and should be documented better. Based on previous work, it had been proposed that the hook lipids are associated with the inner leaflet and that they leave upon (partial) channel opening. In contrast, the present MD simulations indicate these lipids are associated with the outer leaflet and that their association to the channel persists on opening. These critical observations need to be documented better.<br /> i. Do the authors observe hook lipids in the cryoEM structure of the open channel? If yes, data should be shown. If no, then the discrepancy between MD and EM should be explicitly addressed.<br /> ii. Please show the comparison of the position and coordination of the hook lipids in MD simulations and in the closed (and/or open) structures.<br /> iii. The authors acknowledge that the volume of the cavity where the hook lipids are located decreases on channel opening. How does this not affect the association of the hook lipids with the protein?<br /> iv. Past work revealed several lipids in MscS structures near these cavities besides the hook lipids, and their ordered dissociation from the channel was proposed to be important for gating. Do the simulations show lipids in these cavities?<br /> v. Does the occupancy of the hook lipids in MD simulations change between the open and closed conformations? This should be analyzed.<br /> vi. Is the occupancy of other lipids in the nearby cavity altered upon channel opening?<br /> vii. Is the exchange of lipids near Ile150 affected by the conformational change?

I am a bit confused by the claim that "The comparison clearly highlights the reduction in the width of the transmembrane span of the channel upon opening, and how this changed is well matched by the thickness of the corresponding lipid nanodiscs (approximately from 38 to 23 Å)."<br /> i. How was the nanodisc membrane thickness determined? This should be described.<br /> ii. I do not see a ~15A change in the vertical length of the channel protein or of the nanodisc. While the panels in Fig.2 clearly show a vertical compression of the membrane, it appears that the ~15 A claim might be overstated. Adding a panel with measurements would be helpful to quantify this claim. If this is difficult on the membrane, maybe measurements could be performed on the protein.<br /> iii. What happens to the N-terminal cap structure in the open state? What are the rearrangements that allow the extracellular ends of the TM1 to disassemble the cap.

The data shown in Fig. 6 is cryptic and should be explained better in the main text. As it stands there is a cursory mention in pg. 12 and not much else.<br /> i. It would be helpful if the authors showed the position of Ile150 in the structure.<br /> ii. Does the total number of lipids in proximity of Ile150 change over time? Or the fold change represents ~1:1 exchange of lipids in the pocket?<br /> iii. I am confused by the difference in the maximum possible fold-change in unique lipids, does this reflect the difference in total number of lipids in each leaflet in each system? If so, I am a bit confused as to why there is a ~30% difference in the AA simulations whereas the values are nearly identical for the CG one.<br /> iv. Is it possible to quantify the residence time of the lipids in the pocket of each subunit?

The authors state on Pg. 21 "Nevertheless, we question the prevailing view that density signals of this kind are evidence of regulatory lipid binding sites; that is, we do not concur with the assumption that lipids regulate the gating equilibrium of MscS just like an agonist or antagonist would for a ligand-gated receptor-channel." I am a bit confused by this statement. In principle, binding and unbinding of modulatory ligands can happen on relatively fast time scales, so the observation that in MD simulations lipids exchange on a faster time scale than that of channel gating is not sufficient to make this inference. Indeed, there is ample evidence from other channels (i.e. Trp channels, HCN channels etc) where visualization of similar signals led to the identification of modulatory lipid binding sites. Thus, while I do not necessarily disagree with the authors, I would encourage them to tone down the general portion of the statement.

Reviewer #1 (Public Review):

The authors focused on linking physiological data on theta phase precession and spike-timing-dependent plasticity to the more abstract successor representation used in reinforcement learning models of spatial behavior. The model is presented clearly and effectively shows biological mechanisms for learning the successor representation. Thus, it provides an important step toward developing mathematical models that can be used to understand the function of neural circuits for guiding spatial memory behavior.

However, as often happens in the Reinforcement Learning (RL) literature, there is a lack of attention to non-RL models, even though these might be more effective at modeling both hippocampal physiology and its role in behavior. There should be some discussion of the relationship to these other models, without assuming that the successor representation is the only way to model the role of the hippocampus in guiding spatial memory function.

1. Page 1- "coincides with the time window of STDP" - This model shows effectively how theta phase precession allows spikes to fall within the window of spike-timing-dependent synaptic plasticity to form successor representations. However, this combination of precession and STDP has been used in many previous models to allow the storage of sequences useful for guiding behavior (e.g. Jensen and Lisman, Learning and Memory, 1996; Koene, Gorchetchnikov, Cannon, Hasselmo, Neural Networks, 2003). These previous models should be cited here as earlier models using STDP and phase precession to store sequences. They should discuss in terms of what is the advantage of an RL successor representation versus the types of associative sequence coding in these previous models.

2. On this same point, in the introduction, the successor representation is presented as a model that forms representations of space independent of reward. However, this independence of spatial associations and reward has been a feature of most hippocampal models, that then guide behavior based on interactions between a reward representation and the spatial representation (e.g. Redish and Touretzky, Neural Comp. 1998; Burgess, Donnett, Jeffery, O'Keefe, Phil Trans, 1997; Koene et al. Neural Networks 2003; Hasselmo and Eichenbaum, Neural Networks 2005; Erdem and Hasselmo, Eur. J. Neurosci. 2012). The successor representation should not be presented as if it is the only model that ever separated spatial representations and reward. There should be some discussion of what (if any) advantages the successor representation has over these other modeling frameworks (other than connecting to a large body of RL researchers who never read about non-RL hippocampal models). To my knowledge, the successor representation has not been explicitly tested on all the behaviors addressed in these earlier models.

3. Related to this, successes of the successor representation are presented as showing the backward expansion of place cells. But this was modeled at the start by Mehta and colleagues using STDP-type mechanisms during sequence encoding, so why was the successor representation necessary for that? I don't want to turn this into a review paper comparing hippocampal models, but the body of previous models of the role of the hippocampus in behavior warrants at least a paragraph in each of the introduction and discussion sections. In particular, it should not be somehow assumed that the successor representation is the best model, but instead, there should be some comparison with other models and discussion about whether the successor representation resembles or differs from those earlier models.

4. The text seems to interchangeably use the term "successor representation" and "TD trained network" but I think it would be more accurate to contrast the new STDP trained network with a network trained by Temporal Difference learning because one could argue that both of them are creating a successor representation.

Reviewer #1 (Public Review):

In this manuscript, the authors have assembled a reference transcriptome of the whole head of Loligo vulgaris and used it to perform single cell transcriptomics. With about 20,000 cells, they identify 32 clusters corresponding to a few identifiable cell types - neurons, stem cells, sensory cells, and epidermis. They use select marker genes from these clusters and perform HCR in situs on Loligo heads to describe these cell types. Their in situs describe a region similar to the lateral lip seen in other cephalopods where neural progenitors are found and from where neurons migrate into the brain.

All info all of the time available to all makes for a trusting space. Plus, a more active way to help all your people understand what they are seeing.

Reviewer #1 (Public Review):

This work aimed at investigating how a BMI decoding performance is impacted by changing the conditions under which a motor task is performed. They recorded motor cortical activity using multielectrode arrays in two monkeys executing a finger flexion and extension task in four conditions: normal (no load, neutral wrist position), loaded (manipulandum attached to springs or rubber bands to resist flexion), wrist (no load, flexed wrist position) or both (loaded and flexed wrist). They found, as expected, that BMI decoders trained and tested on data sets collected during the same conditions performed better at predicting kinematics and muscle activity than others trained and tested across conditions. They also report that the performance of monkeys a BMI task involving the online control of a virtual hand was almost unaffected by changing either the actual manipulandum conditions as above or switching between decoders trained from data collected under different conditions. As for the neuronal activity, they found a mix of changes across task contexts. Interestingly, a principal component analysis revealed that activity in each context falls within well-aligned manifolds, and that the context-dependent variance in neuronal activity strongly correlated to amplitude of muscle activity.

Strengths:

The current study expands on previous findings about BMI decoders generalizability and contributes scientifically in at least three important ways.

First, their results are obtained from monkeys performing a fine finger control task with up to two degrees of freedom. This provides a powerful setting to investigate fine motor control of the hand in primates. The authors use the accuracy of BMI decoders between data sets as a measure of stationarity in the neurons-to-fingers mapping, which provides a reliable assessment. They show that changes in wrist angle or finger load affect the relationship between cortical neurons and otherwise identical movements. Interestingly, this result hold up for both kinematics and muscle activity predictions, albeit being stronger for the latter.

Second, their results confirming that neuronal activity recorded during different task conditions lies effectively within a common manifold is interesting. It supports prior observations, but in the specific context of finger movements.

Third, the dPCA results provide interesting and perhaps unexpected information about the fact that amplitude of muscle activity (or force) is clearly present in the motor cortical activity. This is possibly one of the most interesting findings because extracting a component from neural activity that can related robustly to muscle activity across context would provide great benefits to the development of BMIs for functional electrical stimulation.<br /> Overall, the analyses are well designed and the interpretation of the results is sound.

Weaknesses:

I found the discussion about the possible reasons why offline decoders are more sensitive to context than online decoders very interesting. Nonetheless, as the authors recognize, the possibility that the BMI itself causes a change in context, "in the plant", limits their interpretation. It could mean for the monkeys to switch from one suboptimal decoder to another, causing a ceiling effect occluding generalization errors.

Overall, several new and original results were obtained through these experiments and analyses. Nonetheless, I found it difficult to extract a clear unique and strong take-home message. The study comes short of proposing a new way to improve BMIs generalizability or precisely identifying factors that influence decoders generalizability.

Reviewer #1 (Public Review):

Chakrabarti et al study inner hair cell synapses using electron tomography of tissue rapidly frozen after optogenetic stimulation. Surprisingly, they find a nearly complete absence of docked vesicles at rest and after stimulation, but upon stimulation vesicles rapidly associate with the ribbon. Interestingly, no changes in vesicle size were found along or near the ribbon. This would have indicated a process of compound fusion prior to plasma membrane fusion, as proposed for retinal bipolar cell ribbons. This lack of compound fusion is used to argue against MVR at the IHC synapse. However, that is only one form of MVR. Another form, coordinated and rapid fusion of multiple docked vesicles at the bottom of the ribbon, is not ruled out. Therefore, I agree that the data set provides good evidence for rapid replenishment of the ribbon-associated vesicles, but I do not find the evidence against MVR convincing. The work provides fundamental insight into the mechanisms of sensory synapses.

Reviewer #1 (Public Review):

Hu et al. present findings that extend the understanding of the cellular and synaptic basis of fast network oscillations in the sensory cortex. They developed the ex vivo model system to study synaptic mechanisms of ultrafast (>400Hz) network oscillation ("ripplets") elicited in layer 4 (L4) of the barrel cortex in the mouse brain slice by optogenetically activating thalamocortical axon terminals at L4, which mimic the thalamic transmission of somatosensory information to the cortex. This model allowed them to reproduce extracellular ripplet oscillations in the slice preparation and investigate the temporal relationship of cellular and synaptic response in fast-spiking (FS) inhibitory interneurons and regular spiking (RS) with extracellular ripplet oscillations to common excitatory inputs at these cells. FS cells show precisely timed firing of spike bursts at ripplet frequency, and these spikes are highly synchronized with neighboring FS cells. Moreover, the phase-locked temporal relationship between the ripplets and responses of FS and RS cells, although different phases, to thalamocortical activation are found to closely coincide with EPSCs in RS cells, which suggests that common excitatory inputs to FS and RS cells and their synaptic connectivity are essential to generate reverberating network activity as ripplet oscillations. Additionally, they show that spikes of FS cells in layer 5 (L5) reduced in the slice with a cut between L4 and L5, proposing that recurrent excitation from L4 excitatory cells induced by thalamocortical optogenetic stimulation is necessary to drive FS spike bursts in layer 5 (L5).

Overall, this study helps extend our knowledge of the synaptic mechanisms of ultrafast oscillations in the sensory cortex. However, it would have been nice if the authors had utilized various methodologies and systems.

Although the overall findings are interesting, the conclusion of the study could have been strengthened according to the following points:

1. The authors investigate the temporal relationship between ripplets and FS and RS cells' response elicited by optogenetic activation of TC axon terminals, which is mainly supported by phase-locked responses of FS and RS cells with local ripplets oscillations to optogenetic activation. They also show highly synchronized FS-FS firing by eliminating electrical gap-junction and inhibitory synaptic connections to this synchrony. Based on these findings, the authors suggest that common excitatory inputs to FS and RS cells in L4 would be essential to generate these local ripplets. However, it interferes with the ability to follow the logical flow for biding other findings of phase-locking responses of FS and RS cells in ripplet oscillations in L4.

2. The authors suggest that the optogenetic activation of TC axon terminal elicits local ripplet oscillations via synchronized spike burst of FS inhibitory interneurons and alternating EPSC-IPSC of RS cells in phase-locked with ripplets in L4 barrel cortex, which would be generated by following common excitatory inputs from the local circuits to these cells at the ripple frequency. Thus they intend to investigate the source of these excitatory inputs at this local network of L4 by suppressing the firing of L4 RS cells. However, they show FS spike bursts in L5B, instead of L4, due to the technical limitations of their experimental setup, as described in the manuscript. Although L5 FS spike bursts decrease after cutting the L4/L5 boundary, supposedly inhibiting excitatory input from L4 as depicted in Fig 6D in the author's manuscript, the interpretation of data seems overly extended because it does not necessarily represent cellular and synaptic activities which are phase-locked with the ripplets observed in L4.

3. Authors suggested a circuit model. It would be recommended that the authors try to perform in silico analysis using the suggested model to explore the function of thalamocortical axons on the fast-spiking and regular-spiking neurons to support their circuit model.

Reviewer #1 (Public Review):

The authors had previously developed a method of determining conformational free energy differences between the alternative DFG-in and DFG-out conformational states of kinases using an energy function based on a Potts model. They did this because direct estimates of this free energy change from molecular simulations, while possible in principle, would in practice be hard to do with sufficient accuracy to be useful for such a large conformational transition. Potts model energies have been shown to be correlated with overall protein stability, so it is reasonable that dividing the contacts into DFG-in and DFG-out sets should allow the estimation of a free energy difference between conformational states. In this work they examine the differences between Tyrosine Kinases (TKs) and Serine/Threonine Kinases (STKs) more closely, finding that the model predicts a small free energy change for converting DFG-in to DFG-out for TKs but a significant unfavorable free energy cost to converting to DFG-out for the STKs. The most insightful part of the paper comes in its analysis of how this conformational change may contribute to the overall binding free energies. Calculating binding free energies for Type II inhibitors (which bind DFG-out) by alchemical methods neglects the contribution from any unfavorable conformational change ("reorganization energy") required to adopt the DFG-out conformation. Thus comparing this calculated binding free energy with the total binding free energy estimated from experiment allows an estimate of the conformational reorganization energy. It is found that this estimate is nicely correlated with the free energy change for conformational rearrangement estimated from the Potts model analysis. Thus an important contribution to Type II inhibitor binding is this conformational transition. The different contributions to Type II binding are analyzed in detail by further dissecting the Potts model.

Reviewer #1 (Public Review):

This paper investigates potential mechanisms underlying the generation of hippocampal theta and gamma rhythms using a combination of several modeling approaches. The authors perform new simulation experiments on the existing large-scale biophysical network model previously published by Bezaire et al. Guided by their analysis of this detailed model, they also develop a strongly reduced, rate-based network model, which allows them to run a much larger number of simulations and systematically explore the effects of varying several key parameters. The combined results from these two in silico approaches allow them to predict which cell types and connections in the hippocampus might be involved in the generation and coupling of theta and gamma oscillations.

In my view, several aspects of the general methodology are exemplary. In the current work as well as several earlier papers, the authors are re-using a large-scale network model that was originally developed in a different laboratory (Bezaire et al., 2016) and that still represents the state-of-the-art in detailed hippocampal modeling. Such model reuse is quite rare in computational neuroscience, which is rather unfortunate given the amount of time and effort required to build and share such a complex model. Very often, and also, in this case, the original publication that describes a detailed model provides only limited validation and analysis of model behavior, and the re-use of the same model in later studies represents a great opportunity to further examine and validate the model.

Combining detailed and simplified models can also be a powerful approach, especially when the correspondence between the two is carefully established. Matching results from the two models, in this case, allow strong arguments about key mechanisms of biological phenomena, where the simplified model allows the identification and characterization of necessary and sufficient components, while the detailed model can firmly anchor the models and their predictions to experimental data.

On the other hand, I have several major concerns about the implementation of these approaches and the interpretation of the results in the current study. First of all, the detailed model of Bezaire et al. is considered strictly equivalent, in all of its relevant details, to biological reality, and no attempt is made to verify or even discuss the validity of this assumption, even when particular details of the model are apparently critical for the results presented. I see this as a fundamental limitation of the current work - the fact that the Bezaire et al. model is the best one we have at the moment does not automatically make it correct in all its details, and features of the model that are essential for the new results certainly deserve careful scrutiny (preferably via detailed comparison with experimental data).

An important case in point is the strength of the interactions between specific neuronal populations. This is represented by different quantities in the detailed and simplified model, but the starting point is always the synaptic weight (conductance) values given by Bezaire et al. (2016), also listed in Tables 2 and 3 of the current manuscript. Looking at these parameters, one can identify a handful of connections whose conductance values are much higher than those of the other connections, and also more than an order of magnitude higher (50-100 nS) than commonly estimated values for cortical synapses (normally less than about 5 nS, except for a few very special types of synapse such as the hippocampal mossy fibers). Not surprisingly, several of these connections (such as the pyramidal cell to pyramidal cell connections, and the CCK+BC to PV+BC connections) were found to be critical for the generation and control of theta and gamma oscillations in the model. Given their importance for the conclusions of the paper, it would be essential to double-check the validity of these parameter values. In this context, it is worth noting that, unlike the anatomical parameters (cell numbers and connectivity) that had been carefully calculated and discussed in Bezaire and Soltesz (2013), biophysical parameters (the densities of neuronal membrane conductances and synaptic conductances) in Bezaire et al. (2016) were obtained by relatively simple (partly manual) fitting procedures whose reliability and robustness are mostly unknown. Specifically for synaptic parameters in CA1, a more systematic review and calculation were recently carried out by Ecker et al. (2020); their estimates for the synaptic conductances in question are typically much lower than those of Bezaire et al. (2016) and appear to be more in line with widely accepted values for cortical (hippocampal) synapses.

Furthermore, some key details concerning the construction of the simplified rate model are unclear in the current manuscript. The process of selecting cell types and connections for inclusion in the rate model is described, and the criteria are mostly clear, although the results are likely to be heavily affected by the problems discussed above, and I do not understand why the strength of external input was included among the selection criteria for cell types (especially if the model is meant to capture the internal dynamics of the isolated CA1 region). However, the main issue is that it remains unclear how the parameters of the rate model (the 24 parameters in Table 4) were obtained. The authors simply state that they "found a set of parameters that give rise to theta-gamma rhythms," and no further explanation is provided. Ideally, the parameters of the rate model should be derived systematically from the detailed biophysical model so that the two models are linked as strongly as possible; but even if this was not the case, the methods used to set these parameters should be described in detail.

An important inaccuracy in the presentation of the results concerns the suggested coupling of theta and gamma oscillations in the models. Although the authors show that theta and gamma oscillations can be simultaneously present in the network under certain conditions, actual coupling of the two rhythms (e.g., in the form of phase-amplitude coupling) is not systematically characterized, and it is therefore not clear under what conditions real coupling is present in the two models (although a probable example can be seen in Figure 1C(ii)).

The Discussion of the paper states that gamma oscillations in the model(s) are generated via a pure interneuronal (ING) mechanism. This is an interesting claim; however, I could not find any findings in the Results section that directly support this conclusion.

Finally, although the authors write that they can "envisage designing experiments to directly test predictions" from their modeling work, no such experimental predictions are explicitly identified in the current manuscript.

Reviewer #1 (Public Review):

This study focuses on the role of polo like kinase 1 (PLK-1) during oocyte meiosis. In mammalian oocytes, Plk1 localizes to chromosomes and spindle poles, and there is evidence that it is required for nuclear envelope breakdown, spindle formation, chromosome segregation, and polar body extrusion. However, how Plk1 is targeted to its various locations and how it performs these functions is not well understood. This study uses C. elegans oocytes as a model to explore PLK-1 function during meiosis. They take advantage of an analogue-sensitive allele of plk-1, which enabled them to bypass nuclear envelope breakdown defects that occur following PLK-1 RNAi. This allowed them to dissect later roles of PLK-1 in oocytes, demonstrating that depletion causes defects in spindle organization, chromosome congression, segregation, and polar body extrusion. Moreover, the authors defined mechanisms by which PLK-1 is targeted to chromosomes, showing that CENP-C (HCP-4) is required for localization to chromosome arms and that BUB-1 is required for targeting to the midbivalent region. Finally, they demonstrate that upon removal of PLK-1 from both domains, there are severe meiotic defects. These findings are interesting. However, there is a need for additional analysis to better support some of their conclusions, and to aid in interpretation of particular phenotypes. Specific comments are below.

- For many important claims of the paper, a single representative image is shown but the n is not noted. This is an issue throughout the paper for much of the localization analysis (e.g. Figure 1B, 1C, 1D, 2A, 2B, 3A, 3B, 3C, etc.); in cases like this, numbers should be included to increase the rigor of the presented data. How many images or movies were analyzed that looked like the one shown? For linescans, were they done only on one image? How many independent experiments were done, etc?.

- In the abstract, it is stated that PLK-1 plays a role in spindle assembly/stability (this is also stated elsewhere, e.g. line 101). This phrasing implies that the authors have demonstrated roles in both spindle assembly and stability. However, to distinguish between these roles, they would have to show that removal of PLK-1 before spindle assembly causes defects, and also that removal of PLK-1 from pre-formed spindles causes collapse. I don't think it is necessary to do this, as the spindle roles of PLK-1 are not a focus of the paper. However, the language should be altered so that it does not imply that the paper has demonstrated roles in both. A good place to do this would be in the section from lines 144-147, where they first discuss the spindle defects. It would be straightforward to explain that their approach does not distinguish between spindle assembly and stability, and that PLK-1 could have a role in either or both.

- It is stated that there is kinetochore localization of PLK-1 (and I do see some dim cup-like localization in images after PLK-1 is removed from the chromosome arms via HCP-4 RNAi). However, this cup-like localization is not clear in most wild-type images (e.g. Figure 1B, 1D, 2A, 3A, etc.). Although I recognize that the chromatin staining might be obscuring kinetochore localization, if PLK-1 was truly a kinetochore protein I would also expect it to localize to filaments within the spindle (as many other kinetochore proteins do), especially since the authors state that BUB-1 targets PLK-1 to the kinetochore (and BUB-1 is in the filaments). In fact, the only images where it looks like PLK-1 may be localized to filaments are in Figure 4C and 6A, when HCP-4 has been depleted (though I don't know if this generally true across all HCP-4 RNAi images). For me, this calls into question the conclusion that PLK-1 truly is on the kinetochore in wild type conditions - could it be that PLK-1 only localizes to the kinetochore (and to the filaments) when HCP-4 is depleted? The authors need to resolve this issue and provide better evidence that PLK-1 normally localizes to the kinetochore, if they want to make this claim. Additionally, the observation that PLK-1 is not on the kinetochore filaments (in wild type conditions) should be addressed in the text somewhere - do the authors think that this is a special type of kinetochore protein that does not localize to the filaments?

- The authors should provide a control experiment, treating wild-type worms with 10uM 3-IB-PP1. This would be important to ensure that the spindle defects seen at this concentration in the plk-1as strain are not non-specific effects of the inhibitor. There is a control in Figure 1 - figure supplement 3 using 1uM 3-IB-PP1 but didn't see a control for 10uM (the concentration at which spindle defects are observed).

- In Figure 2F, the gels for BUB-1+PLK-1 look different in the presence and absence of phosphorylation by Cdk1 - for these data, I agree with the authors that it looks as if the complex elutes at a higher volume if BUB-1 is not phosphorylated (lines 200-204). However, Figure 2G has a repeat of the condition with phosphorylated BUB-1, and in this panel, the complex appears to elute at a higher volume than it did on the gel in panel F. The gel in panel G looks much more similar to the unphosphorylated condition in panel F. The authors need to explain this discrepancy (i.e., Is there a reason why the gels cannot be compared between panels? How reproducible are these data?). Ideally, the authors would include a repeat of the unphosphorylated BUB-1 + PLK-1 condition in panel G, done at the same time as the conditions shown in that panel, to avoid the impression that their results may not be reproducible.

- The authors would need to provide convincing evidence that co-depletion of BUB-1 and HCP-4 delocalizes PLK-1 from the chromosomes entirely, and that this co-depletion condition is more severe than either single depletion alone. Additionally, the bub-1T527A and hcp-4T163A alleles are nice tools to, in theory, more specifically delocalize PLK-1 from the midbivalent and chromosome arms, respectively, to explore the functions of chromosome-associated PLK-1. However, I think the authors cannot rule out the possibility that other proteins are also being depleted from the midbivalent and/or chromosome arms in their conditions, and that this delocalization may contribute to the phenotypes observed. For example, hcp-4 depletion was recently shown to delocalize KLP-19 from the chromosome arms (Horton et.al. 2022), so in the experiment shown in Figure 6E (HCP-4 RNAi in the bub-1 mutant), PLK-1 was likely not the only protein missing from the chromosome arms. Therefore, understanding if other proteins are absent from these domains (in the bub-1T527A and hcp-4T16A3 mutants) would help the reader understand and interpret the presented phenotypes (and how specific they are to PLK-1 loss). Consequently, I think that to better understand the co-depletion analysis presented in Figure 6 (and Figure 6 supplement 1), the authors should analyze other midbivalent and chromosome arm proteins, to determine if any are also delocalized (e.g. SUMO, KLP-19, MCAK, etc.). Additionally, instead of performing a combination of mutant and RNAi analysis (i.e. HCP-4 RNAi in the bub-1 mutant (Figure 6) and BUB-1 RNAi in the hcp-4 mutant (Figure 6 figure supplement 1)), it would be more powerful to generate a double mutant - this has a higher chance of being a more specific depletion condition.

Reviewer #1 (Public Review):

This manuscript presents information that will be of great interest to yeast geneticists - standard gene deletions can lead to misleading phenotypes due to effects on adjacent genes. The experiments carefully document this in one case, for the DBP1 gene, and present additional evidence that it can occur at additional genes. An improved version of the standard gene replacement cassette is described, with evidence that it functions in an improved fashion, insulated from affecting adjacent genes.

Reviewer #1 (Public Review):

This study analyzes the detailed chemical mechanics of the formation of a physiologically important protein multimer. The primary strengths of the study are careful analyses of two distinct methods, CG-MALS a direct measure of multimerization, and environment-sensitive tryptophan fluorescence, that each indicates that Ca2+ activation of the C-lobe alone can change the physical interaction with an SK2 C-terminal peptide. An intriguing finding is that while either the N- or C-lobes alone can interact with the C-terminal peptide, only with full-length CaM can the SK C-terminal peptide be bound by two CaM molecules simultaneously. This study also clearly demonstrates that Ca2+ activation of the N-lobe triggers binding to the SK2 C-terminal peptide. Methods descriptions are thorough and excellent. Discussion of relevance to structures and function are nuanced and free of presumptions. The weaknesses of this manuscript are that the physiological implications of these findings are not clear: CaM interacts with regions of SK channels besides the C-terminal peptide studied here, and no evidence is provided here that C-lobe calcium binding alters channel opening. Overall, the evidence for conformational changes of the complex due to Ca2+ binding to the C-lobe alone is very strong, and physiological importance seems likely. The interpretation of data in this manuscript is mostly cautious and logically crystalline, with alternative interpretations discussed at many junctures.

Reviewer #1 (Public Review):

The overarching hypothesis is that cadherin adhesion molecules specify the code that enables the premotor brainstem breathing circuits to innervate the phrenic motor neurons that control the primary breathing muscle, the diaphragm. The authors show that multiple type 1 and 2 cadherins (N-, 6, 9, 10) are expressed by phrenic motor neurons and are necessary for motor neuron development and breathing, and complementarily, that adhesion signaling in medullary breathing circuits are required for normal breathing. The presented data support a model whereby combinations of redundant adhesion molecules create a code to wire the breathing circuit.

Strengths:<br /> 1) The authors first use a complex, rigorous genetic approach to eliminate N, 6, 9, 10 cadherins from motor neurons and discover using whole body plethysmography that neonates do not breath.<br /> 2) Then, the authors provide a thorough description of the anatomy of the mutant motor neurons and discover that the number of motor neurons decreases, the soma anatomical positions and dendritic arborization shift, and there is decreased innervation of the diaphragm breathing muscle.<br /> 3) That Cdh9 medullary expressing neurons are premotor to Cdh9 expressing phrenic motor neurons.<br /> 4) Cadherin signaling is required for normal breathing.

Weaknesses: The main conclusion that ablation of the cadherin code decreases synaptic connectivity between the rVRG and phrenic motor neurons is never directly shown. This can only be inferred by the data.<br /> 1) Conclusion that the connectivity between rVRG premotor and phrenic nerve motor neurons is "weaker". This conclusion is inferred from several experiments but is never directly demonstrated. Alternative interpretations of the decreased amplitude of the in vitro phrenic nerve burst is that the rootlet contains fewer axons (as predicted by the fewer motor neurons in S3 and innervation of the diaphragm S2). Additionally, the intrinsic electrophysiological properties of the motor neurons might be different. To show this decisively, the authors could use electrophysiological recordings of phrenic motor neurons to directly measure a change in synaptic input (for example, mEPSPs or EPSPs after optogenetic stimulation of rVRG axon terminals). Without a direct measurement, the synaptic connectivity can only be inferred.<br /> 2) Conclusion that the small phenic nerve burst size in Dbx1 deleted cadherin signaling is due to less synaptic input to the motor neurons. Dbx1 is expressed in multiple compartments of the medullary breathing control circuit, like the breathing rhythm generator (preBötC). The smaller burst size could be due to altered activity between preBötC neurons to create a full burst, the transmission of this burst from the preBötC to the rVRG, etc.<br /> 3) In vitro burst size. The authors use 4 bursts from each animal to calculate the average burst size. How were the bursts chosen? Why did the authors use so few bursts? What is the variability of burst size within each animal? What parameters are used to define a burst? This analysis and the level of detail in the figure legend/methods section is inadequate to rigorously establish the conclusion that burst size is altered in the various genotypes.<br /> 4) The authors state that the in vitro frequency in figure 4 is inaccurate, but then the in vitro frequency is used to claim the preBötC is not impacted in Dbx1 mutants (conclusion section "respiratory motor circuit anatomy and assembly"). To directly assess this conclusion, the bursting frequency of the in vitro preBötC rhythm should be measured.<br /> 5) The burst size in picrotoxin/strychnine is used to conclude that the motor neurons intrinsic physiology is not impacted. The bursts are described, and examples are shown, but this is never quantified across many bursts within in a single recordings nor in multiple animals of each genotype.

Reviewer #1 (Public Review):

This paper describes the results of a MEG study where participants listened to classical MIDI music. The authors then use lagged linear regression (with 5-fold cross-validation) to predict the response of the MEG signal using (1) note onsets (2) several additional acoustic features (3) a measure of note surprise computed from one of several models. The authors find that the surprise regressors predict additional variance above and beyond that already predicted by the other note onset and acoustic features (the "baseline" model), which serves as a replication of a recent study by Di Liberto.

They compute note surprisal using four models (1) a hand-crafted Bayesian model designed to reflect some of the dominant statistical properties of Western music (Temperley) (2) an n-gram model trained on one musical piece (IDyOM stm) (3) an n-gram model trained on a much larger corpus (IDyOM ltm) (4) a transformer DNN trained on a mix of polyphonic and monophonic music (MT). For each model, they train the model using varying amounts of context.

They find that the transformer model (MT) and long-term n-gram model (IDyOM stm) give the best neural prediction accuracy, both of which give ~3% improvement in predicted correlation values relative to their baseline model. In addition, they find that for all models, the prediction scores are maximal for contexts of ~2-7 notes. These neural results do not appear to reflect the overall accuracy of the models tested since the short-term n-gram model outperforms the long-term n-gram model and the music transformer's accuracy improves substantially with additional context beyond 7 notes. The authors replicate all these findings in a separate EEG experiment from the Di Liberto paper.

Overall, this is a clean, nicely-conducted study. However, the conclusions do not follow from the results for two main reasons:

1. Different features of natural stimuli are almost always correlated with each other to some extent, and as a consequence, a feature (e.g., surprise) can predict the neural response even if it doesn't drive that response. The standard approach to dealing with this problem, taken here, is to test if a feature improves the prediction accuracy of a model above and beyond that of a baseline model (using cross-validation to avoid over-fitting). If the feature improves prediction accuracy, then one can conclude that the feature contributes additional, unique variance. However, there are two key problems: (1) the space of possible features to control for is vast, and there will almost always be uncontrolled-for features (2) the relationship between the relevant control features and the neural response could be nonlinear. As a consequence, if some new feature (here surprise) contributes a little bit of additional variance, this could easily reflect additional un-controlled features or some nonlinear relationship that was not captured by the linear model. This problem becomes more acute the smaller the effect size since even a small inaccuracy in the control model could explain the resulting finding. This problem is not specific to this study but is a problem nonetheless.

2. The authors make a distinction between "Gestalt-like principles" and "statistical learning" but they never define was is meant by this distinction. The Temperley model encodes a variety of important statistics of Western music, including statistics such as keys that are unlikely to reflect generic Gestalt principles. The Temperley model builds in some additional structure such as the notion of a key, which the n-gram and transformer models must learn from scratch. In general, the models being compared differ in so many ways that it is hard to conclude much about what is driving the observed differences in prediction accuracy, particularly given the small effect sizes. The context manipulation is more controlled, and the fact that neural prediction accuracy dissociates from the model performance is potentially interesting. However, I am not confident that the authors have a good neural index of surprise for the reasons described above, and this limits the conclusions that can be drawn from this manipulation.

Reviewer #1 (Public Review):

The authors used data from extracellular recordings in mouse piriform cortex (PCx) by Bolding & Franks (2018), they examined the strength, timing, and coherence of gamma oscillations with respiration in awake mice. During "spontaneous" activity (i.e. without odor or light stimulation), they observed a large peak in gamma that was driven by respiration and aligned with the spiking of FBIs. TeLC, which blocks synaptic output from principal cells onto other principal cells and FBIs, abolishes gamma. Beta oscillations are evoked while gamma oscillations are induced. Odors strongly affect beta in PCx but have minimal (duration but not amplitude) effects on gamma. Unlike gamma, strong, odor-evoked beta oscillations are observed in TeLC. Using PCA, the authors found a small subset of neurons that conveyed most of the information about the odor (winner cells). Loser cells were more phase-locked to gamma, which matched the time course of inhibition. Odor decoding accuracy closely follows the time course of gamma power.

I think this is an interesting study that uses a publicly available dataset to good effect and advances the field elegantly, especially by selectively analyzing activity in identified principal neurons versus inhibitory interneurons, and by making use of defined circuit perturbations to causally test some of their hypotheses.

Major:

- The authors show odor-specificity at the time of the gamma peak and imply that the gamma coupling is important for odor coding. Is this because gamma oscillations are important or because gamma is strongest when activity in PCx is strongest (i.e. both excitatory and inhibitory activity, which would cancel each other in the population PSTH, which peaks earlier)? To make this claim, the authors could show that odor decoding accuracy - with a small (~10 ms sliding window) - oscillates at approx. gamma frequencies. As is, Fig. 5 just shows that cells respond at slightly different times in the sniff cycle. What time window was used for computing the Odor Specificity Index? Put another way, is it meaningful that decoding is most accurate when gamma oscillations are strongest, or is this just a reflection of total population activity, i.e., when activity is greatest there is more gamma power, and odor decoding accuracy is best?

- The authors say, "assembly recruitment would depend on excitatory-excitatory interactions among winner cells occurring simultaneously during gamma activity." Can the authors test this prediction by examining the TeLC recordings, in which excitatory-excitatory connections are abolished?

- The authors show that gamma oscillations are abolished in the TeLC condition and use this to claim that gamma arises in the PCx. However, PCx neurons also project back to the OB, where they form excitatory connections onto granule cells. Fukunaga et al (2012) showed that granule cells are essential for generating gamma oscillations in the bulb. Can the authors be sure that gamma is generated in the PCx, per se, rather than generated in the bulb by centrifugal inputs from the PCx, and then inherited from the bulb by the PCx?

Reviewer #1 (Public Review):

In this manuscript, the authors built logistic regression prediction models for linear growth faltering using demographic, socioeconomic, and clinical variables, with the objective of developing a clinical prediction rule that could be applied by healthcare workers to identify and treat high-risk children. A model with 2 variables selected by random forest variable importance performed similarly to a model with 10 variables. Age and HAZ at baseline were selected for the 2-variable model, consistent with existing literature. The authors externally validated the 2-variable model and found similar discriminative ability. Based on typical rule-of-thumb cutoffs, model performance was moderate (AUCs of ~0.65-0.75, depending on model specification); models may still be useful in practice, but this should be further discussed by the authors.

Strengths:

Linear growth faltering is a pressing issue with broad, negative impacts on the health, development, and well-being of children worldwide. In this work, the authors applied clearly explained, thoughtful approaches to variable selection, model specification, and model validation, with large, multi-country cohorts used for training and external validation. Appropriate datasets for external validation can be challenging to find, but the MAL-ED data used here is well-suited to the task, with similar predictor and outcome measurements to the GEMS training data. The well-characterized studies allowed the authors to explore a wide range of potential predictors for stunting, including socioeconomic factors, antibiotic use, and diarrheal etiology.

Weaknesses:

This work would benefit from additional discussion around the clinical relevance of the results. For example, what is the current standard of care for prevention of stunting, and how much would this model improve the status quo? Is specificity of 0.47 in the context of sensitivity of 0.80 an acceptable tradeoff with regards to the interventions that would be used? More discussion around these points is necessary to support the authors' conclusions that these models could potentially be used to support clinical decisions and target resources.

In addition to the external validation, further investigation of model performance in key subpopulations would strengthen the importance and applicability of the work. For example, performance of prediction models may vary widely by setting; it would be valuable to show that the model has similar performance in each country. Another key sensitivity analysis would be to show consistent model performance by HAZ at baseline. The authors note that stunting may be challenging to reverse (p.20), and many of the children are already below the typical cutoff of HAZ<-2 at baseline; it would be valuable to show model performance among the subgroup of children for whom treatment would be most beneficial.

Reviewer #1 (Public Review):

The current study uses microbiology, biochemistry, microscopy, and viral vectors to establish a role for prefrontal cortex expression of the immediate early gene NPAS4 in sucrose preference and dendritic spine morphology in the mouse social defeat stress model. The experimental designs are appropriate and the hypotheses addressed are interesting. The paper is generally very well-written and the figures are clear. Most of the statistical analyses are appropriate, and they are reported in clear and useful tables. Thus, the general potential for the studies is quite high. The authors conclusively show that NPAS4 is induced in mPFC in response to social defeat stress and that NPAS4 is important for stress-induced changes in mPFC dendritic spine number. However, some of the key data regarding reward motivation are difficult to properly interpret and do not convincingly demonstrate a behavioral result of NPAS4 knockdown in mPFC. Moreover, the spine morphology and sequencing analyses lack depth. Most importantly, although the authors explore the effects of reducing NPAS4 expression in mPFC, they do not explore the effects of increasing NPAS4 expression or function, and thus the studies seem incomplete and cannot be fully interpreted.

Reviewer #1 (Public Review):

The study by Osei-Owusu and colleagues addresses the mechanism of desensitization of the proton-activated chloride (PAC) channel. In three recent milestone papers, the authors have cloned the channel, identified its cryo-EM structure under high-pH and low-pH conditions, and addressed the mechanism of its pH-dependent activation. Interestingly, despite dramatic rearrangements in the TM domain, both the high- and the low-pH structures showed a closed pore, suggesting that the latter might represent an inactivated state. In the current study, the authors show that prolonged exposure of PAC to an acidic extracellular solution causes inactivation which is rapidly reversible at high pH. They further show that four mutations (H98R, E107R, D109R, H250R) that are predicted to disrupt interactions that stabilize the low-pH structure reduce PAC inactivation. On the other hand, two mutations that accelerate inactivation (D91R, E94R) are predicted to stabilize the low-pH structure based on MD simulations. The work thus functionally supports the earlier hypothesis that the low-pH cryo-EM structure indeed represents an inactivated state. Moreover, it identifies several key titratable residues that are involved in this process.

The choice of the tested residues is based on strong structural evidence, and the electrophysiological data largely seem to support the conclusions, even though the analysis is not always rigorous. (Time constants seem unreliable as they are extracted from decay time courses that are too short to be reliably fitted, but comparisons of the simple parameter "fractional surviving current after 30 s" seem convincing enough.) Some of the mechanistic conclusions are largely based on MD simulations which I am not qualified to assess.

Reviewer #1 (Public Review):

The authors suggest that there is a long-term periodicity of individual antibody response to influenza A (H3N2). The interesting periodicity may be surely appeared. Though the authors assume that the periodicity is driven by pre-existing antibody responses, the authors could provide more supportive data and discuss some possibilities.

1. The authors can investigate whether the periodicity reflects an epidemic/invasion record of A(H2N3) within Guangzhou or the surrounding city, e.g., the numbers of flu-infected people yearly can be referred to.

2. The authors can consider whether the participants are recently/previously vaccinated and/or infected with flu. The remaining antibodies may reflect a long memory but may show a recent activation.

3. The strains inducing high HI titers may have similar mutations and may be reactive to the same antibodies. What are the mutation frequencies among 21 A(H3N2) strains?

Reviewer #1 (Public Review):

While the circuits underlying the computation of directional motion information in the fly brain are very well described, much less is known about the neurons serving the detection of objects. In a previous publication from the same lab, it has been shown that flies perform body saccades to track a moving object during flight. In the current paper, Frighetto and Frye provide evidence that T3 cells, a population of neurons within the optic lobes, are involved in this task. First, they performed 2-photon Calcium imaging from T3 cells to show that these cells respond to moving bars, which they later use in behavioural experiments. They then silenced T3 cells using genetic tools and tested the behavior of these flies in response to a rotating bar using two different setups. In one, the flies are fixed and bilateral changes in wing stroke amplitude are used as a measure for turning, in the other, flies are magnetically tethered such that they can rotate around the vertical body axis. Silencing T3 cells leads to the abolishment of the steering response induced by object position using a bar that is defined by its motion relative to the surround, but leaves the response to object motion intact. In the magnetically tethered flies, it reduces the number of saccades and thus leads to an impairment of bar-tracking behavior. In another set of experiments they optogenetically activated the whole population of T3 neurons (which supposedly impairs their normal function), which leads to an increase in the number of saccades after the activation (when the light stimulus used to activate the cells is turned off). Silencing the neurons necessary for detection of local motion, T4 and T5 cells, in contrast reduces responses elicited by object motion rather than position, but also has an impact on object tracking saccades. The authors provide a simple model, where speed-dependent signals from multiple T3 cells are integrated and trigger a saccade, when a threshold is reached.

The data generally support the conclusion that T3 cells play a role in detecting bar position and in controlling saccades in response to rotating bars. However, there are some inconsistencies in the data that are not sufficiently explored and discussed.

1. In a previous paper from the lab (Keleş et al., 2020), it was shown that T3 cells respond preferentially to small objects, whereas here they robustly respond to elongated bars and even large-field gratings. This discrepancy is not discussed.

2. In a previous paper, the authors showed that integrated positional error rather than bar position is used to elicit bar-tracking saccades and that saccade amplitude is relatively stereotyped. However, here they show, that T3 cells respond much more strongly to a slowly moving stimulus (18{degree sign}/s) rather than to the fast moving stimuli used for the behavioral experiments (> 90{degree sign}/s). This response property plays an important role for the model they propose. My general concern here is that the findings might not be generalizable to slower moving bars, where more precise, position-dependent responses could play a larger role, and that these fast moving bar stimuli represent an extreme situation, where the flies cannot accurately track bar position any more.

3. The claim that T3 cells are tuned to stimulus velocity is not supported by the data in my view. For the bar stimuli, the authors only tested speeds of 18{degree sign}/s and above 90{degree sign}/s, but nothing in between. For the grating motion there seems to be an influence of temporal frequency for the same stimulus velocity (see e.g. Fig.1_1), but this is not quantified.

4. The results from the optogenetic activation experiments are hard to interpret, as it is unclear how a prolonged activation of all T3 cells would affect the downstream circuitry. It is not clear that this experiment is equivalent to a "loss-of-function perturbation" of T3 cells as the authors claim in the text.

Reviewer #1 (Public Review):

This work provides a new general framework for estimating missing data on cervical cancer epidemiology, including sexual behavior, HPV prevalence, and cervical cancer incidence. These data are useful to determine impact projections of cervical cancer prevention. The authors suggest a three-step approach: 1) a clustering method applied on registries with an intermediate level of data availability to cluster cervical cancer incidence based on a Poisson-regression-based CEM algorithm, 2) a classification method applied on registries with a low level of data availability to classify cervical cancer incidence based on a Random Forest, 3) a projection method applied on missing data based on the mean of available data. The authors use India as a case study to implement this new methodology. Results indicate that two patterns of cervical cancer incidence are identified in India (high and low incidence), classifying all Indian states with missing data to a low incidence. From this classification, missing data is approximated using the mean of the available data within each cluster.

A strength of this approach is that this methodology can be applied to regions with missing data, although a minimum set of information is needed. This makes it possible to have individual data for each unit in the region.

One of the weaknesses of this methodology is the need for a minimum set of epidemiological data to enable impact projections. It is true that when epidemiological cervical cancer data is not available, authors mentioned that general indicators (e.g., human development index, geography) can be used but projections will be probably less realistic. As observed with other techniques, countries with fewer resources have less data available and cannot benefit from these types of techniques to have more adequate guidelines.

Imputation of missing data is always a challenging issue. The technique proposed in this manuscript is an interesting new approach to missing data imputation that could be applied with a minimum set of available data. However, we must focus on obtaining reliable data from each region of the world to help local health authorities implement better preventive measures for the local population.

Reviewer #1 (Public Review):

The manuscript by Masschelin et al. describes how Vitamin B2 deficiency affects body composition, energy expenditure, and glucose metabolism. B2 deficient mice have lower O2 consumption, and locomotor activity, with no difference in food intake. These mice also have lower liver FAD levels, which is expected given that B2 is a necessary cofactor for this coenzyme. Additionally, these mice have lower blood glucose levels following pyruvate injection, implying a lower capacity for gluconeogenesis. Using PPAR KO mice, they show that this effect on pyruvate tolerance is due to PPARα activation, though there is still a minor difference between wild-type and KO mice. Importantly, they show that fenofibrate PPARagonism can improve glucose output following pyruvate injection in the absence of B2. The authors also perform robust metabolomics in each experimental condition and phenotype of the mouse well.

1. The authors have yet to explore other explanations of differences in glucose metabolism under B2D +/-Fenofibrate. The canonical targets of PPARα are involved in fatty acid oxidation, ketogenesis, and VLDL/HDL metabolism, in addition to gluconeogenesis (Bougarne et al. 2018). Gluconeogenesis is more of a fasting response due to CREB, FOXO1/PGC1activation rather than PPAR. In response to B2D, the PPARα KO mice have increased plasma TGs, which may suggest a difference in VLDL TG secretion (Suppl. S3). Perhaps lipid metabolism is more directly affected, and changes in glucose metabolism are secondary to that of triglyceride metabolism. Regarding ketogenesis, the fenofibrate+ B2D fed mice have decreased plasma beta-hydroxybutyrate, suggesting decreased ketogenesis, which is a more canonical PPARα pathway (Suppl. S3). Testing each of these processes would help control that this mechanism is specific to gluconeogenesis and not secondary to something else.

2. Is the effect on ISR dependent on PPARα? Is the mechanism of Fenofibrate on the liver, or on another cell type? In Figure 1, the authors state that Riboflavin deficiency alters body composition and energy expenditure, and then focuses on the liver. However, FAD levels are also increased in the heart and kidneys in addition to the liver. These tissues also respond to PPARα agonism, in addition to the muscle which plays a role in regulating glucose metabolism (B2D mice also have a higher lean mass (Fig 1e)). Additionally, the authors haven't shown specifically if the effects of fenofibrate on electron transport and the ISR are dependent on the presence of PPARα (Figure 5, 6).

Reviewer #1 (Public Review):

In their manuscript, Haenelt et al. investigated the structure-function relationship for cortical columns in the in vivo human brain. The example they used is the thick stripe - pale stripe - thin stripe organisation of secondary visual cortex (V2).

The specific strength of the current study lies in the combination of cutting edge imaging protocols for quantitative measurements of myelin-related signals (qMRI) together with functional activation, both at submillimeter resolution at high field (7T). This allowed the visualisation of the stripy organisation of V2 with regards to colour (thin stripes) and binocular disparity (thick stripes) as well as myelination in individual human subjects. The main results suggest higher myelination for the pale stripe regions. This is in line with some earlier studies, across primate species, but not with others.

One potential issue is that the high myelination signal is associated with the compartment in V2 (pale stripes) which was not functionally defined itself but by the absence of specific functional activations. No difference was reported between those stripes that were defined functionally. Other explanations for the differential pattern of a qMRI signals, e.g. ROI distribution for presumed pale stripes is not evenly distributed (more foveal), ROIs with low activations due to some other factor show higher myelin-related signals, cannot be excluded based on the analysis presented.

Another theoretical and practical issue is the question of "ground truth" for the non-invasive qMRI measures, as the authors - as their starting point - roundly dismiss direct histological tissue studies as conflicting, rather than take a critical look at the merit of the conflicting study results and provide a best hypothesis. If so, they need to explain better how they calibrate their non-invasive MR measurements of myelin.

While this paper makes an important contribution to the question of the association of specific myelination patterns defining the columnar architecture in V2, it is not entirely clear whether the authors can fully resolve it with the data presented.

The highly sophisticated methods and detailed analysis show that high resolution investigation of structure-function relationship of the columnar organisation in human visual cortex are feasible and reliable. V2 stripe patterns can be visualised structurally (with quantitative myelin-related measurements) and functionally (based on functional selectivity, which is of considerable importance for the field. The results indicate that in humans, the pale or inter strip regions might be associated with high patterns of myelination.

Reviewer #1 (Public Review):

This manuscript attempts to disclose new insights into barrel cortex cell class-dependent and cell depth-dependent membrane potential (Vm) dynamics during active whisker sensing. The results highlight similarities but also specific differences between different types of cortical neurons. The approach used is very effective and direct: somatosensory stimulation is performed in awake animals without anesthesia, the neurons are recorded with intracellular whole cell patch clamp recording that can provide fast responses with high resolution, and the identification of various neuron types is achieved by using mice expressing genetically defined selective fluorescent markers. The results support the main conclusions. The work is an extension of previous, similar work performed by this group, However, most previous Vm studies in the mouse barrel cortex during behavior have largely focused on superficial neurons located in the upper ~300 μm of the neocortex since these are more easily targeted through two-photon microscopy. In this study, the authors extend current knowledge by investigating Vm dynamics across a greater range of depths including two-photon targeted whole-cell recordings across the upper ~600 μm of the neocortex. I believe that this manuscript uses a demanding, but excellent approach that will be useful to other researchers in the field. The manuscript is likely to be influential.

Reviewer #1 (Public Review):

Proton Activated Chloride (PAC) channels have been recently identified as important contributors to endosomal acidification, and their activity in the plasma membrane increases under certain pathological conditions and can induce cellular death. There is very limited information on the pharmacology of these ion channels. By recording from endogenous PAC channels stimulated with an acidic extracellular solution in HEK293 cells using the patch-clamp technique, this study finds that PAC channels are inhibited physiological concentrations of the soluble short-chain PIP2 analog dic-8-PIP2. Inhibition is quantified for several PIP2-related lipids with different number of headgroup phosphates and shorter or longer acyl chains, and it is found that an acyl chain with more than 8 carbons and a negative headgroup charge are both required for robust inhibition. Importantly, inhibition appears to result from PIP2 incorporated into the outer membrane leaflet, as treatment of the inner leaflet with PIP2 or poly-lysine to either increase or decrease PIP2, respectively, did not have any effect on channel activity, as opposed to when the lipid is extracellularly applied. A structure of the channel in the presence of PIP2 was obtained using single-particle cryo-electron microscopy - the structure resembles a previously observed conformation for PAC channels that likely represents a non-conducting desensitized state, and it contains densities with a shape that is consistent with a bound PIP2 molecule in the outer leaflet. Mutations to alanine based on the channel-lipid interactions observed in the structure were all found to disrupt inhibition of PAC channels by PIP2, consistent with the location of the lipid binding site proposed in the study. By comparing the amino acid sequence of human PAC channels with those of other species, it is found that the proposed lipid binding site is highly conserved except in zebrafish. Notably, zebrafish PAC channels are less susceptible to inhibition by PIP2, and mutation of residues at the binding site to those present in the human channel increases inhibition, consistent with the proposed location of the binding site for PIP2. Finally, it is found that the kinetics of inhibition by PIP2 are positively correlated with the degree of channel activation and also with the kinetics of desensitization, suggesting that PIP2 binds more favorably to the desensitized state of the channel whereas it does not bind to the closed state, providing a possible mechanism for the inhibition.

Results are clearly reported and findings are generally robust. One concern is that most of the electrophysiological characterization of the inhibition of PAC channels by PIP2 lipids was done using endogenously expressed channels. It is unclear why this was done because mutant channels are studied in a PAC KO cell line that could have been used for all experiments. The effects of acidic pH and acidic pH + PIP2 in cells that do not express PAC channels is therefore not shown, but would be important to establish that the measured effects of the lipid are specific to PAC channels.

Another concern for the study is related to the uncertainty in establishing that the bound lipid is indeed PIP2. Although the mutagenesis results are all consistent with the proposed binding site, it remains a possibility that the mutations affect PIP2 inhibition indirectly by e.g. changing the rate of channel desensitization, which was not measured for any of the mutants on Figure 3E. There is not additional analysis performed to determine whether other types of lipids could occupy the density that is proposed to represent PIP2. Although this might be difficult because no density for the headgroup of the lipid was observed.

A final caveat of the study is that effects of PIP2 on the extracellular leaflet might be non-physiological. If this were the case, however, the identification of a non-physiological binding site that favors desensitization might still be beneficial for drug design in the context of this channel.

Reviewer #1 (Public Review):

In this study, Luo, Han, and Yin et al. conduct a fecal microbiota transplant from MSTN KO pigs exhibiting hypertrophy to recipient antibiotic-depleted B6 mice. The microbiota transplants successfully transferred muscle hypertrophy phenotypes to the mice. Aspects of the pig gut microbiome were recapitulated in the recipient mice, including a higher abundance of Romboutsia and valeric acid. The authors then demonstrate that 5 weeks of daily gavage of valerate, but not isobutyrate or water, was sufficient to increase type IIb myofiber growth and GA muscle mass, and protect mice against dexamethasone-induced muscle atrophy. Taken together, these data neatly demonstrate that genetic disruption of the myostatin gene results in a microbiome-dependent increase in valeric acid, which in turn results in significantly altered skeletal muscle growth.

Reviewer #1 (Public Review):

The authors have used eye image data to create an aging clock of the retina in data from eyePACS with validation in the UK Biobank. They show that the clock predicts mortality independently of chronological age and that it is correlated with phenotypic age. Moreover, a GWAS is conducted in the UK Biobank, which identifies novel genetic loci and a top site located in the ALKAL2 region that is functionally validated in a drosophila model. Overall, the study is interesting with sound methodology and is a nice contribution to the field providing a GWAS summary statistic of the eye clock useful for follow-up analyses.

Reviewer #1 (Public Review):

This work leverages single-cell RNA-sequencing to probe changes in various immune functionings within the ovary in aging. The data provided is the most comprehensive of ovarian immune cells at the resolution of single-cell transcriptomics to-date and will be valuable to other researchers. The authors explore four distinct immune functionings:

- The authors identify macrophages and a unique CD3+CD8-CD4- T-cell subpopulation that change in abundance with aging. While these are interesting findings that align with flow cytometry results, the lack of batch correction and application of single-cell differential abundance tools limit the strength of the claims. The authors also do not further probe gene expression changes specific to these populations.

- The authors also analyze changes in global gene expression across cell types using an enrichment analysis; Figure 3B specifically is an excellent visualization summarizing potential global and cell-type specific changes in gene expression programs during aging.

- The authors infer differences in cell-cell communication mediated by various chemokines and cytokines. In this analysis, they claim a decreased inflammatory response due to aging. Here, the global decrease in gene expression in many cell types is not accounted for. Visualizations and quantitative analyses could benefit from existing, specialized in cell-cell communication tools.

- A discussion of changes to the expression of SASP receptors on immune cell types.

While both the data and biology presented are quite interesting, this study is perhaps too wide in breadth such that no individual result is extensively and rigorously explored.

Reviewer #1 (Public Review):

This study presents an implementation of single-particle tomography within the Bayesian framework of the Relion software package. Similar to previously proposed strategies, the approach leverages single-particle analysis tools and tomographic geometric constraints to improve map resolution. Results on the EMPIAR-10164 benchmark dataset appear to match the performance of previous methods, but no maps were made available or deposited, and no direct comparisons with previous results are shown. Consistent with previously published strategies that use 2D projections instead of sub-volumes, the approach performs favorably in terms of resolution when compared to traditional subvolume averaging.

Strengths

- Use of a Bayesian framework for image refinement and reconstruction requires less parameter tweaking.<br /> - By making the new implementation accessible through a GUI already familiar to many SPA users, this tool will make SPT easier to use.<br /> - The implementation of 3D classification could be potentially beneficial to study sample heterogeneity in situ.<br /> - In cases where high resolution can be achieved (better than 3A), the approach has the potential to correct for higher-order optical aberrations.<br /> - Using two cryo-ET datasets, resolution improvements are shown over traditional subvolume averaging (as implemented in the AV3 Matlab suite of programs [Forster et al., 2007] and Dynamo suite [Castano-Diez, 2012]).

Weaknesses

- The approach recapitulates previously proposed strategies for SPT refinement that use raw tomographic projections instead of sub-volumes to improve resolution. Strategies that leverage the increased SNR of average structures to optimize particle pose and deformation, tilt-series alignment, and CTF refinement, were proposed and validated in earlier studies [1,24].<br /> - Compared to end-to-end pipelines for tomography data analysis such as EMAN2 and Dynamo, this approach only implements the subtomogram averaging step, while still relying on external tools for initial tilt-series alignment, CTF estimation, and particle picking.<br /> - In terms of performance, the HIV-1 Gag maps obtained from the benchmark dataset EMPIAR-10164 do not represent an improvement in resolution over previous methods.<br /> - No validation is provided to support the claim that the tool can correct for higher-order optical aberrations of the microscope from cryo-ET data.<br /> - No results are provided to validate the 3D classification routines to study heterogeneity, and no experiments are shown to support the claim that the new approach is more accurate than previous sub-volume classification strategies that compensate for the missing wedge (such as the approach implemented in the earlier version of Relion [4]).

Overall, this implementation of SPT would be a valuable resource for the cryo-ET community.

Reviewer #1 (Public Review):

The authors present a strong set of experiments to uncover what type of role non-mutant stromal cells might be playing in the development of VM and AST, two vascular lesions that share some similarities.

Questions about experimental design.

1) For quantification of gene expression in VM and AST specimens in Figure 2, the methods say qPCR data were normalized to housekeeping genes, but it would be helpful to normalize to endothelial content. It might be that increased TGFa is due to increased endothelium.

2) The mutant allelic frequency for the HUVEC-PIK3CA WT versus HUVEC-PIK3CA H1047R should be provided. This is critically needed for the interpretation of the results.

3) From Figure 5, it appears that the human primary fibroblasts are not required for the mutant ECs to form perfused vessels (panel H). Is it possible that TGFa from the ECs is sufficient to drive vascular malformation?

Reviewer #1 (Public Review):

The current study melds computational and docking methods with functional measurements in a systematic approach: first, they analyze the mechanism of inhibitor binding to EAAT2; second, they mutate ASCT to resemble EAAT and show that the general binding pocket and inhibition mechanism are conserved; third, they perform an in silico screen to identify compounds that bind to the WT ASCT binding pocket; fourth, they perform electrophysiological assays showing that this novel compound allosterically modulates ASCT function. This is a complete and comprehensive study with extensive experimental support for the major conclusions. The authors identify an allosteric ASCT inhibitor, and although only partial inhibition is achieved, this study serves as proof-of-concept that this site can be targeted in diverse SLC-1 transporters as an allosteric inhibitory site.

Reviewer #1 (Public Review):

Using simultaneous EEG-fMRI, authors asked whether neurovascular coupling is already functional in preterm-born neonates, in whom the underlying physiological mechanisms may still be immature at several levels. The question is very interesting and has implications for the study of brain function development as well as early brain injuries. The manuscript reports a correlation between the "mean duration of EEG microstates" and "fMRI BOLD signal-change", through which authors suggest that such a relationship between the EEG activity and BOLD signal highlights the functionality of neurovascular coupling already at the preterm period. The methodology is interesting, but more (not extensive) analysis is required to support the main conclusion and explain the results.

1. The main finding of the study in support of the conclusion comes from relating the inter-individual variability between EEG microstate duration and fMRI BOLD signal change. Given the few subjects (n=13), small even for neuroimaging in infants, studying effects based on inter-individual variability needs to be done with extra care. It is thus important to check whether interindividual variability can be observed for/accounted for by more basic effects in this population :

- The age range is relatively large (age at scan 31 PMA to 36 PMA - but also the age at birth: 29 to 35 weeks) for the number of included infants. Given the intense age-related changes in brain development at this period, it is important to take this factor into account and study it and to have them perhaps explicitly addressed in the manuscript: a ) Does the duration of EEG microstates depend on the age of the infants? b ) Does the time-to-peak in BOLD decrease with increasing age (Arichi et al., 2012)? c) and eventually does the relation between microstate duration and BOLD signal change holds once controlled for their common dependency on the age (i.e. Once partial correlations are used)?

2. The mean/std for the number of epochs per infant can be detailed more. What was the minimum number of epochs? Did such variability in the number of epochs impact microstate properties such as global explained variance/duration? How variable GEV was across infants and would that relate to the variability in duration?

3. Given that sensory-driven changes in microstates follow a sequential pattern (Hu et al., NeuroImage, 2014), could some "microstate syntax" characterize the underlying brain dynamics during stimulation processing in these neonates? Studying the presence of such syntax could be a way to show structured sensorimotor processing, and to further help quantify the inter-individual variability.

4. Is the sleep state monitored (from the EEG signal itself for example)? Given that the sleep state affects EEG activity and in particular EEG microstate properties in newborns (Khazaei et al., Brain topography, 2021), is there a way to rule out that the variability in microstate duration/BOLD signal change is not due to vigilance states?

5. Some of the conclusions/discussion points could be more cautiously stated and developed. On page 7: "However, our results imply that immature neurovascular coupling may not have a significant role in the pathophysiology of cerebral tissue injuries typically seen in preterm born infants (Volpe, 2009); and even that clinical interventions for perinatal brain injury could account for, accommodate, or capitalize on the presence of neurovascular coupling in the preterm human brain to minimize the severity of the injury and its long-term consequences." With an age range covering very preterm infants to late preterm period, generalizing such a conclusion could be potentially misleading for younger infants for example (fNIRS work in younger preterms does not support neurovascular coupling - Nourhashemi et al, Human brain mapping, 2020). As a group - on average - such a pattern may be reported, but the number of infants at each age does not allow us to draw a conclusion about the developmental stage at which such coupling is truly in place. These points could be more directly discussed with regards to the previous literature.

Reviewer #1 (Public Review):

This manuscript reports a series of studies that evaluate the role of long descending propriospinal neurons arising in the cervical spinal cord that project axons to the lumbar spinal cord in locomotor function recovery after spinal cord injury. The experiment uses several different evaluations of gait including BBB, ladder rung walk tests, and kinematics to compare walking before and after synaptic silencing of long descending propriospinal neurons projecting axons to L2. The data reveal that silencing of these neurons mildly improves walking function. The experiments are carefully described and well-controlled. The use of several different methods to evaluate locomotor function is a strength as is a well-thought-out approach to synaptic silencing. The data support the conclusions proposed by the authors. There are caveats to be considered in interpreting the results which are thoughtfully and thoroughly articulated in the discussion.

Public Review:

The study of Choi and collaborators provide novel information about the microstructural morphology and the crystallographic structure of palaeognathid eggshells.<br /> In terms of format and structure, the work is well organized and the extinction of each section is appropriate. All figures, both those from the main text and Supplementary Information, are of good-quality, informative, and useful, facilitating the understanding of the text. The bibliography is very updated, and all essential references are mentioned.

One of the strongest points of the work, in my opinion, is the designee of the study itself, which included specimens from all living palaeognathid birds and several extinct taxa from a large range of lineages.

The methodology used for analysing the crystallographic nature of the studied specimens (EBSD) is appropriate for the goals of the study. The phylogenetic approaches are also right, which are based on the most recent studies about the phylogenetic relationship of ratites.

Despite their complexity, the results are well presented, being relatively easy to understand for a person not versed in the subject. In fact, the ways in which they are described give them the potential to be used as a guideline to anyone interested in eggshell microstructure.

The discussion of the results seems consistent with the data obtained. Despite the phylogenetic relationship between some palaeognathid taxa remains partially instable, authors present different plausible scenario to explain the variability of the eggshell microstructure within a single monophyletic lineage (homology vs homoplasy). In fact, the homoplastic scenario is, perhaps, the most shocking one to me. In part, it is because it intrinsically suggests that all phylogenetic studies based on eggshell morphological features, and conducted during the last 20 years, are potentially artefacts, and they do not represent real phylogenetic relationships. Far from being a criticism, this interpretation has massive implications, especially for those studies where the taxonomic attribution of a fossil egg is based on phylogenetic results (i.e. Montanoolithus, Cairanoolithus).

Although I do not find negative arguments for any special section of the study, I have a question regarding Triprismatoolithu stephensis:

As mentioned in the text, Triprismatoolithu is analysed by the authors, and several pictures are provided in Fig.S12 alongside a brief description in de Supplementary Tex4. But it seems that it is not included in any of the phylogenetic analyses or figures. Why?

If the specimen has no implication for any of the main analyses, there is no need to be considered as "studied material".

Reviewer #1 (Public Review):

Zeng and colleagues investigated the neural underpinnings of visual-vestibular recalibration. Specifically, they measured changes in three monkeys' perception of unisensory heading cues as well as associated changes in neuronal responses to these cues in three different cortical areas following prolonged exposure to systematic visual-vestibular discrepancies. Behavioral responses in a motion direction discrimination task indicate unisensory perceptual shifts in opposite directions that account for the cross-modal discrepancy the monkeys were exposed to. Neuronal firing patterns, related to motion discrimination judgments by means of neurometric functions indicated analogous shifts in neuronal tuning in areas MSTd and PIVC. In contrast, in area VIP tuning for visual heading stimuli shifted in the same direction as tuning for vestibular stimuli and thus in contradiction to the observed perceptual shifts.

The shifts observed in MSTd and PIVC fit nicely with existing theories and results regarding cross-modal recalibration and substitute claims that activity in these areas might underlie perceptual decisions. The shift of visual tuning in VIP is surprising and will certainly spark further investigation.

Overall the results are really interesting, yet, the manuscript in its current form needs revisions along two dimensions, 1) data analysis and 2) writing.

Reviewer #1 (Public Review):

The manuscript by Coates et al. from the Brown lab adds a fascinating and colorful set of tiles to the growing mosaic of small molecule control of the sterol pathway through strategic employment of different parts of the proteostasis pathway. Dr. Brown is an active and creative leader in this field, and this story brings some new and surprising twists to our understanding of the ways that metabolites, and potentially other small molecules, can alter protein processing and life cycle as part of normal cellular function or pathophysiological states. The data are convincing and thorough, and do a great job of revealing many mechanistic aspects of the intriguing observation that hypoxia changes SM processing and activity by altering its degradative fate. The contributing parts of the whole process include altered MARCH 6 E3 ligase activity, new metabolite-ligand regulators (squalene), and ligand-dependent escape from the proteasome to allow the production of a novel form of SM that is freed from the normal regulation of the full-length protein caused by cholesterol, as the authors have previously described. I particularly appreciate three aspects of this study.

First, they test a lot of hypotheses to gain a very full understanding of the gears that are turning to make this hypoxia response machine run. Importantly, these studies also rule out some oxygen sensing mechanisms that work in other contexts, like proline hydroxylation. Second, the authors go to great lengths to integrate the action of the moving parts in a quantitative way, to ascertain if the effects are explained by the coordinated separate changes that are occurring when hypoxia is imposed. And third, the work includes a very well-thought-out set of ideas about why this sort of response is occurring, both in normal cells experiencing either transient or long-term hypoxia, as well as in cancer cells that seem to prefer this form of truncated and alternatively regulated SM.

There is a growing interest in studying and harnessing small molecules to alter and affect protein stability, and these studies add weight to the idea that there are many evolved mechanisms that can teach us lessons both about foundational biology, and new approaches to drug discovery. These beautiful studies will be an important addition to the literature and will be read and referenced by many.

Evidence to claim 1

Claim 1: There are evolving solutions to reduce the carbon foot print

Acho que vale mudar o botão para abrir um popup com o formulário.

Será que vale mudarmos o estilo dessa seção para igual o da home?

Reviewer #1 (Public Review):

Using two openly available multi-task fMRI datasets, the authors decompose thalamic activity into a smaller set of components. They show that voxels with higher loadings on the main components (high task hub property) also have a high participation coefficient as derived from resting state data. Cortical activity patterns can be predicted to some degree from thalamic activity patterns, and generally better than from a number of other cortical areas. This prediction relies mainly on the voxels with high task hub scores. The results are valuable and methodological generally solid, with some aspects being incomplete.

1. The finding that thalamic activity exhibits a low dimension structure is in my opinion less of a finding, but rather an assumption that motivates the use of dimensionality reduction techniques. When the authors ask (line 101) "whether thalamic task activity exhibits similar low dimensional structure", what is the alternative hypothesis? I think it is a foregone conclusion that with a restricted number of tasks, and the intrinsic smoothness of fMRI activity data, there are always K<<N components that capture 50,75, 90% of the variance. If you had measured the spiking of the entire population of thalamic neurons or increased the threshold to 99%, the structure of activity would be more high dimensional. So I believe you can either frame this as an assumption going in, or you build carefully an alternative hypothesis of what a "high-dimensional" structure would look like. Generating activity data i.i.d would be the simplest case, but given that both signal and measurement noise in fMRI are reasonably smooth, this would be a VERY trivial null hypothesis.

2. The measure of "task hub" properties that is central to the paper would need to be much better explained and justified. You motivate the measure to be designed to find voxels that are "more flexibly recruited by multiple thalamic activity components", but it is not clear to me at this point that the measure defined on line 634 does this. First, sum_n w_i^2 is constrained to be the variance of the voxel across tasks, correct? Would sum_n abs(w) be higher when the weights are distributed across components? Given that each w is weighted by the variance (eigenvalue) of the component across the thalamus, would the score not be maximal if the voxel only loaded on the most important eigenvector, rather than being involved in a number of components? Also, the measure is clearly not rotational invariant - so would this result change after some rotation PCA solution? Some toy examples and further demonstrations that show why this measure makes sense (and what it really captures) would be essential. The same holds for the participation index for the resting state analysis.<br /> 3. For the activity flow analysis, the null models (which need to be explained better) appear weak (i.e. no differences across tasks?), and it is no small wonder that the thalamus does significantly better. The Pearson correlations are not overwhelmingly impressive either. To give the reader a feel for how good/bad the prediction actually is, it would be essential that the authors would report noise ceilings - i.e. based on the reliability of the cortical activity patterns and thalamic activity patterns, what correlation would the best model achieve (see King et al., 2022, BioRxiv, as an example).<br /> 4. Overall it has not been made clear what the RDM analysis adds to the prediction of the actual activity patterns. If you predicted the activity patterns themselves up to the noise ceiling, you would also hit the RDM correctly. The opposite is not the case, you could predict the correct RDM, but not the spatial location of the activity. However, the two prediction performances are never related to each other and it remains unclear what is learned from the latter (less specific) analysis.

Reviewer #1 (Public Review):

The authors serendipitously discovered that silencing Reln+ stellate neurons from medial entorhinal cortex layer II (mEC2) transiently by hyperpolarizing them causes them to degenerate. They replicate this result with two different tools to hyperpolarize these neurons, as well as with a tool to inhibit synaptic vesicle release at mECII axon terminals. They gain mechanistic insight into the degeneration process by performing a careful time course of axon morphological changes and caspase activation: somatic hyperpolarization causes axon retraction bulbs, while inhibition of glutamate release causes axon fragmentation. Crucially, they find that, unlike mEC2 neurons, neighboring Wfs1+ pyramidal cells or parasubicular cells do not degenerate when silenced in similar ways.

The vulnerability of mEC2 to inactivity is particularly compelling because the authors use three different tools to demonstrate it, two that hyperpolarize neurons (ivermectin-mediated activation of the modified glycine receptor alpha subunit, expressed transgenically; and Kir2.1 overexpression using AAV stereotaxic injection), and one that inhibits synaptic vesicle release at mEC2 terminals (Tetanus toxin overexpression using AAV stereotaxic injection). Each of these tools has its flaws but taken together the findings are very convincing. A few pieces of evidence that the various tools are achieving exactly what the authors say they are achieving are missing. But again, the convergence of the data between the three tools compensates for this to some extent.

I found the significance of the findings really fundamental and the writing of the paper absolutely remarkable - beautifully structured, crystal clear in its articulations and its implications. This paradigm has the potential to reveal crucial biology about plasticity in the adult, and about degeneration and vulnerability mechanisms. Vulnerability is such an important topic common to most neurodegenerative diseases, with absolutely no hints, until now, of what could render some cells more prone to degeneration, and immense potential for the discovery of central disease mechanisms. Even if degeneration relies here on the overexpression of an exogenous protein, it does not rely on the overexpression of a pathological protein directly associated with neurodegeneration, or on the invalidation of an essential protein. There is nothing trivial about the degeneration phenotype observed here, which makes the observations absolutely fascinating. What's more the authors show here evidence for the Grail of vulnerability: the side-by-side comparison of two similar/neighboring cell types treated in the same way, only one of which undergoing degeneration (Reln+ EC2 neurons Wfs1+ EC2 and parasubiculum neurons). The vulnerable cell type here also happens to be the very cell type that is most vulnerable to degeneration in Alzheimer's disease.

These findings are of major importance for a few different reasons:<br /> - Neuronal excitability is clearly an early event occurring in the EC of incipient Alzheimer's disease. This study suggests that the silencing of certain cells by Alzheimer's lesions might contribute to their degeneration.<br /> - A competition-based mechanism for the survival or degeneration of axons and neurons from EC2, is known to operate during development until the end of critical periods. This study suggests that EC2 neurons, which might be particular for their need to be plastic into adulthood, might use these mechanisms as well.<br /> - Again, they establish a paradigm for the mechanistic study of comparative vulnerability between cell types that can be investigated further to understand the molecular underpinnings of degeneration.

Reviewer #1 (Public Review):

By studying the effect of Treg depletion in a CD8+ T cell-dependent diabetes model the group around Ondrej Stepanek described that in the absence of Treg cells antigen-specific CD8+ OT-I T cells show an activated phenotype and accelerate the development of diabetes in mice. These cells - termed KILR cells - express CD8+ effector and NK cell gene signatures and are identified as CD49d- KLRK1+ CD127+ CD8+ T cells. The authors suggest that the generation of these cells is dependent on TCR stimulation and IL-2 signals, either provided due to the absence of Treg cells or by injection of IL-2 complexed to specific anti-IL-2 mAbs. In vivo, these cells show improved target cell killing properties, while the authors report improved anti-tumor responses of combination treatments with doxorubicin combined with IL-2/JES6 complexes. Finally, the authors identified a similar human subset in publicly available scRNAseq datasets, supporting the translational potential of their findings.

The conclusions are mostly well supported, except for the following two considerations:

1) From Fig. 4A and B it is not conclusively shown, that Tregs limit IL-2 necessary for the expansion of OT-I cells and subsequent induction of diabetes. An IL-2 depletion experiment (e.g. with combined injection of the S4B6 and JES6-1 antibodies) would further strengthen this claim. Along these lines, the authors claim "IL-2Rα expression on T cells can be induced by antigen stimulation or by IL-2 itself in a positive feedback loop [20]. Accordingly, downregulation of IL-2Rα in OT-I T cells in the presence of Tregs might be a consequence of the limited availability of IL-2.". The cited reference 20 did observe CD25 upregulation by IL-2 on T cells but the observed effect might only be caused by upregulation of CD25 on Treg cells, which increases the MFI for the whole T cell population. Did the authors observe significant upregulation of CD25 on effector CD4+ and CD8+ T cells in their experiments with IL-2/S4B6 or IL-2/JES6 treatment?

2) The anti-tumor efficacy of KILR cells is intriguing but currently, it is unclear if it is indeed mediated by KILR cells. Have KILR cells been identified by flow cytometry in the BCL1 and B16F10 models treated with doxorubicin and IL-2/JES6? Were specific KILR cell depletion studies conducted, e.g. with an anti-KLRK1 depleting antibody? Additional experiments addressing these questions would be desirable to further support the authors' claims.

Reviewer #1 (Public Review):

The authors are trying to determine how time is valued by humans relative to energy expenditure during non-steady-state walking - this paper proposes a new cost function in an optimal control framework to predict features of walking bouts that start and stop at rest. This paper's innovation is the addition of a term proportional to the duration of the walking bout in addition to the conventional energetic term. Simulations are used to predict how this additional term affects optimal trajectories, and human subjects experiments are conducted to compare with simulation predictions.

I think the paper's key strengths are its simulation and experimental studies, which I regard as cleverly-conceived and well-executed. I think the paper's key weakness is the connection between these two studies, which I regard as tenuous for reasons I will now discuss in detail.

The Title asserts that "humans dynamically optimize walking speed to save energy and time". Directly substantiating this claim would require independently manipulating the (purported) energy and time cost of walking for human subjects, but these manipulations are not undertaken in the present study. What the Results actually report are two findings:<br /> 1. (simulation) minimizing a linear combination of energy and time in an optimal control problem involving an inverted-pendulum model of walking bouts that (i) start and stop at rest and (ii) walk at constant speed yields a gently-rounded speed-vs-time profile (Fig 2A);<br /> 2. (experiment) human subject walking bouts that started and stopped at rest had self-similar speed-vs-time profiles at several bout lengths after normalizing by the average duration and peak speed of each subject's bouts (Fig 4B).<br /> If the paper established a strong connection between (1.) and (2.), e.g. if speed-vs-time trajectories from the simulation predicted experimental results significantly better than other plausible models (such as the 'steady min-COT' and 'steady accel' models whose trajectories are shown in Fig 2A), this finding could be regarded as providing indirect evidence in support of the claim in the paper's Title. Personally, I would regard this reasoning as rather weak evidence - it would be more accurate to assert 'brief human walking bouts look like trajectories of an inverted-pendulum model that minimize a linear combination of energy and time' (of course this phrasing is too wordy to serve as a replacement Title -- I am just trying to convey what assertion I think can be directly substantiated by the evidence in the paper). But unfortunately, the connection between (1.) and (2.) is only discussed qualitatively, and the other plausible models introduced in the Results are not revisited in the Discussion. To my naive eye, the representative 'steady min-COT' trace in Fig 2A seems like a real contender with the 'Energy-Time' trace for explaining the experimental results in Fig 4, but this candidate is rejected at the end of the third-to-last paragraph in the 'Model Predictions' subsection of Results based on the vague rationale that is never revisited.

An additional limitation of the approach not discussed in the manuscript is that a fixed step length was prescribed in the simulations. The 'Optimal control formulation' subsection in the Methods summarizes the results of a sensitivity analysis conducted by varying the fixed step length, but all results reported here impose a constant-step-length constraint on the optimal control problem. Although this is a reasonable modeling simplification for steady-state walking, it is less well-motivated for the walking bouts considered here that start and stop at rest. For instance, the representative trial from a human subject in Figure 8 clearly shows initiation and termination steps that differ in length from the intermediate steps (visually discernable via the slope of the dashed line interpolating the black dots). Presumably different trajectories would be produced by the model if the constant-step-length constraint were removed. It is unclear whether this change would significantly alter predictions from either the 'Energy-Time' or 'steady min-COT' model candidates, and I imagine that this change would entail substantial work that may be out of scope for the present paper, but I think it is important to discuss this limitation.

With my concerns about the paper's framing and through-line noted as above, I want to emphasize that I regard the computational and empirical work reported here to be top-notch and potentially influential. In particular, the experimental study's use of inexpensive wearable sensors (as opposed to more conventional camera-based motion capture) is an excellent demonstration of efficient study design that other researchers may find instructive. To maximize potential impact, I encourage the authors to release their data, simulations, and details about their experimental apparatus (the first two I regard as essential for reproducibility - the third a selfless act of service to the scientific community).

I think the most important point to emphasize is that the bulk of prior work on human walking has focused on steady-state movement - not because of the real-world relevance (since one study reports 50% of walking bouts in daily life are < 16 steps as summarized in Fig 1B), but rather because steady walking is a convenient behavior to study in the laboratory. Significantly, this paper advances both our theoretical and empirical understanding of the characteristics of non-steady-state walking.

It is also significant to note the relationship between this study, where time was incorporated as an additive term in the cost of walking, with previous studies that incorporated time in a multiplicative discount in the cost of eye and arm movements. There is an emerging consensus that time plays a key role in the generation of movement across the body - future studies will discern whether and when additive or multiplicative effects dominate.

Reviewer #1 (Public Review):

Tomanek and Guet describe the results of an evolution experiment where they allowed the bacterium E. coli to adapt to various concentrations of galactose as an additional carbon source. These conditions impose different degrees of demand for the galK enzyme, whose expression level depends on the promoter sequence and on the number of copies of the galK locus. Given that the initial promoter is random and weak, both amplifications of the locus and mutations in the promoter are expected to be adaptive. The experimental strains of E. coli were equipped with a fluorescent reporter system designed to discriminate between these two types of mutations. Furthermore, two strains, IS+ and IS-, were engineered with high and low rates of duplication around the galK locus, respectively. The main result is that at higher concentrations of galactose, where the demand for galK is high, E. coli adapts by acquiring combinations of both types of adaptive mutations, amplifications, and promoter mutations. In contrast, at low concentrations of galactose, where the demand for galK is low but not zero, E. coli appear to adapt by acquiring either an amplification or a promoter mutation but not their combinations. The observation of apparent interference between the acquisition of these two types of mutations is interesting and novel. The authors provide an intuitive explanation for it: when one mutation is sufficient to achieve the optimal expression of the gene, the mutation that is acquired first makes the other mutation obsolete, i.e., there is negative epistasis (possibly even sign epistasis) between these mutations, in the sense that the second mutation is much less adaptive (or possibly even deleterious) in the presence of the first one, in the low-demand environment. The authors discuss the possible implications of this finding for our understanding of molecular evolution and propose a new Amplification Hindrance hypothesis. This hypothesis states that, since amplifications occur at much higher rates than individual point mutations, they can slow down or even prevent sequence divergence. The amplification hindrance hypothesis stands in contrast to the Innovation-Amplification-Divergence hypothesis which is currently the default paradigm and states that amplifications generally accelerate sequence divergence.

STRENGTHS:

The authors designed a powerful reporter system that allows them to monitor the evolutionary dynamics of amplifications and promoter mutations. They ask an important question: how do early evolutionary dynamics of adaptation to environments with different demands for gene expression look like? The phenotypic data they present looks very interesting and shows the existence of interference between amplifications and point mutations in low-demand but not in high-demand conditions. The Amplification Hindrance hypothesis is a novel and useful intellectual contribution to the field.

WEAKNESSES:

In my opinion, the main weakness of the paper is that, while the interference between amplifications and point mutations in the low-demand condition clearly happens (most convincingly shown in Figure 5), its causes remain unclear. In particular, the authors claim that this interference is caused by negative epistasis, but the possibility of clonal interference without epistasis has not been decisively ruled out. The authors mention clonal interference tangentially in the Discussion, but they do not seriously address this alternative explanation. Yet, understanding the cause of this phenomenon is important because clonal interference and negative epistasis have different implications for long-term evolution.

The authors' main hypothesis is that, in the low-demand conditions, expression-increasing point mutations in the promoter provide much lower fitness benefits (or even incur fitness costs) in strains with galK amplifications compared to the ancestral strain without amplifications. The most direct way to test this hypothesis would be to measure the fitness effects of a point mutation in genetic backgrounds with and without amplifications in conditions with low and high demand for galK. This decisive experiment has unfortunately not been done. Instead, the authors construct an indirect argument, whose essence is as follows.

They show that, over the course of the experiment in the low-demand environment, the IS+ populations have acquired fewer point mutations than IS- populations (Figure 5). In addition, the phenotypic data in Figures 2 and 4 demonstrate that IS+ mutations in the low-demand environment contain three phenotypic classes of cells: ancestral, YFP+ and YFP+CFP+. The YFP+ clones are shown to have only one or two promoter mutations. The YFP+CFP+ cells must have duplications, and it is likely (although not quite certain, see below) that they do not have any promoter mutations. These data demonstrate quite convincingly that, whenever adaptation by duplications is possible, the rate at which point mutations segregate and accumulate declines. These data are consistent with the authors' hypothesis based on negative epistasis. However, they also seem to be consistent with the idea that amplifications and point mutations exhibit clonal interference without negative epistasis.

It may be possible to construct an argument against this alternative hypothesis based on the comparison between different environments, but such an argument would have to take into account the fact that clonal interference depends not only on the rates of mutations (which are presumably the same in all environments) but also on their fitness effects which vary across the environment. Another possibility to argue against clonal interference might be by carrying out simulations, although this approach also seems challenging without knowing some key population genetic parameters. The most direct way to resolve this ambiguity would be to demonstrate negative epistasis as discussed above.

Another, less critical but still important, issue mentioned above concerns the authors' claim that the YFP+CFP+ cells have only duplications but no promoter mutations (e.g., LL. 276-277). This is certainly consistent with intuition since these cells have an increased level of both YFP and CFP relative to the ancestor. However, as far as I can tell, there is no evidence to support this claim directly. My understanding is that the authors base this claim on the fact that YFP+CFP+ cells form a cluster of points on the YFP vs CFP plots that is distinct from the cluster of "mixed" cells, which are shown to have both an amplification and a promoter point mutation (Figure 3). But it is still logically possible that the YFP+CFP+ cells have an amplification and a promoter mutation other than the one found in the "mixed" cells (e.g., weaker). The most direct way to show that YFP+CFP+ cells have no promoter mutations would be to sequence a few of them. Another possibility would be to calibrate the YFP/CFP fluorescence measurements against galK copy number.

Reviewer #1 (Public Review):

Dectin-1 is a known C-type lectin receptor that has a role in recognizing pathogen glycans, particularly beta-glucan. Haji et al. present evidence that Dectin-1 also has an endogenous ligand, another C-type lectin that is enriched on platelets called CLEC-2. Using a Dectin-1 reporter line, they identify human platelets as a source for the ligand, raising a panel of mAbs to human platelets and identify one (6D11) that prevents signalling and use this to identify CLEC2 as the Dectin-1 receptor by immunopurification. The authors go on to further characterise this interaction by showing that it occurs between the human but not mouse orthologues, showing that it is the stalk region of human Dectin-1 that contains the ligand which consists of sialylated core 1 O-linked glycans displayed on Thr 105 and adjacent amino acids. Finally, the authors over-express human Dectin-1 in mice within the context of a null background for another CLEC-2 ligand (podoplanin) and show that this rescues embryonic lethality. They show that Dectin-1-dependent CLEC-2 signalling is not sufficient to induce platelet aggregation but can rescue perinatal lethality in podoplanin-deficient mice.

There is a large amount of work in this manuscript and the experiments are well controlled and the results unequivocal and usually verified by several independent techniques. The paper is very well-written making the volume of data accessible. There is novelty here in that this unusually finds that 2 C-type lectin receptors directly interact and the biochemical characterisation of the glycan binding determinants is very well performed. While the authors show that the interaction occurs between the human but not mice orthologues (because mouse Dectin-1 lacks the critical EDxxT motif that is necessary for displaying the o-linked glycan in the correct context) they were able to investigate the functional role of the interaction by generating mice that overexpressed human Dectin-1 in a podoplanin-deficient background. Normally, podoplanin-deficient mice die at birth due to defects in lymphatic vessel development, but this lethality is rescued by overexpression of human Dectin-1. This xeno-overexpression data can be difficult to interpret but suggests that while human Dectin-1 signalling to mouse platelet CLEC-2 is insufficient to drive platelet activation and thrombus formation, it is sufficient to rescue some aspects of platelet function involved in lymph vessel development. They conclude that human Dectin-1 (which is broadly expressed on different tissues) provides a basal level of tonic signalling through platelet-expressed CLEC-2 to establish platelet signalling thresholds.

I thought the manuscript was comprehensive in its approach, well written, and that the experimental data supported the conclusions.

Reviewer #1 (Public Review):

The authors tackle an interesting problem: how do ant colonies regulate foraging in response to their collective hunger? In previous work, the authors related the colony's response to individual ants sensing their own food levels and its temporal dynamics. Looking more carefully at the spatial dynamics of ants, the authors now find that foragers tend to move toward the depth of the nest when their food load is high and toward the nest exit when it is low. This is an elegant and computationally inexpensive set of rules that explains the spatiotemporal dynamics of the system.

Overall, the paper is written clearly, the methods are sound, and I agree with the interpretation of the results.

I do have a few comments and suggestions:

1) How exactly are the inward outward directions defined? Is it simply, away, or towards the entrance? It is not clear from the text, and since this system is not symmetric (cubic with entrance at one of the corners) the authors should clarify.

2) To analyze the biased random walk analysis of the ants, the authors "coarse-grained" the steps as being "inwards" "outwards" and "stay". It's not clear how this level of granulation is justified. Since the authors have access to the actual trajectories and all trophallaxis events, why not just calculate the actual turning angles between consecutive steps the ants take? This would give an actual assessment of both the bias and the noise imposed on the random walks, which the authors could then use directly in their models.

3) It would be important to better connect the author's previous mechanism (relating the colony's response to individual ants sensing their own food levels and its temporal dynamics) to the new mechanism (spatial-temporal dynamics). Are they mutually exclusive? It would be useful to elaborate on this in the Discussion.

4) It would be useful to add a few supplementary movies from the experiments, showing ants moving toward the entrance with low food loads, and moving away from the entrance with high food loads.

Reviewer #1 (Public Review):

This paper addresses an important issue of how humans select foot placement when running on uneven terrain. The authors examine empirical and model-based data to determine whether runners specifically opt for more level locations on such surfaces. The manuscript suggests potential strategies of how humans mitigate fore-aft impulses during foot contact so as to maintain stability in response to changes in terrain.

Overall, the manuscript provides additional insight into lower-limb mechanics of runners on natural surfaces. The model-based analysis of lower limb compliance is especially useful in the context of stability and understanding human motor control. In addition, participant foot placement analysis using empirical and statistical models is very compelling and provides insight into how much planning occurs during running on uneven terrain. However, there are a few concerns of note:

- A central motivation of the study appears to be that past research did not incorporate height and slope variations when studying gait on uneven terrain. Although some of the past work cited by the authors does focus on step-like terrain, the (Voloshina, Ferris 2015) study had both height and slope variations. In that particular study, the terrain consisted of blocks that were smaller than the dimensions of the average human foot. This means that, during each foot-flat phase, the foot had to span at least two blocks of different heights, placing it at a slope in the fore-aft direction. Similarly, the columns of that terrain layout provided slope variations in the medio-lateral direction. Referring to this surface as "step-like" is inaccurate and potentially misleading. Considering that the terrains in the present study and the study from 2015 likely cause very similar types of perturbations to the runner, the motivation behind the current study is not strongly validated. The authors should consider re-evaluating their results in the context of past studies.

- A primary outcome of the study is the analysis of empirical and model-based fore-aft impulses experienced by runners during foot contact. The authors suggest that this measure is related to stability but do not provide extensive explanations. It would be helpful to include additional background information on how impulse analyses have been used previously and why they are particularly fitting in this context.

- The study evaluates participants running back and forth on a 24m track for up to 10 minutes. This means that participants had to perform many turns during each trial. The authors present metabolic energy expenditure data but do not address how these data may be skewed due to the large instances of directional changes. Measuring metabolic data during such tasks is generally noise-prone, potentially leading to an inaccurate representation of energy expenditure. Considering that this is a comparative study and participants had to perform such turns for each terrain trial, this issue could be minor. However, the authors are encouraged to provide more detail on experimental protocol. Addressing whether or not participants stepped off the terrain to switch directions and providing insight into how this experimental approach could potentially affect outcomes could be particularly helpful.

Reviewer #1 (Public Review):

This is a fascinating paper that takes up an important question with a creative and new approach. We have a few suggestions that we hope are constructive for the authors.

1. One nagging concern is that the category structure in the CNN reflects the category structure baked into color space. Several groups (e.g. Regier, Zaslavsky, et al) have argued that color category structure emerges and evolves from the structure of the color space itself. Other groups have argued that the color category structure recovered with, say, the Munsell space may partially be attributed to variation in saturation across the space (Witzel). How can one show that these properties of the space are not the root cause of the structure recovered by the CNN, independent of the role of the CNN in object recognition?

2. In Figure 1, it could be useful to illustrate the central observation by showing a single example, as in Figure 1 B, C, where the trained color is not in the center of the color category. In other words, if the category structure is immune to the training set, then it should be possible to set up a very unlikely set of training stimuli (ones that are as far away from the center of the color category while still being categorized most of the time as the color category). This is related to what is in E, but is distinctive for two reasons: first, it is a post hoc test of the hypothesis recovered in the data-driven way by E; and second, it would provide an illustration of the key observation, that the category boundaries do not correspond to the median distance between training colors. Figure 5 begins to show something of this sort of a test, but it is bound up with the other control related to shape. Similarly, if the claim is that there are six (or seven?) color categories, regardless of the number of colors used to train the data, it would be helpful to show the result of one iteration of the training that uses say 4 colors for training and another iteration of the training that uses say 9 colors for training. The text asserts that Figure 2 reflects training on a range of color categories (from 4 to 9) but doesn't break them out. This is an issue because the average across these iterations could simply be heavily biased by training on one specific number of categories (e.g. the number used in Figure 1). These considerations also prompt the query: how did you pick 4 and 9 as the limits for the tests? Why not 2 and 20? (the largest range of basic color categories that could plausibly be recovered in the set of all languages)?

3. Regarding the transition points in Figure 2A, indicated by red dots: how strong (transition count) and reliable (consistent across iterations) are these points? The one between red and orange seems especially willfully placed.

4. Figure 2E and Figure 5B are useful tests of the extent to which the categorical structure recovered by the CNNs shifts with the colors used to train the classifier, and it certainly looks like there is some invariance in category boundaries with respect to the specific colors uses to train the classifier, an important and interesting result. But these analyses do not actually address the claim implied by the analyses: that the performance of the CNN matches human performance. The color categories recovered with the CNN are not perfectly invariant, as the authors point out. The analyses presented in the paper (e.g. Figure 2E) tests whether there is as much shift in the boundaries as there is stasis, but that's not quite the test if the goal is to link the categorical behavior of the CNN with human behavior. To evaluate the results, it would be helpful to know what would be expected based on human performance.

5. The paper takes up a test of color categorization invariant to luminance. There are arguments in the literature that hue and luminance cannot be decoupled-that luminance is essential to how color is encoded and to color categorization. Some discussion of this might help the reader who has followed this literature. Related, the argument that "neighboring colors in HSV will be neighboring colors in the RGB space" is not persuasive. Surely this is true of any color space?

6. The paper would benefit from an analysis and discussion of the images used to originally train the CNN. Presumably, there are a large number of images that depict man-made artificially coloured objects. To what extent do the present results reflect statistical patterns in the way the images were created, and/or the colors of the things depicted? How do results on color categorization that derive from images (e.g. trained with neural networks, as in Rosenthal et al and presently) differ (or not) from results that derive from natural scenes (as in Yendrikhovskij?).

7. It could be quite instructive to analyze what's going on in the errors in the output of the classifiers, as e.g. in Figure 1E. There are some interesting effects at the crossover points, where the two green categories seem to split and swap, the cyan band (hue % 20) emerges between orange and green, and the pink/purple boundary seems to have a large number of green/blue results. What is happening here?

8. The second experiment using an evolutionary algorithm to test the location of the color boundaries is potentially valuable, but it is weakened because it pre-determines the number of categories. It would be more powerful if the experiment could recover both the number and location of the categories based on the "categorization principle" (colors within a category are harder to tell apart than colors across a color category boundary). This should be possible by a sensible sampling of the parameter space, even in a very large parameter space.

9. Finally, the paper sets itself up as taking "a different approach by evaluating whether color categorization could be a side effect of learning object recognition", as distinct from the approach of studying "communicative concepts". But these approaches are intimately related. The central observation in Gibson et al. is not the discovery of warm-vs-cool categories (these as the most basic color categories have been known for centuries), but rather the relationship of these categories to the color statistics of objects-those parts of the scene that we care about enough to label. This idea, that color categories reflect the uses to which we put our color-vision system, is extended in Rosenthal et al., where the structure of color space itself is understood in terms of categorizing objects versus backgrounds (u') and the most basic object categorization distinction, animate versus inanimate (v'). The introduction argues, rightly in our view, that "A link between color categories and objects would be able to bridge the discrepancy between models that rely on communicative concepts to incorporate the varying usefulness of color, on the one hand, and the experimental findings laid out in this paragraph on the other". This is precisely the link forged by the observation that the warm-cool category distinction in color naming correlates with object-color statistics (Gibson, 2017; see also Rosenthal et al., 2018). The argument in Gibson and Rosenthal is that color categorization structure emerges because of the color statistics of the world, specifically the color statistics of the parts of the world that we label as objects, which is the same approach adopted by the present work. The use of CNNs is a clever and powerful test of the success of this approach.

Reviewer #1 (Public Review):

This paper uses 2 cohorts from the UK and links a) risk factors associated with low antibody levels after vaccination and b) risk factors for infection. The paper makes the important point that following the third vaccination, risk factors associated with low antibody response after the first vaccination, are less likely to lead to sub-protective levels. This highlights the importance of obtaining a booster shot. Though it is not a primary finding of the paper, the observed discordance between self-reported infection and anti-nucleocapsid positivity is an important finding. While these findings are potentially useful, the presentation of the data is somewhat unfocused, and the message is presented in a diffuse fashion. Moreover, certain key components of the analysis such as the assay threshold and timing of samples after the 2nd vaccine are a bit confusing and require clarification. The use of univariate analyses can be misleading. Finally, the relevance of the findings relative to our current stage of the pandemic with multiple new VOCs requires a clearer explication.

Reviewer #1 (Public Review):

The core conclusions in this manuscript are well supported. First, the genomic data clear support a recent origin of the Baltic and Arctic ecotypes from an Atlantic-like ancestor, without major bottlenecks and with gene exchange at least on the one sampled sympatric location but probably also more widely. The genome scans strongly suggest the involvement of multiple loci in divergence. This is supported by the quantitative trait locus analysis but this support is relatively weak because the number of markers used was small and markers were rather unevenly distributed on the genetic map, leaving little power to detect QTL in some areas. An analysis of the power of the QTL analysis is lacking and there might also be an issue about whether the measured trait truly captures rhythmicity.

The presence of segregating variants across all sampled populations for the loci that appear to underlie local adaptation, plus the absence of strong sweep signatures, is good evidence that adaptation to the Baltic environment was based on standing genetic variation. This is supported by the known presence of local adaptation to tidal regimes within the Atlantic ecotype, providing a mechanism for the maintenance of standing variation. Clustering of putatively adaptive loci in one region of chromosome 1 seems clear, although it is not formally compared to a random distribution.

The authors make an interesting point that it is only when many genes are involved that Gene Ontology enrichment is expected to be informative. Here, they also had clear a priori expectations which are neatly fulfilled, further suggesting that differentiated loci are good candidates for a role in adaptation.

The broader context provided for the analysis of the fascinating marine midge system is brief and could usefully be expanded. It should make clear that, despite some focus on major gene effects that can be assigned to individual loci, there is widespread evidence for polygenic adaptation. Even where structural variants are known to have major effects, many other regions of the genome typically contribute to local adaptation. It would also be helpful to refer to theoretical expectations regarding distributions of effect sizes and clustering of locally-adaptive loci, with or without gene flow.

Reviewer #1 (Public Review):

The authors define regulatory networks across 77 tissue contexts using software they have previously published (PECA2, Duren et al. 2020). Each regulatory network is a set of nodes (transcription factors (TF), target genes (TG), and regulatory elements (RE)) and edges (regulatory scores connecting the nodes). For each context, the authors define context-specific REs, as those that do not overlap REs from any of the other 76 contexts, and context-specific regulatory networks as the collection of TFs, TGs, and REs connected to at least one context-specific RE. This approach essentially creates annotations that are aggregated across genes, elements, and specific contexts. For each tissue, the authors use linkage disequilibrium score regression (LDSC) to calculate enrichment for complex trait heritability within the set of all REs from the corresponding context-specific regulatory network. Heritability enrichments in context-specific regulatory network REs are compared with heritability enrichments in regions defined using other approaches.

Reviewer #1 (Public Review):

Yuan et al. propose that the magnesium transporter unextended (uex) controls Drosophila sleep via Mg2+ efflux, Ca2+-dependent CREB signaling, and a CNK-ERK pathway. UEX protein levels display daily oscillations in fly heads, whereas UEX-depleted flies show long sleep with low levels of Ca2+ and synaptic plasticity. Transgenic expression of wild-type UEX or the mammalian uex homolog CNNM1 possibly rescues the uex mutant sleep, supporting their evolutionary conservation. UEX forms a protein complex with the ERK signaling suppressor CNK, and UEX depletion appears to de-repress ERK activation. As expected, pan-neuronal CNK depletion phenocopies uex mutant sleep. Taken together, the authors suggest a novel mechanism whereby magnesium shapes animal sleep through the specific MAPK pathway. Overall, the authors reported quite a few exciting phenotypes in UEX-depleted flies using a range of analyses (e.g., behaviors, gene expression, Mg2+/neural imaging, and biochemistry). However, evidence for their causal link to sleep regulation is missing, and some key conclusions remain justified by more rigorous analyses. It is noteworthy that Wu et al. have previously demonstrated the Mg2+ efflux transporter activity of UEX and mapped its memory-enhancing function to a specific group of adult neurons in the fly brain (https://pubmed.ncbi.nlm.nih.gov/33242000/). Given the intimate interactions between sleep and memory, these findings may have broad implications in sleep-relevant physiology and disorders. However, a number of important control studies and statistical assessments are not included, but are necessary to conclusively interpret the data.

Reviewer #1 (Public Review):

Modified rabies viral vectors allow high throughput mapping of neuronal circuits with cell type specificity. However, lack of standardization in this field limits extrapolation of useful information, beyond the identity of anatomically connected regions, such as differential input densities and connectivity motifs. in this manuscript, Tran-Van-Minh et al., attempt to develop a statistical approach which will allow consolidation of new, as well as previously-acquired datasets, to yield biologically significant insights into the logic underlying rabies vectors' expansion from single starter cells.

This question the authors address is of high importance and the presentation of this manuscript is timely, as rabies tracing experiments increase at an exponential rate. The authors provide a largely complete description of the main pitfalls and caveats in current analysis approaches and common misconceptions in the interpretation of results. In addition, they correctly diagnose the potential of this methodology to extract pertinent information from such experiments and provide the reader with useful tips on how to better design, analyze and interpret them.

While such a paper is undoubtedly called for, and has the potential to substantially improve circuit mapping experiments, with little cost to the experimenter, there are a few critical flaws in their premises, which will limit a widespread adoption of their analysis approach. While the authors discuss caveats in design and interpretation of results, they do not implement these suggestions into their own experiments, such as the need for accurate estimation of starter cells and automated cell counting which is not biased by strong signal coming from dendritic arbors/axons in densely-labeled regions. While the authors claim that the reduction in residuals and relative conformity across experiments following log-transformation of n(i) and n(s) shows that their approach is robust, the large degree of variability across experiments, the datasets of some are biologically implausible, showing a decrease in n(i) as a function of n(s), suggests that this transformation disposes of useful information, which might help detect anomalies in data acquisition. The fact that the most rigorously-acquired dataset which was presented by the Allen Institute (BRAIN Initiative Cell Census Network, Nature, 2021) is the only one which, does not comply with the transformation, attests further to this caveat. This is probably the reason why the estimated number of individual neurons retrogradely labeled by a single starter cell (~1400) is more than an order of magnitude higher than any previously-published estimation, including those which include tracing from single-cell, and is highly unrealistic.

In addition, the model selected by the authors to fit the various datasets does not take into proper account saturation of n(i), as all proposed functions are growing functions. Here, the most suitable model which should be used to describe the nature of RV expansion is a cumulative distribution function, while the rest are either private cases (e.g. linear fit given low n(s) or zero divergence) or are biologically unrealistic (e.g. exponential fit). Again here, the only dataset which appears to fit this function the best comes from the BRAIN Initiative Cell Census Network (Nature, 2021).

In conclusion, while such work is called for and has the potential of becoming a staple in the connectivity toolbox, many of the premises presented here will need to be significantly adjusted, before the approach could be put into widespread use.

Reviewer #1 (Public Review):

The manuscript by Chen and colleagues contains a number of important findings. First, they showed with careful imaging, cell cycle characterization, and models that replisomes of fast-growing E. coli form factories and super factories (factories involving cousin replisomes), they estimated that factories split after the replication of 1/3 of the chromosome. Second, they used a replication fork block to monitor the interdependence of replisomes of factories. Third, they show that orphaned replisomes replicate slowly and frequently require a repair process to complete the replication cycle. This is the first characterization of an advantage of replicating bacterial genomes in a factory rather than as independent replisomes. This is an important discovery that simultaneously confirms the existence of DNA factories, a much-debated topic, and reveals one of their functions. The existence and characterization of factories were mostly addressed through imaging methods; here the combination of imaging with molecular genomics assays is a real asset for the manuscript.

Reviewer #1 (Public Review):

In this study, the authors investigate multi-attribute economic decisions in humans. More specifically, they investigate the impact of adding a decoy option to a binary choice, the effect of which has been debated in the previous literature with recent studies finding effects in different directions. By re-analyzing large datasets from some of these studies and by applying computational modeling to the data, the authors propose that a subjective encoding rule comparing attributes separately, and not computed as one multiplicative value, explains participants' behavior on different levels of granularity. Context effects were well captured by a selective integration model. The work has many positive features, including praising open science, the analysis of the potential confounds of previously used designs, and both quantitative and qualitative comparisons of several well-justified models.

Reviewer #1 (Public Review):

This manuscript provides the first cellular analysis of how neuronal activity in axons (in this case the optic nerve) regulates the diameter of nearby blood vessels and hence the energy supply to neuronal axons and their associated cells. This is an important subject because, in a variety of neurological disorders, there is damage to the white matter that may result from a lack of sufficient energy supply, and this paper will stimulate work on this important subject.

Axonal spiking is suggested to release glutamate which activates NMDA receptors on myelin-making oligodendrocytes wrapped around the axons: the oligodendrocytes - either directly or indirectly via astrocytes - then generate prostaglandin E2 which relaxes pericytes on capillaries, thus decreasing the resistance of the vascular bed and (presumably) increasing blood flow in the nerve.

Strengths of the paper

The paper identifies some important characteristics of axon-vascular coupling, notably its slow temporal development and long-lasting nature, the involvement of PgE2 in an oxygen-dependent manner, and a role for NMDARs. Rigorous criteria (constriction and dilation of capillaries by pharmacological agents) are used to select functioning pericytes for analysis.

Weaknesses of the paper

The study focuses exclusively on pericytes. It would have been interesting to assess whether arteriolar SMCs also contribute to regulating blood flow.

The slow (~10 minutes) time scale of the responses seen is remarkable when compared to grey matter neurovascular coupling which occurs in seconds. The authors suggest that this reflects movement of messengers along the nerve, but the action potential moves so rapidly down the nerve that it will activate the release of vasodilators essentially at the same time everywhere along the nerve, and there will be no concentration (or voltage) gradient to drive such a movement of messengers (unless there is a spatially localized unique site in the vascular bed where propagating responses are generated). Thus it remains unclear why the responses are so slow.

The activity-evoked dilation is thought to reflect PgE2 release at what is probably a physiological O2 concentration, but not at the hyperoxic 95% O2 used in most of the experiments. The involvement of NMDA receptors is apparently only shown (using OL-specific receptor subunit deletion) at the unphysiological high O2 level. This raises two questions: are NMDA receptors also involved in the response at low O2 (as schematized in Fig 6), and what is the messenger downstream of NMDARs at high O2 (NO would be an obvious candidate, previously shown to contribute to NVC in the grey matter).

Reviewer #1 (Public Review):

The goal of this work is to study whether sleep and wake regulate cerebellar structural plasticity. Scanning electron microscopy and 3D reconstruction were used to characterize structural changes in spines and synapses in the mouse cerebellar cortex. The net number and size of synapses did not change between sleep and wake. However, the number of small portion of spines ("naked" spines without synapses) increased during sleep, and the number of branched spines (contains more than one synapse) increased during wake.<br /> The methodological approach (scanning electron microscopy) is laborious but adequate. However, it cannot follow the same synapses longitudinally, and live imaging, even only in superficial regions, is an ideal approach to achieve the goals.

The results did not find changes in the total number of spines or synapses during sleep and wake. Structural changes were found in small number of spines lacking a synapse. Whether these changes are functionally important is unclear. The results support circuit-specific, rather than global, effect of sleep on synaptic plasticity.

The role of sleep range from cellular maintenance to memory consolidation. Multiple evidence suggests that sleep regulates synaptic plasticity. Although this work did not attempt to understand the functional role of sleep in regulating learning and memory, it discovered that sleep promotes synaptic pruning in specific circuits of the cerebellum. Future similar anatomical studies in additional brain regions, including excitatory and inhibitory circuits, combine with physiological and behavioral assays are expected to provide insights to the role of sleep in regulating synaptic plasticity, learning and memory.

Reviewer #1 (Public Review):

Zebrafish have become a well-established model organism for studies of damage and repair in hair cell sensory organs due to organ accessibility and robust mechanisms for repair and regeneration. While lateral line organs in zebrafish are widely used to study hair cell physiology, zebrafish also contain hair cell organs in their inner ears that have the potential to be more comparable to mammalian counterparts. Using single-cell RNA seq in embryonic through adult stages, this study found that hair cells and supporting cells of the zebrafish inner ear are distinct from those of the lateral line. Additionally, they identified distinct cellular subtypes within the maculae and cristae of the ear.

This work provides some novel findings, including identifying distinct markers in the macula and crista hair cells and supporting cells as well as detecting a domain in the zebrafish utricle that coincides with features of the striolar region in mouse utricle. However, while much of the focus of in situ expression analysis was the spatial separation of calcium-binding proteins capb1b and capb2b in the maculae, it was not clear how Capb1 & 2 expression corresponds to striolar and extrastriolar regions in the mammalian utricle, where Capb2 is expressed throughout. In addition, the authors assumed the zebrafish utricle and saccule perform similar functions (i.e. hearing) and contain hair cells with similar frequency tuning. It would improve this study to consider the unique functions and frequency sensitivities of the utricle and the saccule, given that different expression of voltage-gated channels may give insight into the specific physiology of these two sensory organs.

Reviewer #1 (Public Review):

The authors Rem et al., examine the mechanism of action of APP, a protein implicated in Alzheimer's disease pathology, on GABAB receptor function. It has been reported earlier that soluble APP (sAPP) binds to the Sushi domain 1 of the GABAB1a subunit. In the current manuscript, authors examine this issue in detail and report that sAPP or APP17 interacts with GABABR with nano Molar affinity. However, binding of APP to GABAB receptor does not influence any of the canonical effects such as receptor function, K+ channel currents, spontaneous release of glutamate, or EPSC in vivo. The experimental evidence provided to support the conclusions is thorough and statistically sound. The range of techniques used to address each of the aims has been carefully curated to draw meaningful conclusions.

The authors use HEK293T heterologous cell line to confirm the affinity of APP17 for the receptor, ligand displacement, and receptor activation. They also use this method to study PKA activation downstream of the GPCR. They use slice electrophysiology to measure changes in glutamatergic transmission EPSC and then in vivo 2-photon microscopy to measure functional changes in vivo.

The work is significant for the field of Alzheimer's and also GABAB receptor biology, as it has been assumed for sAPP acts via GABAB receptors to influence neurotransmission in the brain. The results presented here open up the question yet again, what is the physiological function of sAPP in the brain?

The manuscript is clearly written and easy to follow. The main criticism would be that the manuscript fails to identify the mechanism downstream of APP17 interaction with GB1a SD1.

Reviewer #1 (Public Review):

This paper by Hubmann et al investigates the role of a large ECM protein in lymphangiogenesis using the zebrafish model to test genetic interactions and with state-of-the-art reporter readouts to evaluate expression patterns and vascular behaviors. The experiments examine the effects of svep1 deletion on facial lymphatics and produce the surprising result that svep1 and vegfc appear to be required in non-overlapping areas of this lymphatic vascular bed. They then use these tools and mutants for tie1 or tie2 to examine genetic interactions in this area and in parachordal lymphangioblast migration in the trunk, showing that tie1 but not tie2 shows epistasis with svep1. The data are overall rigorous and quantified to show the statistical significance of the stated results. The work provides important insights into a complex signaling pathway that is widely utilized in vascular development. The evidence is convincing in supporting the findings and is predicted to be of interest to vascular biologists and others interested in Tie/Tek signaling such as cancer biologists.

Reviewer #1 (Public Review):

Gormley et al. conduct a comprehensive Mendelian randomisation (MR) analysis to study the causal effect of obesity and metabolic disorder on oral and oropharyngeal cancer. This work follows on from observational studies that found evidence of associations between these variables and continued on from a previous MR study that focused on the effect of circulating lipid traits on these cancer outcomes. In this study, the authors found limited evidence for an effect of obesity-related traits on either cancer type, contradicting the previous observational studies and thus implying that the latter may have been affected by confounding. Sensitivity analyses that adjusted for potential pleiotropy and outlying instrumental variants agreed with the main study findings. Risk factors including smoking, risk-taking (a proxy for sexual behavior), and alcohol consumption were also stratified for, and only evidence was found for smoking as a mediator of the observed effect.

Strengths:<br /> Strengths of this study include the thorough MR analysis conducted, with all required sensitivity analyses and checks conducted and summarised appropriately, including the use of recent MR methods such as SIMEX where required. For the disease outcomes, oral and oropharyngeal cancer, the authors used summary statistics from the single largest trans-ethnic published meta-analysis GWAS available. Known risk factors were stratified for in sensitivity analyses to follow up any results of interest. Where measures of risk factors were not directly available (e.g. sexual behaviour), genetically-correlated proxy measures were used.

Weaknesses:<br /> While this study has several strengths, there are also a number of limitations present, many of which the authors outline in the paper. In particular, the sample size of the outcome GWAS used was rather small which may hinder the power. Also, some of the cancer subtypes of interest that had previously been investigated in observational studies were not available as GWAS, and so could not be included in this study. The authors only used European or trans-ancestry GWAS, as these are the only ancestries presently available, but a future investigation into other ethnicities is warranted. They also used risk-taking (self-defined via questionnaire) as a proxy for sexual behaviour, which despite having a strong genetic correlation, may not be the best substitute.

Reviewer #1 (Public Review):

This is an elegant article where the authors define valuable criteria to identify and classify high-confidence miRNA in fungi. The data supporting the conclusions are solid, and the results are essential to unify the annotation criteria in these organisms. Interestingly, the paper shows that miRNAs in fungi look and position within the genome more similarly to their animal counterpart but may act like plant miRNAs.

The conclusions of this paper are well supported by data, but some aspects need to be clarified and extended.

Reviewer #1 (Public Review):

In this manuscript, the author characterizes the lattice of kinesin-decorated microtubule reconstituted from porcine tubulins in vitro and Xenopus egg extract using cryo-electron tomography and subtomogram averaging. Using the SSTA, they looked at the transition in the lattice of individual microtubules. The authors found that the lattice is not always uniform but contains transitions of different types of lattices. The finding is quite interesting and probably will lead to more investigation of the microtubule lattice inside the cells later on for this kind of lattice transition.

The manuscript is easy to read and well-organized. The supporting data is very well prepared.

Overall, it seems the conclusion of the author is justified. However, the manuscript appears to show a lack of data. Only 4 tomograms are done for the porcine microtubules. Increasing the data number would make the manuscript statistically convincing. In addition, having the same transition with the missing wedge orientation randomly from different subtomograms will allow a better average of transition without the missing wedge artifact.

Another thing that I found lacking is the mapping of the transition region/alignment in the raw data. However, it is not easy for me or the reader to understand how each segment is oriented relative to each other apart from the simplified seam diagrams in the figures, and also the orientation of the seam corresponding to the missing wedge in the average. With these improvements, I think the conclusion of the manuscript will be better justified.

DOI: 10.7554/eLife.82094

Resource: RRID:ZFIN_ZDB-ALT-170110-1

Curator: @MufeiL

SciCrunch record: RRID:ZFIN_ZDB-ALT-170110-1

What is this?

DOI: 10.7554/eLife.82094

Resource: RRID:ZFIN_ZDB-GENO-191211-1

Curator: @MufeiL

SciCrunch record: RRID:ZFIN_ZDB-GENO-191211-1

What is this?

Reviewer #1 (Public Review):

In this manuscript, Garratt and collaborators describe exposure to male vs. female olfactory cues as an intervention modulating mouse lifespan. They use urine exposure (early life) and soiled bedding exposure (after weaning) to determine the impact of sex-specific olfactory cues on lifespan. They use wild-type and Gnao1 neural-specific mutants to determine potential dependency on vomeronasal function as well. They also recorded information about body temperature, body weight, glucose levels, grip strength, and balance. Overall, they identify female-olfactory cues as increasing the lifespan of female but not male mice, regardless of genotype.

This study reports an intriguing new intervention with an impact on mouse lifespan (with a female-specific effect). However, some of the data with negative results are not plotted/shown, and although all experiments were performed in wild-type and mutant background, all the shown data is pooled and not split by genotype. Overall, this study will be valuable to the field provided that a few analytical points are addressed for clarity and reproducibility and that all methodological details are included.

Joint Public Review:

In this work Malis et al introduce a novel spin-labeling MRI sequence to measure cerebrospinal fluid (CSF) outflow. The glymphatic system is of growing interest in a range of diseases, but few studies have been conducted in humans due to the requirement for and invasiveness of contrast injections. By labeling one hemisphere of the brain the authors attempt to assess outflow through the superior sagittal sinus (SSS), one of the major drainage pathways for CSF, signal changes across time were assessed to extract commonly used metrics. Additionally, correlations with age are explored in their cohort of healthy volunteers. The authors report the movement of labeled CSF from the subarachnoid space to the dura mater, parasagittal dura, and ultimately SSS, evidence of leakage from the subarachnoid space to the SSS, and decreases in CSF outflow metrics with older age.

1. I don't think that the description of Parasagittal dura in figure 1 is correct. There is no anatomical structure at the top of SSS that is known as PSD. The location of the lymphatic structures is also incorrect. Please review "Anatomic details of intradural channels in the parasagittal dura: a possible pathway for flow of cerebrospinal fluid" Neurosurgery 1996 Fox at al. There is usually no obvious tissue between the upper wall of the SSS and the calvarium, which can also be seen in the authors' fig 2A and 2B. All of the tissues located lateral to the SSS are known as PSD. Also, the SSS wall is not as thick as the authors stated and is known as PSD in this region. For this reason, the authors need to revise Fig 1 and it should be changed to PSD in the areas referred to as the SSS wall in the article.

2. The authors described tagged CSF in two pathways: from dura mater to PSD and SAS into the SSS and directly from SAS to SSS. Flow from dura mater to PSD and SAS in the main and supplement cannot be seen. Only a flow from PSD to SSS can be seen. Also, regular dura cannot carry flow-collagen-rich fibrous tissue, except parasagittal dura. There is no flow from dura to the CSF in the figures.

3. The authors have conducted many tests to prevent venous contamination. However, measurements were made based on SSS flow rates in all tests. Small parenchymal venous structures, and small cortical-SAS veins might be tagged due to different flow patterns and T2- Relaxation times.

4. The rate of CSF formation in humans is 0.3 - 0.4 ml min-1. ( Brinker et al 2014. Fluids Barriers CNS). We can assume that the absorption rate is also similar to the CSF formation for the entire system brain and Spine. Therefore, the absorption rate of this very small amount of CSF by SSS is very low in seconds. It is hard to detect by MR and especially CSF flow from the PSD to SSS. The authors concluded that using this technique the rate averaged less than a couple of seconds, rather than on the order of hours or days as previously reported with the use of intrathecal administration of GBCA (Ringstad et al., 2020).

5. Overall, I think that the CSF flow from the PSD to the CNS described by the authors - the CSF flow, might be the venous flow that drains into the SSS slowly, predominantly in the rich venous channels, venous lacunae, and previously described channels in the PSD. Additional explanations are needed.

6. The study is generally well described and to the best of my knowledge an innovative approach. The findings are broadly consistent with what might be expected from the literature and the authors make a good argument in support of their findings. However, the lack of validation is a major limitation of the presented work. In introducing a novel technique a comparison with an existing approach, such as Gd enhanced contrast techniques, or phase contrast would have been expected. Several considerations could have been mentioned/addressed in more detail e.g. what effect labeling efficiency, tortuosity of vessels, lack of gating, the effectiveness of the intensity thresholding to remove the signal from blood, etc may have on the quantification, etc. Without a more thorough validation, it is difficult to evaluate the findings. While scans were conducted on two volunteers to assess reproducibility this is a very small sample and it is notable that scans were conducted consecutively, which might be expected to reduce variance relative to scans further apart e.g. on different dates, scanned by a different operator and no information is provided on how the two scans were positioned (i.e. separately vs copied from the first to the second scan), some metrics showed large percentage differences, which were more pronounced in one subject than the other. Without further data, it is difficult to interpret the reproducibility results. No assessment of the effect of physiological parameters e.g. breathing, cardiac pulsations, or factors affecting glymphatic clearance e.g. amount of sleep the evening before was given.

7. Given these limitations it is hard to adequately assess the likely impact or utility. In recent years several groups have published work e.g. doi.org/10.1038/s41467-020-16002-4 , doi.org/10.1016/j.neuroimage.2021.118755 assessing the blood-CSF barrier. However, previous work has generally focused on larger structures, and by labeling in the oblique-sagittal plane it is unclear how drainage and blood flow rates may affect the presented values here.

8. Some validation data would greatly increase the value of the reported work. I would therefore encourage the authors to consider acquiring some additional datasets to compare measures of CSF draining against another method e.g. 2-D or 4-D phase contrast, or Gd-based contrast-enhanced techniques. Some additional points to consider are noted below.

8. Abstract

CSF outflow may also be imaged with phase contrast MRI (albeit in a limited way).<br /> Demographics would fit better in Results, breakdown could be given for the young and old groups i.e. n, ages, sex.<br /> Conclusion - unless further validation can be provided I think some of the claims should be toned down.

9. Introduction

The authors emphasise the role of Nedergaard, however, there was some relevant earlier work (e.g. Rennels et al, PMID: 2396537).

10. Methods

It would be more conventional to summarise the volunteer characteristics in the Results.<br /> Given the age difference between the two groups, and the fact that for conventional ASL we know of differences in labelling efficiency and the need for a different post-labelling duration in more elderly patients how did the authors account for this?<br /> More broadly what would the effect of differences in labeling efficiency be, given the labeling plane is unlikely to be perpendicular to the draining vessels?<br /> While the authors mention circadian effects there is no mention of controlling for other factors before the scan e.g. caffeine consumption, smoking, etc.<br /> Various mechanisms have been hypothesised to drive glymphatic pulsations. Assessing how physiological signals correlated with the flow may have been a useful proof of concept. Why was it not considered necessary to use a gated acquisition? Did the authors consider the potential impact of respiratory and cardiac pulsations on their measurements?<br /> ROI segmentation - manually selected by two raters, was this done independently and blinded? How were consensus ROIs agreed?<br /> Intensity values outwith MEAN +/- 2 SD were excluded from further analyses. This is justified as removing pulsatile blood. However, was this done independently for tag-on and tag-off? Does this mean slight differences were present in the number of voxels between the two?<br /> The starting points and parameter ranges are given in Eq'n 3, how were the ranges defined? Was there a reason for constraining the fit to positive values only, is there a risk of bias from this?<br /> While the main results appear to have a reasonable sample size n=2 for the reproducibility analysis is very limited. Additional datasets would be useful in properly interpreting the results.

11. Results<br /> While the authors have taken some measures to reduce potential contamination from blood I would be concerned about the risk of surface vessels affecting the signal, and there does not seem to be an evaluation of how effective their measures are.<br /> The labeling pulse is applied in the oblique sagittal orientation, but in tandem with differing rates of blood flow and CSF drainage from the labeling plane does that not risk circulating flow from other slices potentially affecting the values?<br /> Figure 4. The authors focus on the parasagittal dura, but in both the subtraction image and panel C showing different slices at TI=1250 ms some movement appears visible in the opposing hemisphere. Similarly in S2 If the signal does represent CSF movement then this seems counterintuitive and should be explained.<br /> In Figures 4 and 5 the angulation of the TIME-SLIP tag pulse seems quite different. What procedure was used to standardise this, and what effect may this have on the results?

12. Discussion<br /> Phrasing error 'which will be assessed in future studies'.<br /> I would suggest that some of the claims of novelty be moderated e.g. 'may facilitate establishment of normative values for CSF outflow' seems a stretch given multiple pathways exist and this is only considered one.<br /> More consideration should be given to some of the points mentioned in the results. The lack of validation should be properly discussed.