Reviewer #2 (Public Review):

The fledgling field of epitranscriptomics has encountered various technical roadblocks with implications for the validity of early epitranscriptomics mapping data. As a prime example, the low specificity of (supposedly) modification-specific antibodies for the enrichment of modified RNAs, has been ignored for quite some time and is only now recognized for its dismal reproducibility (between different labs), which necessitates the development of alternative methods for modification detection. Furthermore, early attempts to map individual epitranscriptomes using sequencing-based techniques are largely characterized by the deliberate avoidance of orthogonal approaches aimed at confirming the existence of RNA modifications that have been originally identified.

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

As for m5C, the subject of the manuscript submitted by Zhou et al., its identification in mRNA goes back to Squires et al., 2012 reporting on >10.000 sites in mRNA of a human cancer cell line, followed by intermittent findings reporting on pretty much every number between 0 to > 100.000 m5C sites in different human cell-derived mRNA transcriptomes. The reason for such discrepancy is most likely of a technical nature. Importantly, all studies reporting on actual transcript numbers that were m5C-modified relied on RNA bisulfite sequencing, an NGS-based method, that can discriminate between methylated and non-methylated Cs after chemical deamination of C but not m5C. RNA bisulfite sequencing has a notoriously high background due to deamination artifacts, which occur largely due to incomplete denaturation of double-stranded regions (denaturing-resistant) of RNA molecules. Furthermore, m5C sites in mRNAs have now been mapped to regions that have not only sequence identity but also structural features of tRNAs. Various studies revealed that the highly conserved m5C RNA methyltransferases NSUN2 and NSUN6 do not only accept tRNAs but also other RNAs (including mRNAs) as methylation substrates, which in combination account for most of the RNA bisulfite-mapped m5C sites in human mRNA transcriptomes. Is m5C in mRNA only a result of the Star activity of tRNA or rRNA modification enzymes, or is their low stoichiometry biologically relevant?

In light of the short-comings of existing tools to robustly determine m5C in transcriptomes, other methods - like DRAM-seq, that allow the mapping of m5C independently of ex-situ RNA treatment with chemicals - are needed to arrive at a more solid "ground state", from which it will be possible to state and test various hypotheses as to the biological function of m5C, especially in lowly abundant RNAs such as mRNA.

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

Comments:

(1) The use of two m5C reader proteins is likely a reason for the high number of edits introduced by the DRAM-Seq method. Both ALYREF and YBX1 are ubiquitous proteins with multiple roles in RNA metabolism including splicing and mRNA export. It is reasonable to assume that both ALYREF and YBX1 bind to many mRNAs that do not contain m5C.

To substantiate the author's claim that ALYREF or YBX1 binds m5C-modified RNAs to an extent that would allow distinguishing its binding to non-modified RNAs from binding to m5C-modified RNAs, it would be recommended to provide data on the affinity of these, supposedly proven, m5C readers to non-modified versus m5C-modified RNAs. To do so, this reviewer suggests performing experiments as described in Slama et al., 2020 (doi: 10.1016/j.ymeth.2018.10.020). However, using dot blots like in so many published studies to show modification of a specific antibody or protein binding, is insufficient as an argument because no antibody, nor protein, encounters nanograms to micrograms of a specific RNA identity in a cell. This issue remains a major caveat in all studies using so-called RNA modification reader proteins as bait for detecting RNA modifications in epitranscriptomics research. It becomes a pertinent problem if used as a platform for base editing similar to the work presented in this manuscript.

(2) Since the authors use a system that results in transient overexpression of base editor fusion proteins, they might introduce advantageous binding of these proteins to RNAs. It is unclear, which promotor is driving construct expression but it stands to reason that part of the data is based on artifacts caused by overexpression. Could the authors attempt testing whether manipulating expression levels of these fusion proteins results in different editing levels at the same RNA substrate?

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

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

(5) The authors state that "plotting the distribution of DRAM-seq editing sites in mRNA segments (5'UTR, CDS, and 3'UTR) highlighted a significant enrichment near the initiation codon (Figure 3F).", which is not true when this reviewer looks at Figure 3F.

(6) The authors state that "In contrast, cells expressing the deaminase exhibited a distinct distribution pattern of editing sites, characterized by a prevalence throughout the 5'UTR.", which is not true when this reviewer looks at Figure 3F.

(7) The authors claim in the final conclusion: "In summary, we developed a novel deaminase and reader protein assisted RNA m5C methylation approach...", which is not what the method entails. The authors deaminate As or Us close to 5mC sites based on the binding of a deaminase-containing protein.

(8) The authors claim that "The data supporting the findings of this study are available within the article and its Supplementary Information." However, no single accession number for the deposited sequencing data can be found in the text or the supplementary data. Without the primary data, none of the claims can be verified.

Reviewer #1 (Public Review):

Summary:

In this manuscript, "PAbFold: Linear Antibody Epitope Prediction using AlphaFold2", the authors generate a python wrapper for the screening of antibody-peptide interactions using AlphaFold, and test the performance of AlphaFold on 3 antibody-peptide complexes. In line with previous observations regarding the ability of AlphaFold to predict antibody structures and antigen binding, the results are mixed. While the authors are able to use AlphaFold to identify and experimentally validate a previously characterized broad binding epitope with impressive precision, they are unable to consistently identify the proper binding registers for their control [Myc-tag, HA-tag] peptides. Further, it appears that the reproducibility and generality of these results are low, with new versions of AlphaFold negatively impacting the predictive power. However, if this reproducibility issue is solved, and the test set is greatly increased, this manuscript could contribute strongly towards our ability to predict antibody-antigen interactions.

Strengths:

Due to the high significance, but difficulty, of the prediction of antibody-antigen interactions, any attempts to break down these predictions into more tractable problems should be applauded. The authors' approach of focusing on linear epitopes (peptides) is clever, reducing some of the complexities inherent to antibody binding. Further, the ability of AlphaFold to narrow down a previously broadly identified experimental epitope is impressive. The subsequent experimental validation of this more precisely identified epitope makes for a nice data point in the assessment of AlphaFold's ability to predict antibody-antigen interactions.

Weaknesses:

Without a larger set of test antibody-peptide interactions, it is unclear whether or not AlphaFold can precisely identify the binding register of a given antibody to a given peptide antigen. Even within the small test set of 3 antibody-peptide complexes, performance is variable and depends upon the scFv scaffold used for unclear reasons. Lastly, the apparent poor reproducibility is concerning, and it is not clear why the results should rely so strongly on which multi-sequence alignment (MSA) version is used, when neither the antibody CDR loops nor the peptide are likely to strongly rely on these MSAs for contact prediction.

Major Point-by-Point Comments:

(1) The central concern for this manuscript is the apparent lack of reproducibility. The way the authors discuss the issue (lines 523-554) it sounds as though they are unable to reproduce their initial results (which are reported in the main text), even when previous versions of AlphaFold2 are used. If this is the case, it does not seem that AlphaFold can be a reliable tool for predicting antibody-peptide interactions.

(2) Aside from the fundamental issue of reproducibility, the number of validating tests is insufficient to assess the ability of AlphaFold to predict antibody-peptide interactions. Given the authors' use of AlphaFold to identify antibody binding to a linear epitope within a whole protein (in the mBG17:SARS-Cov-2 nucleocapsid protein interaction), they should expand their test set well beyond Myc- and HA-tags using antibody-antigen interactions from existing large structural databases.

(3) As discussed in lines 358-361, the authors are unsure if their primary control tests (antibody binding to Myc-tag and HA-tag) are included in the training data. Lines 324-330 suggest that even if the peptides are not included in the AlphaFold training data because they contain fewer than 10 amino acids, the antibody structures may very well be included, with an obvious "void" that would be best filled by a peptide. The authors must confirm that their tests are not included in the AlphaFold training data, or re-run the analysis with these templates removed.

(4) The ability of AlphaFold to refine the linear epitope of antibody mBG17 is quite impressive and robust to the reproducibility issues the authors have run into. However, Figure 4 seems to suggest that the target epitope adopts an alpha-helical structure. This may be why the score is so high and the prediction is so robust. It would be very useful to see along with the pLDDT by residue plots a structure prediction by residue plot. This would help to see if the high confidence pLDDT is coming more from confidence in the docking of the peptide or confidence in the structure of the peptide.

(5) Related to the above comment, pLDDT is insufficient as a metric for assessing antibody-antigen interactions. There is a chance (as is nicely shown in Figure S3C) that AlphaFold can be confident and wrong. Here we see two orange-yellow dots (fairly high confidence) that place the peptide COM far from the true binding region. While running the recommended larger validation above, the authors should also include a peptide RMSD or COM distance metric, to show that the peptide identity is confident, and the peptide placement is roughly correct. These predictions are not nearly as valuable if AlphaFold is getting the right answer for the wrong reasons (i.e. high pLDDT but peptide binding to a non-CDR loop region). Eventual users of the software will likely want to make point mutations or perturb the binding regions identified by the structural predictions (as the authors do in Figure 4).

Reviewer #2 (Public Review):

Summary:

The authors showed the applicability and usefulness of a new AlphaFold2 pipeline called PabFold, which can predict linear antibody epitopes (B-cell epitopes) that can be helpful for the selection of reagents to be applied in competitive ELISA assay.

Strengths:

The authors showed the accuracy of the pipeline to identify correctly the binding epitope for three different antibody-antigen systems (Myc, HA, and Sars-Cov2 nucleocapsid protein). The design of scFvs from Fab of the three antibodies to speed up the analysis time is extremely interesting.

Weaknesses:

The article justifies correctly the findings and no great weaknesses are present. However, it could be useful for a broader audience to show in detail how pLDDT was calculated for both Simple-Max approach (per residue-pLDDT) and Consensus analysis ( average pLDDT for each peptide), with associated equations.

Reviewer #1 (Public Review):

Metabotropic glutamate receptors (mGLuRs) play a key role in regulating neuronal activity and related behaviors. In different brain regions these receptors can be expressed presynaptically and postsynaptically in different classes of neurons. Therefore, it is difficult to predict the effects of systemically applied drugs that act on these receptors. Here, the authors harness the power of photopharmacology, applying modulators that can be activated or inactivated by light with spatial precision, to address this problem. Their stated goal is to determine the role of mGluRs in regulating pain behaviors, and the circuit mechanisms driving this regulation. Their findings suggest that mGluRs acting in medial prefrontal cortex and thalamus drive antinociception in animals with neuropathic pain, whereas these receptors drive pronociception when acting in the amygdala. Their circuit analysis suggests that, in the amygdala, mGluRs act by decreasing feedforward inhibition of the output neurons. These findings have the potential to affect the development of targeted treatment for pain and related disorders. The elegant photopharmacological approaches will likely inform future studies attempting to distinguish the action of neuroactive drugs in different brain regions.

Reducing the impact of these studies are several methodological, analytical, and interpretation issues.

- The authors report that "the effect of optical manipulations of photosensitive mGlu5 NAMs in individual brain regions in pain models has been studied before". It is, therefore, not immediately clear what is novel in the present study.<br /> - The reliance only on reflexive measures of pain, especially in a study that examines the role of "affective and cognitive aspects of pain and pain modulation".<br /> - The inclusion of only males is unfortunate because of known, significant sex differences in neuronal circuits driving pain conditions, in both preclinical models (including form work by the authors) and in clinical populations.<br /> - The elegant slice experiments (especially Fig. 3) were designed to probe circuit mechanisms through which mGluRs act in different brain regions. These experiments also provide a control to assess whether the photopharmacological compounds act as advertised. Surprisingly, the effect size produced by these compounds on neuronal activity are rather small (and, at times, seems driven by outliers). How this small effect affects the interpretation of the behavioral findings is not clear.<br /> - These small effect sizes should also be considered when interpreting the circuit actions studied here.<br /> - Some of the sample sizes are as small as n=3. Without an a priori power analysis, it is difficult to assess the validity of the analyses.<br /> - The authors present intriguing data on changes in InsP levels in some (but not all) animals after injury, but not in sham animals. They also report an increase in the expression of mGLuRs expression in some, but not all brain regions. These findings are not discussed. It is not clear how these selective changes in mGluR expression and activity might affect the interpretation of the photopharmacological results.<br /> - The behavioral data seem to represent discrete, and not continuous variables. The statistical tests applied are likely inappropriate for these analyses.<br /> - The authors assume (and state in the abstract) that they can selectively stimulate BLA afferents to the neocortex. This is technically highly unlikely.<br /> - The results from the experiment on rostroventral medulla (RVM) neurons are less than convincing because only a "trend" towards decreased excitation is reported. As above, without consideration of effect size, it is hard to appreciate the significance of these findings. The absence of a demonstration of a classical ON Cell firing pattern is also unfortunate.

Reviewer #2 (Public Review):

In this study, Notartomaso et al. used optical activation of systemic JF-NP-26, a caged, baseline inactive, negative allosteric modulator (NAM) of mGlu5 receptors, in cingulate, prelimbic and infralimbic cortices, thalamus, and BLA to investigate the roles of these receptors in various brain regions in pain processing. They found that alloswitch-1, an intrinsically active mGlu5 receptor NAM, caused analgesia, but this analgesic effect was reversed by light-induced drug inactivation in the prelimbic and infralimbic cortices, and thalamus. In contrast, these pharmacological effects were reversed in the BLA. They further found that alloswitch-1 increased excitatory synaptic responses in prelimbic pyramidal neurons evoked by stimulation of BLA input, and decreased feedforward inhibition of amygdala output neurons by BLA. They thus concluded that mGlu5 receptors had differential effects in distinct brain regions. mGlu5 receptors are important receptors in pain processing, and their regional specificity has not been studied in detail. Further, this is an interesting study regarding the use of optical activation of pro-drugs, and the findings are timely. The combination of in vivo pharmacology, biochemistry, and slice EP provides complementary results.

Reviewer #3 (Public Review):

In this manuscript, Notartomaso, Antenucci et al. use two different light-sensitive metabotropic glutamate receptor negative allosteric modulators (NAMs) to determine how mGlu5 receptor signaling in distinct brain regions of mice influences mechanical sensitivity in chronic constriction injury (CCI) model of neuropathic pain. This is an extension of their previous work using photocaged mGlu5 negative allosteric modulators and incorporates a systemically active NAM that can be locally photoswitched off and on with violet and green light, respectively. The authors found that NAM signaling in the thalamus and prefrontal cortical regions consistently reduced mechanical hypersensitivity. However, they observed divergent effects on these measures in the basolateral amygdala. The authors attempted to solve the discrepancy in behavioral measurements between mGlu5 signaling in the basolateral amygdala by determining how NAMs modulate synaptic transmission or in vivo firing and found that these effects were projection-dependent.

Strengths:

This study demonstrates the importance of local signaling by mGlu5 receptors across multiple pain-processing circuits in the brain, and the use of optical activation and inactivation strategies is very intriguing.

Weaknesses:

One major limitation is the lack of sham surgery groups and vehicle/light-only controls in behavior and physiology experiments, though the authors did test mechanical sensitivity in the contralateral paw. The reliance on a single behavioral measure in these groups is also problematic. Many of these brain regions are known to influence distinct aspects of somatosensory processing or other behaviors entirely, which may be interpreted as hypersensitivity (e.g. fear or anxiety-like behaviors in the basolateral amygdala). Details on the light intensities used is also absent, and it is important to test whether violet light had any unintended effects on these cells/mice.

While the effort to provide some mechanistic understanding using slice physiology and in vivo recordings is appreciated, they ignore any effects that these NAMs have directly on the excitability of the recorded output neuron. In the models, mGlu5 is proposed to exist on some upstream inhibitory (mPFC) or excitatory (BLA) projection, but no evidence of a direct effect on these synaptic inputs was observed. Given the widespread distribution of mGlu5 in these brain regions, the proposed model seems unlikely. Perhaps CCI induces changes in functional coupling of mGlu5 in different cell types, and this could be revealed with appropriate control experiments.

Overall the broad profiling taken here across multiple brain regions lacks controls and some cohesion, making it challenging to conclude how mGlu5 signaling is acutely impacting these circuits and/or specific cell types.

Reviewer #1 (Public Review):

How does the brain respond to the input of different complexity, and does this ability to respond change with age?

The study by Lalwani et al. tried to address this question by pulling together a number of neuroscientific methodologies (fMRI, MRS, drug challenge, perceptual psychophysics). A major strength of the paper is that it is backed up by robust sample sizes and careful choices in data analysis, translating into a more rigorous understanding of the sensory input as well as the neural metric. The authors apply a novel analysis method developed in human resting-state MRI data on task-based data in the visual cortex, specifically investigating the variability of neural response to stimuli of different levels of visual complexity. A subset of participants took part in a placebo-controlled drug challenge and functional neuroimaging. This experiment showed that increases in GABA have differential effects on participants with different baseline levels of GABA in the visual cortex, possibly modulating the perceptual performance in those with lower baseline GABA. A caveat is that no single cohort has taken part in all study elements, ie visual discrimination with drug challenge and neuroimaging. Hence the causal relationship is limited to the neural variability measure and does not extend to visual performance. Nevertheless, the consistent use of visual stimuli across approaches permits an exceptionally high level of comparability across (computational, behavioural, and fMRI are drawing from the same set of images) modalities. The conclusions that can be made on such a coherent data set are strong.

The community will benefit from the technical advances, esp. the calculation of BOLD variability, in the study when described appropriately, encouraging further linkage between complementary measures of brain activity, neurochemistry, and signal processing.

Reviewer #2 (Public Review):

Lalwani et al. measured BOLD variability during the viewing of houses and faces in groups of young and old healthy adults and measured ventrovisual cortex GABA+ at rest using MR spectroscopy. The influence of the GABA-A agonist lorazepam on BOLD variability during task performance was also assessed, and baseline GABA+ levels were considered as a mediating variable. The relationship of local GABA to changes in variability in BOLD signal, and how both properties change with age, are important and interesting questions. The authors feature the following results: 1) younger adults exhibit greater task-dependent changes in BOLD variability and higher resting visual cortical GABA+ content than older adults, 2) greater BOLD variability scales with GABA+ levels across the combined age groups, 3) administration of a GABA-A agonist increased condition differences in BOLD variability in individuals with lower baseline GABA+ levels but decreased condition differences in BOLD variability in individuals with higher baseline GABA+ levels, and 4) resting GABA+ levels correlated with a measure of visual sensory ability derived from a set of discrimination tasks that incorporated a variety of stimulus categories.

Strengths of the study design include the pharmacological manipulation for gauging a possible causal relationship between GABA activity and task-related adjustments in BOLD variability. The consideration of baseline GABA+ levels for interpreting this relationship is particularly valuable. The assessment of feature-richness across multiple visual stimulus categories provided support for the use of a single visual sensory factor score to examine individual differences in behavioral performance relative to age, GABA, and BOLD measurements. Weaknesses of the study include the absence of an interpretation of the physiological mechanisms that contribute to variability in BOLD signal, particularly for the chosen contrast that compared viewing houses with viewing faces. Whether any of the observed effects can be explained by patterns in mean BOLD signal, independent of variability would be useful to know. The positive correlation between resting GABA+ levels and the task-condition effect on BOLD variability reaches significance at the total group level, when the young and old groups are combined, but not separately within each group. This correlation may be explained by age-related differences since younger adults had higher values than older adults for both types of measurements. This is not to suggest that the relationship is not meaningful or interesting, but that it may be conceptualized differently than presented. Two separate dosages of lorazepam were used across individuals, but the details of why and how this was done are not provided, and the possible effects of the dose are not considered. The observation of greater BOLD variability during the viewing of houses than faces may be specific to these two behavioral conditions, and lingering questions about whether these effects generalize to other types of visual stimuli, or other non-visual behaviors, in old and young adults, limit the generalizability of the immediate findings.

The observed age-related differences in patterns of BOLD activity and ventrovisual cortex GABA+ levels along with the investigation of GABA-agonist effects in the context of baseline GABA+ levels are particularly valuable to the field, and merit follow-up. Assessing background neurochemical levels is generally important for understanding individualized drug effects. Therefore, the data are particularly useful in the fields of aging, neuroimaging, and vision research.

Reviewer #3 (Public Review):

The role of neural variability in various cognitive functions is one of the focal contentions in systems and computational neuroscience. In this study, the authors used a large-scale cohort dataset to investigate the relationship between neural variability measured by fMRI and several factors, including stimulus complexity, GABA levels, aging, and visual performance. Such investigations are valuable because neural variability, as an important topic, is by far mostly studied within animal neurophysiology. There is little evidence in humans. Also, the conclusions are built on a large-scale cohort dataset that includes multi-model data. Such a dataset per se is a big advantage. Pharmacological manipulations and MRS acquisitions are rare in this line of research. Overall, I think this study is well-designed, and the manuscript reads well. I listed my comments below and hope my suggestions can further improve the paper.

Strength:<br /> (1) The study design is astonishingly rich. The authors used task-based fMRI, MRS technique, population contrast (aging vs. control), and psychophysical testing. I appreciate the motivation and efforts for collecting such a rich dataset.<br /> (2) The MRS part is good. I am not an expert in MRS so cannot comment on MRS data acquisition and analyses. But I think linking neural variability to GABA in humans is in general a good idea. There has been a long interest in the cause of neural variability, and inhibition of local neural circuits has been hypothesized as one of the key factors.<br /> (3) The pharmacological manipulation is particularly interesting as it provides at least evidence for the causal effects of GABA and deltaSDBOLD. I think this is quite novel.

Weakness:<br /> (1) I am concerned about the definition of neural variability. In electrophysiological studies, neural variability can be defined as Poisson-like spike count variability. In the fMRI world, however, there is no consensus on what neural variability is. There are at least three definitions. One is the variability (e.g., std) of the voxel response time series as used here and in the resting fMRI world. The second is to regress out the stimulus-evoked activation and only calculate the std of residuals (e.g., background variability). The third is to calculate variability of trial-by-trial variability of beta estimates of general linear modeling. It currently remains unclear the relations between these three types of variability with other factors. It also remains unclear the links between neuronal variability and voxel variability. I don't think the computational principles discovered in neuronal variability also apply to voxel responses. I hope the authors can acknowledge their differences and discuss their differences.<br /> (2) If I understand it correctly, the positive relationship between stimulus complexity and voxel variability has been found in the author's previous work. Thus, the claims in the abstract in lines 14-15, and section 1 in results are exaggerated. The results simply replicate the findings in the previous work. This should be clearly stated.<br /> (3) It is difficult for me to comprehend the U-shaped account of baseline GABA and shift in deltaSDBOLD. If deltaSDBOLD per se is good, as evidenced by the positive relationship between brainscore and visual sensitivity as shown in Fig. 5b and the discussion in lines 432-440, why the brain should decrease deltaSDBOLD ?? or did I miss something? I understand that "average is good, outliers are bad". But a more detailed theory is needed to account for such effects.<br /> (4) Related to the 3rd question, can you should the relationship between the shift of deltaSDBOLD (i.e., the delta of deltaSDBOLD) and visual performance?<br /> (5) Are the dataset openly available ?? I didn't find the data availability statement.

Reviewer #1 (Public Review):

The study is thorough and systematic, and in comparing three well-separated hypotheses about the mechanism leading from grid cells to hexasymmetry it takes a neutral stand above the fray which is to be particularly appreciated. Further, alternative models are considered for the most important additional factor, the type of trajectory taken by the agent whose neural activity is being recorded. Different sets of values, including both "ideal" and "realistic" ones, are considered for the parameters most relevant to each hypothesis. Each of the three hypotheses is found to be viable under some conditions, and less so in others. Having thus given a fair chance to each hypothesis, nevertheless, the study reaches the clear conclusion that the first one, based on conjunctive grid-by-head-direction cells, is much more plausible overall; the hypothesis based on firing rate adaptation has intermediate but rather weak plausibility; and the one based on clustering of cells with similar spatial phases in practice would not really work. I find this conclusion convincing, and the procedure to reach it, a fair comparison, to be the major strength of the study.

What I find less convincing is the implicit a priori discarding of a fourth hypothesis, that is, that the hexasymmetry is unrelated to the presence of grid cells. Full disclosure: we have tried unsuccessfully to detect hexasymmetry in the EEG signal from vowel space and did not find any (Kaya, Soltanipour and Treves, 2020), so I may be ranting off my disappointment, here. I feel, however, that this fourth hypothesis should be at least aired, for a number of reasons. One is that a hexasymmetry signal has been reported also from several other cortical areas, beyond entorhinal cortex (Constantinescu et al, 2016); true, also grid cells in rodents have been reported in other cortical areas as well (Long and Zhang, 2021; Long et al, bioRxiv, 2021), but the exact phenomenology remains to be confirmed. Second, as the authors note, the conjunctive mechanism is based on the tight coupling of a narrow head direction selectivity to one of the grid axes. They compare "ideal" with "Doeller" parameters, but to me the "Doeller" ones appear rather narrower than commonly observed and, crucially, they are applied to all cells in the simulations, whereas in reality only a proportion of cells in mEC are reported to be grid cells, only a proportion of them to be conjunctive, and only some of these to be narrowly conjunctive. Further, Gerlei et al (2020) find that conjunctive grid cells may have each of their fields modulated by different head directions, a truly surprising phenomenon that, if extensive, seems to me to cast doubts on the relation between mass activity hexasymmetry and single grid cells.

Finally, a variant of the fourth hypothesis is that the hexasymmetry might be produced by a clustering of head direction preferences across head direction cells similar to that hypothesized in the first hypothesis, but without such cells having to fire in grid patterns. If head direction selectivity is so clustered, who needs the grids? This would explain why hexasymmetry is ubiquitous, and could easily be explored computationally by, in fact, a simplification of the models considered in this study.

Reviewer #2 (Public Review):

Grid cells - originally discovered in single-cell recordings from the rodent entorhinal cortex, and subsequently identified in single-cell recordings from the human brain - are believed to contribute to a range of cognitive functions including spatial navigation, long-term memory function, and inferential reasoning. Following a landmark study by Doeller et al. (Nature, 2010), a plethora of human neuroimaging studies have hypothesised that grid cell population activity might also be reflected in the six-fold (or 'hexadirectional') modulation of the BOLD signal (following the six-fold rotational symmetry exhibited by individual grid cell firing patterns), or in the amplitude of oscillatory activity recorded using MEG or intracranial EEG. The mechanism by which these network-level dynamics might arise from the firing patterns of individual grid cells remains unclear, however.

In this study, Khalid and colleagues use a combination of computational modelling and mathematical analysis to evaluate three competing hypotheses that describe how the hexadirectional modulation of population firing rates (taken as a simple proxy for the BOLD, MEG, or iEEG signal) might arise from the firing patterns of individual grid cells. They demonstrate that all three mechanisms could account for these network-level dynamics if a specific set of conditions relating to the agent's movement trajectory and the underlying properties of grid cell firing patterns are satisfied.

The computational modelling and mathematic analyses presented here are rigorous, clearly motivated, and intuitively described. In addition, these results are important both for the interpretation of hexadirectional modulation in existing data sets and for the design of future experiments and analyses that aim to probe grid cell population activity. As such, this study is likely to have a significant impact on the field by providing a firmer theoretical basis for the interpretation of neuroimaging data. To my mind, the only weakness is the relatively limited focus on the known properties of grid cells in rodent entorhinal cortex, and the network level activity that these firing patterns might be expected to produce under each hypothesis. Strengthening the link with existing neurobiology would further enhance the importance of these results for those hoping to assay grid cell firing patterns in recordings of ensemble-level neural activity.

Reviewer #3 (Public Review):

This is an interesting and carefully carried out theoretical analysis of potential explanations for hexadirectional modulation of neural population activity that has been reported in the human entorhinal cortex and some other cortical regions. The previously reported hexadirectional modulation is of considerable interest as it has been proposed to be a proxy for the activation of grid cell networks. However, the extent to which this proposal is consistent with the known firing properties of grids hasn't received the attention it perhaps deserves. By comparing the predictions of three different models this study imposes constraints on possible mechanisms and generates predictions that can be tested through future experimentation.

Overall, while the conclusions of the study are convincing, I think the usefulness to the field would be increased if null hypotheses were more carefully considered and if the authors' new metric for hexadirectional modulation (H) could be directly contrasted with previously used metrics. For example, if the effect sizes for hexadirectional modulation in the previous fMRI and EEG data could be more directly compared with those of the models here, then this could help in establishing the extent to which the experimental hexadirectional modulation stands out from path hexasymmetry and how close it comes to the striking modulation observed with the conjunctive models. It could also be helpful to consider scenarios in which hexadirectional modulation is independent of grid firing, for example perhaps with appropriate coordination of head direction cell firing.

Reviewer #1 (Public Review):

In this work, Plaza-Alonso et al. present a collection of volume electron microscopy (EM) reconstructions of human postmortem medial entorhinal cortex (MEC), and they measure properties of MEC cytoarchitecture and synapses as a function of neuroanatomical subdivision. The authors generate a sampling of 9 smaller (≲10 µm/side) EM reconstructions per subdivision to avoid prohibitively large (petabyte) EM volumes, using 3 reconstructions for each of 3 brain donors to control for inter-individual variability. Conducting in-depth analyses for 7 subdivisions (63 reconstructions total), the authors find little significant inter-subdivision variability in structural composition (volume fractions of cell bodies vs. neuropil vs. blood vessels) and multiple synapse properties (spatial distribution, density, area, shape, excitatory/inhibitory type, and postsynaptic cell compartment). They conclude that human MEC connectivity is largely homogeneous, with synapses arranged in a generally random spatial distribution and a large fraction of synapses being asymmetric (putatively excitatory). Their other findings include that asymmetric synapses are larger than symmetric/putatively inhibitory synapses; that asymmetric synapses prefer dendritic spines whereas symmetric synapses prefer dendritic shafts; and that a small fraction of synapses have larger, complex shapes that may suggest increased synaptic efficacy. They note that inhomogeneities may include inter-subdivision variation in asymmetric synapse area and complex-shaped synapse prevalence, and for some reconstructions (12/63), possible substructure in synapse distributions.

Strengths:<br /> The authors have carefully conducted this work, using reasonable methods and comparing their findings with previous volume EM reconstructions where possible. It represents a substantial effort, given the challenges of producing and annotating volume EM data and of collecting human postmortem tissue. They have thus contributed a brain-region-specific characterization of human postmortem tissue with value as both a data resource and an examination of postmortem EM reconstruction quality, given that postmortem tissue is less-studied with volume EM but could be an important source of human brain samples (for example in regions that are surgically inaccessible). Further, some of the authors' measurements may be of added value, as they suggest functional correlates for less-studied synapse structures (such as the differing sizes of complex and simple "macular" synapses formed onto dendritic spines vs. shafts).

Weaknesses:<br /> Despite these strengths, the analysis in this work may be impacted by multiple sources of experimental variability that may have contributed to the observed lack of structural variability, and the potential contributions of these should be addressed in making their claims.

(1) The authors' approach to tissue sampling may have resulted in under-sampling, which may have reduced the detection power of their tests. More specifically, each reconstructed EM volume measured ~10 µm x 7 µm x 6 µm (360 - 502 µm^3) and contained ~300-400 synapses (Lines 211-212, 772-773). Per donor, this amounts to a sampling volume of ~1500 µm^3 for each MEC subdivision or ~1x104 µm^3 total. By contrast, the volume of the adult human MEC is ~1x10^12 µm^3, roughly 1x10^8 times larger [1]. Thus, while these EM reconstructions reflect a substantial effort, it is likely that they represent an under-sampling of MEC structure, especially since multiple excitatory and inhibitory neuron types are likely interspersed throughout (the authors also note this possibility in Lines 640-659).

(2) The authors' measurements are combined across three donors who are biologically diverse (Table S11), including in terms of characteristics that themselves may impact neuronal connectivity. Without controlling for these variables, the possible reduction in stochastic, biological inter-individual variability that could be achieved by combining data across donors may be offset by increases in phenotype-related variability, which could reduce the detectability of true, conserved connectivity variations across MEC subdivisions. Specifically, these donors represent a mix of males and females; a mix of ages (40, 53, and 66 years) that suggest differing degrees of aging-related changes in neuronal connectivity (according to previous work, a majority of people >55 years of age are estimated to have Alzheimer's-associated neurofibrillary tangles, regardless of whether they have dementia symptomatology; see for instance [1]); and one death from metastatic cancer, indicating that for one donor cellular/neuronal abnormalities associated either with cancer itself or related therapies could be present.

These two factors could substantially increase the dispersion of the authors' measurements in each MEC subdivision and lead to a situation with no detectable differences between subdivisions. It would be important to address these impacts when determining whether to interpret a lack of significant differences as true biological homogeneity for human MEC.

One helpful approach would be to explicitly show the variance of each measurement obtained for each EM reconstruction. For example, error bars showing the interquartile range could be added to each data point in Fig. 3C, to show how much synapse areas vary per reconstruction and to allow some comparison across donors and MEC subdivisions.

(3) A third potential source of variability relates to the authors' approach for synapse annotation. They appear to annotate active zones and postsynaptic densities by thresholding synapse images at some user-defined pixel intensity value, taking only pixels darker than that threshold as their annotations (Lines 806 - 812). This technique seems like it could be prone to producing noisy annotations, particularly since in the EM images provided (Figs. S11-16) the pixel intensities of active zones/postsynaptic densities and surrounding neuropil do not appear to be highly distinct.

It would be important for the authors to support their findings by quantifying the variability that may be associated with this technique.

[1] Price, C.C. et al., J. Int. Neuropsychol. Soc., (2010), doi: 10.1017/S135561771000072X.

Reviewer #2 (Public Review):

Plaza-Alanso et al. characterize synaptic properties across human medial entorhinal cortex across layers and, importantly, across multiple individuals. Using an impressive collection of post-mortem autopsy samples, they generate high resolution 3d FIB-SEM volumes across layers and MEC subregions and measure features such as synapse density, spatial distribution, size, shape and target location. The use of volumes permits a richer local context to synaptic reconstructions, and the methods used to count and quantify synapses appear thorough and convincing, although with limited descriptions at times. The core findings suggest few differences in most properties across either layers or individuals, with some modest exceptions in layers 1 and 6. A particular strength of the dataset is the large number of high quality synaptic contact reconstructions.<br /> However, because the volumes have no specific labels and are too small to associate axons or dendrites with individual cells or cell types, it is not clear how to extrapolate these findings to new insights toward the stated goal of a better understanding of the networks and connectivity characteristics of the MEC. Broadly speaking, the paper would benefit from a better explanation of why these specific parameters were chosen and what the authors hoped to gain from them. It might be useful to think of what would need to be the case to see something substantially different. Many of the measures here reflect the properties of dendrites passing through a small volume, which depends on the number of cells of different cell types, the length and thickness of their dendritic arbors, synapse density distributions, local and long range afferents, and more. One interpretation of these results is that these neuropil volumes across layers and individuals are effectively fully packed with dendrites, with a similar ratio of excitatory and inhibitory neurons, dendrites with roughly similar thickness and synaptic input density and local E/I balance. Can the authors disentangle these cellular-scale contributions or constrain their inter-individual variability across individuals? The lack of variability is perhaps the main observation here, and understanding this more clearly could be useful for thinking about larger volumes where fewer replicates are currently possible.

Reviewer #1 (Public Review):

Article strengths:

(1) Detailed data: The authors provided a large amount of clinical data as support, making the analysis results more persuasive and credible.<br /> (2) Scientific method: Appropriate statistical methods were used to analyze the data, which can accurately reflect the internal laws and trends of the data.<br /> (3) Clear conclusions: The conclusions drawn in the article are clear and explicit, easy for readers to understand and accept.<br /> (4) High practicality: The research results have important guiding significance for obstetrics and gynecology clinical practice, helping to improve patient treatment outcomes and quality of life.

Article weaknesses:

Limitations of research methods: Although the authors used statistical methods to analyze the data, they may be limited by factors such as data sources and sample size, leading to some limitations in the research results. It is recommended that the authors further expand the data sources and increase the sample size in subsequent studies to improve the accuracy and reliability of the research.

Reviewer #2 (Public Review):

This prospective study advances our understanding of the predictive value of preoperative serum CA125, CA19-9, CA72-4, CEA, and AFP in endometrial cancer. The evidence supporting the conclusions is convincing with rigorous analysis of the association between prognostic values of several serum markers with the clinical data of endometrial cancer patients. However, there are a few areas in which the article may be improved through further validation of the prognostic value of the risk score in patients with endometrial cancer at different stages. Moreover, the authors should provide a more detailed explanation of the choice of statistical methods in the manuscript. The work will be of broad interest to clinicians, medical researchers and scientists working in endometrial cancer.

(1) The groups of patients with endometrial cancer in the manuscript are classified according to age greater than/less than 60. Please explain why 60 years old is chosen as the boundary value of age.<br /> (2) Among the patients with endometrial cancer selected in the manuscript, AFP outliers accounted for a relatively small proportion. The authors chose the clinical detection outliers of CA-125, CA19-9, AFP and CEA as the dividing line, instead of re-selecting the optimal cut-off value in this population, which should be classified and the prognostic value explored.<br /> (3) In cancer research, stage is an important prognostic factor to guide the treatment of patients in clinical work. Patients with different stages of endometrial cancer have obvious prognostic differences. The authors constructed a new prognostic risk score based on serum level of AFP， CEA and CA125, the prognostic value of the risk score should be validated in patients with endometrial cancer at different stages。

Reviewer #3 (Public Review):

The authors of this study aimed to enhance the prognostic assessment of endometrial cancer (EC) by identifying and validating a set of serum tumor markers (CA125, CEA, and AFP) that could reliably predict progression-free survival (PFS) and overall survival (OS) in patients. By employing a robust methodology that included the use of LASSO Cox regression analysis to construct a predictive model, the study sought to provide a more nuanced tool for clinical decision-making in the management of EC.

Major Strengths:

Methodological Rigor: The study's use of advanced statistical methods to analyze a large dataset of EC patients stands out. The inclusion of a validation cohort enhances the credibility of the prognostic model developed.<br /> Clinical Relevance: The identification of CA125, CEA, and AFP as independent prognostic factors and the creation of a risk score based on these markers offer valuable tools for clinicians. The predictive accuracy of this model could significantly impact patient management and treatment planning.<br /> Weaknesses:

Generalizability: The study is based on a cohort from a single institution, which may limit the applicability of the findings across different populations and healthcare settings.<br /> Loss to Follow-Up: As acknowledged by the authors, the loss to follow-up of some patients introduces a potential source of bias, possibly affecting the study's conclusions.<br /> Achievement of Aims and Support for Conclusions:

The study successfully achieves its aim of developing a prognostic model for EC that integrates serum levels of CA125, CEA, and AFP. The evidence presented supports the authors' conclusions that this model is a robust tool for predicting patient outcomes, evidenced by its performance in both the training and validation cohorts.

Impact and Utility:

This work is poised to make a significant contribution to the field of gynecological oncology, particularly in the management of endometrial cancer. The study's findings provide a practical approach to stratifying patients based on their risk, which could be instrumental in tailoring individualized treatment plans. Moreover, the model's ability to predict PFS and OS with considerable accuracy offers a promising avenue for further research and application in clinical settings.

Additional Context:

Understanding the role of tumor markers in cancer prognosis is a rapidly evolving area of oncology research. This study's focus on combining multiple serum markers into a comprehensive risk score model represents a significant step forward in the quest for more personalized cancer care. Future studies could expand on this work by exploring the integration of such markers with other clinical and molecular data to further refine prognostic models.

Reviewer #1 (Public Review):

The current manuscript investigates the role of microRNA cluster 221/222 (miR221/222) in rheumatoid arthritis synovial fibroblasts (RA SFs) prompted by previous evidence that this cluster is upregulated in these cells. The authors employed multiple genetic mouse models and genomic approaches demonstrating that global overexpression of miR221/222 in huTNFtg polyarthritic mice further expanded SF proliferation and exacerbated RA, whereas global deletion reduced SF proliferation and dampened RA. Mechanistically, the authors provide sufficient evidence that these effects are mediated through the regulation of cell cycle inhibitors (p27 and p57) and the epigenetic regulator Smarca1. In general, these studies offer strong evidence that miR221/222 contributes to the pathogenic mechanisms underlying SF function in RA and provide new critical information to advance the understanding of RA pathology. However, certain important aspects are not addressed. Specifically, limited information related to the immune and inflammatory nature of this mechanism is offered, which is further complicated by limitations of using global overexpression and knockout. For example, it remains unknown to what is the extent of contribution by immune and inflammatory cells as well as what are the SF-derived effectors that propagate tissue damage and erosion

Reviewer #2 (Public Review):

This study focuses on the role of miR221/222 in the pathogenesis of rheumatoid arthritis. Through the use of different murine models and genome-wide techniques, the authors individuate a miR221/222 elicited mechanism leading to synovial fibroblast hyperproliferation. These discoveries may provide a rationale for future targeted therapies for RA treatment.

miR-221 and miR-222 have been linked with arthritis in previous studies from this and other laboratories: miR-221 and miR-222 have been found upregulated in SFs derived from the huTNFtg mouse model and RA patients, where their expression correlates with disease activity. The novelty of the present study resides in the analysis of the role of miR-221/miR-222 in an in vivo system and provides insight into cellular and molecular mechanisms linking miR-221/222 to RA progression.

Reviewer #3 (Public Review):

In this study, Roumelioti et al demonstrate the role of miR-221/222 in synovial fibroblasts (SFs) in inflammatory arthritis, applying a plethora of methods in three transgenic mouse models (huTNFtg, TgColVI-miR-221/222, huTNFtg;TgColVI-miR-221/222). miR-221/222 is upregulated in SFs, upon stimulation with TNF, both in early and established disease, while its gene is activated, as shown by scATAC-seq data. Using RNA sequencing and KEGG pathway analysis, authors showed that overexpression of miR-221 and miR-222 exacerbates arthritis, mainly due to SFs proliferation, driven by cell cycling inhibition and extracellular matrix remodeling. Although the authors suggest the potential utility of miR-221/222 targeting in inflammatory arthritis treatment, this was only examined through miR-221/222 -/- mice generation and not by direct silencing of miR-221/222 by administering a miR-221/222 antagonist.

Reviewer #1 (Public Review):

The authors tested the hypothesis that protein consumption decreases with decreasing mass-specific growth during development. This hypothesis is firmly grounded in the logical premise that as animals progress from periods of reduced activity and rapid growth to phases of increased activity and reduced mass-specific growth during their development, they are likely to adjust their nutrient intake, reducing protein and increasing carbohydrate consumption accordingly. The authors tested their hypothesis using the South American locust Schistocerca cancellata, combining field observations with laboratory experiments. This approach allowed them to discern how variations in activity history and metabolism between field- and laboratory-raised locusts influenced their nutrient requirements.<br /> Their findings, indeed reveal the predicted shift from high protein: carbohydrate consumption to lower protein: carbohydrate intake from the first instar to adult locust - a decline that strongly correlated with a decrease in mass-specific growth rate. Their comparison between field- and laboratory-raised locusts, showed that protein demand was not different, however, carbohydrate consumption rate was >50% higher in the field locusts. These results add depth and significance to the study, shedding light on how environmental factors influence nutrient requirements.<br /> What truly amplifies the strength and novelty of the authors' hypothesis is their anticipation that this observed trend in Schistocerca cancellata could extend to all animals. This anticipation is rooted in the expectation that growth rates scale hypometrically across various body sizes and developmental stages, introducing a universal dimension to their findings that holds great promise for broader ecological and evolutionary understanding.<br /> However, while the study is commendable in its methodology and core findings, there is room for improvement in clarifying the implications of the results. The current lack of clarity is evident in the somewhat shallow questions outlined in lines 358 to 363. For instance, the practice of administering age-specific diets has been commonplace in human and livestock management for ages. Thus, its continued utility may not be the most stimulating question. Instead, a more thought-provoking inquiry might delve into whether variations in global protein availability play a pivotal role in driving niche specialization and the biogeography of animal body sizes and ontogeny, especially considering the potential impacts of climate change. Such inquiries would further elevate the significance of the author's work and its broader implications in the field.

Reviewer #2 (Public Review):

How and why nutritional requirements and intake targets change over development and differ between species are significant questions with wide-ranging implications spanning ecology to health. In this manuscript, Talal et al. set out to address these questions in laboratory and field experiments with grasshoppers and in a comparative analysis of different species.

The authors conclude that the target intake of protein to non-protein energy (in this case carbohydrate) (P:C) falls over developmental stages and that this occurs because of a decline in mass-specific intake of protein whereas mass-specific carbohydrate intake remains more constant. The decrease in mass-specific protein consumption rate is tightly correlated with a decline in specific growth rate. Hence, protein consumption directly reflects requirements for growth, with hypometric scaling of protein intake serving as a useful relationship in nutritional ecology.

The laboratory experiments on the locust, Schistocerca cancellata, provide an elegant dataset in which different instars have been provided with one of two nutritionally complementary food pairings differing in protein to carbohydrate (P: C) content, and their self-selected protein to carbohydrate "intake target" measured.

These lab locust results were then compared with independently collected field data for late instar nymphs of the same locust species, and the conclusion is drawn that field insects ingested similar protein but 50-90% more carbohydrate (with only 23% increased mass-specific resting oxygen consumption rates). Numerous uncontrolled variables between the lab and field studies make meaningful conclusions difficult to draw from this observation.

A graph is then provided showing comparative data across a selection of species, making the case that protein consumption scales similarly both developmentally and across taxa. Questions need to be addressed for this to be convincing, including which criteria were used to select the examples in the graph and how comprehensively do these represent the available literature.

Reviewer #3 (Public Review):

The main goal of this study was to test how and why the intake of two important macronutrients ‒protein and carbon‒ often changes with ontogeny and body size. To do this, authors examined protein and carbon intake in a locusts lab population, across each instar and adult stages. Then, authors examined how the optimal balance of carbon and protein intake in a wild locusts population corresponded to that observed in the laboratory population. Results of these experiments showed that with ontogenic growth, locust decreased protein while increasing carbohydrate intake. Authors concluded that such decrease in the protein: carbohydrate intake may result from reductions in specific growth rates (growth within each instar). The protein: carbohydrate intake in the lab population appeared to be consistent with that observed in a wild locust population. Finally, authors combined their data with that from the literature to examine how protein intake scales with body mass throughout development, within and across different species.

Strengths:<br /> To determine how locusts balance protein: carbohydrate intake, authors applied the Geometric Framework (GF) of nutrition, which is a powerful approach for studying effects of nutrition and understanding the rules of compromise associated with balancing dietary unbalances.

Captivity can change behavior and physiology of most organisms, making it difficult to establish the relevance of laboratory experiments to what happens in the real world. A strength of this paper is that it compares behavior/physiology of lab vs. wild locusts. Finally, this study takes a step further by proposing a new scaling rule based on this study's results and data from the literature on various species.

Weaknesses:<br /> Although the paper has strengths, there seems to be several methodological issues that obscure the interpretation/conclusions presented in the manuscript.<br /> It appears that authors are not actually estimating "Intake Targets", as stated throughout the manuscript. According to the geometric framework, the intake target (IT) is estimated as the point in the nutritional landscape under which performance/fitness is optimized. The geometric framework also predicts that animals can reach their intake targets by feeding selectivity when given a choice of diets that differ in nutrient amounts, which is what authors did here. However, because the relationship between fitness/performance with diet was not established, in the choice experiments authors seem to be assuming (but not testing) that locusts are reaching their intake target.

You estimated a mass-specific protein intake for each instar. It is not clear why mass-specific intake and not just intake of protein was used for analysis. While mass (or size) of an individual may influence food consumption, it seems like authors calculated mass-specific consumption using each instar's final mass, which would make mass a result of protein consumption (and not the opposite). Importantly, the comparison between mass-specific protein consumption and specific growth rate may be problematic, as both variables seem to be estimated using final mass.

Reviewer #1 (Public Review):

Summary:

Marshall and coworkers describe the effects of altering metabotropic glutamate receptor 5 activity on locomotion and related activity of D1 receptor expressing spiny projection neurons in dorsolateral striatum. The authors also examine effects of dSPN-specific constitutive mGlu5 deletion in several motor tests. Effects of inhibiting the degradation of the endocannabinoid 2-arachidonoyl glycerol are also examined. Overall, this study provides intriguing new information with relevance to movement disorders and possibly psychosis. However, there are questions about the interpretation of dSPN activity in relation to movement, as well as the analysis approach. Some aspects of the study are also incomplete.

Strengths:

A nice combination of in vivo cellular calcium imaging, pharmacology, receptor knockout and sophisticated movement analysis are used. The authors conclude that mGlu5 expressed in dSPNs contributes to movement through effects on clustered spatial coactivity of dSPNs. Some data suggesting the story may be different in the other major SPN subpopulation (iSPNs) are also presented. The authors also suggest that mGlu5 stimulation of endocannabinoid signaling may play a role in the receptor effects. Overall, this study provides intriguing new information with relevance to movement disorders and possibly psychosis

Weaknesses:

Major Comments:

(1) The relationship between coactivity and movement in this and the previous study from this group is intriguing. Can the authors offer a hypothesis as to how decreased coactivity promotes increased movement velocity (e.g. as indicated by Figures 2l and 3m, and in the previous study)? Is coactivity during rest part of a "movement preparation" SPN program, or is it simply the case that the actual activity of individual dSPNs starts to contribute to different aspects of movement as velocity increases (given that the majority of neurons appear to show increased event rate during movement).

(2) The authors focus on dSPNs until very late in the study and then provide a little intriguing data suggesting that iSPNs show no difference in coactivity in the mGlu5 cKO mice. However, the basic characterization of the relationship between iSPN coactivity and movement is missing, although Figure 5g does seem to suggest a relationship between coactivity and proximity similar to dSPNs. It would be helpful to include the type of analysis shown in Figure 1 for iMSNs.

(3) The use of the Jaccard similarity index in this study is not intuitive and not fully explained by the methods or the diagram in Figure 1. The more detailed explanations in the previous papers from this group seem to indicate cells are listed as "coactive" if they both show an above-threshold fluorescence increase during a one second time frame after converting signals to a binary "on" or "off" status. However, it seems unlikely that the activity of the neurons would be perfectly or even strongly correlated, as there is bound to be variability in the exact traces from cell to cell. Furthermore, it doesn't seem clear how many frames need to show suprathreshold signals for two neurons to be considered coactive (or does this determine the magnitude of the normalized coactivity y-axis, e.g. in Figure 1i). Thus, while the technique appears to capture some index of coactivity, it does not appear to reveal the true temporal correlations in activity that could be obtained with techniques that use all data points to assess correlations. While this technique may be well suited to determining coactivity based on action potentials, or another all-or-none type biological event, it may not be as optimal for relating calcium transients that have more nuanced features.<br /> Another question is how the one second time frame was chosen. Did the authors run a sensitivity analysis to determine the effect of changing the frame duration on coactivity estimates. This might help determine if the analysis was too conservative in identifying coactive neurons.<br /> These comments may reflect a lack of understanding of the approach on the part of this reviewer. Perhaps a more detailed explanation of the method, maybe including examples of the types of calcium transients that are listed as reflecting coactivity or lack thereof, would clarify the suitability of this technique.

(4) The analysis of a possible 2-AG role in the mGlu5 mediated processes is incomplete and does not add much to the story. As the authors admit, inhibiting MGL globally will have widespread effects on many striatal synapses. Perhaps a dSPN-targeted approach, such as knocking out DAG lipase in dSPNs, would be more informative. For example, one might expect that this knockout would prevent the effects of the JNJ mGlu5 PAM on both movement and dSPN activity. The authors also do not provide any evidence of 2-AG involvement in the synaptic changes they report, although admittedly the role of endocannabinoids in DHPG-induced synaptic depression has been reported in several previous studies.

(5) It would seem to be a simple experiment to examine effects of the mGlu5 NAM in the dSPN mGlu5 cKO mice. If effects of the two manipulations occluded one another this would certainly support the hypothesis that the drug effects are mediated by receptors expressed in dSPNs. A similar argument can be made for examining effects of the JNJ PAM in the cKO mice.

Minor Comments:

(i) The use of CsF-based whole-cell internal solutions has caused concern in some past studies due to possible interference with G-protein, phosphatase and channel function (https://www.sciencedirect.com/science/article/abs/pii/S1044743104000296, https://www.jneurosci.org/content/jneuro/6/10/2915.full.pdf). It is reassuring the DHPG-induced LTD was still observable with this solution. However, it might be worth examining this plasticity with a different internal to ensure that the magnitude of the agonist effect is not altered by this manipulation.

(ii) The Kreitzer and Malenka 2007 paper may not be the best to cite in the context of dSPN-related synaptic plasticity, as these authors claimed that DHPG-induced LTD was restricted to iSPNs (an observation that has not generally been supported by subsequent work in several laboratories).

Reviewer #2 (Public Review):

Strengths are that the topic is of significant interest and understudied and the combination of both genetic and pharmacological approaches. However, while there is great enthusiasm for the need to better understand mGluR5 roles in striatal circuitry, in its present form, there are three overarching concerns that significantly limit the impact of this study. First, while a Jaccard method is used to measure the spatiotemporal dynamics of dSPN activity, collectively the data herein do not support the authors' interpretation of the data that mGluR5 is a modulator of spatiotemporal dSPN dynamics. Specifically, pharmacological and genetic manipulations of mGluR5 do not differentially/preferentially modulate the activity of proximal vs distal dSPNs, therefore, it could also be interpreted that mGluR5 is blanketly boosting/suppressing all dSPN activity as opposed to differential proximal/distal spatial relationships. While this is acknowledged in the manuscript (Figure 2i), it leaves open for question the extent to which mGluR5 is modulating other aspects of dSPN activity independent of the spatiotemporal relationship across dSPNs (i.e. amplitude, firing probability, etc.). Second, while it is a strength that mGluR5 NAM, PAM, and D1 Cre mGluR5-cKO were used to bidirectionally manipulate mGluR5 signaling, the manuscript lacks a clear model of where mGluR5 is acting to affect dSPN activity. This concern can be readily addressed by treating D1 Cre mGluR5-cKO mice with the mGluR5 NAM (as described in Ln. 413-416) to determine the extent to which other sources of mGluR5 are contributing to dSPN activity. The authors' working model predicts that the NAM would have no significant effects on the D1 Grm5 cKO model. Third, there are some concerns about the statistical basis for conclusions that are drawn detailed below that when addressed will strengthen the rigor of the conclusions. Addressing these suggestions should strengthen the mechanistic understanding and further allow the authors to present a more clear working model for their findings.

Reviewer #3 (Public Review):

Summary:

The manuscript by Marshall et al. investigates the role mGluR5 in modulating the coactivity of d1 spiny projection neurons (dSPN) in the dorsolateral striatum through calcium imaging and pharmacological i.p. injections or targeted deletion of mGluR5 in dSPNs. They show a bidirectional modulation by negative and positive allosteric modulators respectively (mainly at rest) on dSPN coactivity, the increase in coactivity by the negative modulator showed qualitative similar effects on coactivity as the deletion of mGluR5 in dSPNs.

Strengths:

Overall the study is well written and easy to read, with the data supporting (most of the time) the conclusion. It brings a new perspective on the role of mGluR5 in the modulation of dSPNs coactivity and its correlation with movement.

Weaknesses:

Some of the experiments would strengthen the solidness of the study providing further information and verifying the claims of the main text with the statistics on the figure legends.

Reviewer #1 (Public Review):

The authors design an automated 24-well Barnes maze with 2 orienting cues inside the maze, then model what strategies the mice use to reach the goal location across multiple days of learning. They consider a set of models and conclude that the animals begin with a large proportion of random choices (choices irrespective of the goal location), which over days of experience becomes a combination of spatial choices (choices targeted around the goal location) and serial choices (successive stepwise choices in a given direction). Moreover, the authors show that after the animal has many days of experience in the maze, they still often began each trial with a random choice, followed by spatial or serial choices.

This study is written concisely and the results are presented concisely. The best fit model provides valuable insight into how the animals solve this task, and therefore offers a quantitative foundation upon which tests of neural mechanisms of the components of the behavioral strategy can be performed. These tests will also benefit from the automated nature of the task.

Reviewer #2 (Public Review):

This paper uses a novel maze design to explore mouse navigation behaviour in an automated analogue of the Barnes maze. A major strength is the novel and clever experimental design which rotates the floor and intramaze cues before the start of each new trial, allowing the previous goal location to become the next starting position. The modelling sampling a Markov chain of navigation strategies is elegant, appropriate and solid, appearing to capture the behavioural data well. This work provides a valuable contribution and I'm excited to see further developments, such as neural correlates of the different strategies and switches between them.

Reviewer #3 (Public Review):

The development of an automated Barnes maze allows for more naturalistic and uninterrupted behavior, facilitating the study of spatial learning and memory, as well as the analysis of the brain's neural networks during behavior when combined with neurophysiological techniques. The system's design has been thoughtfully considered, encompassing numerous intricate details. These details include the incorporation of flexible options for selecting start, goal, and proximal landmark positions, the inclusion of a rotating platform to prevent the accumulation of olfactory cues, and careful attention given to atomization, taking into account specific considerations such as the rotation of the maze without causing wire shortage or breakage. When combined with neurophysiological manipulations or recordings, the system provides a powerful tool for studying spatial navigation system.

The behavioral experiment protocols, along with the analysis of animal behavior, are conducted with care, and the development of behavioral modeling to capture the animal's search strategy is thoughtfully executed. It is intriguing to observe how the integration of these innovative stochastic models can elucidate the evolution of mice's search strategy within a variant of the Barnes maze.

Reviewer #1 (Public Review):

Summary:

The evolution of transporter specificity is currently unclear. Did solute carrier systems evolve independently in response to a cellular need to transport a specific metabolite in combination with a specific ion or counter metabolite, or did they evolve specificity from an ancestral protein that could transport and counter transport most metabolites. The present study addresses this question by applying selective pressure to Saccharomyces cerevisiae and studying the mutational landscape of two well characterised amino acid transporters. The data suggest that AA transporters likely evolved from an ancestral transporter and then specific sub families evolved specificity depending on specific evolutionary pressure.

Strengths:

The work is based on sound logic and the experimental methodology is well thought through. The data appear accurate, and where ambiguity is observed (as in the case of citruline uptake by AGP1), in vitro transport assays are carried out. to verify transport function.

Weaknesses:

The revisions have substantially strengthened the conclusions based on the results of this study. Follow up studies will no doubt try to rationalise/identify if specific mutational hot-spots exist within the APC fold that explain the specialisation observed in mammals (neurotransmitter vs. metabolic) for example.

Reviewer #2 (Public Review):

Summary:

This paper describes evolution experiments performed on yeast amino acid transporters aiming at the enlargement of the substrate range of these proteins. Yeast cells lacking 10 endogenous amino acid transporters and thus being strongly impaired to feed on amino acids were again complemented with amino acid transporters from yeast and grown on media with amino acids as the sole nitrogen source.

In the first set of experiments, complementation was done with seven different yeast amino acid transporters, followed by measuring growth rates. Despite most of them having been described before in other experimental contexts, the authors show that many of them have a broader substrate range than initially thought.

Moving to the evolution experiments, the authors used the OrthoRep system to perform random mutagenesis of the transporter gene while it is actively expressed in yeast. The evolution experiments were conducted such that the medium would allow for poor/slow growth of cells expressing the wt transporters, but much better/faster growth if the amino acid transporter would mutate to efficiently take up a poorly transported (as in case of citrulline and AGP1) or non-transported (as in case of Asp/Glu and PUT4) amino acid.

This way and using Sanger sequencing of plasmids isolated from faster-growing clones, the authors identified a number of mutations that were repeatedly present in biological replicates. When these mutations were re-introduced into the transporter using site-directed mutagenesis, faster growth on the said amino acids was confirmed. Growth phenotype were confirmed by uptake experiments using radioactive amino acids; corresponding correlation plots show that the assays based on growth rates versus radioactive uptake assays indeed can explain the effect of the mutations to a large extent.

When mapped to Alphafold prediction models on the transporters, the mutations mapped to the substrate permeation site, which suggests that the changes allow for more favorable molecular interactions with the newly transported amino acids.<br /> Finally, the authors compared growth rates of the evolved transporter variants with those of the wt transporter and found that some variants exhibit a somewhat diminished capacity to transport its original range of amino acids, while other variants were as fit as the wt transporter in terms of uptake of its original range of amino acids.<br /> Based on these findings, the author conclude that transporters can evolve novel substrates through generalist intermediates, either by increasing a weak activity or by establishing a new one.

Strengths:

The study provides evidence in favour of an evolutionary model, wherein a transporter can "learn" to translocate novel substrates without "forgetting" what it used to transport before. This evolutionary concept has been proposed for enzymes before, and this study shows that it also can apply to transporters. The concept behind the study is easy to understand, i.e. improving growth by uptake of more amino acids as nitrogen source. In addition, the study contains a large and extensive characterization of the transporter variants, including growth assays and radioactive uptake measurements. The authors performed experiments as part of the revision to show that the studied mutations do not greatly change surface expression of the transporters. Further they showed that in the absence of the evolutionary pressure, overexpression of the mutants versus the wildtype transporters does not affect growth rates, which is important to assess. Finally, the authors make careful conclusions saying that in real life, the evolutionary landscape is way more complex than under these "reductive" laboratory conditions with a strain lacking ten natively expressed amino acid transporters and being selected on a single amino acid in a defined medium.

Weaknesses:

The authors took a genetic gain-of-function approach based on random mutagenesis of the transporter. While this experimental approach is suited to find some gain-of-function variants for some of the amino acids, it has also its inherent limitations, the most important being that loss-of-function mutants are not sampled (though they might be interesting) and that mutagenesis is entirely random, thus not targeted. These weaknesses cannot be easily overcome other than by restarting the entire study and conducting for example deep mutational scanning experiments. The authors have done what they could do within the scope of this study to make this manuscript as complete and rigorous as possible.

Reviewer #3 (Public Review):

The goal of the current manuscript is to investigate how changes in transporter substrate specificity emerge in response to a novel selective pressure. The authors investigate the APC family of amino acid transporters, a large family with many related transporters that together cover the spectrum of amino acid uptake in yeast.

The authors use a clever approach for their experimental evolutions. By deleting 10 amino acid uptake transporters in yeast, they develop a strain that relies on amino acid import by introduced APC transporters under nitrogen limiting conditions. They can thus evolve transporters towards transport of new substrates if no other nitrogen source is available. The main takeaway from the paper is that it is relatively easy for the spectrum of substrates in a particular transporter of this family to shift, as a number of single mutants are identified that modulate substrate specificity. In general, transporters evolved towards gain-of-function mutations (better or new activities) also confer transport promiscuity, expanding the range of amino acids transported.

The data in the paper support the conclusions, and the outcomes (evolution towards promiscuity) agree with the literature available for soluble enzymes. The authors do a good job in the discussion of relating the lessons of the current study to natural evolution.

Reviewer #1 (Public Review):

Summary:

Nitric oxide (NO) has been implicated as a neuromodulator in the retina. Specific types of amacrine cells (ACs) produce and release NO in a light-dependent manner. NO diffuses freely through the retina and can modulate intracellular levels of cGMP, or directly modify and modulate proteins via S-nitrosylation, leading to changes in gap-junction coupling, synaptic gain, and adaptation. Although these system-wide effects have been documented, it is not well understood how the physiological function of specific neuronal types is affected by NO. This study aims to address this gap in our knowledge.

Strengths:

NO was expected to produce small effects, and considerable effort was expended in validating the system to ensure that any effects of NO would not be confounded by changes in the state of the preparation. The authors used a paired stimulus protocol to control for changes in the sensitivity of the retina during the extended recording periods. The approach potentially increases the sensitivity of the measurements and allows more subtle effects to be observed.

Neural activity was initially measured by Ca-imaging. Responsive ganglion cells were grouped into 32 types using a clustering analysis. Initial control experiments demonstrated that the cell-types revealed here largely recapitulate those from their earlier landmark study using the same approach (Fig. 2).

Application of NO to the retina strongly modulated responses of a single cluster of cells, labeled G32, while having little effect on the remaining 31 clusters. This result is evident in Fig. 3e.

Separate experiments measured ganglion cell spiking activity on a multi-electrode array (MEA). Clustering analysis of the peri-stimulus spike-time histograms (PSTHs) obtained from the MEA data also revealed 32 clusters. The PSTHs for each cluster were aligned to the Ca-imaging data using a convolution approach. The higher temporal resolution of the MEA recordings indicated that NO increased the speed of sub-cluster 2 responses but had no effect on receptive field size. The physiological significance of the small change in kinetics remains unclear.

Weaknesses:

The G32 cluster was further divided into three sub-types using Bayesian Information Criterion (BIC) based on the temporal properties of the Ca-responses. This sub-clustering result seems questionable due to the small difference in the BIC parameter between 2 and 3 clusters. Three sub-clusters of the G32 cluster were also revealed for the PSTH data, however, the BIC analysis was not applied to further validate this result.

The alignment of sub-clusters 1, 2, and 3 identified in the Ca-imaging and the MEA recordings seemed questionable, because the temporal properties of clusters did not align well, nor did the effects of NO.

The title of the paper indicates that nitric oxide modulates contrast suppression in a subset of mouse retinal ganglion cells, however, this result appears to be inferred from previous results showing that G32 is identified as a "suppressed-by-contrast" cell. The present study does not explicitly evaluate the amount of contrast-suppression in G32 cells.

In its current form, the work is likely to have limited impact, since the morphological and functional properties of the affected sub-cluster remain unknown. The finding that there can be cell-specific adaptation effects during experiments on in vitro retina is important new information for the field.

Reviewer #2 (Public Review):

Neuromodulators are important for circuit function, but their roles in the retinal circuitry are poorly understood. This study by Gonschorek and colleagues aims to determine the modulatory effect of nitric oxide on the response properties of retinal ganglion cells. The authors used two photon calcium imaging and multi-electrode arrays to classify and compare cell responses before and after applying a NO donor DETA-NO. The authors found that DETA-NO selectively increases activity in a subset of contrast-suppressed RGC types. In addition, the authors found cell-type specific changes in light response in the absence of pharmacological manipulation in their calcium imaging paradigm. While this study focuses on an important question and the results are interesting, the following issues need further clarification for better interpretation of the data.

(1) Design of the calcium imaging experiments: the control-control pair has a different time course from the control-drug pair (Fig 1e). First, the control-control pair has a 10 minute interval while the control-drug pair has a 25 minute interval. Second, Control 1 Field 2 was imaged 10 min later than Control 1 Field 1 since the start of the calcium imaging paradigm.

Given that the control dataset is used to control for time-dependent adaptational changes throughout the experiment, I wonder why the authors did not use the same absolute starting time of imaging and the same interval between the first and second round of imaging for both the control-control and the control-drug pairs. This can be readily done in one of the two ways: 1. In a set of experiment, add DETA/NO between "Control 1 Field 1 and "Control 2 Field 1" in Fig. 1e as the drug group; or 2. Omit DETA/NO in the Fig. 1e protocol as the control group to monitor the time course of adaptational changes.

Related to the concern above, to determine NO-specific effect, the authors used the criterion that "the response changes observed for control (ΔR(Ctrl2−Ctrl1)) and NO (ΔR(NO−Ctrl1)) were significantly different". This criterion assumes that without DETA-NO, imaging data obtained at the time points of "Control 1 Field 2" and "DETA/NO Field 2" would give the same value of ΔR as ΔR(Ctrl2−Ctrl1) for all RGC types. It is not obvious to me why this should be the case, because of the unknown time-dependent trajectory of the adaptational change for each RGC type. For example, a RGC type could show stable response in the first 30 min and then change significantly in the following 30 min. DETA/NO may counteract this adaptational change, leading to the same ΔR as the control condition (false negative). Alternatively, DETA/NO may have no effect, but the nonlinear time-dependent response drift can give false positive results.

I also wonder why washing-out, a standard protocol for pharmacological experiments, was not done for the calcium protocol since it was done in the MEA experiments. A reversible effect by washing in and out DETA/NO in the calcium protocol would provide a much stronger support that the observed NO modulation is due to NO and not to other adaptive changes.

(2) Effects of Strychnine: In lines 215-219, " In the light-adapted retina, On-cone BCs boost light-Off responses in Off-cone BCs through cross-over inhibition (83, 84) and hence, strychnine affects Off-response components in RGCs - in line with our observations (Fig. S2)" However, Fig. S2 doesn't seem to show a difference in the Off-response components. Rather, the On response is enhanced with strychnine. In addition, suppressed-by-contrast cells are known to receive glycinergic inhibition from VGluT3 amacrine cells (Tien et al., 2016). However, the G32 cluster in Fig. S2 doesn't seem to show a change with strychnine. More explanation on these discrepancies will be helpful.

(3) This study uses DETA-NO as an NO donor for enhancing NO release. However, a previous study by Thompson et al., Br J Pharmacol. 2009 reported that DETA-NO can rapidly and reversible induce a cation current independent of NO release at the 100 uM used in the current study, which could potentially cause the observed effect in G32 cluster such as reduced contrast suppression and increased activity. This potential caveat should at least be discussed, and ideally excluded by showing the absence of DETA-NO effects in nNOS knockout mice, and/or by using another pharmacological reagent such as the NO donor SNAP or the nNOS inhibitor l-NAME.

(4) Clarification of methods: In the Methods, lines 1119-1127, the authors describe the detrending, baseline subtraction, and averaging. Then, line 1129, " the mean activity r(t) was computed and then traces were normalized such that: max t(|r(t)|) = 1. How is the normalization done? Is it over the entire recording (control and wash in) for each ROI? Or is it normalized based on the mean trace under each imaging session (i.e. twice for each imaging field)?

As for the clustering of RGC types, I assume that each ROI's cluster identity remains unchanged through the comparison. If so, it may be helpful to emphasize this in the text.

Reviewer #1 (Public Review):

Previous studies have used a randomly induced label to estimate the number of hematopoietic precursors that contribute to hematopoiesis. In particular, the McKinney-Freeman lab established a measurable range of precursors of 50-2500 cells using random induction of one of the 4 fluorescent proteins (FPs) of a Confetti reporter in the fetal liver to show that hundreds of precursors establish lifelong hematopoiesis. In the presented work, Liu and colleagues aim to extend the measurable range of precursor numbers previously established and enable measurement in a variety of contexts beyond embryonic development. To this end, the authors investigated whether the random induction of a given Confetti FP follows the principles of binomial distribution such that the variance inversely correlates with the precursor number. They tested their hypothesis using a simplified 2-color in vitro system, paying particular attention to minimizing sources of experimental error (elimination of outliers, sample size, events recorded, etc.) that may obscure the measurement of variance. As a result, the data generated are robust and show that the measurable range of precursors can be extended up to 105 cells. They use tamoxifen-inducible Scl-CreER, which is active in hematopoietic stem and progenitor cells (HSPCs) to induce Confetti labeling, and investigated whether they could extend their model to cell numbers below 50 with in vivo transplantation of high versus low numbers of Confetti total bone marrow (BM) cells. The premise of binomial distribution requires that the number of precursors remains constant within a group of mice. The rare frequency of HSPCs in the BM means that the experimentally generated "low" number recipient animals showed some small variability of seeding number, which does not follow the requirement for binomial distribution. While variance due to differences in precursor numbers still dominates, it is unclear how accurate estimated numbers are when precursor numbers are low (<10).

The authors then apply their model to estimate the number of hematopoietic precursors that contribute to hematopoiesis in a variety of contexts including adult steady state, fetal liver, following myeloablation, and a genetic model of Fanconi anemia. Their modeling shows:

-thousands of precursors (~2400-2600) contribute to adult myelopoiesis, which is in line with results from a previous study (Sun et al, 2014).<br /> -myeloablation (single dose 5-FU), while reducing precursor numbers of myeloid progenitors and HSPCs, was not associated with a reduction in precursor numbers of LT-HSCs.<br /> -no major expansion of precursor number in the fetal liver derived from labeling at E11.5 versus E14.5, consistent with recent findings from Ganuza et al, 2022.<br /> -normal precursor numbers in Fancc-/- mice at steady state and from competitive transplantation of young Fancc-/- BM cells, suggesting that reduced Fancc-/- cell proliferation may underlie the reduced chimerism upon transplantation.<br /> -reduced number of lymphoid precursors following transplantation of BM cells from 9-month-old Fancc-/- animals (beyond this age animals have decreased survival).

Although this system does not permit the tracing of individual clones, the modeling presented allows measurements of clonal activity covering nearly the entire HSPC population (as recently estimated by Cosgrove et al, 2021) and can be applied to a wide range of in vivo contexts with relative ease. The conclusions are generally sound and based on high-quality data. Nevertheless, some results could benefit from further explanation or discussion:

-The estimated number of LT-HSCs that contribute to myelopoiesis is not specifically provided, but from the text, it would be calculated to be 1958/5 = ~391. Data from Busch et al, 2015 suggest that the number of differentiation-active HSCs is 5.2x103, which is considered the maximum limit. There is nevertheless a more than 10-fold difference between these two estimates, and it is unclear how this discrepancy arises.<br /> -Similarly, in Figure 3E, the estimated number of precursors is highest in MPP4, a population typically associated with lymphoid potential and transient myeloid potential, whereas the numbers of MPP3, traditionally associated with myeloid potential, tend to be higher but are not significantly different than those found in HSCs.<br /> -The requirement for estimating precursor numbers at stable levels of Confetti labeling is not well explained. As a result, it is unclear how accurate the estimates of B cell precursors upon transplantation of Fancc-/- cells are. In previous experiments on normal Confetti mice (Figure 3B), the authors do not estimate precursors of lymphopoiesis because Confetti labeling of B cells is not saturated, and this appears to be the case in Fanc-/- animals as well (Fig. 5B).<br /> -Do 9-month-old Fanc-/- animals have reduced lymphoid precursors as well?

Reviewer #2 (Public Review):

Summary:

This manuscript by Liu et al. uses Confetti labeling of hematopoietic stem and progenitor cells in situ to infer the clonal dynamics of adult hematopoiesis. The authors apply a new mathematical framework to analyze the data, allowing them to increase the range of applicability of this tool up to tens of thousands of precursors. With this tool, they (1) provide evidence for the large polyclonality of adult hematopoiesis, (2) offer insights on the expansion dynamics in the fetal liver stage, (3) assess the clonal dynamics in a Fanconi anemia model (Fancc), which has engraftment defects during transplantation.

Strengths:

The manuscript is well written, with beautiful and clear figures, and both methods and mathematical models are clear and easy to understand.

Since 2017, Mikel Ganuza and Shannon McKinney-Freeman have been using these Confetti approaches that rely on calculating the variance across independent biological replicates as a way to infer clonal dynamics. This is a powerful tool and it is a pleasure to see it being implemented in more labs around the world. One of the cool novelties of the current manuscript is using a mathematical model (based on a binomial distribution) to avoid directly regressing the Confetti labeling variance with the number of clones (which only has linearity for a small range of clone numbers). As a result, this current manuscript of Liu et al. methodologically extends the usability of the Confetti approach, allowing them more precise and robust quantification.

They then use this model to revisit some questions from various Ganuza et al. papers, validating most of their conclusions. The application to the clonal dynamics of hematopoiesis in a model of Fanconi anemia (Fancc mice) is very much another novel aspect, and shows the surprising result that clonal dynamics are remarkably similar to the wild-type (in spite of the defect that these Fancc HSCs have during engraftment).<br /> Overall, the manuscript succeeds at what it proposes to do, stretching out the possibilities of this Confetti model, which I believe will be useful for the entire community of stem cell biologists, and possibly make these assays available to other stem cell regenerating systems.

Weaknesses:

My main concern with this work is the choice of CreER driver line, which then relates to some of the conclusions made. Scl-CreER succeeds at being as homogenous as possible in labeling HSC/MPPs... however it is clear that it also labels a subcompartment of HSC clones that become dominant with time... This is seen as the percentage of Confetti-recombined cells never ceases to increase during the 9-month chase of labeled cells, suggesting that non-labeled cells are being replaced by labeled cells. The reason why this is important is that then one cannot really make conclusions about the clonal dynamics of the unlabeled cells (e.g. for estimating the total number of clones, etc.).

I am not sure about the claims that the data shows little precursor expansion from E11 to E14. First, these experiments are done with fewer than 5 replicates, and thus they have much higher error, which is particularly concerning for distinguishing differences of such a small number of clones. Second, the authors do see a ~0.5-1 log difference between E11 and E14 (when looking at months 2-3). When looking at months 5+, there is already a clear decline in the total number of clones in both adult-labeled and embryonic-labeled, so these time points are not as good for estimating the embryonic expansion. In any case, the number of precursors at E11 (which in the end defines the degree of expansion) is always overestimated (and thus, the expansion underestimated) due to the effects of lingering tamoxifen after injection (which continues to cause Confetti allele recombination as stem cell divide). Thus, I think these results are still compatible with expansion in the fetal liver (the degree of which still remains uncertain to me).

Reviewer #3 (Public Review):

Summary:

Liu et al. focus on a mathematical method to quantify active hematopoietic precursors in mice using Confetti reporter mice combined with Cre-lox technology. The paper explores the hematopoietic dynamics in various scenarios, including homeostasis, myeloablation with 5-fluorouracil, Fanconi anemia (FA), and post-transplant environments. The key findings and strengths of the paper include (1) precursor quantification: The study develops a method based on the binomial distribution of fluorescent protein expression to estimate precursor numbers. This method is validated across a wide dynamic range, proving more reliable than previous approaches that suffered from limited range and high variance outside this range; (2) dynamic response analysis: The paper examines how hematopoietic precursors respond to myeloablation and transplantation; (3) application in disease models: The method is applied to the FA mouse model, revealing that these mice maintain normal precursor numbers under steady-state conditions and post-transplantation, which challenges some assumptions about FA pathology. Despite the normal precursor count, a diminished repopulation capability suggests other factors at play, possibly related to cell proliferation or other cellular dysfunctions. In addition, the FA mouse model showed a reduction in active lymphoid precursors post-transplantation, contributing to decreased repopulation capacity as the mice aged. The authors are aware of the limitation of the assumption of uniform expansion. The paper assumes a uniform expansion from active precursor to progenies for quantifying precursor numbers. This assumption may not hold in all biological scenarios, especially in disease states where hematopoietic dynamics can be significantly altered. If non-uniformity is high, this could affect the accuracy of the quantification. Overall, the study underscores the importance of precise quantification of hematopoietic precursors in understanding both normal and pathological states in hematopoiesis, presenting a robust tool that could significantly enhance research in hematopoietic disorders and therapy development. The following concerns should be addressed.

Major Points:

• The authors have shown a wide range of seeded cells (1 to 1e5) (Figure 1D) that follow the linear binomial rule. As the standard deviation converges eventually with more seeded cells, the authors need to address this limitation by seeding the number of cells at which the assumption fails.<br /> • Line 276: This suggests myelopoiesis is preferred when very few precursors are available after irradiation-mediated injury. Did the authors see more myeloid progenitors at 1 month post-transplantation with low precursor number? The authors need to show this data in a supplement.

Minor Points:

• Please cite a reference for line 40: a rare case where a single HSPC clone supports hematopoiesis.<br /> • Line 262-263: "This discrepancy may reflect uneven seeding of precursors to the BM throughout the body after transplantation and the fact that we only sampled a part of the BM (femur, tibia, and pelvis)." Consider citing this paper (https://doi.org/10.1016/j.cell.2023.09.019) that explores the HSPCs migration across different bones.<br /> • Lines 299 and 304. Misspellings of RFP.<br /> • The title is misleading as the paper's main focus is the precursor number estimator using the binomial nature of fluorescent tagging. Using a single-copy cassette of Confetti mice cannot be used to measure clonality.

Reviewer #1 (Public Review):

Freas et al. investigated if the exceedingly dim polarization pattern produced by the moon can be used by animals to guide a genuine navigational task. The sun and moon have long been celestial beacons for directional information, but they can be obscured by clouds, canopy, or the horizon. However, even when hidden from view, these celestial bodies provide directional information through the polarized light patterns in the sky. While the sun's polarization pattern is famously used by many animals for compass orientation, until now it has never been shown that the extremely dim polarization pattern of the moon can be used for navigation. To test this, Freas et al. studied nocturnal bull ants, by placing a linear polarizer in the homing path on freely navigating ants 45 degrees shifted to the moon's natural polarization pattern. They recorded the homing direction of an ant before entering the polarizer, under the polarizer, and again after leaving the area covered by the polarizer. The results very clearly show, that ants walking under the linear polarizer change their homing direction by about 45 degrees in comparison to the homing direction under the natural polarization pattern and change it back after leaving the area covered by the polarizer again. These results can be repeated throughout the lunar month, showing that bull ants can use the moon's polarization pattern even under crescent moon conditions. Finally, the authors show, that the degree in which the ants change their homing direction is dependent on the length of their home vector, just as it is for the solar polarization pattern.

The behavioral experiments are very well designed, and the statistical analyses are appropriate for the data presented. The authors' conclusions are nicely supported by the data and clearly show that nocturnal bull ants use the dim polarization pattern of the moon for homing, in the same way many animals use the sun's polarization pattern during the day. This is the first proof of the use of the lunar polarization pattern in any animal.

Reviewer #2 (Public Review):

Summary:

The authors aimed to understand whether polarised moonlight could be used as a directional cue for nocturnal animals homing at night, particularly at times of night when polarised light is not available from the sun. To do this, the authors used nocturnal ants, and previously established methods, to show that the walking paths of ants can be altered predictably when the angle of polarised moonlight illuminating them from above is turned by a known angle (here +/- 45 degrees).

Strengths:

The behavioural data are very clear and unambiguous. The results clearly show that when the angle of downwelling polarised moonlight is turned, ants turn in the same direction. The data also clearly show that this result is maintained even for different phases (and intensities) of the moon, although during the waning cycle of the moon the ants' turn is considerably less than may be expected.

Weaknesses:

The final section of the results - concerning the weighting of polarised light cues into the path integrator - lacks clarity and should be reworked and expanded in both the Methods and the Results (also possibly with an extra methods figure). I was really unsure of what these experiments were trying to show or what the meaning of the results actually are.

Impact:

The authors have discovered that nocturnal bull ants while homing back to their nest holes at night, are able to use the dim polarised light pattern formed around the moon for path integration. Even though similar methods have previously shown the ability of dung beetles to orient along straight trajectories for short distances using polarised moonlight, this is the first evidence of an animal that uses polarised moonlight in homing. This is quite significant, and their findings are well supported by their data.

Reviewer #3 (Public Review):

Summary:

This manuscript presents a series of experiments aimed at investigating orientation to polarized lunar skylight in a nocturnal ant, the first report of its kind that I am aware of.

Strengths:

The study was conducted carefully and is clearly explained here.

Weaknesses:

I have only a few comments and suggestions, that I hope will make the manuscript clearer and easier to understand.

Time compensation or periodic snapshots

In the introduction, the authors compare their discovery with that in dung beetles, which have only been observed to use lunar skylight to hold their course, not to travel to a specific location as the ants must. It is not entirely clear from the discussion whether the authors are suggesting that the ants navigate home by using a time-compensated lunar compass, or that they update their polarization compass with reference to other cues as the pattern of lunar skylight gradually shifts over the course of the night - though in the discussion they appear to lean towards the latter without addressing the former. Any clues in this direction might help us understand how ants adapted to navigate using solar skylight polarization might adapt use to lunar skylight polarization and account for its different schedule. I would guess that the waxing and waning moon data can be interpreted to this effect.

Effects of moon fullness and phase on precision

As well as the noted effect on shift magnitudes, the distributions of exit headings and reorientations also appear to differ in their precision (i.e., mean vector length) across moon phases, with somewhat shorter vectors for smaller fractions of the moon illuminated. Although these distributions are a composite of the two distributions of angles subtracted from one another to obtain these turn angles, the precision of the resulting distribution should be proportional to the original distributions. It would be interesting to know whether these differences result from poorer overall orientation precision, or more variability in reorientation, on quarter moon and crescent moon nights, and to what extent this might be attributed to sky brightness or degree of polarization.

N.B. The Watson-Williams tests for difference in mean angle are also sensitive to differences in sample variance. This can be ruled out with another variety of the test, also proposed by Watson and Williams, to check for unequal variances, for which the F statistic is = (n2-1)*(n1-R1) / (n1-1)*(n2-R2) or its inverse, whichever is >1.

Reviewer #1 (Public Review):

Summary:

Hartman and Satija's manuscript constitutes a significant contribution to the field of imaging-based spatial transcriptomics (ST) through their comprehensive comparative analysis of six multiplexed in situ gene expression profiling technologies. Their findings provide invaluable insights into the practical considerations and performance of these methods, offering robust evidence for researchers seeking optimal ST technologies. However, given the simultaneous availability of similar preprints, readers should exercise caution when comparing findings to ensure reliable information. Therefore, the authors should revise their manuscript to ensure consistency among all ST technologies compared, considering findings from other preprints as well if possible.

Strengths:

(1) The manuscript offers a comprehensive and systematic comparison of six in situ gene expression profiling technologies, including both commercially available and academically developed methods, which is the most extensive study in this field.

(2) Novel metrics have been proposed by the authors to mitigate molecular artifacts and off-target signals, enhancing the accuracy of sensitivity and specificity comparisons across datasets. By emphasizing the significance of evaluating both sensitivity and specificity, the study addresses the challenge of comparing standard metrics like the number of unique molecules detected per cell, given variations in panel composition and off-target molecular artifacts. This feature is directly connected to their development of novel cell segmentation methods to improve the specificity.

(3) As a result of the analysis performed earlier, the authors illustrate how molecular false positives can distort spatially-aware differential expression analysis, underscoring the necessity for caution in interpreting downstream results.

(4) Offering guidance for the selection, processing, and interpretation of in situ spatial technologies, the study equips researchers in the field with valuable insights.

Weaknesses:

(1) Although focusing on mouse brain datasets broadens the comparison of technologies, it confines the study to a single biological context. Discussing the potential limitations of this approach and advocating for future studies in diverse tissue types would enrich the manuscript, especially for clinical FFPE applications.

(2) Providing more explicit details on the criteria used to select datasets for each technology would ensure a fair and unbiased comparison. Otherwise, it may look like the Hall of Fame for champion data sets to advertise a certain commercial product.

(3) Improving the discussion part by discussing the origins of non-specific signals and molecular artifacts, alongside the challenges related to cell segmentation across different tissue types and cell morphologies, would enrich its content. Note that all of these experimental sets have been obtained from thin mouse brain slices, which are actually 3D although they are thin like 10-20 um. As a result, there might be a chance to have partial cell overlap in the z-axis, potentially leading to transcript mixing. Additionally, many cells are probably cut so their actual transcriptomes are inherently partial information, which makes direct comparison to scRNA-seq unfair. These aspects should be included for fair comparison issues.

(4) Expanding on the potential implications of the findings for developing new computational methods to address non-specific biases in downstream analyses would augment the manuscript's impact and relevance.

Reviewer #2 (Public Review):

Summary:

In the manuscript, Hartman et al. present a detailed comparison of 6 distinct multiplexed in situ gene expression profiling technologies, including both academic and commercial systems.

The main concept of the study is to evaluate publicly accessible mouse brain datasets provided by the platforms' developers, where optimal performance in showcasing their technologies is expected. The authors stress the difficulty of making a comparison with standard metrics, e.g., the count of total molecules per cell, considering the differences in gene panel sizes across platforms. To make a fair comparison, the authors conceived a metric of specificity performance, which is called "MECR", an average of mutually exclusive gene co-expression rates in the sample. The authors found that the rate mainly depends on the choice of cell segmentation method, thus reanalyzed 5 of these datasets (excluding STARmap PLUS, due to the lack of molecule location information) with an independent cell segmentation algorithm (i.e., Baysor). Based on the reanalysis, the authors clearly suggest the best-performing platform at the end of the manuscript.

Strengths:

I consider that the paper is a valuable contribution to the community, for the following two reasons:

(1) As the authors mentioned, I fully agree that the spatial transcriptomics community indeed needs better metrics in terms of comparison across technologies, rather than traditional metrics, e.g., molecule counts per cell. In that regard, I believe introducing a new metric, MECR, is quite valuable.

(2) This work highlights the differences in results based on the choice of cell segmentation used for each platform, which suggests a need for trying out different segmentation algorithms to derive the right results. I believe this is an urgent warning that should be widespread in the community as soon as possible.

Weaknesses:

I disagree with the conclusion of the manuscript where the authors compare the technologies and suggest the best-performing ones, because of the following major points:

(1) As the authors mentioned, MECR is a measure of "specificity" not "sensitivity". Still, the comparison of sensitivity was done with the mean counts per cell (Figure 3e). However, I strongly disagree with using the mean counts per cell as a measure of sensitivity because the comparison was done with different gene panels. The counts per cell can be highly dependent on the choice of genes, especially due to optical crowding.

(2) The authors compared sensitivity based on the Baysor cell segmentation, but in fact, Baysor uses spatial gene expression for cell segmentation, which depends on the sensitivity of the platform. Thus, a comparison of sensitivity based on an algorithm that is based on sensitivity seems to be nonsensical.

Reviewer #3 (Public Review):

This paper concerns whether synaptic scaling (or homeostatic synaptic plasticity; HSP) occurs similarly at GABA and Glu synapses and comes to the surprising conclusion that these can be regulated independently. In fact, under the conditions used in this study, only the GABAergic synapses show HSP and the glutamatergic synapses don't change. This is surprising because these were thought to be co-regulated during HSP and in fact, the major mechanisms thought to underlie downscaling (TTX or CNQX driven), retinoic acid and TNF, have been shown to regulate both GABARs and AMPARs directly. Thus, the main result, that GABA HSP is dissociable from Glu HSP, is novel and exciting. This suggests either different mechanisms underlie the two processes, or that under certain conditions, another mechanism is engaged that scales one type of synapse and not the other. Given that glutamatergic synapses are unchanged in their conditions, that later seems more likely - a novel form of HSP exists that only scale GABAergic synapses. Whether glutamatergic and GABAergic synapses scale independently during HSP affecting both types of synapses remains to be addressed. It would be necessary to demonstrate the dissociation in the same system, under conditions where both types of synapses are changing. But because the form of HSP studied here appears different than that studied in Fong et al., the authors should be careful when comparing the two results. There seems to be an implicit underlying assumption that there is a simple form of HSP, when the overall literature (and the two studies from this lab) supports the idea of many forms of HSP.

The homeostatic changes at GABAergic synapses do seem to be more consistent in amplitude across the bulk of the synapses, which does suggest that true scaling (a proportional change to all synapses on a cell) is occurring. This may represent a major difference in how homeostatic changes occur at the two types of synapses.

The second finding is that this form of HSP seems more regulated by action potential firing than conventional HSP - previous work from this lab had shown that restoring AP firing during AMPA receptor blockade did not prevent scaling of glutamatergic synapses (it should be noted these experiments were done in rat cultures, not mouse, used a higher concentration of CNQX, and used a different optogenetic stimulation paradigm). Restoring AP firing rates under the conditions used here (and thus the form of HSP only affecting GABA synapses), on the other hand, did prevent the homeostatic response. This suggests that this GABA-only form of HSP is more attuned to spiking rates than other forms.

However, details in the data may suggest that spiking is not the (or the only) homeostat, as TTX and CNQX causes identical changes in mIPSC amplitude but have different effects on spiking (although TTX may be driving a different form of HSP). Further, in Fig 5, CTZ had a minimal effect on spiking but a large effect on mIPSCs. Similar issues appear in Fig 6, where the induction of increased spiking is highly variable, with many cells showing control levels or lower spiking rates. Yet the synaptic changes are robust, across all cells. Overall, more will need to be done to conclude that spiking is the homeostat for GABA synapses.

The paper also suggests that the GABA changes are leading to the recovery of the spiking rates, but while they have the time course of the spiking changes and recovery, they only have the 24h time point for synaptic changes. It is not yet possible to conclude how the time courses align without more data, nor can we assume that cells that did not recover to control firing rates would do so eventually.

Reviewer #1 (Public Review):

The inferior colliculus (IC) is the central auditory system's major hub. It integrates ascending brainstem signals to provide acoustic information to the auditory thalamus. The superficial layers of the IC ("shell" IC regions as defined in the current manuscript) also receive a massive descending projection from the auditory cortex. This auditory cortico-collicular pathway has long fascinated the hearing field, as it may provide a route to funnel "high-level" cortical signals and impart behavioral salience upon an otherwise behaviorally agnostic midbrain circuit.

Accordingly, IC neurons can respond differently to the same sound depending on whether animals engage in a behavioral task (Ryan and Miller 1977; Ryan et al., 1984; Slee & David, 2015; Saderi et al., 2021; De Franceschi & Barkat, 2021). Many studies also report a rich variety of non-auditory responses in the IC, far beyond the simple acoustic responses one expects to find in a "low-level" region (Sakurai, 1990; Metzger et al., 2006; Porter et al., 2007). A tacit assumption is that the behaviorally relevant activity of IC neurons is inherited from the auditory cortico-collicular pathway. However, this assumption has never been tested, owing to two main limitations of past studies:

(1) Prior studies could not confirm if data were obtained from IC neurons that receive monosynaptic input from the auditory cortex.

(2) Many studies have tested how auditory cortical inactivation impacts IC neuron activity; the consequence of cortical silencing is sometimes quite modest. However, all prior inactivation studies were conducted in anesthetized or passively listening animals. These conditions may not fully engage the auditory cortico-collicular pathway. Moreover, the extent of cortical inactivation in prior studies was sometimes ambiguous, which complicates interpreting modest or negative results.

Here, the authors' goal is to directly test if the auditory cortex is necessary for behaviorally relevant activity in IC neurons. They conclude that surprisingly, task relevant activity in cortico-recipient IC neuron persists in absence of auditory cortico-collicular transmission. To this end, a major strength of the paper is that the authors combine a sound-detection behavior with clever approaches that unambiguously overcome the limitations of past studies.

First the authors inject a transsynaptic virus into the auditory cortex, thereby expressing a genetically encoded calcium indicator in the auditory cortex's postsynaptic targets in the IC. This powerful approach enables 2-photon Ca2+ imaging from IC neurons that unambiguously receive monosynaptic input from auditory cortex. Thus, any effect of cortical silencing should be maximally observable in this neuronal population. Second, they abrogate auditory cortico-collicular transmission using lesions of auditory cortex. This "sledgehammer" approach is arguably the most direct test of whether cortico-recipient IC neurons will continue to encode task-relevant information in absence of descending feedback. Indeed, their method circumvents the known limitations of more modern optogenetic or chemogenetic silencing, e.g. variable efficacy.

The authors have revised their manuscript and adequately addressed the major concerns. Although more in depth analyses of these rich datasets are definitely possible, the current results nevertheless stand on their own. Indeed, the work serves as a beacon to move away from the idea that cortico-collicular projections function primarily to impart behavioral relevance upon auditory midbrain neurons. This knowledge inspires a search for alternative explanations as to the role of auditory cortico-collicular synapses in behavior.

Reviewer #2 (Public Review):

Summary:

This study takes a new approach to studying the role of corticofugal projections from auditory cortex to inferior colliculus. The authors performed two-photon imaging of cortico-recipient IC neurons during a click detection task in mice with and without lesions of auditory cortex. In both groups of animals, they observed similar task performance and relatively small differences in the encoding of task-response variables in the IC population. They conclude that non-cortical inputs to the IC can provide substantial task-related modulation, at least when AC is absent.

Strengths:

This study provides valuable new insight into big and challenging questions around top-down modulation of activity in the IC. The approach here is novel and appears to have been executed thoughtfully. Thus, it should be of interest to the community.

Weaknesses:

Analysis of single unit activity is limited in its scope.

Reviewer #3 (Public Review):

Summary:

This study aims to demonstrate that cortical feedback is not necessary to signal behavioral outcome to shell neurons of the inferior colliculus during a sound detection task. The demonstration is achieved in a very clear manner by the observation of the activity of cortico-recepient neurons in animals which have received lesions of the auditory cortex. The experiment shows that neither behavior performance nor neuronal responses are significantly impacted by cortical lesions except for the case of partial lesions which seem to have a disruptive effect on behavioral outcome signaling.

Strengths:

The demonstration of the main conclusions is based on state-of-the-art, carefully controlled methods and is highly convincing. There is an in depth discussion of the different effects of auditory cortical lesions on sound detection behavior.

Weaknesses:

The description of feedback signals could be more detailed although it is difficult to achieve good temporal resolution with the calcium imaging technique necessary for targeting cortico-recipient neurons.

Reviewer #1 (Public Review):

Summary:

Through an unbiased genomewide KO screen, the authors identified loss of DBT to suppress MG132-mediated death of cultured RPE cells. Further analyses suggested that DBT reduces ubiquitinated proteins by promoting autophagy. Mechanistic studies indicated that DBT loss promotes autophagy via AMPK and its downstream ULK and mTOR signaling. Furthermore, loss of DBT suppresses polyglutamine- or TDP-43-mediated cytotoxicity and/or neurodegeneration in fly models. Finally, the authors showed that DBT proteins are increased in ALS patient tissues, compared to non-neurological controls.

Strengths:

The idea is novel, the evidence is convincing, and the data are clean. The findings have implications for human diseases.

Weaknesses:

None.

Reviewer #2 (Public Review):

Summary:

Hwang, Ran-Der et al utilized a CRISPR-Cas9 knockout in human retinal pigment epithelium (RPE1) cells to evaluate for suppressors of toxicity by the proteasome inhibitor MG132 and identified that knockout of dihydrolipoamide branched chain transacylase E2 (DBT) suppressed cell death. They show that DBT knockout in RPE1 cells does not alter proteasome or autophagy function at baseline. However, with MG132 treatment, they show a reduction in ubiquitinated proteins but with no change in proteasome function. Instead, they show that DBT knockout cells treated with MG132 have improved autophagy flux compared to wildtype cells treated with MG132. They show that MG132 treatment decreases ATP/ADP ratios to a greater extent in DBT knockout cells, and in accordance causes activation of AMPK. They then show downstream altered autophagy signaling in DBT knockout cells treated with MG132 compared to wild-type cells treated with MG132. Then they express the ALS mutant TDP43 M337 or expanded polyglutamine repeats to model Huntington's disease and show that knockdown of DBT improves cell survival in RPE1 cells with improved autophagic flux. They also utilize a Drosophila models and show that utilizing either a RNAi or CRISPR-Cas9 knockout of DBT improves eye pigment in TDP43M337V and polyglutamine repeat-expressing transgenic flies. Finally, they show evidence for increased DBT in postmortem spinal cord tissue from patients with ALS via both immunoblotting and immunofluorescence.

Strengths:

This is a mechanistic and well-designed paper that identifies DBT as a novel regulator of proteotoxicity via activating autophagy in the setting of proteasome inhibition. Major strengths include careful delineation of a mechanistic pathway to define how DBT is protective. These conclusions are well-justified.

Weaknesses:

None

Reviewer #1 (Public Review):

Summary:

The study introduces and validates the Cyclic Homogeneous Oscillation (CHO) detection method to precisely determine the duration, location, and fundamental frequency of non-sinusoidal neural oscillations. Traditional spectral analysis methods face challenges in distinguishing the fundamental frequency of non-sinusoidal oscillations from their harmonics, leading to potential inaccuracies. The authors implement an underexplored approach, using the auto-correlation structure to identify the characteristic frequency of an oscillation. By combining this strategy with existing time-frequency tools to identify when oscillations occur, the authors strive to solve outstanding challenges involving spurious harmonic peaks detected in time-frequency representations. Empirical tests using electrocorticographic (ECoG) and electroencephalographic (EEG) signals further support the efficacy of CHO in detecting neural oscillations.

Strengths:

The paper puts important emphasis on the 'identity' question of oscillatory identification. The field primarily identifies oscillations through frequency, space (brain region), and time (length, and relative to task or rest). However, more tools that claim to further characterize oscillations by their defining/identifying traits are needed, in addition to data-driven studies about what the identifiable traits of neural oscillations are beyond frequency, location, and time. Such tools are useful for potentially distinguishing between circuit mechanistic generators underlying signals that may not otherwise be distinguished. This paper states this problem well and puts forth a new type of objective for neural signal processing methods.

The paper uses synthetic data and multimodal recordings at multiple scales to validate the tool, suggesting CHO's robustness and applicability in various real-data scenarios. The figures illustratively demonstrate how CHO works on such synthetic and real examples, depicting in both time and frequency domains. The synthetic data are well-designed, and capable of producing transient oscillatory bursts with non-sinusoidal characteristics within 1/f noise. Using both non-invasive and invasive signals exposes CHO to conditions which may differ in the extent and quality of harmonic signal structure. An interesting follow-up question is whether the utility demonstrated here holds for MEG signals, as well as source-reconstructed signals from non-invasive recordings.

This study is accompanied by open-source code and data for use by the community.

Weaknesses:

The criteria that the authors use for neural oscillations embody some operating assumptions underlying their characteristics, perhaps informed by immediate use cases intended by the authors (e.g., hippocampal bursts). The extent to which these assumptions hold in all circumstances should be investigated. For instance, the notion of consistent auto-correlation breaks down in scenarios where instantaneous frequency fluctuates significantly at the scale of a few cycles. Imagine an alpha-beta complex without harmonics (Jones 2009). If oscillations change phase position within a timeframe of a few cycles, it would be difficult for a single peak in the auto-correlation structure to elucidate the complex time-varying peak frequency in a dynamic fashion. Likewise, it is unclear whether bounding boxes with a pre-specified overlap can capture complexes that manoeuvre across peak frequencies.

This method appears to lack the implementation of statistical inferential techniques for estimating and interpreting auto-correlation and spectral structure. In standard practice, auto-correlation functions and spectral measures can be subjected to statistical inference to establish confidence intervals, often helping to determine the significance of the estimates. Doing so would be useful for expressing the likelihood that an oscillation and its harmonic has the same auto-correlation structure and fundamental frequency, or more robustly identifying harmonic peaks in the presence of spectral noise. Here, the authors appear to use auto-correlation and time-frequency decomposition more as a deterministic tool rather than an inferential one. Overall, an inferential approach would help differentiate between true effects and those that might spuriously occur due to the nature of the data. Ultimately, a more statistically principled approach might estimate harmonic structure in the presence of noise in a unified manner transmitted throughout the methodological steps.

Reviewer #2 (Public Review):

Summary:

A new toolbox is presented that builds on previous toolboxes to distinguish between real and spurious oscillatory activity, which can be induced by non-sinusoidal waveshapes. Whilst there are many toolboxes that help to distinguish between 1/f noise and oscillations, not many tools are available that help to distinguish true oscillatory activity from spurious oscillatory activity induced in harmonics of the fundamental frequency by non-sinusoidal waveshapes. The authors present a new algorithm which is based on autocorrelation to separate real from spurious oscillatory activity. The algorithm is extensively validated using synthetic (simulated) data, and various empirical datasets from EEG, and intracranial EEG in various locations and domains (i.e. auditory cortex, hippocampus, etc.).

Strengths:

Distinguishing real from spurious oscillatory activity due to non-sinusoidal waveshapes is an issue that has plagued the field for quite a long time. The presented toolbox addresses this fundamental problem which will be of great use for the community. The paper is written in a very accessible and clear way so that readers less familiar with the intricacies of Fourier transform and signal processing will also be able to follow it. A particular strength is the broad validation of the toolbox, using synthetic, scalp EEG, EcoG, and stereotactic EEG in various locations and paradigms.

Weaknesses:

A weakness is that the algorithm seems to be quite conservative in identifying oscillatory activity which may render it only useful for analyzing very strong oscillatory signals (i.e. alpha), but less suitable for weaker oscillatory signals (i.e. gamma).

Reviewer #2 (Public Review):

Summary:

The authors set out to non-invasively track neuronal development in rat neonates, which they achieved with notable success. However, the direct relationship between the results and broader conclusions regarding developmental biology and potential human implications is somewhat overstretched without further validation.

Strengths:

If adequately revised and validated, this work could have a significant impact on the field, providing a non-invasive tool for longitudinal studies of brain development and neurodevelopmental disorders in preclinical settings.

Weaknesses:

(1) Consistency and Logical Flow:

- The manuscript suffers from a lack of strategic flow in some sections. Specifically, transitions between major findings and methodological discussions need refinement to ensure a logical progression of ideas. For example, the jump from the introduction of developmental trajectories and the technicalities of MRS (Magnetic Resonance Spectroscopy) processing on page 3 could benefit from a bridging paragraph that explicitly states the study's hypotheses based on existing literature gaps.

(2) Scientific Rigour:

- While the novel application of diffusion-weighted MRS is commendable, there's a notable gap in the rigorous validation of this approach against gold-standard histological or molecular techniques. Particularly, the assertions regarding the sphere fraction and morphological changes inferred from biophysical modelling mandates direct validation to solidify the claims made. A study comparing these in vivo findings with ex vivo confirmation in at least a subset of samples would significantly enhance the reliability of these conclusions.

(3) Clarity and Novelty:

- The manuscript often delves deeply into technical specifics at the expense of accessibility to readers not deeply familiar with MRS technology. The introduction and discussions would benefit from a clearer elucidation of why these specific metabolite markers were chosen and their known relevance to neuronal and glial cells, placing this in the context of what is novel compared to existing literature.<br /> - The novelty aspect could be reinforced by a more structured discussion on how this method could change the current understanding or practices within neurodevelopmental research, compared to the current state of the art.

(4) Completeness:

- The Discussion section requires expansion to offer a more comprehensive interpretation of how these findings impact the broader field of neurodevelopment and psychiatric disorders. Specifically, the implications for human studies or clinical translation are touched upon but not fully explored.<br /> - Further, while supplementary material provides necessary detail on methodology, key findings from these analyses should be summarized and discussed in the main text to ensure the manuscript stands complete on its own.

(5) Grammar, Style, Orthography:

- There are sporadic grammatical and typographical errors throughout the text which, while minor, detract from the overall readability. For example, inconsistencies in metabolite abbreviations (e.g., tCr vs Cr+PCr) should be standardized.

(6) References and Additional Context:

- The current reference list is extensive but lacks integration into the narrative. Direct comparisons with existing studies, especially those with conflicting or supportive findings, are scant. More dedicated effort to contextualize this work within the existing body of knowledge would be beneficial.

Reviewer #1 (Public Review):

In this work, Ligneul and coauthors implemented diffusion-weighted MRS in young rats to follow longitudinally and in vivo the microstructural changes occurring during brain development. Diffusion-weighted MRS is here instrumental in assessing microstructure in a cell-specific manner, as opposed to the claimed gold-standard (manganese-enhanced MRI) that can only probe changes in brain volume. Differential microstructure and complexification of the cerebellum and the thalamus during rat brain development were observed non-invasively. In particular, lower metabolite ADC with increasing age were measured in both brain regions, reflecting increasing cellular restriction with brain maturation. Higher sphere (representing cell bodies) fraction for neuronal metabolites (total NAA, glutamate) and total creatine and taurine in the cerebellum compared to the thalamus were estimated, reflecting the unique structure of the cerebellar granular layer with a high density of cell bodies. Decreasing sphere fraction with age was observed in the cerebellum, reflecting the development of the dendritic tree of Purkinje cells and Bergmann glia. From morphometric analyses, the authors could probe non-monotonic branching evolution in the cerebellum, matching 3D representations of Purkinje cells expansion and complexification with age. Finally, the authors highlighted taurine as a potential new marker of cerebellar development.

From a technical standpoint, this work clearly demonstrates the potential of diffusion-weighted MRS at probing microstructure changes of the developing brain non-invasively, paving the way for its application in pathological cases. Ligneul and coauthors also show that diffusion-weighted MRS acquisitions in neonates are feasible, despite the known technical challenges of such measurements, even in adult rats. They also provide all necessary resources to reproduce and build upon their work, which is highly valuable for the community.

From a biological standpoint, claims are well supported by the microstructure parameters derived from advanced biophysical modelling of the diffusion MRS data. The assumption of metabolite compartmentation, forming the basis of cell-specific microstructure interpretation of dMRS data, remains debated and should be considered with care (Rae, Neurochem Res, 2014, https://doi.org/10.1007/s11064-013-1199-5). External cross-validation of some of the authors' claims, in particular taurine in the thalamus switching from neurons to astrocytes during brain development, would be a highly valuable addition to this study.

Specific strengths:

(1) The interpretation of dMRS data in terms of cell-specific microstructure through advanced biophysical modelling (e.g. the sphere fraction, modelling the fraction of cell bodies versus neuronal or astrocytic processes) is a strong asset of the study, going beyond the more commonly used signal representation metrics such as the apparent diffusion coefficient, which lacks specificity to biological phenomena.<br /> (2) The fairly good data quality despite the complexity of the experimental framework should be praised: diffusion-weighted MRS was acquired in two brain regions (although not in the same animals) and longitudinally, in neonates, including data at high b-values and multiple diffusion times, which altogether constitutes a large-scale dataset of high value for the diffusion-weighted MRS community.<br /> (3) The authors have shared publicly data and codes used for processing and fitting, which will allow one to reproduce or extend the scope of this work to disease populations, and which goes in line with the current effort of the MR(S) community for data sharing.

Specific weaknesses:

(1) This work lacks an introduction and a discussion about diffusion MRI, which is already a validated technique to assess brain development non-invasively. Although water lacks cell-specificity compared to metabolites, several studies have reported a decrease in water ADC and increased fractional anisotropy with brain maturation, associated with the myelination process and decreased water content (overview in Hüppi, Chapt. 30 of "Diffusion MRI: Theory, Methods, and Applications", Oxford University Press, 2010). Interestingly, the same observations are found in this work (decreased ADC with age for most metabolites in both brain regions), which should have been commented on. Moreover, the authors could have reported water diffusion properties in addition to metabolites', as I believe the water signal, used for coil combination and/or Eddy currents corrections, is usually naturally acquired during diffusion-weighted MRS scans.<br /> (2) It is unclear why the authors have normalized metabolite concentrations (measured from low b-values diffusion-weighted MRS spectra) to the macromolecule concentrations. First, it is not specified whether in vivo macromolecules were acquired at each age or just at one time point. Second, such ratios are not standard practice in the MRS community so this choice should have been explained. Third, the macromolecule content was reported to change with age (Tkac et al., Magn Reson Med, 2003), therefore a change in metabolite to macromolecule ratio with age cannot be interpreted unequivocally.<br /> (3) Some discussion is missing about the choice of the analytical biophysical model (although a few are compared in Supplementary Materials), in particular: is a model of macroscopic anisotropy relevant in cerebellum, made of a large fraction of oriented white matter tracks, and does the model remain valid at different ages given white matter maturation and the ongoing myelination process?

Joint Public Review:

Summary:

The authors of the study investigated the generalization capabilities of a deep learning brain age model across different age groups within the Singaporean population, encompassing both elderly individuals aged 55 to 88 years and children aged 4 to 11 years. The model, originally trained on a dataset primarily consisting of Caucasian adults, demonstrated a varying degree of adaptability across these age groups. For the elderly, the authors observed that the model could be applied with minimal modifications, whereas for children, significant fine-tuning was necessary to achieve accurate predictions. Through their analysis, the authors established a correlation between changes in the brain age gap and future executive function performance across both demographics. Additionally, they identified distinct neuroanatomical predictors for brain age in each group: lateral ventricles and frontal areas were key in elderly participants, while white matter and posterior brain regions played a crucial role in children. These findings underscore the authors' conclusion that brain age models hold the potential for generalization across diverse populations, further emphasizing the significance of brain age progression as an indicator of cognitive development and aging processes.

Strengths:

(1) The study tackles a crucial research gap by exploring the adaptability of a brain age model across Asian demographics (Chinese, Malay, and Indian Singaporeans), enriching our knowledge of brain aging beyond Western populations.<br /> (2) It uncovers distinct anatomical predictors of brain aging between elderly and younger individuals, highlighting a significant finding in the understanding of age-related changes and ethnic differences.

Weaknesses:

(1) Clarity in describing the fine-tuning process is essential for improved comprehension.<br /> (2) The analysis often limits its findings to p-values, omitting the effect sizes crucial for understanding the relationship with cognition.<br /> (3) Employing a predictive framework for cognition using brain age could offer more insight than mere statistical correlations.<br /> (4) Expanding the study's scope to evaluate the model's generalisability to unseen Caucasian samples is vital for establishing a comparative baseline.

In summary, this paper underscores the critical need to include diverse ethnicities in model testing and estimation.

Reviewer #1 (Public Review):

Summary:

In this manuscript, the authors attempt to reconstitute some active zone properties by introducing synaptic ribbon proteins into HEK cells. This "ground-up" approach can be valuable for assessing the necessity of specific proteins in synaptic function. Here, the authors co-transfect a membrane-targeted bassoon, RBP2, calcium channel subunits and Ribeye to generate what they call "synthetic ribbons". The resultant structures show an ability to cluster calcium channels (Figure 4B) and a modest ability to concentrate calcium entry locations (figure 7J). At the light level, the ribeye aggregates look spherical and localize to the membrane through its interaction with the membrane-targeted bassoon. It is a nice proof-of-principle in establishing a useful experimental system for studying calcium channel localization. However, the impact of the study is modest. No new biology is discovered and to call these structures "synthetic ribbons" is an overstatement in the absence of an ultrastructural analysis.

Strengths:

(1) The authors establish a new experimental system for the study of calcium channel localization to active zones.<br /> (2) The clustering of calcium channels to bassoon via RBP2 is a nice confirmation of a previously described interaction between bassoon and calcium channels in a cell-based system<br /> (3) The "ground-up" approach is an attractive one and theoretically allows one to learn a lot about the essential interactions for building a ribbon structure.

Weaknesses:

(1) Are these truly "synthetic ribbons". The ribbon synapse is traditionally defined by its morphology at the EM level. To what extent these structures recapitulate ribbons is not shown. It has been previously shown that Ribeye forms aggregates on its own. Do these structures look any more ribbon-like than ribeye aggregates in the absence of its binding partners?<br /> (2) No new biology is discovered here. The clustering of channels is accomplished by taking advantage of previously described interactions between RBP2, Ca channels and bassoon. The localization of Ribeye to bassoon takes advantage of a previously described interaction between the two. Even the membrane localization of the complexes required the introduction of a membrane-anchoring motif.<br /> (3) The only thing ribbon-specific about these "syn-ribbons" is the expression of ribeye and ribeye does not seem to participate in the localization of other proteins in these complexes. Bsn, Cav1.3 and RBP2 can be found in other neurons.<br /> (4) As the authors point out, RBP2 is not necessary for some Ca channel clustering in hair cells, yet seems to be essential for clustering to bassoon here.<br /> (5) The difference in Ca imaging between SyRibbons and other locations is extremely subtle.<br /> (6) The effect of the expression of palm-Bsn, RBP2 and the combination of the two on Ca-current is ambiguous. It appears that while the combination is larger than the control, it probably isn't significantly different from either of the other two alone (Fig 5). Moreover, expression of Ribeye + the other two showed no effect on Ca current (Figure 7). Also, why is the IV curve right shifted in Figure 7 vs Figure 5?<br /> (7) While some of the IHC is quantified, some of it is simply shown as single images. EV2, EV3 and Figure 4a in particular (4b looks convincing enough on its own, but could also benefit from a larger sample size and quantification)

Reviewer #2 (Public Review):

Summary:

The authors show that co-expression of bassoon, RIBEYE, Cav1.3-alpha1, Cav-beta3, Cav-alpha2delta1, and RBP2 in a heterologus system (HEK293 cells) is sufficient to generate a protein complex resembling a presyanptic ribbon-type active zone both in morphology and in function (in clustering voltage-gated Ca channels and creating sites for localized Ca2+ entry). If the 3 separate Cav gene products are taken as a single protein (i.e. a Ca channel), the conclusion is that the core of a ribbon synapse comprises 4 proteins: bassoon holds the RIBEYE-containing ribbon to the plasma membrane, and RPB2 binds to bassoon and Ca channels, tethering the Ca channels to the presynaptic active zone.

Strengths:

Good use of a heterologous system with generally appropriate controls provides convincing evidence that a presynaptic ribbon-type active zone (without the ability to support exocytosis), with the ability to support localized Ca2+ entry (a key feature of ribbon-type pre-synapses) can be assembled from a few proteins.

Weaknesses:

(1) Relies on over-expression, which almost certainly diminishes the experimentally-measured parameters (e.g. pre-synapse clustering, localization of Ca2+ entry).<br /> (2) Are HEK cells the best model? HEK cells secrete substances and have a studied-endocytitic pathway, but they do not create neurosecretory vesicles. Why didn't the authors try to reconstitute a ribbon synapse in a cell that makes neurosecretory vesicles like a PC12 cell?<br /> (3) Related to 1 and 2: the Ca channel localization observed is significant but not so striking given the presence of Cav protein and measurements of Ca2+ influx distributed across the membrane. Presumably, this is the result of overexpression and an absence of pathways for pre-synaptic targeting of Ca channels. But, still, it was surprising that Ca channel localization was so diffuse. I suppose that the authors tried to reduce the effect of over-expression by using an inducible Cav1.3? Even so, the accessory subunits were constitutively over-expressed.

Reviewer #3 (Public Review):

Summary:

Ribbon synapses are complex molecular assemblies responsible for synaptic vesicle trafficking in sensory cells of the eye and the inner ear. The Ca2+-dependent exocytosis occurs at the active zone (AZ), however, the molecular mechanisms orchestrating the structure and function of the AZs of ribbon synapses are not well understood. To advance in the understanding of those mechanisms, the authors present a novel and interesting experimental strategy pursuing the reconstitution of a minimal active zone of a ribbon synapse within a synapse-naïve cell line: HEK293 cells. The authors have used stably transfected HEK293 cells that express voltage-gated Ca2+ channels subunits (constitutive -CaV beta3 and CaV alpha2 beta1- and inducible CaV1.3 alpha1). They have expressed in those cells several proteins of the ribbon synapse active zone: (1) RIBEYE, (2) a modified version of Bassoon that binds to the plasma membrane through artificial palmitoylation (Palm-Bassoon) and (3) RIM-binding protein 2 (RBP2) to induce the formation of a minimal active zone that they called SyRibbons. The formation of such structures is convincing, however, the evidence of such structures having an impact enhancing Ca2+-currents, as the authors claim, is rather weak in the present version of the study.

Strengths of the study:

(1) The study is carefully carried out using a remarkable combination of (1) superresolution microscopy, to analyze the formation and subcellular distribution of molecular assemblies and (2) functional assessment of voltage-gated Ca2+ channels using patch-clamp recording of Ca2+-currents and fluorometry to correlate Ca2+ influx with the molecular assemblies formed by AZ proteins. The results are of high quality and are in general accompanied of required control experiments.<br /> (2) The method opens new opportunities to further investigate the minimal and basic properties of AZ proteins that are difficult to study using in vivo systems. The cells that operate through ribbon synapses (e.g. photoreceptors and hair cells) are particularly difficult to manipulate, so setting up and validating the use of a heterologous system more suitable for molecular manipulations is highly valuable.<br /> (3) The structures formed by RIBEYE and Palm-Bassoon in HEK293 cells identified by STED nanoscopy are strikingly similar to the AZs of ribbon synapses found in rat inner hair cells (Figure 2).

Weaknesses of the study:

(1) The results obtained in a heterologous system (HEK293 cells) need to be interpreted with caution. They will importantly speed the generation of models and hypothesis that will, however, require in vivo validation.<br /> (2) The authors analyzed the distribution of RIBEYE clusters in different membrane compartments and correctly conclude that RIBEYE clusters are not trapped in any of those compartments, but it is soluble instead. The authors, however, did not carry out a similar analysis for Palm-Bassoon. It is therefore unknown if Palm-Bassoon binds to other membrane compartments besides the plasma membrane. That could occur because in non-neuronal cells GAP43 has been described to be in internal membrane compartments. This should be investigated to document the existence of ectopic internal Synribbons beyond the plasma membrane because it might have implications for interpreting functional data in case Ca2+-channels become part of those internal Synribbons.<br /> (3) The co-expression of RBP2 and Palm-Bassoon induces a rather minor but significant increase in Ca2+-currents (Figure 5). Such an increase does not occur upon expression of (1) Palm-Bassoon alone, (2) RBP2 alone or (3) RIBEYE alone (Figure 5). Intriguingly, the concomitant expression of Palm-Bassoon, RBP2 and RIBEYE does not translate into an increase of Ca2+-currents either (Figure 7).<br /> (4) The authors claim that Ca2+-imaging reveals increased CA2+-signal intensity at synthetic ribbon-type AZs. That claim is a subject of concern because the increase is rather small and it does not correlate with an increase in Ca2+-currents.

Reviewer #1 (Public Review):

Summary:

Protein conformational changes are often critical to protein function, but obtaining structural information about conformational ensembles is a challenge. Over a number of years, the authors of the current manuscript have developed and improved an algorithm, qFit protein, that models multiple conformations into high resolution electron density maps in an automated way. The current manuscript describes the latest improvements to the program, and analyzes the performance of qFit protein in a number of test cases, including classical statistical metrics of data fit like Rfree and the gap between Rwork and Rfree, model geometry, and global and case-by-case assessment of qFit performance at different data resolution cutoffs. The authors have also updated qFit to handle cryo-EM datasets, although the analysis of its performance is more limited due to a limited number of high-resolution test cases and less standardization of deposited/processed data.

Strengths:

The strengths of the manuscript are the careful and extensive analysis of qFit's performance over a variety of metrics and a diversity of test cases, as well as careful discussion of the limitations of qFit. This manuscript also serves as a very useful guide for users in evaluating if and when qFit should be applied during structural refinement.

Reviewer #2 (Public Review):

Summary

The manuscript "Uncovering Protein Ensembles: Automated Multiconformer Model building for X-ray Crystallography and Cryo-EM" by Wankowicz et al. describes updates to qFit, an algorithm for the characterization of conformational heterogeneity of protein molecules based on X-ray diffraction of Cryo-EM data. The work provides a clear description of the algorithm used by qFit. The authors then proceed to validate the performance of qFit by comparing to deposited X-ray entries in the PDB in the 1.2-1.5 Å resolution range as quantified by Rfree, Rwork-Rfree, detailed examination of the conformations introduced by qFit, and performance on stereochemical measures (MolProbity scores). To examine the effect of experimental resolution of X-ray diffraction data, they start from an ultra high-resolution structure (SARS-CoV2 Nsp3 macrodomain) to determine how the loss of resolution (introduced artificially) degrades the ability of qFit to correctly infer the nature and presence of alternate conformations. The authors observe a gradual loss of ability to correctly infer alternate conformations as resolution degrades past 2 Å. The authors repeat this analysis for a larger set of entries in a more automated fashion and again observe that qFit works well for structures with resolutions better than 2 Å, with a rapid loss of accuracy at lower resolution. Finally, the authors examine the performance of qFit on cryo-EM data. Despite a few prominent examples, the authors find only a handful (8) of datasets for which they can confirm a resolution better than 2.0 Å. The performance of qFit on these maps is encouraging and will be of much interest because cryo-EM maps will, presumably, continue to improve and because of the rapid increase in the availability of such data for many supramolecular biological assemblies. As the authors note, practices in cryo-EM analysis are far from uniform, hampering the development and assessment of tools like qFit.

Strengths

qFit improves the quality of refined structures at resolutions better than 2.0 A, in terms of reflecting true conformational heterogeneity and geometry. The algorithm is well-designed and does not introduce spurious or unnecessary conformational heterogeneity. I was able to install and run the program without a problem within a computing cluster environment. The paper is well-written and the validation thorough.<br /> I found the section on cryo-EM particularly enlightening, both because it demonstrates the potential for discovery of conformational heterogeneity from such data by qFit, and because it clearly explains the hurdles towards this becoming common practice, including lack of uniformity in reporting resolution, and differences in map and solvent treatment.

Weaknesses

Due to limitations of past software engineering, the paper lacks a careful comparison to past versions of qFit. In light of the extensive assessment of the current version of qFit, this is a minor concern.

Although qFit can handle supramolecular assemblies and bound organic molecules, analysis in the manuscript is limited to single-chain X-ray structures. I look forward to demonstration of its utility in such cases in future work.

Appraisal & Discussion

Overall, the authors convincingly demonstrate that qFit provides a reliable means to detect and model conformational heterogeneity within high-resolution X-ray diffraction datasets and (based on a smaller sample) in cryo-EM density maps. This represents the state of the art in the field and will be of interest to any structural biologist or biochemist seeking to attain an understanding of the structural basis of the function of their system of interest, including potential allosteric mechanisms-an area where there are still few good solutions. That is, I expect qFit to find widespread use.

Reviewer #3 (Public Review):

Summary:

The authors address a very important issue of going beyond a single-copy model obtained by the two principal experimental methods of structural biology, macromolecular crystallography and cryo electron microscopy (cryo-EM). Such multiconformer model is based on the fact that experimental data from both these methods represent a space- and time-average of a huge number of the molecules in a sample, or even in several samples, and that the respective distributions can be multimodal. Differently from structure prediction methods, this approach is strongly based on accurate high-resolution experimental information and requires validated single-copy high-quality models as input. In overall, the results support the authors' conclusions.

In fact, the method addresses two problems which could be considered separately:

- an automation of construction of multiple conformations when they can be identified visually;<br /> - a determination of multiple conformations when their visual identification is difficult or impossible.

The former is a known problem, when missing alternative conformations may cost a few percent in R-factors. While these conformations are relatively easy to detect and build manually, the current procedure may save significant time being quite efficient, as the test results show. It is an indisputably useful tool for such a goal. The second problem is important from the physical point of view and has been considered first thirty years ago by Burling & Brünger. The manuscript does not specify clearly how much the current tool addresses the second case. To model such maps, the authors introduced errors in structure factors, however, being independent, as in this work, such errors, even quite high, may leave the maps reasonably well interpretable. Obviously, it is impossible to model all kinds of errors and this modeling of noise is appreciated but it would helpful for understanding if the manuscript shows, for example, the worst map when the procedure was successful.

The new procedure deals with a second-order variation in the R-factors, of about 1% or less, like placing riding hydrogen atoms, modeling density deformation or variation of the bulk solvent. In such situations, it is hard to justify model improvement. Keeping Rfree values or their marginal decreasing can be considered as a sign that the model does not overfit data but hardly as a strong argument in favor of the model.

In general, global targets are less appropriate for this kind of problems and local characteristics may be better indicators. Improvement of the model geometry is a good choice. Indeed, yet Cruickshank (1956) showed that averaged density images may lead to a shortening of covalent bonds when interpreting such maps by a single model. However, a total absence of geometric outliers is not necessarily required for the structures solved at a high resolution where diffraction data should have a more freedom to place the atoms where the experiments "see" them.

The key local characteristic for multicomformer models is a closeness of the model map to the experimental one. Actually, the procedure uses a kind of such measure, the Bayesian information criteria (BIC). Unfortunately, the manuscript does not describe how sharply it identifies the best model and how much it changes between the initial and final models; in general, there is no feeling about its values. The Q-score (page 17) can be an appropriate tool for the first problem where the multiple conformations and individual atomic images are clearly separated and not for the second problem where the contributions from neighboring conformations and atoms are merged. In addition to BIC or to even more conventional global target functions such as LS or map correlation, the extreme values of the local difference maps may help to validate, or not, the model.

This described method with the results presented is a strong argument for a need in experimental data and information they contain, differently from a pure structure prediction. This tool is important to produce user-unbiased multiconformer models rapidly and automatically. At the same time, absence of strong density-based validation components may limit its impact.

Strengths:<br /> Addressing an important problem and automatisation of model construction for alternative conformations using high-resolution experimental data.

Weaknesses:<br /> An insufficient validation of the models when no discrete alternative conformations visible and insufficiency of local real-space validation indicators.

Reviewer #1 (Public Review):

Summary:

The authors try to use a gene therapy approach to cure urofacial symptoms in an HSPE2 mutant mouse model.

Strengths:

The authors have convincingly shown the expression of AAV9/HSPE2 in pelvic ganglion and liver tissues. They have also shown the defects in urethra relaxation and bladder muscle contraction in response to EFS in mutant mice, which were reversed in treated mice.

Weaknesses:

It is easy to understand that high expression levels of HPSE2 in the bladder tissue lead to bladder dysfunction in human patients, however, the undetectable level of HPSE2 in AAV9 transfected mice bladders is a big question for the functional correction in those HPSE2 mutated mice.

Reviewer #2 (Public Review):

In this study, Lopes and colleagues provide evidence to support the potential for gene therapy to restore expression of heparanase-2 (Hpse2) in mice mutant for this gene, as occurs in urofacial syndrome. Building on prior studies describing the nature of urinary tract dysfunction in Hpse2 mutant mice, the authors applied a gene therapy approach to determine whether gene replacement could be achieved, and if so, whether restoration of HPSE2 expression could mitigate the urinary tract dysfunction. Using a viral vector-based strategy, shown to be successful for gene replacement in humans, the authors demonstrated dose-dependent viral transduction of pelvic ganglia and liver in wild type mice. No impact on body weight or liver health was noted suggesting the approach was safe. Administration of AAV9/HPSE2 to Hpse2 mutant mice was associated with similar transduction of pelvic ganglia and a corresponding increase in heparanase-2 protein expression in this site. Analysis of bladder outflow tract and bladder body physiology using organ bath studies showed that re-expression of heparanase-2 in Hpse2 mutant mice was associated with restored neurogenic relaxation of the outflow tract and nerve-evoked contraction of the bladder body, albeit with notable variability in the response at lower frequencies across replicates. Differences were noted in the evoked response to carbachol with bladders from Hpse2 mutant male mice showing increased sensitivity upon HPSE2 replacement compared to wild type, but bladders from female mice showing no difference. Based on these findings the authors concluded that AAV9-based HPSE2 replacement is feasible and safe, mitigates some physiological deficits in outflow tract and bladder tissue from Hpse2 mutant mice and provides proof-of-principle for gene replacement approaches for other genes implicated in lower urinary tract disorders. Strengths include a solid experimental design and data in support of some of the conclusions, and discussion of limitations of the approach. Weaknesses include the variability, albeit acknowledged, in some of the functional assessments, and the limited investigation of bladder tissue morphology in Hpse2 mutant mice.

Reviewer #1 (Public Review):

Summary:

This paper suggests to apply intrinsically-motivated exploration for the discovery of robust goal states in gene regulatory networks.

Strengths:

The paper is well written. The biological motivation and the need for such methods are formulated extraordinarily well. The battery of experimental models is impressive.

Weaknesses:

(1) The proposed method is compared to the random search. That says little about the performance with regard to the true steady-state goal sets. The latter could be calculated at least for a few simple ODE (e.g., BIOMD0000000454, `Metabolic Control Analysis: Rereading Reder'). The experiment with 'oscillator circuits' may not be directly interpolated to the other models.

The lack of comparison to the ground truth goal set (attractors of ODE) from arbitrary initial conditions makes it hard to evaluate the true performance/contribution of the method. A part of the used models can be analyzed numerically using JAX, while there are models that can be analyzed analytically.

"...The true versatility of the GRN is unknown and can only be inferred through empirical exploration and proxy metrics....": one could perform a sensitivity analysis of the ODEs, identifying stable equilibria. That could provide a proxy for the ground truth 'versatility'.

(2) The proposed method is based on `Intrinsically Motivated Goal Exploration Processes with Automatic Curriculum Learning', which assumes state action trajectories [s_{t_0:t}, a_{t_0:t}], (2.1 Notations and Assumptions' in the IMGEP paper). However, the models used in the current work do not include external control actions, but rather only the initial conditions can be set. It is not clear from the methods whether IMGEP was adapted to this setting, and how the exploration policy was designed w/o actual time-dependent actions. What does "...generates candidate intervention parameters to achieve the current goal...."<br /> mean considering that interventions 'Sets the initial state...' as explained in Table 2?

(3) Fig 2 shows the phase space for (ERK, RKIPP_RP) without mentioning the typical full scale of ERK, RKIPP_RP. It is unclear whether the path from (0, 0) to (~0.575, ~3.75) at t=1000 is significant on the typical scale of this phase space. is it significant on the typical scale of this phase space?

(4) Table 2:<br /> (a) Where is 'effective intervention' used in the method?<br /> (b) In my opinion 'controllability', 'trainability', and 'versatility' are different terms. If there correspondence is important I would suggest to extend/enhance the column "Proposed Isomorphism". otherwise, it may be confusing. I don't see how this table generalizes generalizes "concepts from dynamical complex systems and behavioral sciences under a common navigation task perspective".

Reviewer #2 (Public Review):

Summary:

Etcheverry et al. present two computational frameworks for exploring the functional capabilities of gene regulatory networks (GRNs). The first is a framework based on intrinsically motivated exploration, here used to reveal the set of steady states achievable by a given gene regulatory network as a function of initial conditions. The second is a behaviorist framework, here used to assess the robustness of steady states to dynamical perturbations experienced along typical trajectories to those steady states. In Figs. 1-5, the authors convincingly show how these frameworks can explore and quantify the diversity of behaviors that can be displayed by GRNs. In Figs. 6-9, the authors present applications of their framework to the analysis and control of GRNs, but the support presented for their case studies is often incomplete.

Following revision, my overall perspective of the paper remains unchanged. The first half of the paper provides solid evidence to support an important conceptual framework. The evidence presented for the use cases in the latter half is incomplete; as the authors note, they are preliminary and meant to be built on in future work. I have included my first round comments below.

Strengths:

Overall, the paper presents an important development for exploring and understanding GRNs/dynamical systems broadly, with solid evidence supporting the first half of their paper in a narratively clear way.

The behaviorist point of view for robustness is potentially of interest to a broad community, and to my knowledge introduces novel considerations for defining robustness in the GRN context.

Some specific weaknesses, mostly concerning incomplete analyses in the second half of the paper:

(1) The analysis presented in Fig. 6 is exciting but preliminary. Are there other appropriate methods for constructing energy landscapes from dynamical trajectories in gene regulatory networks? How do the results in this particular case study compare to other GRNs studied in the paper?

Additionally, it is unclear whether the analysis presented in Fig. 6C is appropriate. In particular, if the pseudopotential landscapes are constructed from statistics of visited states along trajectories to the steady state, then the trajectories derived from dynamical perturbations do not only reflect the underlying pseudo-landscape of the GRN. Instead, they also include contributions from the perturbations themselves.

(2) In Fig. 7, I'm not sure how much is possible to take away from the results as given here, as they depend sensitively on the cohort of 432 (GRN, Z) pairs used. The comparison against random networks is well-motivated. However, as the authors note, comparison between organismal categories is more difficult due to low sample size; for instance, the "plant" and "slime mold" categories each only has 1 associated GRN. Additionally, the "n/a" category is difficult to interpret.

(3) In Fig. 8, it is unclear whether the behavioral catalog generated is important to the intervention design problem of moving a system in one attractor basin to another. The authors note that evolutionary searches or SGD could also be used to solve the problem. Is the analysis somehow enabled by the behavioral catalog in a way that is complementary to those methods? If not, comparison against those methods (or others e.g. optimal control) would strengthen the paper.

(4) The analysis presented in Fig. 9 also is preliminary. The authors note that there exist many algorithms for choosing/identifying the parameter values of a dynamical system that give rise to a desired time series. It would be a stronger result to compare their approach to more sophisticated methods, as opposed to random search and SGD. Other options from the recent literature include Bayesian techniques, sparse nonlinear regression techniques (e.g. SINDy), and evolutionary searches. The authors note that some methods require fine-tuning in order to be successful, but even so, it would be good to know the degree of fine-tuning which is necessary compared to their method. [second round: the authors have included a comparison against CMA-ES, an evolutionary algorithm]

Reviewer #1 (Public Review):

The mechanisms underlying the generation and maintenance of LLPCs have been one of the unresolved issues. In the last few years, several groups have independently generated new genetic tools or models and addressed how LLPCs are generated or maintained in homeostatic conditions or upon immunization or infection. Here, Jing et al. have also established a new PC time stamping system and tried to address the issues above. The authors have found that LLPCs accumulated in the BM PC pool, along with aging, and that LLPCs had unique sufacetome, transcriptome, and BCR clonality. These observations have already been made by other groups (Xu et al. 2020, Robinson et al. 2022, Liu et al. 2022, Koike et al. 2023, Robinson et al. 2023, plus Tellier et al., 2024), therefore it is hard to find significant conceptual advances there. In my opinion, however, genetic analysis of the role of CXCR4 on PC localization or survival in BM (Figure 4 and 5) provided new aspects which have not been addressed in previous studies. Importantly, CXCR4 was required for the maintenance of plasma cells in bone marrow survival niches, conditional loss of which led to rapid mobilization from the bone marrow, reduced plasma cell survival, and reduced antibody titer. Thus, these data suggest that CXCR4-CXCL12 axis is not only important for plasma cell recruitment to the bone marrow but also essential for their lodging on the niches. I think the study is of high quality and the findings should be widely shared in the field.

Reviewer #2 (Public Review):

In this study by Jing, Fooksman, and colleagues, a Blimp1-CreERT2-based genetic tracing study is employed to label plasma cells. Over the course of several months post-tamoxifen treatment, the only remaining labeled cells are long-lived plasma cells. This system provides a way to sort live long-lived plasma cells and compare them to unlabeled plasma cells, which contain a range of short-to-long-lived cells. From this analysis, several observations are made: 1) the turnover rate of plasma cells is greater in the spleen than in the bone marrow; 2) the turnover rate is highest early in life; 3) subtle transcriptional and cell surface marker differences distinguish long- from shorter-lived plasma cells; 4) long-lived plasma cells in the bone marrow are sessile and localize in clusters with each other; 5) CXCR4 is required for plasma cell retention in these clusters and in the bone marrow; 6) Repertoire analysis hints that the selection of long-lived plasma cells is not random for any cell that lands in the bone marrow.

Strengths:

(1) The genetic timestamping approach is a clever and functional way to separate plasma cells of differing longevities.

(2) This approach led to the identification of several markers that could help prospective separation of long-lived plasma cells from others.

(3) Functional labeling of long-lived plasma cells allowed for a higher resolution analysis of transcriptomes and motility than was previously possible.

(4) The genetic system allowed for a revisitation of the importance of CXCR4 in plasma cell retention and survival.

Weaknesses:

(1) Most of the labeling studies, likely for practical reasons, were done on polyclonal rather than antigen-specific plasma cells. The triggers of these responses could vary based on age at the time of exposure, anatomical sites, etc. How these differences might influence markers and transcriptomes, independently of longevity, is not completely known.

(2) The fraction of long-lived plasma cells in the unlabeled fraction varies with age, potentially diluting differences between long- and short-lived plasma cells.

(3) The authors suggest their data favors a model by which plasma cells compete for niche space. Yet there is no evidence presented here that these niches are limiting. While a finite number of plasma cells may occupy a single niche (Figure 2), it may be that these niches overall are abundant in the bone marrow and do not restrict LLPC numbers. Robinson...Tarlinton and colleagues (Immunity, 2023) in fact provide experimental evidence against an extrinsic limit.

(4) The functional importance of the observed transcriptome differences between long- and shorter-lived plasma cells is unknown. An assessment as to whether these differences are conserved in human long- and short-lived bone marrow plasma cells might provide circumstantial supporting evidence that these changes are important for longevity.

Reviewer #3 (Public Review):

Summary:

Long-lived PCs are maintained in a CXCR4-dependent manner.

Strengths:

The reporter mice for fate-mapping can clearly distinguish long-lived PCs from total PCs and greatly contribute to the identification of long-lived PCs.

Reviewer #1 (Public Review):

Summary:

In this study, a chromosome-level genome of the rose-grain aphid M. dirhodum was assembled with high quality, and A-to-I RNA-editing sites were systematically identified. The authors then demonstrated that: 1) Wing dimorphism induced by crowding in M. dirhodum is regulated by 20E (ecdysone signaling pathway); 2) an A-to-I RNA editing prevents the binding of miR-3036-5p to CYP18A1 (the enzyme required for 20E degradation), thus elevating CYP18A1 expression, decreasing 20E titer, and finally regulating the wing dimorphism of offspring.

Strengths:

The authors present both genome and A-to-I RNA editing data. An interesting finding is that a A-to-I RNA editing site in CYP18A1 ruin the miRNA binding site of miR-3036-5p. And loss of miR-3036-5p regulation lead to less 20E and winged offspring.

Weaknesses:

How crowding represses the miR-3036-5p is still unclear.

Reviewer #2 (Public Review):

Summary:

Environmental influences on development are ubiquitous, affecting many phenotypes in organisms. However molecular genetic and cellular mechanisms transducing environmental signals are still only barely understood. This study examines part of one such intracellular mechanism in a polyphenic (or dimorphic) aphid.

Strengths:

While other published reports have linked phenotypic plasticity to RNA editing before, this study reports such an interaction in insects. The study uses a wide array of molecular tools to identify connections upstream and downstream of the RNA editing to elucidate the regulatory mechanism, which is illuminating.

Weaknesses:

While this system is intriguing, this report does not foster confidence in its conclusions. Many of the analyses seem based on very small sample sizes. It is itself problematic that sample sizes are not obvious in most figures, although based on Methods section covering RNAseq, they seem to be either 3, 6 or 9, depending on whether stages were pooled, but that point is not made clear. With such small sample sizes, statistical tests of any kind are unreliable. Besides the ambiguity on sample sizes, it's unclear what error bars or whiskers show in plots throughout this study. When sample sizes are small estimates of variance are not reliable. Student's t-test is not appropriate for comparisons with such small sample sizes. Presently, it is not possible to replicate the tests shown in Figures 3, 4 and 6. (Besides the HT-seq reads, other data should also be made publicly available, following the journal's recommendations.) Regardless, effect sizes in some comparisons (Fig 3J, 4A-C, 6E,H) are clearly not large, making confidence in conclusions low. The authors should be cautious about over-interpreting these data.

Reviewer #2 (Public Review):

Summary:

The dominant paradigm in the past decade for modeling the ventral visual stream's response to images has been to train deep neural networks on object classification tasks and regress neural responses from units of these networks. While object classification performance is correlated to variance explained in the neural data, this approach has recently hit a plateau of variance explained, beyond which increases in classification performance do not yield improvements in neural predictivity. This suggests that classification performance may not be a sufficient objective for building better models of the ventral stream. Lindsey & Issa study the role of factorization in predicting neural responses to images, where factorization is the degree to which variables such as object pose and lighting are represented independently in orthogonal subspaces. They propose factorization as a candidate objective for breaking through the plateau suffered by models trained only on object classification. They show the degree of factorization in a model captures aspects of neural variance that classification accuracy alone does not capture, hence factorization may be an objective that could lead to better models of ventral stream. I think the most important figure for a reader to see is Fig. 6.

Strengths:

This paper challenges the dominant approach to modeling neural responses in the ventral stream, which itself is valuable for diversifying the space of ideas.

This paper uses a wide variety of datasets, spanning multiple brain areas and species. The results are consistent across the datasets, which is a great sign of robustness.

The paper uses a large set of models from many prior works. This is impressively thorough and rigorous.

The authors are very transparent, particularly in the supplementary material, showing results on all datasets. This is excellent practice.

Weaknesses:

The authors have addressed many of the weaknesses in the original review. The weaknesses that remain are limitations of the work that cannot be easily addressed. In addition to the limitations stated at the end of the discussion, I'll add two:

(1) This work shows that factorization is correlated with neural similarity, and notably explains some variance in neural similarity that classification accuracy does not explain. This suggests that factorization could be used as an objective (along with classification accuracy) to build better models of the brain. However, this paper does not do that - using factorization to build better models of the brain is left to future work.

Reviewer #3 (Public Review):

Summary:

Object classification serves as a vital normative principle in both the study of the primate ventral visual stream and deep learning. Different models exhibit varying classification performances and organize information differently. Consequently, a thriving research area in computational neuroscience involves identifying meaningful properties of neural representations that act as bridges connecting performance and neural implementation. In the work of Lindsey and Issa, the concept of factorization is explored, which has strong connections with emerging concepts like disentanglement [1,2,3] and abstraction [4,5]. Their primary contributions encompass two facets: (1) The proposition of a straightforward method for quantifying the degree of factorization in visual representations. (2) A comprehensive examination of this quantification through correlation analysis across deep learning models.

To elaborate, their methodology, inspired by prior studies [6], employs visual inputs featuring a foreground object superimposed onto natural backgrounds. Four types of scene variables, such as object pose, are manipulated to induce variations. To assess the level of factorization within a model, they systematically alter one of the scene variables of interest and estimate the proportion of encoding variances attributable to the parameter under consideration.

The central assertion of this research is that factorization represents a normative principle governing biological visual representation. The authors substantiate this claim by demonstrating an increase in factorization from macaque V4 to IT, supported by evidence from correlated analyses revealing a positive correlation between factorization and decoding performance. Furthermore, they advocate for the inclusion of factorization as part of the objective function for training artificial neural networks. To validate this proposal, the authors systematically conduct correlation analyses across a wide spectrum of deep neural networks and datasets sourced from human and monkey subjects. Specifically, their findings indicate that the degree of factorization in a deep model positively correlates with its predictability concerning neural data (i.e., goodness of fit).

Strengths:

The primary strength of this paper is the authors' efforts in systematically conducting analysis across different organisms and recording methods. Also, the definition of factorization is simple and intuitive to understand.

Weaknesses:

Comments on revised version:

I thank the authors for addressing the weaknesses I brought up regarding the manuscript.

Reviewer #1 (Public Review):

Summary:

The authors of the study are trying to show that RNAseq can be used for neoantigen prediction and that the machine learning approach to the prediction can reveal very useful information for the selection of neoantigens for personalized antitumor vaccination.

Strengths:

The authors demonstrated that RNA expression of a neoantigen is a very important factor in the selection of peptides for the creation of personalized vaccines. They proved in vivo that in silico-predicted neoantigens can trigger an antitumor response in mice.

Weaknesses:

The selection of the peptides for vaccination is not clear. Some peptides were selected before and some after processing. What processing is also not clear. The authors didn't provide the full list of peptides before and after processing, please add those. And it wasn't clear that these peptides were previously published. Looking at the previously published table with peptide from B16 F10 (https://www.nature.com/articles/s41598-021-89927-5/tables/3), there are other genes with high expression, e.g. Tab2, Tm9sf3 that have higher expression than Herc6, please clarify the choice.

It's not clear how many mice were used for each group in each experiment, please add this information to the text and figures. It would be good to add this, to aid the understanding of a broader audience.

Please provide information about what software was used for statistical analysis.

Reviewer #2 (Public Review):

Summary:

The authors develop a new neoantigen prediction tool (NAP-CNB) which primarily predicts neoantigens based on expression (RNAseq) and ranks mutations using binding affinity. The validated predicted neoantigens in mice demonstrate that neoantigens with higher expression (but not necessarily the highest immunogenicity) lead to the greatest tumor control.

Strengths:

There is in vivo validation of the neoantigens.<br /> Demonstrates comparability to other prediction algorithms that are commonly used.<br /> Demonstrates that expression holds a higher value than T-cell responses in actual tumor control.

Weaknesses:

Binding affinity does not always predict immune responses or tumor control in vivo which is used as part of the selection criteria.

Reviewer #1 (Public Review):

In this paper, Wu et al. investigated the physiological roles of CCDC113 in sperm flagellum and HTCA stabilization by using CRISPR/Cas knockouts mouse models, co-IP, and single sperm imaging. They find that CCDC113 localizes in the linker region among radial spokes, the nexin-dynein regulatory complex (N-DRC), and doublet microtubules (DMTs) RS, N-DRC, and DMTs and interacts with axoneme-associated proteins CFAP57 and CFAP91, acting as an adaptor protein that facilitates the linkage between RS, N-DRC, and DMTs within the sperm axoneme. They show the disruption of CCDC113 produced spermatozoa with disorganized sperm flagella and CFAP91, DRC2 could not colocalize with DMTs in Ccdc113-/- spermatozoa. Interestingly, the data also indicate that CCDC113 could localize on the HTCA region, and interact with HTCA-associated proteins. The knockout of Ccdc113 could also produce acephalic spermatozoa. By using Sun5 and Centlein knockout mouse models, the authors further find SUN5 and CENTLEIN are indispensable for the docking of CCDC113 to the implantation site on the sperm head. Overall, the experiments were designed properly and performed well to support the authors' observation in each part. Furthermore, the study's findings offer valuable insights into the physiological and developmental roles of CCDC113 in the male germ line, which can provide insight into impaired sperm development and male infertility. The conclusions of this paper are mostly well supported by data, but some points need to be clarified and discussed.<br /> (1) In Figure 1, a sperm flagellum protein, which is far away from CCDC113, should be selected as a negative control to exclude artificial effects in co-IP experiments.<br /> (2) Whether the detachment of sperm head and tail in Ccdc113-/- mice is a secondary effect of the sperm flagellum defects? The author should discuss this point.<br /> (3) Given that some cytoplasm materials could be observed in Ccdc113-/- spermatozoa (Fig. 5A), whether CCDC113 is also essential for cytoplasmic removal?<br /> (4) Although CCDC113 could not bind to PMFBP1, the localization of CCDC113 in Pmfbp1-/- spermatozoa should be also detected to clarify the relationship between CCDC113 and SUN5-CENTLEIN-PMFBP1.

Reviewer #2 (Public Review):

Summary:

In the present study, the authors select the coiled-coil protein CCDC113 and revealed its expression in the stages of spermatogenesis in the testis as well as in the different steps of spermiogenesis with expression also mapped in the different parts of the epididymis. Gene deletion led to male infertility in CRISPR-Cas9 KO mice and PAS staining showed defects mapped in the different stages of the seminiferous cycle and through the different steps of spermiogenesis. EM and IF with several markers of testis germ cells and spermatozoa in the epididymis indicated defects in flagella and head-to-tail coupling for flagella as well as acephaly. The authors' co-IP experiments of expressed CCDC113 in HEK293T cells indicated an association with CFAP91 and DRC2 as well as SUN5 and CENTLEIN.

The authors propose that CCDC113 connects CFAP91 and DRC2 to doublet microtubules of the axoneme and CCDC113's association with SUN5 and CENTLEIN to stabilize the sperm flagellum head-to-tail coupling apparatus. Extensive experiments mapping CCDC13 during postnatal development are reported as well as negative co-IP experiments and studies with SUN5 KO mice as well as CENTLEIN KO mice.

Strengths:

The authors provide compelling observations to indicate the relevance of CCDC113 to flagellum formation with potential protein partners. The data are relevant to sperm flagella formation and its coupling to the sperm head.

Weaknesses:

The authors' observations are consistent with the model proposed but the authors' conclusions for the mechanism may require direct demonstration in sperm flagella. The Walton et al paper shows human CCDC96/113 in cilia of human respiratory epithelia. An application of such methodology to the proteins indicated by Wu et al for the sperm axoneme and head-tail coupling apparatus is eagerly awaited as a follow-up study.

Reviewer #1 (Public Review):

Summary:

The authors provide a genome annotation resource of 33 insects using a motif-blind prediction method for tissue-specific cis-regulatory modules. This is a welcome addition that may facilitate further research in new laboratory systems, and the approach seems to be relatively accurate, although it should be combined with other sources of evidence to be practical.

Strengths:

The paper clearly presents the resource, including the testing of candidate enhancers identified from various insects in Drosophila. This cross-species analysis, and the inherent suggestion that training datasets generated in flies can predict a cis-regulatory activity in distant insects, is interesting. While I can not be sure this approach will prevail in the future, for example with approaches that leverage the prediction of TF binding motifs, the SCRMShaw tool is certainly useful and worth consideration for the large community of genome scientists working on insects.

Weaknesses:

While the authors made the effort to provide access to the SCRMShaw annotations via the RedFly database, the usefulness of this resource is somewhat limited at the moment. First, it is possible to generate tables of annotated elements with coordinates, but it would be more useful to allow downloads of the 33 genome annotations in GFF (or equivalent) format, with SCRMshaw predictions appearing as a new feature. Also, I should note that unlike most species some annotations seem to have issues in the current RedFly implementation. For example, Vcar and Jcoen turn empty.

Reviewer #2 (Public Review):

Summary:

The ability of researchers to identify and compare enhancers across different species is an important facet of understanding gene regulation across development and evolution. Many traditional methods of enhancer identification involve sequence alignments and manual annotations, limiting the ability to expand the scope of regulatory investigations into many species. In order to overcome this obstacle, the authors apply a previously published machine learning method called SCRMshaw to predict enhancers across 33 insect species, using D. melanogaster as a reference. SCRMshaw operates through the selection of a few dozen training loci in a reference genome, marking genomic loci in other species that are significantly enriched with similar k-mer distributions relative to randomly selected genomic backgrounds. Upon identification of predicted enhancer regions, the authors perform post-processing step filtering and identify the most likely predicted enhancer candidates based on the proximity of an orthologous target gene. They then perform reporter gene analysis to validate selected predicted enhancers from other species in D. melanogaster. The analysis of the expression patterns returned variable results across the selected predicted regions.

Strengths:

The authors provide annotations of predicted regions across dozens of insect species, with the intention of expanding and refining the annotations for use by the scientific field. This is useful, as researchers will be able to use the identified annotations for their own work or as a benchmark for future methods. This work also showcases the flexible and versatile nature of SCRMshaw, which can readily obtain predictions using training sets of genomic loci requiring only a few dozen annotations as input. SCRMshaw does not require sequence alignments of the enhancers and can operate without prior knowledge of the cis-regulatory sequence rules such as transcription factor binding motifs, making it a useful tool to explore the evolution of enhancers in further distant and less well-studied species.

Weaknesses:

This work provides predicted enhancer annotations across many insect species, with reporter gene analysis being conducted on selected regions to test the predictions. However, the code for the SCRMshaw analysis pipeline used in this work is not made available, making reproducibility of this work difficult. Additionally, while the authors claim the predicted enhancers are available within the REDfly database, the predicted enhancer coordinates are currently not downloadable as Supplementary Material or from a linked resource.

The authors do not validate or benchmark the application of SCRMshaw against other published methods, nor do they seek to apply SCRMshaw under a variety of conditions to confirm the robustness of the returned predicted enhancers across species. Since SCRMshaw relies on an established k-mer enrichment of the training loci, its performance is presumably highly sensitive to the selection of training regions as well as the statistical power of the given k-mer counts. The authors do not justify their selection of training regions by which they perform predictions.

While there is an attempt made to report and validate the annotated predicted enhancers using previously published data and tools, the validation lacks the depth to conclude with confidence that the predicted set of regions across each species is of high quality. In vivo, reporter assays were conducted to anecdotally confirm the validity of a few selected regions experimentally, but even these results are difficult to interpret. There is no large-scale attempt to assess the conservation of enhancer function across all annotated species.

Lastly, it is suggested that predicted regions are derived from the shared presence of sequence features such as transcription factor binding motifs, detected through k-mer enrichment via SCRMshaw. This assumption has not been examined, although there are public motif discovery tools that would be appropriate to discover whether SCRMshaw is assigning predicted regions based on previously understood motif grammar, or due to other sequence patterns captured by k-mer count distributions. Understanding the sequence-derived nature of what drives predictions is within the scope of this work and would boost confidence in the predicted enhancers, even if it is limited to a few training examples for the sake of clarity of interpretation.

Reviewer #3 (Public Review):

Summary:

In this ambitious paper, the authors develop an unparalleled community resource of insect genome regulatory annotations spanning five insect orders. They employ their previously-developed SCRMshaw method for computational cross-species enhancer prediction, drawing on available training datasets of validated enhancer sequence and expression from Drosophila melanogaster, which had been previously shown to perform well across select holometabolous insects (representing 160-345MY divergence). In this work, they expand regulatory sequence annotation to 33 insect genomes spanning Holometabola and Hemiptera, which is even more distantly related to the fly model. They perform multiple downstream analyses of sets of predicted enhancers to assess the true-positive rate of predictions; the independent comparisons of real predictions with simulated predictions and with chromatin accessibility data, as well as the functional validation through reporter gene analysis, strengthen their conclusions that their annotation pipeline achieves a high true-positive rate and can be used across long divergence times to computationally annotate regulatory genome regions, an ability that has been previously inaccessible for non-model insects and now is possible across the many newly-sequenced insect scaffold-level genomes.

Strengths:

This work fills a large gap in current methods and resources for predicting regulatory regions of the genome, a task that has long lagged behind that of coding region prediction and analysis.

Despite technical constraints in working outside of well-developed model insect systems, the authors creatively draw on existing resources to scaffold a pipeline and independently assess the likelihood of prediction validity.

The established database will be a welcome community resource in its current state, and even more so as the authors continue to expand their annotations to more insect genomes as they indicate. Their available analysis pipeline itself will be useful to the community as well for research groups that may want to undertake their own regulatory genome annotation.

Weaknesses:

The rates of predicted true positive enhancer identification vary widely across the genomes included here based on the simulations and comparison to datasets of accessible chromatin in a manner that doesn't map neatly onto phylogenetic distance. At this point, it is unclear why these patterns may arise, although this may become more clear as regulatory annotation is undertaken for more genomes.

Functional assessment of predicted enhancers was performed through reporter gene assays primarily in Drosophila melanogaster imaginal discs, a system amenable to transgenics. Unfortunately, this mode of canonical imaginal disc development is only representative of a subset of all holometabolous insects; therefore, it is difficult to interpret reporter gene expression in a fly imaginal disc as evidence of a true positive enhancer that would be active in its native species whose adult appendages develop differently through the larval stage (for example, Coleopteran and Lepidopteran legs). However, the reporter gene assays from other tissues do offer strong evidence of true positive enhancer detection, and constraints on transgenic experiments in other systems mean that this approach is the best available.

Reviewer #3 (Public Review):

Overall:

ExoIII has been described and commercialized as a dsDNA specific nuclease. Several lines of evidence, albeit incomplete, have indicated this may not be entirely true. Therefore, Wang et al comprehensively characterize the endonuclease and exonuclease enzymatic activities of ExoIII on ssDNA. A strength of the manuscript is the testing of popular kits that utilize ExoIII and coming up with and testing practical solutions (e.g., addition of SSB proteins ExoIII variants such as K121A and varied assay conditions).

Comments:

(1) The footprint of ExoIII on DNA is expected to be quite a bit larger than 5-nt, see structure in manuscript reference #5. Therefore, the substrate design in Figure 1A seems inappropriate for studying the enzymatic activity and it seems likely that ExoIII would be interacting with the FAM and/or BHQ1 ends as well as the DNA. Could this cause quenching? Would this represent real ssDNA activity? Is this figure/data necessary for the manuscript?<br /> (2) Based on the descriptions in the text, it seems there is activity with some of the other nucleases in 1C, 1F, and 1I other than ExoIII and Cas12a. Can this be plotted on a scale that allows the reader to see these relative to one other?<br /> (3) The sequence alignment in Figure 2N and corresponding text indicate a region of ExoIII lacking in APE1 that may be responsible for their differences in substrate specificity in regards to ssDNA. Does the mutational analysis support this hypothesis?

Reviewer #2 (Public Review):

Summary:

This paper describes some experiments addressing 3' exonuclease and 3' trimming activity of bacterial exonuclease III. The quantitative activity is in fact very low, despite claims to the contrary. The work is of low interest with regard to biology, but possibly of use for methods development. Thus the paper seems better suited to a methods forum.

Strengths:

Technical approaches.

Comments on revised version:

All concerns have been addressed.

Reviewer #1 (Public Review):

Summary:

Ciliary rootlet is a structure associated with the ciliary basal body (centriole) with beautiful striation observed by electron microscopy. It has been known for more than a century, but its function and protein arrangement is still unknown. This work reconstructed near-atomic resolution 3D structure of the rootlet using cryo-electron tomography, discovered a number of interesting filamentous structures inside and built molecular model of the rootlet.

Strengths:

The authors exploited the current possible ability of cryo-ET and used it appropriately to describe 3D structure of the rootlet. They carefully conducted subtomogram averaging and classification, which enabled an unprecedented detailed view of this structure. The dual use of (nearly) intact rootlet from cilia and extracted (demembraned) rootlet enabled them to describe with confidence how D1/D2/A bands form periodic structures and cross with longitudinal filaments, which are likely coiled-coil.

Weaknesses:

Some more clarifications in the method and indications in figures were needed in the original version. The authors addressed them in the revision.

Reviewer #3 (Public Review):

Summary:

The study offers a compelling molecular model for the organization of rootlets, a critical organelle that links cilia to the basal body. Striations have been observed in rootlets, but their assembly, composition, and function remain unknown. While previous research has explored rootlet structure and organization, this study delivers an unprecedented level of resolution, valuable to the centrosome and cilia field. The authors isolated rootlets from mice's eyes. They apply EM to partially purified rootlets (first negative stain, then cryoET). From these micrographs, they observed striations along the membranes along the rootlet but no regular spacing was observed.

The thickness of the sample and membranes prevented good contrast in the tomograms. Thus they further purified the rootlets using detergent, which allowed them to obtain cryoET micrographs of the rootlets with greater details. The tomograms were segmented and further processed to improve the features of the rootlet structures. From their analysis, they described 3 regular cross-striations and amorphous densities, which are connected perpendicularly to filaments along the length of the rootlets. They propose that various proteins provide the striations and rootletin (mouse homolog of human c-nap1) forms parallel coiled coils that run along the rootlet. Overall their data provide a detailed model for the molecular organization of the rootlet.

The major strength is that this high-quality study uses state-of-the-art cryo-electron tomography, sub-tomogram averaging, and image analysis to provide a model of the molecular organization of rootlets. The micrographs are exceptional, with excellent contrast and details, which also implies the sample preparation was well optimized to provide excellent samples for cryo-ET. The manuscript is also clear and accessible.

This research marks a significant step forward in our understanding of rootlets' molecular organization.

Reviewer #1 (Public Review):

Summary:

This finding shows a connection between cancer associated beta-catenin mutations extracellular vesicle secretion. A link between the beta-catenin mutation and expression of trafficking and exocytosis machinery. They used a multidisciplinary approach to explore expression levels of relevant proteins and single particle imaging to directly explore the release of extracellular vesicles. These results suggest a role of extracellular vesicles in immune evasion in liver cancer with the role needing to be further explored in other forms of cancer. I find this work to be compelling and of strong significance.

Strengths:

This paper uses multidisciplinary methods to demonstrate a compelling role of beta-catenin mutations in suppressing EV secretion in tumors. The results and imaging are extremely convincing and compelling.

Reviewer #2 (Public Review):

Summary:

Dantzer and colleagues are investigating the pivotal role of ß-catenin, a gene that undergoes mutation in various cancer cells, and its influence on promoting the evasion of immune cells. In their initial experiments, the authors developed a HepG2 mutated ß-catenin KD model, conducting transcriptional and proteomic analyses. The results revealed that the silencing of mutated ß-catenin in HepG2 cells led to an up-regulation in the expression of exosome biogenesis genes.

Furthermore, the researchers verified that these KD cells exhibited an increased production of exosomes, with the mutant form of ß-catenin concurrently decreasing the expression of SDC4 and Rab27a. Intriguingly, applying a GSK inhibitor to the cells resulted in reduced expression of SDC4 and Rab27a. Subsequent findings indicated that mutated ß-catenin actively facilitates immune escape through exosomes, and silencing exosome biogenesis correlates with a decrease in immune cell infiltration.<br /> In a crucial clinical correlation, the study demonstrated that patients with ß-catenin mutations exhibited low levels of exosome biogenesis.

Strengths:

Overall, the data robustly supports the outlined conclusions, and the study is commendably designed and executed. However, there are a few suggestions for manuscript improvement.

Weaknesses: No weakness

Reviewer #3 (Public Review):

Summary:

In this very important study by Dantzer et al., 'Emerging role of oncogenic b-catenin in exosome biogenesis as a driver of immune escape in hepatocellular carcinoma' the authors define a role for oncogenic b-catenin on exosome biology and explore the link between reduce exosome secretion and tumor immune cell evasion. Using transcriptional and proteomic analysis of hepatocellular carcinoma cells with either oncogenic or wildtype b-catenin the authors find that oncogenic b-catenin negatively regulates exosome biogenesis.

The authors can provide compelling evidence that oncogenic b-catenin in different hepatocellular carcinoma cells negatively regulates exosome biogenesis and secretion, by downregulation of, amongst others, SDC4 and RAB27A, two proteins involved in exosome biogenesis. The authors corroborate these results by inducing b-catenin activation using CHIR99021 in a hepatocarcinoma cell line with non-oncogenic bCatenin (Huh7 cells). The authors can further demonstrate convincingly that reduction in exosome release by hepatocarcinoma spheroids leads to a reduction in immune cell infiltration into the tumor spheroid.

Strengths:

This is a very important and well-conceived study, that appeals to a readership beyond the field of hepatocarcinoma. The authors demonstrate a compelling link between oncogenic bCatenin and exosome biogenesis. Their results are convincing and with well-designed control experiments. The authors included various complementary lines of investigation to verify their findings.

Weaknesses:

One limitation of this study is that the mechanistic relationship of exosome release and how they affect immune cells remains to be elucidated. In this context, the authors conclusions rest on the assumption that hepatocarcinoma immune evasion is based exclusively on the reduced number of exosomes. However, the authors do not analyze exosome composition between exosomes of wildtype and oncogenic background, which could be different.

Reviewer #1 (Public Review):

Summary:

The manuscript authored by Stockner and colleagues delves into the molecular simulations of Na+ binding pathway and the ionic interactions at the two known sodium binding sites site 1 and site 2. They further identify a patch of two acidic residues in TM6 that seemingly populate the Na+ ions prior to entry into the vestibule. These results highlight the importance of studying the ion-entry pathways through computational approaches and the authors also validate some of their findings through experimental work. They observe that sodium site 1 binding is stabilized by the presence of the substrate in the s1 site and this is particularly vital as the GABA carboxylate is involved in coordinating the Na+ ion unlike other monoamine transporters and binding of sodium to the Na2 site stabilizes the conformation of the GAT1 by reducing flexibility among the helical bundles involved in alternating access.

Strengths:

The study displays results that are generally consistent with available information from experiments on SLC6 transporters particularly GAT1 and puts forth the importance of this added patch of residues in the extracellular vestibule that could be of importance to the ion permeation in SLC6 transporters. This is a nicely performed study and could be improved if the authors could comment on and fix the following queries.

Comments on revised version:

The authors have satisfactorily addressed my comments and this has significantly improved the clarity of the manuscript.

The only point that I would like to inquire about is the role of EL4 in modulating Na+ entry. In the simulations do the authors see no role of EL4 in controlling Na+ entry. It is particularly intriguing as some studies in the recent past displayed charged mutations in EL4 of dDAT, SERT and GAT1 as being detrimental for substrate entry/uptake. It would therefore be nice to add a small discussion if there is any role for EL4 in Na+ entry.

Reviewer #2 (Public Review):

Summary

Starting from an AlphaFold2 model of the outward-facing conformation of the GAT1 transporter, the authors primarily use state-of-the-art MD simulations to dissect the role of the two Na+ ions that are known to be co-transported with the substrate, GABA (and a co-transported Cl- ion). The simulations indicated that Na+ binding to OF GAT depends on the electrostatic environment. The authors identify an extracellular recruiting site including residues D281 and E283 which they hypothesized to increase transport by locally increasing the available Na+ concentration and thus increasing binding of Na+ to the canonical binding sites NA1 and NA2. The charge-neutralizing double mutant D281A-E283A showed decreased binding in simulations. The authors performed GABA uptake experiments and whole-cell patch clamp experiments that taken together validated the hypothesis that the Na+ staging site is important for transport due to its role in pulling in Na+.

Detailed analysis of the MD simulations indicated that Na+ binding to NA2 has multiple structural effects: The binding site becomes more compact (reminiscent of induced fit binding) and there is some evidence that it stabilizes the outward-facing conformation.

Binding to NA1 appears to require the presence of the substrate, GABA, whose carboxylate moiety participates in Na+ binding; thus the simulations predict cooperativity between binding of GABA and Na+ binding to NA1.

Strengths

- MD simulations were used to propose a hypothesis (the existence of the staging Na+ site) and then tested with a mutant in simulations AND in experiments. This is an excellent use of simulations in combination with experiments.

- A large number of repeat MD simulations are generally able to provide a consistent picture of Na+ binding. Simulations are performed according to current best practices and different analyses illuminate the details of the molecular process from different angles.

- The role of GABA in cooperatively stabilizing Na+ binding to the NA1 site looks convincing and intriguing.

Weaknesses

- Assessing the effects of Na+ binding on the large scale motions of the transporter is more speculative because the PCA does not clearly cover all of the conformational space and the use of an AlphaFold2 model may have introduced structural inconsistencies. For example, it is not clear if movements of the inner gate are due to a AF2 model that's not well packed or really a feature of the open outward conformation.

- Quantitative analyses are difficult with the existing data; for example, the tICA "free energy" landscape is probably not converged because unbinding events haven't been observed.

Reviewer #1 (Public Review):

Summary:

This study investigated the phosphoryl transfer mechanism of the enzyme adenylate kinase, using SCC-DFTB quantum mechanical/molecular mechanical (QM/MM) simulations, along with kinetic studies exploring the temperature and pH dependence of the enzyme's activity, as well as the effects of various active site mutants. Based on a broad free energy landscape near the transition state, the authors proposed the existence of wide transition states (TS), characterized by the transferring phosphoryl group adopting a meta-phosphate-like geometry with asymmetric bond distances to the nucleophilic and leaving oxygens. In support of this finding, kinetic experiments were conducted with Ca2+ ions at different temperatures and pH, which revealed a reduced entropy of activation and unique pH-dependence of the catalyzed reaction.

Strengths:

A combined application of simulation and experiments is a strength.

Weaknesses:

The conclusion that the enzyme-catalyzed reaction involves a wide transition state is not sufficiently clarified with some concerns about the determined free energy profiles compared to the experimental estimate. (See Recommendations for the authors.)

Reviewer #2 (Public Review):

Summary:

The authors report results of QM/MM simulations and kinetic measurements for the phosphoryl-transfer step in adenylate kinase. The main assertion of the paper is that a wide transition state ensemble is a key concept in enzyme catalysis as a strategy to circumvent entropic barriers. This assertion is based on observation of a "structurally wide" set of energetically equivalent configurations that lie along the reaction coordinate in QM/MM simulations, together with kinetic measurements that suggest a decrease of the entropy of activation.

Strengths:

The study combines theoretical calculations and supporting experiments.

Weaknesses:

The current paper hypothesizes a "wide" transition state ensemble as a catalytic strategy and key concept in enzyme catalysis. Overall, it is not clear the degree to which this hypothesis is fully supported by the data. The reasons are as follows:

(1) Enzyme catalysis reflects a rate enhancement with respect to a baseline reaction in solution. In order to assert that something is part of a catalytic strategy of an enzyme, it would be necessary to demonstrate from simulations that the activation entropy for the baseline reaction is indeed greater and the transition state ensemble less "wide". Alternatively stated, when indicating there is a "wide transition state ensemble" for the enzyme system - one needs to indicate that is with respect to the non-enzymatic reaction. However, these simulations were not performed and the comparisons not demonstrated. The authors state "This chemical step would take about 7000 years without the enzyme" making it impossible to measure; nonetheless, the simulations of the nonenzymatic reaction would be fairly straight forward to perform in order to demonstrate this key concept that is central to the paper. Rather, the authors examine the reaction in the absence of a catalytically important Mg ion.

(2) The observation of a "wide conformational ensemble" is not a quantitative measure of entropy. In order to make a meaningful computational prediction of the entropic contribution to the activation free energy, one would need to perform free energy simulations over a range of temperatures (for the enzymatic and non-enzymatic systems). Such simulations were not performed, and the entropy of activation was thus not quantified by the computational predictions. The authors instead use a wider TS ensemble as a proxy for larger entropy, and miss an opportunity to compare directly to the experimental measurements.

Reviewer #3 (Public Review):

Summary:

By conducting QM/MM free energy simulations, the authors aimed to characterize the mechanism and transition state for the phosphoryl transfer in adenylate kinase. The qualitative reliability of the QM/MM results has been supported by several interesting experimental kinetic studies. However, the interpretation of the QM/MM results is not well supported by the current calculations.

Strengths:

The QM/MM free energy simulations have been carefully conducted. The accuracy of the semi-empirical QM/MM results was further supported by DFT/MM calculations, as well as qualitatively by several experimental studies.

Weaknesses:

(1) One key issue is the definition of the transition state ensemble. The authors appear to define this by simply considering structures that lie within a given free energy range from the barrier. However, this is not the rigorous definition of transition state ensemble, which should be defined in terms of committor distribution. This is not simply an issue of semantics, since only a rigorous definition allows a fair comparison between different cases - such as the transition state in an enzyme vs in solution, or with and without the metal ion. For a chemical reaction in a complex environment, it is also possible that many other variables (in addition to the breaking and forming P-O bonds) should be considered when one measures the diversity in the conformational ensemble.

In the revised ms, the authors included committor analysis. However, the discussion of the result is very brief. In particular, if we use the common definition of the transition state ensemble (TSE) as those featuring the committor around 0.5, the reaction coordinate of the TSE would span a much narrower range than those listed in Table 1. This point should be carefully addressed.

(2) While the experimental observation that the activation entropy differs significantly with and without the Ca2+ ion is interesting, it is difficult to connect this result with the "wide" transition state ensemble observed in the QM/MM simulations so far. Even without considering the definition of the transition state ensemble mentioned above, it is unlikely that a broader range of P-O distances would explain the substantial difference in the activation entropy measured in the experiment. Since the difference is sufficiently large, it should be possible to compute the value by repeating the free energy simulations at different temperatures, which would lead to a much more direct evaluation of the QM/MM model/result and the interpretation.

Reviewer #1 (Public Review):

Continuous attractor networks endowed with some sort of adaptation in the dynamics, whether that be through synaptic depression or firing rate adaptation, are fast becoming the leading candidate models to explain many aspects of hippocampal place cell dynamics, from hippocampal replay during immobility to theta sequences during run. Here, the authors show that a continuous attractor network endowed with spike frequency adaptation and subject to feedforward external inputs is able to account for several previously unaccounted aspects of theta sequences, including (1) sequences that move both forwards and backwards, (2) sequences that alternate between two arms of a T-maze, (3) speed modulation of place cell firing frequency, and (4) the persistence of phase information across hippocampal inactivations.

I think the main result of the paper (findings (1) and (2)) are likely to be of interest to the hippocampal community, as well as to the wider community interested in mechanisms of neural sequences. In addition, the manuscript is generally well written and the analytics are impressive. However, several issues should be addressed, which I outline below.

Major comments:

In real data, population firing rate is strongly modulated by theta (i.e., cells collectively prefer a certain phase of theta - see review paper Buzsaki, 2002) and largely oscillates at theta frequency during run. With respect to this cyclical firing rate, theta sweeps resemble "Nike" check marks, with the sweep backwards preceding the sweep forwards within each cycle before the activity is quenched at the end of the cycle. I am concerned that (1) the summed population firing rate of the model does not oscillate at theta frequency, and (2) as the authors state, the oscillatory tracking state must begin with a forward sweep. With regards to (1), can the authors show theta phase spike preference plots for the population to see if they match data? With regards to (2), can the authors show what happens if the bump is made to sweep backwards first, as it appears to do within each cycle?

I could not find the width of the external input mentioned anywhere in the text or in the table of parameters. The implication is that it is unclear to me whether, during the oscillatory tracking state, the external input is large compared to the size of the bump, so that the bump lives within a window circumscribed by the external input and so bounces off the interior walls of the input during the oscillatory tracking phase, or whether the bump is continuously pulled back and forth by the external input, in which case it could be comparable to the size of the bump. My guess based on Fig 2c is that it is the latter. Please clarify and comment.

I would argue that the "constant cycling" of theta sweeps down the arms of a T-maze was roughly predicted by Romani & Tsodyks, 2015, Figure 7. While their cycling spans several theta cycles, it nonetheless alternates by a similar mechanism, in that adaptation (in this case synaptic depression) prevents the subsequent sweep of activity from taking the same arm as the previous sweep. I believe the authors should cite this model in this context and consider the fact that both synaptic depression and spike frequency adaptation are both possible mechanisms for this phenomenon. But I certainly give the authors credit for showing how this constant cycling can occur across individual theta cycles.

The authors make an unsubstantiated claim in the paragraph beginning with line 413 that the Tsodyks and Romani (2015) model could not account for forwards and backwards sweeps. Both the firing rate adaptation and synaptic depression are symmetry breaking models that should in theory be able to push sweeps of activity in both directions, so it is far from obvious to me that both forward and backward sweeps are not possible in the Tsodyks and Romani model. The authors should either prove that this is the case (with theory or simulation) or excise this statement from the manuscript.

The section on the speed dependence of theta (starting with line 327) was very hard to understand. Can the authors show a more graphical explanation of the phenomenon? Perhaps a version of Fig 2f for slow and fast speeds, and point out that cells in the latter case fire with higher frequency than in the former?

I had a hard time understanding how the Zugaro et al., (2005) hippocampal inactivation experiment was accounted for by the model. My intuition is that while the bump position is determined partially by the location of the external input, it is also determined by the immediate history of the bump dynamics as computed via the local dynamics within the hippocampus (recurrent dynamics and spike rate adaptation). So that if the hippocampus is inactivated for an arbitrary length of time, there is nothing to keep track of where the bump should be when the activity comes back on line. Can the authors please explain more how the model accounts for this?

Can the authors comment on why the sweep lengths oscillate in the bottom panel of Fig 5b during starting at time 0.5 seconds before crossing the choice point of the T-maze? Is this oscillation in sweep length another prediction of the model? If so, it should definitely be remarked upon and included in the discussion section.

Perhaps I missed this, but I'm curious whether the authors have considered what factors might modulate the adaptation strength. In particular, might rat speed modulate adaptation strength? If so, would have interesting predictions for theta sequences at low vs high speeds.

I think the paper has a number of predictions that would be especially interesting to experimentalists but are sort of scattered throughout the manuscript. It would be beneficial to have them listed more prominently in a separate section in the discussion. This should include (1) a prediction that the bump height in the forward direction should be higher than in the backward direction, (2) predictions about bimodal and unimodal cells starting with line 366, (3) prediction of another possible kind of theta cycling, this time in the form of sweep length (see comment above), etc.

Reviewer #2 (Public Review):

In this work, the authors elaborate on an analytically tractable, continuous-attractor model to study an idealized neural network with realistic spiking phase precession/procession. The key ingredient of this analysis is the inclusion of a mechanism for slow firing-rate adaptation in addition to the otherwise fast continuous-attractor dynamics. The latter continuous-attractor dynamics classically arises from a combination of translation invariance and nonlinear rate normalization.

For strong adaptation/weak external input, the network naturally exhibits an internally generated, travelling-wave dynamics along the attractor with some characteristic speed. For small adaptation/strong external stimulus, the network recovers the classical externally driven continuous-attractor dynamics. Crucially, when both adaptation and external input are moderate, there is a competition with the internally generated and externally generated mechanisms leading to an oscillatory tracking regime. In this tracking regime, the population firing profile oscillates around the neural field tracking the position of the stimulus. The authors demonstrate by a combination of analytical and computational arguments that oscillatory tracking corresponds to realistic phase precession/procession. In particular the authors can account for the emergence of unimodal and bimodal cells, as well as some other experimental observations with respect the dependence of phase precession/procession on the animal's locomotion.

The strengths of this work are at least three-fold: 1) Given its simplicity, the proposed model has a surprisingly large explanatory power of the various experimental observations. 2) The mechanism responsible for the emergence of precession/procession can be understood as a simple yet rather illuminating competition between internally driven and externally driven dynamical trends. 3) Amazingly, and under some adequate simplifying assumptions, a great deal of analysis can be treated exactly, which allows for a detailed understanding of all parametric dependencies. This exact treatment culminates with a full characterization of the phase space of the network dynamics, as well as the computation of various quantities of interest, including characteristic speeds and oscillating frequencies.

As mentioned by the authors themselves, the main limitation of this work is that it deals with a very idealized model and it remains to see how the proposed dynamical behaviors would persists in more realistic models. For example, the model is based on a continuous attractor model that assumes perfect translation-invariance of the network connectivity pattern. Would the oscillating tracking behavior persist in the presence of connection heterogeneities? Another limitation is that the system needs to be tuned to exhibit oscillation within the theta range and that this tuning involves a priori variable parameters such as the external input strength. Is the oscillating-tracking behavior overtly sensitive to input strength variations? The author mentioned that an external pacemaker can serve to drive oscillation within the desired theta band but there is no evidence presented supporting this. A final and perhaps secondary limitation has to do with the choice of parameter, namely the time constant of neural firing which is chosen around 3ms. This seems rather short given that the fast time scale of rate models (excluding synaptic processes) is usually given by the membrane time constant, which is typically about 15ms. I suspect this latter point can easily be addressed.

Reviewer #1 (Public Review):

This study exploits novel agent (IMT) that inhibits mitochondrial activity in combination with venetoclax. While the concept is not novel, the agent is novel (inhibitor of the mitochondrial RNA polymerase, described in Nature in other tumor models), and quest for safe mitochondrial inhibitors is highly warranted. The strength is in vivo activity data shown in CLDX and in one of the two AML PDX models tested, and the apparent safety of the combination. However, the impact on survival is impressive in CLDX but not in PDX, and unclear why Ven-sensitive PDX is resistant to combination (opposite what cell line data show). The paper is lacking mechanistic data beyond Seahorse and standard apoptosis assays, and even transcriptome analysis from PDX cells is poorly analyzed. There is no real evidence that this agent overcome Ven resistance, which could be done for example in primary AML cells. Finally, no on-target pharmacodynamic endpoints are measured in vivo to support the activity of the compound on mitochondrial activity at the doses used (which are safe). These multiple weaknesses significantly reduce my enthusiasm for this manuscript.

The cell line data show additive/synergistic effects of IMT and Ven on cell viability in p53-WT cells. However, no mechanisms of synergy beyond OCR are shown, which is a missed opportunity.

No data are shown in primary AML cells in vitro. This could address venetoclax-resistant AML cells with distinct genomic profiles.

The in vivo CLDX model (MV4;11) data is quite impressive, showing reduction of tumor burden and meaningful extension of survival in combination cohort. It is unclear why venetoclax used at highest dose normally sued in vivo (100mg/kg) did not show any impact on survival in this Ven-sensitive model. It is disappointing that no biomarkers of mitochondrial activity (for example, simple pAMPK, or levels of mitochondrial subunits) are shown to support on-target pharmacodynamic activity. However, efficacy in human PDX is less impressive, for example in Fig 6C the combination has extended survival from 96 to 112 days, possibly due to early stopping of treatment (around day 30); and no extension of survival is seen in another PDX in Fig 7. Still, this is indicative of combinatorial activity in TP53-mutant PDX. There is however discrepancy with in vitro studies that show no impact of combination in TP53 mutant cells and synergy in TP53-wt cells, and the opposite findings in vivo, which is not explained. Overall, the activity of the combination is modest. The safety is encouraging, but again, no pharmacodynamic measurements are shown to support that IMT at least partially inhibited mitochondrial activity in AML cells.

In Discussion the statement that inhibition of POLRMT can overcome venetoclax resistance is not supported by the data, as no additive effects are seen in vitro in TP53 mutant cells, and no other resistant models (such as primary AML cells) are tested. In vivo as stated above there is some activity in TP53 mutant PDX but this alone cannot be sued to justify this strong statement. Also, the sentence that "...we were able to reduce the tumor burden in all (cell- and patient-derived) xenografted mice treated with a combination of IMT and venetoclax" is not supported by data in Fig 7.

Reviewer #2 (Public Review):

Summary:

The manuscript by Arabanian and colleagues presents studies showing how inhibition of mitochondrial transcription and replication with a novel inhibitor of the mitochondrial polymerase, IMT, can promote AML cell death in combination with the Bcl2 inhibitor venetoclax. They further show that this combinatorial efficacy is evident in vivo in both the AML cell line MV411 and in a PDX model. Given the multiple studies showing the importance of Oxphos in maintaining AML cell survival, the current studies provide an additional strategy to inhibit Oxphos and thus improve the therapeutic management of AML.

Strengths:

A novel aspect of this work is that IMT is a new class of mitochondrial inhibitor that acts by inhibiting the mitochondrial polymerase. In addition, the demonstration of therapeutic efficacy both in vitro and in vivo (including with PDX), together with some data showing minimal toxicity, adds to the impact of this work. Their overall conclusion that IMT increases the potency of Vex in treating AMLs is supported.

Weaknesses:

There are several deficiencies that should be addressed to substantiate the rigor and impact of this study. Of most importance, they need to show that IMT actually inhibits the mitochondrial polymerase in AML cells, and there are additional concerns with their models that if addressed would improve the ability of IMT to be developed clinically.

Reviewer #1 (Public Review):

Rebecca R.G. et al. set to determine the function of grid cells. They present an interesting case claiming that the spatial periodicity seen in the grid pattern provides a parsimonious solution to the task of coding 2D trajectories using sequential cell activation. Thus, this work defines a probable function grid cells may serve (here, the function is coding 2D trajectories), and proves that the grid pattern is a solution to that function. This approach is somewhat reminiscent in concept to previous works that defined a probable function of grid cells (e.g., path integration) and constructed normative models for that function that yield a grid pattern. However, the model presented here gives clear geometric reasoning to its case.

Stemming from 4 axioms, the authors present a concise demonstration of the mathematical reasoning underlying their case. The argument is interesting and the reasoning is valid, and this work is a valuable addition to the ongoing body of work discussing the function of grid cells.

However, the case uses several assumptions that need to be clearly stated as assumptions, clarified, and elaborated on: Most importantly, the choice of grid function is grounded in two assumptions:<br /> (1) that the grid function relies on the activation of cell sequences, and<br /> (2) that the grid function is related to the coding of trajectories. While these are interesting and valid suggestions, since they are used as the basis of the argument, the current justification could be strengthened (references 28-30 deal with the hippocampus, reference 31 is interesting but cannot hold the whole case).

The work further leans on the assumption that sequences in the same direction should be similar regardless of their position in space, it is not clear why that should necessarily be the case, and how the position is extracted for similar sequences in different positions. The authors also strengthen their model with the requirement that grid cells should code for infinite space. However, the grid pattern anchors to borders and might be used to code navigated areas locally. Finally, referencing ref. 14, the authors claim that no existing theory for the emergence of grid cell firing that unifies the experimental observations on periodic firing patterns and their distortions under a single framework. However, that same reference presents exactly that - a mathematical model of pairwise interactions that unifies experimental observations. The authors should clarify this point.

Reviewer #2 (Public Review):

Summary:

In this work, the authors consider why grid cells might exhibit hexagonal symmetry - i.e., for what behavioral function might this hexagonal pattern be uniquely suited? The authors propose that this function is the encoding of spatial trajectories in 2D space. To support their argument, the authors first introduce a set of definitions and axioms, which then lead to their conclusion that a hexagonal pattern is the most efficient or parsimonious pattern one could use to uniquely label different 2D trajectories using sequences of cells. The authors then go through a set of classic experimental results in the grid cell literature - e.g. that the grid modules exhibit a multiplicative scaling, that the grid pattern expands with novelty or is warped by reward, etc. - and describe how these results are either consistent with or predicted by their theory. Overall, this paper asks a very interesting question and provides an intriguing answer. However, the theory appears to be extremely flexible and very similar to ideas that have been previously proposed regarding grid cell function.

Major strengths:

The general idea behind the paper is very interesting - why *does* the grid pattern take the form of a hexagonal grid? This is a question that has been raised many times; finding a truly satisfying answer is difficult but of great interest to many in the field. The authors' main assertion that the answer to this question has to do with the ability of a hexagonal arrangement of neurons to uniquely encode 2D trajectories is an intriguing suggestion. It is also impressive that the authors considered such a wide range of experimental results in relation to their theory.

Major weaknesses:

One major weakness I perceive is that the paper overstates what it delivers, to an extent that I think it can be a bit confusing to determine what the contributions of the paper are. In the introduction, the authors claim to provide "mathematical proof that ... the nature of the problem being solved by grid cells is coding of trajectories in 2-D space using cell sequences. By doing so, we offer a specific answer to the question of why grid cell firing patterns are observed in the mammalian brain." This paper does not provide proof of what grid cells are doing to support behavior or provide the true answer as to why grid patterns are found in the brain. The authors offer some intriguing suggestions or proposals as to why this might be based on what hexagonal patterns could be good for, but I believe that the language should be clarified to be more in line with what the authors present and what the strength of their evidence is.

Relatedly, the authors claim that they find a teleological reason for the existence of grid cells - that is, discover the function that they are used for. However, in the paper, they seem to instead assume a function based on what is known and generally predicted for grid cells (encode position), and then show that for this specific function, grid cells have several attractive properties.

There is also some other work that seems very relevant, as it discusses specific computational advantages of a grid cell code but was not cited here: https://www.nature.com/articles/nn.2901.

A second major weakness was that some of the claims in the section in which they compared their theory to data seemed either confusing or a bit weak. I am not a mathematician, so I was not able to follow all of the logic of the various axioms, remarks, or definitions to understand how the authors got to their final conclusion, so perhaps that is part of the problem. But below I list some specific examples where I could not follow why their theory predicted the experimental result, or how their theory ultimately operated any differently from the conventional understanding of grid cell coding. In some cases, it also seemed that the general idea was so flexible that it perhaps didn't hold much predictive power, as extra details seemed to be added as necessary to make the theory fit with the data.

I don't quite follow how, for at least some of their model predictions, the 'sequence code of trajectories' theory differs from the general attractor network theory. It seems from the introduction that these theories are meant to serve different purposes, but the section of the paper in which the authors claim that various experimental results are predicted by their theory makes this comparison difficult for me to understand. For example, in the section describing the effect of environmental manipulations in a familiar environment, the authors state that the experimental results make sense if one assumes that sequences are anchored to landmarks. But this sounds just like the classic attractor-network interpretation of grid cell activity - that it's a spatial metric that becomes anchored to landmarks.

It was not clear to me why their theory predicted the field size/spacing ratio or the orientation of the grid pattern to the wall.

I don't understand how repeated advancement of one unit to the next, as shown in Figure 4E, would cause the change in grid spacing near a reward.

I don't follow how this theory predicts the finding that the grid pattern expands with novelty. The authors propose that this occurs because the animals are not paying attention to fine spatial details, and thus only need a low-resolution spatial map that eventually turns into a higher-resolution one. But it's not clear to me why one needs to invoke the sequence coding hypothesis to make this point.

The last section, which describes that the grid spacing of different modules is scaled by the square root of 2, says that this is predicted if the resolution is doubled or halved. I am not sure if this is specifically a prediction of the sequence coding theory the authors put forth though since it's unclear why the resolution should be doubled or halved across modules (as opposed to changed by another factor).

Reviewer #3 (Public Review):

The manuscript presents an intriguing explanation for why grid cell firing fields do {\em not} lie on a lattice whose axes aligned to the walls of a square arena. This observation, by itself, merits the manuscript's dissemination to the journals audience.

The presentation is quirky (but keep the quirkiness!).

But let me recast the problem presented by the authors as one of combinatorics. Given repeating, spatially separated firing fields across cells, one obtains temporal sequences of grid cells firing. Label these cells by integers from $[n]$. Any two cells firing in succession should uniquely identify one of six directions (from the hexagonal lattice) in which the agent is currently moving.

Now, take the symmetric group $\Sigma$ of cyclic permutations on $n$ elements.<br /> We ask whether there are cyclic permutations of $[n]$ such that

So, for instance, $(4,2,3,1)$ would not be counted as a valid permutation of $(1,2,3,4)$, as $(2,3)$ and $(1,4)$ are adjacent.

Furthermore, given $[n]$, are there two distinct cyclic permutations such that {\em no} adjacencies are preserved when considering any pair of permutations (among the triple of the original ordered sequence and the two permutations)? In other words, if we consider the permutation required to take the first permutation into the second, that permutation should not preserve any adjacencies.

{\bf Key question}: is there any difference between the solution to the combinatorics problem sketched above and the result in the manuscript? Specifically, the text argues that for $n=7$ there is only {\em one} solution.

Ideally, one would strive to obtain a closed-form solution for the number of such permutations as a function of $n$.

Joint Public Review:

The overall goal of this manuscript is to understand how Notch signaling is activated in specific regions of the endocardium, including the OFT and AVC, that undergo EMT to form the endocardial cushions. Using dofetilide to transiently block circulation in E9.5 mice, the authors show that Notch receptor cleavage still occurs in the valve-forming regions due to mechanical sheer stress as Notch ligand expression and oxygen levels are unaffected. The authors go on to show that changes in lipid membrane structure activate mTOR signaling, which causes phosphorylation of PKC and Notch receptor cleavage.

The strengths of the manuscript include the dual pharmacological and genetic approaches to block blood flow in the mouse, the inclusion of many controls including those for hypoxia, the quality of the imaging, and the clarity of the text. However, several weaknesses were noted surrounding the main claims where the supporting data are incomplete.

PKC - Notch1 activation:

(1) Does deletion of Prkce and Prkch affect blood flow, and if so, might that be suppressing Notch1 activation indirectly?

(2) It would be helpful to visualize the expression of prkce and prkch by in situ hybridization in E9.5 embryos.

(3) PMA experiments: Line 223-224: A major concern is related to the conclusion that "blood flow activates Notch in the cushion endocardium via the mTORC2-PKC signaling pathway". To make that claim, the authors show that a pharmacological activation with a potent PKC activator, PMA, rescues NICD levels in the AVC in dofetilide-treated embryos. This claim would also need proof that a lack of blood flow alters the activity of mTORC2 to phosphorylate the targets of PKC phosphorylation. Also, this observation does not explain the link between PKC activity and Notch activation.

(4) In addition, the authors hypothesise that shear stress lies upstream of PKC and Notch activation, and that because shear stress is highest at the valve-forming regions, PKC and Notch activity is localised to the valve-forming regions. Since PMA treatment affects the entire endocardium which expresses Notch1, NICD should be seen in areas outside of the AVC in the PMA+dofetilide condition. Please clarify.

Lipid Membrane:

(1) It is not clear how the authors think that the addition of cholesterol changes the lipid membrane structure or alters Cav-1 distribution. Can this be addressed? Does adding cholesterol make the membrane more stiff? Does increased stiffness result from higher shear stress?

(2) The loss of blood flow apparently affects Cav1 membrane localization and causes a redistribution from the luminal compartment to lateral cell adhesion sites. Cholesterol treatment of dofetilide-treated hearts (lacking blood flow) rescued Cav1 localization to luminal membrane microdomains and rescued NICD expression. It remains unclear how the general addition of cholesterol would result in a rescue of regionalized membrane distribution within the AVC and in high-shear stress areas.

(3) The authors do not show the entire heart in that rescue treatment condition (cholesterol in dofetilide-treated hearts). Also, there is no quantification of that rescue in Figure 4B. Currently, only overview images of the heart are shown but high-resolution images on a subcellular scale (such as electron microscopy) are needed to resolve and show membrane microdomains of caveolae with Cav1 distribution. This is important because Cav-1could have functions independent of caveolae (eg. Lolo et al., https://doi.org/10.1038/s41556-022-01034-3).

Figure Legends, missing data, and clarity:

(1) The number of embryos used in each experiment is not clear in the text or figure legends. In general, figure legends are incomplete (for instance in Figure 1).

(2) Line 204: The authors refer to unpublished endocardial RNAseq data from E9.5 embryos. These data must be provided with this manuscript if it is referred to in any way in the text.

(3) Figure 1 shows Dll4 transcript levels, which do not necessarily correlate with protein levels. It would be important to show quantifications of these patterns as Notch/Dll4 levels are cycling and may vary with time and between different hearts.

(4) Line 212-214: The authors describe cardiac cushion defects due to the loss of blood flow and refer to some quantifications that are not completely shown in Figure 3. For instance, quantifications for cushion cellularity and cardiac defects at three hours (after the start of treatment?) are missing.

(5) Related to Figure 5. The work would be strengthened by quantification of the effects of dofetilide and verapamil on heartbeat at the doses applied. Is the verapamil dosage used here similar to the dose used in the clinic?

Overstated Claims:

(1) The authors claim that the lipid microstructure/mTORC2/PKC/Notch pathway is responsive to shear stress, rather than other mechanical forces or myocardial function. Their conclusions seem to be extrapolated from various in vitro studies using non-endocardial cells. To solidify this claim, the authors would need additional biomechanical data, which could be obtained via theoretical modelling or using mouse heart valve explants. This issue could also be addressed by the authors simply softening their conclusions.

(2) Line 263-264: In the discussion, the authors conclude that "Strong fluid shear stress in the AVC and OFT promotes the formation of caveolae on the luminal surface of the endocardial cells, which enhances PKCε phosphorylation by mTORC2." This link was shown rather indirectly, rather than by direct evidence, and therefore the conclusion should be softened. For example, the authors could state that their data are consistent with this model.

(3) In the Discussion, it says: "Mammalian embryonic endocardium undergoes extensive EMT to form valve primordia while zebrafish valves are primarily the product of endocardial infolding (Duchemin et al., 2019)." In the paper cited, Duchemin and colleagues described the formation of the zebrafish outflow tract valve. The zebrafish atrioventricular valve primordia is formed via partial EMT through Dll-Notch signaling (Paolini et al. Cell Reports 2021) and the collective cell migration of endocardial cells into the cardiac jelly. Then, a small subset of cells that have migrated into the cardiac jelly give rise to the valve interstitial cells, while the remainder undergo mesenchymal-to-endothelial transition and become endothelial cells that line the sinus of the atrioventricular valve (Chow et al., doi: 10.1371/journal.pbio.3001505). The authors should modify this part of the Discussion and cite the relevant zebrafish literature.

Reviewer #2 (Public Review):

Summary:

The authors demonstrated that maternal choline supplementation (MCS) improved spatial memory, reduced a marker of hyperexcitability/epilepsy (FosB expression), and reduced oxidative stress (as measured by restored NeuN expression) in an Alzheimer's disease mouse model. This multidisciplinary study spanned behavior, EEG, and histological measures and constituted a large amount of work. Overall, the results supported that MCS does have important effects on hippocampal function, which may substantially impact human AD.

Strengths:

The strength of the group was the ability to monitor the incidence of interictal spikes (IIS) over the course of 1.2-6 months in the Tg2576 Alzheimer's disease model, combined with meaningful behavioral and histological measures. The authors were able to demonstrate MCS had protective effects in Tg2576 mice, which was particularly convincing in the hippocampal novel object location task.

Weaknesses:

Although choline deficiency was associated with impaired learning and elevated FosB expression, consistent with increased hyperexcitability, IIS was reduced with both low and high choline diets. Although not necessarily a weakness, it complicates the interpretation and requires further evaluation.

Joint Public Review:

Chartampila et al. describe the effect of early-life choline supplementation on cognitive functions and epileptic activity in a mouse model of Alzheimer's disease. The cognitive abilities were assessed by the novel object recognition test and the novel object location test, performed in the same cohort of mice at 3 months and 6 months of age. Neuronal loss was tested using NeuN immunoreactivity, and neuronal hyperexcitability was examined using deltaFosB and video-EEG recordings, providing multi-level correlations between these different parameters.

The study was designed as a 6-month follow-up, with repeated behavioral and EEG measurements through disease development and multilevel correlations providing valuable and interesting findings on AD progression and the effect of early-life choline supplementation. Moreover, the behavioral data that suggest an adverse effect of low choline in WT mice are interesting and important also beyond the context of AD, highlighting the dramatic effect of diet on the phenotypes of animal.

Reviewer #1 (Public Review):

Summary:

The authors propose a new method to quantitatively assess morphogenetic processes during organismal development. They apply their method to ascidian morphogenesis and thus find that gastrulation is a two-step process.

The method applies to morphogenetic changes of surfaces. It consists of the following steps: first, surface deformations are quantified based on microscopy images without requiring cellular segmentation and tracking. This is achieved by mapping, at each time point, a polygonal mesh initially defined on a sphere to the surface of the embryo. The mapped vertices of this polygonal mesh then serve as (Lagrangian) markers for the embryonic surface. From these, one can infer the deformation of the surface, which can be expressed in terms of the strain tensor at each point of the surface. Changes in the strain tensor give the strain rate, which captures the morphogenetic processes. Second, at each time point, the strain rate field is decomposed in terms of spherical harmonics. Finally, the evolution of the weights of the various spherical harmonics in the decomposition is analysed via wavelet analysis. The authors apply their workflow to ascidian development between 4 and 8.7 hpf. From their analysis, they find clear indications for gastrulation and neurulation and identify two sub-phases of gastrulation, namely, endoderm invagination and 'blastophore closure'.

Strengths:

The combination of various tools allows the authors to obtain a quantitative description of the developing embryo without the necessity of identifying fiducial markers. Visual inspection shows that their method works well. Furthermore, this quantification then allows for an unbiased identification of different morphogenetic phases.

Weaknesses:

At times, the explanation of the method is hard to follow, unless the reader is already familiar with concepts like level-set methods or wavelet transforms. Furthermore, the software for performing the determination of Lagrangian markers or the subsequent spectral analysis does not seem to be available to the readers.

Reviewer #2 (Public Review):

Summary:

In this manuscript, the authors proposed a method to quantitatively analyze 3D live imaging data of early developing embryos, using ascidian development as an example. For this purpose, the previously proposed level set method was used to computationally track the temporal evolution of reference points introduced on the embryo surface. Then, from the obtained three-dimensional trajectories, the velocity field was obtained, from which the strain rate field was computed according to the idea of continuum mechanics. The information in the strain rate field was reduced to a scalar field, determined by taking the square root of the sum of the squares of the eigenvalues. The scalar field is then further decomposed into a spectrum using spherical harmonics. In this paper, the authors focused on the modes with lower order with real coefficients. The time evolution of these modes was analyzed using wavelet transforms. The authors claimed that the results reflected the developmental stages of ascidian embryos.

Strengths:

In this way, this manuscript proposes a pipeline of analyses combining various methods. The strength of this method lies in its ability to quantitatively analyze the deformation of the entire embryo without the requirement for cellular segmentation and tracking.

Weaknesses:

The limitations of the proposed analysis pipeline are not clearly indicated. Claims such as the identification of developmental stages need more quantitative validation. In addition, it is not clearly shown how the proposed method can distinguish between the superposition of individual cell behavior and the collective behavior of cells.

Reviewer #1 (Public Review):

Summary:

In this human neuroimaging and electrophysiology study, the authors aimed to characterize the effects of a period of visual deprivation in the sensitive period on excitatory and inhibitory balance in the visual cortex. They attempted to do so by comparing neurochemistry conditions ('eyes open', 'eyes closed') and resting state, and visually evoked EEG activity between ten congenital cataract patients with recovered sight (CC), and ten age-matched control participants (SC) with normal sight.

First, they used magnetic resonance spectroscopy to measure in vivo neurochemistry from two locations, the primary location of interest in the visual cortex, and a control location in the frontal cortex. Such voxels are used to provide a control for the spatial specificity of any effects because the single-voxel MRS method provides a single sampling location. Using MR-visible proxies of excitatory and inhibitory neurotransmission, Glx and GABA+ respectively, the authors report no group effects in GABA+ or Glx, no difference in the functional conditions 'eyes closed' and 'eyes open'. They found an effect of the group in the ratio of Glx/GABA+ and no similar effect in the control voxel location. They then performed multiple exploratory correlations between MRS measures and visual acuity, and reported a weak positive correlation between the 'eyes open' condition and visual acuity in CC participants.

The same participants then took part in an EEG experiment. The authors selected only two electrodes placed in the visual cortex for analysis and reported a group difference in an EEG index of neural activity, the aperiodic intercept, as well as the aperiodic slope, considered a proxy for cortical inhibition. They report an exploratory correlation between the aperiodic intercept and Glx in one out of three EEG conditions.

The authors report the difference in E/I ratio, and interpret the lower E/I ratio as representing an adaptation to visual deprivation, which would have initially caused a higher E/I ratio. Although intriguing, the strength of evidence in support of this view is not strong. Amongst the limitations are the low sample size, a critical control cohort that could provide evidence for a higher E/I ratio in CC patients without recovered sight for example, and lower data quality in the control voxel.

Strengths of study:

How sensitive period experience shapes the developing brain is an enduring and important question in neuroscience. This question has been particularly difficult to investigate in humans. The authors recruited a small number of sight-recovered participants with bilateral congenital cataracts to investigate the effect of sensitive period deprivation on the balance of excitation and inhibition in the visual brain using measures of brain chemistry and brain electrophysiology. The research is novel, and the paper was interesting and well-written.

Limitations:

- Low sample size. Ten for CC and ten for SC, and a further two SC participants were rejected due to a lack of frontal control voxel data. The sample size limits the statistical power of the dataset and increases the likelihood of effect inflation.

- Lack of specific control cohort. The control cohort has normal vision. The control cohort is not specific enough to distinguish between people with sight loss due to different causes and patients with congenital cataracts with co-morbidities. Further data from more specific populations, such as patients whose cataracts have not been removed, with developmental cataracts, or congenitally blind participants, would greatly improve the interpretability of the main finding. The lack of a more specific control cohort is a major caveat that limits a conclusive interpretation of the results.

- MRS data quality differences. Data quality in the control voxel appears worse than in the visual cortex voxel. The frontal cortex MRS spectrum shows far broader linewidth than the visual cortex (Supplementary Figures). Compared to the visual voxel, the frontal cortex voxel has less defined Glx and GABA+ peaks; lower GABA+ and Glx concentrations, lower NAA SNR values; lower NAA concentrations. If the data quality is a lot worse in the FC, then small effects may not be detectable.

- Because of the direction of the difference in E/I, the authors interpret their findings as representing signatures of sight improvement after surgery without further evidence, either within the study or from the literature. However, the literature suggests that plasticity and visual deprivation drive the E/I index up rather than down. Decreasing GABA+ is thought to facilitate experience-dependent remodelling. What evidence is there that cortical inhibition increases in response to a visual cortex that is over-sensitised due to congenital cataracts? Without further experimental or literature support this interpretation remains very speculative.

- Heterogeneity in the patient group. Congenital cataract (CC) patients experienced a variety of duration of visual impairment and were of different ages. They presented with co-morbidities (absorbed lens, strabismus, nystagmus). Strabismus has been associated with abnormalities in GABAergic inhibition in the visual cortex. The possible interactions with residual vision and confounds of co-morbidities are not experimentally controlled for in the correlations, and not discussed.

- Multiple exploratory correlations were performed to relate MRS measures to visual acuity (shown in Supplementary Materials), and only specific ones were shown in the main document. The authors describe the analysis as exploratory in the 'Methods' section. Furthermore, the correlation between visual acuity and E/I metric is weak, and not corrected for multiple comparisons. The results should be presented as preliminary, as no strong conclusions can be made from them. They can provide a hypothesis to test in a future study.

- P.16 Given the correlation of the aperiodic intercept with age ("Age negatively correlated with the aperiodic intercept across CC and SC individuals, that is, a flattening of the intercept was observed with age"), age needs to be controlled for in the correlation between neurochemistry and the aperiodic intercept. Glx has also been shown to negatively correlate with age.

- Multiple exploratory correlations were performed to relate MRS to EEG measures (shown in Supplementary Materials), and only specific ones were shown in the main document. Given the multiple measures from the MRS, the correlations with the EEG measures were exploratory, as stated in the text, p.16, and in Figure 4. Yet the introduction said that there was a prior hypothesis "We further hypothesized that neurotransmitter changes would relate to changes in the slope and intercept of the EEG aperiodic activity in the same subjects." It would be great if the text could be revised for consistency and the analysis described as exploratory.

- The analysis for the EEG needs to take more advantage of the available data. As far as I understand, only two electrodes were used, yet far more were available as seen in their previous study (Ossandon et al., 2023). The spatial specificity is not established. The authors could use the frontal cortex electrode (FP1, FP2) signals as a control for spatial specificity in the group effects, or even better, all available electrodes and correct for multiple comparisons. Furthermore, they could use the aperiodic intercept vs Glx in SC to evaluate the specificity of the correlation to CC.

Reviewer #2 (Public Review):

Summary:

The manuscript reports non-invasive measures of activity and neurochemical profiles of the visual cortex in congenitally blind patients who recovered vision through the surgical removal of bilateral dense cataracts. The declared aim of the study is to find out how restoring visual function after several months or years of complete blindness impacts the balance between excitation and inhibition in the visual cortex.

Strengths:

The findings are undoubtedly useful for the community, as they contribute towards characterising the many ways this special population differs from normally sighted individuals. The combination of MRS and EEG measures is a promising strategy to estimate a fundamental physiological parameter - the balance between excitation and inhibition in the visual cortex, which animal studies show to be heavily dependent upon early visual experience. Thus, the reported results pave the way for further studies, which may use a similar approach to evaluate more patients and control groups.

Weaknesses:

The main issue is the lack of an appropriate comparison group or condition to delineate the effect of sight recovery (as opposed to the effect of congenital blindness). Few previous studies suggested an increased excitation/Inhibition ratio in the visual cortex of congenitally blind patients; the present study reports a decreased E/I ratio instead. The authors claim that this implies a change of E/I ratio following sight recovery. However, supporting this claim would require showing a shift of E/I after vs. before the sight-recovery surgery, or at least it would require comparing patients who did and did not undergo the sight-recovery surgery (as common in the field).

MR Spectroscopy shows a reduced GLX/GABA ratio in patients vs. sighted controls; however, this finding remains rather isolated, not corroborated by other observations. The difference between patients and controls only emerges for the GLX/GABA ratio, but there is no accompanying difference in either the GLX or the GABA concentrations. There is an attempt to relate the MRS data with acuity measurements and electrophysiological indices, but the explorative correlational analyses do not help to build a coherent picture. A bland correlation between GLX/GABA and visual impairment is reported, but this is specific to the patients' group (N=10) and would not hold across groups (the correlation is positive, predicting the lowest GLX/GABA ratio values for the sighted controls - the opposite of what is found). There is also a strong correlation between GLX concentrations and the EEG power at the lowest temporal frequencies. Although this relation is intriguing, it only holds for a very specific combination of parameters (of the many tested): only with eyes open, only in the patient group.

For these reasons, the reported findings do not allow us to draw firm conclusions on the relation between EEG parameters and E/I ratio or on the impact of early (vs. late) visual experience on the excitation/inhibition ratio of the human visual cortex.

Reviewer #3 (Public Review):

This manuscript examines the impact of congenital visual deprivation on the excitatory/inhibitory (E/I) ratio in the visual cortex using Magnetic Resonance Spectroscopy (MRS) and electroencephalography (EEG) in individuals whose sight was restored. Ten individuals with reversed congenital cataracts were compared to age-matched, normally sighted controls, assessing the cortical E/I balance and its interrelationship to visual acuity. The study reveals that the Glx/GABA ratio in the visual cortex and the intercept and aperiodic signal are significantly altered in those with a history of early visual deprivation, suggesting persistent neurophysiological changes despite visual restoration.

My expertise is in EEG (particularly in the decomposition of periodic and aperiodic activity) and statistical methods. I have several major concerns in terms of methodological and statistical approaches along with the (over)interpretation of the results. These major concerns are detailed below.

(1) Variability in visual deprivation:

- The document states a large variability in the duration of visual deprivation (probably also the age at restoration), with significant implications for the sensitivity period's impact on visual circuit development. The variability and its potential effects on the outcomes need thorough exploration and discussion.

(2) Sample size:

- The small sample size is a major concern as it may not provide sufficient power to detect subtle effects and/or overestimate significant effects, which then tend not to generalize to new data. One of the biggest drivers of the replication crisis in neuroscience.

- The main problem with the correlation analyses between MRS and EEG measures is that the sample size is simply too small to conduct such an analysis. Moreover, it is unclear from the methods section that this analysis was only conducted in the patient group (which the reviewer assumed from the plots), and not explained why this was done only in the patient group. I would highly recommend removing these correlation analyses.

(3) Statistical concerns:

- The statistical analyses, particularly the correlations drawn from a small sample, may not provide reliable estimates (see https://www.sciencedirect.com/science/article/pii/S0092656613000858, which clearly describes this problem).

- Statistical analyses for the MRS: The authors should consider some additional permutation statistics, which are more suitable for small sample sizes. The current statistical model (2x2) design ANOVA is not ideal for such small sample sizes. Moreover, it is unclear why the condition (EO & EC) was chosen as a predictor and not the brain region (visual & frontal) or neurochemicals. Finally, the authors did not provide any information on the alpha level nor any information on correction for multiple comparisons (in the methods section). Finally, even if the groups are matched w.r.t. age, the time between surgery and measurement, the duration of visual deprivation, (and sex?), these should be included as covariates as it has been shown that these are highly related to the measurements of interest (especially for the EEG measurements) and the age range of the current study is large.

- EEG statistical analyses: The same critique as for the MRS statistical analyses applies to the EEG analysis. In addition: was the 2x3 ANOVA conducted for EO and EC independently? This seems to be inconsistent with the approach in the MRS analyses, in which the authors chose EO & EC as predictors in their 2x2 ANOVA.

- Figure 4: The authors report a p-value of >0.999 with a correlation coefficient of -0.42 with a sample size of 10 subjects. This can't be correct (it should be around: p = 0.22). All statistical analyses should be checked.

- Figure 2c. Eyes closed condition: The highest score of the *Glx/GABA ratio seems to be ~3.6. In subplot 2a, there seem to be 3 subjects that show a Glx/GABA ratio score > 3.6. How can this be explained? There is also a discrepancy for the eyes-closed condition.

(4) Interpretation of aperiodic signal:

- Several recent papers demonstrated that the aperiodic signal measured in EEG or ECoG is related to various important aspects such as age, skull thickness, electrode impedance, as well as cognition. Thus, currently, very little is known about the underlying effects which influence the aperiodic intercept and slope. The entire interpretation of the aperiodic slope as a proxy for E/I is based on a computational model and simulation (as described in the Gao et al. paper).

- Especially the aperiodic intercept is a very sensitive measure to many influences (e.g. skull thickness, electrode impedance...). As crucial results (correlation aperiodic intercept and MRS measures) are facing this problem, this needs to be reevaluated. It is safer to make statements on the aperiodic slope than intercept. In theory, some of the potentially confounding measures are available to the authors (e.g. skull thickness can be computed from T1w images; electrode impedances are usually acquired alongside the EEG data) and could be therefore controlled.

- The authors wrote: "Higher frequencies (such as 20-40 Hz) have been predominantly associated with local circuit activity and feedforward signaling (Bastos et al., 2018; Van Kerkoerle et al., 2014); the increased 20-40 Hz slope may therefore signal increased spontaneous spiking activity in local networks. We speculate that the steeper slope of the aperiodic activity for the lower frequency range (1-20 Hz) in CC individuals reflects the concomitant increase in inhibition." The authors confuse the interpretation of periodic and aperiodic signals. This section refers to the interpretation of the periodic signal (higher frequencies). This interpretation can not simply be translated to the aperiodic signal (slope).

- The authors further wrote: We used the slope of the aperiodic (1/f) component of the EEG spectrum as an estimate of E/I ratio (Gao et al., 2017; Medel et al., 2020; Muthukumaraswamy & Liley, 2018). This is a highly speculative interpretation with very little empirical evidence. These papers were conducted with ECoG data (mostly in animals) and mostly under anesthesia. Thus, these studies only allow an indirect interpretation by what the 1/f slope in EEG measurements is actually influenced.

(5) Problems with EEG preprocessing and analysis:

- It seems that the authors did not identify bad channels nor address the line noise issue (even a problem if a low pass filter of below-the-line noise was applied).

- What was the percentage of segments that needed to be rejected due to the 120μV criteria? This should be reported specifically for EO & EC and controls and patients.

- The authors downsampled the data to 60Hz to "to match the stimulation rate". What is the intention of this? Because the subsequent spectral analyses are conflated by this choice (see Nyquist theorem).

- "Subsequently, baseline removal was conducted by subtracting the mean activity across the length of an epoch from every data point." The actual baseline time segment should be specified.

- "We excluded the alpha range (8-14 Hz) for this fit to avoid biasing the results due to documented differences in alpha activity between CC and SC individuals (Bottari et al., 2016; Ossandón et al., 2023; Pant et al., 2023)." This does not really make sense, as the FOOOF algorithm first fits the 1/f slope, for which the alpha activity is not relevant.

- The model fits of the 1/f fitting for EO, EC, and both participant groups should be reported.

(6) Validity of GABA measurements and results:

- According the a newer study by the authors of the Gannet toolbox (https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/abs/10.1002/nbm.5076), the reliability and reproducibility of the gamma-aminobutyric acid (GABA) measurement can vary significantly depending on acquisition and modeling parameter. Thus, did the author address these challenges? Furthermore, the authors wrote: "We confirmed the within-subject stability of metabolite quantification by testing a subset of the sighted controls (n=6) 2-4 weeks apart. Looking at the supplementary Figure 5 (which would be rather plotted as ICC or Blant-Altman plots), the within-subject stability compared to between-subject variability seems not to be great. Furthermore, I don't think such a small sample size qualifies for a rigorous assessment of stability.

- "Why might an enhanced inhibitory drive, as indicated by the lower Glx/GABA ratio" Is this interpretation really warranted, as the results of the group differences in the Glx/GABA ratio seem to be rather driven by a decreased Glx concentration in CC rather than an increased GABA (see Figure 2).

- Glx concentration predicted the aperiodic intercept in CC individuals' visual cortices during ambient and flickering visual stimulation. Why specifically investigate the Glx concentration, when the paper is about E/I ratio?

(7) Interpretation of the correlation between MRS measurements and EEG aperiodic signal:

- The authors wrote: "The intercept of the aperiodic activity was highly correlated with the Glx concentration during rest with eyes open and during flickering stimulation (also see Supplementary Material S11). Based on the assumption that the aperiodic intercept reflects broadband firing (Manning et al., 2009; Winawer et al., 2013), this suggests that the Glx concentration might be related to broadband firing in CC individuals during active and passive visual stimulation." These results should not be interpreted (or with very caution) for several reasons (see also problem with influences on aperiodic intercept and small sample size). This is a result of the exploratory analyses of correlating every EEG parameter with every MRS parameter. This requires well-powered replication before any interpretation can be provided. Furthermore and importantly: why should this be specifically only in CC patients, but not in the SC control group?

(8) Language and presentation:

- The manuscript requires language improvements and correction of numerous typos. Over-simplifications and unclear statements are present, which could mislead or confuse readers (see also interpretation of aperiodic signal).

- The authors state that "Together, the present results provide strong evidence for experience-dependent development of the E/I ratio in the human visual cortex, with consequences for behavior." The results of the study do not provide any strong evidence, because of the small sample size and exploratory analyses approach and not accounting for possible confounding factors.

- "Our results imply a change in neurotransmitter concentrations as a consequence of *restoring* vision following congenital blindness." This is a speculative statement to infer a causal relationship on cross-sectional data.

- In the limitation section, the authors wrote: "The sample size of the present study is relatively high for the rare population , but undoubtedly, overall, rather small." This sentence should be rewritten, as the study is plein underpowered. The further justification "We nevertheless think that our results are valid. Our findings neurochemically (Glx andGABA+ concentration), and anatomically (visual cortex) specific. The MRS parameters varied with parameters of the aperiodic EEG activity and visual acuity. The group differences for the EEG assessments corresponded to those of a larger sample of CC individuals (n=38) (Ossandón et al., 2023), and effects of chronological age were as expected from the literature." These statements do not provide any validation or justification of small samples. Furthermore, the current data set is a subset of an earlier published paper by the same authors "The EEG data sets reported here were part of data published earlier (Ossandón et al., 2023; Pant et al., 2023)." Thus, the statement "The group differences for the EEG assessments corresponded to those of a larger sample of CC individuals (n=38) " is a circular argument and should be avoided.

Reviewer #1 (Public Review):

This study identifies two behavioral processes that underlie learned pathogen avoidance behavior in C. elegans: exiting and re-entry of pathogenic bacterial lawns. Long-term behavioral tracking indicates that animals increase the prevalence of both behaviors over long-term exposure to the pathogen Pseudomonas aeruginosa. Using an optogenetic silencing screen, the authors identify groups of neurons, whose activity regulates lawn occupancy. Surprisingly, they find that optogenetic inhibition of neurons during only the first two hours of pathogen exposure can establish subsequent long-term changes in pathogen aversion. By leveraging a compressed sensing approach, the authors define a set of neurons involved in either lawn exit or lawn re-entry behavior using a constrained set of transgenic lines that drive Arch-3 expression in overlapping groups of neurons. They then measure the calcium activity of the candidate neurons involved in lawn re-entry in freely moving animals using GCaMP, and observe a reduction in their neural activity after exposure to a pathogen. Optogenetic inhibition of AIY and SIA neurons during acute pathogen exposure in naïve animals delays lawn entry whereas activating these neurons in animals previously exposed to pathogen enhances lawn entry, albeit transiently.

This work is missing several controls that are necessary to substantiate their claims. My most important concern is that the optogenetic screen for neurons that alter pathogenic lawn occupancy does not have an accompanying control on non-pathogenic OP50 bacteria. Hence, it remains unclear whether these neuronal inhibition experiments lead to pathogen-specific or generalized lawn-leaving alterations. For strains that show statistical differences between - and + ATR conditions, the authors should perform follow-up validation experiments on non-pathogenic OP50 lawns to ensure that the observed effect is PA14-specific. Similarly, neuronal inhibition experiments in Figures 5E and H are only performed with naïve animals on PA14 - we need to see the latency to re-entry on OP50 as well, to make general conclusions about these neurons' role in pathogen-specific avoidance.

My second major concern is regarding the calcium imaging experiments of candidate neurons involved in lawn re-entry behavior. Although the data shows that AIY, AVK, and SIA/SIB neurons all show reduced activity following pathogen exposure, the authors do not relate these activity changes to changes in behavior. Given the well-established links between these cells and forward locomotion, it is essential to not only report differences in activity but also in the relationship between this activity and locomotory behavior. If animals are paused outside of the pathogen lawn, these neurons may show low activity simply because the animals are not moving forward. Other forward-modulated neurons may also show this pattern of reduced activity if the animals remain paused. Given that the authors have recorded neural activity before and after contact with pathogenic bacteria in freely moving animals, they should also provide an analysis of the relationship between proximity to the lawn and the activity of these neurons.

This work is missing methodological descriptions that are necessary for the correct interpretation of the results shown here. Figure 2 suggests that the determination of statistical significance across the optogenetic inhibition screen will be found in the Methods, but this information is not to be found there. At various points in the text, authors refer to "exit rate", "rate constant", and "entry rate". These metrics seem derived from an averaged measurement across many individual animals in one lawn evacuation assay plate. However "latency to re-entry" is only defined on a per-animal basis in the lawn re-exposure assay. These differences should be clearly stated in the methods section to avoid confusion and to ensure that statistics are computed correctly.

This work also contains mislabeled graphs and incorrect correspondence with the text, which make it difficult to follow the authors 'claims. The text suggests that Pdop-2::Arch3 and Pmpz-1::Arch3 show increased exit rates, whereas Figure 2 shows that Pflp-4::Arch3 but not Pmpz-1::Arch3 has increased exit rate. The authors should also make a greater effort to correctly and clearly label which type of behavioral experiment is used to generate each figure and describe the differences in experimental design in the main text, figure legends, and methods. Figure 2E depicts trajectories of animals leaving a lawn over a 2.5-minute interval but it is unclear when this time window occurs within the 18-hour lawn leaving assay. Likewise, Figure 2H depicts a 30-minute time window which has an unclear relationship to the overall time course of lawn leaving. This figure legend is also mislabeled as "Infected/Healthy", whereas it should be labeled "-/+ ATR".

This work raises the interesting possibility that different sets of neurons control lawn exit and lawn re-entry behaviors following pathogen exposure. However, the authors never directly test this claim. To rigorously show this, the authors would need to show that lawn-exit-promoting neurons (CEPs, HSNs, RIAs, RIDs, SIAs) are dispensable for lawn re-entry behavior and that lawn re-entry promoting neurons (AVK, SIA, AIY, MI) are dispensable for lawn exit behavior in pathogen-exposed animals. The authors identify AVK neurons as important for modulating lawn re-entry behavior by brief inhibition at the start of pathogen exposure but fail to find that these neurons are required for increased latency to re-entry in naïve animals (Figure 5D). Recent work from Marquina-Solis et al (2024) shows that chronic silencing of these neurons delays pathogen lawn leaving, due to impaired release of flp-1 neuropeptide. Authors may wish to connect their work more closely with the existing literature by investigating the behavioral process by which AVK contributes to lawn evacuation.

If the authors work through these criticisms, this work can become an important contribution to the field of pathogen learning in C. elegans. However, in its current form, this work remains incomplete.

Reviewer #2 (Public Review):

In this manuscript, Hallacy et al. used a compressed sensing-based optogenetic screening method to investigate the crucial neurons that regulate pathogenic avoidance behavior in C. elegans. They further substantiate their findings using complementary optogenetic activation and imaging techniques to confirm the roles of the key neurons identified through extensive screening efforts. Notably, they identified AIY and SIA as pivotal neurons in the dynamic process of pathogenic avoidance. Their significant discovery is the delayed or stalled reentry process, which drives avoidance behavior; to my knowledge, this dynamic has not been previously documented. Additionally, the successful integration of quantitative optogenetic tools and compressed sensing algorithms is noteworthy, demonstrating the potential for obtaining highly quantitative data from the C. elegans nervous system. This approach is quite rare in this field, yet it represents a promising direction for studying this simple nervous system.

However, the paper's main weakness lies in its lack of a detailed mechanism explaining how the delayed reentry process directly influences the actual locomotor output that results in avoidance. The term 'delayed reentry' is used as a dynamic metric for quantifying the screening, yet the causal link between this metric and the mechanistic output remains unclear. Despite this, the study is well-structured, with comprehensive control experiments, and is very well constructed.

Reviewer #3 (Public Review):

Summary:

Using a compressed sensing-based approach applied previously by the author's group, the authors conducted an initial screen for neurons that when optogenetically down-regulated, influenced learned pathogen avoidance consisting of two component behaviors, exit from the bacterial lawn and lawn re-entry. Authors found that 4 classes of neurons AVK, SIA, AIY, and MI were inferred over a wide range of sparsity parameters, thereby indicating the importance of lawn re-entry. They found six classes of neurons required for lawn exit. The authors then went on to further analyze the neurons for the re-entry behavior, and conducted calcium imaging of those neurons in the freely behaving animals. They found that the activities of AIY and SIA neurons decreased after the animals that had been exposed to the pathogenic bacteria tried to re-enter the bacterial lawn. They also found that when those neurons of the animals that had not been exposed to pathogenic bacteria were downregulated by optogenetics, those operated animals increased the latency of the re-entry, which is a similar behavioral modification to that of the animals that had been exposed to the pathogen. Conversely, those neurons of the animals that were exposed to pathogenic bacteria were up-regulated by optogenetics, those animals showed a shortened latency of the re-entry, which is similar to the behavior observed in the animals not exposed to pathogen.

Strengths:

This is overall a very nice piece of work. Most importantly, an initial screening of neurons was conducted by a compressed sensing-based approach previously applied by the same group. It is also worth emphasizing that this compressed analysis is applicable when the behavior of interest involves a small number of neurons, as the authors pointed out in the Introduction Session. Therefore, the readers should keep in mind that the validation and significance of this work heavily depend on the justification of scarcity parameters that the authors chose. Nevertheless, this work is well justified because neurons identified by the initial screening were thoroughly analyzed by various methods including calcium imaging and optogenetic manipulation of neuronal activities and behavioral analyses using an animal-tracking system.

Weaknesses:

My only concern is that the authors should be more careful about describing their "compressed sensing-based approach". Authors often cite their previous Nature Methods paper, but should explain more because this method is critical for this manuscript. Also, this analysis is based on the hypothesis that only a small number of neurons are responsible for a given behavior. Authors should explain more about how to determine scarcity parameters, for example.

Reviewer #1 (Public Review):

Summary:

The study characterized the cellular and molecular mechanisms of spike timing-dependent long-term depression (t-LTD) at the synapses between excitatory afferents from lateral (LPP) and medial (MPP) perforant pathways to granule cells (GC) of the dentate gyrus (DG) in mice.

Strengths:

The electrophysiological experiments are thorough. The experiments are systematically reported and support the conclusions drawn.<br /> This study extends current knowledge by elucidating additional plasticity mechanisms at PP-GC synapses, complementing existing literature.

Weaknesses:

To more conclusively define the pivotal role of astrocytes in modulating t-LTD at MPP and LPP GC synapses through SNARE protein-dependent glutamate release, as posited in this study, the authors could adopt additional methods, such as alternative mouse models designed to regulate SNARE-dependent exocytosis, as well as optogenetic or chemogenetic strategies for precise astrocyte manipulation during t-LTD induction. This would provide more direct evidence of the influence of astrocytic activity on synaptic plasticity.

Reviewer #2 (Public Review):

Summary:

This work reports the existence of spike timing-dependent long-term depression (t-LTD) of excitatory synaptic strength at two synapses of the dentate gyrus granule cell, which are differently connected to the entorhinal cortex via either the lateral or medial perforant pathways (LPP or MPP, respectively). Using patch-clamp electrophysiological recording of tLTD in combination with either pharmacology or a genetically modified mouse model, they provide information on the differences in the molecular mechanism underlying this t-LTD at the two synapses.

Strengths:

The two synapses analyzed in this study have been understudied. This new data thus provides interesting new information on a plasticity process at these synapses, and the authors demonstrate subtle differences in the underlying molecular mechanisms at play. Experiments are in general well controlled and provide robust data that are properly interpreted.

Weaknesses:

- Caution should be taken in the interpretation of the results to extrapolate to adult brain as the data were obtained in P13-21 days old mice, a period during which synapses are still maturing and highly plastic.<br /> - In experiments where the drug FK506 or thapsigargin are loaded intracellularly, the concentrations used are as high as for extracellular application. Could there be an error of interpretation when stating that the targeted actors are necessarily in the post-synaptic neuron? Is it not possible for the drug to diffuse out of the cell as it is evident that it can enter the cell when applied extracellularly?<br /> - The experiments implicating glutamate release from astrocytes in t-LTD would require additional controls to better support the conclusions made by the authors. As the data stand, it is not clear how the authors identified astrocytes to load BAPTA and if dnSNARE expression in astrocytes does not indirectly perturb glutamate release in neurons.

Significance:

While this is the first report of t-LTD at these synapses, this plasticity process has been mechanistically well investigated at other synapses in the hippocampus and in the cortex. Nevertheless, this new data suggests that mechanistic differences in the induction of t-LTD at these two DG synapses could contribute to the differences in the physiological influence of the LPP and MPP pathways.

Reviewer #3 (Public Review):

Coatl et al. investigated the mechanisms of synaptic plasticity of two important hippocampal synapses, the excitatory afferents from lateral and medial perforant pathways (LPP and MPP, respectively) of the entorhinal cortex (EC) connecting to granule cells of the hippocampal dentate gyrus (DG). They find that these two different EC-DG synaptic connections in mice show a presynaptically expressed form of long-term depression (LTD) requiring postsynaptic calcium, eCB synthesis, CB1R activation, astrocyte activity, and metabotropic glutamate receptor activation. Interestingly, LTD at MPP-GC synapses requires ionotropic NMDAR activation whereas LTD at LPP-GC synapse is NMDAR independent. Thus, they discovered two novel forms of t-LTD that require astrocytes at EC-GC synapses. Although plasticity of EC-DG granule cell (GC) synapses has been studied using classical protocols, These are the first analysis of the synaptic plasticity induced by spike timing dependent protocols at these synapses. Interestingly, the data also indicate that t-LTD at each type of synapse require different group I mGluRs, with LPP-GC synapses dependent on mGluR5 and MPP-GC t-LTD requiring mGluR1.

The authors performed a detailed analysis of the coefficient of variation of the EPSP slopes, miniature responses and different approaches (failure rate, PPRs, CV, and mEPSP frequency and amplitude analysis) they demonstrate a decrease in the probability of neurotransmitter release and a presynaptic locus for these two forms of LTD at both types of synapses. By using elegant electrophysiological experiments and taking advantage of the conditional dominant-negative (dn) SNARE mice in which doxycycline administration blocks exocytosis and impairs vesicle release by astrocytes, they demonstrate that both LTD forms require the release of gliotransmitters from astrocytes. These data add in an interesting way to the ongoing discussion on whether LTD induced by STDP participates in refining synapses potentially weakening excitatory synapses under the control of different astrocytic networks. The conclusions of this paper are mostly well supported by data, but some aspects the results must be clarified and extended.

(1) It should be clarified whether present results are obtained with or without the functional inhibitory synapse activation. It is not clear if GABAergic synapses are blocked or not. If GABAergic synapses are not blocked authors must discuss whether the LTD of the EPSPs is due to a decrease in glutamatergic receptor activation or an increase in GABAergic receptor activation. Moreover, it should be recommended to analyze not only the EPSPs but also the EPSCs to address whether the decrease in synaptic transmission is caused by a decrease in the input resistance or by a decrease in the space constant (lambda).<br /> (2) Authors show that Thapsigargin loaded in the postsynaptic neuron prevents the induction of LTD at both synapses. Analyzing the effects of blocking postsynaptic IP3Rs (Heparin in the patch pipette) and Ryanodine receptors (Ruthenium red in the patch pipette) is recommended for a deeper analysis of the mechanism implicated in the induction of this novel forms of LTD in the hippocampus.<br /> (3) Authors nicely demonstrate that CB1R activation is required in these forms of LTD by blocking CB1Rs with AM251, however an interesting unanswered question is whether CB1R activation is sufficient to induce this synaptic plasticity. This reviewer suggests studying whether applying puffs of the CB1R agonist, WIN 55,212-2, could induce these forms of LTD.<br /> (4) Finally, adding a last figure with a cartoon summarizing the proposed model of action in these novel forms of LTD would add a positive value and would help the reading of the manuscript, especially in those aspects related with the discussion of the results.

The extension of these results would improve the manuscript which provides interesting results showing two novel forms of presynaptic t-LTD in the brain synapses with different action mechanisms probably implicated in the different aspects of information processing.

Reviewer #1 (Public Review):

Summary:

The authors provide solid evidence with a mouse model as well as supporting in vitro and analysis of clinical samples that loss of Fak increases the development of BRAF V600E-induced dysplastic lesions and carcinomas in the cecum via downregulation of Egfr-mediated Erk phosphorylation. This fine-tuning of Erk phosphorylation increases the expression of Lrg4 mRNA expression and promotes Lrg4 stability through downregulation of the E3 ubiquitin ligase Nedd4. The high Lrg4 expression correlates with an increased intestinal stem cell transcriptional signature that the authors suggest drives higher rates of transformation. This provides important insight that factors such as FAK may be able to modulate MAPK-driven tumorigenesis in specific circumstances. The data presented here are largely specific to the cecum. While these specific findings may ultimately have practical implications for human CRC outside the cecum and even therapeutic implications, these remain unexplored and will be a point for future investigations.

Strengths:

The authors use a mouse model (intestinal specific BRAF V600E +/- Fak knockout) as well as supporting in vitro analyses and clinical sample characterization to support their model. For both in vitro and in vivo studies, the authors use a combination of genetic and pharmacologic (including EGFR, FAK, and MEK inhibitors) tools to modulate the MAPK pathway. They also use a combination of transcriptional (RNA-Seq) and protein (IHC and Western blotting) readouts to support their proposed model. Importantly, they use a distinct mouse model (mutant Kras) to demonstrate their findings with Fak loss are specific to instances where EGFR can modulate ERK activation, providing strong evidence for their model. Finally, they also correlate their findings in the murine model with patient samples and with trends in the TCGA database. Collectively, these create a solid and convincing basis for their proposed model.

Weaknesses:

(1) The murine data is largely confined to the cecum. While the analysis of the cecum is appropriate based on the cecum specificity of their phenotype, they often use these findings to make broader generalizations about the nature of tumorigenesis in the intestinal epithelia and in CRC more generally. In my opinion, there was insufficient evidence presented supporting the extension of the proposed model beyond the cecum. While this is a weakness, it could be part of a growing effort to characterize left and right-sided malignancies as related but separate disease processes.

(2) The authors generally do a good job of focusing their analysis on the cecum and supporting their model. For example, Figure 5A examines different colon compartments, including the cecum. However, the authors fail to demonstrate that Fak loss only promotes Lrg4 upregulation in the cecum, where they observe an increase in BRAF V600E dysplasia and carcinoma. This is again seen in Figure 6A, where they only characterize Nedd4 expression in the cecum and not other compartments of the colon.

(3) The authors evaluate a broad range of tissues, including normal colonic mucosa, polyps, pre-cancerous dysplastic lesions, adenocarcinomas, and adenocarcinoma cell lines. While this breadth is a strength of the paper, the authors, at times, equate experimental observations in each of these conditions, despite the difference in the biology of these tissues/cells. For example, in their mouse model, they equate the development of dysplastic lesions and carcinoma lesions. This makes it difficult to accurately interpret their data and conclusions.

(4) In Figure 5i, this experiment was only completed in one cell line (HT29), despite the conclusion that Lrg4 expression is increased by decreased ERK phosphorylation due to protein stabilization. HT29 cells are a transformed human CRC cell line, quite different than a pre-malignant cecum intestinal epithelial cell. While convincing, the authors could have performed this key experiment in non-transformed murine cecal organoids (as they did for other experiments in Figure 5E), which would better recapitulate the mouse and pre-malignant setting to explain their mouse phenotype.

(5) While a large portion of the discussion focusses on the therapeutic implications of these findings, the authors only really investigate tumorigenesis. They likely have additional investigations planned for future manuscripts.

Reviewer #2 (Public Review):

Summary:

The manuscript by Gao et al. described a study identifying the role of FAK in fine-tuning the activation levels of ERK signaling in BRAF-V600E-driven colorectal cancer. The authors generated new mouse models combining Vill-Cre mediated BRAF-V600E expression with FAK deletion. Analyses of intestinal tumor phenotypes revealed that FAK-loss promotes BRAF-V600E-induced tumor formation, specifically in the cecum. Interestingly, these tumors closely resemble human sessile serrated adenoma/polyps. Using bioinformatics analysis, the authors found that FAK deletion upregulates the intestinal stem cell and fetal-type transcriptomic signatures compared to mice expressing BRAF-V600E alone. In addition, FAK-loss decreases the phosphorylation of ERK whereas it increases the expression of Lgr4 at both mRNA and protein levels. To mechanistically connect FAK-mediated downregulation of ERK and upregulation of Lgr4 in the context of BRAF-V600E mutation, results from biochemical experiments showed that MEK inhibitor treatment decreases the expression of NEDD4, a previously identified ubiquitin E3 ligase of Lgr4, which coincides with increased Lgr4 protein expression both in cells and in vivo. Moreover, the FAK-dependent modulation of ERK signaling is specific to BRAF-V600E-driven tumorigenesis only as knockout of FAK has no effect in Vill-Cre/KRAS-G12D mice. Collectively, the authors proposed a "just right" model in that a tunable FAK expression controls the optimal level of ERK pathway output needed for BRAF-V600E-induced cecal tumor formation.

Strengths:

This study provides new insights into the mechanisms underlying the serrated pathway-driven tumorigenesis in colorectal cancer. The newly established mouse model with compound mutations of BRAF and FAK offers a useful resource for future studies of the serrated pathway. The conclusions of this paper are mostly supported by data.

Weaknesses:

However, some aspects of the paper can be strengthened with additional mechanistically focused experiments.

(1) Some of the conclusions of the paper mainly rely on bioinformatic analyses of RNA-seq data. For example, it has been noted in several places in the paper that the knockout of FAK in Vill-Cre/BRAF-V600E mice does not affect the transcriptional outcome downstream of ERK while ERK phosphorylation levels are decreased. This statement is based on the lack of significant difference in the MAPK signature according to GSEA. However, whereas a significant enrichment of certain pathways can be used as support evidence, the lack of enrichment does not necessarily indicate those pathways are not involved. Other experiments are needed to examine the expression of ERK target genes to confirm. Similarly, the upregulation of fetal stem cell signature in FAK knockout mice needs to be verified using other methods besides GSEA.

(2) According to Figure 5i, the half-life of Lgr4 is around 48 hours in HT29 cells. However, it has been reported by at least two other publications cited in this paper (Ref. 44 and 45) that the half-life of Lgr4 is much shorter. This discrepancy is not explained.

(3) The effect of decreased ERK signaling on NEDD4 expression has only been briefly explored in Figure 6. The mechanisms by which FAK-loss and/or inhibition of MEK/ERK activity regulate NEDD4 expression are currently unclear. Moreover, the levels of NEDD4 expression are only analyzed in one mouse per group in Figure 6a. Quantitative analysis of NEDD4 as well as Lgr4 expression in additional numbers of mice will provide more solid support for the inverse correlation between NEDD4 and Lgr4 proteins. Since MEK inhibitor treatment also increases Lgr4 mRNA expression as shown in Figure 5f-g, the relative contribution of this altered mRNA expression vs. NEDD4L-mediated ubiquitination has not been investigated.

(4) It is an interesting finding that knockout FAK has no effect on KRAS-G12D-driven hyperplasia as shown in Figure 7. However, additional studies are needed to further explore the potential mechanisms by which FAK-loss specifically decreases EGFR/ERK signaling in the context of BRAF-V600E mutation.

Reviewer #3 (Public Review):

Summary:

Right-sided colorectal Cancer (CRC) is very different from left-sided CRC. Therefore it is important to model this cancer in mice and find new molecular targets. A broad set of data exists on FAK (Focal Adhesion Kinase) being important in colorectal cancer. However, this has focussed on APC mutant CRC which tends to be left-sided. BRAF mutation is common in right-sided CRC (and is rarely mutated with APC). Therefore the authors have tested whether FAK is important in this context. The authors show that FAK deletion surprisingly accelerates BRAF mutant CRC. Tumours arise in the proximal colon (which recapitulates BRAF mutant right-sided CRC). There are low for Lgr5 and high for foetal programmes. Mechanistically they suggest a pathway from FAK to NEDD4 to Lgr4 may underpin this phenotype.

Strengths:

Strong genetic data from FAK revealed that there is an acceleration of tumourigenesis and mice now develop proximal colon tumours and can be viewed as a good model of right-sided CRC.<br /> The expression data between humans and mice is strong.

Weaknesses:

The functional mechanism of how FAK loss promotes tumourigenesis is still quite correlative. An alternative hypothesis is that it drives inflammation in the proximal colon that drives tumourigenesis.

We still did not know the functional role for LGR4 (loss leads to a loss of paneth cells in homeostasis) so I'm not sure you can hypothesise a stem cell role.

Reviewer #2 (Public Review):

Summary:

This interesting study challenges the dogma regarding the link between bacterial metabolism decrease and tolerance to aminoglycosides (AG). The authors demonstrate that mutants well-known for being tolerant to AG, such as those of complexes I and II, are not so due to a decrease in the proton motive force (PMF) and thus antibiotic uptake, as previously reported in the literature.

Strengths:

This is a complete study that employs several read-outs.

In this revised version, the authors have carefully addressed all the reviewers' comments. I appreciate the effort made in this new version to clarify that this study does not refute the PMF-dependent mechanism of aminoglycoside uptake (in the discussion_ lines 731-734_).

The addition of the requested experiments using lower concentrations of aminoglycosides is a considerable improvement as it allows for comparison with previously published results.

Reviewer #1 (Public Review):

Summary:

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

Strengths:

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

Weaknesses:

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

Reviewer #2 (Public Review):

Summary:

In this study, Wang and colleagues study the potential probiotic effects of Bacillus velezensis. Bacillus species have potential benefit to serve as probiotics due to their ability to form endospores and synthesize secondary metabolites. B. velezensis has been shown to have probiotic effects in plants and animals but data for human use are scarce, particularly with respect to salmonella-induced colitis. In this work, the authors identify a strain of B. velezensis and test it for its ability to control colitis in mice.

Key findings:

(1) The authors sequence an isolate for B. velezensis - HBXN2020 and describe its genome (roughly 4 mb, 46% GC-content etc).<br /> (2) The authors next describe the growth of this strain in broth culture and survival under acid and temperature stress. The susceptibility of HBXN2020 was tested against various antibiotics and against various pathogenic bacteria. In the case of the latter, the authors set out to determine if HBXN2020 could directly inhibit the growth of pathogenic bacteria. Convincing data, indicating that this is indeed the case, are presented.<br /> (3) To determine the safety profile of BHXN2020 (for possible use as a probiotic), the authors infected the strain in mice and monitored weight, together with cytokine profiles. Infected mice displayed no significant weight loss and expression of inflammatory cytokines remained unchanged. Blood cell profiles of infected mice were consistent with that of uninfected mice. No significant differences in tissues, including the colon were observed.<br /> (4) Next, the authors tested the ability to HBXN2020 to inhibit growth of Salmonella typhimurium (STm) and demonstrate that HBXN2020 inhibits STm in a dose dependent manner. Following this, the authors infect mice with STm to induce colitis and measure the ability of HBXN2020 to control colitis. The first outcome measure was a reduction in STm in faeces. Consistent with this, HBXN2020 reduced STm loads in the ileum, cecum, and colon. Colon length was also affected by HBXN2020 treatment. In addition, treatment with HBXN2020 reduced the appearance colon pathological features associated with colitis, together with a reduction in inflammatory cytokines.<br /> (5) After noting the beneficial (and anti-inflammatory effects) of HBXN2020, the authors set out to investigate effects on microbiota during treatment. Using a variety of algorithms, the authors demonstrate that upon HXBN2020 treatment, microbiota composition is restored to levels akin to that seen in healthy mice.<br /> (6) Finally, the authors assessed the effect of using HBXN2020 as prophylactic treatment for colitis by first treating mice with the spores and then infecting with STm. Their data indicate that treatment with HBXN2020 reduced colitis. A similar beneficial impact was seen with the gut microbiota.

Strengths:

(1) Good use of in vitro and animal models to demonstrate a beneficial probiotic effect.<br /> (2) Most observations are supported using multiple approaches.<br /> (3) Mouse experiments are very convincing.

Weaknesses:

(1) Whilst a beneficial effect is observed, there no investigation of the mechanism that underpins this.<br /> (2) Mouse experiments would have benefited from the use of standard anti-inflammatory therapies to control colitis. That way the authors could compare their approach of using bacillus spores that current gold standard for treatment.

Reviewer #3 (Public Review):

Summary:

The manuscript by Wang et al. investigates the effects of B. velezensis HBXN2020 in alleviating S. Typhimurium-induced mouse colitis. The results showed that B. velezensis HBXN2020 could alleviate bacterial colitis by enhancing intestinal homeostasis (decreasing harmful bacteria and enhancing the abundance of Lactobacillus and Akkermansia) and gut barrier integrity and reducing inflammation.

Strengths:

B. velezensis HBXN2020 is a novel species of Bacillus that can produce a great variety of secondary metabolites and exhibit high antibacterial activity against several pathogens. B. velezensis HBXN2020 is able to form endospores and has strong anti-stress capabilities. B. velezensis HBXN2020 has a synergistic effect with other beneficial microorganisms, which can improve intestinal homeostasis.

Weaknesses:

Few studies about the clinical application of Bacillus velezensis. Thus, more studies are still needed to explore the effectiveness of Bacillus velezensis before clinical application.

Reviewer #1 (Public Review):

Summary:

Duan et al analyzed brain imaging data in UKBK and found a pattern in brain structure changes by aging. They identified two patterns and found links that can be differentiated by the categorization.

Strengths:

This discovery harbors substantial impacts in aging and brain structure and function.

Weaknesses:

Therefore, the study requires more validation efforts. Most importantly, data underlying the stratification of two groups are not obvious and lack further details. Can they also stratified by different method? i.e. PCA?

Any external data can be used for validation?

Other previous discoveries or claims supporting the results of the study should be explored to support the conclusion.

Sex was merely used as a covariate. Were there sex-differences during brain aging? Sex ratio difference in group 1 and 2?

Although statistically significant, Fig 3 shows minimal differences. LTL and phenoAge is displayed in adjusted values but what is the actual values that differ between pattern 1 and 2?

It is not intuitive to link gene expression result shown in Fig 8 and brain structure and functional differences between pattern 1 and 2. Any overlap of genes identified from analyses shown in Fig 6 (GWAS) and 8 (gene expression)?

Reviewer #2 (Public Review):

Summary:

The authors aimed to understand the heterogeneity of brain aging by analyzing brain imaging data. Based on the concept of structural brain aging, they divided participants into two groups based on the volume and rate of decrease of gray matter volume (GMV). The group with rapid brain aging showed accelerated biological aging and cognitive decline and was found to be vulnerable to certain neuropsychiatric disorders. Furthermore, the authors claimed the existence of a "last in, first out" mirroring pattern between brain aging and brain development, which they argued is more pronounced in the group with rapid brain aging. Lastly, the authors identified genetic differences between the two groups and speculated that the cause of rapid brain aging may lie in genetic differences.

Strengths:

The authors supported their claims by analyzing a large amount of data using various statistical techniques. There seems to be no doubt about the quality and quantity of the data. Additionally, they demonstrated their strength in integrating diverse data through various analysis techniques to conclude.

Weaknesses:

The authors provided appropriate answers to the reviewers' questions and revised the manuscript accordingly, and as a result, the paper has been edited to be more easily understood.

Reviewer #1 (Public Review):

Summary:

In previous work the Elias group has shown that leptin sensing PMv neurons make connections with the neuroendocrine reproductive axis and are involved in reproductive function/s. Sáenz de Miera et al. build on this body of work to investigate the sufficiency of leptin sensing PMv neurons to evoke the release of luteinizing hormone. The team further investigates how glutamate signaling from leptin-sensing neurons can influence pubertal timing in females, along with mature estrous cycles. Genetic ablation of Slc17a6 (Vglut2) from LepRb-expressing cells resulted in a delay of the first estrus cycle post pubertal transition, along with a significantly lengthened estrous cycle in mature females. However, this deficit did not lengthen the latency to birth of the first litter in experimental dams. Restoration of leptin signaling in LepRb PMv neurons that was previously shown to induce puberty and instate reproductive function in LepRb knock-out female mice (Mahany et al., 2018). Here, Sáenz de Miera et al. use a combined genetic and viral strategy to demonstrate that glutamate signaling in LepRb PMv neurons is required for sexual maturation in LepRb knock-out female mice.

Strengths:

Most of the experiments performed in this manuscript are well justified and rigorously tested. The genetic method to simultaneously remove glutamate signaling and restore the leptin receptor in LepRb PMv neurons was well executed and showed that glutamate signaling in LepRb PMv neurons is necessary for leptin-dependent fertility.

Weaknesses:

Analysis of experimentally induced luteinizing hormone release could be confounded by spontaneous pulses of luteinizing hormone that are independent of LepRb PMv neurons.

Reviewer #2 (Public Review):

Summary:

This is a very well-written manuscript by Saenz de Meira and colleagues on a careful study reporting on the key role of glutamate transporter vGlut2 expression in the neurons of the ventral perimammillary nucleus (PMv) of the hypothalamus expressing the leptin receptor LepRb in energy homeostasis, puberty, and estrous cyclicity. The authors first show using cre-dependent chemogenetic viral tools that the selective activation of the PMv LepRb induces luteinizing hormone (LH) release. Then the authors demonstrate that the selective invalidation of vGlut2 in LepRb-expressing cells in the all body induces obesity and mild alteration of sexual maturation in both sexes and blunted estrous cyclicity in females. Finally, the authors knock out vGlut2 in PMv neurons in which they reintroduce LepRb expression in an otherwise LepRb-null background using an AAV Cre approach. This latter very elegant experiment shows that while the sole re-expression of LepRb in PMv neurons in LepRb-null mice was shown before to restore puberty onset, deleting vGlut2 in LepRb-expressing PMv neurons blunts this effect.

Strengths:

The authors employ state-of-the-art methods and their conclusions are robustly supported by the results.

Weaknesses:

None identified. Only minor comments have been formulated.

Reviewer #3 (Public Review):

Summary:

The authors examined the effects of glutamate release from PMv LepR neurons in the regulation of puberty and reproduction in female mice.

Strengths:

Multiple genetic mouse models were utilized to either manipulate PMv LepR neuron activities or to delete glutamate vesicle transporters from LepR neurons. The authors have been quite rigorous in validating these models and exploring potential contaminations. Most of the data presented are solid and convincing and support the conclusion.

Comments on revised version:

The authors have addressed most of my comments.

Reviewer #2 (Public Review):

In this study, Sekulovski and colleagues report refinements to an in vitro model of human amnion formation. Working with 3D cultures and BMP4 to induce differentiation, the authors chart the time course of amnion induction in human pluripotent stem cells in their system using immunofluorescence and RNA-seq. They carry out validation through comparison of their data to existing embryo datasets, and through immunostaining of post-implantation marmoset embryos. Functional experiments show that the transcription factor TFAP2C drives the amnion differentiation program once it has been initiated.

There is currently great interest in the development of in vitro models of human embryonic development. While it is known that the amnion plays an important structural supporting role for the embryo, its other functions, such as morphogen production and differentiation potential, are not fully understood. Since a number of aspects of amnion development are specific to primates, models of amniogenesis will be valuable for the study of human development. Advantages of this model include its efficiency and the purity of the cell populations produced, a significant degree of synchrony in the differentiation process, benchmarking with single-cell data and immunocytochemistry from primate embryos, and identification of key markers of specific phases of differentiation. Weaknesses are the absence of other embryonic tissues in the model, and overinterpretation of certain findings, in particular relating bulk RNA-seq results to scRNA-seq data from published analyses of primate embryos and results from limited (though high quality) embryo immunostainings.

Reviewer #3 (Public Review):

In this work, the authors tried to profile time-dependent changes in gene and protein expression during BMP-induced amnion differentiation from hPSCs. The authors depicted a GATA3 - TFAP2A - ISL1/HAND1 order of amniotic gene activation, which provides a more detailed temporary trajectory of amnion differentiation compared to previous works. As a primary goal of this study, the above temporal gene/protein activation order is amply supported by experimental data. However, the mechanistic insights on amniotic fate decision, as well as the transcriptomic analysis comparing amnion-like cells from this work and other works remain limited. While this work allows us to see more details of amnion differentiation and understand how different transcription factors were turned on in a sequence and might be useful for benchmarking the identity of amnion in ex utero cultured human embryos/embryoids, it provides limited insights on how amnion cells might diverge from primitive streak / mesoderm-like cells, despite some transcriptional similarity they shared, during early development.

[Editors' note: In the revised manuscript, the authors have added new results and made textual revisions that address the reviewers' concerns. These changes have significantly enhanced the clarity, quality, and impact of the study. ]

Reviewer #1 (Public Review):

Summary:

The work by Zeng et al. comprehensively explored the differences in the effects of leaf and soil microbes on the seed germination, seedling survival and seedling growth of an invasive forb, Ageratina Adenophora, and found evidence of stronger adverse effects of leaf microbes on Ageratina compared with soil microbes. By further DNA sequencing and fungal strain cultivation, the authors were able to identify some of the key microbial guilds that may facilitate such negative and positive feedbacks.

Strengths:

(1) The theoretic framework is well-established;<br /> (2) Relating the direction of plant-microbe feedback to certain microbial guild is always hard, but the authors had done a great job in identifying and interpreting such relationships.

Weaknesses:

(1) Allelopathic effects can't be directly accounted for;<br /> (2) The fungal strains accumulated in dead seedlings may also accumulate in live seedlings, thus more evidence is needed to validate the claim by the authors that Allophoma and Alternaria can increase seedling mortality.

Reviewer #1 (Public Review):

Summary:

The authors addressed the influence of DKK2 on colorectal cancer (CRC) metastasis to the liver using an orthotopic model transferring AKP-mutant organoids into the spleens of wild-type animals. They found that DKK2 expression in tumor cells led to enhanced liver metastasis and poor survival in mice. Mechanistically, they associate Dkk2-deficiency in donor AKP tumor organoids with reduced Paneth-like cell properties, particularly Lz1 and Lyz2, and defects in glycolysis. Quantitative gene expression analysis showed no significant changes in Hnf4a1 expression upon Dkk2 deletion. Ingenuity Pathway Analysis of RNA-Seq data and ATAC-seq data point to a Hnf4a1 motif as a potential target. They also show that HNF4a binds to the promoter region of Sox9, which leads to LYZ expression and upregulation of Paneth-like properties. By analyzing available scRNA data from human CRC data, the authors found higher expression of LYZ in metastatic and primary tumor samples compared to normal colonic tissue; reinforcing their proposed link, HNF4a was highly expressed in LYZ+ cancer cells compared to LYZ- cancer cells.

Strengths:

Overall, this study contributes a novel mechanistic pathway that may be related to metastatic progression in CRC.

Weaknesses:

The main concerns are related to incremental gains, missing in vivo support for several of their conclusions in murine models, and missing human data analyses. Additionally, methods and statistical analyses require further clarification.

Main comments:

(1) Novelty<br /> The authors previously described the role of DKK2 in primary CRC, correlating increased DKK2 levels to higher Src phosphorylation and HNF4a1 degradation, which in turn enhances LGR5 expression and "stemness" of cancer cells, resulting in tumor progression (PMID: 33997693). A role for DKK2 in metastasis has also been previously described (sarcoma, PMID: 23204234).

(2) Mouse data<br /> a) The authors analyzed liver mets, but the main differences between AKT and AKP/Dkk2 KO organoids could arise during the initial tumor cell egress from the intestinal tissue (which cannot be addressed in their splenic injection model), or during pre-liver stages, such as endothelial attachment. While the analysis of liver mets is interesting, given that Paneths cells play a role in the intestinal stem cell niche, it is questionable whether a study that does not involve the intestine can appropriately address this pathway in CRC metastasis.<br /> b) The overall number of Paneth cells found in the scRNA-seq analysis of liver mets was strikingly low (17 cells, Figure 3), and assuming that these cells are driving the differences seems somewhat far-fetched. Adding to this concern is inappropriate gating in the flow plot shown in Figure 6. This should be addressed experimentally and in the interpretation of data.<br /> c) Figures 3, 5, and 6 show the individual gene analyses with unclear statistical data. It seems that the p-values were not adjusted, and it is unclear how they reached significance in several graphs. Additionally, it was not stated how many animals per group and cells per animal/group were included in the analyses.<br /> d) Figure 6 suggests a signaling cascade in which the absence of DKK2 leads to enhanced HNF4A expression, which in turn results in reduced Sox9 expression and hence reduced expression of Paneth cell properties. It is therefore crucial that the authors perform in vivo (splenic organoid injection) loss-of-function experiments, knockdown of Sox9 expression in AKP organoids, and Sox9 overexpression experiments in AKP/Dkk2 KO organoids to demonstrate Sox9 as the central downstream transcription factor regulating liver CRC metastasis.<br /> e) Given the previous description of the role of DKK2 in primary CRC, it is important to define the step of liver metastasis affected by Dkk2 deficiency in the metastasis model. Does it affect extravasation, liver survival, etc.?

(3) Human data<br /> Can the authors address whether the expression of Dkk2 changes in human CRC and whether mutations in Dkk2 as correlated with metastatic disease or CRC stage?

(4) Bioinformatic analysis<br /> The authors did not provide sufficient information on bioinformatic analyses. The authors did not include information about the software, cutoffs, or scripts used to make their analyses or output those figures in the manuscript, which challenges the interpretation and assessment of the results. Terms like "Quantitative gene expression analyses" (line 136) "visualized in a Uniform Approximation and Projection" (line 178) do not explain what was inputted and the analyses that were executed. There are multiple forms to align, preprocess, and visualize bulk, single cell, ATAC, and ChIP-seq data, and depending on which was used, the results vary greatly. For example, in the single-cell data, the authors did not inform how many cells were sequenced, nor how many cells had after alignment and quality filtering (RNA count, mt count, etc.), so the result on Paneth+ to Goblet+ percent in lines 184 and 185 cannot be reached because it depends on this information. The absence of a clustering cutoff for the single-cell data is concerning since this greatly affects the resulting cluster number (https://www.nature.com/articles/s41592-023-01933-9). The authors should provide a comprehensive explanation of all the data analyses and the steps used to obtain those results.

(5) Clarity of methods and experimental approaches<br /> The methods were incomplete and they require clarification.

Reviewer #2 (Public Review):

Summary:

The authors propose that DKK2 is necessary for the metastasis of colon cancer organoids. They then claim that DKK2 mediates this effect by permitting the generation of lysozyme-positive Paneth-like cells within the tumor microenvironmental niche. They argue that these lysozyme-positive cells have Paneth-like properties in both mouse and human contexts. They then implicate HNF4A as the causal factor responsive to DKK2 to generate lysozyme-positive cells through Sox9.

Strengths:

The use of a genetically defined organoid line is state-of-the-art. The data in Figure 1 and the dependence of DKK2 for splenic injection and liver engraftment, as well as the long-term effect on animal survival, are interesting and convincing. The rescue using DKK2 administration for some of their phenotype in vitro is good. The inclusion and analysis of human data sets help explore the role of DKK2 in human cancer and help ground the overall work in a clinical context.

Weaknesses:

In this work by Shin et al., the authors expand upon prior work regarding the role of Dickkopf-2 in colorectal cancer (CRC) progression and the necessity of a Paneth-like population in driving CRC metastasis. The general topic of metastatic requirements for colon cancer is of general interest. However, much of the work focuses on characterizing cell populations in a mouse model of hepatic outgrowth via splenic transplantation. In particular, the concept of Paneth-like cells is primarily based on transcriptional programs seen in single-cell RNA sequencing data and needs more validation. Although including human samples is important for potential generality, the strength could be improved by doing immunohistochemistry in primary and metastatic lesions for Lyz+ cancer cells. Experiments that further bolster the causal role of Paneth-like CRC cells in metastasis are needed.

Reviewer #1 (Public Review):

This study by Popli et al. evaluated the function of Atg14, an autophagy protein, in reproductive function using a conditional knockout mouse model. The authors showed that female mice lacking Atg14 were infertile partly due to defective embryo transport function of the oviduct and faulty uterine receptivity and decidualization using PgrCre/+;Atg14f/f mice. The findings from this work are exciting and novel. The authors demonstrated that a loss of Atg14 led to an excessive pyroptosis in the oviductal epithelial cells that compromises cellular integrity and structure, impeding the transport function of the oviduct. In addition, the authors use both genetic and pharmacological approaches to test the hypothesis. Therefore, the findings from this study are high-impact and likely reproducible. However, there are multiple major concerns that need to be addressed to improve the quality of the work.

Reviewer #2 (Public Review):

Summary:

In this manuscript, Popli et al investigated the roles of the autophagy-related gene, Atg14, in the female reproductive tract (FRT) using conditional knockout mouse models. By ablation of Atg14 in both oviduct and uterus with PR-Cre (Atg14 cKO), the authors discovered that such females are completely infertile. They went on to show that Atg14 cKO females have impaired embryo implantation and uterus receptivity due to impaired response to P4 stimulation and stromal decidualization. In addition to the uterus defect, the authors also discovered that early embryos are trapped inside the oviduct and cannot be efficiently transported to the uterus in these females. They went on to show that oviduct epithelium in Atg14 cKO females showed increased pyroptosis, which disrupts oviduct epithelial integrity and leads to obstructive oviduct lumen and impaired embryo transport. Therefore, the authors concluded that autophagy is critical for maintaining the oviduct homeostasis and keeping the inflammation under check to enable proper embryo transport.

Strengths:

This study revealed an important and unexpected role of the autophagy-related gene Atg14 in preventing pyroptosis and maintaining oviduct epithelial integrity, which is poorly studied in the field of reproductive biology. The study is well designed to test the roles of ATG14 in mouse oviduct and uterus. The experimental data in general support the conclusion and the interpretations are mostly accurate. This work should be of interest to reproductive biologists and scientists in the field of autophagy and pyroptosis.

Weaknesses:

Despite the strengths, there are several major weaknesses raising concerns. In addition, the mismatched figure panels, the undefined acronyms, and the poor description/presentation of some of the data significantly hinder the readability of the manuscript.

(1) In the abstract, the authors stated that "autophagy is critical for maintaining the oviduct homeostasis and keeping the inflammation under check to enable embryo transport". This statement is not substantiated. Although Atg14 is an autophagy-related gene and plays a critical role in oviduct homeostasis, the authors did not show a direct link between autophagy and pyroptosis/oviduct integrity. In addition, the authors pointed out in the last paragraph of the introduction that none of the other autophagy-related genes (ATG16L, FIP200, BECN1) exhibited any discernable impact on oviduct function. Therefore, the oviduct defect is caused by Atg14 specifically, not necessarily by autophagy.

(2) In lines 412-414, the authors stated that "Atg14 ablation in the oviduct causes activation of pyroptosis", which is also not supported by the experimental data. The authors did not show that Atg14 is expressed in oviduct cells. PR-Cre is also not specific in oviduct cells. It is possible that Atg14 knockout in other PR-expressing tissues (such as the uterus) indirectly activates pyroptosis in the oviduct. More experiments will be required to support this claim. In line with the no defect when Atg14 is knocked out in oviduct ciliary cells, it will be good to use the secretory cells Cre, such as Pax8-Cre, to demonstrate that Atg14 functions in the secretory cells of the oviduct thus supporting this conclusion.

(3) With FOXJ1-Cre, the authors attempted to specifically knockout Atg14 in ciliary cells, but there are no clear fertility and embryo implantation defects in Foxj1/Atg14 cKO mice. The author should provide the verification data to show that Atg14 had been effectively depleted in ciliary cells if Atg14 is normally expressed.

(4) In lines 307-313, the author tested whether ATG14 is required for the decidualization of HESCs. The author stated that "Control siRNA transfected cells when treated with EPC seemed to change their morphological transformation from fibroblastic to epithelioid (Fig. 2E) and had increased expression of the decidualization markers IGFBP1 and PRL by day three only (Fig. 2F)". First, the labels in Figure 2 are not corresponding to the description in the text. Second, the morphology of the HESCs in control and Atg14 siRNA group showed no obvious difference even at day 3 and day 6. The author should point out the difference in each panel and explain in the text or figure legend.

(5) In lines 332-336, the authors pointed out that the cKO mice oviduct lining shows marked eosinophilic cytoplasmic change, but there's no data to support the claim. In addition, the authors further described that "some of the cells showed degenerative changes with cytoplasmic vacuolization and nuclear pyknosis, loss of nuclear polarity, and loss of distinct cell borders giving an appearance of fusion of cells (Fig. 3D)". First, Figure 3D did not show all these phenotypes and it is likely a mismatch to Figure 3E. Even in Figure 3E, it is not obvious to notice all the phenotypes described here. The figure legend is overly simple, and there's no explanation of the arrowheads in the panel. More data/images are required to support the claim here and provide a clear indication and explanation in the figure legend.

(6) In lines 317-325, it is rather confusing about the description of the portion of embryos from the oviduct and uterus. In addition, the total number of embryos was not provided. I would recommend presenting the numerical data to show the average embryos from the oviduct and uterus instead of using the percentage data in Figures 3A and 5G.

(7) In lines 389-391, authors tested whether Polyphyllin VI treatment led to activated pyroptosis and blocked embryo transport. Although Figures 5F-G showed the expected embryo transport defect, the authors did not show the pyroptosis and oviduct morphology. It will be important to show that the Polyphyllin VI treatment indeed led to oviduct pyroptosis and lumen disruption.

(8) In line 378, it would be better to include a description of pyroptosis and its molecular mechanisms to help readers to better understand your experiments. Alternatively, you can add it in the introduction.

(9) Please make sure to provide definitions for the acronyms such as FRT, HESCs, GSDMD, etc.

(10) It is rather confusing to use oviducal cell plasticity in this manuscript. The work illustrated the oviducal epithelial integrity, not the plasticity.

Reviewer #3 (Public Review):

Summary:

The manuscript by Pooja Popli and co-authors tested the importance of Atg14 in the female reproductive tract by conditionally deleting Atg14 using PrCre and also Foxj1cre. The authors showed that loss of Atg14 leads to infertility due to the retention of embryos within the oviduct. The authors further concluded that the retention of embryos within the oviduct is due to pyroptosis in oviduct cells leading to defective cellular integrity. The manuscript has some interesting findings, however there are also areas that could be improved.

Strengths:

The importance of Atg14 and autophagy in the female reproductive tract is incompletely understood. The manuscript also provides partial evidence about a new mechanism linking Atg14 to pyropotosis.

Weaknesses:

(1) It is not clear why the loss of Atg14 selectively induces Pyroptosis within oviduct cells but not in other cellular compartments. The authors should demonstrate that these events are not happening in uterine cells.

(2) The manuscript never showed any effect on the autophagy upon loss of Atg14. Is there any effect on autophagy upon Atg14 loss? If so does that contribute to the observation?

(3) It is not clear what the authors meant by cellular plasticity and integrity. There is no evidence provided in that aspect that the plasticity of oviduct cells is lost. Similarly, more experimental evidence is necessary for the conclusion about cellular integrity.

(4) The mitochondrial phenotype shown in Figure 3 didn't appear as severe as it is described in the results section. The analyses should be more thorough. They should include multiple frames (in supplemental information) showing mitochondrial morphology in multiple cells. The authors should also test that aspect in uterine cells. The authors should measure Feret's diagram. Difference in membrane potential etc. for a definitive conclusion.

(5) The comment that the loss of Atg14 and pyroptosis leads to the narrowing of the lumen in the oviduct should be experimentally shown.

(6) The manuscript never showed the proper mechanism through which Atg14 loss induces pyroptosis. The authors should link the mechanism.

Reviewer #1 (Public Review):

This paper discusses the identification of viral genes in publicly available DNA and RNA sequencing datasets. In many cases, these datasets have been assembled into contigs. Many viral genes were identified and contigs containing genes from more than one type of virus were more common than expected. The analysis appears to be sound and the results presented will be of great interest to the community.

The strengths of the paper are in the analysis itself, which is detailed, complex, and on a very large scale. To my knowledge, the identification of DNA viral proteins in sequencing datasets not deliberately infected with viruses has not previously been performed on this scale. Many proteins were identified which are at the limit of our current capacity to detect divergent proteins. I think the use of multiple methodologies strengthens the study, as it increases the depth of the results. The authors are also clear about the limitations of their study and give many caveats about their results, which is excellent.

I have two major concerns about the study. The first is the presentation, which in places makes it difficult to tell exactly how and why the analysis has been performed. I do not think it would be possible to reproduce this analysis based only on the information presented in the Materials and Methods section. This makes it difficult to assess the exact details of the method and whether they are appropriate. I would appreciate something like a flow chart to show, for each SRA dataset and each assembled contig, the exact steps taken for classification and the hierarchy of tools, plus the threshold values, applied to the results. An overview of the results at the beginning of the results section would also be helpful - how many proteins were identified, what were their host species, how many contigs were assembled and how many of these were chimeric, etc.

My second concern is that it is not clear how each protein was determined to be either viral or non-viral or how contigs were assigned as chimeric or non-chimeric. Positive and negative controls are not mentioned and false positive or negative rates are not calculated. Given that many of the identified proteins are highly divergent from known viral proteins, it would be good to see how likely it is that a random protein would be assigned as viral, or a viral protein as non-viral. Chimeric contigs could occur due to misassembly or endogenous viral elements, it seems like viruses in these categories may have been filtered using Cenote Taker but no checks are described to confirm that the filtering was successful.

Overall, I think that the study is useful and of interest, but I think more clarity in the presentation of the results would increase the value of the paper for many readers.

Reviewer #2 (Public Review):

Summary:

A large-scale computational analysis of published sequences of various animal species provides evidence for extensive gene transfer amongst DNA viruses.

Strengths:

The study provides evidence for a large number of previously uncharacterized DNA viruses and supports a model whereby DNA viruses have evolved by combining distinct shared replication modules and some of these evolutionary oddities likely remain in the biosphere. The work provides a useful repository and potential framework for additional virus discovery efforts.

Weaknesses:

This is an entirely computational story, with very limited experimental validation. A large number of often confusing new acronyms are introduced that may be "cute" (such as the reference to the delicious half-smoke sausage) but are not particularly useful. This is not helped by the somewhat "telegraphic" presentation of the data that is sometimes difficult to digest. Not all paragraphs deliver what they promise. For example under the title "Polyomaviruses and papillomaviruses" there is no discussion of papillomaviruses. Overall, however, these weaknesses do not diminish my enthusiasm for this paper, which will be an important resource for computational and non-computational virus hunters.

Reviewer #3 (Public Review):

Summary:

Buck et al., set out to characterize small DNA tumor viruses through the generation and analysis of ~100,000 public sequencing datasets from the SRA and other databases. Using a variety of powerful bioinformatic methods including alignment-based searches, statistical modelling, and structure-aware detection, the authors successfully classify novel protein sequences which support the occurrence of evolutionary gene transfer between DNA virus families. The authors propose a naming scheme to better capture viral diversity and uncover novel chimeric viruses, those containing genes from multiple established virus families. Additional analysis using the generated dataset was performed to search for DNA and RNA viruses of interest, demonstrating the utility of generated datasets for exploratory screens. The assembled sequencing datasets are publicly available, providing invaluable resources for current and future investigations within this subfield.

Strengths:

The scope of data analysis (100,000+ SRA records and additional libraries) is substantial, and the authors have contributed to further insight into the modularity of previously uncharacterized viral genomes, through computationally demanding advanced bioinformatics analyses in addition to extensive manual inspection.

The publicly available resources generated as a result of these analyses provide useful data for further experiments to inspect viral diversity and modularity. Other scanning experiments and further investigation of biologically relevant viruses using these contigs may uncover, for example, animal reservoirs or novel recombinant viruses of significance.

Novel instances of genomic modularity provide excellent starting points for understanding virus evolutionary pathways and gene transfer events.

Weaknesses:

Overall, the methods section of this paper requires more detail.

The inclusion criteria for which "SRA" datasets were or were not utilized within this study are poorly defined. This means the comprehensiveness of the study for a given search space of the SRA is not defined, and the results are ultimately not reproducible, or expandable. For example, are all vertebrate RNA-seq samples processed? Or just aquatic vertebrate RNA-seq? Were samples randomly sampled from a more comprehensive data set? What is the make-up of the search space and how much was DNA-seq or RNA-seq? This section should be expanded and explicit accounting provided for how dataset selection was performed. This would provide additional confidence in the results and conclusions, as well as allow for future analysis to be conducted.

Hallmark virus genes require further clarification, as it is unclear what genes are utilized as bait, or in the initial search process. The reported "Hallmark gene sets" are not described in a systematic way. What is the sensitivity and specificity of these gene sets? Was there a validation of the performance characteristics (ROC) for this gene set with different tools? How is this expected to be utilized? Which kinds of viruses are excluded/missed? Are viroids included?

For the Tailtomavirus, additional information is needed for sufficient confidence. Was this "chimeric" genomic arrangement detected in a single library? This raises a greater issue of how technical artifacts, which may appear as chimeric assemblies, are ruled out in the workflow. If two viral genomes share a k-mer of length greater than the assembly k, the graph may become merged. Are there read pairs that span all regions of the genome? Is there evidence for multiple homologous viruses with synteny between them that supports the combination of these genes as an evolving genome, or is this an anomalous observation? Read alignments should be included and Bandage graph visualization for all cases of chimeric assemblies and active steps to disprove the baseline hypotheses that these are technical artifacts of genome assembly.

Justification for exclusion of endogenized sequences is not included and must be described, as small DNA tumor viruses may endogenize into the host genome as part of their life cycle. How is such an integration resolved from an evolutionary "endogenization"? What's the biological justification for this step?

Additional supporting information, clear presentation, and context are needed to strengthen results and conclusions.

Basic reporting of global statistics, such as the total number of viruses found per family, should be included in the main text to better support the scope of the results. How many viruses (per family) were previously known, and therefore what is the magnitude of the expansion performed here?

Additional parameters and information should be included in bioinformatic tool outputs to provide greater clarity and interpretation of results. For example, reporting the "BLASTp E-val", as for the PolB homology (BLASTp 6E-12) is not informative, and does not tell the reader this is (we assume) an expectancy value. For each such case please report, the top database hit accession, percent identity, query coverage, and E-value. Otherwise, a judgment cannot be adequately made regarding the quality of evidence for homology. Similarly, for HHpred what does the number represent - confidence, identity, or coverage?

Some findings described in the Results section may require revision. Several of the Nidoviruses (Nidovirus takifugu, Nidovirus hypomesus, Nidovirus ambystoma, etc...) have been previously described by three groups, first by Edgar et al., (https://www.nature.com/articles/s41586-021-04332-2), then Miller et al., (https://academic.oup.com/ve/article/7/2/veab050/6290018) and then Lauber et al., (https://journals.plos.org/plospathogens/article?id=10.1371/journal.ppat.1012163). This is now the 4th description of the same set of viruses. These sequences are in GenBank (https://www.ncbi.nlm.nih.gov/nuccore/OV442424.1), although it is unclear why they're not returned as BLAST hits. Miller also described the Togavirus co-segment previously.

It is also uncertain what is being described with HelPol/maldviruses which was not previously described in distantly similar relatives. How many were described in the previous literature and how many are described by this work?

Co-phylogenies should be used to convey gene transfer and flow clearly to support the conclusions made in the text.

Statements such as, "The group encompasses a surprising degree of genomic diversity...", should be supported by additional information to strengthen conclusions (e.g., what the expected diversity is). What is the measurement for genomic diversity here, and why is this surprising? There is overall a lack of quantification to support the conclusions made throughout the paper.

Reviewer #1 (Public Review):

Summary of the work: In this work, Fruchard et. al. study the enzyme Tgt and how it modifies guanine in tRNAs to queuosine (Q), essential for Vibrio cholerae's growth under aminoglycoside stress. Q's role in codon decoding efficiency and its proteomic effects during antibiotic exposure is examined, revealing Q modification impacts tyrosine codon decoding and influences RsxA translation, affecting the SoxR oxidative stress response. The research proposes Q modification's regulation under environmental cues reprograms the translation of genes with tyrosine codon bias, including DNA repair factors, crucial for bacterial antibiotic response.

The experiments are well-designed and conducted and the conclusions, for the most part, are well supported by the data. However, a few clarifications will significantly strengthen the manuscript.

Major:<br /> Figure S4 A-D. These growth curves are important data and should be presented in the main figures. Moreover, given that it is not possible to make a rsxA mutant, I wonder if it would be possible to connect rsx and tgt using the following experiment: expression of tgt results in resistance to TOB (in B), while expression of only rsx lower resistance to TOB (in D). Then simultaneous overexpression of both tgt/rsx in the WT strain should have either no effect on TOB resistance or increased resistance, relative to the WT. Perhaps the authors have done this, and if so, the data should be included as it will significantly strengthen their model.

Figure S4 - Is there a rationale for why it is possible to make rsx mutants in E. coli, but not in V. cholerae? For example, does E. coli have a second gene/protein that is redundant in function to rsxA, while V. cholerae does not? I think your data hint at this, since in the right panel growth data, your double mutant does not fully rescue back to rsx single mutant levels, suggesting another factor in tgt mutant also acts to lower resistance to TOB. If so, perhaps a line or two in text will be helpful for readers.

-For growth curves in Figure 2 and relative comparisons like in Figure 5D and Figure S4 (and others in the paper), statistics and error bars, along with replicate information should be provided.

-Figure 6A - Is the transcript fold change in linear or log? If linear, then tgt expression should not be classified as being upregulated in TOB. It is barely up by ~2-fold with TOB- 0.6....which is a mild phenotype, at best.

-Line 779- 780: "This indicates that sub-MIC TOB possibly induces tgt expression through the stringent response activation." To me, the data presented in this figure, do not support this statement. The experiment is indirect.

-Figure 3B and D. - These samples only have tobramycin, correct? The legend says both carbenicillin and tobramycin.

-Figure 5. The color schemes in bars do not match up with the color scheme in cartoons below panels B and C. That makes it confusing to read. Please fix.

-A lot of abbreviations have been used. This makes reading a bit cumbersome. Ideally, less abbreviations will be used.

Reviewer #2 (Public Review):

Fruchard et al. investigate the role of the queuosine (Q) modification of the tRNA (Q-tRNA) in the human pathogen Vibrio cholerae. First, the authors state that the absence of Q-modified tRNAs (tgt mutant) increases the translation of TAT codons and proteins with a high TAT codon bias. Second, the absence of Q increases rsxA translation, because rsxA gene has a high TAT codon bias. Third, increased RsxA in the absence of Q inhibits SoxR response, reducing resistance towards the antibiotic tobramycin (TOB). Authors also predict in silico which genes harbor a higher TAT bias and found that among them are some involved in DNA repair, experimentally observing that a tgt mutant is more resistant to UV than the wt strain. It is worth noting that authors employ a wide variety of techniques, both experimental and bioinformatic. However, some aspects of the work need to be clarified or reevaluated.

(1) The statement that the absence of Q increases the translation of TAT codons and proteins encoded by TAT-enriched genes presents the following problems that should be addressed:

(1.1) The increase in TAT codon translation in the absence of Q is not supported by proteomics, since there was no detected statistical difference for TAT codon usage in proteins differentially expressed. Furthermore, there are some problems regarding the statistics of proteomics. Some proteins shown in Table S1 have adjusted p-values higher than their p-values, which makes no sense. Maybe there is a mistake in the adjusted p-value calculation. In addition, it is not common to assume that proteins that are quantitatively present in one condition and absent in another are differentially abundant proteins. Proteomics data software typically addresses this issue and applies some corrections. It would be advisable to review that.

(1.2) Problems with the interpretation of Ribo-seq data (Figure 4D). On the one hand, the Ribo-seq data should be corrected (normalized) with the RNA-seq data in each of the conditions to obtain ribosome profiling data, since some genes could have more transcription in some of the conditions studied. In other articles in which this technique is used (such as in Tuorto et al., EMBO J. 2018; doi: 10.15252/embj.201899777), it is interpreted that those positions in which the ribosome moves most slowly and therefore less efficiently translated), are the most abundant. Assuming this interpretation, according to the hypothesis proposed in this work, the fragments enriched in TAT codons should have been less abundant in the absence of Q-tRNA (tgt mutant) in the Rib-seq experiment. However, what is observed is that TAT-enriched fragments are more abundant in the tgt mutant, and yet the Ribo-seq results are interpreted as RNA-seq, stating that this is because the genes corresponding to those sequences have greater expression in the absence of Q. On the other hand, it would be interesting to calculate the mean of the protein levels encoded by the transcripts with high and low ribosome profiling data.

(1.3) This statement is contrary to most previously reported studies on this topic in eukaryotes and bacteria, in which ribosome profiling experiments, among others, indicate that translation of TAT codons is slower (or unaffected) than translation of the TAC codons, and the same phenomenon is observed for the rest of the NAC/T codons. This is completely opposed to the results showed in Figure 4. However, the results of these studies are either not mentioned or not discussed in this work. Some examples of articles that should be discussed in this work:<br /> - "Queuosine-modified tRNAs confer nutritional control of protein translation" (Tuorto et al., 2018; 10.15252/embj.201899777)<br /> - "Preferential import of queuosine-modified tRNAs into Trypanosoma brucei mitochondrion is critical for organellar protein synthesis" (Kulkarni et al., 2021; doi:10.1093/nar/gkab567.<br /> - "Queuosine-tRNA promotes sex-dependent learning and memory formation by maintaining codon-biased translation elongation speed" (Cirzi et al., 2023; 10.15252/embj.2022112507)<br /> - "Glycosylated queuosines in tRNAs optimize translational rate and post-embryonic growth" (Zhao et al., 2023; 10.1016/j.cell.2023.10.026)<br /> - "tRNA queuosine modification is involved in biofilm formation and virulence in bacteria" (Diaz-Rullo and Gonzalez-Pastor, 2023; doi: 10.1093/nar/gkad667). In this work, the authors indicate that Q-tRNA increases NAT codon translation in most bacterial species. Could the regulation of TAT codon-enriched proteins by Q-tRNAs in V. cholerae an exception? In addition, authors use a bioinformatic method to identify genes enriched in NAT codons similar to the one used in this work, and to find in which biological process are involved the genes whose expression is affected by Q-tRNAs (as discussed for the phenotype of UV resistance). It will be worth discussing all of this.

(1.4) It is proposed that the stress produced by the TOB antibiotic causes greater translation of genes enriched in TAT codons. On the one hand, it is shown that the GFP-TAT version (gene enriched in TAT codons) and the RsxA-TAT-GFP protein (native gene naturally enriched in TAT) are expressed more, compared to their versions enriched in TAC in a tgt mutant than in a wt, in the presence of TBO (Fig. 5C). However, in the absence of TOB, and in a wt context, although the two versions of GFP have a similar expression level (Fig. 3SD), the same does not occur with RsxA, whose RsxA-TAT form (the native one) is expressed significantly more than the RsxA-TAC version (Fig. 3SA). How can it be explained that in a wt context, in which there are also tRNA Q-modification, a gene naturally enriched in TAT is translated better than the same gene enriched in TAC? It would be expected that in the presence of Q-tRNAs the two versions would be translated equally (as happens with GFP) or even the TAT version would be less translated. On the other hand, in the presence of TOB the fluorescence of WT GFP(TAT) is higher than the fluorescence of WT GFP(TAC) (Figure S3E) (mean fluorescence data for RsxA-GFP version in the presence of TOB is not shown). These results may indicate that the apparent better translation of TAT versions could be due to indirect effects rather from TAT codon translation.

(2) Another problem is related to the already known role of Q in prevention of stop codon readthrough, which is not discuss at all in the work. In the absence of Q, stop codon readthrough is increased. In addition, it is known that aminoglycosides (such as tobramycin) also increase stop codon readthrough ("Stop codon context influences genome-wide stimulation of termination codon readthrough by aminoglycosides"; Wanger and Green, 2023; 10.7554/eLife.52611). Absence of Q and presence of aminoglycosides can be synergic, producing devastating increases in stop codon readthrough and a large alteration of global gene expression. All of these needs to be discussed in the work. Moreover, it is known that stop codon readthrough can alter gene expression and mRNA sequence context all influence the likelihood of stop codon readthrough. Thus, this process could also affect to the expression of recoded GFP and RsxA versions.

(3) The statement about that the TOB resistance depends on RsxA translation, which is related to the presence of Q, also presents some problems:

(3.1) It is observed that the absence of tgt produces a growth defect in V. cholerae when exposed to TOB (Figure 1A), and it is stated that this is mediated by an increase in the translation of RsxA, because its gene is TAT enriched. However, in Figure S4F, it is shown that the same phenotype is observed in E. coli, but its rsxA gene is not enriched in TAT codons. Therefore, the growth defect observed in the tgt mutant in the presence of TOB may not be due to the increase in the translation of TAT codons of the rsxA gene in the absence of Q. This phenotype is very interesting, but it may be related to another molecular process regulated by Q. Maybe the role of Q in preventing stop codon readthrough is important in this process, reducing cellular stress in the presence of TOB and growing better.

(3.2) All experiments related to the effect of Q on the translation of TAT codons have been performed with the tgt mutant strain. Considering that the authors have a pSEVA-tgt plasmid to overexpress this gene, they would have to show whether tgt overexpression in a wt strain produces a decrease in the translation of proteins encoded by TAT-enriched genes such as RsxA. This experiment would allow them to conclude that Q reduces RsxA levels, increasing resistance to TOB.

(3.3) On the other hand, Fig. 1B shows that when the wt and tgt strains compete, both overexpressing tgt, the tgt mutant strain grows better in the presence of TOB. This result is not very well understood, since according to the hypothesis proposed, the absence of modification by Q of the tRNA would increase the translation of genes enriched in TAT, therefore, a strain with a higher proportion of Q-modified tRNAs as in the case of the wt strain overexpressing tgt would express the rsxA gene less than the tgt strain overexpressing tgt and would therefore grow better in the presence of TOB. For all these reasons, it would be necessary to evaluate the effect of tgt overexpression on the translation of RsxA.

(3.4) According to Figure 1I, the overexpression of tRNA-Tyr(GUA) caused a better growth of tgt mutant in comparison to WT. If the growth defect observed in tgt mutant in the presence of TOB is due to a better translation of the TAT codons of rsxA gene, the overexpression of tRNA-Tyr(GUA) in the tgt mutant should have resulted in even better RsxA translation a worse growth, but not the opposite result.

(4) It cannot be stated that DNA repair is more efficient in the tgt mutant of V. cholerae, as indicated in the text of the article and in Fig 7. The authors only observe that the tgt mutant is more resistant to UV radiation and it is suggested that the reason may be TAT bias of DNA repair genes. To validate the hypothesis that UV resistance is increased because DNA repair genes are TAT biased, it would be necessary to check if DNA repair is affected by Q. UV not only produces DNA damage, but also oxidative stress. Therefore, maybe this phenotype is due to the increase in proteins related to oxidative stress controlled by RsxA, such as the superoxide dismutase encoded by sodA. It is also stated that these repair genes were found up for the tgt mutant in the Ribo-seq data, with unchanged transcription levels. Again, it is necessary to clarify this interpretation of the Ribo-seq data, since the fact that they are more represented in a tgt mutant perhaps means that translation is slower in those transcripts. Has it been observed in proteomics (wt vs tgt in the absence of TOB) whether these proteins involved in repair are more expressed in a tgt mutant?

(5) The authors demonstrate that in E. coli the tgt mutant does not show greater resistance to UV radiation (Fig. 7D), unlike what happens in V. cholerae. It should be discussed that in previous works it has been observed that overexpression in E. coli of the tgt gene or the queF gene (Q biosynthesis) is involved in greater resistance to UV radiation (Morgante et al., Environ Microbiol, 2015 doi: 10.1111/1462-2920.12505; and Díaz-Rullo et al., Front Microbiol. 2021 doi: 10.3389/fmicb.2021.723874). As an explanation, it was proposed (Diaz-Rullo and Gonzalez-Pastor, NAR 2023 doi: 10.1093/nar/gkad667) that the observed increase in the capacity to form biofilms in strains that overexpress genes related to Q modification of tRNA would be related to this greater resistance to UV radiation.

Reviewer #3 (Public Review):

Summary:

In this manuscript the authors begin with the interesting phenotype of sub-inhibitory concentrations of the aminoglycoside tobramycin proving toxic to a knockout of the tRNA-guanine transglycosylase (Tgt) of the important human pathogen, Vibrio cholerae. Tgt is important for incorporating queuosine (Q) in place of guanosine at the wobble position of GUN codons. The authors go on to define a mechanism of action where environmental stressors control expression of tgt to control translational decoding of particularly tyrosine codons, skewing the balance from TAC towards TAT decoding in the absence of the enzyme. The authors use advanced proteomics and ribosome profiling to reveal that the loss of tgt results in increased translation of proteins like RsxA and a cohort of DNA repair factors, whose genes harbor an excess of TAT codons in many cases. These findings are bolstered by a series of molecular reporters, mass spectrometry, and tRNA overexpression strains to provide support for a model where Tgt serves as a molecular pivot point to reprogram translational output in response to stress.

Strengths:

The manuscript has many strengths. The authors use a variety of strains, assays, and advanced techniques to discover a mechanism of action for Tgt in mediating tolerance to sub-inhibitory concentrations of tobramycin. They observe a clear phenotype for a tRNA modification in facilitating reprogramming of the translational response, and the manuscript certainly has value in defining how microbes tolerate antibiotics.

Weaknesses:

The conclusions of the manuscript are mostly very well-supported by the data, but in some places control experiments or peripheral findings cloud precise conclusions. Some additional clarification, discussion, or even experimental extension could be useful in strengthening these areas.

(1) The authors have created and used a variety of relevant molecular tools. In some cases, using these tools in additional assays as controls would be helpful. For example, testing for compensation of the observed phenotypes by overexpression of the Tyrosine tRNA(GUA) in Figure 2A with the 6xTAT strain, Figure 5C with the rxsA-GFP fusion, and/or Figure 7B with UV stress would provide additional information of the ability of tRNA overexpression to compensate for the defect in these situations.<br /> (2) The authors present a clear story with a reprogramming towards TAT codons in the knockout strain, particularly regarding tobramycin treatment. The control experiments often hint at other codons also contributing to the observed phenotypes (e.g., His or Asp), yet these effects are mostly ignored in the discussion. It would be helpful to discuss these findings at a minimum in the discussion section, or possibly experimentally address the role of His or Asp by overexpression of these tRNAs together with Tyrosine tRNA(GUA) in an experiment like that of Figure 1I to see if a more "wild type" phenotype would present. In fact, the synergy of Tyr, His, and/or Asp codons likely helps to explain the effects observed with the DNA repair genes in later experiments.<br /> (3) Regarding Figure 6D, the APB northern blot feels like an afterthought. It was loaded with different amounts of RNA as input and some samples are repeated three times, but Δcrp only once. Collectively, it makes this experiment very difficult to assess.

Minor Points:<br /> (4) Fig S2B, do the authors have a hypothesis why the Asp and Phe tRNAs lead to a growth decrease in the untreated samples? It appears like Phe(GAA) partially compensates for the defect.<br /> (5) Lines 655 to 660 seem more appropriate as speculation in the discussion rather than as a conclusion in the results, where no direct experiments are performed. The authors might take advantage of the "Ideas and Speculation" section that eLife allows.

Reviewer #1 (Public Review):

Summary:

In this paper, the researchers aimed to address whether bees causally understand string-pulling through a series of experiments. I first briefly summarize what they did:

- In experiment 1, the researchers trained bees without string and then presented them with flowers in the test phase that either had connected or disconnected strings, to determine what their preference was without any training. Bees did not show any preference.

- In experiment 2, bees were trained to have experience with string and then tested on their choice between connected vs. disconnected string.

- experiment 3 was similar except that instead of having one option which was an attached string broken in the middle, the string was completely disconnected from the flower.

- In experiment 4, bees were trained on green strings and tested on white strings to determine if they generalize across color.

- In experiment 5, bees were trained on blue strings and tested on white strings.

- In experiment 6, bees were trained where black tape covered the area between the string and the flower (i.e. so they would not be able to see/ learn whether it was connected or disconnected).

- In experiments 2-6, bees chose the connected string in the test phase.

- In experiment 7, bees were trained as in experiment 3 and then tested where the string was either disconnected or coiled i.e. still being 'functional' but appearing different.

- In experiment 8, bees were trained as before and then tested on a string that was in a different coiled orientation, either connected or disconnected.

- In experiments 7 and 8 the bees showed no preference.

Strengths:

I appreciate the amount of work that has gone into this study and think it contains a nice, thorough set of experiments. I enjoyed reading the paper and felt that overall it was well-written and clear. I think experiment 1 shows that bees do not have an untrained understanding of the function of the string in this context. The rest of the experiments indicate that with training, bees have a preference for unbroken over broken string and likely use visual cues learned during training to make this choice. They also show that as in other contexts, bees readily generalize across different colors.

Weaknesses:

(1) I think there are 2 key pieces of information that can be taken from the test phase - the bees' first choice and then their behavior across the whole test. I think the first choice is critical in terms of what the bee has learned from the training phase - then their behavior from this point is informed by the feedback they obtain during the test phase. I think both pieces of information are worth considering, but their behavior across the entire test phase is giving different information than their first choice, and this distinction could be made more explicit.

In addition, while the bees' first choice is reported, no statistics are presented for their preferences.

(2) It seemed to me that the bees might not only be using visual feedback but also motor feedback. This would not explain their behavior in the first test choice, but could explain some of their subsequent behavior. For example, bees might learn during training that there is some friction/weight associated with pulling the string, but in cases where the string is separated from the flower, this would presumably feel different to the bee in terms of the physical feedback it is receiving. I'd be interested to see some of these test videos (perhaps these could be shared as supplementary material, in addition to the training videos already uploaded), to see what the bees' behavior looks like after they attempt to pull a disconnected string.

(3) I think the statistics section needs to be made clearer (more in private comments).

(4) I think the paper would be made stronger by considering the natural context in which the bee performs this behavior. Bees manipulate flowers in all kinds of contexts and scrabble with their legs to achieve nectar rewards. Rather than thinking that it is pulling a string, my guess would be that the bee learns that a particular motor pattern within their usual foraging repertoire (scrabbling with legs), leads to a reward. I don't think this makes the behavior any less interesting - in fact, I think considering the behavior through an ecological lens can help make better sense of it.

Reviewer #2 (Public Review):

Summary:

The authors wanted to see if bumblebees could succeed in the string-pulling paradigm with broken strings. They found that bumblebees can learn to pull strings and that they have a preference to pull on intact strings vs broken ones. The authors conclude that bumblebees use image matching to complete the string-pulling task.

Strengths:

The study has an excellent experimental design and contributes to our understanding of what information bumblebees use to solve a string-pulling task.

Weaknesses:

Overall, I think the manuscript is good, but it is missing some context. Why do bumblebees rely on image matching rather than causal reasoning? Could it have something to do with their ecology? And how is the task relevant for bumblebees in the wild? Does the test translate to any real-life situations? Is pulling a natural behaviour that bees do? Does image matching have adaptive significance?

Reviewer #3 (Public Review):

Summary:

This paper presents bees with varying levels of experience with a choice task where bees have to choose to pull either a connected or unconnected string, each attached to a yellow flower containing sugar water. Bees without experience of string pulling did not choose the connected string above chance (experiment 1), but with experience of horizontal string pulling (as in the right-hand panel of Figure 4) bees did choose the connected string above chance (experiments 2-3), even when the string colour changed between training and test (experiments 4-5). Bees that were not provided with perceptual-motor feedback (i.e they could not observe that each pull of the string moved the flower) during training still learned to string pull and then chose the connected string option above chance (experiment 6). Bees with normal experience of string pulling then failed to discriminate between connected and unconnected strings when the strings were coiled or looped, rather than presented straight (experiments 7-8).

Weaknesses:

The authors have only provided video of some of the conditions where the bees succeeded. In general, I think a video explaining each condition and then showing a clip of a typical performance would make it much easier to follow the study designs for scholars. Videos of the conditions bees failed at would be highly useful in order to compare different hypotheses for how the bees are solving this problem. I also think it is highly important to code the videos for switching behaviours. When solving the connected vs unconnected string tasks, when bees were observed pulling the unconnected string, did they quickly switch to the other string? Or did they continue to pull the wrong string? This would help discriminate the use of perceptual-motor feedback from other hypotheses.

The experiments are also not described well, for my below comments I have assumed that different groups of bees were tested for experiments 1-8, and that experiment 6 was run as described in line 331, where bees were given string-pulling training without perceptual feedback rather than how it is described in Figure 4B, which describes bees as receiving string pulling training with feedback.

The authors suggest the bees' performance is best explained by what they term 'image matching'. However, experiment 6 does not seem to support this without assuming retroactive image matching after the problem is solved. The logic of experiment 6 is described as "This was to ensure that the bees could not see the familiar "lollipop shape" while pulling strings....If the bees prefer to pull the connected strings, this would indicate that bees memorize the arrangement of strings-connected flowers in this task." I disagree with this second sentence, removing perceptual feedback during training would prevent bees memorising the lollipop shape, because, while solving the task, they don't actually see a string connected to a yellow flower, due to the black barrier. At the end of the task, the string is now behind the bee, so unless the bee is turning around and encoding this object retrospectively as the image to match, it seems hard to imagine how the bee learns the lollipop shape.

Despite this, the authors go on to describe image matching as one of their main findings. For this claim, I would suggest the authors run another experiment, identical to experiment 6 but with a black panel behind the bee, such that the string the bee pulls behind itself disappears from view. There is now no image to match at any point from the bee's perspective so it should now fail the connectivity task.

Strengths:

Despite these issues, this is a fascinating dataset. Experiments 1 and 2 show that the bees are not learning to discriminate between connected and unconnected stimuli rapidly in the first trials of the test. Instead, it is clear that experience in string pulling is needed to discriminate between connected and unconnected strings. What aspect of this experience is important? Experiment 6 suggests it is not image matching (when no image is provided during problem-solving, but only afterward, bees still attend to string connectivity) and casts doubt on perceptual-motor feedback (unless from the bee's perspective, they do actually get feedback that pulling the string moves the flower, video is needed here). Experiments 7 and 8 rule out means-end understanding because if the bees are capable of imagining the effect of their actions on the string and then planning out their actions (as hypotheses such as insight, means-end understanding and string connectivity suggest), they should solve these tasks.

If the authors can compare the bees' performance in a more detailed way to other species, and run the experiment suggested, this will be a highly exciting paper

Reviewer #1 (Public Review):

Syngnathid fishes (seahorses, pipefishes, and seadragons) present very particular and elaborated features among teleosts and a major challenge is to understand the cellular and molecular mechanisms that permitted such innovations and adaptations. The study provides a valuable new resource to investigate the morphogenetic basis of four main traits characterizing syngnathids, including the elongated snout, toothlessness, dermal armor, and male pregnancy. More particularly, the authors have focused on a late stage of pipefish organogenesis to perform single-cell RNA-sequencing (scRNA-seq) completed by in situ hybridization analyses to identify molecular pathways implicated in the formation of the different specific traits.

The first set of data explores the scRNA-seq atlas composed of 35,785 cells from two samples of gulf pipefish embryos that authors have been able to classify into major cell types characterizing vertebrate organogenesis, including epithelial, connective, neural, and muscle progenitors. To affirm identities and discover potential properties of clusters, authors primarily use KEGG analysis that reveals enriched genetic pathways in each cell types. While the analysis is informative and could be useful for the community, some interpretations appear superficial and data must be completed to confirm identities and properties. Notably, supplementary information should be provided to show quality control data corresponding to the final cell atlas including the UMAP showing the sample source of the cells, violin plots of gene count, UMI count, and mitochondrial fraction for the overall dataset and by cluster, and expression profiles on UMAP of selected markers characterizing cluster identities.

The second set of data aims to correlate the scRNA-seq analysis with in situ hybridizations (ISH) in two different pipefish (gulf and bay) species to identify and characterize markers spatially, and validate cell types and signaling pathways active in them. While the approach is rational, the authors must complete the data and optimize labeling protocols to support their statements. One major concern is the quality of ISH stainings and images; embryos show a high degree of pigmentation that could hide part of the expression profile, and only subparts and hardly detectable tissues/stainings are presented. The authors should provide clear and good-quality images of ISH labeling on whole-mount specimens, highlighting the magnification regions and all other organs/structures (positive controls) expressing the marker of interest along the axis. Moreover, ISH probes have been designed and produced on gulf pipefish genome and cDNA respectively, while ISH labeling has been performed indifferently on bay or gulf pipefish embryos and larvae. The authors should specify stages and species on figure panels and should ensure sequence alignment of the probe-targeted sequences in the two species to validate ISH stainings in the bay pipefish. Moreover, spatiotemporal gene expression being a very dynamic process during embryogenesis, interpretations based on undefined embryonic and larval stages of pipefish development and compared to 3dpf zebrafish are insufficient to hypothesize on developmental specificities of pipefish features, such as on the absence of tooth primordia that could represent a very discrete and transient cell population. The ISH analyses would require a clean and precise spatiotemporal expression comparison of markers at the level of the entire pipefish and zebrafish specimens at well-defined stages, otherwise, the arguments proposed on teleost innovations and adaptations turn out to be very speculative.

To conclude, whereas the scRNA-seq dataset in this unconventional model organism will be useful for the community, the spatiotemporal and comparative expression analyses have to be thoroughly pushed forward to support the claims. Addressing these points is absolutely necessary to validate the data and to give new insights to understand the extraordinary evolution of the Syngnathidae family.

Reviewer #2 (Public Review):

Summary:

The authors present the first single-cell atlas for syngathid fishes, providing a resource for future evolution & development studies in this group.

Strengths:

The concept here is simple and I find the manuscript to be well written. I like the in situ hybridization of marker genes - this is really nice. I also appreciate the gene co-expression analysis to identify modules of expression. There are no explicit hypotheses tested in the manuscript, but the discovery of these cell types should have value in this organism and in the determination of morphological novelties in seahorses and their relatives.

Weaknesses:

I think there are a few computational analyses that might improve the generality of the results.

(1) The cell types: The authors use marker gene analysis and KEGG pathways to identify cell types. I'd suggest a tool like SAMap (https://elifesciences.org/articles/66747) which compares single-cell data sets from distinct organisms to identify 'homologous' cell types -- I imagine the zebrafish developmental atlases could serve as a reasonable comparative reference.

(2) Trajectory analyses: The authors suggest that their analyses might identify progenitor cell states and perhaps related differentiated states. They might explore cytoTRACE and/or pseudotime-based trajectory analyses to more fully delineate these ideas.

(3) Cell-cell communication: I think it's very difficult to identify 'tooth primordium' cell types, because cell types won't be defined by an organ in this way. For instance, dental glia will cluster with other glia, and dental mesenchyme will likely cluster with other mesenchymal cell types. So the histology and ISH is most convincing in this regard. Having said this, given the known signaling interactions in the developing tooth (and in development generally) the authors might explore cell-cell communication analysis (e.g., CellChat) to identify cell types that may be interacting.

Reviewer #3 (Public Review):

Summary:

This study established a single-cell RNA sequencing atlas of pipefish embryos. The results obtained identified unique gene expression patterns for pipefish-specific characteristics, such as fgf22 in the tip of the palatoquadrate and Meckel's cartilage, broadly informing the genetic mechanisms underlying morphological novelty in teleost fishes. The data obtained are unique and novel, potentially important in understanding fish diversity. Thus, I would enthusiastically support this manuscript if the authors improve it to generate stronger and more convincing conclusions than the current forms.

Weaknesses:

Regarding the expression of sfrp1a and bmp4 dorsal to the elongating ethmoid plate and surrounding the ceratohyal: are their expression patterns spatially extended or broader compared to the pipefish ancestor? Is there a much closer species available to compare gene expression patterns with pipefish? Did the authors consider using other species closely related to pipefish for ISH? Sfrp1a and bmp4 may be expressed in the same regions of much more closely related species without face elongation. I understand that embryos of such species are not always accessible, but it is also hard to argue responsible genes for a specific phenotype by only comparing gene expression patterns between distantly related species (e.g., pipefish vs. zebrafish). Due to the same reason, I would not directly compare/argue gene expression patterns between pipefish and mice, although I should admit that mice gene expression patterns are sometimes helpful to make a hypothesis of fish evolution. Alternatively, can the authors conduct ISH in other species of pipefish? If the expression patterns of sfrp1a and bmp4 are common among fishes with face elongation, the conclusion would become more solid. If these embryos are not available, is it possible to reduce the amount of Wnt and BMP signal using Crispr/Cas, MO, or chemical inhibitor? I do think that there are several ways to test the Wnt and/or BMP hypothesis in face elongation.

Reviewer #1 (Public Review):

Summary:

Given the cost of producing action potentials and transmitting them along axons, it has always seemed a bit strange that there are synaptic failures: when a spike arrives at a synapse, about half the time nothing happens. This paper proposes a perfectly reasonable explanation: reducing failures (or, more generally, reducing noise) is costly. Four possible mechanisms are proposed, each associated with a different cost, with costs of the form 1/sigma_i^rho where sigma_i is the failure-induced variability at synapse i and rho is an exponent. The four different mechanisms produce four different values of rho.

What is interesting about the study is that the model makes experimental predictions about the relationship between learning rate, variability and presynaptic firing rate. Those predictions are consistent with experimental data, making it a strong candidate model. The fact that the predictions come from reasonable biological mechanisms make it a very strong candidate model and suggest several experiments to test it further.

Interestingly, the predictions made by this model are nearly indistinguishable from the predictions made by a normative model (Synaptic plasticity as Bayesian inference. Aitchison it al., Nature Neurosci. 24:565-571 (2021). As pointed out by the authors, working out whether the brain is using Bayesian inference to tune learning rules, or it just looks like it's Bayesian inference but the root cause is cost minimization, will be an interesting avenue for future research.

Finally, the authors relate their cost of reliability to the cost used in variational Bayesian inference. Intriguingly, the biophysical cost provides an upper bound on the variational cost. This is intellectually satisfying, as it answers a "why" question: why would evolution evolve to produce the kind of costs seen in the brain?

Strengths:

This paper provides a strong mix of theoretical analysis, simulations and comparison to experiments. And the extended appendices, which are very easy to read, provide additional mathematical insight.

Weaknesses:

None.

Reviewer #2 (Public Review):

Summary

This manuscript argues about the similarity between two frameworks describing synaptic plasticity. In the Bayesian inference perspective, due to the noise and the limited available pre- and postsynaptic information, synapses can only have an estimate of what should be their weight. The belief about those weights is described by their mean and variance. In the energy efficient perspective, synaptic parameters (individual means and variances) are adapted such that the neural network achieves some task while penalizing large mean weights as well as small weight variances. Interestingly, the authors show both numerically and analytically the strong link between those two frameworks. In particular, both frameworks predict that (a) synaptic variances should decrease when the input firing rate increases and (b) that the learning rate should increase when the weight variances increase. Both predictions have some experimental support.

Strengths

(1) Overall, the paper is very well written and the arguments are clearly presented.

(2) The tight link between the Bayesian inference perspective and the energy efficiency perspective is elegant and well supported, both with numerical simulations as well as with analytical arguments.

(3) I also particularly appreciate the derivation of the reliability cost terms as a function of the different biophysical mechanisms (calcium efflux, vesicle membrane, actin and trafficking). Independently of the proposed mapping between the Bayesian inference perspective and the energy efficiency perspective, those reliability costs (expressed as power-law relationships) will be important for further studies on synaptic energetics.

Weaknesses

(1) As recognised by the authors, the correspondence between the entropy term in the variational inference description and the reliability cost in the energetic description is strong, but not perfect. Indeed, the entropy term scales as -log(sigma) while reliability cost scales as sigma^(-rho).

(2) Even though this is not the main point of the paper, I appreciate the effort made by the authors to look for experimental data that could in principle validate the Bayesian/energetic frameworks. A stronger validation will be an interesting avenue for future research.

Reviewer #1 (Public Review):

This study conducted a series of experiments to comprehensively support the allocentric rather than egocentric visual spatial reference updating for the path-integration mechanism in the control of target-oriented locomotion. Authors firstly manipulated the waiting time before walking to tease apart the influence from spatial working memory in guiding locomotion. They demonstrated that the intrinsic bias in perceiving distance remained constant during walking and that the establishment of a new spatial layout in the brain took a relatively longer time beyond the visual-spatial working memory. In the following experiments, the authors then uncovered that the strength of the intrinsic bias in distance perception along the horizontal direction is reduced when participants' attention is distracted, implying that world-centered path integration requires attentional effort. This study also revealed horizontal-vertical asymmetry in a spatial coding scheme that bears a resemblance to the locomotion control in other animal species such as desert ants.

The revised version of the study effectively situates the research within the broader context of terrestrial navigation, focusing on the movement of land-based creatures and offers a clearer explanation for the potential neurological basis of the human brain's allocentric odometer. Previous feedback has been thoroughly considered, and additional details have been incorporated into the presentation of the results.

Reviewer #3 (Public Review):

This study investigated what kind of reference (allocentric or egocentric) frame we used for perception in darkness. This question is essential and was not addressed much before. The authors compared the perception in the walking condition with that in the stationary condition, which successfully separated the contribution of self-movement to the spatial representation. In addition, the authors also carefully manipulated the contribution of the waiting period, attentional load, vestibular input, testing task, and walking direction (forward or backward) to examine the nature of the reference frame in darkness systematically.

I am a bit confused by Figure 2b. Allocentric coordinate refers to the representation of the distance and direction of an object relative to other objects but not relative to the observer. In Figure 2, however, the authors assumed that the perceived target was located on the interception between the intrinsic bias curve and the viewing line from the NEW eye position to the target. This suggests that the perceived object depends on the observer's new location, which seems odd with the allocentric coordinate hypothesis.

According to Fig 2b, the perceived size should be left-shifted and lifted up in the walking condition compared to that in the stationary condition. However, in Figure 3C and Fig 4, the perceived size was the same height as that in the baseline condition.

Is the left-shifted perceived distance possibly reflecting a kind of compensation mechanism? Participants could not see the target's location but knew they had moved forward. Therefore, their brain automatically compensates for this self-movement when judging the location of a target. This would perfectly predict the left-shifted but not upward-shifted data in Fig 3C. A similar compensation mechanism exists for size constancy in which we tend to compensate for distance in computing object size.

According to Fig 2a, the target, perceived target, and eye should be aligned in one straight line. This means that connecting the physical targets and the corresponding perceived target results in straight lines that converge at the eye position. This seems, however, unlikely in Figure 3c.

Reviewer #2 (Public Review):

Summary:

The goal of this study is to clarify how the brain simultaneously represents item-specific temporal information and item-independent boundary information. The authors report spectral EEG data from intracranial patients performing a delayed free recall task. They perform cosine similarity analyses on principal components derived from gamma band power across stimulus duration. The authors find that similarity between items in serial position 1 (SP1) and all other within-list items decreases as a function of serial position, consistent with temporal context models. The authors find that across-list item similarity to SP1 is greatest for SP1 items relative to items from other serial positions, an effect that is greater in medial parietal lobe compared to lateral temporal cortex and hippocampus. The authors conclude that their findings suggest that perceptual boundary information is represented in medial parietal lobe. Despite a robust dataset, the methodological limitations of the study design prevent strong interpretations from being made from these data. The same-serial position across-list similarity may be driven by attentional mechanisms that are distinct from boundary information.

Strengths:

(1) The motivation of the study is strong as how both temporal contextual drift and event boundaries contribute to memory mechanisms is an important open question.

(2) The dataset of spectral EEG data from 99 intracranial patients provides the opportunity for precise spatiotemporal investigation of neural memory mechanisms.

Weaknesses:

The goal of reconciling temporal context and event boundary mechanisms is timely and would be of interest; however, an attentional account can still be used to explain the findings. This alternative account is not considered in the manuscript.

(1) The issue related to interpreting the SP1 similarity effects as reflecting boundary specific representations remains in the revised manuscript. The authors suggest that because cross-list SP1 similarity is found in recalled items that this supports the boundary interpretation. However, the effects could still be explained by variability in attention that is not specific to an event-boundary per se. As both subsequently recalled items and primacy items tend to recruit more gamma power than non-recalled and non-primacy items, recalled items will tend to have greater similarity with one another. It does not necessarily follow though that that this similarity is due to a "boundary representation."

(2) The authors partly addressed my concern regarding the comparison of recalled pairs. How did the authors account for the fact that the same participants do not contribute equally to all ROIs? If only participants who have electrodes in all ROIs are included, are the effects consistent?

Reviewer #1 (Public Review):

Summary:

This study applied pattern similarity analyses to intracranial EEG recordings to determine how neural drift is related to memory performance in a free recall task. The authors compared neural similarity within and across lists, in order to contrast signals related to contextual drift vs. the onset of event boundaries. They find that within-list neural differentiation in the lateral temporal cortex correlates with probability of word recall; in contrast, across-list pattern similarity in the medial parietal lobe correlates with recall for items near event boundaries (early-list serial positions). This primacy effect persists for the first three items of a list. Medial parietal similarity is also enhanced across lists for end-of-list items, however this effect then predicts forgetting. The authors do not find that within- or across-list pattern similarity in the hippocampus is related to recall probability.

Strengths:

The authors use a large dataset of human intracranial electrophysiological recordings, which gives them high statistical power to compare neural activity and memory across three important memory encoding regions. In so doing, the authors seek to address a timely and important question about the neural mechanisms that underlie the formation of memories for events.

The use of both within and across event pattern similarity analyses, combined with linear mixed effects modeling, is a marriage of techniques that is novel and translatable in principle to other types of data.

Weaknesses:

In several instances the paper does not address apparent inconsistencies between the prior literature and the findings. For example, the first main finding is that recalled items have more differentiated lateral temporal cortex representations within lists than not recalled items. This seems to be the opposite of the prediction from temporal context models that are used to motivate the paper-context models would predict that greater contextual similarity within a list should lead to greater memory through enhanced temporal clustering in recall. This is what El-Kalliny et al (2019) found, using a highly similar design (free recall, intracranial recordings from the lateral temporal lobe). The authors never address this contradiction in any depth in order to reconcile it with the previous literature and with the motivating theoretical model.

The way that the authors conduct the analysis of medial parietal neural similarity at boundaries leads to results that cannot be conclusively interpreted. The authors report enhanced similarity across lists for the first item in each list, which they interpret as reflecting a qualitatively distinct boundary signal. However, this finding can readily be explained by contextual drift if one assumes that whatever happens at the start of each list is similar or identical across lists (for example, a get ready prompt or reminder of instructions). In other words, this is analogous to presenting the same item at the start of every single list, in which case it is not surprising that the parietal (or any neural) representation would be similar to itself at the start of every list. So, a qualitatively unique boundary representation would not be necessary to explain this result. The authors do not include analyses to rule this out, which makes it difficult to interpret a key finding.

There is a similar absence of interpretation with respect to the previous literature for the data showing enhanced boundary-related similarity in the medial parietal cortex. The authors' interpretation seems to be that they have identified a boundary-specific signal that reflects a large and abrupt change in context, however another plausible interpretation is that enhanced similarity in the medial parietal cortex is related to a representation of a schema for the task structure that has been acquired across repeated instances.

The authors do not directly compare their model to other models that could explain how variability in neural activity predicts memory. One example is the neural fatigue hypothesis, which the authors mention, however there are no analyses or data to suggest that their data is better fit by a boundary/contextual drift mechanism as opposed to neural fatigue.

Reviewer #3 (Public Review):

Summary:

In this study, the authors analyzed data from 99 individuals with implanted electrodes who were performing a word-list recall task. Because the task involves successively encoding and then recalling 25 lists in a row, they were able to measure the similarity in neural responses for items within the same list as well as items across different lists, allowing them to test hypotheses about the impact of between-list boundaries on neural responses. They find that, in addition to slow drift in responses across items within a list and changes across lists, there is boundary-related structure in the medial parietal lobe such that early items in each list show similarity (for recalled items) and late items in each list show similarity (for not recalled items).

Strengths:

The dataset used in this paper is substantially larger than most iEEG datasets, allowing for the detection of nuanced differences between item positions and for analyses of individual differences in boundary-related responses. There are excellent visualizations of the similarity structure between items for each region, and this work connects to a growing literature on the role of event boundaries in structuring neural responses.

Weaknesses:

(1) The visualization in Fig 1B claims that the prediction of the temporal context model is that nearby items in the presented sequence should have similar representations; that is, nearby items within a list should be similar, and the end of a list should look similar to the beginning of the next list. First, it's unclear to me if this is exactly what TCM would predict for this dataset, since lists are separated by ~60 seconds of distractor and retrieval tasks, rather than simply by a brief event boundary. Second, the authors do not actually test this model of continuous similarity across lists. After examining smooth drift in the within-list analysis (Fig 2), the across-list analyses (Figs 3-5) use a model with a "list distance" regressor that predicts discrete changes between lists. The authors state that it is not possible to replace this list distance regressor with an item distance regressor (which would be a straight line in Fig 3D rather than stair-steps) because this would be too collinear with the boundary proximity regressor, but I do not understand why these regressors would be collinear at all (since the boundary proximity regressor does not systematically increase or decrease across items).

(2) There is no theoretical or quantitative justification for the specific forms of the boundary proximity models, For initial items, a model of e^(1-d) is used (with d being serial position), but it is not stated how the falloff scale of this model was selected (as opposed to e.g. e^((1-d)/2)). For final items, a different linear model of d/#items is used, which seems to have a somewhat different interpretation, since it changes at a constant rate across all items rather than only modeling items near the final boundary. Confusingly, the schematic in Fig 1B shows symmetric effects at initial and final boundaries, despite two different models being used and the authors' assertion in their response that they do not believe these processes are symmetric.

(3) It is unclear to me whether the authors believe that the observed similarity after boundaries is due to an active process in which "the medial parietal lobe uses drift-resets" to reinstate a boundary-related context, or that this similarity is simply because "the context for the first item may be the boundary itself", and therefore this effect would emerge naturally from a temporal context model that incorporates the full task structure as the "items."

Reviewer #3 (Public Review):

Summary:

The authors food-deprived male and female mice and observed a much stronger reduction of leptin levels, energy consumption in the visual cortex, and visual coding performance in males than females. This indicates a sex-specific strategy for the regulation of the energy budget in the face of low food availability.

Strengths:

This study extends a previous study demonstrating the effect of food deprivation on visual processing in males, by providing a set of clear experimental results, demonstrating the sex-specific difference. It also provides hypotheses about the strategy used by females to reduce energy budget based on the literature.

Weaknesses:

The authors do not provide evidence that females are not impacted by visually guided behaviors contrary to what was shown in males in the previous study.

Reviewer #1 (Public Review):

Padamsey et al. followed up on their previous study in which they found that male mice sacrifice visual cortex computation precision to save energy in periods of food restriction (Padamsey et al. 2021, Neuron). In the present study, the authors find that female mice show much lower levels of adaptation in response to food restriction on the level of metabolic signaling and visual cortex computation. This is an important finding for understanding sex differences in adaptation to food scarcity and also impacts the interpretation of studies employing food restriction in behavioral analyses and learning paradigms.

Strengths:

The manuscript is, in general, very clear and the conclusions are straightforward. The experiments are performed in the same conditions for males and females and the authors did not find differences in the behavioral states of male and female mice that could explain differences in energy consumption. Moreover, they show that visual cortex in both males and females does not change its baseline energy consumption in the dark, therefore the adjustment of energy budget in males only targets visual processing.

Weaknesses:

The number of experiments is insufficient to compare the effects of food restriction in males and females directly, which is discussed by the authors: to address this point they use Bayes factor analysis to provide an estimate of the likelihood that females and males indeed differ in terms of energy metabolism and sensory processing adaptions during food restriction.

Reviewer #2 (Public Review):

Summary:

Padamsey et al build up on previous significant work from the same group which demonstrated robust changes in the visual cortex in male mice from long-term (2-3 weeks) food restriction. Here, the authors extend this finding and reveal striking sex-specific differences in the way the brain responds to food restriction. The measures included the whole-body measure of serum leptin levels, and V1-specific measures of activity of key molecular players (AMPK and PPARα), gene expression patterns, ATP usage in V1, and the sharpness of visual stimulus encoding (orientation tuning). All measures supported the conclusion that the female mouse brain (unlike in males) does not change its energy usage and cortical functional properties on comparable food restriction.

While the effect of food restriction on more peripheral tissue such as muscle and bones has been well studied, this result contributes to our understanding of how the brain responds to food restriction. This result is particularly significant given that the brain consumes a large fraction of the body's energy consumption (20%), with the cortex accounting for half of that amount. The sex-specific differences found here are also relevant for studies using food restriction to investigate cortical function.

Strengths:

The study uses a wide range of approaches mentioned above which converge on the same conclusion, strengthening the core claim of the study.

Weaknesses:

Since the absence of a significant effect does not prove the absence of any changes, the study cannot claim that the female mouse brain does not change in response to food restriction. However, the authors do not make this claim. Instead, they make the well-supported claim that there is a sex-specific difference in the response of V1 to food restriction.

Reviewer #2 (Public Review):

Summary:

The study investigates whether speech and music processing involve specific or shared brain networks. Using intracranial EEG recordings from 18 epilepsy patients, it examines neural responses to speech and music. The authors found that most neural activity is shared between speech and music processing, without specific regional brain selectivity. Furthermore, domain-selective responses to speech or music are limited to frequency-specific coherent oscillations. The findings challenge the notion of anatomically distinct regions for different cognitive functions in the auditory process.

Strengths:

(1) This study uses a relatively large corpus of intracranial EEG data, which provides high spatiotemporal resolution neural recordings, allowing for more precise and dynamic analysis of brain responses. The use of continuous speech and music enhances ecological validity compared to artificial or segmented stimuli.

(2) This study uses multiple frequency bands in addition to just high-frequency activity (HFA), which has been the focus of many existing studies in the literature. This allows for a more comprehensive analysis of neural processing across the entire spectrum. The heterogeneity across different frequency bands also indicates that different frequency components of the neural activity may reflect different underlying neural computations.

(3) This study also adds empirical evidence towards distributed representation versus domain-specificity. It challenges the traditional view of highly specialized, anatomically distinct regions for different cognitive functions. Instead, the study suggests a more integrated and overlapping neural network for processing complex stimuli like speech and music.

Weaknesses:

While this study is overall convincing, there are still some weaknesses in the methods and analyses that limit the implication of the work.

The study's main approach, focusing primarily on the grand comparison of response amplitudes between speech and music, may overlook intricate details in neural coding. Speech and music are not entirely orthogonal with each other at different levels of analysis: at the high-level abstraction, these are two different categories of cognitive processes; at the low-level acoustics, they overlap a lot; at intermediate levels, they may also share similar features. For example, the study doesn't adequately address whether purely melodic elements in music correlate with intonations in speech at the neural level. A more granular analysis, dissecting stimuli into distinct features like pitch, phonetics, timbre, and linguistic elements, could unveil more nuanced shared, and unique neural processes between speech and music. Prior research indicates potential overlap in neural coding for certain intermediate features in speech and music (Sankaran et al. 2023), suggesting that a simple averaged response comparison might not fully capture the complexity of neural encoding. Further delineation of phonetic, melodic, linguistic, and other coding, along with an analysis of how different informational aspects (phonetic, linguistic, melodic, etc) are represented in shared neural activities, could enhance our understanding of these processes and strengthen the study's conclusions.

While classifying electrodes into 3 categories provides valuable insights, it may not fully capture the complexity of the neural response distribution to speech and music. A more nuanced and continuous approach could reveal subtler gradations in neural response, rather than imposing categorical boundaries. This could be done by computing continuous metrics, like unique variances explained by each category or by each acoustic feature, etc. Incorporating such a continuum could enhance our understanding of the neural representation of speech and music, providing a more detailed and comprehensive picture of cortical processing. This goes back to my first comment that the selected set of stimuli may not fully exploit the entire space of speech and music, and there are possible exemplars that violate the preference map here. For example, this study only considered a specific set of multi-instrumental music, it is not clear to me if other types of music would result in different response profiles in individual channels. It is also not clear if a foreign language that the listeners cannot comprehend would evoke similar response profiles. On the contrary, breaking down into the neural coding of more fundamental feature representations that constitute speech and music, and analyzing the unique contribution of each feature would give a more comprehensive understanding.

The paper's emphasis on shared and overlapping neural activity, as observed through sEEG electrodes, provides valuable insights. It is probably true that domain-specificity for speech and music does not exist at such a macro scale. However, it's important to consider that each electrode records from a large neuronal population, encompassing thousands of neurons. This broad recording scope might mask more granular, non-overlapping feature representations at the single neuron level. Thus, while the study suggests shared neural underpinnings for speech and music perception at a macroscopic level, it cannot definitively rule out the possibility of distinct, non-overlapping neural representations at the microscale of local neuronal circuits for features that are distinctly associated with speech and music. This distinction is crucial for fully understanding the neural mechanisms underlying speech and music perception that merit future endeavors with more advanced large-scale neuronal recordings.

Reviewer #1 (Public Review):

Summary:

In this study, the authors examined the extent to which processing of speech and music depends on neural networks that are either specific to a domain or general in nature. They conducted comprehensive intracranial EEG recordings on 18 epilepsy patients as they listened to natural, continuous forms of speech and music. This enabled an exploration of brain activity at both the frequency-specific and network levels across a broad spectrum. Utilizing statistical methods, the researchers classified neural responses to auditory stimuli into categories of shared, preferred, and domain-selective types. It was observed that a significant portion of both focal and network-level brain activity is commonly shared between the processing of speech and music. However, neural responses that are selectively responsive to speech or music are confined to distributed, frequency-specific areas. The authors highlight the crucial role of using natural auditory stimuli in research and the need to explore the extensive spectral characteristics inherent in the processing of speech and music.

Strengths:

The study's strengths include its high-quality sEEG data from a substantial number of patients, covering a majority of brain regions. This extensive cortical coverage grants the authors the ability to address their research questions with high spatial resolution, marking an advantage over previous studies. They performed thorough analyses across the entire cortical coverage and a wide frequency range of neural signals. The primary analyses, including spectral analysis, temporal response function calculation, and connectivity analysis, are presented straightforwardly. These analyses, as well as figures, innovatively display how neural responses, in each frequency band and region/electrode, are 'selective' (according to the authors' definition) to speech or music stimuli. The findings are summarized in a manner that efficiently communicates information to readers. This research offers valuable insights into the cortical selectivity of speech and music processing, making it a noteworthy reference for those interested in this field. Overall, this research offers a valuable dataset and carries out extensive yet clear analyses, amounting to an impressive empirical investigation into the cortical selectivity of speech and music. It is recommended for readers who are keen on understanding the nuances of selectivity and generality in the processing of speech and music to refer to this study's data and its summarized findings.

Weaknesses:

(1) The study employed longer speech and music stimuli, thereby promising improved ecological validity as compared to prior research, a point emphasized by the authors. However, it failed to differentiate between neural responses to the diverse content or local structures within speech and music. The authors considered the potential limitation of treating these extensive speech and music stimuli as stationary signals, neglecting their complex musical or linguistic structural details and temporal variations across local structures such as sentences and phrases. This balanced perspective offered by the authors aids readers in better understanding the context of the study and highlights potential areas for expansion and further considerations.

(2) In contrast to previous studies that employed short stimulus segments along with various control stimuli to ensure that observed selectivity for speech or music was not merely due to low-level acoustic properties, this study used longer, ecological stimuli. However, the control stimuli used in this study, such as tone or syllable sequences, do not align with the low-level acoustic properties of the speech and music stimuli. This mismatch raises concerns that the differences or selectivity between speech and music observed in this study might be attributable to these basic acoustic characteristics rather than to more complex processing factors specific to speech or music. However, this should not deter readers from recognizing the study's strengths, namely, the use of iEEG recordings that offer high spatial resolution and extensive cortical coverage.

(3) The concept of selectivity - shared, preferred, and domain-selective - may not present sufficient theoretical accuracy. It is appreciated that the authors put effort into clearly defining their operational measurement on 'selectivity'. Later, the authors further mentioned the specific indication of their analyses. However, the authors' categorization of neural sites/regions as shared, preferred, or domain-selective regarding speech and music processing essentially resembles a traditional ANOVA test with posthoc analysis. While this categorization gives meaningful context to the results, the mere presence of significant differences among control stimuli, a segment of speech, and a piece of music does not present a strong case that a region is specifically selective to a type of stimulus like speech. The narrative of the manuscript could potentially lead to an overgeneralized interpretation of their findings as being broadly applicable to speech or music, if a reader does not delve into the details.

(4) The authors' approach, akin to mapping a 'receptive field' by correlating stimulus properties with neural responses to ascertain functional selectivity for speech and music, presents potential issues. If cortical regions exhibit heightened responses to one type of stimulus over another, it doesn't automatically imply selectivity or preference for that stimulus. The explanation could lie in functional aspects, such as a region's sensitivity to temporal units of a specific duration, be it music, speech, or even movie segments, and its role in chunking such units (e.g., around 500 ms), which might be more prevalent in music than in speech, or vice versa in the current study. This study does not delve into the functional mechanisms of how speech and music are processed across different musical or linguistic hierarchical levels but merely demonstrates differences in neural responses to various stimuli over a 10-minute span.

Reviewer #3 (Public Review):

Summary:

Te Rietmolen et al., investigated the selectivity of cortical responses to speech and music stimuli using neurosurgical stereo EEG in humans. The authors address two basic questions: 1. Are speech and music responses localized in the brain or distributed; 2. Are these responses selective and domain specific or rather domain general and shared. To investigate this, the study proposes a nomenclature of shared responses (speech and music responses are not significantly different), domain selective (one domain is significant from baseline and the other is not), domain preferred (both are significant from baseline but one is larger than the other and significantly different from each other). The authors employ this framework using neural responses across the spectrum (rather than focusing on high gamma), providing evidence for a low level of selectivity across spectral signatures. To investigate the nature of the underlying representations they use encoding models to predict neural responses (low and high frequency) given a feature space of the stimulus envelope or peak rate (by time delay) and find stronger encoding for both in the low frequency neural responses. The top encoding electrodes are used as seeds for a pair-wise connectivity (coherence) in order to repeat the shared/selective/preferred analysis across the spectra, suggesting low selectivity. Spectral power and connectivity are also analyzed on the level of regional patient population to rule out (and depict) any effects driven by a select few patients. Across analyses the authors consistently show a paucity of domain selective responses and when evident these selective responses were not represented across the entire cortical region. The authors argue that speech and music mostly rely on shared neural resources.

Strengths:

I found this manuscript to be rigorous providing compelling and clear evidence towards shared neural signatures for speech and music. The use of intracranial recordings provides an important spatial and temporal resolution that lends itself to the power, connectivity and encoding analyses. The statistics and methods employed are rigorous and reliable, estimated based on permutation approaches and cross-validation/regularization was employed and reported properly. The analysis of measures across the entire spectra in both power, coherence and encoding models provides a comprehensive view of responses that no doubt will benefit the community as an invaluable resource. Analysis on the level of patient population (feasible with their high N) per region also supports the generalizability of the conclusions across a relatively large cohort of patients. Last but not least, I believe the framework of selective, preferred, and shared is a welcome lens through which to investigate cortical function.

Weaknesses:

I did not find methodological weaknesses in the current version of the manuscript. I do believe that it is important to highlight that the data is limited to passively listening to naturalistic speech and music. The speech and music stimuli are not completely controlled with varying key acoustic features (inherent to the different domains). Overall, I found the differences in stimulus and lack of attentional controls (passive listening) to be minor weaknesses that would not dramatically change the results or conclusions.

Reviewer #1 (Public Review):

Summary:

In this article, the authors investigate whether the connectivity of the hippocampus is altered in individuals with aphantasia ¬- people who have reduced mental imagery abilities and where some describe having no imagery, and others describe having vague and dim imagery. The study investigated this question using a fMRI paradigm, where 14 people with aphantasia and 14 controls were tested, and the researchers were particularly interested in the key regions of the hippocampus and the visual-perceptual cortices. Participants were interviewed using the Autobiographical Interview regarding their autobiographical memories (AMs), and internal and external details were scored. In addition, participants were queried on their perceived difficulty in recalling memories, imagining, and spatial navigation, and their confidence regarding autobiographical memories was also measured. Results showed that participants with aphantasia reported significantly fewer internal details (but not external details) compared to controls; that they had lower confidence in their AMs; and that they reported finding remembering and imagining in general more difficult than controls. Results from the fMRI section showed that people with aphantasia displayed decreased hippocampal and increased visual-perceptual cortex activation during AM retrieval compared to controls. In contrast, controls showed strong negative functional connectivity between hippocampus and the visual cortex. Moreover, resting state connectivity between the hippocampus and visual cortex predicted better visualisation skills. The authors conclude that their study provides evidence for the important role of visual imagery in detail-rich vivid AM, and that this function is supported by the connectivity between the hippocampus and visual cortex. This study extends previous findings of reduced episodic memory details in people with aphantasia, and enables us to start theorising about the neural underpinnings of this finding.

The data provided good support for the conclusion that the authors draw, namely that there is a 'tight link between visual imagery and our ability to retrieve vivid and detail-rich personal past events'. However, as the authors also point out, the exact nature of this relationship is difficult to infer from this study alone, as the slow temporal resolution of fMRI cannot establish the directionality between the hippocampus and the visual-perceptual cortex. This is an exciting future avenue to explore.

Strengths:

A great strength of this study is that it introduces a fMRI paradigm in addition to the autobiographical interview, paralleling work done on episodic memory in cognitive science (e.g. Addis and Schacter, 2007, https://doi.org/10.1016%2Fj.neuropsychologia.2006.10.016 ), which has examined episodic and semantic memory in relation to imagination (future simulation) in non-aphantasic participants as well as clinical populations. Future work could build on this study, and for example use the recombination paradigm (Addis et al. 2009, 10.1016/j.neuropsychologia.2008.10.026 ), which would shed further light on the ability of people with aphantasia to both remember and imagine events. Future work could also build on the interesting findings regarding spatial navigation, which together with previous findings in aphantasia (e.g. Bainbridge et al., 2021, https://doi.org/10.1016/j.cortex.2020.11.014 ) strongly suggests that spatial abilities in people with aphantasia are unaffected. This can shed further light on the different neural pathways of spatial and object memory in general. In general, this study opens up a multitude of new avenues to explore and is likely to have a great impact on the field of aphantasia research.

Weaknesses:

A weakness of the study is that some of the questions used are a bit vague, and no objective measure is used, which could have been more informative. For example, the spatial navigation question (reported as 'How difficult is it typically for you to orient you spatially?' could have been more nuanced to tap into whether participants relied mostly on cognitive maps (likely supported by the hippocampus) or landmarks. It would also have been interesting to conduct a spatial navigation task, as participants do not necessarily have insight to their spatial navigation abilities (they could have been overconfident or underconfident in their abilities). Secondly, the question 'how difficult is it typically for you to use your imagination?' could also be more nuanced, as imagination is used in a variety of ways, and we only have reason to hypothesise that people with aphantasia might have difficulties in some cases (i.e. sensory imagination involving perceptual details). It is unlikely that people with aphantasia would have more difficulty than controls to use their imagination to imagine counterfactual situations and engage in counterfactual thought (de Brigard et al., 2013, https://doi.org/10.1016%2Fj.neuropsychologia.2013.01.015) due to its non-sensory nature, but the question used does not distinguish between these types of imagination. Again, this is a ripe area for future research. The general phrasing of 'how difficult is [x]' could also potentially bias participants towards more negative answers, something which ought to be controlled for in future research.

Reviewer #2 (Public Review):

Summary:

This study investigates to what extent neural processing of autobiographical memory retrieval is altered in people who are unable to generate mental images ('aphantasia'). Self-report as well as objective measures were used to establish that the aphantasia group indeed had lower imagery vividness than the control group. The aphantasia group also reported fewer sensory and emotional details of autobiographical memories. In terms of brain activity, compared to controls, aphantasics had a reduction in activity in the hippocampus and an increase in the activity in visual cortex during autobiographical memory retrieval. For controls, these two regions were also functionally connected during autobiographical memory retrieval, which did not seem to be the case for aphantasics. Finally, resting-state connectivity between visual cortex and hippocampus was positively related to autobiographical vividness in the control group but negatively in the aphantasia group. The results are in line with the idea that aphantasia is caused by an increase in noise within the visual system combined with a decrease in top-down communication from the hippocampus.

Recent years have seen a lot of interest in the influence of aphantasia on other cognitive functions and one of the most consistent findings is deficits in autobiographical memory. This is one of the first studies to investigate the neural correlates underlying this difference, thereby substantially increasing our understanding of aphantasia and the relationship between mental imagery and autobiographical memory.

Strengths:

One of the major strengths of this study is the use of both self-report as well as objective measures to quantify imagery ability. Furthermore, the fMRI analyses are hypothesis-driven and reveal unambiguous results, with alterations in hippocampal and visual cortex processing seeming to underlie the deficits in autobiographical memory.

Weaknesses:

In terms of weaknesses, the control task, doing mathematical sums, also differs from the autobiographical memory task in aspects that are unrelated to imagery or memory, such as self-relevance and emotional salience, which makes it hard to conclude that the differences in activity are reflecting only the cognitive processes under investigation. However, given that the most important comparisons are between groups of participants, this does not diminish the main conclusions about aphantasia.

Overall, I believe that this is a timely and important contribution to the field and will inspire novel avenues for further investigation.

Reviewer #1 (Public Review):

Summary:

This is an experimentally soundly designed work and a very well-written manuscript. There is a very clear logic that drives the reader from one experiment to the next, the experimental design is clearly explained throughout and the relevance of the acquired data is well analyzed and supports the claims made by the authors. The authors made an evident effort to combine imaging, genetic, and molecular data to describe previously unknown early embryonic movement patterns and to identify regulatory mechanisms that control several aspects of it.

Strengths:

The authors develop a new method to analyze, quantitatively, the onset of movement during the latter embryonic stages of Drosophila development. This setup allows for a high throughput analysis of general movement dynamics based on the capture of variations of light intensity reflected by the embryo. This setup is capable of imaging several embryos simultaneously and provides a detailed measure of movement over time, which proves to be very useful for further discoveries in the manuscript. This setup already provides a thorough and quantifiable description of a process that is little known and identifies two different phases during late embryonic movements: a myogenic phase and a neurogenic phase, which they elegantly prove is dependent on neuronal activity by knocking down action potentials across the nervous system.

However, in this system, movement is detected as a whole, and no further description of the type of movement is provided beyond frequency and amplitude; it would be interesting to know from the authors if a more precise description of the movements that take place at this stage can be achieved with this method (e.g. motion patterns across the A-P body axis).

Importantly, this highly quantitative experimental setup is an excellent system for performing screenings of motion regulators during late embryonic development, and its use could be extended to search for different modulators of the process, beyond miRNAs (genetic mutants, drugs, etc.).

Using their newly established motion detection pipeline, the authors identify miR-2b-1 as required for proper larval and embryonic motion, and identify an overall reduction in the quantity of both myogenic and neurogenic movements, as well as an increased frequency in neurogenic movement "pulses".

Focusing on the neurogenic movement phenotype the authors use in situ probes and perform RT-PCR on FACS-sorted CNS cells to unambiguously detect miR-2b-1 expression in the embryonic nervous system. The neurogenic motion defects observed in miR-2b-1 mutant embryos and early larvae can be completely rescued by the expression of ectopic miR-2b-1 specifically in the nervous system, providing solid evidence of the requirement and sufficiency of miR-2b-1 expressed in the nervous system to regulate these phases of movement.

To explore the mechanism through which miR-2b-1 impacts embryonic movement, the authors use a state-of-the-art bioinformatic approach to identify potential targets of miR-2b-1, and find that the expression levels of an uncharacterized gene, CG3638, are indeed regulated by miR-2b-1. Furthermore, they prove that by knocking down the expression of CG3638 in a miR-2b-1 mutant background, the neurogenic embryonic movement defects are rescued, pointing that the repression of CG3638 by miR-2b-1 is necessary for correct motion patterns in wild-type embryos. Therefore, this paper provides the first functional characterization of CG3638, and names this gene Motor.

Finally, the authors aim to discriminate which elements of the embryonic motor system miR-2b-1/Motor are required. Using directed overexpression of miR-2b-1 and Motor knockdown in the motor neurons and the chordotonal (sensory) organs, they prove that the miR-2b-1/Motor regulatory axis is specifically required in the sensory organs to promote normal embryonic and larval movement.

Weaknesses:

The initial screening to identify miRNAs involved in motion behaviors is performed in early larval movement. The logic presented by the authors is clear - it is assumed that early larval movement cannot proceed normally in the absence of previous embryonic motion - and ultimately helped them identify a miRNA required for modulation of embryonic movement. However, it is possible that certain miRNAs play a role in the modulation of embryonic movement while being dispensable for early L1 behaviors. Such regulators might have been missed with the current screening setup.

Reviewer #2 (Public Review):

Summary:<br /> The manuscript, "A microRNA that controls the emergence of embryonic movement" by Menzies, Chagas, and Alonso provides evidence that Drosophila miR-2b-1 is expressed in neurons and controls the expression of the predicted chloride channel CG3638, here named "Motor". Loss of the miRNA leads to movement phenotypes that can be rescued by downregulation of Motor; using specific drivers, the authors show that a larval movement phenotype (slower movement) can be rescued by knockdown of Motor in the chordotonal organs, suggesting that the increase in Motor found in the chordotonal organs is likely the root of the movement defects. Overall, I found the data presented in the manuscript of reasonable quality and are well enough supported by the presented data.

The genetic and phenotypic analysis seems to be correct. The nicest part of the manuscript is the connection between the loss of a miRNA and finding its likely target in generating a phenotype. The authors also develop some protocols for the analysis of the movement phenotypes which may be useful for others.

Reviewer #1 (Public Review):

Summary:

The authors in this manuscript performed scRNA-seq on a cohort of 15 early-stage cervical cancer patients with a mixture of adeno- and squamous cell carcinoma, HPV status, and several samples that were upstaged at the time of surgery. From their analyses they identified differential cell populations in both immune and tumour subsets related to stage, HPV status, and whether a sample was adenocarcinoma or squamous cell. Putative microenvironmental signaling was explored as a potential explanation for their differential cell populations. Through these analyses the authors also identified SLC26A3 as a potential biomarker for later stage/lymph node metastasis which was verified by IHC and IF. The dataset is likely useful for the community, however, the strong claims made are not adequately supported by the data and would require additional functional validation.

Strengths:

The dataset could be useful for the community.<br /> SLC26A3 could potentially be a useful marker to predict lymph node metastasis with further study.

Weaknesses:

The link between the background in the introduction and the actual study and findings is often tenuous or not clearly explained. A re-working of the intro to better set up and link to the study questions would be beneficial.

For the sequencing, which kit was used on the Novaseq6000?

Additional details are needed for the analysis pipeline. How were batch effects identified/dealt with, what were the precise functions and settings for each step of the analysis, how was clustering performed and how were clusters validated etc. Currently, all that is given is software and sometimes function names which are entirely inadequate to be able to assess the validity of the analysis pipeline. This could alternatively be answered by providing annotated copies of the scripts used for analysis as a supplement.

For Cell type annotation, please provide the complete list of "selected gene markers" that were used for annotation.

No statistics are given for the claims on cell proportion differences throughout the paper (for cell types early, epithelial sub-clusters later, and immune cell subsets further on). This should be a multivariate analysis to account for ADC/SCC, HPV+/- and Early/Late stage.

The Y-axis label is missing from the proportion histograms in Figure 2D. In these same panels, the bars change widths on the right side. If these are exclusively in ADC, show it with a 0 bar for SCC, not doubling the width which visually makes them appear more important by taking up more area on the plot.

Throughout the manuscript, informatic predictions (differentiation potential, malignancy score, stemness, and trajectory) are presented as though they're concrete facts rather than the predictions they are. Strong conclusions are drawn on the basis of these predictions which do not have adequate data to support. These conclusions which touch on essentially all of the major claims made in the manuscript would need functional data to validate, or the claims need to be very substantially softened as they lack concrete support. Indeed, the fact that most of the genes examined that were characteristic of a given cluster did not show the expected expression patterns in IHC highlights the fact that such predictions require validation to be able to draw proper inferences.

The cluster Epi_10_CYSTM1 which is the basis for much of the paper is present in a single individual (with a single cell coming from another person), and heavily unconnected from the rest of the epithelial populations. If so much emphasis is placed on it, the existence of this cluster as a true subset of cells requires validation.

Claims based on survival analysis of TCGA for Epi_10_CYSTM1 are based on a non-significant p-value, though there is a slight trend in that direction.

The claim "The identification of Epi_10_CYSTM1 as the only cell cluster found in patients with stage IIICp raises the possibility that this cluster may be a potential marker to diagnose patients with lymph node metastasis." This is incorrect according to the sample distributions which clearly show cells from the patient who has EPI_10_CYSTM1 in multiple other clusters. This is then used as justification for SLC26A3 which appears to be associated with associated with late stage, however, in the images SLC26A3 appears to be broadly expressed in later tumours rather than restricted to a minor subset as it should be if it were actually related to the EPI_10_CYSTM1 cluster.

The authors claim that cytotoxic T cells express KRT17, and KRT19. This likely represents a mis-clustering of epithelial cells.

Multiple claims are made for specific activities based on GO term biological process analysis which while not contradictory to the data, certainly are by no means the only explanation for it, nor directly supported.

Reviewer #2 (Public Review):

Summary:

Peng et al. present a study using scRNA-seq to examine phenotypic properties of cervical cancer, contrasting features of both adenocarcinomas (ADC) and squamous cell carcinoma (SCC), and HPV-positive and negative tumours. They propose several key findings: unique malignant phenotypes in ADC with elevated stemness and aggressive features, interactions of these populations with immune cells to promote an immunosuppressive TME, and SLC26A3 as a biomarker for metastatic (>=Stage III ) tumours.

Strengths:

This study provides a valuable resource of scRNA-seq data from a well-curated collection of patient samples. The analysis provides a high-level view of the cellular composition of cervical cancers. The authors introduce some mechanistic explanations of immunosuppression and the involvement of regulatory T cells that are intriguing.

Weaknesses:

I believe that many of the proposed conclusions are over-interpretations or unwarranted generalizations of the single-cell analysis. These conclusions are often based on populations in the scRNA-seq data that are described as enriched or specific to a given group of samples (eg. ADC). This conclusion is based on the percentage of cells in that population belonging to the given group; for example, a cluster of cells that dominantly come from ADC. The data includes multiple samples for each group, but statistical approaches are never used to demonstrate the reproducibility of these claims.

This leads to problematic conclusions. For example, the "ADC-specific" Epi_10_CYSTM1 cluster, which is a central focus of the paper, only contains cells from one of the 11 ADC samples and represents only a small fraction of the malignant cells from that sample (Sample 7, Figure 2A). Yet, this population is used to derive SLC26A3 as a potential biomarker. SLC26A3 transcripts were only detected in this small population of cells (none of the other ADC samples), which makes me question the specificity of the IHC staining on the validation cohort.

This is compounded by technical aspects of the analysis that hinder interpretation. For example, it is clear that the clustering does not perfectly segregate cell types. In Figures 2B and D, it is evident that C4 and C5 contain mixtures of cell type (eg. half of C4 is EPCAM+/CD3-, the other half EPCAM-/CD3+). These contaminations are carried forward into subclustering and are not addressed. Rather, it is claimed that there is a T cell population that is CD3- and EPCAM+, which does not seem likely.

Reviewer #1 (Public Review):

Summary:

This manuscript from Clayton and co-authors, entitled "Mechanism of dimer selectivity and binding cooperativity of BRAF inhibitors", aims at clarifying the molecular mechanism of BRAF dimer selectivity. Indeed, first generation BRAF inhibitors, targeting monomeric BRAFV600E, are ineffective in treating resistant dimeric BRAF isoforms. Here, the authors employed molecular dynamics simulations to study the conformational dynamics of monomeric and dimeric BRAF, in the presence and absence of inhibitors. Multi-microseconds MD simulations showed an inward shift of the αC helix in the BRAFV600E mutant dimer. This helped identify a hydrogen bond between the inhibitors and the BRAF residue Glu501 as critical for dimer compatibility. The stability of the aforementioned interaction seems to be important to distinguish between dimer-selective and equipotent inhibitors.

Strengths:

The study is overall valuable and robust. The authors used the recently developed particle mesh Ewald constant pH molecular dynamics, a state-of-the-art method, to investigate the correct histidines protonation considering the dynamics of the protein. Then, multi-microsecond simulations showed differences in the flexibility of the αC helix and DFG motif. The dimerization restricts the αC position in the inward conformation, in agreement with the result that dimer-compatible inhibitors are able to stabilize the αC-in state. Noteworthy, the MD simulations were used to study the interactions between the inhibitors and the protein, suggesting a critical role for a hydrogen bond with Glu501. Finally, simulations of a mixed state of BRAF (one protomer bound to the inhibitor and the other apo) indicate that the ability to stabilize the inward αC state of the apo protomer could be at the basis of the positive cooperativity of PHI1.

Weaknesses:

Regarding the analyses of the mixed state simulations, the DFG dihedral probability densities for the apo protomer (Fig. 5a right) are highly overlapping. It is not convincing that a slight shift can support the conclusion that the binding in one protomer is enough to shift the DFG motif outward allosterically. Moreover, the DFG dihedral time-series for the apo protomer (Supplementary Figure 9) clearly shows that the measured quantities are affected by significant fluctuations and poor consistency between the three replicates. The apo protomer of the mixed state simulations could be affected by the same problem that the authors pointed out in the case of the apo dimer simulations, where the amount of sampling is insufficient to model the DFG-out/-in transition properly. There is similar concern with the Lys483-Glu501 salt bridge measured for the apo protomers of the mixed simulations. As it can be observed from the probabilities bar plot (Fig. 5a middle), the standard deviation is too high to support a significant role for this interaction in the allosteric modulation of the apo protomer.

Reviewer #2 (Public Review):

Summary:

The authors employ molecular dynamics simulations to understand the selectivity of FDA approved inhibitors within dimeric and monomeric BRAF species. Through these comprehensive simulations, they shed light on the selectivity of BRAF inhibitors by delineating the main structural changes occurring during dimerization and inhibitor action. Notably, they identify the two pivotal elements in this process: the movement and conformational changes involving the alpha-C helix and the formation of a hydrogen bond involving the Glu-501 residue. These findings find support in the analyses of various structures crystallized from dimers and co-crystallized monomers in the presence of inhibitors. The elucidation of this mechanism holds significant potential for advancing our understanding of kinase signalling and the development of future BRAF inhibitor drugs.

Strengths:

The authors employ a diverse array of computational techniques to characterize the binding sites and interactions between inhibitors and the active site of BRAF in both dimeric and monomeric forms. They combine traditional and advanced molecular dynamics simulation techniques such as CpHMD (All-atom continuous constant pH molecular dynamics) to provide mechanistic explanations. Additionally, the paper introduces methods for identifying and characterizing the formation of the hydrogen bond involving the Glu501 residue without the need for extensive molecular dynamics simulations. This approach facilitates the rapid identification of future BRAF inhibitor candidates.

Weaknesses:

Despite the use of molecular dynamics yields crucial structural insights and outlines a mechanism to elucidate dimer selectivity and cooperativity in these systems, the authors could consider adoption of free energy methods to estimate the values of hydrogen bond energies and hydrophobic interactions, thereby enhancing the depth of their analysis.

Reviewer #1 (Public Review):

Summary:

In the manuscript by Tie et.al., the authors couple the methodology which they have developed to measure LQ (localization quotient) of proteins within the Golgi apparatus along with RUSH based cargo release to quantify the speed of different cargos traveling through Golgi stacks in nocodazole induced Golgi ministacks to differentiate between cisternal progression vs stable compartment model of the Golgi apparatus. The debate between cisternal progression model and stable compartment model has been intense and going on for decades and important to understand the basic way of function/organization of the Golgi apparatus. As per the stable compartment model, cisterna are stable structures and cargo moves along the Golgi apparatus in vesicular carriers. While as per cisternal progression model, Golgi cisterna themselves mature acquiring new identity from the cis face to the trans face and act as transport carriers themselves. In this work, authors provide a missing part regarding intra-Golgi speed for transport of different cargoes as well as the speed of TGN exit and based on the differences in the transport velocities for different cargoes tested favor a stable compartment model. The argument which authors make is that if there is cisternal progression, all the cargoes should have a similar intra-Golgi transport speed which is essentially the rate at which the Golgi cisterna mature. Furthermore, using a combination of BFA and Nocodazole treatments authors show that the compartments remain stable in cells for at least 30-60 minutes after BFA treatment.

Strengths:

The method to accurately measure localization of a protein within the Golgi stack is rigorously tested in the previous publications from the same authors and in combination with pulse chase approaches has been used to quantify transport velocities of cargoes through the Golgi. This is a novel aspect in this paper and differences in intra-Golgi velocities for different cargoes tested makes a case for a stable compartment model.

Weaknesses:

Experiments are only tested in one cell line (HeLa cells) and predominantly derived from experimental paradigm using RUSH assays where a secretory cargo is released in a wave (not the most physiological condition) and therefore additional approaches would make a more compelling case for the model.

Reviewer #2 (Public Review):

Summary:

This manuscript describes the use of quantitative imaging approaches, which have been a key element of the labs work over the past years, to address one of the major unresolved discussions in trafficking: intra-Golgi transport. The approach used has been clearly described in the labs previous papers, and is thus clearly described. The authors clearly address the weaknesses in this manuscript and do not overstate the conclusions drawn from the data. The only weakness not addressed is the concept of blocking COPI transport with BFA, which is a strong inhibitor and causes general disruption of the system. This is an interesting element of the paper, which I think could be improved upon by using more specific COPI inhibitors instead, although I understand that this is not necessarily straightforward.

I commend the authors on their clear and precise presentation of this body of work, incorporating mathematical modelling with a fundamental question in cell biology. In all, I think that this is a very robust body of work, that provides a sound conclusion in support of the stable compartment model for the Golgi.

General points:

The manuscript contains a lot of background in its results sections, and the authors may wish to consider rebalancing the text: The section beginning at Line 175 is about 90% background and 10% data. Could some data currently in supplementary be included here to redress this balance, or this part combined with another?

Reviewer #3 (Public Review):

The manuscript by Tie et al. provides a quantitative assessment of intra-Golgi transport of diverse cargos. Quantitative approaches using fluorescence microscopy of RUSH synchronized cargos, namely GLIM and measurement of Golgi residence time, previously developed by the author's team (publications from 20216 to 2022), are being used here.

Most of the results have been already published by the same team in 2016, 2017, 2020 and 2021. In this manuscript, very few new data have been added. The authors have put together measurements of intra-Golgi transport kinetics and Golgi residence time of many cargos. The quantitative results are supported by a large number of Golgi mini-stacks/cells analyzed. They are discussed with regard to the intra-Golgi transport models being debated in the field, namely the cisternal maturation/progression model and the stable compartments model. However, over the past decades, the cisternal progression model has been mostly accepted thanks to many experimental data.

The authors show that different cargos have distinct intra-Golgi transport kinetics and that the Golgi residence time of glycosyltransferases is high. From this and the experiment using brefeldinA, the authors suggest that the rim progression model, adapted from the stable compartments model, fits with their experimental data.

Strengths:

The major strength of this manuscript is to put together many quantitative results that the authors previously obtained and to discuss them to give food for thought about the intra-Golgi transport mechanism.<br /> The analysis by fluorescence microscopy of intra-Golgi transport is tough and is a tour de force of the authors even if their approach show limitations, which are clearly stated. Their work is remarkable in regards to the numbers of Golgi markers and secretory cargos which have been analyzed.

Weaknesses:

As previously mentioned, most of the data provided here were already published and thus accessible for the community. Is there is a need to publish them again?<br /> The authors' discussion about the intra-Golgi transport model is rather simplistic. In the introduction, there is no mention of the most recent models, namely the rapid partitioning and the rim progression models. To my opinion, the tubular connections between cisternae and the diffusion/biochemical properties of cargos are not enough taken into account to interpret the results. Indeed, tubular connections and biochemical properties of the cargos may affect their transit through the Golgi and the kinetics with which they reach the TGN for Golgi exit.<br /> Nocodazole is being used to form Golgi mini-stacks, which are necessary to allow intra-Golgi measurement. The use of nocodazole might affect cellular homeostasis but this is clearly stated by the authors and is acceptable as we need to perturb the system to conduct this analysis. However, the manual selection of the Golgi mini-stack being analyzed raises a major concern. As far as I understood, the authors select the mini-stacks where the cargo and the Golgi reference markers are clearly detectable and separated, which might introduce a bias in the analysis.<br /> The terms 'Golgi residence time ' is being used but it corresponds to the residence time in the trans-cisterna only as the cargo has been accumulated in the trans-Golgi thanks to a 20{degree sign}C block. The kinetics of disappearance of the protein of interest is then monitored after 20{degree sign}C to 37{degree sign}C switch.<br /> Another concern also lies in the differences that would be introduced by different expression levels of the cargo on the kinetics of their intra-Golgi transport and of their packaging into post-Golgi carriers.