Reviewer #1 (Public Review):

This manuscript presents the first evidence for a plastic enhancement in the response of pial cortical arterioles to external stimulation. Specifically, they show (p8; Figure 3A-C) that repeated application of a visual stimulus at 0.25 Hz, at the upper edge of the vasomotor response, leads to a greater change in the diameter of pial arterioles at that frequency. This adds to the earlier, referenced work of Mateo et al (2017) that showed locking - or entrainment of pial arteriole vasomotion - by stimuli at different (0.0 to 0.3 Hz) frequencies.

The manuscript has a major flaw. Much as there is plasticity that leads to an increase in the amplitude of vasomotion at the drive frequency, the authors need to show reversibility. This could possibly be accomplished by driving the visual system at a different frequency, say 0.15 Hz, and observing if the 0.25 Hz response is then diminished. The authors could then test if their observation is repeatable by again driving at 0.25 Hz. Unless I missed the presentation on this point, there is no evidence for reversibility.

Drew, P. J., A. Y. Shih, J. D. Driscoll, P. M. Knutsen, D. Davalos, P. Blinder, K. Akassoglou, P. S. Tsai, and D. Kleinfeld. 2010. 'Chronic optical access through a polished and reinforced thinned skull', Nature Methods, 7: 981-84.<br /> Morii, S., A. C. Ngai, and H. R. Winn. 1986. 'Reactivity of rat pial arterioles and venules to adenosine and carbon dioxide: With detailed description of the closed cranial window technique in rats', Journal of Cerebral Blood Flow & Metabolism, 6: 34-41.

Reviewer #2 (Public Review):

Sasaki et al. investigated methods to entrain vasomotion in awake wild-type mice across multiple regions of the brain using a horizontally oscillating visual pattern which induces an optokinetic response (HOKR) eye movement. They found that spontaneous vasomotion could be detected in individual vessels of their wild-type mice through either a thinned cranial window or intact skull preparation using a widefield macro-zoom microscope. They showed that low-resolution autofluorescence signals coming from the brain parenchyma could be used to capture vasomotion activity using a macro-zoom microscope or optical fibre, as this signal correlates well with the intensity profile of fluorescently-labelled single vessels. They show that vasomotion can also be entrained across the cortical surface using an oscillating visual stimulus with a range of parameters (with varying temporal frequencies, amplitudes, or spatial cycles), and that the amplitude spectrum of the detected vasomotion frequency increases with repeated training sessions. The authors include some control experiments to rule out fluorescence fluctuations being due to artifacts of eye movement or screen luminance and attempt to demonstrate some functional benefit of vasomotion entraining as HOKR performance improves after repeat training. These data add in an interesting way to the current knowledge base on vasomotion, as the authors demonstrate the ability to entrain vasomotion across multiple brain areas and show some functional significance to vasomotion with regards to information processing as HOKR task performance correlates well with vascular oscillation amplitudes.

The aims of the paper are mostly well supported by the data, but some streamlining of the data presentation would improve overall clarity. The third aim to establish the functional significance of vasomotion in relation to plasticity in information processing could be better supported by the inclusion of some additional control experiments. Specifically:

1) The clarity and comprehensibility of the paper could be significantly enhanced by incorporating additional details in both the introduction and discussion sections. In the introduction, a succinct definition of the frequency range of vasomotion should be provided, as well as a better description of the horizontal optokinetic response (i.e. as they have in the results section in the first paragraph below the 'Entrainment of vasomotion with visual stimuli presentation' sub-heading). The discussion would benefit from the inclusion of a clear summary of the results presented at the start, and the inclusion of stronger justification (i.e. more citations) with regards to the speculation about vasomotion and neuronal plasticity (e.g. paragraph 5 includes no citations).

2) The novel methods for detecting vasomotion using low-resolution imaging techniques are discussed across the first four figures, but this gets a little bit confusing to follow as the authors jump back and forth between the different imaging and analysis techniques they have employed to capture vasomotion. The data presentation could be better streamlined - for instance by presenting only the methods most relevant for the functional dataset (in Figures 5-7), with the additional information regarding the various controls to establish the use of autofluorescence intensity imaging as a valid method for capturing vasomotion reduced to fewer figure panels, or moved to supplementary figures so as to not detract from the main novel findings contributed in this study.

3) The authors heavily rely on representative traces from individual vessels to illustrate their findings, particularly evident in Figures 1-4. While these traces offer a valuable visualization, augmenting their approach by presenting individual data points across the entire dataset, encompassing all animals and vessels, would significantly enhance the robustness of their claims. For instance, in Figures 1 and 2, where average basal and dilated traces are depicted for a representative vessel, supplementing these with graphs showcasing peak values across all measured vessels would enable the authors to convey a more holistic representation of their data. Or in Figure 3, where the amplitude spectrum is presented for individual Texas red fluorescence intensity changes in V1 across novice, trained, and expert mice, incorporating a summary graph featuring the amplitude spectrum value at 0.25Hz for each individual trace (across animals/imaging sessions), followed by statistical analysis, would fortify the strength of their assertions. Moreover, providing explicit details on sample sizes for each individual figure panel (where not a representative trace), including the number of animals or vessels/imaging sessions, would contribute to transparency and aid readers in assessing the generalisability of the findings.

4) In the experiments where mice are classed as "novice", "trained" or "expert", the inclusion of the specific range of the number of training sessions for each category would improve replicability.

5) The authors don't state whether mice were habituated to the imaging set-up prior to the first data collection, as head-fixation and restraint can be stress-inducing for animals, especially upon first exposure, which could impact their neurovascular coupling responses differentially in "novice" versus "trained" imaging sessions (e.g. see Han et al., 2020, DOI: https://doi.org/10.1523/JNEUROSCI.1553-20.2020). The stress associated with a tail vein injection prior to imaging could also partially explain why mice didn't learn very well if Texas Red was injected before the training session. If no habituation was conducted in these experiments, the study would benefit from the inclusion of some control experiments where "novice" responses were compared between habituated and non-habituated animals.

6) The experiments regarding the brain-wide vasomotion entrainment across the cortical surface would benefit from some additional information about how brain regions were identified (e.g. particularly how V1 and V2 were distinguished given how close together they are).

7) Whilst the authors show that HOKR task performance and vasomotion amplitude are increased with repeated training to provide some support to their aim of investigating the functional significance of vasomotion with regards to information processing plasticity, the inclusion of some additional control experiments would provide stronger evidence to address this aim. For instance, if vasomotion signalling is blocked or reduced (e.g. using optogenetics or in an AD mouse model where arteriole amyloid load restricts vasomotion capacity), does flocculus-dependent task performance (e.g. HOKR eye movements) still improve with repeated exposure to the external stimulus.

Reviewer #3 (Public Review):

Summary:<br /> Here the authors show global synchronization of cerebral blood flow (CBF) induced by oscillating visual stimuli in the mouse brain. The study validates the use of endogenous autofluorescence to quantify the vessel "shadow" to assess the magnitude of frequency-locked cerebral blood flow changes. This approach enables straightforward estimation of artery diameter fluctuations in wild-type mice, employing either low magnification wide-field microscopy or deep-brain fibre photometry. For the visual stimuli, awake mice were exposed to vertically oscillating stripes at a low temporal frequency (0.25 Hz), resulting in oscillatory changes in artery diameter synchronized to the visual stimulation frequency. This phenomenon occurred not only in the primary visual cortex but also across a broad cortical and cerebellar surface. The induced CBF changes adapted to various stimulation parameters, and interestingly, repeated trials led to plastic entrainment. The authors control for different artefacts that may have confounded the measurements such as light contamination and eye movements but found no influence of these variables. The study also tested horizontally oscillating visual stimuli, which induce the horizontal optokinetic response (HOKR). The amplitude of eye movement, known to increase with repeated training sessions, showed a strong correlation with CBF entrainment magnitude in the cerebellar flocculus. The authors suggest that parallel plasticity in CBF and neuronal circuits is occurring. Overall, the study proposes that entrained "vasomotion" contributes to meeting the increased energy demand associated with coordinated neuronal activity and subsequent neuronal circuit reorganization.

Strengths:<br /> -The paper describes a simple and useful method for tracking vasomotion in awake mice through an intact skull.<br /> -The work controls for artefacts in their primary measurements.<br /> -There are some interesting observations, including the nearly brain-wide synchronization of cerebral blood flow oscillations to visual stimuli and that this process only occurs after mice are trained in a visual task.<br /> -This topic is interesting to many in the CBF, functional imaging, and dementia fields.

Weaknesses:<br /> -I have concerns with the main concepts put forward, regarding whether the authors are actually studying vasomotion as they state, as opposed to functional hyperemia which is sensory-induced changes in blood flow, which is what they are actually doing. I recommend several additional experiments/analyses for them to explore. This is mostly further characterizing their effect which will benefit the interpretations.

-Neuronal calcium imaging would also benefit the study and improve the interpretations.

-The plastic effects in vasomotion synchronization that occur with training are interesting but they could use an additional control for stress. Is this really a plastic effect, or is it caused by progressively decreasing stress as trials and progress? I recommend a habituation control experiment.

Appraisal<br /> I think the authors have an interesting effect that requires further characterization and controls. Their interpretations are likely sound and additional experiments will continue to support the main hypothesis. If brain-wide synchrony of blood flow can be trained and entrained by external stimuli, this may have interesting therapeutic potential to help clear out toxic proteins from the brain as seen in several neurodegenerative diseases.

Reviewer #1 (Public Review):

This manuscript by Tyler and colleagues describes a thorough analysis of IR-induced changes in nascent RNA transcripts, and a genome-wide screening effort to identify the responsible proteins. The findings extend previous work describing DNA damage-induced transcriptional repression from DNA breaks in cis to bulk genomic DNA damage. A significant discovery is the inability of arrested cells to undergo DNA damage-induced gene silencing, which, at least at the rDNA locus, is attributed to an inability to mediate ATM-induced transcriptional repression. While the findings add to our knowledge of how DNA damage affects gene expression, there are several limitations to the current study that remain inadequately addressed. In addition, some of the proposed conclusions seem speculative and should be marked as such, omitted, or experimentally supported.

Two major concerns are as follows:

1) The CIRSPR screen designed to detect regulators of damage-induced transcriptional repression is based on EU incorporation following a 7-day selection of stable knockout cells. As the authors point out, cell cycle arrest reduces rDNA transcription on its own. The screen, which assesses changes in sgRNA distribution in EU high cells, is thus likely to be dominated by factors that affect cell cycle progression. This is exemplified in the analyses of top hits related to neddylation. The screen's limitations in terms of identifying DDR effectors of damage-induced silencing need to be clearly stated.

2) The authors confirm previous findings of DNA damage-induced repression of rDNA and histone gene transcription. The authors propose that these highly transcribed genes are more susceptible to silencing than the bulk of protein-coding genes and propose a global damage-induced signaling event that is independent of DNA breaks in cis. While this is possible, it is not demonstrated in this manuscript, and the authors should acknowledge alternative explanations. For example, the loci found to be repressed by bulk IR are highly repetitive gene arrays that tend to form nuclear sub-compartments (nucleoli, histone bodies). As such, their likelihood of being in the vicinity of DNA damage is high, at least for a fraction of gene copies. The findings, therefore, remain consistent with cis-induced silencing. Moreover, silencing may spread through the relevant nuclear sub-compartments, consistent with the formation of DNA damage compartments described recently (PMID: 37853125).

Other comments:<br /> 1) The statement that silencing is due to transcription initiation rather than elongation is not sufficiently supported by the data. Could equivalent nascent transcript reduction not be the result of the suppression of elongating RNA PolII? To draw the proposed conclusion, the authors would need to demonstrate that RNA PolII initiation is altered, using RNA PollII ChIP and/or analysis of relevant RNA PolII phosphorylation patterns.

2) The lack of rDNA silencing in arrested cells is interesting, though the underlying mechanism remains unclear. To further corroborate the proposed defect in ATM-mediated signaling, the authors should look directly at ATM and Treacle phosphorylation upstream of TOPBP1.

3) The "change in relative heights of the EU low (G1) and EU high (S/G2) peaks" in Figures 5D, 5E, and 6B is central to the proposed model of transcriptional changes being affected by cell cycle arrest. These differences should be visualized more clearly and quantified across independent experiments. Ideally, the cell cycle stage should be dissected as in Figure 2B. How do the authors envision cell cycle arrest triggers the defect in transcriptional silencing?

Reviewer #2 (Public Review):

Summary:<br /> In this manuscript, the authors attempted to study mechanisms of transcription inhibition in cells treated with IR. They observed that, unlike histone chaperone HIRA-dependent transcription inhibition during UV-induced damage, IR-induced transcription inhibition does not depend on HIRA. Through the CRISPR/Cas9 screen, they identified protein neddylation is important for transcription inhibition. By sequencing nascent RNA, they observed that down-regulated transcripts upon IR treatment are largely highly transcribed genes including histone genes and rDNA.

Strengths:<br /> The authors utilized comprehensive approaches to fill in the knowledge gap of IR-induced transcription inhibition.

Weaknesses:<br /> it is not clear that inhibition of histone genes by IR is due to a reduction of S phase progression.

Reviewer #1 (Public Review):

Summary:

This valuable study by Gui Yu and colleagues aims to investigate the function of a subtype of hepatocyte, which expressed Twist2 at some point in its lineage. First, using reporter mice, they show that hepatocytes can be labelled using Twist2-cre mice. Importantly, Twist2-cre also labels liver mesenchymal cells. Using scRNA seq of P1 and P14 Twist2-Cre tomato-labelled cells, they identify both mesenchymal cells and hepatocytes, and (using trajectory analyses) propose that Twist-traced hepatocytes and mesenchymal cells are derived from Epcam+ progenitors. The authors propose that the Twist2-traced hepatocytes occupy the midzone, and are polyploid. Using partial hepatectomy and Ccl4 as models for regeneration, the authors show that, after insult, there is an increase in Twist2-Tomato+ cells, which are more proliferative. Next, the authors propose that Notch signaling is suppressed during regeneration (based on the downregulation of HES1 protein in midzone hepatocytes during regeneration). They therefore knock out Notch1 in Twist2-expressing cells and show that this leads to hepatocyte proliferation (but fewer liver lobes) in homeostatic conditions. Finally, the authors also interfere with mTor and VEGF signaling and show that both interventions suppresses the excess hepatocyte proliferation in Twist2-conditional Notch1-knockout mice.

Strengths:

Overall, the data show that Twist2 is expressed at some point during hepatocyte development or homeostasis, and that Notch1 in hepatocytes or mesenchymal cells plays a role in limiting homeostatic hepatocyte proliferation.

Weaknesses:

The study relies heavily on the use of Twist2-Cre mice, which labels both mesenchymal cells and hepatocytes. Several experiments on hepatocytes (such as scRNA seq or bulk RNA seq) could be confounded by doublets or contamination with mesenchymal cells.

Reviewer #2 (Public Review):

Summary:

There are two potential contributions made by this study, both of which are not fully supported by the data presented. First, that Twist-positive hepatocytes in the midlobular zone are derived from Twist2-expressing cells in embryonic livers via intermediate EpCAM-expressing cells. Second, that there is a population of hepatocytes with mesenchymal features that drive regeneration after various injuries. The concept that mid-lobular hepatocytes are more regenerative in adult injury settings has already been established and this paper further supports that body of knowledge.

Strengths:

There are copious scRNA-seq data that are supportive of the claims, but these analyses were not definitive.

Weaknesses:

1. There is not sufficient evidence to support the following assertion: "markers identified a mesenchymal-hepatocyte hybrid population (13.7% of total hepatocytes) that express signature genes of both lineages." Twist-Cre reporter mice mark hepatocytes and mesenchymal populations, but it is not clear whether or not this means that the hepatocyte population labeled by Twist is mesenchymal. It is very possible for hepatocytes to express mesenchymal genes without being a true hybrid population. There is not much evidence that zone 2 cells are a mix of hepatocyte and mesenchymal. The idea of a hybrid population needs to be defined. The definition probably needs to involve the concept that hybrid cells must have morphologic or functional features of mesenchymal cells, rather than just expressing some genes from each cell type.

Related to this, the authors claim that co-expression of Twist and EpCAM in E10.5 liver cells might support the existence of a hepatomesenchymal cell type. This is possible, but one should note that adult hepatocytes can express EpCAM, especially during ductular reactions, so it is not necessarily a mesenchymal marker per se.

2. The authors assert several times that Twist-Cre mice appear to have no effect on overall liver regeneration phenotypes. They use this to suggest a lack of an effect for heterozygous deletion of Twist by the Cre allele. It is still possible for these mice to have altered lineage tracing results. It is very difficult to rule this out. For example, Axin2-CreER mice did not have any overt liver function or regeneration phenotypes, but the lineage tracing results from these mice differed from other CreER mice.

3. The central problem with this study is that the authors use a Cre strain and not a CreER strain. With a Cre strain, there could be new labeling of Twist-positive cells at multiple later time points. Thus, it is very difficult to assert that the Tomato-positive population at later time points are really descendants of the originally labeled population. It is very difficult to interpret the results of Cre-based lineage tracing experiments.

With this technical limitation in mind, I do not think that there is enough evidence to support the assertion made on page 6: "These findings suggest that EpCAMlow progenitor cells give rise to hepatocytes and MCs." The authors use scRNA-seq trajectory analysis to come to the conclusion that mesenchymal cells give rise to hepatocytes between p1 and p14. Much more evidence is needed before the authors can arrive at this conclusion. It is much more likely that midlobular hepatocytes arise from other hepatocytes. To support their arguments, the authors would have to use a CreER line that exclusively labels mesenchymal cells in the liver, then lineage traces them until p14 to determine if they become hepatocytes. Without such an experiment, I do not think the current experiments are interpretable.

4. The injury experiments are again limited in their interpretability because they do not use CreER. It is very possible that Twist is turned on after CCl4 or surgical injury, and thus new hepatocytes might activate Tomato. It is unclear if previously Tomato-positive midzone hepatocytes were proliferating to increase the Tomato positive population. The authors use expression-based studies to argue against ectopic activation of Twist, but it is very difficult to exclude Cre activation using these types of studies.

Reviewer #1 (Public Review):

In this study, the authors investigate the role of triglycerides in spermatogenesis. This work is based on their previous study (PMID: 31961851) on triglyceride sex differences in which they showed that somatic testicular cells play a role in whole body triglyceride homeostasis. In the current study, they show that lipid droplets (LDs) are significantly higher in the stem and progenitor cell (pre-meiotic) zone of the adult testis than in the meiotic spermatocyte stages. The distribution of LDs anti-correlates with the expression of the triglyceride lipase Brummer (Bmm), which has higher expression in spermatocytes than early germline stages. Analysis of a bmm mutant (bmm[1]) - a P-element insertion that is likely a hypomorphic - and its revertant (bmm[rev]) as a control shows that bmm acts autonomously in the germline to regulate LDs. In particular, the number of LDs is significantly higher in spermatocytes from bmm[1] mutants than from bmm[rev] controls. Testes from males with global loss of bmm (bmm[1]) are shorter than controls and have fewer differentiated spermatids. The zone of bam expression, typically close to the niche/hub in WT, is now many cell diameters away from the hub in bmm[1] mutants. There is an increase in the number of GSCs in bmm[1] homozygotes, but this phenotype is probably due to the enlarged hub. However, clonal analyses of GSCs lacking bmm indicate that a greater percentage of the GSC pool is composed of bmm[1]-mutant clones than of bmm[rev]-clones. This suggests that loss of bmm could impart a competitive advantage to GSCs, but this is not explored in greater detail. Despite the increase in number of GSCs that are bmm[1]-mutant clones, there is a significant reduction in the number of bmm[1]-mutant spermatocyte and post-meiotic clones. This suggests that fewer bmm[1]-mutant germ cells differentiate than controls. To gain insights into triglyceride homeostasis in the absence of bmm, they perform mass spec-based lipidomic profiling. Analyses of these data support their model that triglycerides are the class of lipid most affected by loss of bmm, supporting their model that excess triglycerides are the cause of spermatogenetic defects in bmm[1]. Consistent with their model, a double mutant of bmm[1] and a diacylglycerol O-acyltransferase 1 called midway (mdy) reverts the bmm-mutant germline phenotypes.

There are numerous strengths of this paper. First, the authors report rigorous measurements and statistical analyses throughout the study. Second, the authors utilize robust genetic analyses with loss-of-function mutants and lineage-specific knockdown. Third, they demonstrate the appropriate use of controls and markers. Fourth, they show rigorous lipidomic profiling. Lastly, their conclusions are appropriate for the results. In other words, they don't over-state the results. Overall, the rigorously quantified results support the major aim that appropriate regulation of triglycerides are needed in a germline cell-autonomous manner for spermatogenesis.

This paper should have a positive impact on the field. First and foremost, there is limited knowledge about the role of lipid metabolism in spermatogenesis. The lipidomic data will be useful to researchers in the field who study various lipid species. Going forward, it will be very interesting to determine what triglycerides regulate in germline biology. In other words, what functions/pathways/processes in germ cells are negatively impacted by elevated triglycerides. And as the authors point out in the discussion, it will be important to determine what regulates bmm expression such that bmm is higher in later stages of germline differentiation.

Reviewer #2 (Public Review):

Summary:

Here, the authors show that neutral lipids play a role in spermatogenesis. Neutral lipids are components of lipid droplets, which are known to maintain lipid homeostasis, and to be involved in non-gonadal differentiation, survival, and energy. Lipid droplets are present in the testis in mice and Drosophila, but not much is known about the role of lipid droplets during spermatogenesis. The authors show that lipid droplets are present in early differentiating germ cells, and absent in spermatocytes. They further show a cell autonomous role for the lipase brummer in regulating lipid droplets and, in turn, spermatogenesis in the Drosophila testis. The data presented show that a relationship between lipid metabolism and spermatogenesis is congruous in mammals and flies, supporting Drosophila spermatogenesis as an effective model to uncover the role lipid droplets play in the testis.

Strengths and weaknesses:

The authors do a commendably thorough characterization of where lipid droplets are detected in normal testes: located in young somatic cells, and early differentiating germ cells. They use multiple control backgrounds in their analysis, including w[1118], Canton S, and Oregon R, which adds rigor to their interpretations. The authors employ markers that identify which lipid droplets are in somatic cells, and which are in germ cells. The authors use these markers to present measured distances of somatic and germ cell-derived lipid droplets from the hub. Because they can also measure the distance of somatic and germ cells with age-specific markers from the hub, these results allow the authors to correlate position of lipid droplets with the age of cells in which they are present. This analysis is clearly shown and well quantified.

The quantification of lipid droplet distance from the hub is applied well in comparing brummer mutant testes to wild type controls. The authors measure the number of lipid droplets of specific diameters, and the spatial distribution of lipid droplets as a function of distance from the hub. These measurements quantitatively support their findings that lipid droplets are present in an expanded population of cells further from the hub in brummer mutants. The authors further quantify lipid droplets in germline clones of specified ages; the quantitative analysis here is displayed clearly and supports a cell autonomous role for brummer in regulating lipid droplets in spermatocytes.

Data examining testis size and number of spermatids in brummer mutants clearly indicates the importance of regulating lipid droplets to spermatogenesis. The authors show beautiful images supported by rigorous quantification supporting their findings that brummer mutants have both smaller testes with fewer spermatids at both 29 and 25C. There is also significant data supporting defects in testis size, but not spermatid number, in 14-day-old brummer mutant animals compared to controls. Their analysis clearly shows an expanded region beyond the testis apex that includes younger germ cells, supporting a role for lipid droplets influencing germ cell differentiation during spermatogenesis.

The authors present a series of data exploring a cell autonomous role for brummer in the germline, including clonal analysis and tissue specific manipulations. The clonal data indicating increased lipid droplets in spermatocyte clones, and a higher proportion of brummer mutant GSCs at the hub are convincing and supported by quantitation. The authors also show a tissue specific rescue of the brummer testis size phenotype by knocking down mdy specifically in germ cells, which is also supported by statistically significant quantitation. The authors present data examining the number of spermatocyte and post-meiotic clones 14 days after clonal induction. Their finding is significant with a p-value of 0.0496, which they acknowledge is less robust than their other data reported in this study, and could be a result of a low sample size. They indicate that future studies might validate these results with additional samples.

The authors do a beautiful job of validating where they detect brummer-GFP by presenting their own pseudotime analysis of publicly available single cell RNA sequencing data. Their data is presented very clearly, and supports expression of brummer in older somatic and germline cells of the age when lipid droplets are normally not detected. The authors also present a thorough lipidomic analysis of animals lacking brummer to identify triglycerides as an important lipid droplet component regulating spermatogenesis.

Impact:

The authors present data supporting the broad significance of their findings across phyla. This data represents a key strength of this manuscript. The authors show that loss of a conserved triglyceride lipase impacts testis development and spermatogenesis, and that these impacts can be rescued by supplementing diet with medium-chain triglycerides. The authors point out that these findings represent a biological similarity between Drosophila and mice, supporting the relevance of the Drosophila testis as a model for understanding the role of lipid droplets in spermatogenesis. The connection buttresses the relevance of these findings and this model to a broad scientific community.

Reviewer #1 (Public Review):

Summary:<br /> Deletion of the hrp2 and hrp3 loci in P. falciparum poses an immediate public health threat. This manuscript provides a more complete understanding of the dynamic nature with which these deletions are generated. By delving into the likely mechanisms behind their generation, the authors also provide interesting insight into general Plasmodium biology that can inform our broader understanding of the parasite's genomic evolution.

Strengths:<br /> The sub-telomeric regions of P. falciparum (where hrp2 and hrp3 are located) are notoriously difficult to study with short-read sequence data. The authors take an appropriate, targeted approach toward studying the loci of interest, which includes read-depth analysis and local haplotype reconstruction. They additionally use both long-read and short-read data to validate their major findings. There is an extensive set of supplementary plots, which helps clarify several aspects of the data.

Weaknesses:<br /> In this first version, there are a few factors that hinder a full assessment of the robustness and replicability of the results. First, a number of the analyses lack basic details in the methods; for instance, one must visit the authors' personal website to find some of the tools used. Second, there are several tricky methodological points that are not fully documented. Read depths are treated (and plotted) discretely as 0/1/2 without any discussion of how thresholds were used and determined. For read mapping to standard vs hybrid chromosomes, there is no documentation on how assignments were made if partially ambiguous or how final sample calls were determined when some reads were discordant. There is no mention of how missing data were handled. Without this, it is difficult to know when conclusions were based on analyses that were more quantitative (for instance, using pre-determined read thresholds) or more subjective (with patterns being extracted visually). Third, while a new method is employed for local haplotype reconstruction (PathWeaver), the manuscript does not include details on this approach or benchmarking data with which to evaluate its performance and understand any potential artifacts.

Reviewer #2 (Public Review):

This work investigates the mechanisms, patterns, and geographical distribution of pfhrp2 and pfhrp3 deletions in Plasmodium falciparum. Rapid diagnostic tests (RDTs) detect P. falciparum histidine-rich protein 2 (PfHRP2) and its paralog PfHRP3 located in subtelomeric regions. However, laboratory and field isolates with deletions of pfhrp2 and pfhrp3 that can escape diagnosis by RDTs are spreading in some regions of Africa. They find that pfhrp2 deletions are less common and likely occur through chromosomal breakage with subsequent telomeric healing. Pfhrp3 deletions are more common and show three distinct patterns: loss of chromosome 13 from pfhrp3 to the telomere with evidence of telomere healing at breakpoint (Asia; Pattern 13-); duplication of a chromosome 5 segment containing pfhrp1 on chromosome 13 through non-allelic homologous recombination (NAHR) (Asia; Pattern 13-5++); and the most common pattern, duplication of a chromosome 11 segment on chromosome 13 through NAHR (Americas/Africa; Pattern 13-11++). The loss of these genes impacts the sensitivity of RDTs, and knowing these patterns and geographic distribution makes it possible to make better decisions for malaria control.

Reviewer #3 (Public Review):

Summary:<br /> The study provides a detailed analysis of the chromosomal rearrangements related to the deletions of histidine-rich protein 2 (pfhrp2) and pfhrp3 genes in P. falciparum that have clinical significance since malaria rapid diagnostic tests detect these parasite proteins. A large number of publicly available short sequence reads for the whole genome of the parasite were analyzed, and data on coverage and discordant mapping allowed the authors to identify deletions, duplications, and chromosomal rearrangements related to pfhrp3 deletions. Long-read sequences showed support for the presence of a normal chromosome 11 and a hybrid 13-11 chromosome lacking pfhrp3 in some of the pfhrp3-deleted parasites. The findings support that these translocations have repeatedly occurred in natural populations. The authors discuss the implications of these findings and how they do or do not support previous hypotheses on the emergence of these deletions and the possible selective pressures involved.

Strengths:<br /> The genomic regions where these genes are located are challenging to study since they are highly repetitive and paralogous and the use of long-read sequencing allowed to span the duplicated regions, giving support to the identification of the hybrid 13-11 chromosome.

All publicly available whole-genome sequences of the malaria parasite from around the world were analysed which allowed an overview of the worldwide variability, even though this analysis is biased by the availability of sequences, as the authors recognize.

Despite the reduced sample size, the detailed analysis of haplotypes and identification of the location of breakpoints gives support to a single origin event for the 13-5++ parasites.

The analysis of haplotype variation across the duplicated chromosome-11 segment identified breakpoints at varied locations that support multiple translocation events in natural populations. The authors suggest these translocations may be occurring at high frequency in meiosis in natural populations but are strongly selected against in most circumstances, which remains to be tested.

Weaknesses:<br /> Relying on sequence data publicly available, that were collected based on diagnostic test positivity and that are limited by sequencing availability, limits the interpretation of the occurrence and relative frequency of the deletions. In the discussion, caution is needed when identifying the least common and most common mechanisms and their geographical associations. The identification of only one type of deletion pattern for Pfhrp2 may be related to these biases.

The specific objectives of the study are not stated clearly, and it is sometimes difficult to know which findings are new to this study. Is it the first study analyzing all the worldwide available sequences? Is it the first one to do long-read sequencing to span the entire duplicated region?

Another aspect that should be explained in the introduction is that there was previous information about the association of the deletions to patterns found in chromosomes 5 and 11. In the short-read sequences results, it is not clear if these chromosomes were analysed because of the associations found in this study (and no associations were found to putative duplications or deletions in other chromosomes), or if they were specifically included in the analysis because of the previous information (and the other chromosomes were not analysed).

An interesting statement in the discussion is that existing pfhrp3 deletions in a low-transmission environment may provide a genetic background on which less frequent pfhrp2 deletion events can occur. Does it mean that the occurrence of pfhrp3 deletions would favor the pfhrp2 deletion events? How, and is there any evidence for that?

Reviewer #1 (Public Review):

The authors aim to develop an easy-to-use image analysis tool for the mother machine that is used for single-cell time-lapse imaging. Compared with related software, they tried to make this software more user-friendly for non-experts with a design of "What You Put Is What You Get". This software is implemented as a plugin of Napari, which is an emerging microscopy image analysis platform. The users can interactively adjust the parameters in the pipeline with good visualization and interaction interface.

Strengths:<br /> - Updated platform with great 2D/3D visualization and annotation support.<br /> - Integrated one-stop pipeline for mather machine image processing.<br /> - Interactive user-friendly interface.<br /> - The users can have a visualization of intermediate results and adjust the parameters.

Weaknesses:<br /> - Based on the presentation of the manuscript, it is not clear that the goals are fully achieved.<br /> - Although there is great potential, there is little evidence that this tool has been adopted by other labs.<br /> - the diversity of datasets used in this study is limited.<br /> - Some paragraphs in the Discussion section are like blogs with general recommendations. Although the suggestions look pretty useful, it is not the focus of this manuscript. It might be more appropriate to put it in the GitHub repo or a documentation page. The discussion should still focus on the software, such as features, software maintenance, software development roadmap, and community adoption.

A discussion of the likely impact of the work on the field, and the utility of the methods and data to the community.<br /> - The impact of this work depends on the adoption of the software MM3. Napari is a promising platform with an expanding community. With good software user experience and long-term support, there is a good chance that this tool could be widely adopted in the mother machine image analysis community.<br /> - The data analysis in this manuscript is used as a demo of MM3 features, rather than scientific research.

Reviewer #2 (Public Review):

The authors present an image-analysis pipeline for mother-machine data, i.e., for time-lapses of single bacterial cells growing for many generations in one-dimensional microfluidic channels. The pipeline is available as a plugin of the python-based image-analysis platform Napari. The tool comes with two different previously published methods to segment cells (classical image transformation and thresholding as well as UNet-based analysis), which compare qualitatively and quantitatively well with the results of widely accessible tools developed by others (BACNET, DelTA, Omnipose). The tool comes with a graphical user interface and example scripts, which should make it valuable for other mother-machine users, even if this has not been demonstrated yet.

The authors also add a practical overview of how to prepare and conduct mother-machine experiments, citing their previous work, referring to detailed instructions on their github page, and giving more advice on how to load cells using centrifugation.

Finally, the authors emphasize that machine-learning methods for image segmentation reproduce average quantities of training datasets, such as the length at birth or division. Therefore, differences in training can propagate to differences in measured average quantities. This result is not surprising but good to remember before interpreting absolute measurements of cell shape.

Reviewer #1 (Public Review):

Summary:<br /> This work addresses how to quantify functional compensation throughout the aging process and identifies brain regions that engage in compensatory mechanisms during the Cattell task, a measure of fluid cognition. The authors find that regions of the frontal cortex and cuneus showed unique effects of both age and performance. Interestingly, these two regions demonstrated differential activation patterns taking into account both age and performance. Specifically, the researchers found that the relationship between performance and activation in the cuneal ROI was strongest in older adults, however, this was not found in younger adults. These findings suggest that specifically within the cuneus, greater activation is needed by older adults to maintain performance, suggestive of functional compensation.

Strengths:<br /> The conclusions derived from the study are well supported by the data. The authors validated the use of the in-scanner Cattell task by demonstrating high reliability in the same sample with the standard out-of-scanner version. Some strengths of the study include the large sample size and wide age range of participants. The authors use a stringent Bayes factor of 20 to assess the strength of evidence. The authors used a whole-brain approach to define regions of interest (ROIs) based on activation patterns that were jointly related to age and performance. Overall, the methods are technically sound and support the authors' conclusions.

Weaknesses:<br /> While the manuscript is methodologically sound, the following aspects of image acquisition and data analysis need to be clarified to ensure replicability and reproducibility. The authors state that the sample is a "population-derived adult lifespan sample", the lack of demographic information makes it impossible to know if the sample is truly representative. Though this may seem inconsequential, education may impact both cognitive performance and functional activation patterns. Moreover, the authors do not report race/ethnicity in the manuscript. This information is essential to ensure representativeness in the sample. It is imperative that barriers to study participation within minoritized groups are addressed to ensure rigor and reproducibility of findings.

For the whole-brain analysis in which the ROIs were derived, the authors used a threshold-free cluster enhancement (TFCE; Smith & Nichols 2009). The methodological paper cited suggests that individuals' TCFE image should still be corrected for multiple comparisons using the following: "to correct for multiple comparisons, one [...] has to build up the null distribution (across permutations of the input data) of the maximum (across voxels) TFCE score, and then test the actual TFCE image against that. Once the 95th percentile in the null distribution is found then the TFCE image is simply thresholded at this level to give inference at the p < 0.05 (corrected) level." (Smith & Nichols, 2009). Although the authors mention that clusters were estimated using 2000 permutations, there is no mention of the TFCE image itself being thresholded. While this would impact the overall size of the ROIs used in the study, the remaining analyses are methodologically sound.

Reviewer #2 (Public Review):

This work by Knights et al., makes use of the Cam-CAN dataset to investigate functional compensation during a fluid processing task in older adults, in a fairly large sample of approximately 200 healthy adults ranging from 19 to 87. Using univariate methods, the authors identify two brain regions in which activity increases as a function of both age and performance and conduct further investigations to assess whether the activity of these regions provides information regarding task difficulty. The authors conclude that the cuneal cortex - a region of the brain previously implicated in visual attention - shows evidence of compensation in older adults.

The conclusions of the paper are well supported by the data, and the authors use appropriate statistical analyses. The use of multivariate methods over the last 20 years has demonstrated many effects that would have been missed using more traditional univariate analysis techniques. The data set is also of an appropriate size, and as the authors note, fluid processing is an extremely important domain in the field of cognition in aging, due to its steep decline over aging. However, it might have been nice to see an analysis of a more crystallised intelligence task included too, as a contrast since this is an area that does not demonstrate such a decline (and perhaps continues to improve over aging).

Reviewer #3 (Public Review):

This neuroimaging study investigated how brain activity related to visual pattern-based reasoning changes over the adult lifespan, addressing the topic of functional compensation in older age. To this end, the authors employed a version of the Cattell task, which probes visual pattern recognition for identifying commonalities and differences within sets of abstract objects in order to infer the odd object among a given set. Using a state-of-the-art univariate analysis approach on fMRI data from a large lifespan sample, the authors identified brain regions in which the activation contrast between hard and easy Cattell task conditions was modulated by both age and performance. Regions identified comprised prefrontal areas and bilateral cuneus. Applying a multivariate decoding approach to activity in these regions, the authors went on to show that only in older adults, the cuneus, but not the prefrontal regions, carried information about the task condition (hard vs. easy) beyond that already provided by activity patterns of voxels that showed a univariate main effect of task difficulty. This was taken as compelling evidence for task-specific compensatory activity in the cuneus in advanced age.

The study is well-motivated and well-written. The authors used appropriate, rigorous methods that allowed them to control for a range of possible confounds or alternative explanations. Laudable aspects include the large sample with a wide and even age distribution, the validation of the in-scanner task performance against previous results obtained with a more standard version outside the scanner, and the control for vascular age-related differences in hemodynamic activity via a BOLD signal amplitude measure obtained from a separate resting-state fMRI scan. Overall, the conclusions are well-supported by the data.

In the following, I list some points of discussion that I would like to see addressed by the authors in a revision:

1) I don't quite follow the argumentation that compensatory recruitment would need to show via non-redundant information carried by any given non-MDN region (cf. p14). Wouldn't the fact that a non-MDN region carries task-related information be sufficient to infer that it is involved in the task and, if activated increasingly with increasing age, that its stronger recruitment reflects compensation, rather than inefficiency or dedifferentiation? Put differently, wouldn't "more of the same" in an additional region suffice to qualify as compensation, as compared to the "additional information in an additional region" requirement set by the authors? As a consequence, in my honest opinion, showing that decoding task difficulty from non-MDN ROIs works better with higher age would already count as evidence for compensation, rather than asking for age-related increases in decoding boosts obtained from adding such ROIs. It would be interesting to see whether the arguably redundant frontal ROI would satisfy this less demanding criterion. At any rate, it seems useful to show whether the difference in log evidence for the real vs. shuffled models is also related to age.

2) Relatedly, does the observed boost in decoding by adding the cuneal ROI (in older adults) really reflect "additional, non-redundant" information carried by this ROI? Or could it be that this boost is just a statistical phenomenon that is obtained because the cuneus just happens to show a more clear-cut, less noisy difference in hard vs. easy task activation patterns than does the MDN (which itself may suffer from increased neural inefficiency in older age), and thus the cuneaus improves decoding performance without containing additional (novel) pieces of information (but just more reliable ones)? If so, the compensation account could still be maintained by reference to the less demanding rationale for what constitutes compensation laid out above.

3) On page 21, the authors state that "...traditional univariate criteria alone are not sufficient for identifying functional compensation." To me, this conclusion is quite bold as I'd think that this depends on the unvariate criterion used. For instance, it could be argued that compensation should be more clearly indicated by an over additive interaction as observed for the relationship of cuneal activity with age and performance (i.e., the activity increase with better performance becomes stronger with age), rather than by an additive effect of age and performance as observed for the prefrontal ROI (see Fig. 2C). In any case, I'd appreciate it if the authors discussed this issue and the relationship between univariate and multivariate results in more detail (e.g. how many differences in sensitivity between the two approaches have contributed), in particular since the sophisticated multivariate approach used here is not widely established in the field yet.

4) As to the exclusion of poorly performing participants (see p24): If only based on the absolute number of errors, wouldn't you miss those who worked (overly) slowly but made few errors (possibly because of adjusting their speed-accuracy tradeoff)? Wouldn't it be reasonable to define a criterion based on the same performance measure (correct - incorrect) as used in the main behavioural analyses?

5) Did the authors consider testing for negative relationships between performance and brain activity, given that there is some literature arguing that neural efficiency (i.e. less activation) is the hallmark of high intelligence (i.e. high performance levels in the Cattell task)? If that were true, at least for some regions, the set of ROIs putatively carrying task-related information could be expanded beyond that examined here. If no such regions were found, it would provide some evidence bearing on the neural efficiency hypothesis.

Reviewer #1 (Public Review):

In this work, the authors address a fundamental question in the biological physics of many marine organisms, across a range of sizes: what is the mechanism by which they measure and respond to pressure. Such responses are classed under the term "barotaxis", with a specific response termed "barokinesis", in which swimming speed increases with depth (hence with pressure). While macroscopic structures such as gas-filled bladders are known to be relevant in fish, the mechanism for smaller organisms has remained unclear. In this work, the authors use ciliated larvae of the marine annelid Platynereis dumerilii to investigate this question. This organism has previously been of great importance in unravelling the mechanism of multicellular phototaxis associated with a ciliated band of tissue directed by light falling on photoreceptors.

In the present work, the authors use a bespoke system to apply controlled pressure changes to organisms in water and to monitor their transient response in terms of swimming speed and characteristics of swimming trajectories. They establish that those changes are based on relative pressure, and are reflected in changes in the ciliary beating. Significantly, by imaging neuronal activity during pressure stimulation, it was shown that ciliary photoreceptor cells are activated during the pressure response. That these photoreceptors are implicated in the response was verified by the reduced response of certain mutants, which appear to have defective cilia. Finally, serotinin was implicated in the synaptic response of those neurons.

This work is an impressive and synergistic combination of a number of different biological and physical probes into this complex problem. The ultimate result, that ciliary photoreceptors are implicated, is fascinating and suggests an interesting interplay between photoreception and pressure detection. I see no obvious weaknesses.

Reviewer #2 (Public Review):

Summary:

Bezares Calderon et al demonstrate that the planktonic larva of marine annelid Platynereis dumerii responds to increased pressure in the water column by swimming upward. The authors show the larvae do so via their ciliated photoreceptors that recruit serotoninergic motor neurons to elicit swimming via an increased ciliary beat frequency of the multiciliary band of their head.

Strengths:

The authors built original setups to increase water pressure and monitor behavior or calcium activity in the cells. Using their original setups, they combined behavioral and imaging experiments on wild type and mutant larvae for an opsin to show how photoreceptors encode the response to pressure and recruit in response serotoninergic motor neurons that increase the ciliary beating frequency of the multiciliary band in the head.

Weaknesses:

Technical note:<br /> The authors should use DF/F to quantify over time the calcium response in photoreceptors. Furthermore, they should show that there is no concern of motion artifact when the pressure changes - as it could be a concern.

The authors have not shown<br /> 1- how the off response to decrease of pressure is mediated<br /> 2- which receptor/channel mediates in photoreceptors the response to increased pressure,<br /> 3- nor how the integration of light and pressure information is integrated by photoreceptors in order to guide the behavior of the larvae.

These points are beyond the scope of the study. However, if possible within a short time frame, it would be really interesting to find out whether conflicting stimuli or converging stimuli (light & pressure) can cancel each other out or synergize. In particular since the authors cite unpublished results in the discussion: "Our unpublished results indeed suggest that green light determines the direction of swimming and can override upward swimming induced by pressure, which only influences the speed of swimming (LABC and GJ, unpublished)." Showing in one panel this very cool phenomenon would be exciting & open tons of questions for the field.

Reviewer #1 (Public Review):

Summary:<br /> In this study of metabolism using Xenopus, explanted porcine hearts and limbs, and human organs-on-chips, Sperry et al studied the ability of WB3 to slow metabolism and mobility. The group developed WB3, an analog of SNC80, void of SNC80's delta-opioid receptor binding capacity, and studied its metabolic impact. The authors concluded that SNC80 and its analog WB3 can induce "biostasis" and produce a hypometabolic state which holds promise for prolonging organ viability in transplant surgery as well as other potential clinical benefits.

Strengths:<br /> This study also opens new avenues for therapeutic possibilities in areas such as trauma, acute infection, and brain injuries. The overall methodology is acceptable, but certain concerns should be addressed.

Weaknesses:<br /> 1. In cardiac and renal transplantation, cold preservation in ice remains a common practice for transporting explanted hearts to donors which remains a cheap and easily accessible way of preserving organs. While ex-vivo mechanical circulatory platforms have been developed and are increasingly being utilized to prolong organ viability, cold preservation remains widely used. The authors perfused explanted hearts with oxygenated perfusion preservation devices at subnormothermic temperatures (20-23C) which is even much lower than routinely used in clinical cardiopulmonary bypass scenarios (28-32C) (in the discussion, the authors allude to SNC80's possible "protective effect" in cardiac bypass). It is unclear how much of the hypometabolic state is related to WB3 administration versus hypothermia. The study will benefit from a comparison of WB3 administration and hypothermia in Xenopus, explanted porcine organs versus cold preservation alone to show distinction in biostasis parameters.

2. The authors selected SNC80 based on a literature survey where it was identified based on its ability to induce hypothermia and protect against the effects of spinal cord ischemia in rodents. While this makes sense, were other drugs (eg. Puerarin) considered? The induction of hypothermia and spinal cord protective effect of SNC80 may be multifactorial and not necessarily related to its biostatic effects as the authors describe. Please provide some more context into the background of SNC80.

3. In most of the models, the primary metric that the authors utilize to characterize metabolic activity is oxygen consumption, which is a somewhat limited indicator. For instance, this does not provide any information, however, on anaerobic metabolic activity. In addition, the ATP/ADP ratio was found to decrease in the organ chips where SNC80 was utilized, but similar findings were not presented for the other models.

4. The authors should provide a more detailed explanation of SNC80's mechanisms of interaction with proteins related to transmembrane transport, mitochondrial activity, and metabolic processes. What is the impact of SNC80 on mitochondrial function, particularly ATP production and mitochondrial respiration? Are there changes in mitochondrial membrane potential, electron transport chain activity, or oxidative phosphorylation? In this context, the authors discuss the potential role of NCX1 as a binding target for SNC80 and its various mechanisms in slowing metabolism. However, no experiments have been done to confirm this binding in the present study. Co-immunoprecipitation studies using appropriate antibodies against SNC80 and NCX1 should be considered to demonstrate their direct binding. Additionally, surface plasmon resonance (SPR) or isothermal titration calorimetry (ITC) experiments could be employed to quantify the binding affinity between SNC80 and NCX1, providing further evidence of their interaction. These experiments would elucidate the binding mechanism between SNC80 and NCX1 and reveal more information on the mechanism of action for SNC80.

5. The manuscript notes that histological analysis was conducted, but it seems that only example images are provided, such as Figure 4f. Quantified histological data would provide a more thorough understanding of tissue integrity.

6. Some of the points mentioned in the discussion and conclusion are rather strong and based on possible associations such as SNC80's potential vasodilatory capacity conferring a cardioprotective effect, and ability to reversibly suppress metabolism across different temperatures and species. Please tone this down and stay limited to the organs studied. Further, the reversibility of the findings may be more objectively assessed by biomarkers with decreased immunofluorescence in response to ischemia such as troponin I for the heart and albumin for the liver. Additionally, an investigation of proteins involved in inflammation, hypoxia, and key cell death pathways using immunohistochemistry analysis can better describe the impact of treatment on apoptosis/necroptosis.

7. What could be the underlying cause of the observed increase in intercellular spacing after SNC80 administration in porcine limbs which also seems to be evident in the heart histology samples? This seems to be more prominent in the SNC80 compared to the vehicle group.

8. In the Discussion section, it would be valuable to provide a concise interpretation of the lipidomic data, particularly explaining how changes in acylcarnitine and cholesterol ester levels may relate to tadpole metabolism, hibernation, or other biological processes.

9. What are the limitations or disadvantages of the study? Does SNC80 possess any immunomodulatory properties that might affect the outcomes of organ transplantation? Are there specific organs for which SNC80 may not be a suitable preservation agent, and if so, what are the reasons behind this?

Reviewer #2 (Public Review):

Summary:<br /> This manuscript titled "Identification of pharmacological inducers of a reversible hypometabolic state for whole organ preservation" reports the effects of delta opioid receptor activator SNC80 and its modified analog WB3 with ~1,000 times less delta opioid receptor binding activity on metabolic state.

Strengths:<br /> This is an interesting study with potentially broad implications for organ preservation.

Weaknesses:<br /> There are several limitations that raise concerns.

1. The authors developed an analog of a known delta opioid receptor activator SNC80 with three orders of magnitude lesser binding with the delta opioid receptor WB3. This will likely reduce the undesirable effects of SNC80 while preserving the metabolic slowing needed for organ preservation. Yet, most experiments were done with SNC80, not the superior modification, WB3, shown in only a limited set of experiments, Figure 3.

2. The heart is one of the most challenging organs to preserve, and some experiments are done to establish the metabolic effects of SNC80. However, the biodistribution study, shown in Figure 2, conspicuously omitted the heart.

3. I do not understand the design of the electrophysiology and contractility experiments with the porcine hearts. How did you defibrillate the hearts after removal and establishing perfusion? Lines 173-175 on Page 7 state: "After defibrillation with epinephrine, the P and QRS waveforms were visible in ECGs from 3 of 4 SNC80-treated hearts (Table S1), suggesting that those hearts regain atrial and ventricular polarization." Please clarify. Defibrillation is done with an electric shock. Also, please show the ECG recordings to support your conclusions about "polarization." What did you mean by "polarization"? Depolarization? Repolarization? Or resting potential. To establish a normal physiological state, please show ECG waveforms and present data on basic ECG characteristics: heart rate, PQ and QT intervals, and P and QRS durations. I recommend perfusion of the porcine heart with WB3, not only SNC80.

4. Pathology data also raises concerns. The histology images shown in Figure 4f are not quantified, and they show apparently higher levels of tissue disruption in SNC80-treated tissue vs vehicle-treated. The test (lines 169-171) confirms this concern: "In some hearts treated with SNC80, greater waviness of muscle fibers was observed, possibly indicating a state of muscle contraction." It will be helpful to measure markers of apoptosis and necrosis and to apply TTC viability staining.

5. The apparent state of contracture suggests a higher degree of myocardial damage and a high intracellular calcium level in SNC80-treated hearts. The authors suggested that the sodium-calcium exchanger NCX is a possible target of SNC80 and could be responsible for the "hypometabolic state." However, NCX1 is critically important in the extrusion of cytosolic Ca2+ during the diastolic phase. Failure to remove excessive calcium and restore ionic homeostasis would lead to calcium overload and heart failure.

6. I am surprised the authors did not consider using the gold standard assay for measuring mitochondrial function in cells by the Seahorse Cell Mito Stress Test.

Reviewer #3 (Public Review):

In this manuscript, Sperry and colleagues identify SNC80 as a compound that can slow metabolism and mimic hibernation, thereby prolonging tissue viability in organ transplantation and cardiovascular disease settings. Overall, the use of varied and relevant model systems is a strength of this study.

The authors perform a literature search to identify SNC80 as a promising hit. However, the details of the literature search, a list of other potential hits, and the criteria for identification of SNC80 are not described. The hypometabolic effect of SNC80 exposure is well-characterized in the Xenopus model. Furthermore, the authors show that SNC80 localises to the brain, but do not discuss several studies that have pointed to convulsions induced by exposure to high doses of SCN80, and whether this would be apparent in the Xenopus studies. The authors have promising data on the WB3 morpholino that retains or even improves on the hypometabolism phenotype of SCN80 while likely not retaining delta opioid activity. However, this is not functionally demonstrated. Moreover, WB3 is not used in any of the other assays and models used in the study. In the setting of cardiac transplant surgery, co-administration of SNC80 reduces metabolic activity and inflammation, although it is unclear if there is an improvement in recovery of organ function due to SCN80. The reversible induction of hypometabolic status is also demonstrated in two different organ chips. These models could identify the differential response of epithelial cells and vascular cells to drug perfusion, but the authors have mostly focused on the former. Finally, the authors identify specific targets for the hypometabolic effect of SNC80, which is a valuable resource for other screening studies and can form the basis for future work.

Reviewer #1 (Public Review):

In this work, the authors have explored how treating C. albicans fungal cells with EDTA affects their growth and virulence potential. They then explore the use of EDTA-treated yeast as a whole-cell vaccine in a mouse model of systemic infection. In general, the results of the paper are unsurprising. Treating yeast cells with EDTA affects their growth and the addition of metals rescues the phenotype. Because of the significant growth defects of the cells, they don't infect mice and you see reduced virulence. Injection with these cells effectively immunises the mice, in the same way that heat-killed yeast cells would. The data is fairly sound and mostly well-presented, and the paper is easy to follow. However, I feel the data is an incremental advance at best, and the immune analysis in the paper is very basic and descriptive.

Strengths:

Detailed analysis of EDTA-treated yeast cells

Weaknesses:

- Basic immune data with little advance in knowledge.<br /> - No comparison between their whole-cell vaccine and others tried in the field.<br /> - The data is largely unsurprising and not novel.

Reviewer #2 (Public Review):

Summary:

Invasive fungal infections are very difficult to treat with limited drug options. With the increasing concern of drug resistance, developing an antifungal vaccine is a high priority. In this study, the authors studied the metal metabolism in Candida albicans by testing some chelators, including EDTA, to block the metal acquisition and metabolism by the fungus. Interestingly, they found EDTA-treated yeast cells grew poorly in vitro and non-pathogenic in vivo in a murine model. Mice immunized by EDTA-treated Candida (CAET) were protected against challenge with wild-type Candida cells. RNA-Seq analysis to survey the gene expression profile in response to EDTA treatment in vitro revealed upregulation of genes in metal homeostasis and downregulation of ribosome biogenesis. They also revealed an induction of both pro- and anti-inflammatory cytokines involved in Th1, Th2 and Th17 host immune response in response to CAET immunization. Overall, this is an interesting study with translational potential.

Strengths:

The main strength of the report is that the authors identified a potential whole-cell live vaccine strain that can provide full protection against candidiasis. Abundant data both on in vitro phenotype, gene expression profile, and host immune response have been presented.

Weaknesses:

A weakness is that the immune mechanism of CAET-mediated host protection remains unclear. The immune data is somewhat confusing. The authors only checked cytokines and chemokines in blood. The immune response in infected tissues and antibody response may be investigated.

Reviewer #3 (Public Review):

Summary:

The authors are trying to find a vaccine solution for invasive candidiasis.

Strengths:

The testing of the antifungal activity of EDTA on Candida is not new as many other papers have examined this effect. The novelty here is the use of this EDTA-treated strain as a vaccine to protect against a secondary challenge with wild-type Candida.

Weaknesses:

However, data presented in Figure 5 and Figure 6 are not convincing and need further experimental controls and analysis as the authors do not show a time-dependent effect on the CFU of their vaccine formulation.

The methodology used is also an issue. As it stands, the impact is minor.

Reviewer #1 (Public Review):

The paper by Perovic and colleagues describes how important blood vessels called collaterals form during development and remodel/expand upon injury to the brain. These vessels are conduits between arteries that do not have strong blood flow physiologically but upon injury can compensate for conduit loss. Published work by others is largely descriptive and does not address the cellular sources of collaterals over time. Here elegant lineage tracing is used to better understand the source of vascular endothelial cells during embryonic development, and how these lineages contribute to remodeling upon injury. The work is ambitious and important as collateral capacity can strongly influence the trajectory of outcomes with vascular blockage. The work reveals that proliferative arterial EC is the primary contributor to the collaterals developmentally, with a small contribution from capillary/venous EC, and that this shifts to almost completely arterial contribution from birth onward. There are several aspects of the work that, if addressed, would strengthen the study and better support the interesting and novel conclusions, including analysis of non-collateral lineage contributions, more careful interpretation of fixed image data, and more careful annotation of the image panels.

Reviewer #2 (Public Review):

Pial collateral vessels are anastomotic connections that cross-connect distal arterioles of the middle, anterior, and posterior cerebral arteries. With respect to ischemic stroke, good pial collateral flow positively correlates with decreased infarct volume and improved recovery; accordingly, optimizing collateral flow represents an important intervention for limiting stroke damage. The goal of this study was to determine the endothelial cell (EC) subtype(s) that contribute to the embryonic and neonatal development of pial collaterals and their expansion in response to stroke. To this end, the authors used lineage tracing methods in the mouse, labeling arterial endothelial cells (using Bmx-CreERT on switch line, R26mTmG) or venous and microvascular endothelial cells (using Vegfr3-CreERT on R26mTmG) and assessing pial collaterals via confocal microscopy. The authors convincingly demonstrate that arterial-lineage ECs comprise the majority of pial collateral ECs during development and in adulthood, with a minor contribution from pial plexus-derived microvascular ECs that decline over time. They also convincingly demonstrate that pial collateral outward remodeling after experimentally-induced stroke (distal middle cerebral artery occlusion, or dMCAO) involves, at least in part, local proliferation of arterial-lineage ECs. The latter is intriguing given that arterial ECs generally leave the cell cycle. While these conclusions are quite solid, some key details are missing that could improve analysis, and some important caveats are not addressed. Moreover, less convincing are mechanistic claims that pial collaterals form via a migratory process of "mosaic colonization" of a preexisting vessel.

1. It is difficult to understand whether individual collaterals are truly mosaic vessels, or whether arterial or venous/microvascular lineage ECs predominate in any particular region of the pial collateral vasculature. This is due to a number of methodological reasons: arterial and venous/microvascular contributions to pial collaterals were assessed independently, only a few (and in some cases, just one) collaterals were analyzed in each mouse, and regionality/location of collaterals was not addressed. Additionally, the inefficiency and variability of EC labeling, especially with the Vegfr3-CreERT line (Fig. S1, ~6-30%), compounds this problem.

2. The identification of "pre-collateral" vessels requires further support. The authors define these vessels by their connection to the feeding artery, their (often) larger diameter, and their more pronounced ICAM2 expression. While most of these criteria are demonstrated in Figure S3, it is not apparent how these vessels were defined in Figure 4, which lacks specific annotation of each of these identifying criteria. As the identification of these novel vessels is one of the key findings of this paper, a more robust method of unambiguously defining them is warranted.

3. The conclusion that collateral-forming ECs migrate in the direction of flow into preexisting vessels is not well supported. The authors state that the presence of filopodial projections (Figure 4) supports this conclusion. However, filopodia number and directional polarization/orientation were not quantified, and "intercalation movements"/migration, per se, cannot be inferred from these static images.

4. In Figure 5, the simplest explanation for relative Cx40 expression in different vessels is the absence (low expression) or presence (high expression) of flow. This figure provides little mechanistic insight beyond this already-known relationship, and it is unclear how many times this experiment was performed (there is no N, no quantification or correlation).

5. There is no statistical analysis in this work. This is justified by the authors by their admission that the study is of a "descriptive nature and...exploratory design."

Reviewer #3 (Public Review):

Summary:<br /> These studies focus on a very interesting, understudied phenomenon in vascular development - the formation of pial collaterals between cerebral arteries. Understanding the mechanism(s) that regulates this process during normal development could provide important insights for the treatment of adult stroke patients, for which repair is highly dependent on collateral formation. Insights may also be relevant to other collateral-dependent diseases, such as heart disease and chronic peripheral ischemia.

Strengths:<br /> The investigators use lineage tracing and 3D imaging to show that, in mouse embryos, endothelial cells (ECs) predominantly from Bmx+ arteries and some from the Vegfr3+ microvasculature, invade pre-existing pre-collateral vascular structures in a process they termed "mosaic colonization", and arterialization of the vessel segments is said to occur concurrently with colonization, although details about EC phenotypes are lacking. Growth of the collaterals in response to ischemic injury relies on local replication of the ECs within the collaterals and not further recruitment from veins and the microvasculature. Although detailed molecular mechanisms are not provided, demonstration of the "cellular mechanism" of pial collateral vascularization is novel.

Weaknesses:<br /> Nonetheless, there are some issues that should be addressed, particularly to clarify the phenotype of the ECs forming the collaterals and expanding in response to injury; only their "origin" was traced and not their identity/growth after labeling in Bmx+ vessels.

Reviewer #2 (Public Review):

The authors present here a mathematical and computational study of the topological/graph theory requirements to obtain sustained oscillations in neural network models. A first approach mathematically demonstrates that, for a given network of interconnected neural populations (understood in the sense of dynamical systems) requires an odd number of inhibitory populations to sustain oscillations. The authors extend this result via numerical simulations of (i) a simplified set of Wilson-Cowan networks, (ii) a simplified circuit of the cortico-basal ganglia network, and (iii) a more complex, spike-based neural network of basal ganglia network, which provides insight on experimental findings regarding abnormal synchrony levels in Parkinson's Disease (PD).

The work elegantly and effectively combines a solid mathematical proof with careful numerical simulations at different levels of description, which is uncommon and provides additional layers of confidence to the study. Furthermore, the authors included detailed sections to provide intuition about the mathematical proof, which will be helpful for readers less inclined to the perusal of mathematical derivations. Its insightful and well-informed connection with a practical neuroscience problem, the presence of strong beta rhythms in PD, elevates the potential influence of the study and provides testable predictions.

In its updated form, the authors have solved the most pressing issues of the study, by acknowledging the limitations of their work regarding the effects of delays in oscillations, and addressing some of these effects in new simulations. Although some interesting simulations are still not presented in the revised version, they could constitute the focus of future work to complement the conclusions presented here. The absence of explanations for some of the figures and panels has been corrected, and the issues with grammar and lack of clarity have been improved. This important work is therefore now improved.

Reviewer #1 (Public Review):

Summary:

Authors study appearance of oscillations in motifs of linear threshold systems, coupled in specific topologies. They derive analytically conditions for appearance of oscillations, in the context of excitatory and inhibitory links. They also emphasize the higher importance of the topology, compared to the strength of the links, though it is not straightforward to apply this for brain networks where the weights can be distributed several orders of magnitude. Finally the results are confirmed with WC oscillators. The findings are to some extent confirmed with spiking neurons, though here results are less clear.

Overall, the results are sound from a theoretical perspective, but I still find hard to believe that they are of significant relevance for biological networks, or in particular for the oscillations of BG-thalamus-cortex loop in PD. I find motifs in general to be too simplistic for multiscale and generally large networks as it is the case in the brain. Moreover, the division on regions is more or less arbitrary by definition, and having such a strong dependence on odd/even number of inhibitory links is far from reality. Another limitation is the fact that the cortex is considered as a single node. Similarly, decomposing even such a coarse network in all possible (238 in this case) motifs doesn't seem of much relevance, when I'd assume that the emergence of pathological rhythms is more of an emergent phenomena.

Strengths:

From the point of nonlinear dynamics, the results are solid, and the intuition behind the proofs of the theorems is well explained.

Weaknesses:

As stated in the summary, I find the work to be too theoretical without a real application for the brain dynamics, where the networks are generally very large. The odd/even number rule is too strict, and talking about fixed and definite number of cycles in actual brain seems too simplistic. Moreover, the cortex is considered as a single node, and finally the impact of the delays is ignored even though they define the synchronizability of the brain network, and previous works on the amplitude reduction due to the time-delays in difference-coupled networks of oscillators is not mentioned.

Reviewer #1 (Public Review):

Summary:

The authors define a new metric for visual displays, derived from psychophysical response times, called visual homogeneity (VH). They attempt to show that VH is explanatory of response times across multiple visual tasks. They use fMRI to find visual cortex regions with VH-correlated activity. On this basis, they declare a new visual region in the human brain, area VH, whose purpose is to represent VH for the purpose of visual search and symmetry tasks.

Strengths:

The authors present carefully designed experiments, combining multiple types of visual judgments and multiple types of visual stimuli with concurrent fMRI measurements. This is a rich dataset with many possibilities for analysis and interpretation.

Weaknesses:

The datasets presented here should provide a rich basis for analysis. However, in this version of the manuscript, I believe that there are major problems with the logic underlying the authors' new theory of visual homogeneity (VH), with the specific methods they used to calculate VH, and with their interpretation of psychophysical results using these methods. These problems with the coherency of VH as a theoretical construct and metric value make it hard to interpret the fMRI results based on searchlight analysis of neural activity correlated with VH. In addition, the large regions of VH correlations identified in Experiments 1 and 2 vs. Experiments 3 and 4 are barely overlapping. This undermines the claim that VH is a universal quantity, represented in a newly discovered area of the visual cortex, that underlies a wide variety of visual tasks and functions.

Maybe I have missed something, or there is some flaw in my logic. But, absent that, I think the authors should radically reconsider their theory, analyses, and interpretations, in light of the detailed comments below, to make the best use of their extensive and valuable datasets combining behavior and fMRI. I think doing so could lead to a much more coherent and convincing paper, albeit possibly supporting less novel conclusions.

THEORY AND ANALYSIS OF VH

1) VH is an unnecessary, complex proxy for response time and target-distractor similarity.

VH is defined as a novel visual quality, calculable for both arrays of objects (as studied in Experiments 1-3) and individual objects (as studied in Experiment 4). It is derived from a center-to-distance calculation in a perceptual space. That space in turn is derived from the multi-dimensional scaling of response times for target-distractor pairs in an oddball detection task (Experiments 1 and 2) or in a same-different task (Experiments 3 and 4). Proximity of objects in the space is inversely proportional to response times for arrays in which they were paired. These response times are higher for more similar objects. Hence, proximity is proportional to similarity. This is visible in Fig. 2B as the close clustering of complex, confusable animal shapes.

VH, i.e. distance-to-center, for target-present arrays, is calculated as shown in Fig. 1C, based on a point on the line connecting the target and distractors. The authors justify this idea with previous findings that responses to multiple stimuli are an average of responses to the constituent individual stimuli. The distance of the connecting line to the center is inversely proportional to the distance between the two stimuli in the pair, as shown in Fig. 2D. As a result, VH is inversely proportional to the distance between the stimuli and thus to stimulus similarity and response times. But this just makes VH a highly derived, unnecessarily complex proxy for target-distractor similarity and response time. The original response times on which the perceptual space is based are far more simple and direct measures of similarity for predicting response times.

2) The use of VH derived from Experiment 1 to predict response times in Experiment 2 is circular and does not validate the VH theory.

The use of VH, a response time proxy, to predict response times in other, similar tasks, using the same stimuli, is circular. In effect, response times are being used to predict response times across two similar experiments using the same stimuli. Experiment 1 and the target present condition of Experiment 2 involve the same essential task of oddball detection. The results of Experiment 1 are converted into VH values as described above, and these are used to predict response times in Experiment 2 (Fig. 2F). Since VH is a derived proxy for response values in Experiment 1, this prediction is circular, and the observed correlation shows only consistency between two oddball detection tasks in two experiments using the same stimuli.

3) The negative correlation of target-absent response times with VH as it is defined for target-absent arrays, based on the distance of a single stimulus from the center, is uninterpretable without understanding the effects of center-fitting. Most likely, center-fitting and the different VH metrics for target-absent trials produce an inverse correlation of VH with target-distractor similarity.

The construction of the VH perceptual space also involves fitting a "center" point such that distances to center predict response times as closely as possible. The effect of this fitting process on distance-to-center values for individual objects or clusters of objects is unknowable from what is presented here. These effects would depend on the residual errors after fitting response times with the connecting line distances. The center point location and its effects on the distance-to-center of single objects and object clusters are not discussed or reported here.

Yet, this uninterpretable distance-to-center of single objects is chosen as the metric for VH of target-absent displays (VHabsent). This is justified by the idea that arrays of a single stimulus will produce an average response equal to one stimulus of the same kind. However, it is not logically clear why response strength to a stimulus should be a metric for homogeneity of arrays constructed from that stimulus, or even what homogeneity could mean for a single stimulus from this set. It is not clear how this VHabsent metric based on single stimuli can be equated to the connecting line VH metric for stimulus pairs, i.e. VHpresent, or how both could be plotted on a single continuum.

It is clear, however, what *should* be correlated with difficulty and response time in the target-absent trials, and that is the complexity of the stimuli and the numerosity of similar distractors in the overall stimulus set. The complexity of the target, similarity with potential distractors, and the number of such similar distractors all make ruling out distractor presence more difficult. The correlation seen in Fig. 2G must reflect these kinds of effects, with higher response times for complex animal shapes with lots of similar distractors and lower response times for simpler round shapes with fewer similar distractors.

The example points in Fig. 2G seem to bear this out, with higher response times for the deer stimulus (complex, many close distractors in the Fig. 2B perceptual space) and lower response times for the coffee cup (simple, few close distractors in the perceptual space). While the meaning of the VH scale in Fig. 2G, and its relationship to the scale in Fig. 2F, are unknown, it seems like the Fig. 2G scale has an inverse relationship to stimulus complexity, in contrast to the expected positive relationship for Fig. 2F. This is presumably what creates the observed negative correlation in Fig. 2G.

Taken together, points 1-3 suggest that VHpresent and VHabsent are complex, unnecessary, and disconnected metrics for understanding target detection response times. The standard, simple explanation should stand. Task difficulty and response time in target detection tasks, in both present and absent trials, are positively correlated with target-distractor similarity.

I think my interpretations apply to Experiments 3 and 4 as well, although I find the analysis in Fig. 4 especially hard to understand. The VH space in this case is based on Experiment 3 oddball detection in a stimulus set that included both symmetric and asymmetric objects. However, the response times for a very different task in Experiment 4, a symmetric/asymmetric judgment, are plotted against the axes derived from Experiment 3 (Fig. 4F and 4G). It is not clear to me why a measure based on oddball detection that requires no use of symmetry information should be predictive of within-stimulus symmetry detection response times. If it is, that requires a theoretical explanation not provided here.

4) Contrary to the VH theory, same/different tasks are unlikely to depend on a decision boundary in the middle of a similarity or homogeneity continuum.

The authors interpret the inverse relationship of response times with VHpresent and VHabsent, described above, as evidence for their theory. They hypothesize, in Fig. 1G, that VHpresent and VHabsent occupy a single scale, with maximum VHpresent falling at the same point as minimum VHabsent. This is not borne out by their analysis, since the VHpresent and VHabsent value scales are mainly overlapping, not only in Experiments 1 and 2 but also in Experiments 3 and 4. The authors dismiss this problem by saying that their analyses are a first pass that will require future refinement. Instead, the failure to conform to this basic part of the theory should be a red flag calling for revision of the theory.

The reason for this single scale is that the authors think of target detection as a boundary decision task, along a single scale, with a decision boundary somewhere in the middle, separating present and absent. This model makes sense for decision dimensions or spaces where there are two categories (right/left motion; cats vs. dogs), separated by an inherent boundary (equal left/right motion; training-defined cat/dog boundary). In these cases, there is less information near the boundary, leading to reduced speed/accuracy and producing a pattern like that shown in Fig. 1G.

This logic does not hold for target detection tasks. There is no inherent middle point boundary between target present and target absent. Instead, in both types of trials, maximum information is present when the target and distractors are most dissimilar, and minimum information is present when the target and distractors are most similar. The point of greatest similarity occurs at the limit of any metric for similarity. Correspondingly, there is no middle point dip in information that would produce greater difficulty and higher response times. Instead, task difficulty and response times increase monotonically with the similarity between targets and distractors, for both target present and target absent decisions. Thus, in Figs. 2F and 2G, response times appear to be highest for animals, which share the largest numbers of closely similar distractors.

DEFINITION OF AREA VH USING fMRI

1) The area VH boundaries from different experiments are nearly completely non-overlapping.

In line with their theory that VH is a single continuum with a decision boundary somewhere in the middle, the authors use fMRI searchlight to find an area whose responses positively correlate with homogeneity, as calculated across all of their target present and target absent arrays. They report VH-correlated activity in regions anterior to LO. However, the VH defined by symmetry Experiments 3 and 4 (VHsymmetry) is substantially anterior to LO, while the VH defined by target detection Experiments 1 and 2 (VHdetection) is almost immediately adjacent to LO. Fig. S13 shows that VHsymmetry and VHdetection are nearly non-overlapping. This is a fundamental problem with the claim of discovering a new area that represents a new quantity that explains response times across multiple visual tasks. In addition, it is hard to understand why VHsymmetry does not show up in a straightforward subtraction between symmetric and asymmetric objects, which should show a clear difference in homogeneity.

2) It is hard to understand how neural responses can be correlated with both VHpresent and VHabsent.

The main paper results for VHdetection are based on both target-present and target-absent trials, considered together. It is hard to interpret the observed correlations, since the VHpresent and VHabsent metrics are calculated in such different ways and have opposite correlations with target similarity, task difficulty, and response times (see above). It may be that one or the other dominates the observed correlations. It would be clarifying to analyze correlations for target-present and target-absent trials separately, to see if they are both positive and correlated with each other.

3) The definition of the boundaries and purpose of a new visual area in the brain requires circumspection, abundant and convergent evidence, and careful controls.

Even if the VH metric, as defined and calculated by the authors here, is a meaningful quantity, it is a bold claim that a large cortical area just anterior to LO is devoted to calculating this metric as its major task. Vision involves much more than target detection and symmetry detection. The cortex anterior to LO is bound to perform a much wider range of visual functionalities. If the reported correlations can be clarified and supported, it would be more circumspect to treat them as one byproduct of unknown visual processing in the cortex anterior to LO, rather than treating them as the defining purpose for a large area of the visual cortex.

Reviewer #2 (Public Review):

Summary:

This study proposes visual homogeneity as a novel visual property that enables observers perform to several seemingly disparate visual tasks, such as finding an odd item, deciding if two items are the same, or judging if an object is symmetric. In Experiment 1, the reaction times on several objects were measured in human subjects. In Experiment 2, the visual homogeneity of each object was calculated based on the reaction time data. The visual homogeneity scores predicted reaction times. This value was also correlated with the BOLD signals in a specific region anterior to LO. Similar methods were used to analyze reaction time and fMRI data in a symmetry detection task. It is concluded that visual homogeneity is an important feature that enables observers to solve these two tasks.

Strengths:

1) The writing is very clear. The presentation of the study is informative.<br /> 2) This study includes several behavioral and fMRI experiments. I appreciate the scientific rigor of the authors.

Weaknesses:

1) My main concern with this paper is the way visual homogeneity is computed. On page 10, lines 188-192, it says: "we then asked if there is any point in this multidimensional representation such that distances from this point to the target-present and target-absent response vectors can accurately predict the target-present and target-absent response times with a positive and negative correlation respectively (see Methods)". This is also true for the symmetry detection task. If I understand correctly, the reference point in this perceptual space was found by deliberating satisfying the negative and positive correlations in response times. And then on page 10, lines 200-205, it shows that the positive and negative correlations actually exist. This logic is confusing. The positive and negative correlations emerge only because this method is optimized to do so. It seems more reasonable to identify the reference point of this perceptual space independently, without using the reaction time data. Otherwise, the inference process sounds circular. A simple way is to just use the mean point of all objects in Exp 1, without any optimization towards reaction time data.

2) On page 11, lines 214-221. It says: "these findings are non-trivial for several reasons". However, the first reason is confusing. It is unclear to me why "it suggests that there are highly specific computations that can be performed on perceptual space to solve oddball tasks". In fact, these two sentences provide no specific explanation for the results.

3) The second reason is interesting. Reaction times in target-present trials can be easily explained by target-distractor similarity. But why does reaction time vary substantially across target-absent stimuli? One possible explanation is that the objects that are distant from the feature distribution elicit shorter reaction times. Here, all objects constitute a statistical distribution in the feature (perceptual) space. There is certainly a mean of this distribution. Some objects look like outliers and these outliers elicit shorter reaction times in the target-absent trials because outlier detection is very salient.

One might argue that the above account is merely a rephrasing of the idea of visual homogeneity proposed in this study. If so, feature saliency is not a new account. In other words, the idea of visual homogeneity is another way of reiterating the old feature saliency theory.

4) One way to reject the feature saliency theory is to compare the reaction times of the objects that are very different from other objects (i.e., no surrounding objects in the perceptual space, e.g., the wheel in the lower right corner of Fig. 2B) with the objects that are surrounded by several similar objects (e.g., the horse in the upper part of Fig. 2B). Also, please choose the two objects with similar distance from the reference point. I predict that the latter will elicit longer reaction times because they can be easily confounded by surrounding similar objects (i.e., four-legged horses can be easily confounded by four-legged dogs). If the density of object distribution per se influences the visual homogeneity score, I would say that the "visual homogeneity" is essentially another way of describing the distributional density of the perceptual space.

5) The searchlight analysis looks strange to me. One can easily perform a parametric modulation by setting visual homogeneity as the trial-by-trial parametric modulator and reaction times as a covariate. This parametric modulation produces a brain map with the correlation of every voxel in the brain. On page 17 lines 340-343, it is unclear to me what the "mean activation" is.

Minor points:

1) In the intro, it says: "using simple neural rules..." actually it is very confusing what "neural rules" are here. Better to change it to "computational principles" or "neural network models"??

2) In the intro, it says: "while machine vision algorithms are extremely successful in solving feature-based tasks like object categorization (Serre, 2019), they struggle to solve these generic tasks (Kim et al., 2018; Ricci et al. 2021). These are not generic tasks. They are just a specific type of visual task-judging relationship between multiple objects. Moreover, a large number of studies in machine vision have shown that DNNs are capable of solving these tasks and even more difficult tasks. Two survey papers are listed here.

Wu, Q., Teney, D., Wang, P., Shen, C., Dick, A., & Van Den Hengel, A. (2017). Visual question answering: A survey of methods and datasets. Computer Vision and Image Understanding, 163, 21-40.

Małkiński, M., & Mańdziuk, J. (2022). Deep Learning Methods for Abstract Visual Reasoning: A Survey on Raven's Progressive Matrices. arXiv preprint arXiv:2201.12382.

Joint Public Review:

This study describes a group of CRH-releasing neurons, located in the paraventricular nucleus of the hypothalamus, which, in mice, affects both the state of sevoflurane anesthesia and a grooming behavior observed after it. PVHCRH neurons showed elevated calcium activity during the post-anesthesia period. Optogenetic activation of these PVHCRH neurons during sevoflurane anesthesia shifts the EEG from burst-suppression to a seemingly activated state (an apparent arousal effect), although without a behavioral correlate. Chemogenetic activation of the PVHCRH neurons delays sevoflurane-induced loss of righting reflex (another apparent arousal effect). On the other hand, chemogenetic inhibition of PVHCRH neurons delays recovery of righting reflex and decreases sevoflurane-induced stress (an apparent decrease in the arousal effect). The authors conclude that PVHCRH neurons "integrate" sevoflurane-induced anesthesia and stress. The authors also claim that their findings show that sevoflurane itself produces a post-anesthesia stress response that is independent of any surgical trauma, such as an incision. In its revised form, the article does not achieve its intended goal and will not have impact on the clinical practice of anesthesiology nor on anesthesiology research.

Strengths:

The manuscript uses targeted manipulation of the PVHCRH neurons with state-of-the-art methods, and is technically sound. Also, the number of experiments is substantial.

Weaknesses:

The most significant weaknesses remain: a) overinterpretation of the significance of their findings b) the failure to use another anesthetic as a control, c) a failure to compellingly link their post-sevoflurane measures in mice to anything measured in humans, and d) limitations in the novelty of the findings. These weaknesses are related to the primary concerns described below:

Concerns about the primary conclusion that PVHCRH neurons integrate the anesthetic effects and post-anesthesia stress response of sevoflurane GA:

1) After revision, their remain multiple places where it is claimed that PVHCRH neurons mediate the anesthetic effects of sevoflurane (impact statement: we explain "how sevoflurane-induced general anesthesia works..."; introduction: "the neuronal mechanisms that mediate the anesthetic effects...of sevoflurane GA remain poorly understood" and "PVHCRH neurons may act as a crucial node integrating the anesthetic effect and stress response of sevoflurane"). The manuscript simply does not support these statements. The authors show that a short duration exposure to sevoflurane inhibits PVHCRH neurons, but this is followed by hyperexcitability of these neurons for a short period after anesthesia is terminated. They show that the induction and recovery from sevoflurane anesthesia can be modulated by PVHCRH neuronal activity, most likely through changes in brain state (measured by EEG). They also show that PVHCRH neuronal activity modulates corticosterone levels and grooming behavior observed post-anesthesia (which the authors argue are two stress responses). These two things (effects during anesthesia and effects post-anesthesia) may be mechanistically unrelated to each other. None of these observations relate to the primary mechanism of action for sevoflurane. All claims relating to "anesthetic effects" should be removed. Even the term "integration" seems wrong-it implies the PVH is combining information about the anesthetic effect and post-anesthesia stress responses.

2) It is important to compare the effects of sevoflurane with at least one other inhaled ether anesthetic as one step towards elevating the impact of this paper. Isoflurane, desflurane, and enflurane are ether anesthetics that are very similar to each other, as well as being similar to sevoflurane. For example, one study cited by the authors (Marana et al. 2013) concludes that there is weak evidence for differences in stress-related hormones between sevoflurane and desflurane, with lower levels of cortisol and ACTH observed during the desflurane intraoperative period. It is important to determine whether desflurane activates PVHCRH neurons in the post-anesthesia period, and whether this is accompanied by excess grooming in the mice, because this will distinguish whether the effects of sevoflurane generalize to other inhaled anesthestics, or, alternatively, relate to unique idiosyncratic properties of this gas that may not be a part of its anesthetic properties.

Concerns about the clinical relevance of the experiments:

In anesthesiology practice, perioperative stress observed in patients is more commonly related to the trauma of the surgical intervention, with inadequate levels of antinociception or unconsciousness intraoperatively and/or poor post-operative pain control. The authors seem to be suggesting that the sevoflurane itself is causing stress because their mice receive sevoflurane but no invasive procedures, but there is no evidence of sevoflurane inducing stress in human patients. It is important to know whether sevoflurane effectively produces behavioral stress in the recovery room in patients that could be related to the putative stress response (excess grooming) observed in mice. For example, in surgeries or procedures which required only a brief period of unconsciousness that could be achieved by administering sevoflurane alone (comparable to the 30 min administered to the mice), is there clinical evidence of post-operative stress? It is also important to describe a rationale for using a 30 min sevoflurane exposure. What proportion of human surgeries using sevoflurane use exposure times that are comparable to this?

It is the experience of one of the reviewers that human patients who receive sevoflurane as the primary anesthetic do not wake up more stressed than if they had had one of the other GABAergic anesthetics. If there were signs of stress upon emergence (increased heart rate, blood pressure, thrashing movements) from general anesthesia, this would be treated immediately. The most likely cause of post-operative stress behaviors in humans is probably inadequate anti-nociception during the procedure, which translates into inadequate post-op analgesia and likely delirium. It is the case that children receiving sevoflurane do have a higher likelihood of post-operative delirium. Perhaps the authors' studies address a mechanism for delirium associated with sevoflurane, but this is barely mentioned. Delirium seems likely to be the closest clinical phenomenon to what was studied. As noted by the Besnier et al (2017) article cited by the authors, surgery can elevate postoperative glucocorticoid stress hormones, but it generally correlates with the intensity of the surgical procedure. Besnier et al also note the elevation of glucocorticoids is generally considered to be adaptive. Thus, reducing glucocorticoids during surgery with sevoflurane may hamper recovery, especially as it relates to tissue damage, which was not measured or considered here. This paper only considers glucocorticoid release as a negative factor, which causes "immunosuppression", "proteolysis", and "delays postoperative recovery and...leads to increased morbidity".

It is also the case that there are explicit published findings showing that mild and moderate surgical procedures in children receiving sevoflurane (which might be the closest human proxy to the brief 30 minute sevoflurane exposure used here) do not have elevated cortisol (Taylor et al, J Clin Endocrinol Metab, 2013). This again raises the question of whether the enhanced grooming or elevated corticosterone observed in the mice here has any relevance to humans.

Concerns about the novelty of the findings:

The key finding here is that CRH neurons mediate measures of arousal, and arousal modulates sevoflurane anesthesia induction and recovery. However, CRH is associated with arousal in numerous studies. In fact, the authors' own work, published in eLife in 2021, showed that stimulating the hypothalamic CRH cells lead to arousal and their inhibition promoted hypersomnia. In both papers the authors use fos expression in CRH cells during a specific event to implicate the cells, then manipulate them and measure EEG responses. In the previous work, the cells were active during wakefulness; here- they were active in the awake state the follows anesthesia (Figure 1). Thus, the findings in the current work are incremental and not particularly impactful. Claims like "Here, a core hypothalamic ensemble, corticotropin-releasing hormone neurons in the paraventricular nucleus of the hypothalamus, is discovered" are overstated. PVHCRH cell populations were discovered in the 1980s. Suggesting that it is novel to identify that hypothalamic CRH cells regulate post-anesthesia stress is unfounded as well: this PVH population has been shown over four decades to regulate a plethora of different responses to stress. Anesthesia stress is no different. Their role in arousal is not being discovered in this paper. Even their role in grooming is not discovered in this paper.

The activation of CRH cells in PVH has already been shown to result in grooming by Jaideep Bains (a paper cited by the authors). Thus, the involvement of these cells in this behavior is not surprising. The authors perform elaborate manipulations of CRH cells and numerous analyses of grooming and related behaviors. For example, they compare grooming and paw licking after anesthesia with those after other stressors such as forced swim, spraying mice with water, physical attack and restraint. The authors have identified a behavioral phenomenon in a rodent model that does not have a clear correlation with a behavior state observed in humans during the use of sevoflurane as part of an anesthetic regimen. The grooming behaviors are not a model of the emergence delirium or the cognitive dysfunction observed commonly in patients receiving sevoflurane for general anesthesia. Emergence delirium is commonly seen in children after sevoflurane is used as part of general anesthesia and cognitive dysfunction is commonly observed in adults-particularly the elderly-- following general anesthesia. No features of delirium or cognitive dysfunction are measured here.

Other concerns:

In Figure 2, cFos was measured in the PVH at different points before, during and after sevoflurane. The greatest cFos expression was seen in Post 2, the latest time point after anesthesia. However, this may simply reflect the fact that there is a delay between activity levels and expression of cFos (as noted by the authors, 2-3 hours). Thus, sacrificing mice 30 minutes after the onset of sevoflurane application would be expected to drive minimal cFos expression, and the cFos observed at 30 minutes would not accurately reflect the activity levels during the sevoflurane. Also, the authors state that the hyperactivity, as measured by cFos, lasted "approximately 1 hours before returning to baseline", but there is no data to support this return to baseline.

In Figure 7, the number of animals appears to change from panel to panel even though they are supposed to show animals from the same groups. For example, cort was measured in only 3 saline-treated O2 animals (Fig 7E), but cFos and CRH were assessed in 4 (Fig C,D). Similarly, grooming time and time spent in open arms was measured in 6 saline-treated O2 controls (Fig 7F,H) but central distance was measured in 8 (Fig 7G). There are other group number discrepancies in this figure-- the number of data points in the plots do not match what is reported in the legend for numerous groups. Similarly, Figure 4 has a mismatch between the Ns reported in the legend and the number of points plotted per bar. For example, there were 10 animals in the hM3Di group; all are shown for the LORR and time to emergence plots, but only 8 were used for time to induction. The legends reported N=7 for the mCherry group, yet 9 are shown for the time to emergence panel. No reason for exclusions is cited. These figures (and their statistics) should be corrected.

Reviewer #1 (Public Review):

Summary & Assessment:

The catalytic core of the eukaryotic decapping complex consists of the decapping enzyme DCP2 and its key activator DCP1. In humans, there are two paralogs of DCP1, DCP1a, and DCP1b, that are known to interact with DCP2 and recruit additional cofactors or coactivators to the decapping complex; however, the mechanisms by which DCP1 activates decapping and the specific roles of DCP1a versus DCP1b, remain poorly defined. In this manuscript, the authors used CRISPR/Cas9-generated DCP1a/b knockout cells to begin to unravel some of the differential roles of human DCP1a and DCP1b in mRNA decapping, gene regulation, and cellular metabolism. While this manuscript presents some new and interesting observations on human DCP1 (e.g. human DCP1a/b KO cells are viable and can be used to investigate DCP1 function; only the EVH1 domain, and not its disordered C-terminal region which recruits many decapping cofactors, is apparently required for efficient decapping in cells; DCP1a and b target different subsets of mRNAs for decay and may regulate different aspects of metabolism), there are several major issues that undercut some of the main conclusions of the paper, and some key claims that are incompletely or inconsistently supported by the presented data.

Strengths & well-supported claims:

• Through in vivo tethering assays in CRISPR/Cas9-generated DCP1a/b knockout cells, the authors show that DCP1 depletion leads to significant defects in decapping and the accumulation of capped, deadenylated mRNA decay intermediates.

• DCP1 truncation experiments reveal that only the EVH1 domain of DCP1 is necessary to rescue decapping defects in DCP1a/b KO cells.

• RNA and protein immunoprecipitation experiments suggest that DCP1 acts as a scaffold to help recruit multiple decapping cofactors to the decapping complex (e.g. EDC3, DDX6, PATL1 PNRC1, and PNRC2), but that none of these cofactors are essential for DCP2-mediated decapping in cells.

• The authors investigated the differential roles of DCP1a and DCP1b in gene regulation through transcriptomic and metabolomic analysis and found that these DCP1 paralogs target different mRNA transcripts for decapping and have different roles in cellular metabolism and their apparent links to human cancers. (Although I will note that I can't comment on the experimental details and/or rigor of the transcriptomic and metabolomic analyses, as these are outside my expertise.)

Weaknesses & incompletely supported claims:

1) A central mechanistic claim of the paper is that "DCP1a can regulate DCP2's cellular decapping activity by enhancing DCP2's affinity to RNA, in addition to bridging the interactions of DCP2 with other decapping factors. This represents a pivotal molecular mechanism by which DCP1a exerts its regulatory control over the mRNA decapping process." Similar versions of this claim are repeated in the abstract and discussion sections. However, this appears to be entirely at odds with the observation from in vitro decapping assays with immunoprecipitated DCP2 that showed DCP1 knockout does not significantly affect the enzymatic activity of DCP2 (Figures 2B-D; I note that there may be a very small change in DCP2 activity shown in panel C, but this may be due to slightly different amounts of immunoprecipitated DCP2 used in the assay, as suggested by panel D). If DCP1 pivotally regulates decapping activity by enhancing RNA binding to DCP2, why is no difference in decapping activity observed in the absence of DCP1? Furthermore, the authors show only weak changes in relative RNA levels immunoprecipitated by DCP2 with versus without DCP1 (~2-3 fold change; consistent with the Valkov 2016 NSMB paper, which shows what looks like only modest changes in RNA binding affinity for yeast Dcp2 +/- Dcp1). Is the argument that only a 2-3 fold change in RNA binding affinity is responsible for the sizable decapping defects and significant accumulation of deadenylated intermediates observed in cells upon Dcp1 depletion? (and if so, why is this the case for in-cell data, but not the immunoprecipitated in vitro data?)

The authors acknowledge this apparent discrepancy between the in vitro DCP2 decapping assays and in-cell decapping data, writing: "this observation could be attributed to the inherent constraints of in vitro assays, which often fall short of faithfully replicating the complexity of the cellular environment where multiple factors and cofactors are at play. To determine the underlying cause, we postulated that the observed cellular decapping defect in DCP1a/b knockout cells might be attributed to DCP1 functioning as a scaffold." This is fair. They next show that DCP1 acts as a scaffold to recruit multiple factors to DCP2 in cells (EDC3, DDX6, PatL1, and PNRC1 and 2). However, while DCP1 is shown to recruit multiple cofactors to DCP2 (consistent with other studies in the decapping field, and primarily through motifs in the Dcp1 C-terminal tail), the authors ultimately show that *none* of these cofactors are actually essential for DCP2-mediated decapping in cells (Figures 3A-F). More specifically, the authors showed that the EVH1 domain was sufficient to rescue decapping defects in DCP1a/b knockout cells, that PNRC1 and PNRC2 were the only cofactors that interact with the EVH1 domain, and finally that shRNA-mediated PNRC1 or PNCR2 knockdown has no effect on in-cell decapping (Figures 3E and F). Therefore, based on the presented data, while DCP1 certainly does act as a scaffold, it doesn't seem to be the case that the major cellular decapping defect observed in DCP1a/b knockout is due to DCP1's ability to recruit specific cofactors to DCP2.

So as far as I can tell, the discrepancy between the in vitro (DCP1 not required) and in-cell (DCP1 required) decapping data, remains entirely unresolved. Therefore, I don't think that the conclusions that DCP1 regulates decapping by (a) changing RNA binding affinity (authors show this doesn't matter in vitro, and that the change in RNA binding affinity is very small) or (b) by bridging interactions of cofactors with DCP2 (authors show all tested cofactors are dispensable for robust in-cell decapping activity), are supported by the evidence presented in the paper (or convincingly supported by previous structural and functional studies of the decapping complex).

2) Related to the RNA binding claims mentioned above, are the differences shown in Figure 3H statistically significant? Why are there no error bars shown for the MBP control? (I understand this was normalized to 1, but presumably, there were 3 biological replicates here that have some spread of values?). The individual data points for each replicate should be displayed for each bar so that readers can better assess the spread of data and the significance of the observed differences. I've listed these points as major because of the key mechanistic claim that DCP1 enhances RNA binding to DCP2 hinges in large part on this data.

3) Also related to point (1) above, the kinetic analysis presented in Figure 2C shows that the large majority of transcript is mostly decapped at the first 5-minute timepoint; it may be that DCP2-mediated decapping activity is actually different in vitro with or without DCP1, but that this is being missed because the reaction is basically done in less than 5 minutes under the conditions being assayed (i.e. these are basically endpoint assays under these conditions). It may be that if kinetics were done under conditions to slow down the reaction somewhat (e.g. lower Dcp2 concentration, lower temperatures), so that more of the kinetic behavior is captured, the apparent discrepancy between in vitro and in-cell data would be much less. Indeed, previous studies have shown that in yeast, Dcp1 strongly activates the catalytic step (kcat) of decapping by ~10-fold, and reduces the KM by only ~2 fold (Floor et al, NSMB 2010). It might be beneficial to use purified proteins here (only a Western blot is used in Figure 2D to show the presence of DCP2 and/or DCP1, but do these complexes have other, and different, components immunoprecipitated along with them?), if possible, to better control reaction conditions.

This contradiction between the in vitro and in-cell decapping data undercuts one of the main mechanistic takeaways from the first half of the paper. This needs to be addressed/resolved with further experiments to better define the role of DCP1-mediated activation, or the mechanistic conclusions significantly changed or removed.

4) The second half of the paper compares the transcriptomic and metabolic profiles of DCP1a versus DCP1b knockouts to reveal that these target a different subset of mRNAs for degradation and have different levels of cellular metabolites. This is a great application of the DCP1a/b KO cells developed in this paper and provides new information about DCP1a vs b function in metazoans, which to my knowledge has not really been explored at all. However, the analysis of DCP1 function/expression levels in human cancer seems superficial and inconclusive: for example, the authors conclude that "...these findings indicate that DCP1a and DCP1b likely have distinct and non-redundant roles in the development and progression of cancer", but what is the evidence for this? I see that DCP1a and b levels vary in different cancer cell types, but is there any evidence that these changes are actually linked to cancer development, progression, or tumorigenesis? If not, these broader conclusions should be removed.

5) The authors used CRISPR-Cas9 to introduce frameshift mutations that result in premature termination codons in DCP1a/b knockout cells (verified by Sanger sequencing). They then use Western blotting with DCP1a or DCP1b antibodies to confirm the absence of DCP1 in the knockout cell lines. However, the DCP1a antibody used in this study (Sigma D5444) is targeted to the C-terminal end of DCP1a. Can the authors conclusively rule out that the CRISPR/Cas-generated mutations do not result in the production of truncated DCP1a that is just unable to be detected by the C-terminally targeted antibody? While it is likely the introduced premature termination codon in the DCP1a gene results in nonsense-mediated decay of the resulting transcript, this outcome is indeed supported by the knockout results showing large defects in cellular decapping which can be rescued by the addition of the EVH1 domain, it would be better to carefully validate the success of the DCP1a knockout and conclusively show no truncated DCP1a is produced by using N-terminally targeted DCP1a antibodies (as was the case for DCP1b).

Some additional minor comments:

• More information would be helpful on the choice of DCP1 truncation boundaries; why was 1-254 chosen as one of the truncations?<br /> • Figure S2D is a pretty important experiment because it suggests that the observed deadenylated intermediates are in fact still capped; can a positive control be added to these experiments to show that removal of cap results in rapid terminator-mediated degradation?

Reviewer #2 (Public Review):

Summary:

Chen et al., investigate the role of DCP1 paralogs in regulating RNA decay in human tissue culture cells. They assess the impact of the absence of DCP1a and/or DCP1b on the interaction of DCP2 with mRNA and other members of the decapping complex. In vitro RNA decay assays were performed to demonstrate that DCP1a/b plays a minor role in DCP2-mediated decapping and decay. The impacts of DCP1a and/or DCP1b knockout on the transcriptome and metabolome were determined.

Strengths:

Analysis of RNA abundance and metabolite differences in human tissue culture cells lacking DCP1a and/or DCP1b was performed.

The protein-protein interactions between DCP2 and other members of the decapping machinery mediated by DCP1a and/or DCP1b were assessed.

The functional role of DCP1a and/or DCP1b in mediating mRNA decapping/decay in human tissue culture cell extracts was determined.

Human tissue culture cells lacking DCP1a and/or DCP1b appear to have altered metabolomes, however, the significance and meaning of these differences are not clear.

Weaknesses:

The direct targets of DCP1a and/or DCP1b were not determined as the analysis was restricted to RNA-seq to assess RNA abundance, which can be a result of direct or indirect regulation by DCP1a/b.

P-bodies appear to be larger in human cells lacking DCP1a and DCP1b but a lack of image quantification prevents this conclusion from being drawn.

The lack of details in the methodology and figure legends limit reader understanding.

Reviewer #3 (Public Review):

Summary:

Machhua et al. in their work focused on unravelling the molecular mechanism of daptomycin binding and interaction with bacterial cell membranes. Daptomycin (Dap) is an acidic, cyclic lipopeptide composed of 13 amino acids, known for preferential binding to anionic lipids, particularly phosphatidylglycerol (PG), which are prevalent components in the membranes of Gram-positive bacteria. The process of binding and antimicrobial efficacy of Dap is significantly influenced by the ionic composition of the surrounding environment, especially the presence of Ca2+ ions. The authors underscore the presence of significant knowledge gaps in our understanding of daptomycin's mode of action. Several critical questions remain unanswered, including the basis for selective recognition and accumulation in membranes of Gram-positive strains, the specific role of Ca2+ ions in this process, and the mechanisms by which daptomycin binds to and inserts into the cell membrane.

Dap is intrinsically fluorescent due to its kynurenine residue (Kyn-13) and this property allows direct imaging of Dap binding to model cell membranes without the need for additional labeling. Taking advantage of this Dap autofluorescence, authors monitored the emission intensity of micelles, composed of varying DMPG content upon their exposure to Dap and compared it with the kinetics of fluorescence observed for zwitterionic DMPC and other negatively charged lipids such as cardiolipin (CA), POPA and POPS. The authors noted that the linear relationship between DMPG content and Dap fluorescence is strongly lipid-specific, as it was not observed for other anionic lipids. The manuscript sheds light on the specificity of Dap's interaction with CA and DMPG lipids. Through Ca2+ sequestration with EGTA, the authors demonstrated that the binding of Dap with CA is reversible, while its interaction with DMPG results in the irreversible insertion of Dap into the lipid membrane structure, caused by the significant conformational change of this lipopeptide. The formation of a stable DMPG-Dap complex was also verified in bacterial cells isolated from Gram-positive bacteria B. subtilis, where Dap exhibited a permanent binding to PG lipids.

Altogether, the authors endeavored to illuminate novel insights into the molecular basis of Dap binding, interaction, and the mechanism of insertion into bacterial cell membranes. Such understanding holds promise for the development of innovative strategies in combating drug resistance and the emergence of the so-called superbugs.

Strengths:

- The manuscript by Machhua et al. provides a comprehensive analysis of the Dap mechanism of binding and interaction with the membrane. It discusses various aspects of this, only apparently trivial interactions such as the importance of PG presence in the membrane, the impact of Ca2+ ions, and different mechanisms of Dap binding with other negatively charged lipids.

- The authors focused not only on model membranes (micelles) but also extended their research to bacterial cell membranes obtained from B. subtilis.

- The research is not only a report of the experimental findings but tries to give potential hypotheses explaining the molecular mechanisms behind the observed results.

Weaknesses:

- The authors overestimate their findings, stating that they propose a novel mechanism of Dap interaction with bacterial cell membranes. In fact, they rather extend the already reported hypotheses.

- The literature study was not done as thoroughly as it should be. Many publications discussing the importance and mechanism of action of Ca2+ ions or conformational changes of daptomycin were not cited.

Reviewer #1 (Public Review):

Summary:

In this manuscript, the molecular mechanism of interaction of daptomycin (DAP) with bacterial membrane phospholipids has been explored by fluorescence and CD spectroscopy, mass spectrometry, and RP-HPLC. The mechanism of binding was found to be a two-step process. A fast reversible step of binding to the surface and a slow irreversible step of membrane insertion. Fluorescence-based titrations were performed and analysed to infer that daptomycin bound simultaneously two molecules of PG with nanomolar affinity in the presence of calcium. Conformational change but not membrane insertion was observed for DAP in the presence of cardiolipin and calcium.

Strengths:

The strength of the study is the skillful execution of biophysical experiments, especially stopped-flow kinetics that capture the first surface binding event, and the careful delineation of the stoichiometry.

Weaknesses:

The weakness of the study is that it does not add substantially to the previously known information and fails to provide additional molecular details. The current study provides incremental information on DAP-PG-calcium association but fails to capture the complex in mass spectrometry. The ITC and NMR studies with G3P are inconclusive There are no structural models presented. Another aspect missing from the study is the reconciliation between PG in the monomer, micellar, and membrane forms.

Reviewer #2 (Public Review):

The authors provide evidence for the early events of the lipopeptide daptomycin inserting into bacterial membranes. The authors utilize several biochemical and biophysical methods to characterize the nature of daptomycin interactions with a diverse set of phospholipids. The authors found that daptomycin, when complexed with calcium ions, can transiently interact with the headgroups of anionic phospholipids. In particular, the authors found that daptomycin rapidly interacts with the headgroup of cardiolipin and that this interaction is reversible and dependent on calcium. The authors provide evidence supporting previously published data that daptomycin interacts with phosphatidylglycerol (PG) with high affinity in a 1:2 ratio. The authors showed that this interaction includes both a calcium-dependent headgroup interaction (denoted the pre-insertion complex) and a distinct, irreversible interaction that is likely occurring between the hydrophobic tail of daptomycin with the tails of the PG molecules (denoted the quaternary complex of daptomycin, calcium, and 2 PG). The authors also isolated a daptomycin-containing complex from Bacillus subtilis cells following exposure to daptomycin and calcium. PG was identified from the isolated complex, albeit with a different acyl chain length from that used in vitro. Taken together, these data deepen our understanding of the stages of daptomycin interaction and intercalation in a membrane and can contribute to translational research on the development of structural analogs that could augment the efficacy of daptomycin treatment.

The authors have provided sufficient evidence to support a very specific interaction between daptomycin and PG, but their conclusions drawn from the data are exceedingly broad. In particular, the role of lipid II and lipid II precursors in the insertion and flipping events of daptomycin in the membrane are only briefly addressed despite the recently described pivotal role assigned to lipid II in the formation of a membrane-active daptomycin complex (Grein et al. Nature Communications 2020). While the authors put forth an intriguing and probable hypothesis that there are potentially multiple complexes and conformations of daptomycin as it incorporates within the membrane, the strength of the study's results and conclusions lies in its examination of the early headgroup interactions and distinctive PG interaction rather than the later events of daptomycin insertion in the membrane. The in vivo data presented supports the authors' model, but the conclusions do not address critical differences between the two very different systems i.e., in the behavior of micelles versus cell bilayer membranes.

Reviewer #1 (Public Review):

Summary:

Axon growth is of course essential to the formation of neural connections. Adhesion is generally needed to anchor and rectify such motion, but whether the tenacity or forces of adhesion must be optimal for maximal axon extension is unknown. Measurements and contributing factors are generally lacking and are pursued here with a laser-induced shock wave approach near the axon growth cone. The authors claim to make measurements of the pressure required to detach axons from low to high matrix density. The results seem to support the authors' conclusions, and the work - with further support - is likely to impact the field of cell adhesion. In particular, there could be some utility of the methods for the adhesion and those interested in aspects of axon growth.

Strengths:

A potential ability to control the pressure simply via proximity of the laser spot is convenient and perhaps reasonable. The 0 to 1 scale for matrix density is a good and appropriate measure for comparing adhesion and other results. The attention to detachment speed, time, F-actin, and adhesion protein mutant provides key supporting evidence. Lastly, the final figure of traction force microscopy with matrix varied on a gel is reasonable and more physiological because neural tissue is soft (cite PMID: 16923388); an optimum in Fig.6 also perhaps aligns with axon length results in Fig.5.

Weaknesses:

The results seem incomplete and less than convincing. This is because the force calibration curve seems to be from a >10 yr old paper without any more recent checks or validating measurements. Secondly, the claimed effect of pressure on the detachment of the growth cone does not consider other effects such as cavitation or temperature, and certainly needs validation with additional methods that overcome such uncertainties. The authors need to check whether the laser perturbs the matrix, particularly local density. A relation between traction stresses of ~20-50 pN/um2 in Fig.6 and the adhesion pressure of 3-5 kPa of FIg.3 needs to be carefully explained; the former units equate to 0.02-0.05 kPa, and would perhaps suggest cells cannot detach themselves and move forward.

The authors need to measure axon length on gels (Fig.6) as more physiological because neural tissue is soft. The studies are also limited to a rudimentary in vitro model without clear relevance to in vivo.

Reviewer #2 (Public Review):

Summary:

The authors measure axon outgrowth rate, laminin adhesion strength, and actin rearward flow rate. They find that the axon outgrowth rate has a biphasic dependence on adhesion strength. In interpreting the results, they suggest that the results "imply that adhesion modulation is key to the regulation of axon guidance"; however, they measure elongation rate, not guidance.

Strengths:

The measurements of adhesion strength by laser-induced shock waves are reasonable as is the measurement of actin flow rates by speckle microscopy.

Weaknesses:

They only measure the length of the axons after 3 days and have no measurements of the actual rate of growth cone movements when they are moving. They do not measure the rate of actin growth at the leading edge to know its contribution to the extension rate. This is inadequate.

These studies are unlikely to have an impact on the field because the measurement of axon growth rate at short times is missing.

Reviewer #3 (Public Review):

Summary:

Yamada et al. build on classic and more recent studies (Chen et al., 2023; Lemmon et al., 1992; Nichol et al., 2016; Zheng et al., 1994; Schense and Hubbell, 2000) to better understand the relationship between substrate adhesion and neurite outgrowth.

Strengths:

The primary strength of the manuscript lies in developing a method for investigating the role of adhesion in axon outgrowth and traction force generation using a femtosecond laser technique. The most exciting finding is that both outgrowth and traction force generation have a biphasic relationship with laminin concentration.

Weaknesses:

The primary weaknesses are a lack of discussion of prior studies that have directly measured the strength of growth cone adhesions to the substrate (Zheng et al., 1994) and traction forces (Koch et al., 2012), the inverse correlation between retrograde flow rate and outgrowth (Nichol et al., 2016), and prior studies noting a biphasic effect of substrate concentration of neurite outgrowth (Schense and Hubbell, 2000).

Overall, the claims and conclusions are well justified by the data. The main exception is that the data is more relevant to how the rate of neurite outgrowth is controlled rather than axonal guidance.

This manuscript will help foster interest in the interrelationship between neurite outgrowth, traction forces, and substrate adhesion, and the use of a novel method to study this problem.

Reviewer #1 (Public Review):

Summary:<br /> This study provides an incremental advance to the scavenger receptor field by reporting the crystal structures of the domains of SCARF1 that bind modified LDL such as oxidized LDL and acylated LDL. The crystal packing reveals a new interface for the homodimerization of SCARF1. The authors characterize SCARF1 binding to modified LDL using flow cytometry, ELISA, and fluorescent microscopy. They identify a positively charged surface on the structure that they predict will bind the LDLs, and they support this hypothesis with a number of mutant constructs in binding experiments.

Strengths:<br /> The authors have crystallized domains of an understudied scavenger receptor and used the structure to identify a putative binding site for modified LDL particles. An especially interesting set of experiments is the SCARF1 and SCARF2 chimeras, where they confer binding of modified LDLs to SCARF2, a related protein that does not bind modified LDLs, and use show that the key residues in SCARF1 are not conserved in SCARF2.

Weaknesses:<br /> While the data largely support the conclusions, the figures describing the structure are cursory and do not provide enough detail to interpret the model or quality of the experimental X-ray structure data. Additionally, many of the flow cytometry experiments lack negative controls for non-specific LDL staining and controls for cell surface expression of the SCARF constructs. In several cases, the authors interpret single data points as increased or decreased affinity, but these statements need dose-response analysis to support them. These deficiencies should be readily addressable by the authors in the revision.

The paper is a straightforward set of experiments that identify the likely binding site of modified LDL on SCARF1 but adds little in the way of explaining or predicting other binding interactions. That a positively charged surface on the protein could mediate binding to LDL particles is not particularly surprising. This paper would be of greater importance if the authors could explain the specificity of the binding of SCARF1 to the various lipoparticles that it does or does not bind. Incorporating these mutants into an assay for the biological role of SCARF1 would be powerful.

Reviewer #2 (Public Review):

Summary:<br /> The manuscript by Wang and colleagues provided mechanistic insights into SCARF1 and its interactions with the lipoprotein ligands. The authors reported two crystal structures of the N-terminal fragments of SCARF1 ectodomain (ECD). On the basis of the structural analysis, the authors further investigated the interactions between SCARF1 and modified LDLs using cell-based assays and biochemical experiments. Together with the two structures and supporting data, this work provided new insights into the diverse mechanisms of scavenger receptors and especially the crucial role of SCARF1 in lipid metabolism.

Strengths:<br /> The authors started by determining the crystal structures of two fragments of SCARF1 ECD. The superposition of the two high-resolution structures, together with the predicted model by AlphaFold, revealed that the ECD of SCARF1 adopts a long-curved conformation with multiple EGF-like domains arranged in tandem. Non-crystallographic and crystallographic two-fold symmetries were observed in crystals of f1 and f2 respectively, indicating the formation of SCARF1 homodimers. Structural analysis identified critical residues involved in dimerization, which were validated through mutational experiments. In addition, the authors conducted flow cytometry and confocal experiments to characterize cellular interactions of SCARF1 with lipoproteins. The results revealed the vital role of the 133-221aa region in the binding between SCARF1 and modified LDLs. Moreover, four arginine residues were identified as crucial for modified LDL recognition, highlighting the contribution of charge interactions in SCARF1-lipoprotein binding. The lipoprotein binding region is further validated by designing SCARF1/SCARF2 chimeric molecules. Interestingly, the interaction between SCARF1 and modified LDLs could be inhibited by teichoic acid, indicating potential overlap in or sharing of binding sites on SCARF1 ECD.

The author employed a nice collection of techniques, namely crystallographic, SEC, DLS, flow cytometry, ELISA, and confocal imaging. The experiments are technically sound and the results are clearly written, with a few concerns as outlined below. Overall, this research represents an advancement in the mechanistic investigation of SCARF1 and its interaction with ligands. The role of scavenger receptors is critical in lipid homeostasis, making this work of interest.

Reviewer #3 (Public Review):

Summary:<br /> The manuscript by Wang et. al. described the crystal structures of the N-terminal fragments of Scavenger receptor class F member 1 (SCARF1) ectodomains. SCARF1 recognizes modified LDLs, including acetylated LDL and oxidized LDL, and it plays an important role in both innate and adaptive immune responses. They characterized the dimerization of SCARF1 and the interaction of SCARF1 with modified lipoproteins by mutational and biochemical studies. The authors identified the critical residues for dimerization and demonstrated that SCARF1 may function as homodimers. They further characterized the interaction between SCARF1 and LDLs and identified the lipoprotein ligand recognition sites, the highly positively charged areas. Their data suggested that the teichoic acid inhibitors may interact with SCARF1 in the same areas as LDLs.

Strengths:<br /> The crystal structures of SCARF1 were high quality. The authors performed extensive site-specific mutagenesis studies using soluble proteins for ELISA assays and surface-expressed proteins for flow cytometry.

Weaknesses:<br /> 1. The schematic drawing of human SCARF1 and SCARF2 in Fig 1A did not show the differences between them. It would be useful to have a sequence alignment showing the polymorphic regions.<br /> 2. The description of structure determination was confusing. The f1 crystal structure was determined by SAD with Pt derivatives. Why did they need molecular replacement with a native data set? The f2 crystal structure was solved by molecular replacement using the structure of the f1 fragment. Why did they need to use EGF-like fragments predicted by AlphaFold as search models?<br /> 3. It's interesting to observe that SCARA1 binds modified LDLs in a Ca2+-independent manner. The authors performed the binding assays between SCARF1 and modified LDLs in the presence of Ca2+ or EDTA on Page 9. However, EDTA is not an efficient Ca2+ chelator. The authors should have performed the binding assays in the presence of EGTA instead.<br /> 4. The authors claimed that SCARF1Δ353-415, the deletion of a C-terminal region of the ectodomain, might change the conformation of the molecule and generate hinderance for the C-terminal regions. Why didn't SCARF1Δ222-353 have a similar effect? Could the deletion change the interaction between SCARF1 and the membrane? Is SCARF1Δ353-415 region hydrophobic?<br /> 5. What was the point of having Figure 8? Showing the SCARF1 homodimers could form two types of dimers on the membrane surface proposed? The authors didn't have any data to support that.

Reviewer #1 (Public Review):

Summary:

TOR complex 1 (TORC1) is a key regulator cell growth in response to nutrients, and it therefore integrates inputs from multiple nutrient-sensing regulators. However, we still do not understand how each upstream regulatory branch contributes to TORC1 activity under different nutrient conditions. The authors set out to answer this question using budding yeast (Saccharomyces cerevisiae) as a model eukaryote. Yeast TORC1 is activated by two upstream regulators: the highly conserved GTPases Gtr1/2 and the PI3P-binding protein Pib2. The cooperation of these regulators towards TORC1 activation has been unclear, with some studies suggesting that they act in parallel (i.e. redundantly), and others suggesting a more complex picture. By exploring the dependence of different TORC1 substrates on Gtr1/2 and Pib2 activity, the authors have discovered that Gtr1/2 and Pib2 do not act redundantly, but instead are part of a mechanism that drives the TORC1 pathways into three distinct activity levels: i) both Gtr1/2 and Pib2 ON in rich nutrients (leading to the highest TORC1 activity), ii) Gtr1/2 OFF and Pib2 ON in poor quality nitrogen sources (intermediate TORC1 activity), and iii) both Gtr1/2 and Pib2 OFF under starvation conditions (lowest TORC1 activity).

Strengths:

The relation between Gtr1/2 and Pib2 has remained a mystery for a long time, making it difficult to interpret the results of experiments in which one of the two regulators is inactive or missing. By employing a phosphoproteomics assay, the authors were able to monitor the phosphorylation of multiple TORC1 substrates in response to TORC1 inhibition (via rapamycin) and in mutants carrying deletions of Gtr1/2 or Pib2. In this way, they could identify two groups of substrates: those that require the activity of both regulators, and those that remain active when a single regulator is active. These data clearly demonstrate the non-redundancy of the Gtr1/2 and Pib2, especially since the different groups of substrates seem to correspond to groups of proteins with distinct functions.

Weaknesses:

- The first section of the Results contains an analysis of Gtr1/2- and Pib2-dependent signaling using Rps6 as a TORC1 reporter. I do not think that Rps6 is an appropriate readout for this type of work, as it is not a direct TORC1 substrate, and it also lies downstream of TORC2 [Yerlikaya et al. 2016]. The authors obtain several puzzling results with Rps6, and later on (pg. 8) remark that the level of Rps6 phosphorylation does not always correspond to TORC1 activity. While this is an interesting finding in its own right and will certainly be interesting for the yeast TOR community, I do not see why the Results need to open with such a confusing section, and why Rps6 features so prominently throughout the manuscript.<br /> - There is very large ambiguity regarding the types of media and strains that are used (prototrophic vs auxotrophic). The authors use SC medium which, if I understand correctly, contains ammonium and a supplement of amino acids. They then use single amino acid dropouts (e.g. SC -gln and SC -leu) to probe TORC1 activity under "partial starvation" conditions. However, the cells are anything but starved in these experiments, and I do not know how to interpret results obtained with such media. Even when amino acids are completely removed, the cells are still able to grow on ammonium. The matter gets further complicated because it appears that the authors use prototrophic strains with single nitrogen source media, but not with complete or "partial starvation" media. Since this study aims to elucidate the roles of nutrient-sensing regulators upstream of TORC1, I would expect that matters related to media composition and strain usage should be addressed more carefully and described more explicitly in the text, especially since nutritional complementation of auxotrophic strains is not always equivalent to genetic complementation [Pronk, 2002].<br /> - A recent publication (Zeng et al. 2023, doi: 10.1016/j.celrep.2023.113599) identified Ser33 and Ser3 as TORC1 substrates and examined their dependence on Pib2 activity. More importantly, the publication addressed a question that is very similar to the one addressed here (i.e. how different amino acids require Gtr1/2 or Pib2 to activate TORC1). I would recommend that the authors cite that publication and compare their findings with the results reported there.<br /> - The GO analysis of TORC1 substrates (from Fig.4) is mentioned in the text but is not shown. The authors should present the GO analysis more explicitly, e.g. in a supplementary table.<br /> - Similar to Rps6, it should be kept in mind that Par32 is not a TORC1 substrate. While I understand the rationale behind the choice of Par32 as a readout, this point needs to be emphasized more. Additionally, previous work [Brito et al. 2019, doi: 10.1016/j.isci.2019.09.025] has suggested that Npr1 and Par32 are implicated in a feedback loop with Pib2. The potential relevance of that work should be discussed more here.<br /> - Besides Sch9, Tod6 phosphorylation is also regulated by PKA [Huber et al. 2011, doi: 10.1038/emboj.2011.221]. This point should be discussed and taken into account in the interpretation of the Tod6 results. I also find it puzzling that Tod6 persists one hour after rapamycin treatment, because the protein seems to be unstable and gets quickly degraded when TORC1 activity is lost [Kusama 2022, doi: 10.1016/j.isci.2022.103986].<br /> - Given the points raised above, I remain skeptical about the three-state model proposed by the authors. On a conceptual level, the intermediate activity state of TORC1 proposed here seems to depend absolutely on Pib2 (since Gtr1/2 appear to be off in that state). The authors make a similar point in the Discussion, where they claim that yeast growth on poor nitrogen sources can be halted by deletion of Pib2. However, they do not test this conjecture experimentally.<br /> - Fig. 6F compares the growth of different strains on different media, but the doubling times are not quantified.<br /> - The Introduction describes regulatory pathways of mTORC1, several of which do not exist in budding yeast. The transition from the second to third paragraph is very abrupt and confusing.

Reviewer #2 (Public Review):

This work examines the roles of Gtr1/Gtr2 and Pib2 in activation of TORC1 in S cerevisiae and proposes they are non-redundant in activating TORC1. Previous work from many groups has suggested that the Gtr complex and Pib2 activate TORC1 in a parallel manner. One contribution of this study is the suggestion that using the standard readout(s) of TORC1 activation are not sufficient to assess the separate roles of these two components in the complex network of amino acid and starvation response signaling. The overall conclusion of the work, based on phosphoproteome analyses of deletion strains and comparison to rapamycin treatment, with some supporting experimentation, is that Pib2 signaling sustains the starvation response in poor amino acid/nitrogen sources, whereas the additional activation of the Gtr complex is required for the full spectrum of TORC1 effects on growth.

At first, the authors recapitulate and extend studies on TORC1 inactivation using the Rps6 reporter. Here, Pib2 could inactivate TORC1 on glutamine starvation only if the Gtr complex is partially compromised. The authors speculated that Gtr and Pib2 do lead to different responses, but these cannot be detected by monitoring the phospho state of Rps6.

The authors determined the phosphoproteome in wild type cells and a variety of knockout strains, in rich media and in the presence of rapamycin. The authors identified 175 phosphosites that are downregulated on rapamycin treatment, at least under these conditions. Many were dependent on both Pib2 and the Gtr complex but, of particular interest for this work , were the phosphosites on Ser33, that were dependent on the presence of Pib2 but not the Gtr complex. The authors noted that phosphosites not dependent on Pib2 or Gtr1/2 included Sch9 and other common readouts of TORC1 activation.

Focusing on Ser33, the authors next show that rapamycin, amino acid and nitrogen starvation result in loss of Ser33 phosphorylation. Further analysis showed that the Ser33 phosphorylation status depends on the quality of the amino acid and nitrogen source.

Then the authors use this to develop a model where TORC1 has three states depending on whether either Gtr1/2, or Pib2, or both are active in signaling to TORC1, depending on the nutrient state and quality of amino acids/nitrogen available. The new state is state III, where TORC1 is active to promote growth and the starvation response remains active, via the Npr1/Par32 branch. The remainder of the work involves developing tools to assess the growth (Sch9) and starvation (Par32) branches under various amino acid/nutrient states. While moving from media with an excess of all amino acids to glutamine or leucine led to only transient occupation of state III, the new state was already occupied when the cells were in a poor amino acid/nitrogen source and moved to a better one. In other words, the Pib2 signalling permitted aspects of a starvation response to be maintained in the background of a Sch9 growth signal.

Finally, the authors address a puzzle: Sch9 phosphorylation does not have the dynamic range to account for the difference in growth rates of yeast cells in SC or proline medium. Tod6 was dephosphorylated in the absence of Gtr1/Gtr2 or Pib2 in the phosphoproteomics and is the likely connection, as it moves to the nucleus on growth on proline media (or on rapamycin), where it may control the chromatin accessibility of ribosome growth and biogenesis genes.

Overall, the core of this work, the phosphoproteome analyses, convincingly demonstrates that activation of TORC1 relies on a nuanced interplay of signaling pathways and that to fully appreciate and dissect the consequences of the Gtr- and Pib2-responsive signaling pathways a more comprehensive range of readouts is required. The work elegantly shows a scenario where Pib2-based signaling is active, required to sustain some growth even when the amino acid/nitrogen mix is poor.

There are some areas, however, where the work could be strengthened. The model proposed in this work is based on nuanced signaling responses to various states of nitrogen/amino acid starvation. However, the phosphoproteome was determined in a synthetic rich background, supplemented with rapamycin where relevant, and comparing the phosphoproteome of pib2 del and gtr1 del/gtr2 del to this. The phosphoproteome is by far the strongest data in this work suggesting multi-level regulation so an appropriately matched phosphoproteome condition screen would likely significantly substantiate the model: the conditions used might miss all the nuanced signaling responses the authors develop throughout the paper. Not unrelated, the authors show that Pib2 can transmit glutamine starvation signals to TORC1 in the presence of a partial Gtr1/2 complex (gtr1 del or gtr2 del) but not a complete deletion of the complex (Fig. 2). Similar to the above comment, the phosphoproteome was determined only with full loss of the gtr complex, and then only in a rich background, which may miss this entire branch of Pib2 signaling. Perhaps in support of this, Pib2Ser113 phosphorylation apparently decreased significantly on rapamycin treatment but not on loss of the Gtr complex (TableS1), whereas other Pib2 phospho sites were not similarly affected by rapamycin treatment. Adding to the notion of complexity, the other sites may themselves be subject to other signaling pathways that could regulate Pib2 - and these may change on nutrient starvation.

The data showing the enrichment of Pib2 with Ser33 is weak (Fig. 5G, mostly because of the significant precipitation of Ser33 in the absence of Pib2), particularly without the contribution of the immunopurifications of Fig5S1. Assessing the binding of Ser3 may be a better candidate?

Reviewer #3 (Public Review):

Summary:<br /> This work addresses an important question of how Gtr1/2 small GTPases and Pib2, two major regulators of the TORC1 cell growth controller, differentially operate in yeast. They found not all the TORC1 downstream targets respond to Gtr1/2 and Pib2 equally. In fact, they demonstrate that TORC1-dependent phosphorylation of Ser33, a 3-phosphoglycerate dehydrogenase, is responsive to only Pib2. They attributed this specificity to the physical interaction between Ser33 and Pib2. This part is novel and important, revising the canonical view in the field that Gtr1/2 and Pib2 branches act towards the same TORC1 downstream targets. Of note, this claim largely agrees with a recent independent study (PMID: 38127619).

Moving on, the authors describe different behaviors of TORC1 downstream readouts in intermediate nutrient conditions with a poor nitrogen source, with some readouts still active while others inactive. They argue that selective activation of certain TORC1 downstream targets reflects the "Gtr1/2 off, Pib2 on" state. However, this claim is not sufficiently supported by the presented data.

Strengths:<br /> The data presented in this paper has high value to the TOR community. In particular, a rigorous and comprehensive phospho-proteomic dataset that compares the Gtr1/2- and Pib2-dependency of diverse TORC1 downstream targets is very informative, potentially stimulating follow-up studies on each target.

Identification of Ser33 as a Pib2-specific TORC1 downstream is important and convincing (although whether Ser33 is a direct substrate of TORC1 was not addressed in this work). Physical interaction between Ser33 and Pib2 could represent a novel layer of TORC1 signaling regulation, in line with the mammalian Rag-TFEB interaction model, as discussed by the authors.

Weaknesses:<br /> The authors' three-state model, particularly the claim that cells are in the "Gtr1/2 off, Pib2 on" state in a poor nitrogen condition (e.g., proline medium), is not convincing enough because of the following reasons.

1) The "Pib2 on" claim contradicts with the observation that Ser33, Pib2-specific readout, is hypo-phosphorylated in proline medium (Fig 5F).

2) In the genetic experiments (Figure 8), the authors compare pib2D with Gtr1/2OFF. This is not appropriate, because GTR1/2OFF (GTR1-GDP and Gtr2-GTP) actively inhibits TORC1, differing from the null nature of pib2D. pib2D should be compared with gtr1/2D instead.

3) In general, diverse behaviors of TORC1 targets are not unexpected because their phosphorylation levels should have different dynamic ranges depending on how "good" they are as TORC1 substrates, with some requiring a higher TORC1 activity than others to be detectably phosphorylated. Although this aspect can be physiologically meaningful, and it is indeed important to look at multiple substrates as the authors suggest, this approach does not inform whether the signal is coming from Gtr1/2 or Pib2. An informative way in this context would be to look at the Gtr1/2- or Pib2-specific targets, but the former has not been identified, and observations on the latter, Ser33, do not support the "Pib2 on" claim as mentioned in the above 1).

4) In addition, comparisons made between direct TORC1 substrates (e.g., Sch9) and indirect downstream targets (e.g., Rps6 and Par32) are not very informative, because indirect targets can be impacted by TORC1-independent regulation of the mediating factors (e.g., Ypk3 for Rps6 and Npr1 for Par32).

In summary, the presented data do not tell us which of the two branches (Gtr1/2 or Pib2) is "more active" in the poor nitrogen condition. Their observations do not necessarily prefer their 3-state on/off model (Figure 8) over the more natural assumption that both branches have the gradation of activity depending on the nutrient status.

Reviewer #1 (Public Review):

Summary:<br /> In this study, the authors engineer the endogenous left boundary of the Drosophila eve TAD, replacing the endogenous Nhomie boundary by either a neutral DNA, a wildtype Nhomie boundary, an inverted Nhomie boundary, or a second copy of the Homie boundary. They perform Micro-C on young embryos and conclude that endogenous Nhomie and Homie boundaries flanking eve pair with head-to-tail directionality to form a chromosomal stem loop. Abrogating the Nhomie boundary leads to ectopic activation of genes in the former neighboring TAD by eve embryonic stripe enhancers. Replacing Nhomie by an inverted version or by Homie (which pairs with itself head-to-head) transformed the stem loop into a circle loop. An important finding was that stem and circle loops differentially impact endogenous gene regulation both within the eve TAD and in the TADs bracketing eve. Intriguingly, an eve TAD with a circle loop configuration leads to ectopic activation of flanking genes by eve enhancers - indicating compromised regulatory boundary activity despite the presence of an eve TAD with intact left and right boundaries.

Strengths:<br /> Overall, the results obtained are of high-quality and are meticulously discussed. This work advances our fundamental understanding of how 3D genome topologies affect enhancer-promoter communication.

Weaknesses:<br /> Though convincingly demonstrated at eve, the generalizability of TAD formation by directional boundary pairing remains unclear, though the authors propose this mechanism could underly the formation of all TADs in Drosophila and possibly even in mammals. Strong and ample evidence has been obtained to date that cohesin-mediated chromosomal loop extrusion explains the formation of a large fraction of TADs in mammals. Moreover, given the unique specificity with which Nhomie and Homie are known to pair (and exhibit "homing" activity), it is conceivable that formation of the eve TAD by boundary pairing represents a phenomenon observed at exceptional loci rather than a universal rule of TAD formation. Indeed, characteristic Micro-C features of the eve TAD are only observed at a restricted number of loci in the fly genome, and many TADs lack focal 3D interactions between their boundaries.

Reviewer #2 (Public Review):

"Chromatin Structure II: Stem-loops and circle-loops" by Ke*, Fujioka*, Schedl, and Jaynes reports a set of experiments and subsequent analyses focusing on the role of Drosophila boundary elements in shaping 3D genome structure and regulating gene expression. The authors primarily focus on the region of the fly genome containing the even skipped (eve) gene; eve is expressed in a canonical spatial pattern in fly embryos and its locus is flanked by the well-characterized neighbor of homie (nhomie) and homie boundary elements. The main focus of investigation is the orientation dependence of these boundary elements, which had been observed previously using reporter assays. In this study, the authors use Crispr/Cas9 editing followed by recombination-mediated cassette exchange to create a series of recombinant fly lines in which the nhomie boundary element is either replaced with exongenous sequence from phage 𝝀, an inversion of nhomie, or a copy of homie that has the same orientation as the endogenous homie sequence. The nhomie sequence is also regenerated in its native orientation to control for effects introduced by the transgenesis process.

The authors then perform high-resolution Micro-C to analyze 3D structure and couple this with fluorescent and colorimetric RNA in situ hybridization experiments to measure the expression of eve and nearby genes during different stages of fly development. The major findings of these experiments are that total loss of boundary sequence (replacement with 𝝀 DNA) results in major 3D structure changes and the most prominent observed gene changes, while inversion of the nhomie boundary or replacement with homie resulted in more modest effects in terms of 3D structure and gene expression changes and a distinct pattern of gene expression change from the 𝝀 DNA replacement. As the samples in which the nhomie boundary is inverted or replaced with homie have similar Micro-C profiles at the eve locus and show similar patterns of a spurious gene activation relative to the control, the observed effects appear to be driven by the relative orientation of the nhomie and homie boundary elements to one another.

Collectively, the findings reported in the manuscript are of broad interest to the 3D genome field. Although extensive work has gone into characterizing the patterns of 3D genome organization in a whole host of species, the underlying mechanisms that structure genomes and their functional consequences are still poorly understood. The perhaps best understood system, mechanistically, is the coordinated action of CTCF with the cohesin complex, which in vertebrates appears to shape 3D contact maps through a loop extrusion-pausing mechanism that relies on orientation-dependent sequence elements found at the boundaries of interacting chromatin loops. Despite having a CTCF paralog and cohesin, the Drosophila genome does not appear to be structure by loop extrusion-pausing. The identification of orientation-dependent elements with pronounced structural effects on genome folding thus may shed light on alternative mechanisms used to regulated genome structure, which in turn may yield insights into the significance of particular folding patterns.

On the whole, this study is comprehensive and represents a useful contribution to the 3D genome field. The transgenic lines and Micro-C datasets generated in the course of the work will be valuable resources for the research community. Moreover, the manuscript, while dense in places, is generally clearly written and comprehensive in its description of the work. However, I have a number of comments and critiques of the manuscript, mainly centering on the framing of the experiments and presentation of the Micro-C results and on manner in which the data are analyzed and reported. They are as follows:

Major Points:

1. The authors motivate much of the introduction and results with hypothetical "stem loop" and "circle loop" models of chromosome confirmation, which they argue are reflected in the Micro-C data and help to explain the observed ISH patterns. While such structures may possibly form, the support for these specific models vs. the many alternatives is not in any way justified. For instance, no consideration is given to important biophysical properties such as persistence length, packing/scaling, and conformational entropy. As the biophysical properties of chromatin are a very trafficked topic both in terms of experimentation and computational modeling and generally considered in the analysis of chromosome conformation data, the study would be strengthened by acknowledgement of this body of work and more direct integration of its findings.

2. Similar to Point 1, while there is a fair amount of discussion of how the observed results are or are not consistent with loop extrusion, there is no discussion of the biophysical forces that are thought to underly compartmentalization such as block-polymer co-segregation and their potential influence. I found this absence surprising, as it is generally accepted that A/B compartmentalization essentially can explain the contact maps observed in Drosophila and other non-vertebrate eukaryotes (Rowley, ..., Corces 2017; PMID 28826674). The manuscript would be strengthened by consideration of this phenomenon.

3. The contact maps presented in the study represent many cells and distinct cell types. It is clear from single-cell Hi-C and multiplexed FISH experiments that chromosome conformation is highly variable even within populations of the same cell, let alone between cell types, with structures such as TADs being entirely absent at the single cell level and only appearing upon pseudobulking. It is difficult to square these observations with the models of relatively static structures depicted here. The authors should provide commentary on this point.

4. The analysis of the Micro-C data appears to be largely qualitative. Key information about the number of reads sequenced, reaps mapped, and data quality are not presented. No quantitative framework for identifying features such as the "plumes" is described. The study and its findings would be strengthened by a more rigorous analysis of these rich datasets, including the use of systematic thresholds for calling patterns of organization in the data.

5. Related to Point 4, the lack of quantitative details about the Micro-C data make it difficult to evaluate if the changes observed are due to biological or technical factors. It is essential that the authors provide quantitative means of controlling for factors like sampling depth, normalization, and data quality between the samples.

6. The ISH effects reported are modest, especially in the case of the HCR. The details provided for how the imaging data were acquired and analyzed are minimal, which makes evaluating them challenging. It would strengthen the study to provide much more detail about the acquisition and analysis and to include depiction of intermediates in the analysis process, e.g. the showing segmentation of stripes.

Reviewer #1 (Public Review):

Summary:<br /> The authors addressed how long-range interactions between boundary elements are established and influence their function in enhancer specificity. Briefly, the authors placed two different reporters separated by a boundary element. They inserted this construct ectopically ~140 kb away from an endogenous locus that contains the same boundary element. The authors used expression patterns driven by nearby enhancers as an output to determine which enhancers the reporters interact with. They complemented this analysis with 3D DNA contact mapping. The authors found that the orientation of the boundary element determined which enhancers each reporter interacted with. They proposed that the 3D interaction topology, whether being circular or stem configuration, distinguished whether the interaction was cohesin mediated or through an independent mechanism termed pairing.

Strengths:<br /> The transgene expression assays are built upon prior knowledge of the enhancer activities. The 3D DNA contacts confirm that transgene expression correlates with the contacts. Using 4 different orientations covers all combinations of the reporter genes and the boundary placement.

Weaknesses:<br /> The interpretation of the data as a refusal of loop extrusion playing a role in TAD formation is not warranted, as the authors did not deplete the loop extruders to show that what they measure is independent. As the authors show, the single long DNA loop mediated by cohesin loop extrusion connecting the ectopic and endogenous boundary is clearly inconsistent with the results, therefore the main conclusion of the paper that the 3D topology of the boundary elements a consequence of pairing is strong. However, the loop extrusion and pairing are not mutually exclusive models for the formation of TADs. Loop-extruding cohesin complexes need not make a 140 kb loop, multiple smaller loops could bring together the two boundary elements, which are then held together by pairing proteins that can make circular topologies.

Reviewer #2 (Public Review):

In Bing et al, the authors analyze micro-C data from NC14 fly embryos, focusing on the eve locus, to assess different models of chromatin looping. They conclude that fly TADs are less consistent with conventional cohesin-based loop extrusion models and instead rely more heavily on boundary-boundary pairings in an orientation-dependent manner.

Overall, I found the manuscript to be interesting and thought-provoking. However, this paper reads much more like a perspective than a research article. I strongly suggest the authors spend some time editing their introduction to the most salient points as well as organizing their results section in a more conventional way with conclusion-based titles. It was very difficult to follow the authors' logic throughout the manuscript as written. It was also not clear as written which experiments were performed as part of this study and which were reanalyzed but published elsewhere. This should be made clearer throughout.

It has been shown several times that Drosophila Hi-C maps do not contain all of the features (frequent corner peaks, stripes, etc.) observed when compared to mammalian cells. Considering these features are thought to be products of extrusion events, it is not an entirely new concept that Drosophila domains form via mechanisms other than extrusion. That being said, the authors' analyses do not distinguish between the formation and the maintenance of domains. It is not clear to this reviewer why a single mechanism should explain the formation of the complex structures observed in static Hi-C heatmaps from a population of cells at a single developmental time point. For example, how can the authors rule out that extrusion initially provides the necessary proximity and possibly the cis preference of contacts required for boundary-boundary pairing whereas the latter may more reflect the structures observed at maintenance? Future work aimed at analyzing micro-C data in cohesin-depleted cells might shed additional light on this.

Additional mechanisms at play include compartment-level interactions driven by chromatin states. Indeed, in mammalian cells, these interactions often manifest as a "plume" on Hi-C maps similar to what the authors attribute to boundary interactions in this manuscript. How do the chromatin states in the neighboring domains of the eve locus impact the model if at all?

How does intrachromosomal homolog pairing impact the models proposed in this manuscript (Abed et al. 2019; Erceg et al., 2019). Several papers recently have shown that somatic homolog pairing is not uniform and shows significant variation across the genome with evidence for both tight pairing regions and loose pairing regions. Might loose pairing interactions have the capacity to alter the cis configuration of the eve locus?<br /> In summary, the transgenic experiments are extensive and elegant and fully support the authors' models. However, in my opinion, they do not completely rule out additional models at play, including extrusion-based mechanisms. Indeed, my major issue is the limited conceptual advance in this manuscript. The authors essentially repeat many of their previous work and analyses. The authors make no attempt to dissect the mechanism of this process by modifying extrusion components directly. Some discussion of Rollins et al., 1999 on the discovery of Nipped-B and its role in enhancer-promoter communication should also be made to reconcile their conclusions in the proposed absence of extrusion events.

Reviewer #3 (Public Review):

Bing et al. attempt to address fundamental mechanisms of TAD formation in Drosophila by analyzing gene expression and 3D conformation within the vicinity of the eve TAD after insertion of a transgene harboring a Homie insulator sequence 142 kb away in different orientations. These transgenes along with spatial gene expression analysis were previously published in Fujioka et al. 2016, and the underlying interpretations regarding resulting DNA configuration in this genomic region were also previously published. This manuscript repeats the expression analysis using smFISH probes in order to achieve more quantitative analysis, but the main results are the same as previously published. The only new data are the Micro-C and an additional modeling/analysis of what they refer to as the 'Z3' orientation of the transgenes. The rest of the manuscript merely synthesizes further interpretation with the goal of addressing whether loop extrusion may be occurring or if boundary:boundary pairing without loop extrusion is responsible for TAD formation. The authors conclude that their results are more consistent with boundary:boundary pairing and not loop extrusion; however, most of this imaging data seems to support both loop extrusion and the boundary:boundary models. This manuscript lacks support, especially new data, for its conclusions. Furthermore, there are many parts of the manuscript that are difficult to follow. There are some minor errors in the labelling of the figures that if fixed would help elevate understanding. Lastly, there are several major points that if elaborated on, would potentially be helpful for the clarity of the manuscript.

Major Points:<br /> 1. The authors suggest and attempt to visualize in the supplemental figures, that loop extrusion mechanisms would appear during crosslinking and show as vertical stripes in the micro-C data. In order to see stripes, a majority of the nuclei would need to undergo loop extrusion at the same rate, starting from exactly the same spots, and the loops would also have to be released and restarted at the same rate. If these patterns truly result from loop extrusion, the authors should provide experimental evidence from another organism undergoing loop extrusion.<br /> 2. On lines 311-314, the authors discuss that stem-loops generated by cohesin extrusion would possibly be expected to have more next-next-door neighbor contacts than next-door neighbor contacts and site their models in Figure 1. Based on the boundary:boundary pairing models in the same figure would the stem-loops created by head-to-tail pairing also have the same phenotype? Making possible enrichment of next-next-door neighbor contacts possible in both situations? The concepts in the text are not clear, and the diagrams are not well-labeled relative to the two models.<br /> 3. The authors appear to cite Chen et al., 2018 as a reference for the location of these transgenes being 700nM away in a majority of the nuclei. However, the exact transgenes in this manuscript do not appear to have been measured for distance. The authors could do this experiment and include expression measurements.<br /> 4. The authors discuss the possible importance of CTCF orientation in forming the roadblock to cohesin extrusion and discuss that Homie orientation in the transgene may impact Homie function as an effective roadblock. However, the Homie region inserted in the transgene does not contain the CTCF motif. Can the authors elaborate on why they feel the orientation of Homie is important in its ability to function as a roadblock if the CTCF motif is not present? Trans-acting factors responsible for Homie function have not been identified and this point is not discussed in the manuscript.<br /> 5. The imaging results seem to be consistent with both boundary:boundary interaction and loop extrusion stem looping.<br /> 6. The authors suggest that the eveMa TAD could only be formed by extrusion after the breakthrough of Nhomie and several other roadblocks. Additionally, the overall long-range interactions with Nhomie appear to be less than the interactions with endogenous Homie (Figures 7, 8, and supplemental 5). Is it possible that in some cases boundary:boundary pairing is occurring between only the transgenic Homie and endogenous Homie and not including Nhomie?<br /> 7. In Figure 4E, the GFP hebe expression shown in the LhomieG Z5 transgenic embryo does not appear in the same locations as the LlambdaG Z5 control. Is this actually hebe expression or just a background signal?<br /> 8. Figure 6- The LhomieG Z3 late-stage embryo appears to be showing the ventral orientation of the embryo rather than the lateral side of the embryo as was shown in the previous figure. Is this for a reason? Additionally, there are no statistics shown for the Z3 transgenic images. Were these images analyzed in the same way as the Z5 line images?<br /> 9. Do the Micro-C data align with the developmental time points used in the smFISH probe assays?

Reviewer #1 (Public Review):

Summary: This work is an extension of their earlier work published in Sci Adv in 2021, wherein they showed that DTD2 deacylates N-ethyl-D-aminoacyl-tRNAs arising from acetaldehyde toxicity. The authors (Kumar et al.) in this study, investigate the role of archaeal/plant DTD2 in the deacylation/detoxification of D-Tyr-tRNATyr modified by multiple other aldehydes and methylglyoxal (produced by plants). Importantly, the authors take their biochemical observations to plants, to show that deletion of DTD2 gene from a model plant (Arabidopsis thaliana) makes them sensitive to the aldehyde supplementation in the media especially in the presence of D-Tyr. These conclusions are further supported by the observation that the model plant shows increased tolerance to the aldehyde stress when DTD2 is overproduced from the CaMV 35S promoter. The authors propose a model for the role of DTD2 in the evolution of land plants. Finally, the authors suggest that the transgenic crops carrying DTD2 may offer a strategy for stress-tolerant crop development. Overall, the authors present a convincing story, and the data are supportive of the central theme of the story.

Strengths: Data are novel and they provide a new perspective on the role of DTD2, and propose possible use of the DTD2 lines in crop improvement.

Weaknesses: (a) Data obtained from a single aminoacyl-tRNA (D-Tyr-tRNATyr) have been generalized to imply that what is relevant to this model substrate is true for all other D-aa-tRNAs (term modified aa-tRNAs has been used synonymously with the modified Tyr-tRNATyr). This is not a risk-free extrapolation. For example, the authors see that DTD2 removes modified D-Tyr from tRNATyr in a chain-length dependent manner of the modifier. Why do the authors believe that the length of the amino acid side chain will not matter in the activity of DTD2? (b) While the use of EFTu supports that the ternary complex formation by the elongation factor can resist modifications of L-Tyr-tRNATyr by the aldehydes or other agents, in the context of the present work on the role of DTD2 in plants, one would want to see the data using eEF1alpha. This is particularly relevant because there are likely to be differences in the way EFTu and eEF1alpha may protect aminoacyl-tRNAs (for example see description in the latter half of the article by Wolfson and Knight 2005, FEBS Letters 579, 3467-3472).

Note added after revision: The authors have addressed all my concerns by doing additional experiments and by providing convincing arguments. I am happy to conclude that all my concerns on the weaknesses of the work have been nicely addressed. The already convincing story is now stronger.

Reviewer #2 (Public Review):

In bacteria and mammals, metabolically generated aldehydes become toxic at high concentrations because they irreversibly modify the free amino group of various essential biological macromolecules. However, these aldehydes can be present in extremely high amounts in archaea and plants without causing major toxic side effects. This fact suggests that archaea and plants have evolved specialized mechanisms to prevent the harmful effects of aldehyde accumulation.

In this manuscript, the authors show that the plant enzyme DTD2, originating from archaea, functions as a D-aminoacyl-tRNA deacylase. This enzyme effectively removes stable D-aminoacyl adducts from tRNAs, enabling these molecules to be recycled for translation. Furthermore, they demonstrate that DTD2 serves as a broad detoxifier for various aldehydes in vivo, extending its function beyond acetaldehyde, as previously believed. Finally, the authors suggest a potential application of their findings by showing that the absence of DTD2 renders plants more susceptible to reactive aldehydes, while its overexpression provides protection against them.

Overall, this study provides a molecular explanation for the remarkable efficiency of plants in handling reactive aldehydes. However, direct evidence that translation is impaired in plants lacking DTD2 experience is currently lacking. Furthermore, because root morphology of DTD2-overexpressing plants appears to differ from that of WT, a thorough phenotypic analysis of DTD2-overexpressing plants will be essential to accurately assess the potential translational application of this enzyme for engineering stress-tolerant plants.

Reviewer #1 (Public Review):

1. I suggest that the author's choose a different term in their title, abstract and manuscript to describe the phenotypes associated with ufd-1 and npl-4 knockdown other than an "inflammation-like response." Inflammation is a pathological term with four cardinal signs: redness (rubor), swelling (tumor), warmth (calor) and pain (dolor). These are not symptoms know to occur in C. elegans. The authors could consider using "tolerance" instead, as this term may better describe their findings.

2. It would help the reader to better understand the novelty of the findings in this study if the authors include a paragraph in their introduction to put their results in context of the published literature that has examined the relationship between immune activation and nematode health and survival. In particular, I suggest that the authors discuss doi:10.7554/eLife.74206 (2022), a study that charcterized a similar observation to what the authors are reporting. This study found that low cholesterol reduces pathogen tolerance and host survival during pathogen infection. Cholesterol scarcity increases p38 PMK-1 phosphorylation, priming immune effector induction in a manner that reduces pathogen accumulation in the intestine during a subsequent infection. I also suggest that the authors highlight in this introductory paragraph that the toxic effects of inappropriate immune activation in C. elegans has been widely catalogued. For example: doi.org/10.1371/journal.ppat.1011120 (2023); doi:10.1186/s12915-016-0320-z (2016).; doi:10.1126/science.1203411 (2011); doi:10.1534/g3.115.025650 (2016).

In this context, the authors could consider re-wording their novelty claim in the abstract and introduction to take into account this previous body of work.

3. The authors rely on the use of RNAi of ufd-1 and npl-4 to study their effect on P. aeruginosa colonization and pathogen resistance throughout the manuscript. To address the possibility of off-target effects of the RNAi, the authors should consider both (i) showing with qRT-PCR that these genes are indeed targeted during RNAi, and (ii) confirming their phenotypes with an orthologous technique, preferably by studying ufd-1 and npl-4 loss-of-function mutants [both in the wild-type and sek-1(km4) backgrounds]. If mutation of these genes is lethal, the authors could use Auxin Inducible Degron (AID) technology to induce the degradation of these proteins in post-developmental animals.

4. I am confused about the authors explanation regarding their observation that inhibition of the UFD-1/ NPL-4 complex extends the lifespan of sek-1(km25) animals, but not pmk-1(km25) animals, as SEK-1 is the MAPKK that functions immediately upstream of the p38 MAPK PMK-1 to promote pathogen resistance.

I am also confused why their RNA-seq experiment revealed a signature of intracellular pathogen response genes and not PMK-1 targets, which the authors propose is accounting for toxic immune activation. Activation of which immune response leads to toxicity?

5. The authors did not test alternative explanations for why UFD-1/ NPL-4 complex inhibition compromises survival during pathogen infection, other than exuberant immune activation. For example, it is possible that inhibition of this proteosome complex shortens lifespan by compromising the general health/ normal physiology of nematodes. Immune responses could be activated as a secondary consequence of this stress, and not be a direct cause of early morality. Does sek-1(km4) mutant suppress the lifespan shortened lifespan of ufd-1 and npl-4 knockdown? This experiment should also be done with loss-of-function mutants, as noted in point 3.

6. The conclusion of Figure 6 hinges on an experiments that uses double RNAi to knockdown two genes at the same time (Fig. 6D and 6G), an approach that is inherently fraught in C. elegans biology owing the likelihood that the efficiency of RNAi-mediated gene knockdown is compromised and may account for the observed phenotypes. The proper control for double RNAi is not empty vector + ufd-1(RNAi), but rather gfp(RNAi) + ufd-1(RNAi), as the introduction of a second hairpin RNA is what may compromise knockdown efficiency. In this context, it is important to confirm that knockdown of both genes occurs as expected (with qRT-PCR) and to confirm this phenotype using available elt-2 loss-of-function mutants.

7. A supplementary table with the source data for at least three replications (mean lifespan, n, statistical comparison) for each pathogenesis assay should be included in this manuscript.

Reviewer #2 (Public Review):

Summary:<br /> The authors aimed to uncover what role, if any, the UFD1/NPL4 complex might play in the innate immune responses of the nematode C. elegans. The authors find that loss of the complex renders animals more sensitive to both pathogenic and non-pathogenic bacteria. However, there appears to be a complex interplay with known innate immune pathways since the loss of UFD1/NPL4 actually results in increased survival of animals lacking the canonical innate immune pathways.

Strengths:<br /> The authors perform robust genetic analysis to exclude and include possible mechanisms by which the UFD1/NPL4 pathway acts in the innate immune response.

Weaknesses:<br /> The argument that the loss of the UFD1/NPL4 complex triggers a response that mimics that of an intracellular pathogen has not been thoroughly investigated. Additionally, the finding of a role of the GATA transcription factor, ELT-2, in this response is suggestive, but experiments showing sufficiency in the context of loss of the UFD1/NPL4 complex need to be explored.

Reviewer #1 (Public Review):

Summary:<br /> The authors identify a mechanical model of activation of Abelson kinase involving the modification of stability of an alpha helix by mutations and different classes of inhibitors. They use NMR chemical shifts of mutant sequences of the alpha helix in a model of Abelson kinase including the regulatory and kinase domains.

Strengths:<br /> The mechanism of inhibition of this important drug target is highly complex involving multiple domains' interactions, While crystal structures can establish end states well, the details of more dynamic interactions among the components can be assessed by NMR studies, The authors previously established {Sonti, 2018, PMID29319304} that different inhibitors and assembled states result from changes of stabilisation of the assembly involving the kinase and the SH3 domain. This is extended here to illuminate the role of the kinase C terminal alpha helic I' to the domains' interface, expanding the previous identification of this area of the protein as key to agonist/antagonist action at the allosteric myristlylation binding site.

Reviewer #2 (Public Review):

In this paper, Paladini and colleagues investigate the concerted motions within the Abl kinase that control its conformational transition between the active (disassembled) and inactive (assembled state). This work follows their previously published findings that binding of the type II inhibitor, imatinib to the active site of Abl, leads to kinase core disassembly via the force imposed by the P-loop and other regions of the N-lobe on the SH3 domain. Interestingly, imatinib-induced disassembly is prevented when an allosteric inhibitor, asciminib, binds to the myristate-binding pocket. Key to asciminib and myristate binding are motions of helix I, located in the C-lobe, and thus, helix I is hypothesized to be the sensor of the imatinib-induced changes. Specifically, bending of helix I upon engagement of myristate or asciminib was postulated to be important for re-assembly of the autoinhibited Abl core, and thus, reducing the "force" with which kinase N-lobe pushes against the SH2 domain upon binding imatinib.

The authors use NMR to measure conformational transitions in the several 15N-labeled Abl kinase constructs that display different degrees of helix I truncations. This analysis is slightly limited by the instability of the constructs that carry truncations beyond the helix I "bend". Nevertheless, it is sufficient to establish that truncation of helix I that removes its fragment, which is in contact with myristate or asciminib ligands, results in loss of the ability of helix I to impose "force" on the SH2 domain that results in kinase core disassembly, even in the presence of imatinib binding. In the absence of this force, the allosteric coupling between the helix I/SH2 and KD/SH3 interfaces is compromised. Principle component analysis is used to analyze the NMR data, and it is very clear and convincing.

A compelling evidence in support of the proposed allosteric mechanism comes from the analysis of the E528K disease mutation, identified in the Abl1 malformation syndrome. The authors show that this mutant, poised to break a salt bridge formed between E528 in the C-terminal portion of helix I and R479 on the kinase domain, increases helix I outward motions resulting in core disassembly and higher Abl kinase activity. Together, these results reinforce that helix I motions are central to the mechanism of kinase activation via core disassembly.

Joint Public Review

This paper shows that networks of binary neurons can exhibit power law behavior (including "crackling", which refers to a particular relationship among the power law exponents) without fine tuning. If, as is standard, we equate power law behavior to criticality, then criticality can arise in networks of neurons without fine tuning. The network model used to show this was extremely simple: a population of completely uncoupled neurons was driven by a small number of slowly varying "hidden" variables (either 1 or 5). This caused the firing rate of every neuron to change slowly over time, in a correlated fashion. Criticality was observed over a large range of couplings, time constants, and average firing rates.

This paper is extremely important in light of the hypothesis that criticality in the brain is both special, in the sense that it requires fine tuning, and that it leads to optimal information processing. As mentioned above, this paper shows that fine tuning is not required. It also shows that criticality does not imply optimal information transmission. This does not, of course, rule out the above "critical brain" hypothesis. But it does show that simply observing power law behavior is not enough to draw conclusions about either fine tuning or function.

These authors are not the first to show that slowly varying firing rates can give rise to power law behavior (see, for example, Touboul and Destexhe, 2017; Priesemann and Shriki, 2018). However, to our knowledge they are the first to show crackling, and to compute information transmission in, and out of, the critical state.

References:

Touboul and Destexhe, 2017: Touboul J, Destexhe A. Power-law statistics and universal scaling in the absence of criticality. Phys Rev E. 2017 95:012413, 2017.

Priesemann and Shriki, 2018: Priesemann V, Shriki O. PLOS Comp. Bio. 14:1-29, 2018.

Joint Public Review:

This paper shows that signatures of criticality -- in particular, power law behavior and "crackling" (the latter referring to a particular relationship among critical exponents) -- emerge from a biologically reasonable model that has nothing to do with criticality. Instead, the firing rate of a population of "neurons" (taken to be binary units) varies slowly in time. Importantly, conditioned on firing rate, the activity of each neuron (whether or not it emits a "spike") is independent of the activity of all the other neurons.

To put this result in broader context, we need to be clear what critically is and is not. Critically is a very specific set of phenomena in physics in which fundamentally local interactions produce unexpected long-range behavior. The model in this paper has no such local interactions. Instead, each neuron is coupled to a small number of latent dynamical modes (which in turn produce slowly varying firing rates). Thus, signatures of criticality emerge through fundamentally non-critical mechanisms. Consequently, such signatures of criticality observed in the brain can be misleading: they might not be evidence that the brain is critical at all; instead, they might just be evidence that neural activity is mirroring a small number of dynamical latent variables.

While this does not rule out criticality in the brain, it decidedly weakens the evidence for it, which was based on the following logic: critical systems give rise to power law behavior; power law behavior is observed in cortical networks; therefore, cortical networks operate near a critical point. Given, as shown in this paper, that power laws can arise from non-critical processes, the logic breaks. Moreover, the authors show that criticality does not imply optimal information transmission (one of its proposed functions). This highlights the necessity for more rigorous analyses to affirm criticality in the brain. In particular, it suggests that attention should be focused on the question "does the brain implement a dynamical latent variable model?".

These authors are not the first to show that slowly varying firing rates can give rise to power law behavior (see, for example, Touboul and Destexhe, 2017; Priesemann and Shriki, 2018). However, to our knowledge they are the first to show crackling, and to compute information transmission in the critical state.

Major comments:

1) For many readers, the essential messages of the paper may not be immediately clear. For example, is the paper criticizing the criticality hypothesis of cortical networks, or does the criticism extend deeper, to the theoretical predictions of "crackling" relationships in physical systems as they can emerge without criticality? Statements like "We show that a system coupled to one or many dynamical latent variables can generate avalanche criticality ..." could be misinterpreted as affirming criticality. A more accurate language is needed; for instance, the paper could state that the model generates relationships observed in critical systems. The paper should provide a clearer conclusion and interpretation of the findings in the context of the criticality hypothesis of cortical dynamics.

2) On lines 97-99, the authors state that "We are agnostic as to the origin of these inputs: they may be externally driven from other brain areas, or they may arise from recurrent dynamics locally". This idea is also repeated at the beginning of the Summary section. Perhaps being agnostic isn't such a good idea: it's possible that the recurrent dynamics is in a critical regime, which would just push the problem upstream. Presumably you're thinking of recurrent dynamics with slow timescales that's not critical? Or are you happy if it's in the critical regime? This should be clarified.

3) Even though the model in Equation 2 has been described in a previous publication and the Methods section, more details regarding the origin and justification of this model in the context of cortical networks would be helpful in the Results section. Was it chosen just for simplicity, or was there a deeper reason?

4) The Methods section (paragraph starting on lie 340) connects the time scale to actual time scales in neuronal systems, stating that "The timescales of latent variables examined range from about 3 seconds to 3000 seconds, assuming 3-ms bins". While bins of 3 ms are relevant for electrophysiological data from LFPs or high-density EEG/MEG, time scales above 10 seconds are difficult to generate through biophysically clear processes like ionic channels and synaptic transmission. The paper suggests that slow time scales of the latent variables are crucial for obtaining power law behavior resembling criticality. Yet, one way to generate such slow time scales is via critical slowing down, implying that some brain areas providing input to the network under study may operate near criticality. This pushes the problem toward explaining the criticality of those external networks. Hence, discussing potential sources for slow time scales in latent variables is crucial. One possibility you might want to consider is sources external to the organism, which could easily have time scales in the 1-24 hour range.

5) It is common in neuronal avalanche analysis to calculate the branching parameter using the ratio of events in consecutive bins. Near-critical systems should display values close to 1, especially in simulations without subsampling. Including the estimated values of the branching parameter for the different cases investigated in this study could provide more comprehensive data. While the paper acknowledges that the obtained exponents in the model differ from those in a critical branching process, it would still be beneficial to offer the branching parameter of the observed avalanches for comparison.

6) In the Discussion (l 269), the paper suggests potential differences between networks cultured in vitro and in vivo. While significant differences indeed exist, it's worth noting that exponents consistent with a critical branching process have also been observed in vivo (Petermann et al 2009; Hahn et al. 2010), as well as in large-scale human data.

References:

Touboul and Destexhe, 2017: Touboul J, Destexhe A. Power-law statistics and universal scaling in the absence of criticality. Phys Rev E. 2017 95:012413, 2017.

Priesemann and Shriki, 2018: Priesemann V, Shriki O. PLOS Comp. Bio. 14:1-29, 2018.

Petermann et al 2009: Oetermann, T., Thiagarajan, T. C., Lebedev, M. A., Nicolelis, M. A., Chialvo, D. R., and Plenz, D. PNAS 106:15921-15926, 2009.

Hahn et al. 2010: Hahn, G., Petermann, T., Havenith, M. N., Yu, S., Singer, W., Plenz, D., and Nikolic, D. J. Neurophys. 104:3312-3322, 2010.

Minor comments:

1) The term 'latent variable' should be rigorously explained, as it is likely to be unfamiliar to some readers.

2) There's a relatively important typo in the equations: Eq. 2 and Eq. 6 differ by a minus sign in the exponent. Eqs. 3 and 4 use the plus sign, but epsilon_0 on line 198 uses the minus sign. All very confusing until we figured out what was going on. But easy to fix.

3) In Eq. 7, the left hand side is zeta'/zeta', which is equal to 1. Maybe it should be zeta'/zeta?

Reviewer #1 (Public Review):

Henault et al build on their own previous work investigating the longstanding hypothesis that hybridization between divergent populations can activate transposable element mobilization (transposition). Previously they created crosses of increasing sequence divergence, using both intra- and inter-species hybrids and passaged them neutrally for hundreds of generations. Their previous work showed that neither hybrids isolated from natural environments nor hybrids from their mutation accumulation lines showed consistent evidence of increased transposable element content. Here, they sequence and assemble long read genomes of 127 of their mutation-accumulation lines and annotate all existing and de novo transposable elements. They find only a handful of de novo transposition events, and instead demonstrate that structural variation (ploidy, aneuploidy, loss of heterozygosity) plays a much larger role in the transposable element load in a given strain. They then created transposable element reporter constructs using two different Ty1 elements from S. paradoxus lineages and measured transposition rate in a number of intraspecific crosses. They demonstrate that transposition rate is dependent on both the Ty1 sequence and the copy number of genomic transposable elements, the latter of which is consistent with what has been observed in the literature of transposable element copy number control in Saccharomyces. To my knowledge, others have not directly tested the effect of Ty1 sequence itself (have not created diverse Ty1 reporter constructs), and so this is an interesting advance. Finally, the authors show that mitotype has a moderate effect on transposition rate, which is an intriguing finding that will be interesting to explore in future work.

The authors state that their results from their current work support results taken from their previous study using short read sequencing data of the same lines. The argument that follows is whether the authors gained anything novel from long read sequencing. While major results did not change from their previous work, the addition of long read sequencing did provide novel insight into the comparison of de novo transposition and structural variation that was not possible with short read sequencing. Additionally, this allowed the authors to compare estimates of transposition from two methods (inferred from mutation accumulation lines and from reporter assays).

Overall, this study represents a large effort to investigate how genetic background can influence transposable element load and transposition rate. The long read sequencing, assembly, and annotation, and the creation of these reporter constructs is non-trivial. Their results are straightforward, well supported, and are a nice addition to the literature.

Reviewer #2 (Public Review):

This is an interesting followup study that uses long read sequencing to examine previously constructed mutation accumulation lines between wild populations of S. cerevisiae and S. paradoxus. They also complement this work with reporter assays in hybrid backgrounds. The authors are attempting to test the hypothesis that hybridization leads to genome shock and unrestrained transposition. The paper largely confirms previous results (suggesting hybridization does not increase transposition) that are well cited and discussed in the paper, both from this group and from the Smukowski Heil/Dunham group but extends them to a new set of species/hybrids and with some additional resolution via the long read sequencing. The paper is well written and clear and I have no serious complaints.

In the abstract, the authors make three primary claims:

Structural variation plays a strong role in TE load.<br /> Transposition plays only a minor role in shaping the TE landscape in MA lines.<br /> Transposition rates are not increased by hybridization but are affected by genotype specific factors.

Comments on revised submission:

I found all three claims supported, albeit with some minor questions. Those questions were answered by the authors in revision. I appreciate the authors revisions and feel the paper is now in better shape than upon the original submission.

Reviewer #2 (Public Review):

Summary:<br /> The authors report the successful retrieval of mitogenomes from extinct Pleistocene megafauna (woolly Mammoth and woolly rhino) from recent sediment cores from two close Siberian lakes. The cores are too recent to represent real time points of these two extinct species (known to have been extinct for several thousands of years) and therefore, the most plausible interpretation is that permafrost thawing and similar physical processes in the lakes have made surface old ancient DNA, maybe from nearby, deep-buried carcasses.

They have answered the comments and questions I raised in my review. I agree with them on the complexities or separating a potential mixing of different Mammoth mito genomes retrieved.

Reviewer #3 (Public Review):

Summary:<br /> In this study, the researchers used ancient environmental DNA (aeDNA) retrieved from sediment cores, from two lakes in the Arctic, on the Yamal peninsula, in Siberia. The dating of one of the cores, showed that the sediment layers were very recent (ranging between the years 2019 - 1895). From this core they sequenced 23 libraries which were enriched for mammal mitochondrial genomes. They found a high proportion of two species that have been extinct for thousands of years, the mammoth and the woolly rhinoceros. The highest proportion of mammoth reads were found in very young layer (~81 years old) and as this initial finding does not match the temporal occurrence of the species, they confirmed the identification with several other methods. Additionally, they applied a different dating method on some samples and found that the aging of the samples was not completely congruent. The authors suggest the that the presence of these two Pleistocene megafauna in such recent sediment layers is a consequence of physical processes, specific to the study site, and that the high quality of the aeDNA recovered is a result of permafrost preservation.

The strengths of the study are in the rigorous confirmation of the identification of the taxa with four different PCR and sequencing techniques being used, the initial enrichment panel, and then subsequent metabarcoding PCRs, and taxa specific PCR for COI and cytB. Along with the ancient DNA protocol applied, this is therefore very convincing that the DNA detected in the samples is indeed from the Pleistocene mammals. Additionally, two methods were used to age the sediment cores, and although the depth of the samples tested do not overlap, they give reasonable ages (apart from the anomalous sample) and all together these are robust results.

There is now an analysis supporting the idea that there are multiple individual mammoths in the sample as well as a figure to display the locations of the haplotypes. The authors also confirm that the woolly rhinocerous did not recover enough sequences for analysis. The aims have been clarified and no longer states that they are looking at mammal biodiversity through time, so the papers focus is now more specifically on just the mammoth. But a supplementary table of the reads from common mammals has been added.

Overall the results support that there has been some movement of DNA throughout the sediment core which may impact the dating of the last occurrence of particular extinct taxa. As highlighted, though the geological processes by which this may have arisen are specific to this particular lake and may not be broadly relevant, therefore highlighting that knowledge of each system is important to understanding DNA distribution.

Reviewer #2 (Public Review):

In their manuscript, Keramidioti and co-authors investigate the cellular architecture of the nervous system in the freshwater polyp Hydra. Specifically, the authors attempt to improve the resolution, which is lacking in the previous studies, yet to generate a comprehensive overview of the entire nervous system's spatial organization and to infer communication between cells. To this end, Keramidioti et al. use state-of-the-art imaging approaches, such as confocal microscopy combined with the use of transgenic animals, transmission electron microscopy, and block face scanning electron microscopy. The authors present three major observations: i) A novel PNab antibody may be used to detect the entire nervous system of Hydra; ii) Nerve cells in the ectoderm and in the endoderm are organized in two separate nerve nets, which do not interact; iii) Both nerve nets are composed of bundles of overlapping nerve processes.

The manuscript addresses a long-standing and currently intensively studied question in developmental neurobiology biology - it attempts to reveal structural properties and principles that govern the function of the nervous systems in non-bilaterian animals. Hence, this study contributes to understanding the nervous system evolution trajectories. Therefore, the manuscript may represent interest to researchers interested in evolutionary and developmental neurobiology.

The manuscript reports a remarkably meticulous study and presents stunning imaging results.

Reviewer #3 (Public Review):

In this paper by Keramidioti et al, the authors have characterized a polyclonal antibody from rabbit, which was raised against a peptide of the intracellular domain of the Hydra Cadherin. This antibody unexpectedly recognizes presumably all neurons in the Hydra polyp but the specificity of the antibody was not investigated. Regardless, the antibody can be used to visualize and study the nerve net under a variety of conditions. The authors find that the endodermal and ectodermal nerve net do not make any contacts through the mesoglea, in contrast to earlier assumptions and data. They show that ectodermal neurons make close contacts to the myoepithelial muscles, in contrast to the endodermal muscles. Furthermore, they show that tentacle endoderm surprisingly does not have any neurons. Finally, a very nice tool to visualize the connections between the neurons is the staining of mosaic nGreen transgenic lines. This showed that the neurites align in parallel forming bundles of neurites over longer stretches, in particular in the ectoderm, which offers a mechanism how new neurons are added laterally to the existing nerve net. This has important implications about the way the neurons might communicate with each other.

Taken together, this paper adds to our knowledge of the Hydra nerve net and provides a new experimental tool. Although most of the study is rather descriptive the pictures are of spectacular quality, providing fascinating new insights into the arrangement and topology of the nerve net.

Reviewer #1 (Public Review):

Summary:<br /> The manuscript by Dubicka and co-workers on calcification in miliolid foraminifera presents an interesting piece of work. The study uses confocal and electron microscopy to show that the traditional picture of calcification in porcelaneous foraminifera is incorrect.

Strengths:<br /> The authors present high-quality images and an original approach to a relatively solid (so I thought) model of calcification.

Weaknesses:<br /> There are several major shortcomings. Despite the interesting subject and the wonderful images, the conclusions of this manuscript are simply not supported at all by the results. The fluorescent images may not have any relation to the process of calcification and should therefore not be part of this manuscript. The SEM images, however, do point to an outdated idea of miliolid calcification. I think the manuscript would be much stronger with the focus on the SEM images and with the speculation of the physiological processes greatly reduced.

Reviewer #2 (Public Review):

Summary:<br /> Dubicka et al. in their paper entitled " Biocalcification in porcelaneous foraminifera" suggest that in contrast to the traditionally claimed two different modes of test calcification by rotallid and porcelaneous miliolid formaminifera, both groups produce calcareous tests via the intravesicular mineral precursors (Mg-rich amorphous calcium carbonate). These precursors are proposed to be supplied by endocytosed seawater and deposited in situ as mesocrystals formed at the site of new wall formation within the organic matrix. The authors did not observe the calcification of the needles within the transported vesicles, which challenges the previous model of miliolid mineralization. Although the authors argue that these two groups of foraminifera utilize the same calcification mechanism, they also suggest that these calcification pathways evolved independently in the Paleozoic.

Strengths:<br /> The authors document various unknown aspects of calcification of Pseudolachlanella eburnea and elucidate some poorly explained phenomena (e.g., translucent properties of the freshly formed test) however there are several problematic observations/interpretations which in my opinion should be carefully addressed.

Weaknesses:<br /> 1. The authors (line 122) suggest that "characteristic autofluorescence indicates the carbonate content of the vesicles (Fig. S2), which are considered to be Mg-ACCs (amorphous MgCaCO3) (Fig. 2, Movies S4 and S5)". Figure S2 which the authors refer to shows only broken sections of organic sheath at different stages of mineralization. Movie S4 shows that only in a few regions some vesicles exhibit red autofluorescence interpreted as Mg-ACC (S5 is missing but probably the authors were referring to S3). In their previous paper (Dubicka et al 2023: Heliyon), the authors used exactly the same methodology to suggest that these are intracellularly formed Mg-rich amorphous calcium carbonate particles that transform into a stable mineral phase in rotaliid Aphistegina lessonii. However, in Figure 1D (Dubicka et al 2023) the apparently carbonate-loaded vesicles show the same red autofluorescence as the test, whereas in their current paper, no evidence of autofluorescence of Mg-ACC grains accumulated within the "gel-like" organic matrix is given. The S3 and S4 movies show circulation of various fluorescing components, but no initial phase of test formation is observable (numerous mineral grains embedded within the organic matrix - Figures 3A and B - should be clearly observed also as autofluorescence of the whole layer). Thus the crucial argument supporting the calcification model (Figure 5) is missing. There is no support for the following interpretation (lines 199-203) "The existence of intracellular, vesicular intermediate amorphous phase (Mg-ACC pools), which supply successive doses of carbonate material to shell production, was supported by autofluorescence (excitation at 405 nm; Fig. 2; Movies S3 and S4; see Dubicka et al., 2023) and a high content of Ca and Mg quantified from the area of cytoplasm by SEM-EDS analysis (Fig. S6)."

2. The authors suggest that "no organic matter was detected between the needles of the porcelain structures (Figures 3E; 3E; S4C, and S5A)". Such a suggestion, which is highly unusual considering that biogenic minerals almost by definition contain various organic components, was made based only on FE-SEM observation. The authors should either provide clearcut evidence of the lack of organic matter (unlikely) or may suggest that intense calcium carbonate precipitation within organic matrix gel ultimately results in a decrease of the amount of the organic phase (but not its complete elimination), alike the pure calcium carbonate crystals are separated from the remaining liquid with impurities ("mother liquor"). On the other hand, if (249-250) "organic matrix involved in the biomineralization of foraminiferal shells may contain collagen-like networks", such "laminar" organization of the organic matrix may partly explain the arrangement of carbonate fibers parallel to the surface as observed in Fig. 3E1.

3. The author's observations indeed do not show the formation of individual skeletal crystallites within intracellular vesicles, however, do not explain either what is the structure of individual skeletal crystallites and how they are formed. Especially, what are the structures observed in polarized light (and interpreted as calcite crystallites) by De Nooijer et al. 2009? The author's explanation of the process (lines 213-216) is not particularly convincing "we suspect that the OM was removed from the test wall and recycled by the cell itself".

4. The following passage (lines 296-304) which deals with the concept of mesocrystals is not supported by the authors' methodology or observations. The authors state that miliolid needles "assembled with calcite nanoparticles, are unique examples of biogenic mesocrystals (see Cölfen and Antonietti, 2005), forming distinct geometric shapes limited by planar crystalline faces" (later in the same passage the authors say that "mesocrystals are common biogenic components in the skeletons of marine organisms" (are they thus unique or are they common)? It is my suggestion to completely eliminate this concept here until various crystallographic details of the miliolid test formation are well documented.

Reviewer #1 (Public Review):

Cheng et al investigated how vascular cells of the zebrafish Circle of Willis arteries differentiate using live imaging of transgenic zebrafish embryos. They find an anterior-to-posterior gradient in the differentiation of pdgfrb+ progenitors into acta2+ smooth muscle cells (SMCs). Computational modeling suggests that blood flow velocity and wall shear stress are higher in the anterior Circle of Willis arteries. Using pharmacological manipulations, they show that blood flow is required for the differentiation of SMCs but not for the short-term maintenance of the SMC differentiation state. They provide evidence that the increased expression of the flow-responsive Klf2 transcription factor in endothelial cells predates SMC differentiation, with the same anterior-to-posterior gradient, and that Klf2 expression is required for SMC differentiation.

Overall, the study is very well-conducted and the paper is well-written. These important data point to hemodynamics as an important driver of artery muscularization in the Circle of Willis.

Reviewer #2 (Public Review):

Summary:<br /> Cheng et al. explore the development of the arteries that form the Circle of Willis and investigate how blood flow pulsatility influences vascular smooth muscle cell (VSMC) differentiation. Using live confocal imaging of the developing zebrafish, the authors show that endothelial cells in the Circle of Willis arteries transition from venous to arterial identity between 54 hours post-fertilization (hpf) and 3 days post-fertilization (dpf), and that this coincides with pdgfrb+ mural cell progenitor differentiation into acta2+ arterial VSMCs. They find that the anterior portions of the Circle of Willis, including the internal carotid arteries (CaDI), establish acta2 expression earlier than posterior aspects, likely due to faster flow rate and increased pulsatility through the CaDI. Then, using computational fluid dynamics, an in vitro co-culture assay, and genetic and drug manipulations of blood flow, the authors provide evidence that pdgfrb+ differentiation is dependent upon pulsatile blood flow and klf2a activation. The results add to our understanding of vascular development and suggest that deficits in pulsatile flow could be potential drivers of arteriopathies.

Strengths:<br /> 1) Longitudinal confocal imaging of live developing zebrafish makes the timeline of arterial development in the Circle of Willis easy to understand. This is a strong approach to studying how vascular networks are altered with genetic and pharmacological manipulations.

2) Rigorous use of multiple techniques to test the hypothesis that pulsatile blood flow is required for smooth muscle cell differentiation. The microangiography experiment, in vitro co-culture assay, and genetic and drug manipulations of heart rate at various developmental time points yield outcomes that are consistent with the hypothesis.

Reviewer #3 (Public Review):

Cheng et al. studied if and how blood flow regulates the differentiation of vascular smooth muscle cells (VSMC) in the Circle of Willis (CW) in zebrafish embryos. They show that CW vessels gradually acquire an arterial identity. VSMCs also undergo gradual differentiation, which correlates with blood flow velocity. Using cell culture they show that pulsatile blood flow promotes pericyte differentiation into smooth muscle cells. They further identify transcription factor klf2a as differentially regulated by blood flow, and show that klf2a inhibition results in VSMC differentiation. The authors conclude that pulsatile flow promotes VSMC differentiation through klf2a activation.

Overall this is an important study, because VSMC differentiation in CW has not been previously studied, although analogous observations regarding the role of blood flow and klf2 involvement have been previously made in other systems and other vascular beds, for example, mouse klf2 mutants, which have deficient VSMC coverage of the dorsal aorta (Wu et al., 2008, JBC 283: 3942-50). The results convincingly show that VSMC differentiation in CW depends on the blood flow and that klf2a flow-dependent function regulates VSMC differentiation.

Reviewer #1 (Public Review):

Summary:<br /> In this manuscript, Bell et al. provide an exhaustive and clear description of the diversity of a new class of predicted type IV restriction systems that the authors denote as CoCoNuTs, for their characteristic presence of coiled-coil segments and nuclease tandems. Along with a comprehensive analysis that includes phylogenetics, protein structure prediction, extensive protein domain annotations, and an in-depth investigation of encoding genomic contexts, they also provide detailed hypotheses about the biological activity and molecular functions of the members of this class of predicted systems. This work is highly relevant, it underscores the wide diversity of defence systems that are used by prokaryotes and demonstrates that there are still many systems to be discovered. The work is sound and backed-up by a clear and reasonable bioinformatics approach. I do not have any major issues with the manuscript, but only some minor comments.

Strengths:<br /> The analysis provided by the authors is extensive and covers the three most important aspects that can be covered computationally when analysing a new family/superfamily: phylogenetics, genomic context analysis, and protein-structure-based domain content annotation. With this, one can directly have an idea about the superfamily of the predicted system and infer their biological role. The bioinformatics approach is sound and makes use of the most current advances in the fields of protein evolution and structural bioinformatics.

Weaknesses:<br /> It is not clear how coiled-coil segments were assigned if only based on AF2-predicted models or also backed by sequence analysis, as no description is provided in the methods. The structure prediction quality assessment is based solely on the average pLDDT of the obtained models (with a threshold of 80 or better). However, this is not enough, particularly when multimeric models are used. The PAE matrix should be used to evaluate relative orientations, particularly in the case where there is a prediction that parts from 2 proteins are interacting. In the case of multimers, interface quality scores, such as the ipTM or pDockQ, should also be considered and, at minimum, reported.

Reviewer #2 (Public Review):

Summary:<br /> In this work, using in-depth computational analysis, Bell et al. explore the diverse repertoire of type IV McrBC modification-dependent restriction systems. The prototypical two-component McrBC system has been structurally and functionally characterised and is known to act as a defence by restricting phage and foreign DNA containing methylated cytosines. Here, the authors find previously unanticipated complexity and versatility of these systems and focus on detailed analysis and classification of a distinct branch, the so-called CoCoNut, named after its composition of coiled-coil structures and tandem nucleases. These CoCoNut systems are predicted to target RNA as well as DNA and to utilise defence mechanisms with some similarity to type III CRISPR-Cas systems.

Strengths:<br /> This work is enriched with a plethora of ideas and a myriad of compelling hypotheses that now await experimental verification. The study comes from the group that was amongst the first to describe, characterize, and classify CRISPR-Cas systems. By analogy, the findings described here can similarly promote ingenious experimental and conceptual research that could further drive technological advances. It could also instigate vigorous scientific debates that will ultimately benefit the community.

Weaknesses:<br /> The multi-component systems described here function in the context of large oligomeric complexes. Some of the single chain AF2 predictions shown in this work are not compatible, for example, with homohexameric complex formation due to incompatible orientation of domains. The recent advances in protein structure prediction, in particular AlphaFold2 (AF2) multimer, now allow us to confidently probe potential protein-protein interactions and protein complex formation. This predictive power could be exploited here to produce a better glimpse of these multimeric protein systems. It can also provide a more sound explanation for some of the observed differences amongst different McrBC types.

Reviewer #1 (Public Review):

In this article, different machine learning models (pan-specific, peptide-specific, pre-trained, and ensemble models) are tested to predict TCR-specificity from a paired-chain peptide-TCR dataset. The data consists of 6,358 positive observations across 26 peptides (as compared to six peptides in NetTCR version 2.1) after several pre-processing steps (filtering and redundancy reduction). For each positive sample, five negative samples were generated by swapping TCRs of a given peptide with TCRs binding to other peptides. The weighted loss function is used to deal with the imbalanced dataset in pan-specific models.

The results demonstrate that the redundant data introduced during training did not lead to performance gain; rather, a decrease in performance was observed for the pan-specific model. The removal of outliers leads to better performance.

To further improve the peptide-specific model performance, an architecture is created to combine pan-specific and peptide-specific models, where the pan-specific model is trained on pan-specfic data while keeping the peptide-specfic part of the model frozen, and the peptide-specific model is trained on a peptide-specific dataset while keeping the pan-specific part of the model frozen. This model surpassed the performance of individual pan-specific and peptide-specific models. Finally, sequence similarity-based predictions of TCRbase are integrated into the pre-trained CNN model, which further improved the model performance (mostly due to the better discrimination of binders and non-binders).

The prediction for unseen peptides is still low in a pan-specific model; however, an improvement in prediction is observed for peptides with high similarity to the ones in the training dataset. Furthermore, it is shown that 15 observations shows satisfactory performance as compared to the ~150 recommended in the literature.

Models are evaluated on the external dataset (IMMREP benchmark). Peptide-specific models performed competitively with the best models in the benchmark. The pre-trained model performed worst, which the authors suggested could be because of positive and negative sample swapping across training and testing sets. To resolve this issue, they applied the redundancy removal technique to the IMMER dataset. The results agreed with earlier conclusion that the pre-trained models surpassed peptide-specific models and the integration of similarity-based methods leads to performance boost. It highlights the need for the creation of a new benchmark without data redundancy or leakage problems.

The manuscript is well written, clear and easy to understand. The data is effectively presented. The results validate the drawn conclusions.

Reviewer #2 (Public Review):

Summary:<br /> The authors describe a novel ML approach to predict binding between MHC-bound peptides and T-Cell receptors. Such approaches are particularly useful for predicting the binding of peptide sequences with low similarity when compared to existing data sets. The authors focus on improving dataset quality and optimizing model architecture to achieve a pan-specific predictive model in hopes of achieving a high precision model for novel peptide sequences.

Strengths:<br /> Since assuring the quality of training datasets is the first major step in any ML training project, the extensive human curation and computational analysis and enhancements made in this manuscript represent a major contribution to the field. Moreover, the systematic approach to testing redundancy reduction and data augmentation is exemplary, and will significantly help future research in the field.

The authors also highlight how their model can identify outliers and how that can be used to improve the model around known sequences, which can help the creation and optimization of future datasets for peptide binding.

The new models presented here are novel and built using paired α/β TCR sequence data to predict peptide-specific TCR binding, and have been extensively and rigorously tested.

Weaknesses:<br /> Achieving an accurate pan-specific model is an ambitious goal, and the authors have significant difficulties when trying to achieve non-random performance for prediction of TCR binding to novel peptides. This is the most challenging task for this kind of model, but also the most desirable when applying such models to biotechnological and bioengineering projects.

The manuscript is a highly technical and extremely detailed computational work, which can make the achievements and impact of the work hard to parse for application-oriented researchers, and still hard to translate to real-world use-cases for TCR specificity predictions.

Reviewer #1 (Public Review):

Summary:<br /> The manuscript submitted by Langenbacher et al., entitled " Rtf1-dependent transcriptional pausing regulates cardiogenesis", describes very interesting and highly impactful observations about the function of Rtf-1 in cardiac development. Over the last few years, the Chen lab has published novel insights into the genes involved in cardiac morphogenesis. Here, they used the mouse model, the zebrafish model, cellular assays, single cell transcription, chemical inhibition, and pathway analysis to provide a comprehensive view of Rtf1 in RNAPII (Pol2) transcription pausing during cardiac development. They also conducted knockdown-rescue experiments to dissect the functions of Rtf1 domains.

Strengths:<br /> The most interesting discovery is the connection between Rtf1 and CDK9 in regulating Pol2 pausing as an essential step in normal heart development. The design and execution of these experiments also demonstrate a thorough approach to revealing a previously underappreciated role of Pol2 transcription pausing in cardiac development. This study also highlights the potential amelioration of related cardiac deficiencies using small molecule inhibitors against cyclin dependent kinases, many of which are already clinically approved, while many other specific inhibitors are at various preclinical stages of development for the treatment of other human diseases. Thus, this work is impactful and highly significant.

Reviewer #2 (Public Review):

Summary:

Langenbacher at el. examine the requirement of Rtf1, a component of the PAF1C, which regulates transcriptional pausing in cardiac development. The authors first confirm their previous morphant study with newly generated rtf1 mutant alleles, which recapitulate the defects in cardiac progenitor and differentiation gene expression observed previously in morphants. They then examine the conservation of Rtf1 in mouse embryos and embryonic stem cell-derived cardiomyocytes. Conditional loss of Rtf1 in mesodermal lineages and depletion in murine ESCs demonstrates a failure to turn on cardiac progenitor and differentiation marker genes, supporting conservation of Rtf1 in promoting cardiac development. The authors subsequently employ bulk RNA-seq on flow-sorted hand2:GFP+ cells and multiomic single-cell RNA-seq on whole Rtf1-depleted embryos at the 10-12 stage. These experiments corroborate that genes associated with cardiac and muscle development are lost. Furthermore, the differentiation trajectories suggest that the expression of genes associated with cardiac maturation is not initiated. Structure-function analysis supports that the Plus3 domain is necessary for its function in promoting cardiac progenitor formation. ChIP-seq for RNA Pol II on 10-12 somite stage embryos suggests that Rtf1 is required for proper promoter pausing. This defect can partially be rescued through use of a pharmacological inhibitor for Cdk9, which inhibits elongation, can partially restore elongation in rtf1 mutants.

Strengths:

Many aspects of the data are strong, which support the basic conclusions of the authors that Rtf1 is required for transcriptional pausing and has a conserved requirement in vertebrate cardiac development. Areas of strength include the genetic data supporting the conserved requirement for Rtf1 in promoting cardiac development, the complementary bulk and single-cell RNA-sequencing approaches providing some insight into the gene expression changes of the cardiac progenitors, the structure-function analysis supporting the requirement of the Plus3 domain, and the pharmacological epistasis combined with the RNA Pol II ChIP-seq, supporting the mechanism implicating Cdk9 in the Rtf1 dependent mechanism of RNA Pol II pausing.

Weaknesses:

While most of the basic conclusions are supported by the data, there are a number of analyses that are confusing as to why they chose to perform the experiments the way they did and some places where the interpretations presently do not support the interpretations. One of the conclusions is that the phenotype affects the maturation of the cardiomyocytes and they are arresting in an immature state. However, this seems to be mostly derived from picking a few candidates from the single cell data in Fig. 6. If that were the case, wouldn't the expectation be to observe relatively normal expression of earlier marker genes required for specification, such as Nkx2.5 and Gata5/6? The in situ expression analysis from fish and mice (Fig. 2 and Fig. 3) and bulk RNA-seq (Fig. 5) seems to suggest that there are pretty early specification and differentiation defects. While some genes associated with cardiac development are not changed, many of these are not specific to cardiomyocyte progenitors and expressed broadly throughout the ALPM. Similarly, it is not clear why a consistent set of cardiac progenitor genes (for instance mef2ca, nkx2.5, and tbx20) was analyzed for all the experiments, in particular with the single cell analysis.

The point of the multiomic analysis is confusing. RNA- and ATAC-seq were apparently done at the same time. Yet, the focus of the analysis that is presented is on a small part of the RNA-seq data. This data set could have been more thoroughly analyzed, particularly in light of how chromatin changes may be associated with the transcriptional pausing. This seems to be a lost opportunity. Additionally, how the single cell data is covered in Supplemental Fig. 2 and 3 is confusing. There is no indication of what the different clusters are in the Figure or the legend.

While the effect of Rtf1 loss on cardiomyocyte markers is certainly dramatic, it is not clear how well the mutant fish have been analyzed and how specific the effect is to this population. It is interpreted that the effects on cardiomyocytes are not due to "transfating" of other cell fates, yet supplemental Fig. 4 shows numerous effects on potentially adjacent cell populations. Minimally, additional data needs to be provided showing the live fish at these stages and marker analysis to support these statements. In some images, it is not clear the embryos are the same stage (one can see pigmentation in the eyes of controls that is not in the mutants/morphants), causing some concern about developmental delay in the mutants.

With respect to the transcriptional pausing defects in the Rtf1 deficient embryos, it is not clear from the data how this effect relates to the expression of the cardiac markers. This could have been directly analyzed with some additional sequencing, such as PRO-seq, which would provide a direct analysis of transcriptional elongation.

Some additional minor issues include the rationale that sequence conservation suggests an important requirement of a gene (line 137), which there are many examples this isn't the case, referencing figures panels out of order in Figs. 4, 7, and 8) as described in the text, and using the morphants for some experiments, such as the rescue, that could have been done in a blinded manner with the mutants.

Reviewer #1 (Public Review):

The current manuscript revisits previous reports in the literature. The human Pannexin 1 channel is regulated by phosphorylation at two residues by Src kinase. From this series of experiments, the authors conclude that PANX-1 is not phosphorylated at these residues.

Strengths of the manuscript:<br /> The biggest strength of the manuscript is the comprehensiveness of the approach. The authors recapitulate prior experiments in the literature, and also add a series of new, orthogonal experiments that all examine the claim of PANX-1 phosphorylation. The breadth of the reported experiments extends over multiple cell lines and protein constructs, in vitro purified proteins, mass spec, different phosphorylation detection reagents and antibodies, and functional electrophysiology assays that show that the addition of Src does not impact gating. The combined weight of all these data strongly suggests that the field should re-examine the claim that PANX-1 is regulated by phosphorylation at Y199 and Y309.

Another strength is that the authors go beyond simply showing that the antibodies do not recognize phosphorylated PANX-1. They also provide potential mechanisms for how the antibodies may be misleading. Both antibodies recognize phosphorylated Src-1. In the case of anti-PANX1-pY308, the authors provide solid mutagenesis evidence that the antibody also weakly recognizes a non-phosphorylated epitope of PANX1 in the same region as the tyrosine. This helps make a convincing case.

Such experiments, while not glamorous, have great practical importance for developing an accurate understanding of how Pannexin channels are regulated.

Reviewer #2 (Public Review):

Summary:<br /> The widely distributed pannexin 1 (PANX1) is an ATP-permeable channel that plays an important role in intercellular communication and has been implicated in various pathophysiological processes and diseases. Previous studies have demonstrated that PANX1 can be phosphorylated at two molecular sites via the non-receptor kinase Src, thereby leading to channel opening and ATP release. In this paper, the authors used a variety of methods to detect tyrosine phosphorylation modification of PANX1 channel protein, however, their results showed that commercially available antibodies against the two phosphorylation sites used in previous studies did not work well, in other words, phosphorylation changes in PANX1 could not be detected by those antibodies. Therefore, the authors call for the re-examination and evaluation of previous research results.

Strengths:<br /> In general, this is a meticulous study, using different detection methods and different expression systems.

Reviewer #3 (Public Review):

Summary:<br /> It has been proposed in the literature, that the ATP release channel Panx1 can be activated in various ways, including by tyrosine phosphorylation of the Panx1 protein. The present study reexamines the commercial antibodies used previously in support of the phosphorylation hypothesis and the presented data indicate that the antibodies may recognize proteins unrelated to Panx1. Consequently, the authors caution about the use and interpretation of results obtained with these antibodies.

Strengths:<br /> The manuscript by Ruan et al. addresses an important issue in Panx1 research, i.e. the activation of the channel formed by Panx1 via protein phosphorylation. If the authors' conclusions are correct, the previous claims for Panx1 phosphorylation on the basis of the commercial anti-phospho-Panx1 antibodies would be in question.

This is a very detailed and comprehensive analysis making use of state-of-the-art techniques, including mass spectrometry and phos-tag gel electrophoresis.

In general, the study is well-controlled as relating to negative controls.

The value of this manuscript is, that it could spawn new, more function-oriented studies on the activation of Panx1 channels.

Weaknesses:<br /> Although the manuscript addresses an important issue, the activation of the ATP-release channel Panx1 by protein phosphorylation, the data provided do not support the firm conclusion that such activation does not exist. The failure to reproduce published data obtained with commercial anti-phospho Panx1 antibodies can only be of limited interest for a subfield.

1. The title claiming that "Panx1 is NOT phosphorylated..." is not justified by the failure to reproduce previously published data obtained with these antibodies. If, as claimed, the antibodies do not recognize Panx1, their failure cannot be used to exclude tyrosine phosphorylation of the Panx1 protein. There is no positive control for the antibodies.

2. The authors claim that exogenous SRC expression does not phosphorylate Y198. DeLalio et al. 2019 show that Panx1 is constitutively phosphorylated at Y198, so an effect of exogenous SRC expression is not necessarily expected.

3. The authors argue that the GFP tag of Panx1at the COOH terminus does not interfere with folding since the COOH modified (thrombin cleavage site) Panx1 folds properly, forming an amorphous glob in the cryo-EM structure. However, they do not show that the COOH-modified Panx1 folds properly. It may not, because functional data strongly suggest that the terminal cysteine dives deep into the pore. For example, the terminal cysteine, C426, can form a disulfide bond with an engineered cysteine at position F54 (Sandilos et al. 2012).

4. The authors dismiss the additional arguments for tyrosine phosphorylation of Panx1 given by the various previous studies on Panx1 phosphorylation. These studies did not, as implied, solely rely on the commercial anti-phospho-Panx1 antibodies, but also presented a wealth of independent supporting data. Contrary to the authors' assertion, in the previous papers the pY198 and pY308 antibodies recognized two protein bands in the size range of glycosylated and partial glycosylated Panx1.

5. A phosphorylation step triggering channel activity of Panx1 would be expected to occur exclusively on proteins embedded in the plasma membrane. The membrane-bound fraction is small in relation to the total protein, which is particularly true for exogenously expressed proteins. Thus, any phosphorylated protein may escape detection when total protein is analyzed. Furthermore, to be of functional consequence, only a small fraction of the channels present in the plasma membrane need to be in the open state. Consequently, only a fraction of the Panx1 protein in the plasma membrane may need to be phosphorylated. Even the high resolution of mass spectroscopy may not be sufficient to detect phosphorylated Panx1 in the absence of enrichment processes.

6. In the electrophysiology experiments described in Figure 7, there is no evidence that the GFP-tagged Panx1 is in the plasma membrane. Instead, the image in Figure 7a shows prominent fluorescence in the cytoplasm. In addition, there is no evidence that the CBX-sensitive currents in 7b are mediated by Panx1-GFP and are not endogenous Panx1. Previous literature suggests that the hPanx1 protein needs to be cleaved (Chiu et al. 2014) or mutated at the amino terminus (Michalski et al 2018) to see voltage-activated currents, so it is not clear that the currents represent hPANX1 voltage-activated currents.

Reviewer #1 (Public Review):

The microtubule cytoskeleton is essential for basic cell functions, enabling intracellular transport, and establishment of cell polarity and motility. Microtubule-associated proteins (MAPs) contribute to the regulation of microtubule dynamics and stability - mechanisms that are specifically important for the development and physiological function of neurons. Here, the authors aimed to elucidate the neuronal function of the MAP Hmmr, which they had previously identified in a quantitative study of the proteome associated with neuronal microtubules.

The authors conduct well-controlled experiments to demonstrate the localization of endogenous as well as exogenous Hmmr on microtubules within the soma as well as all neurites of hippocampal neurons. Functional analysis using gain- and loss-of-function approaches demonstrates that Hmmr levels are crucial for neuronal morphogenesis, as the length of both dendrites and axons decreases upon loss of Hmmr and increases upon Hmmr overexpression. In addition to length alterations, the branching pattern of neurites changes with Hmmr levels. To uncover the mechanism of how Hmmr influences neuronal morphology, the authors follow the lead that Hmmr overexpression induces looped microtubules in the soma, indicative of an increase in microtubule stability. Microtubule acetylation indeed decreases and increases with Hmmr LOF and GOF, respectively. Together with a rescue of nocodazole-induced microtubule destabilization by Hmmr GOF, these results argue that Hmmr regulates microtubule stability. Highlighted by the altered movement of a plus-end-associated protein, Hmmr also has an effect on the dynamic nature of microtubules. The authors present evidence suggesting that the nucleation frequency of neuronal microtubules depends on Hmmr's ability to recruit the microtubule nucleator Tpx2. Together, these data add novel insight into MAP-mediated regulation of microtubules as a prerequisite for neuronal morphogenesis. While the data shown support the author's conclusions, the study also has several weaknesses:

- The study appears incomplete as the initial proteomics analysis which is referenced as an entry into the study is not presented. This surely is the authors' choice, however, without presenting this data set, it would make more sense if the authors first showed the localization of Hmmr on neuronal microtubules and then started with the functional analysis.

- Neurite branching is quantified, but the methods used are not consistent (normalized branch density vs. Sholl analysis) and there is no distinction between alterations of branching in dendrites vs. axons. This information should be added as it could prove informative with respect to the physiological function of Hmmr in neurite branching.

- The authors show that altered Hmmr levels affect neurite branching and identify an effect on microtubule stability and dynamics as a molecular mechanism. However, how branching correlates with or is regulated by Hmmr-mediated microtubule dynamics is neither addressed experimentally nor discussed by the authors. The physiological significance of altered neuronal morphogenesis also lacks discussion.

- Multiple times, the manuscript lacks a rationale for an experimental approach, choice of cell type, time points, regions of interest, etc. Also, a meaningful description of the methods and for how data were analyzed is missing, making the paper hard to read for someone not directly from the field.

Reviewer #2 (Public Review):

The mechanism of microtubule formation, stabilization, and organization in neurites is important for neuronal function. In this manuscript, the authors examine the phenotype of neurons following alteration in the level of the protein HMMR, a microtubule-associated protein with established roles in mitosis. Neurite morphology is measured as well as microtubule stability and dynamic parameters using standard assays. A binding partner of HMMR, TPX2, is localized. The results support a role for HMMR in neurons.

The work presented in this manuscript seeks to determine if a MAP called HMMR contributes to microtubule dynamics in neurons. Several steps, including validation of the RNAi, additional statistical analysis, use of cells at the same age in culture, and better documentation in figures, would increase the impact of the work.

In many places, the data can be improved which might make the story more convincing. As presented, the results show that HMMR is distributed as puncta on neurons with data coming from a single HMMR antibody, and some background staining that was not discussed. In the discussion the authors state that HMMR impacts microtubule stability, which was evaluated by the presence of post-translational modification and resistance to nocodazole; the data are suggestive but not entirely convincing. The discussion also states that HMMR increases the "amount" of growing microtubules which was measured as the frequency of comet appearance. The authors did not comment on how the number of growing microtubules results in the observed morphological changes.

Reviewer #1 (Public Review):

Summary:<br /> The article "Chemoproteomics validates selective targeting of Plasmodium M1 alanyl aminopeptidase as a cross-species strategy to treat malaria" presents a series of biochemical methods based on proteomics and metabolomics, as a means to:<br /> (1) validate the specific targeting of biologically active molecules (MIPS2673) towards a defined (unique) protein target within a parasite<br /> and<br /> (2) to explore whether by quantifying the perturbations generated at the level of the parasite metabolome, it is possible to extrapolate which metabolic pathway has been disrupted by using this biologically active molecule and whether this may further confirm selective targeting in parasites of the expected (or in-vitro targeted) enzyme (here PfA-1).

The inhibitor used in this work by the authors (MIPS2673) is to my knowledge a novel one, although belonging to a chemical series previously explored by the authors, which recently enabled them to discover a specific PfA-M17 inhibitor, MIPS2571 (Edgard et al., 2022, ref 11 of this current work). Indeed, inhibitors specifically targeting either PfA-M1 or PfA-M17 (and not both, as currently done in the past) are scarce today, and highly needed to functionally characterize these two zinc-aminopeptidases. MIPS2673, blocks the development of erythrocytic stages of Plasmodium falciparum with an EC50 of 324 nM, blocks the parasite development at the young trophozoite stage at 5x EC50 (but at ring stages at 10xEC50, figure 1E), and inhibits the enzymatic activity of PfA-M1 (and its ortholog Pv-M1) but not of the related malarial metallo-aminopeptidases (M17 and M18 families) nor the human metalloenzymes from closely related enzymatic families, supporting its selective targeting of PfA-M1 (and Pv-M1).

All experiments are carried out in vitro (e.g. biochemical studies such as enzymology, proteomics, metabolomics) and on cultured parasites (erythrocyte stages of Plasmodium falciparum and several gametocytes stages obtained in vitro); there are no in vivo manipulations. The work related to Plasmodium vivax, which justifies the "cross-species" indication in the title of the article, is restricted to using a recombinant form of the M1-family aminopeptidase in enzymatic assays. The rest of the work concerns only Plasmodium falciparum. While I found globally that this work is original and brings new data and above all proposes chemical validation approaches that could be used for other target validations under similar limiting conditions (impossibility of KO of the gene), I have some specific questions to address to the authors.

Strengths and weaknesses:<br /> -The chemoproteomic approach, that explores the ability of MIPS2673 to more significantly "protect" the putative target (PfA-M1) against thermal degradation or enzymatic attack (by proteinase K), to document its selective targeting towards PfA-M1 (the inhibitor, once associated with its target, is expected to stabilize its structure or prevent the action of end proteases), uses several concentrations of MIPS2673 and provides convincing results. My main criticism is that these tests are carried out with parasite extracts enriched in 30-38 hours old forms, and restricted to the fraction of soluble proteins isolated from these parasitic forms, which still limits the scope of the analysis. It is clear that this methodological approach is a choice that can be argued both biologically (PfA-M1 is well expressed in these stages of the parasite development) and biochemically (it is difficult to do proteomic analyses on insoluble proteins) but I regret that the authors do not discuss these limitations further, notably, I would have expected (from Figure 1E) some targets to be also present at ring stages.

-The metabolomic approach, by documenting the ability of MIPS2673 to selectively increase the number of non-hydrolyzed dipeptides in treated versus untreated parasites is another argument in favor of the selective targeting of PfA-M1 by MIPS2673, in particular by its broad-spectrum aminopeptidase action preferentially targeting peptides resulting from the degradation of hemoglobin by the parasite. The relative contribution of peptides derived from host hemoglobin versus other parasite proteins is, however, little discussed.

The work as a whole remains highly interesting, both for the specific topic of PfA-M1's role in parasite biology and for the method, applicable to other malarial drug contexts.

Reviewer #2 (Public Review):

In this manuscript, the authors first developed a new small molecular inhibitor that could target specifically the M1 metalloproteases of both important malaria parasite species Plasmodium falciparum and P. vivax. This was done by a chemical modification of a previously developed molecule that targets PfM1 as well as PfM17 and possibly other Plasmodial metalloproteases. After the successful chemical synthesis, the authors showed that the derived inhibitor, named MIPS2673, has a strong antiparasitic activity with IC50 342 nM and it is highly specific for M1. With this in mind, the authors first carried out two large-scale proteomics to confirm the MIPS2673 interaction with PfM1 in the context of the total P. falciparum protein lysate. This was done first by using thermal shift profiling and subsequently limited proteolysis. While the first demonstrated overall interaction, the latter (limited proteolysis) could map more specifically the site of MIPS2673-PfM1 interaction, presumably the active site. Subsequent metabolomics analysis showed that MIPS2673 cytotoxic inhibitory effect leads to the accumulation of short peptides many of which originate from hemoglobin. Based on that the authors argue that the MIPS2673 mode of action (MOA) involves inhibition of hemoglobin digestion that in turn inhibits the parasite growth and development.

Reviewer #3 (Public Review):

This is a manuscript that attempts to validate Plasmodium M1 alanyl aminopeptidase as a target for antimalarial drug development. The authors provide evidence that MIPS2673 inhibits recombinant enzymes from both Pf and Pv and is selective over other proteases. There is in vitro antimalarial activity. Chemoproteomic experiments demonstrate selective targeting of the PfA-M1 protease.

This is a continuation of previous work focused on designing inhibitors for aminopeptidases by a subset of these authors. Medicinal chemistry explorations resulted in the synthesis of MIPS2673 which has improved properties including potent inhibition of PfA-M1 and PvA-M1 with selectivity over a closed related peptidase. The compound also demonstrated selectivity over several human aminopeptidases and was not toxic to HEK293 cells at 40 uM. The activity against P. falciparum blood-stage parasites was about 300 nM.

Thermal stability studies confirmed that PfA-M1 was a binding target, however, there were other proteins consistently identified in the thermal stability studies. This raises the question as to their potential role as additional targets of this inhibitor. The authors dismiss these because they are not metalloproteases, but further analysis is warranted. This is particularly important as the authors were not able to generate mutants using in vitro evolution of resistance strategies. This often indicates that the inhibitor has more than one target.

The next set of experiments focused on a limited proteolysis approach. Again several proteins were identified as interacting with MIPS2673 including metalloproteases. The authors go on to analyze the LiP-MS data to identify the peptide from PfA-M1 which putatively interacts with MIPS2673. The authors are clearly focused on PfA-M1 as the target, but a further analysis of the other proteins identified by this method would be warranted and would provide evidence to either support or refute the authors' conclusions.

The final set of experiments was an untargeted metabolomics analysis. They identified 97 peptides as significantly dysregulated after MIPS2673 treatment of infected cells and most of these peptides were derived from one of the hemoglobin chains. The accumulation of peptides was consistent with a block in hemoglobin digestion. This experiment does reveal a potential functional confirmation, but questions remain as to specificity.

Overall, this is an interesting series of experiments that have identified a putative inhibitor of PfA-M1 and PvA-M1. The work would be significantly strengthened by structure-aided analysis. It is unclear why putative binding sites cannot be analyzed via specific mutagenesis of the recombinant enzyme. In the thermal stability and LiP -MS analysis, other proteins were consistently identified in addition to PfA-M1 and yet no additional analysis was undertaken to explore these as potential targets. The metabolomics experiments were potentially interesting, but without significant additional work including different lengths of treatment and different stages of the parasite, the conclusions drawn are overstated. Many treatments disrupt hemoglobin digestion - either directly or indirectly and from the data presented here it is premature to conclude that treatment with MIPS2673 directly inhibits hemoglobin digestion. Finally, the potency of this compound on parasites grown in vitro is 300 nM - this would need improvements in potency and demonstration of in vivo efficacy in the SCID mouse model to consider this a candidate for a drug.

Summary:<br /> Overall, this is an interesting series of experiments that have identified a putative inhibitor of the Plasmodium M1 alanyl aminopeptidases, PfA-M1 and PvA-M1.

Strengths:<br /> The main strengths include the synthesis of MIPS2673 which is selectively active against the enzymes and in whole-cell assay.

Weaknesses:<br /> The weaknesses include the lack of additional analysis of additional targets identified in the chemoproteomic approaches.

Reviewer #1 (Public Review):

Trenker et al. report cryo-EM structures of HER4/HER2 heterodimers and HER4 homodimers bound to Neuregulin-1β (Nrg1β) and Betacellulin (BTC). As observed for prior cryo-EM structures of full-length or near full-length HER-family receptors only the extracellular regions are visualized, presumably owing to flexibility in the relative orientation of extra- and intra-cellular regions. The authors observe no appreciable differences between Nrg1β and BTC bound heterodimers, both ligands in this case being high-affinity ligands, and modest "scissor-like" differences in the subunit relationships in HER4 homodimers with Nrg1β and BTC bound.

The authors also show that, as they showed for HER3, the HER4 dimerization arm is not indispensable for forming heterodimers with HER2 despite the HER4 dimerization arm forming a more canonical interaction with HER2. Perhaps most interestingly, the authors observe glycan interactions that appear to stabilize intra- and inter-subunit interactions in HER4 homodimers but that inter-subunit glycans are not present in HER2/HER4 heterodimers. The authors speculate that these glycan interactions may contribute to the apparent propensity of HER4 to homodimerize vs. heterodimerize with HER2.

Reviewer 2 (Public Review):

With the data presented in this manuscript, the authors help complete the set of high resolution HER2- associated complex heterodimer structures as well as HER4 homodimer structures in the presence of NRG1b and BTC. Purification of HER2-HER4 heterodimers appears to be inherently challenging due to the propensity of HER4 to form homodimers. The authors have used an effective scheme to isolate these HER2-HER4 heterodimers and have employed graphene-oxide grid chemistry to presumably overcome the issues of low sample yield for solving cryo-EM structures of these complexes. The authors conclude HER2-HER4 heterodimers with either ligand is conformationally homogeneous relative to the HER4 homodimers. The HER2-HER4 heterodimers also appear to be better stabilized compared to other published HER2 heterodimers. The ability to model glycans in the context of HER4 homodimers is exciting to see and provides a strong rationale for the stability of these structures. Overall, the work is of great interest and the methods described in this work would benefit a wide variety of structural biology projects.

Joint Public Review:

Yamanaka et al.'s research investigates into the impact of volatile organic compounds (VOCs), particularly diacetyl, on gene expression changes. By inhibiting histone acetylase (HDACs) enzymes, the authors were able to observe changes in the transcriptome of various models, including cell lines, flies, and mice. The study reveals that HDAC inhibitors not only reduce cancer cell proliferation but also provide relief from neurodegeneration in fly Huntington's disease models. The revised manuscript addresses the key queries raised in the initial reviews.

Reviewer #1 (Public Review):

This work seeks to understand how behaviour-related information is represented in the neural activity of the primate motor cortex. To this end, a statistical model of neural activity is presented that enables a non-linear separation of behaviour-related from unrelated activity. As a generative model, it enables the separate analysis of these two activity modes, here primarily done by assessing the decoding performance of hand movements the monkeys perform in the experiments. Several lines of analysis are presented to show that while the neurons with significant tuning to movements strongly contribute to the behaviourally-relevant activity subspace, less or un-tuned neurons also carry decodable information. It is further shown that the discovered subspaces enable linear decoding, leading the authors to conclude that motor cortex read-out can be linear.

Strengths:

In my opinion, using an expressive generative model to analyse neural state spaces is an interesting approach to understand neural population coding. While potentially sacrificing interpretability, this approach allows capturing both redundancies and synergies in the code as done in this paper. The model presented here is a natural non-linear extension of a previous linear model PSID) and uses weak supervision in a manner similar to a previous non-linear model (TNDM).

Weaknesses:

This revised version provides additional evidence to support the author's claims regarding model performance and interpretation of the structure of the resulting latent spaces, in particular the distributed neural code over the whole recorded population, not just the well-tuned neurons. The improved ability to linearly decode behaviour from the relevant subspace and the analysis of the linear subspace projections in my opinion convincingly demonstrates that the model picks up behaviour-relevant dynamics, and that these are distributed widely across the population. As reviewer 3 also points out, I would, however, caution to interpret this as evidence for linear read-out of the motor system - your model performs a non-linear transformation, and while this is indeed linearly decodable, the motor system would need to do something similar first to achieve the same. In fact to me it seems to show the opposite, that behaviour-related information may not be generally accessible to linear decoders (including to down-stream brain areas).

As in my initial review, I would also caution against making strong claims about identifiability although this work and TNDM seem to show that in practise such methods work quite well. CEBRA, in contrast, offers some theoretical guarantees, but it is not a generative model, so would not allow the type of analysis done in this paper. In your model there is a para,eter \alpha to balance between neural and behaviour reconstruction. This seems very similar to TNDM and has to be optimised - if this is correct, then there is manual intervention required to identify a good model.

Somewhat related, I also found that the now comprehensive comparison with related models shows that the using decoding performance (R2) as a metric for model comparison may be problematic: the R2 values reported in Figure 2 (e.g. the MC_RTT dataset) should be compared to the values reported in the neural latent benchmark, which represent well-tuned models (e.g. AutoLFADS). The numbers (difficult to see, a table with numbers in the appendix would be useful, see: https://eval.ai/web/challenges/challenge-page/1256/leaderboard) seem lower than what can be obtained with models without latent space disentanglement. While this does not necessarily invalidate the conclusions drawn here, it shows that decoding performance can depend on a variety of model choices, and may not be ideal to discriminate between models. I'm also surprised by the low neural R2 for LFADS I assume this is condition-averaged) - LFADS tends to perform very well on this metric.

One statement I still cannot follow is how the prior of the variational distribution is modelled. You say you depart from the usual Gaussian prior, but equation 7 seems to suggest there is a normal prior. Are the parameters of this distribution learned? As I pointed out earlier, I however suspect this may not matter much as you give the prior a very low weight. I also still am not sure how you generate a sample from the variational distribution, do you just draw one for each pass?

Summary:

This paper presents a very interesting analysis, but some concerns remain that mainly stem from the complexity of deep learning models. It would be good to acknowledge these as readers without relevant background need to understand where the possible caveats are.

Reviewer #2 (Public Review):

Li et al present a method to extract "behaviorally relevant" signals from neural activity. The method is meant to solve a problem which likely has high utility for neuroscience researchers. There are numerous existing methods to achieve this goal some of which the authors compare their method to-thankfully, the revised version includes one of the major previous omissions (TNDM). However, I still believe that d-VAE is a promising approach that has its own advantages. Still, I have issues with the paper as-is. The authors have made relatively few modifications to the text based on my previous comments, and the responses have largely just dismissed my feedback and restated claims from the paper. Nearly all of my previous comments remain relevant for this revised manuscript. As such, they have done little to assuage my concerns, the most important of which I will restate here using the labels/notation (Q1, Q2, etc) from the reviewer response.

Q1) I still remain unconvinced that the core findings of the paper are "unexpected". In the response to my previous Specific Comment #1, they say "We use the term 'unexpected' due to the disparity between our findings and the prior understanding concerning neural encoding and decoding." However, they provide no citations or grounding for why they make those claims. What prior understanding makes it unexpected that encoding is more complex than decoding given the entropy, sparseness, and high dimensionality of neural signals (the "encoding") compared to the smoothness and low dimensionality of typical behavioural signals (the "decoding")?

Q2) I still take issue with the premise that signals in the brain are "irrelevant" simply because they do not correlate with a fixed temporal lag with a particular behavioural feature hand-chosen by the experimenter. In the response to my previous review, the authors say "we employ terms like 'behaviorally-relevant' and 'behaviorally-irrelevant' only regarding behavioral variables of interest measured within a given task, such as arm kinematics during a motor control task.". This is just a restatement of their definition, not a response to my concern, and does not address my concern that the method requires a fixed temporal lag and continual decoding/encoding. My example of reward signals remains. There is a huge body of literature dating back to the 70s on the linear relationships between neural and activity and arm kinematics; in a sense, the authors have chosen the "variable of interest" that proves their point. This all ties back to the previous comment: this is mostly expected, not unexpected, when relating apparently-stochastic, discrete action potential events to smoothly varying limb kinematics.

Q5) The authors seem to have missed the spirit of my critique: to say "linear readout is performed in motor cortex" is an over-interpretation of what their model can show.

Q7) Agreeing with my critique is not sufficient; please provide the data or simulations that provides the context for the reference in the fano factor. I believe my critique is still valid.

Q8) Thank you for comparing to TNDM, it's a useful benchmark.

Reviewer #4 (Public Review):

I am a new reviewer for this manuscript, which has been reviewed before. The authors provide a variational autoencoder that has three objectives in the loss: linear reconstruction of behavior from embeddings, reconstruction of neural data, and KL divergence term related to the variational model elements. They take the output of the VAE as the "behaviorally relevant" part of neural data and call the residual "behaviorally irrelevant". Results aim to inspect the linear versus nonlinear behavior decoding using the original raw neural data versus the inferred behaviorally relevant and irrelevant parts of the signal.

Overall, studying neural computations that are behaviorally relevant or not is an important problem, which several previous studies have explored (for example PSID in (Sani et al. 2021), TNDM in (Hurwitz et al. 2021), TAME-GP in (Balzani et al. 2023), pi-VAE in (Zhou and Wei 2020), and dPCA in (Kobak et al. 2016), etc). However, this manuscript does not properly put their work in the context of such prior works. For example, the abstract states "One solution is to accurately separate behaviorally-relevant and irrelevant signals, but this approach remains elusive", which is not the case given that these prior works have done that. The same is true for various claims in the main text, for example "Furthermore, we found that the dimensionality of primary subspace of raw signals (26, 64, and 45 for datasets A, B, and C) is significantly higher than that of behaviorally-relevant signals (7, 13, and 9), indicating that using raw signals to estimate the neural dimensionality of behaviors leads to an overestimation" (line 321). This finding was presented in (Sani et al. 2021) and (Hurwitz et al. 2021), which is not clarified here. This issue of putting the work in context has been brought up by other reviewers previously but seems to remain largely unaddressed. The introduction is inaccurate also in that it mixes up methods that were designed for separation of behaviorally relevant information with those that are unsupervised and do not aim to do so (e.g., LFADS). The introduction should be significantly revised to explicitly discuss prior models/works that specifically formulated this behavior separation and what these prior studies found, and how this study differs.

Beyond the above, some of the main claims/conclusions made by the manuscript are not properly supported by the analyses and results, which has also been brought up by other reviewers but not fully addressed. First, the analyses here do not support the linear readout from the motor cortex because i) by construction, the VAE here is trained to have a linear readout from its embedding in its loss, which can bias its outputs toward doing well with a linear decoder/readout, and ii) the overall mapping from neural data to behavior includes both the VAE and the linear readout and thus is always nonlinear (even when a linear Kalman filter is used for decoding). This claim is also vague as there is no definition of readout from "motor cortex" or what it means. Why is the readout from the bottleneck of this particular VAE the readout of motor cortex? Second, other claims about properties of individual neurons are also confounded because the VAE is a population-level model that extracts the bottleneck from all neurons. Thus, information can leak from any set of neurons to other sets of neurons during the inference of behaviorally relevant parts of signals. Overall, the results do not convincingly support the claims, and thus the claims should be carefully revised and significantly tempered to avoid misinterpretation by readers.

Below I briefly expand on these as well as other issues, and provide suggestions:

1) Claims about linearity of "motor cortex" readout are not supported by results yet stated even in the abstract. Instead, what the results support is that for decoding behavior from the output of the dVAE model -- that is trained specifically to have a linear behavior readout from its embedding -- a nonlinear readout does not help. This result can be biased by the very construction of the dVAE's loss that encourages a linear readout/decoding from embeddings, and thus does not imply a finding about motor cortex.

2) Related to the above, it is unclear what the manuscript means by readout from motor cortex. A clearer definition of "readout" (a mapping from what to what?) in general is needed. The mapping that the linearity/nonlinearity claims refer to is from the *inferred* behaviorally relevant neural signals, which themselves are inferred nonlinearly using the VAE. This should be explicitly clarified in all claims, i.e., that only the mapping from distilled signals to behavior is linear, not the whole mapping from neural data to behavior. Again, to say the readout from motor cortex is linear is not supported, including in the abstract.

3) Claims about individual neurons are also confounded. The d-VAE distilling processing is a population level embedding so the individual distilled neurons are not obtainable on their own without using the population data. This population level approach also raises the possibility that information can leak from one neuron to another during distillation, which is indeed what the authors hope would recover true information about individual neurons that wasn't there in the recording (the pixel denoising example). The authors acknowledge the possibility that information could leak to a neuron that didn't truly have that information and try to rule it out to some extent with some simulations and by comparing the distilled behaviorally relevant signals to the original neural signals. But ultimately, the distilled signals are different enough from the original signals to substantially improve decoding of low information neurons, and one cannot be sure if all of the information in distilled signals from any individual neuron truly belongs to that neuron. It is still quite likely that some of the improved behavior prediction of the distilled version of low-information neurons is due to leakage of behaviorally relevant information from other neurons, not the former's inherent behavioral information. This should be explicitly acknowledged in the manuscript.

4) Given the nuances involved in appropriate comparisons across methods and since two of the datasets are public, the authors should provide their complete code (not just the dVAE method code), including the code for data loading, data preprocessing, model fitting and model evaluation for all methods and public datasets. This will alleviate concerns and allow readers to confirm conclusions (e.g., figure 2) for themselves down the line.

5) Related to 1) above, the authors should explore the results if the affine network h(.) (from embedding to behavior) was replaced with a nonlinear ANN. Perhaps linear decoders would no longer be as close to nonlinear decoders. Regardless, the claim of linearity should be revised as described in 1) and 2) above, and all caveats should be discussed.

6) The beginning of the section on the "smaller R2 neurons" should clearly define what R2 is being discussed. Based on the response to previous reviewers, this R2 "signifies the proportion of neuronal activity variance explained by the linear encoding model, calculated using raw signals". This should be mentioned and made clear in the main text whenever this R2 is referred to.

7) Various terms require clear definitions. The authors sometimes use vague terminology (e.g., "useless") without a clear definition. Similarly, discussions regarding dimensionality could benefit from more precise definitions. How is neural dimensionality defined? For example, how is "neural dimensionality of specific behaviors" (line 590) defined? Related to this, I agree with Reviewer 2 that a clear definition of irrelevant should be mentioned that clarifies that relevance is roughly taken as "correlated or predictive with a fixed time lag". The analyses do not explore relevance with arbitrary time lags between neural and behavior data.

8) CEBRA itself doesn't provide a neural reconstruction from its embeddings, but one could obtain one via a regression from extracted CEBRA embeddings to neural data. In addition to decoding results of CEBRA (figure S3), the neural reconstruction of CEBRA should be computed and CEBRA should be added to Figure 2 to see how the behaviorally relevant and irrelevant signals from CEBRA compare to other methods.

References:

Kobak, Dmitry, Wieland Brendel, Christos Constantinidis, Claudia E Feierstein, Adam Kepecs, Zachary F Mainen, Xue-Lian Qi, Ranulfo Romo, Naoshige Uchida, and Christian K Machens. 2016. "Demixed Principal Component Analysis of Neural Population Data." Edited by Mark CW van Rossum. eLife 5 (April): e10989. https://doi.org/10.7554/eLife.10989.

Sani, Omid G., Hamidreza Abbaspourazad, Yan T. Wong, Bijan Pesaran, and Maryam M. Shanechi. 2021. "Modeling Behaviorally Relevant Neural Dynamics Enabled by Preferential Subspace Identification." Nature Neuroscience 24 (1): 140-49. https://doi.org/10.1038/s41593-020-00733-0.

Zhou, Ding, and Xue-Xin Wei. 2020. "Learning Identifiable and Interpretable Latent Models of High-Dimensional Neural Activity Using Pi-VAE." In Advances in Neural Information Processing Systems, 33:7234-47. Curran Associates, Inc. https://proceedings.neurips.cc/paper/2020/hash/510f2318f324cf07fce24c3a4b89c771-Abstract.html.

Hurwitz, Cole, Akash Srivastava, Kai Xu, Justin Jude, Matthew Perich, Lee Miller, and Matthias Hennig. 2021. "Targeted Neural Dynamical Modeling." In Advances in Neural Information Processing Systems. Vol. 34. https://proceedings.neurips.cc/paper/2021/hash/f5cfbc876972bd0d031c8abc37344c28-Abstract.html.

Balzani, Edoardo, Jean-Paul G. Noel, Pedro Herrero-Vidal, Dora E. Angelaki, and Cristina Savin. 2023. "A Probabilistic Framework for Task-Aligned Intra- and Inter-Area Neural Manifold Estimation." In . https://openreview.net/forum?id=kt-dcBQcSA.

Reviewer #1 (Public Review):

Summary:<br /> The paper investigates visual processing in primates and deep neural networks (DNNs), focusing on factorization in the encoding of scene parameters. It challenges the conventional view that object classification is the primary function of the ventral visual stream, suggesting instead that the visual system employs a nuanced strategy involving both factorization and invariance. The study also presents empirical findings suggesting a correlation between high factorization scores and good neural predictivity.

Strengths:

1. Novel Perspective: The paper introduces a fresh viewpoint on visual processing by emphasizing the factorization of non-class information.

2. Methodology: The use of diverse datasets from primates and humans, alongside various computational models, strengthens the validity of the findings.

3. Detailed Analysis: The paper suggests metrics for factorization and invariance, contributing to a future understanding & measurements of these concepts.

Weaknesses:

1. Vagueness (Perceptual or Neural Invariance?): The paper uses the term 'invariance', typically referring to perceptual stability despite stimulus variability [1], as the complete discarding of nuisance information in neural activity. This oversimplification overlooks the nuanced distinction between perceptual invariance (e.g., invariant object recognition) and neural invariance (e.g., no change in neural activity). It seems that by 'invariance' the authors mean 'neural' invariance (rather than 'perceptual' invariance) in this paper, which is vague. The paper could benefit from changing what is called 'invariance' in the paper to 'neural invariance' and distinguish it from 'perceptual invariance,' to avoid potential confusion for future readers. The assignment of 'compact' representation to 'invariance' in Figure 1A is misleading (although it can be addressed by the clarification on the term invariance). [1] DiCarlo JJ, Cox DD. Untangling invariant object recognition. Trends in cognitive sciences. 2007 Aug 1;11(8):333-41.

2. Details on Metrics: The paper's explanation of factorization as encoding variance independently or uncorrelatedly needs more justification and elaboration. The definition of 'factorization' in Figure 1B seems to be potentially misleading, as the metric for factorization in the paper seems to be defined regardless of class information (can be defined within a single class). Does the factorization metric as defined in the paper (orthogonality of different sources of variation) warrant that responses for different object classes are aligned/parallel like in 1B (middle)? More clarification around this point could make the paper much richer and more interesting.

3. Factorization vs. Invariance: Is it fair to present invariance vs. factorization as mutually exclusive options in representational hypothesis space? Perhaps a more fair comparison would be factorization vs. object recognition, as it is possible to have different levels of neural variability (or neural invariance) underlying both factorization and object recognition tasks.

4. Potential Confounding Factors in Empirical Findings: The correlation observed in Figure 3 between factorization and neural predictivity might be influenced by data dimensionality, rather than factorization per se [2]. Incorporating discussions around this recent finding could strengthen the paper.

[2] Elmoznino E, Bonner MF. High-performing neural network models of the visual cortex benefit from high latent dimensionality. bioRxiv. 2022 Jul 13:2022-07.

Conclusion:<br /> The paper offers insightful empirical research with useful implications for understanding visual processing in primates and DNNs. The paper would benefit from a more nuanced discussion of perceptual and neural invariance, as well as a deeper discussion of the coexistence of factorization, recognition, and invariance in neural representation geometry. Additionally, addressing the potential confounding factors in the empirical findings on the correlation between factorization and neural predictivity would strengthen the paper's conclusions.

Reviewer #2 (Public Review):

Summary:<br /> 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 the 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 claim that (i) maintaining these non-class variables in a factorized manner yields better neural predictivity than ignoring non-class information entirely, and (ii) factorization may be a representational strategy used by the brain.

The first of these claims is supported by their data. The second claim does not seem well-supported, and the usefulness of their observations is not entirely clear.

Strengths:<br /> 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:<br /> 1. The primary weakness of this paper is a lack of clarity about what exactly is the contribution. I see two main interpretations: (1-A) As introducing a heuristic for predicting neural responses that improve over-classification accuracy, and (1-B) as a model of the brain's representational strategy. These two interpretations are distinct goals, each of which is valuable. However, I don't think the paper in its current form supports either of them very well:

(1-A) Heuristic for neural predictivity. The claim here is that by optimizing for factorization, we could improve models' neural predictivity to break through the current predictivity plateau. To frame the paper in this way, the key contribution should be a new heuristic that correlates with neural predictivity better than classification accuracy. The paper currently does not do this. The main piece of evidence that factorization may yield a more useful heuristic than classification accuracy alone comes from Figure 5. However, in Figure 5 it seems that factorization along some factors is more useful than others, and different linear combinations of factorization and classification may be best for different data. There is no single heuristic presented and defended. If the authors want to frame this paper as a new heuristic for neural predictivity, I recommend the authors present and defend a specific heuristic that others can use, e.g. [K * factorization_of_pose + classification] for some constant K, and show that (i) this correlates with neural predictivity better than classification alone, and (ii) this can be used to build models with higher neural predictivity. For (ii), they could fine-tune a state-of-the-art model to improve this heuristic and show that doing so achieves a new state-of-the-art neural predictivity. That would be convincing evidence that their contribution is useful.

(1-B) Model of representation in the brain. The claim here is that factorization is a general principle of representation in the brain. However, neural predictivity is not a suitable metric for this, because (i) neural predictivity allows arbitrary linear decoders, hence is invariant to the orthogonality requirement of factorization, and (ii) neural predictivity does not match the network representation to the brain representation. A better metric is representational dissimilarity matrices. However, the RDM results in Figure S4 actually seem to show that factorization does not do a very good job of predicting neural similarity (though the comparison to classification accuracy is not shown), which suggests that factorization may not be a general principle of the brain. If the authors want to frame the paper in terms of discovering a general principle of the brain, I suggest they use a metric (or suite of metrics) of brain similarity that is sensitive to the desiderata of factorization, e.g. doesn't apply arbitrary linear transformations, and compare to classification accuracy in addition to invariance.

Overall, I suggest the authors clarify exactly what their claim is, then focus on that claim and present results to justify it. If neither of the claims above can be supported by evidence, then this paper still has value as an idea that they spent effort trying to test, but they should not suggest these claims in the paper. In that case, it may also be possible to increase the value of the contribution by characterizing how the structure of class-free variable representations impacts correlation with neural fit, instead of just comparing existence vs absence (invariance) of this information. For example, evaluate the degree to which local or global orthogonality matters, or the degree to which curvature of the embedding matters.

2. I think the comparison to invariance, which is pervasive throughout the paper, is not very informative. First, it is not surprising that invariance is more weakly correlated with neural predictivity than factorization, because invariant representations lose information compared to factorized representations. Second, there has long been extensive evidence that responses throughout the ventral stream are not invariant to the factors the authors consider, so we already knew that invariance is not a good characterization of ventral stream data.

3. The formalization of the factorization metric is not particularly elegant, because it relies on computing top K principal components for the other-parameter space, where K is arbitrarily chosen as 10. While the authors do show that in their datasets the results are not very sensitive to K (Figure S5), that is not guaranteed to be the case in general. I suggest the authors try to come up with a formalization that doesn't have arbitrary constants. For example, one possibility that comes to mind is E[delta_a x delta_b], where 'x' is the normalized cross product, delta_a, and delta_b are deltas in representation space induced by perturbations of factors a and b, and the expectation is taken over all base points and deltas. This is just the first thing that comes to mind, and I'm sure the authors can come up with something better. The literature on disentangling metrics in machine learning may be useful for ideas on measuring factorization.

4. The authors defined the term "factorization" according to their metric. I think introducing this new term is not necessary and can be confusing because the term "factorization" is vague and used by different researchers in different ways. Perhaps a better term is "orthogonality", because that is clear and seems to be what the authors' metric is measuring.

5. One general weakness of the factorization paradigm is the reliance on a choice of factors. This is a subjective choice and becomes an issue as you scale to more complex images where the choice of factors is not obvious. While this choice of factors cannot be avoided, I suggest the authors add two things: First, an analysis of how sensitive the results are to the choice of factors (e.g. transform the basis set of factors and re-run the metric); second, include some discussion about how factors may be chosen in general (e.g. based on temporal statistics of the world, independent components analysis, or something else).

Reviewer #3 (Public Review):

Summary:<br /> 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:<br /> 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:<br /> This work exhibits two primary weaknesses that warrant attention: (i) the definition of factorization and its comparison to previous, relevant definitions, and (ii) the chosen analysis method.

Firstly, the definition of factorization presented in this paper is founded upon the variances of representations under different stimuli variations. However, this definition can be seen as a structural assumption rather than capturing the effective geometric properties pertinent to computation. More precisely, the definition here is primarily statistical in nature, whereas previous methodologies incorporate computational aspects such as deviation from ideal regressors [1], symmetry transformations [3], generalization [5], among others. It would greatly enhance the paper's depth and clarity if the authors devoted a section to comparing their approach with previous methodologies [1,2,3,4,5], elucidating any novel insights and advantages stemming from this new definition.

Secondly, in order to establish a meaningful connection between factorization and computation, the authors rely on a straightforward synthetic model (Figure 1c) and employ multiple correlation analyses to investigate relationships between the degree of factorization, decoding performance, and goodness of fit. Nevertheless, the results derived from the synthetic model are limited to the low training-sample regime. It remains unclear whether the biological datasets under consideration fall within this low training-sample regime or not.

[1] Eastwood, Cian, and Christopher KI Williams. "A framework for the quantitative evaluation of disentangled representations." International conference on learning representations. 2018.<br /> [2] Kim, Hyunjik, and Andriy Mnih. "Disentangling by factorising." International Conference on Machine Learning. PMLR, 2018.<br /> [3] Higgins, Irina, et al. "Towards a definition of disentangled representations." arXiv preprint arXiv:1812.02230 (2018).<br /> [4] Bernardi, Silvia, et al. "The geometry of abstraction in the hippocampus and prefrontal cortex." Cell 183.4 (2020): 954-967.<br /> [5] Johnston, W. Jeffrey, and Stefano Fusi. "Abstract representations emerge naturally in neural networks trained to perform multiple tasks." Nature Communications 14.1 (2023): 1040.<br /> [6] Majaj, Najib J., et al. "Simple learned weighted sums of inferior temporal neuronal firing rates accurately predict human core object recognition performance." Journal of Neuroscience 35.39 (2015): 13402-13418.

Reviewer #1 (Public Review):

Summary:<br /> This study examines the cortical modular functional organization of visual texture in comparison with that of color and disparity. While color, disparity, and orientation have been shown to exhibit clear functional organizations within the thin, thick, and thick/pale stripes of V2, whether the feature of texture is also organized within V2 is unknown. Using ultrahigh field 7T fMRI in humans viewing color-, disparity-, and texture-specific visual stimuli, the authors find that, unlike color and disparity, texture does not exhibit stripe-specific organization in V2. Moreover, using laminar imaging methods and calculations of informational connectivity, they find V2 color and disparity stripes exhibit the expected feedforward and feedback relationships with V1 & V4, and with V1 & V3ab, respectively. In contrast, texture activation, found predominantly in the deep layers of V2, is driven preferentially by feedback from V4. Based on these findings, the authors suggest that texture is a visual feature computed in higher-order areas and not generated by local intra-V2 computation.

Strengths:<br /> This study poses an interesting and fundamental question regarding the relationship between functional modularity and the hierarchical origin of computed properties. This question is thus highly significant and deserves study. The methodology is appropriate for the question and the areal and laminar resolution achieved across 10 subjects is commendable. The combination of high-resolution functional imaging and informational connectivity analysis introduces a useful way for examining feedforward and feedback relationships in mesoscale imaging data.

Weaknesses:<br /> While the data are suggestive, further controls are needed.

To support the finding that texture is not represented in a modular fashion, additional possibilities must be considered. These include the effectiveness and specificity of the texture stimulus and control stimuli, (b) further analysis of possible structure in images that may have been missed, and (c) limitations of imaging resolution.

More in-depth analysis of subject data is needed. The apparent structure in the texture images in peripheral fields of some subjects calls for more detailed analysis. e.g Relationship to eccentricity and the need for a 'modularity index' to quantify the degree of modularity. A possible relationship to eccentricity should also be considered.

Given what is known as a modular organization in V4 and V3 (e.g. for color, orientation, curvature), did images reveal these organizations? If so, connectivity analysis would be improved based on such ROIs. This would further strengthen the hierarchical scheme.

Reviewer #2 (Public Review):

High-resolution functional magnetic resonance imaging (fMRI) at ultra-high magnetic field strengths (7 T and above) can potentially study cortical functioning at the mesoscopic scale, i.e., at the spatial scale of cortical columns and layers. The authors of the study entitled "Mesoscale functional organization and connectivity of color, disparity, and naturalistic texture in human second visual area" remarkably show the current possibilities of high-resolution fMRI methods by studying the columnar and laminar organization for the processing of color, binocular disparity, and naturalistic texture in human secondary visual cortex (V2).

The study could robustly show color-selective and disparity-selective stripes in human V2. While this was already demonstrated in several in vivo studies using fMRI (Nasr et al., 2016, J Neurosci, 36, 1841-1857; Dumoulin et al., 2017, Sci Rep, 7, 733; Tootell et al., 2021, Cereb Cortex, 31, 1163-1181; Navarro et al., 2021, NeuroImage, 225, 117520; Kennedy et al., 2023, Prog Neurobiol, 220, 102374; Haenelt et al., 2023, eLife, 12, e78756), the strength, in my opinion, of the current study is three-fold:

1. Previous studies mainly focused on the columnar architecture of the stripe architecture in V2, neglecting any information across cortical depth. This study included a laminar analysis, which showcases the current possibilities of high-resolution fMRI methods that target the cortical local circuitry at the mesoscopic level.

2. The successful mapping of color-selective and disparity-selective stripes in V2 was corroborated by an innovative connectivity analysis, which shows the expected higher connectivity of color-selective clusters in V2 with area V4 and binocular disparity with area V3ab.

3. Furthermore, in addition to color-selective and disparity-selective stripes in V2 that were already shown in several studies at the columnar level (but without a laminar analysis), this study included naturalistic textures and analyzed the mesoscopic processing in V2. As expected, they showed greater sensitivity for texture selectivity in higher-order areas such as V4 and V3ab. In addition, due to the laminar analysis, feedforward and feedback connectivity were shown to be differentiable, demonstrating that feedback processes from higher-order areas rather drive texture processing in V2.

Overall, the study shows interesting results that are valuable for the general neuroscientific community. In addition, the manuscript is understandable and clearly written.

However, a few points might be worth discussing:

1. In lines 162-163, it is stated that no clear columnar organization exists for naturalistic texture processing in V2. In my opinion, this should be rephrased. As far as I understand, Figure 2B refers to the analysis used to support the conclusion. The left and middle bar plots only show a circular analysis since ROIs were based on the color and disparity contrast used to define thin and thick stripes. The interesting graph is the right plot, which shows no statistically significant overlap of texture processing with thin, thick, and pale stripe ROIs. It should be pointed out that this analysis does not dismiss a columnar organization per se but instead only supports the conclusion of no coincidence with the CO-stripe architecture.

2. In Figure 3, cortical depth-dependent analyses are presented for color, disparity, and texture processing. I acknowledge that the authors took care of venous effects by excluding outlier voxels. However, the GE-BOLD signal at high magnetic fields is still biased to extravascular contributions from around larger veins. Therefore, the highest color selectivity in superficial layers might also result from the bias to draining veins and might not be of neuronal origin. Furthermore, it is interesting that cortical profiles with the highest selectivity in superficial layers show overall higher selectivity across cortical depth. Could the missing increase toward the pial surface in other profiles result from the ROI definition or overall smaller signal changes (effect size) of selected voxels? At least, a more careful interpretation and discussion would be helpful for the reader.

3. I was slightly surprised that no retinotopy data was acquired. The ROI definition in the manuscript was based on a retinotopy atlas plus manual stripe segmentation of single columns. Both steps have disadvantages because they neglect individual differences and are based on subjective assessment. A few points might be worth discussing: (1) In lines 467-468, the authors state that V2 was defined based on the extent of stripes. This classical definition of area V2 was questioned by a recent publication (Nasr et al., 2016, J Neurosci, 36, 1841-1857), which showed that stripes might extend into V3. Could this have been a problem in the present analysis, e.g., in the connectivity analysis? (2) The manual segmentation depends on the chosen threshold value, which is inevitably arbitrary. Which value was used?

4. The use of 1-mm isotropic voxels is relatively coarse for cortical depth-dependent analyses, especially in the early visual cortex, which is highly convoluted and has a small cortical thickness. For example, most layer-fMRI studies use a voxel size of around isotropic 0.8 mm, which has half the voxel volume of 1 mm isotropic voxels. With increasing voxel volume, partial volume effects become more pronounced. For example, partial volume with CSF might confound the analysis by introducing pulsatility effects.

5. The SVM analysis included a feature selection step stated in lines 531-533. Although this step is reasonable for the training of a machine learning classifier, it would be interesting to know if the authors think this step could have reintroduced some bias to remaining draining vein contributions.

Reviewer #3 (Public Review):

Summary:<br /> Ai et al. studied texture, color, and disparity selectivity in the human visual cortex at the mesoscale level using high-resolution fMRI. They reproduced earlier monkey and human studies showing interdigitated color-selective and disparity-selective sub-compartments within area V2, likely corresponding to thin and thick stripes, respectively. At least with the stimuli used, no clear evidence for texture-selective mesoscale activations was observed in area V2. The most interesting and novel part of this study focused on cortical-depth-dependent connectivity analyses across areas. The data suggest feedback and feedforward functional connectivity between V1 and V3A for disparity signals and feedback from V4 to the deep layers of V2 for textures.

Strengths:<br /> High-resolution fMRI and highly interesting layer-specific informational connectivity analyses.

Weaknesses:<br /> The authors tend to overclaim their results.

Reviewer #1 (Public Review):

Summary:<br /> The authors aim to test the sensory recruitment theory of visual memory, which assumes that visual sensory areas are recruited for working memory, and that these sensory areas represent visual memories in a similar fashion to how perceptual inputs are represented. To test the overlap between working memory (WM) and perception, the authors use coarse stimulus (aperture) biases that are known to account for (some) orientation decoding in the visual cortex (i.e., stimulus energy is higher for parts of an image where a grating orientation is perpendicular to an aperture edge, and stimulus energy drives decoding). Specifically, the authors show gratings (with a given "carrier" orientation) behind two different apertures: one is a radial modulator (with maximal energy aligned with the carrier orientation) and the other an angular modulator (with maximal energy orthogonal to the carrier orientation). When the subject detects contrast changes in these stimuli (the perceptual task), orientation decoding only works when training and testing within each modulator, but not across modulators, showing the impact of stimulus energy on decoding performance. Instead, when subjects remember the orientation over a 12s delay, orientation decoding works irrespective of the modulator used. The authors conclude that representations during WM are therefore not "sensory-like", given that they are immune to aperture biases. This invalidates the sensory recruitment hypothesis, or at least the part assuming that when sensory areas are recruited during WM, they are recruited in a manner that resembles how these areas are used during perception.

Strengths:<br /> Duan and Curtis very convincingly show that aperture effects that are present during perception, do not appear to be present during the working memory delay. Especially when the debate about "why can we decode orientations from human visual cortex" was in full swing, many may have quietly assumed this to be true (e.g., "the memory delay has no stimuli, and ergo no stimulus aperture effects"), but it is definitely not self-evident and nobody ever thought to test it directly until now. In addition to the clear absence of aperture effects during the delay, Duan and Curtis also show that when stimulus energy aligns with the carrier orientation, cross-generalization between perception and memory does work (which could explain why perception-to-memory cross-decoding also works). All in all, this is a clever manipulation, and I'm glad someone did it, and did it well.

Weaknesses:<br /> There seems to be a major possible confound that prohibits strong conclusions about "abstractions" into "line-like" representation, which is spatial attention. What if subjects simply attend the endpoints of the carrier grating, or attend to the edge of the screen where the carrier orientation "intersects" in order to do the task? This may also result in reconstructions that have higher bold at areas close to the stimulus/screen edges along the carrier orientation. The question then would be if this is truly an "abstracted representation", or if subjects are merely using spatial attention to do the task.

Alternatively (and this reaches back to the "fine vs coarse" debate), another argument could be that during memory, what we are decoding is indeed fine-scale inhomogenous sampling of orientation preferences across many voxels. This is clearly not the most convincing argument, as the spatial reconstructions (e.g., Figure 3A and C) show higher BOLD for voxels with receptive fields that are aligned to the remembered orientation (which is in itself a form of coarse-scale bias), but could still play a role.

To conclude that the spatial reconstruction from the data indeed comes from a line-like representation, you'd need to generate modeled reconstructions of all possible stimuli and representations. Yes, Figure 4 shows that line results in a modeled spatial map that resembles the WM data, but many other stimuli might too, and some may better match the data. For example, the alternative hypothesis (attention to grating endpoints) may very well lead to a very comparable model output to the one from a line. However testing this would not suffice, as there may be an inherent inverse problem (with multiple stimuli that can lead to the same visual field model).

The main conclusion, and title of the paper, that visual working memories are abstractions of percepts, is therefore not supported. Subjects could be using spatial attention, for example. Furthermore, even if it is true that gratings are abstracted into lines, this form of abstraction would not generalize to any non-spatial feature (e.g., color cannot become a line, contrast cannot become a line, etc.), which means it has limited explanatory power.

Additional context:<br /> The working memory and perception tasks are rather different. In this case, the perception task does not require the subject to process the carrier orientation (which is largely occluded, and possibly not that obvious without paying attention to it), but attention is paid to contrast. In this scenario, stimulus energy may dominate the signal. In the WM task, subjects have to work out what orientation is shown to do the task. Given that the sensory stimulus in both tasks is brief (1.5s during memory encoding, and 2.5s total in the perceptual task), it would be interesting to look at decoding (and reconstructions) for the WM stimulus epoch. If abstraction (into a line) happens in working memory, then this perceptual part of the task should still be susceptible to aperture biases. It allows the authors to show that it is indeed during memory (and not merely the task or attentional state of the subject) that abstraction occurs.

What's also interesting is what happens in the passive perceptual condition, and the fact that spatial reconstructions for areas beyond V1 and V2 (i.e., V3, V3AB, and IPS0-1) align with (implied) grating endpoints, even when an angular modulator is used (Figure 3C). Are these areas also "abstracting" the stimulus (in a line-like format)?

Reviewer #2 (Public Review):

Summary:<br /> According to the sensory recruitment model, the contents of working memory (WM) are maintained by activity in the same sensory cortical regions responsible for processing perceptual inputs. A strong version of the sensory recruitment model predicts that stimulus-specific activity patterns measured in sensory brain areas during WM storage should be identical to those measured during perceptual processing. Previous research casts doubt on this hypothesis, but little is known about how stimulus-specific activity patterns during perception and memory differ. Through clever experimental design and rigorous analyses, Duan & Curtis convincingly demonstrate that stimulus-specific representations of remembered items are highly abstracted versions of representations measured during perceptual processing and that these abstracted representations are immune to aperture biases that contribute to fMRI feature decoding. The paper provides converging evidence that neural states responsible for representing information during perception and WM are fundamentally different, and provides a potential explanation for this difference.

Strengths:<br /> 1. The generation of stimuli with matching vs. orthogonal orientations and aperture biases is clever and sets up a straightforward test regarding whether and how aperture biases contribute to orientation decoding during perception and WM. The demonstration that orientation decoding during perception is driven primarily by aperture bias while during WM it is driven primarily by orientation is compelling.

2. The paper suggests a reason why orientation decoding during WM might be immune to aperture biases: by weighting multivoxel patterns measured during WM storage by spatial population receptive field estimates from a different task the authors show that remembered - but not actively viewed - orientations form "line-like" patterns in retinotopic cortical space.

Weaknesses:<br /> 1. The paper tests a strong version of the sensory recruitment model, where neural states representing information during WM are presumed to be identical to neural states representing the same information during perceptual processing. As the paper acknowledges, there is already ample reason to doubt this prediction (see, e.g., earlier work by Kok & de Lange, Curr Biol 2014; Bloem et al., Psych Sci, 2018; Rademaker et al., Nat Neurosci, 2019; among others). Still, the demonstration that orientation decoding during WM is immune to aperture biases known to drive orientation decoding during perception makes for a compelling demonstration.

2. Earlier work by the same group has reported line-like representations of orientations during memory storage but not during perception (e.g., Kwak & Curtis, Neuron, 2022). It's nice to see that result replicated during explicit perceptual and WM tasks in the current study, but I question whether the findings provide fundamental new insights into the neural bases of WM. That would require a model or explanation describing how stimulus-specific activation patterns measured during perception are transformed into the "line-like" patterns seen during WM, which the authors acknowledge is an important goal for future research.

Reviewer #3 (Public Review):

Summary:<br /> In this work, Duan and Curtis addressed an important issue related to the nature of working memory representations. This work is motivated by findings illustrating that orientation decoding performance for perceptual representations can be biased by the stimulus aperture (modulator). Here, the authors examined whether the decoding performance for working memory representations is similarly influenced by these aperture biases. The results provide convincing evidence that working memory representations have a different representational structure, as the decoding performance was not influenced by the type of stimulus aperture.

Strengths:<br /> The strength of this work lies in the direct comparison of decoding performance for perceptual representations with working memory representations. The authors take a well-motivated approach and illustrate that perceptual and working memory representations do not share a similar representational structure. The authors test a clear question, with a rigorous approach and provide convincing evidence. First, the presented oriented stimuli are carefully manipulated to create orthogonal biases introduced by the stimulus aperture (radial or angular modulator), regardless of the stimulus carrier orientation. Second, the authors implement advanced methods to decode the orientation information present, in visual and parietal cortical regions, when directly perceiving or holding an oriented stimulus in memory. The data illustrates that working memory decoding is not influenced by the type of aperture, while this is the case in perception. In sum, the main claims are important and shed light on the nature of working memory representations.

Weaknesses:<br /> I have a few minor concerns that, although they don't affect the main conclusion of the paper, should still be addressed.

1. Theoretical framing in the introduction: Recent work has shown that decoding of orientation during perception does reflect orientation selectivity, and it is not only driven by the stimulus aperture (Roth, Kay & Merriam, 2022).

2. Figure 1C illustrates the principle of how the radial and angular modulators bias the contrast energy extracted by the V1 model, which in turn would influence orientation decoding. It would be informative if the carrier orientations used in the experiment were shown in this figure, or at a minimum it would be mentioned in the legend that the experiment used 3 carrier orientations (15{degree sign}, 75{degree sign}, 135{degree sign}) clockwise from vertical. Related, when trying to find more information regarding the carrier orientation, the 'Stimuli' section of the Methods incorrectly mentions that 180 orientations are used as the carrier orientation.

3. The description of the image computable V1 model in the Methods is incomplete, and at times inaccurate. i) The model implements 6 orientation channels, which is inaccurately referred to as a bandwidth of 60{degree sign} (should be 180/6=30). ii) The steerable pyramid combines information across phase pairs to obtain a measure of contrast energy for a given stimulus.<br /> Here, it is only mentioned that the model contains different orientation and spatial scale channels. I assume there were also 2 phase pairs, and they were combined in some manner (squared and summed to create contrast energy). Currently, it is unclear what the model output represents. iii) The spatial scale channel with the maximal response differences between the 2 modulators was chosen as the final model output. What spatial frequency does this channel refer to, and how does this spatial frequency relate to the stimulus?

4. It is not clear from the Methods how the difficulty in the perceptual control task was controlled. How were the levels of task difficulty created?

Reviewer #1 (Public Review):

The main focus of the current study is to identify the anatomical core of an expiratory oscillator in the medulla using pharmacological disinhibition. Although expiration is passive in normal eupneic conditions, activation of the parafacial (pFL) region is believed to evoke active expiration in conditions of elevated ventilatory demands. The authors and others in the field have previously attempted to map this region using pharmacological, optogenetic, and chemogenetic approaches, which present their own challenges.

In the present study, the authors take a systematic approach to determine the precise anatomical location within the ventral medulla's rostrocaudal axis where the expiratory oscillator is located. The authors used a bicuculline (a GABA-A receptor antagonist) and fluorobeads solution at 5 distinct anatomical locations to study the effects on neuronal excitability and functional circuitry in the pFL. The effects of bicuculline on different phases of the respiratory cycle were characterized using a multidimensional cycle-by-cycle analysis. This analysis involved measuring the differences in airflow, diaphragm electromyography (EMG), and abdominal EMG signals, as well as using a phase-plane analysis to analyze the combined differences of these respiratory signals. Anatomical immunostaining techniques were also used to complement the functional mapping of the pFL.

Major strengths of this work include a robust study design, complementary neurophysiological and immunohistochemical methods, and the use of a novel phase-plane analysis. The authors construct a comprehensive functional map revealing functional nuances in respiratory responses to bicuculline along the rostrocaudal axis of the parafacial region. They convincingly show that although bicuculline injections at all coordinates of the pFL generated an expiratory response, the most rostral locations in the lateral parafacial region play the strongest role in generating active expiration. These were characterized by a strong impact on the duration and strength of ABD activation and a robust change in tidal volume and minute ventilation. The authors also confirmed histologically that none of the injection sites overlapped grossly with PHOX2B+ neurons, thus confirming the specificity of the injections in the pFL and not the neighboring RTN.

Collectively, these findings advance our understanding of the presumed expiratory oscillator, the pFL, and highlight the functional heterogeneity in the functional response of this anatomical structure.

Reviewer #2 (Public Review):

Summary:<br /> Pisanski and colleagues map regions of the brainstem that produce the rhythm for active expiratory breathing movements and influence their motor patterns. While the neural origins of inspiration are very well understood, the neural bases for expiration lag considerably. The problem is important and new knowledge pertaining to the neural origins of expiration is welcome.

The authors perturb the parafacial lateral (pFL) respiratory group of the brainstem with microinjection of bicuculline, to elucidate how disinhibition in specific locations of the pFL influences active expiration (and breathing in general) in anesthetized rats. They provide valuable, if not definitive, evidence that the borders of the pFL appear to extend more rostrally than previously appreciated. Prior research suggests that the expiratory pFL exists at the caudal pole of the facial cranial nucleus (VIIc). Here, the authors show that its borders probably extend as much as 1 mm rostral to VIIc. The evidence is convincing albeit with caveats.

Strengths:<br /> The authors achieve their aim in terms of showing that the borders of the expiratory pFL are not well understood at present and that it (the pFL) extends more rostrally. The results support that point. The data are strong enough to cause many respiratory neurobiologists to look at the sites rostral to the VIIc for expiratory rhythmogenic neurons and characterize their properties and mechanisms. At present my view is that most respiratory neurobiologists overlook the regions rostral to VIIc in their studies of expiratory rhythm and pattern.

Weaknesses:<br /> The injection of bicuculline has indiscriminate effects on excitatory and inhibitory neurons, and the parafacial region is populated by excitatory neurons that are expiratory rhythmogenic and GABA and glycinergic neurons whose roles in producing active expiration are contradictory (Flor et al. J Physiol, 2020, DOI: 10.1113/JP280243). It remains unclear how the microinjections of bicuculline differentially affect all three populations. A more selective approach would be able to disinhibit the populations separately. Nevertheless, for the main point at hand, the data do suggest that we should reconsider the borders of the expiratory pFL nucleus and begin to examine its physiology up to 1 mm rostral to VIIc.

The control experiment showed that bicuculline microinjections induced cFos expression in the pFL, which is good, but again we don't know which neurons were disinhibited: glutamatergic, GABAergic, or glycinergic.

The manuscript characterizes how bicuculline microinjections affect breathing parameters such as tidal volume, frequency, ventilation, inspiratory and expiratory time, as well as oxygen consumption. Those aspects of the manuscript are a bit tedious and sometimes overanalyzed. Plus, there was no predictive framework established at the outset for how one should expect disinhibition to affect breathing parameters. In other words, if the authors are seeking to map the pFL borders, then why analyze the breathing patterns so much? Does doing so provide more insight into the borders of pFL? I did not think it was compellingly argued.

Further, lines 382-386 make a point about decreasing inspiratory time even though the data do not meet the statistical threshold.

In lines 386-395, the reporting appears to reach significance (line 388) but not reach significance (line 389). I had trouble making sense of that disparity.

The other statistical hiccups include "tended towards significance" (line 454), "were found to only reach significance for a short portion of the response" (line 486-7), "did not reach the level of significance" (line 506), which gives one the sense of cherry picking or over-analysis. Frankly, this reviewer finds the paper much more compelling when just asking whether the microinjections evoke active expiration. If yes, then the site is probably part of the pFL.

I encourage the authors to consider the fickleness of p-values in general and urge them to consider not just p but also effect size.

Reviewer #3 (Public Review):

Summary:<br /> The study conducted by Pisanski et al investigates the role of the lateral parafacial area (pFL) in controlling active expiration. Stereotactic injections of bicuculline were utilized to map various pFL sites and their impact on respiration. The results indicate that injections at more rostral pFL locations induce the most robust changes in tidal volume, minute ventilation, and combined respiratory responses. The study indicates that the rostrocaudal organization of the pFL and its influence on breathing is not simple and uniform.

Strengths:<br /> The data provide novel insights into the importance of rostral locations in controlling active expiration. The authors use innovative analytic methods to characterize the respiratory effects of bicuculline injections into various areas of the pFL.

Weaknesses:<br /> Bicuculline injections increase the excitability of neurons. Aside from blocking GABA receptors, bicuculline also inhibits calcium-activated potassium currents and potentiates NMDA current, thus insights into the role of GABAergic inhibition are limited.

Increasing the excitability of neurons provides little insights into the activity pattern and function of the activated neurons. Without recording from the activated neurons, it is impossible to know whether an effect on active expiration or any other respiratory phase is caused by bicuculline acting on rhythmogenic neurons or tonic neurons that modulate respiration. While this approach is inappropriate to study the functional extent of the conditional "oscillator" for active expiration, it provides valuable insights into this region's complex role in controlling breathing.

Reviewer #1 (Public Review)

Cav1.4 calcium channels control voltage-dependent calcium influx at photoreceptor synapses, and congenital loss of Cav1.4 function causes stationary night blindness CSNB2. Based on a broad portfolio of methodological approaches - genetic mouse models, immunolabeling and microscopic imaging, serial block-face-SEM, ERGs, and electrophysiology - the authors show that cone photoreceptor synapse development is strongly perturbed in the absence of Cav1.4 protein, and that expression of a nonconducting Cav1.4 channel mitigates these perturbations. Further data indicate that Cav3 channels are present, which, according to the authors, may compensate for the loss of Cav1.4 calcium currents and thus maintain cone synaptic transmission. These data, which are in agreement with a similar study by the same authors on rod photoreceptor synapses, help to explain what functional defects exactly cause CSNB2 and why it is accompanied by only mild visual impairment.

The strengths of the present study are its conceptual and experimental soundness, the broad spectrum of cutting-edge methodological approaches pursued, and the convincing differential analysis of mutant phenotypes.

Weaknesses mainly concern the experiments and arguments leading to the authors' notion that Cav3 channels may partially compensate for the loss of Cav1.4 calcium currents in cone synapses. It is possible that the non-conducting Cav1.4 variant supports synapse development and the Cav3 channel then provides the calcium influx. However, in its current state, the study does not unequivocally assess Cav3 expression in wild-type cones, it lacks direct evidence of Cav3 expression and upregulation, e.g. via single cell transcriptomics, immunolabeling, or an elaboration on electrophysiology, and it does not test the authors' earlier idea that Cav1.4 might couple to intracellular calcium stores at photoreceptor synapses

Reviewer #2 (Public Review)

Summary:<br /> This paper by Maddox et al. presents the results of a study of Ca channel function in mouse cone photoreceptor synaptic terminals. It builds on earlier work by the same authors (Maddox et al. 2020 in eLife) which demonstrated that a non-conducting but voltage-sensing variant of Cav1.4 (G369i knock-in, or KI) could substitute for WT Cav1.4 to promote relatively normal rod synapse development despite an inability to support Ca2+-dependent glutamatergic transmission to postsynaptic bipolar cells. Cav1.4 knock-out (KO) rod synapses, however, were completely disorganized, indicating that the presence of Cav1.4 protein is critical for synaptic organization. Here, the authors extend their study of the G369i-KI retina to demonstrate that G369i-KI cones develop working (though disrupted and sometimes aberrant) synapses that support some visual function owing to compensatory expression of Cav3-containing Ca channels that can mediate some Ca2+-dependent transmission from cones to postsynaptic cells. This compensatory expression of a low voltage-activated Ca conductance was not noted previously (Maddox et al. 2020) in G369i-KI rods.

Strengths:<br /> In all, this is a scientifically sound study that shows obvious differences between synaptic terminal morphology and organization, macroscopic Ca currents, transmission to postsynaptic horizontal and bipolar cells (with whole-cell recording and ERG, respectively), and visually-guided behavior in experimental groups.

Weaknesses:<br /> The major criticism that I have of the study is that it infers Ca channel molecular composition based solely on pharmacological analysis, which, as the authors note, is confounded by the cross-reactivity of many of the "specific" channel-type antagonists. The authors note that Cav3 mRNAs have been found in cones, but here, they do not perform any analysis to examine Cav3 transcript expression after G369i-KI nor do they examine Ca channel transcript expression in monkey or squirrel cones, which serve as controls of sorts for the G369i-KI (i.e. like WT mouse cones, cones of these other species do not seem to exhibit LVA Ca currents).

Secondarily, in Maddox et al. 2020, the authors raise the possibility that G369i-KI, by virtue of having a functional voltage-sensing domain-might couple to intracellular Ca2+ stores, and it seems appropriate that this possibility be considered experimentally here.

As a minor point: the authors might wish to note - in comparison to another retinal ribbon synapse-that Zhang et al. 2022 (in J. Neuroscience) performed a study of mouse rod bipolar cells found a number of LVA and HVA Ca conductances in addition to the typical L-type conductance mediated by Cav1-containing channels.

Reviewer #3 (Public Review)

Summary<br /> This is an important study that tests the hypothesis that Cav1.4 calcium channels do more than provide a voltage-dependent influx of Ca2+ into photoreceptors. The relevant background can be divided into two tranches. First, deletion of Cav1.4 channels (Cav1.4 knock-out) disrupts rod and cone photoreceptors and their synapses in the outer plexiform layer. Second, knock-in of a non-conducting Cav1.4 channel (Cav1.4 knock-in) partially spares the organization of the outer plexiform layer and photoreceptor synapses (Maddox et al., eLife 2020), which is remarkable considering the disruption of the outer plexiform layer in the Cav1.4 knock-out. In addition, phototransduction, assessed by scotopic and phototopic electroretinography (a-wave amplitude) in the Cav1.4 knock-in retina was partially spared for rods and only slightly impaired for cones. However, the non-conducting Cav1.4 channel of the Cav1.4 knock-in failed to rescue synaptic transmission across the outer retina (electroretinography: b-wave amplitude, Maddox et al., eLife 2020). The 2020 Maddox et al. (eLife) focused more on the rod pathway, while the current work addressed the cone pathway.

Strengths<br /> The study addresses the important question of how disruption of Cav1.4 function in both rod and cone photoreceptors leads to impairment primarily of the rod pathway for scotopic vision. This is clinically relevant as human mutations lead to stationary night blindness rather than blindness. The work relevance provides excellent single-cell electrophysiological recordings of Ca2+ currents from cones of wild-type, Cav1.4 knock-out, and Cav1.4 knock-in mice and, in addition, from ground squirrel and monkey cones. To make these recordings successfully in the various species and the compromised retinas (Cav1.4 knock-out and Cav1.4 knock-in) is very impressive. The findings clearly advance our understanding of Ca2+ channel function in cones. In addition, the study presents high-quality electron microscopy reconstructions of cones and further physiological and behavioral data related to the cone pathway.

Weaknesses<br /> The major critiques are related to the description of the Cav1.4 knock-in mouse as "sparing" function, which can be remedied in part by a simple rewrite, and in certain places, the data may need to be examined more critically. In particular, the authors should address features in the data presented in Figures 6 and 7 that seem to indicate that the retina of the Cav1.4 knock-in is not intact, but the interpretation given by the authors as "intact" is not appropriate and made without rigorous statistical testing.

Reviewer #4 (Public Review)

Summary:<br /> Cav1.4 voltage-gated calcium channels play an important role in neurotransmission at mammalian photoreceptor synapses. Mutations in the CACNA1f gene lead to congenital stationary night blindness that particularly affects the rod pathway. Mouse Cav1.4 knockout and Cav1.4 knockin models suggest that Cav1.4 is also important for the cone pathway. Deletion of Cav1.4 in the knockout models leads to signaling malfunctions and to abundant morphological re-arrangements of the synapse suggesting that the channel not only has a role in the influx of Ca2+ but also in the morphological organization of the photoreceptor synapse. Of note, also additional Cav-channels have been previously detected in cone synapses by different groups, including L-type Cav1.3 (Wu et al., 2007; pmid; Kersten et al., 2020; pmid), and also T-type Cav3.2 (Davison et al., 2021; pmid 35803735).

In order to study a conductivity-independent role of Cav1.4 in the morphological organization of photoreceptor synapses, the authors generated the knockin (KI) mouse Cav1.4 G369i in a previous study (Maddox et al., eLife 2020; pmid 32940604). The Cav1.4 G369i KI channel no longer works as a Ca2+-conducting channel due to the insertion of a glycine in the pore-forming unit (Madox et al. elife 2020; pmid 32940604). In this previous study (Madox et al. elife 2020; pmid 32940604), the authors analyzed Cav1.4 G369i in rod photoreceptor synapses. In the present study, the authors analyzed cone synapses in this KI mouse.

For this purpose, the authors performed a comprehensive set of experimental methods including immunohistochemistry with antibodies (also with quantitative analyses), electrophysiological measurements of presynaptic Ca2+ currents from cone photoreceptors in the presence/absence of inhibitors of L-type- and T-type- calcium channels, electron microscopy (FIB-SEM), ERG recordings and visual behavior tests of the Cav G369i KI in comparison to the Cav1.4 knockout and wild-type control mice.

The authors found that the non-conducting Cav channel is properly localized in cone synapses and demonstrated that there are no gross morphological alterations (e.g., sprouting of postsynaptic components that are typically observed in the Cav1.4 knockout). These findings demonstrate that cone synaptogenesis relies on the presence Cav1.4 protein but not on its Ca2+ conductivity. This result, obtained at cone synapses in the present study, is similar to the previously reported results observed for rod synapses (Maddox et al., eLife 2020, pmid 32940604). No further mechanistic insights or molecular mechanisms were provided that demonstrated how the presence of the Cav channels could orchestrate the building of the cone synapse.

Strengths:<br /> The study has been expertly performed. A comprehensive set of experimental methods including immunohistochemistry with antibodies (also with quantitative analyses), electrophysiological measurements of presynaptic Ca2+ currents from cone photoreceptors in the presence/absence of inhibitors of L-type- and T-type- calcium channels, electron microscopy (FIB-SEM), ERG recordings and visual behavior tests of the Cav G369i KI in comparison to the Cav1.4 knockout and wild-type control mice.

Weaknesses:<br /> The study has been expertly performed but remains descriptive without deciphering the underlying molecular mechanisms of the observed phenomena, including the proposed homeostatic switch of synaptic calcium channels. Furthermore, a relevant part of the data in the present paper (presence of T-type calcium channels in cone photoreceptors) has already been identified/presented by previous studies of different groups (Macosko et al., 2015; pmid 26000488; Davison et al., 2021; pmid 35803735; Williams et al., 2022; pmid 35650675). The degree of novelty of the present paper thus appears limited.

Reviewer #1 (Public Review):

Summary:<br /> This manuscript aims at a quantitative model of how visual stimuli, given as time-dependent light intensity signals, are transduced into electrical currents in photoreceptors of macaque and mouse retina. Based on prior knowledge of the fundamental biophysical steps of the transduction cascade and a relatively small number of free parameters, the resulting model is found to fairly accurately capture measured photoreceptor currents under a range of diverse visual stimuli and with parameters that are (mostly) identical for photoreceptors of the same type.

Furthermore, as the model is invertible, the authors show that it can be used to derive visual stimuli that result in a desired, predetermined photoreceptor response. As demonstrated with several examples, this can be used to probe how the dynamics of phototransduction affect downstream signals in retinal ganglion cells, for example, by manipulating the visual stimuli in such a way that photoreceptor signals are linear or have reduced or altered adaptation. This innovative approach had already previously been used by the same lab to probe the contribution of photoreceptor adaptation to differences between On and Off parasol cells (Yu et al, eLife 2022), but the present paper extends this by describing and testing the photoreceptor model more generally and in both macaque and mouse as well as for both rods and cones.

Strengths:<br /> The presentation of the model is thorough and convincing, and the ability to capture responses to stimuli as different as white noise with varying mean intensity and flashes with a common set of model parameters across cells is impressive. Also, the suggested approach of applying the model to modify visual stimuli that effectively alter photoreceptor signal processing is thought-provoking and should be a powerful tool for future investigations of retinal circuit function. The examples of how this approach can be applied are convincing and corroborate, for example, previous findings that adaptation to ambient light in the primate retina, as measured by responses to light flashes, mostly originates in photoreceptors.

Weaknesses:<br /> In the current form of the presentation, it doesn't become fully clear how easily the approach is applicable at different mean light levels and where exactly the limits for the model inversion are at high frequency. Also, accessibility and applicability by others could be strengthened by including more details about how parameters are fixed and what consensus values are selected.

Reviewer #2 (Public Review):

Summary:<br /> This manuscript proposes a modeling approach to capture nonlinear processes of photocurrents in mammalian (mouse, primate) rod and cone photoreceptors. The ultimate goal is to separate these nonlinearities at the level of photocurrent from subsequent nonlinear processing that occurs in retinal circuitry. The authors devised a strategy to generate stimuli that cancel the major nonlinearities in photocurrents. For example, modified stimuli would generate genuine sinusoidal modulation of the photocurrent, whereas a sinusoidal stimulus would not (i.e., because of asymmetries in the photocurrent to light vs. dark changes); and modified stimuli that could cancel the effects of light adaptation at the photocurrent level. Using these modified stimuli, one could record downstream neurons, knowing that any nonlinearities that emerge must happen post-photocurrent. This could be a useful method for separating nonlinear mechanisms across different stages of retinal processing, although there are some apparent limitations to the overall strategy.

Strengths:<br /> 1. This is a very quantitative and thoughtful approach and addresses a long-standing problem in the field: determining the location of nonlinearities within a complex circuit, including asymmetric responses to different polarities of contrast, adaptation, etc.<br /> 2. The study presents data for two primary models of mammalian retina, mouse, and primate, and shows that the basic strategy works in each case.<br /> 3. Ideally, the present results would generalize to the work in other labs and possibly other sensory systems. How easy would this be? Would one lab have to be able to record both receptor and post-receptor neurons? Would in vitro recordings be useful for interpreting in vivo studies? It would be useful to comment on how well the current strategy could be generalized.

Weaknesses:<br /> 1. The model is limited to describing photoreceptor responses at the level of photocurrents, as opposed to the output of the cell, which takes into account voltage-dependent mechanisms, horizontal cell feedback, etc., as the authors acknowledge. How would one distinguish nonlinearities that emerge at the level of post-photocurrent processing within the photoreceptor as opposed to downstream mechanisms? It would seem as if one is back to the earlier approach, recording at multiple levels of the circuit (e.g., Dunn et al., 2006, 2007).<br /> 2. It would have been nice to see additional confirmations of the approach beyond what is presented in Figure 9. This is limited by the sample (n = 1 horizontal cell) and the number of conditions (1). It would have been interesting to at least see the same test at a dimmer light level, where the major adaptation mechanisms are supposed to occur beyond the photoreceptors (Dunn et al., 2007).

Reviewer #3 (Public Review):

Summary:<br /> The authors propose to invert a mechanistic model of phototransduction in mouse and rod photoreceptors to derive stimuli that compensate for nonlinearities in these cells. They fit the model to a large set of photoreceptor recordings and show in additional data that the compensation works. This can allow the exclusion of photoreceptors as a source of nonlinear computation in the retina, as desired to pinpoint nonlinearities in retinal computation. Overall, the recordings made by the authors are impressive and I appreciate the simplicity and elegance of the idea. The data support the authors' conclusions but the presentation can be improved.

Strengths:<br /> - The authors collected an impressive set of recordings from mouse and primate photoreceptors, which is very challenging to obtain.<br /> - The authors propose to exploit mechanistic mathematical models of well-understood phototransduction to design light stimuli that compensate for nonlinearities.<br /> - The authors demonstrate through additional experiments that their proposed approach works.

Weaknesses:<br /> - The authors use numerical optimization for fitting the parameters of the photoreceptor model to the data. Recently, the field of simulation-based inference has developed methods to do so, including quantification of the uncertainty of the resulting estimates. Since the authors state that two different procedures were used due to the different amounts of data collected from different cells, it may be worthwhile to rather test these methods, as implemented e.g. in the SBI toolbox (https://joss.theoj.org/papers/10.21105/joss.02505). This would also allow them to directly identify dependencies between parameters, and obtain associated uncertainty estimates. This would also make the discussion of how well constrained the parameters are by the data or how much they vary more principled because the SBI uncertainty estimates could be used.

- In several places, the authors refer the reader to look up specific values e.g. of parameters in the associated MATLAB code. I don't think this is appropriate, important values/findings/facts should be in the paper (lines 142, 114, 168). I would even find the precise values that the authors measure interesting, so I think the authors should show them in a figure/table. In general, I would like to see also the average variance explained by different models summarized in a table and precise mean/median values for all important quantities (like the response amplitude ratios in Figures 6/9).

- If the proposed model is supposed to model photoreceptor adaptation on a longer time scale, I fail to see why this can be an invertible model. Could the authors explain this better? I suspect that the model is mainly about nonlinearities as the authors also discuss in lines 360ff.

- The important Figures 6-8 are very hard to read, as it is not easy to see what the stimulus is, the modified stimulus, the response with and without modification, what the desired output looks like, and what is measured for part B. Reworking these figures would be highly recommended.

- If I understand Figure 6 correctly, part B is about quantifying the relative size of the response to the little first flash to the little second flash. While clearly, the response amplitude of the second flash is only 50% for the second flash compared to the first flash in primate rod and cones in the original condition, the modified stimulus seems to overcompensate and result in 130% response for the second flash. How do the authors explain this? A similar effect occurs in Figure 9, which the authors should also discuss.

Joint Public Review:

Summary:

In this interesting work, the authors investigated an important topical question: when we see travelling waves in cortical activity, is this due to true wave-like spread, or due to sequentially activated sources? In simulations, it is shown that sequential brain module activation can show up as a travelling wave - even in improved methods such as phase delay maps - and a variety of parameters is investigated. Then, in ex-vivo turtle eye-brain preparations, the authors show that visual cortex waves observable in local field potentials are in fact often better explained as areas D1 and D2 being sequentially activated. This has implications for how we think about travelling wave methodology and relevant analytical tools.

Strengths:

I enjoyed reading the discussion. The authors are careful in their claims, and point out that some phenomena may still indeed be genuine travelling waves, but we should have a higher evidence bar to claim this for a particular process in light of this paper and Zhigalov & Jensen (2023) (ref 44). Given this careful discussion, the claims made are well-supported by the experimental results. The discussion also gives a nice overview of potential options in light of this and future directions.

The illustration of different gaussian covariances leading to very different latency maps was interesting to see.

Furthermore, the methods are detailed and clearly structured and the Supplementary Figures, particularly single trial results, are useful and convincing.

Reviewer #1 (Public Review):

Summary:<br /> The authors explore mechanisms through which T-regs attenuate acute pain using a heat sensitivity paradigm. Analysis of available transcriptomic data revealed expression on the proenkephalin (Penk) gene in T-regs. The authors explore the contribution of T-reg Penk in the resolution of heat sensitivity.

Strengths:<br /> Investigating the potential role of T-reg Penk in the resolution of acute pain is a strength.

Weaknesses:<br /> The overall experimental design is superficial and lacks sufficient rigor to draw any meaningful conclusions.

For instance:<br /> 1) The were no TAM controls. What is the evidence that TAM does not alter heat-sensitive receptors.<br /> 2) There are no controls demonstrating that recombination actually occurred. How do the authors know a single dose of TAM is sufficient?<br /> 3) Why was only heat sensitivity assessed? The behavioral tests are inadequate to derive any meaningful conclusions. Further, why wasn't the behavioral data plotted longitudinally

Reviewer #2 (Public Review):

Summary:<br /> The present study addresses the role of enkephalins, which are specifically expressed by regulatory T cells (Treg), in sensory perception in mice. The authors used a combination of transcriptomic databases available online to characterize the molecular signature of Treg. The proenkephalin gene Penk is among the most enriched transcripts, suggesting that Treg plays an analgesic role through the release of endogenous opioids. In addition, in silico analysis suggests that Penk is regulated by the TNFR superfamily; this being experimentally confirmed. Using flow cytometry analysis, the authors then show that Penk is mostly expressed in Treg of the skin and colon, compared to other immune cells. Finally, genetic conditional excision of Penk, selectively in Treg, results in heat hypersensitivity, as assessed by behavior analysis.

Strengths:<br /> The manuscript is clear and reveals a previously unappreciated role of enkephalins, as released by immune cells, in sensory perception. The rationale in this manuscript is easy to follow, and conclusions are well supported by data.

Weaknesses:<br /> The sensory deficit of Penk cKO appears to be quite limited compared to control littermates.

Reviewer #3 (Public Review):

Summary:<br /> Aubert et al investigated the role of PENK in regulatory T cells. Through the mining of publicly available transcriptome data, the authors confirmed that PENK expression is selectively enriched in regulatory but not conventional T cells. Further data mining suggested that OX40, 4-1BB as well as BATF, can regulate PENK expression in Tregs. The authors generated fate-mapping mice to confirm selective PENK expression in Tregs and activated effector T cells in the colon and spleen. Interestingly, transgenic mice with conditional deletion of PENK in Tregs resulted in hypersensitivity to heat, which the authors attributed to heat hyperalgesia.

Strengths:<br /> The generation of transgenic mice with conditional deletion of PENK in foxp3 and PENK fate-mapping is novel and can potentially yield significant findings. The identification of upstream signals that regulate PENK is interesting but unlikely to be the main reason why PENK is predominantly expressed in Tregs as both BATF and TNFR are expressed in effector T cells.

Weaknesses:<br /> There is a lack of direct evidence and detailed analysis of Tregs in the control and transgenic mice to support the authors' hypothesis. PENK was previously reported to be expressed in skin Tregs and play a significant role in regulating skin homeostasis: this should be considered as an alternative mechanism that may explain the changed sensitivity to heat observed in the paper.

Reviewer #1 (Public Review):

In this paper by Lui and colleagues, the authors examine the role of locus coeruleus (LC)-noradrenaline (NA) neurons in the extinction of appetitive instrumental conditioning. They report that optogenetic activation of global LC-NA neurons during the conditioned stimulus (CS) period of extinction enhances long-term extinction memory without affecting within-session extinction. In contrast, LC-NA activation during the intertrial interval doesn't affect extinction and long-term memory. They then show that optogenetic activation of LC-NA neurons doesn't induce conditioned place preference/avoidance. Finally, they assess the necessity of LC-NA neurons in appetitive extinction and find that optogenetic inactivation of LC-NA neurons during CS period results in enhancement of within-session extinction. The experiments are well-designed, including offset control in the optogenetic activation study. I think this study adds new insight into the LC-NA system in the context of appetitive extinction.

Strength:<br /> ・These studies identify the artificial activation of LC-NA neurons enhances long-term memory of appetitive extinction while this activation can't induce long-term conditioned place aversion. Thus, optogenetic activation of LC-NA neurons can inhibit spontaneous recovery of appetitive extinction without causing long-term aversive memory.<br /> ・Optoinhibition study demonstrates the reduction of conditioned response of within-session extinction. Therefore, LC-NA neuronal activity at the CS period of extinction could act as anti-extinction or be important for the expression of conditioned response.

Weakness:<br /> ・It is unclear how LC-NA neurons behave during the CS period of appetitive extinction from this study. This weakens the importance of the optogenetic inactivation result.<br /> ・While authors manipulate global LC-NA neurons, many people find functionally heterogeneous populations in the LC. It remains unsolved if there is specific LC-NA subpopulation responsible for appetitive extinction.

Reviewer #2 (Public Review):

Understanding how the LC/noradrenaline system controls basic cognitive processes is important and timely. This study aims to understand the role Locus Coeurelus /noradrenaline system in extinction of conditioned responding. The authors used a discriminative appetitive procedure to show that photoexcitation of noradrenergic neurons of the Locus Coeruleus has no effect on the performance during extinction but impacts expression of extinguished responding through a decreased spontaneous recovery. This study is appropriately designed and the results are well analysed. Therefore, it provides an important and timely addition to the field

Reviewer #3 (Public Review):

The introduction/background is excellent. It reviews evidence showing that extinction of conditioned responding is regulated by noradrenaline and suggesting that the locus coeruleus (LC) may be a critical locus of this regulation. This naturally leads to the aim of the study: to determine whether the locus coeruleus is involved in extinction of an appetitive conditioned response. Overall, the study is well designed, nicely conducted and the results advance our understanding of the role of the LC in extinction of conditioned behaviour. Future studies may provide more fine-grained analyses of behavioral data to clarify the impact of the LC manipulations (stimulation and inhibition) on performance in the task.

Reviewer #1 (Public Review):

Summary:<br /> The Roco proteins are a family of GTPases characterized by the conserved presence of an ROC-COR tandem domain. How GTP binding alters the structure and activity of Roco proteins remains unclear. In this study, Galicia C et al. took advantage of conformation-specific nanobodies to trap CtRoco, a bacterial Roco, in an active monomeric state and determined its high-resolution structure by cryo-EM. This study, in combination with the previous inactive dimeric CtRoco, revealed the molecular basis of CtRoco activation through GTP-binding and dimer-to-monomer transition.

Strengths:<br /> The reviewer is impressed by the authors' deep understanding of the CtRoco protein. Capturing Roco proteins in a GTP-bound state is a major breakthrough in the mechanistic understanding of the activation mechanism of Roco proteins and shows similarity with the activation mechanism of LRRK2, a key molecule in Parkinson's disease. Furthermore, the methodology the authors used in this manuscript - using conformation-specific nanobodies to trap the active conformation, which is otherwise flexible and resistant to single-particle average - is highly valuable and inspiring.

Weakness:<br /> Though written with good clarity, the paper will benefit from some clarifications.

1. The angular distribution of particles for the 3D reconstructions should be provided (Figure 1 - Sup. 1 & Sup. 2).

2. The B-factors for protein and ligand of the model, Map sharpening factor, and molprobity score should be provided (Table 1).

3. A supplemental Figure to Figure 2B, illustrating how a0-helix interacts with COR-A&LRR before and after GTP binding in atomic details, will be helpful for the readers to understand the critical role of a0-helix during CtRoco activation.

4. For the following statement, "On the other hand, only relatively small changes are observed in the orientation of the Roc a3 helix. This helix, which was previously suggested to be an important element in the activation of LRRK2 (Kalogeropulou et al., 2022), is located at the interface of the Roc and CORB domains and harbors the residues H554 and Y558, orthologous to the LRRK2 PD mutation sites N1337 and R1441, respectively."<br /> It is not surprising the a3-helix of the ROC domain only has small changes when the ROC domain is aligned (Figure 2E). However, in the study by Zhu et al (DOI: 10.1126/science.adi9926), it was shown that a3-helix has a "see-saw" motion when the COR-B domain is aligned. Is this motion conserved in CtRoco from inactive to active state?

5. A supplemental figure showing the positions of and distances between NbRoco1 K91 and Roc K443, K583, and K611 would help the following statement. "Also multiple crosslinks between the Nbs and CtRoco, as well as between both nanobodies were found. ... NbRoco1-K69 also forms crosslinks with two lysines within the Roc domain (K583 and K611), and NbRoco1-K91 is crosslinked to K583".

6. It would be informative to show the position of CtRoco-L487 in the NF and GTP-bound state and comment on why this mutation favors GTP hydrolysis.

Reviewer #2 (Public Review):

Summary<br /> The manuscript by Galicia et al describes the structure of the bacterial GTPyS-bound CtRoco protein in the presence of nanobodies. The major relevance of this study is in the fact that the CtRoco protein is a homolog of the human LRRK2 protein with mutations that are associated with Parkinson's disease. The structure and activation mechanisms of these proteins are very complex and not well understood. Especially lacking is a structure of the protein in the GTP-bound state. Previously the authors have shown that two conformational nanobodies can be used to bring/stabilize the protein in a monomer-GTPyS-bound state. In this manuscript, the authors use these nanobodies to obtain the GTPyS-bound structure and importantly discuss their results in the context of the mammalian LRRK2 activation mechanism and mutations leading to Parkinson's disease. The work is well performed and clearly described. In general, the conclusions on the structure are reasonable and well-discussed in the context of the LRRK2 activation mechanism.

Strengths:<br /> The strong points are the innovative use of nanobodies to stabilize the otherwise flexible protein and the new GTPyS-bound structure that helps enormously in understanding the activation cycle of these proteins.

Weakness:<br /> The strong point of the use of nanobodies is also a potential weak point; these nanobodies may have induced some conformational changes in a part of the protein that will not be present in a GTPyS-bound protein in the absence of nanobodies.

Two major points need further attention.

1. Several parts of the protein are very flexible during the monomer-dimer activity cycle. This flexibility is crucial for protein function, but obviously hampers structure resolution. Forced experiments to reduce flexibility may allow better structure resolution, but at the same time may impede the activation cycle. Therefore, careful experiments and interpretation are very critical for this type of work. This especially relates to the influence of the nanobodies on the structure that may not occur during the "normal" monomer-dimer activation cycle in the absence of the nanobodies (see also point 2). So what is the evidence that the nanobody-bound GTPyS-bound state is biochemically a reliable representative of the "normal" GTP-bound state in the absence of nanobodies, and therefore the obtained structure can be confidentially used to interpret the activation mechanism as done in the manuscript.

2. The obtained structure with two nanobodies reveals that the nanobodies NbRoco1 and NbRoco2 bind to parts of the protein by which a dimer is impossible, respectively to a0-helix of the linker between Roc-COR and LRR, and to the cavity of the LRR that in the dimer binds to the dimerizing domain CORB. It is likely the open monomer GTP-bound structure is recognized by the nanobodies in the camelid, suggesting that overall the open monomer structure is a true GTP-bound state. However, it is also likely that the binding energy of the nanobody is used to stabilize the monomer structure. It is not automatically obvious that in the details the obtained nonobody-Roco-GTPyS structure will be identical to the "normal" Roco-GTPyS structure. What is the influence of nanobody-binding on the conformation of the domains where they bind; the binding energy may be used to stabilize a conformation that is not present in the absence of the nanobody. For instance, NbRoco1 binds to the a0 helix of the linker; what is here the "normal" active state of the Roco protein, and is e.g. the angle between RocCOR and LRR also rotated by 135 degrees? Furthermore, nanobody NbRoco2 in the LRR domain is expected to stabilize the LRR domain; it may allow a position of the LRR domain relative to the rest of the protein that is not present without nanobody in the LRR domain. I am convinced that the observed open structure is a correct representation of the active state, but many important details have to be supported by e,g, their CX-MS experiments, and in the end probably need confirmation by more structures of other active Roco proteins or confirmation by a more dynamic sampling of the active states by e.g. molecular dynamics or NMR.

Reviewer #1 (Public Review):

Summary:<br /> Medina et al, 2023 investigated the peripheral blood transcriptional responses in patients with diversifying disease outcomes. The authors characterized the blood transcriptome of four non-hospitalized individuals presenting mild disease and four patients hospitalized with severe disease. These individuals were observed longitudinally at three time points (0-, 7-, and 28-days post recruitment), and distinct transcriptional responses were observed between severe hospitalized patients and mild non-hospitalized individuals, especially during 0- and 7-day collection time points. Particularly, the authors found that increased expression of genes associated with NK cell cytotoxicity is associated with mild outcomes. Additional co-regulated gene network analyses positively correlate T cell activity with mild disease and neutrophil degranulation with severe disease.

Strengths:<br /> The longitudinal measurements in individual participants at consistent collection intervals can offer an added dimension to the dataset that involves temporal trajectories of genes associated with disease outcomes and is a key strength of the study. The use of co-expressed gene networks specific to the cohort to complement enrichment results obtained from pre-determined genesets can offer valuable insights into new associations/networks associated with disease progression and warrants further analyses on the biological functions enriched within these co-expressed network modules.

Weaknesses:<br /> There is a large difference in terms of infection timeline (onset of symptom to recruitment) between mild and severe patient cohorts. As immune responses during early infection can be highly dynamic, the differences in infection timeline may contribute to differences in transcriptional signatures. The study is also limited by a small cohort size.

Reviewer #2 (Public Review):

In their manuscript, Medina and colleagues investigate transcriptional differences between mild and severe SARS-CoV-2 infections. Their analyses are very comprehensive incorporating a multitude of bioinformatics tools ranging from PCA plots, GSEA and DEG analysis, protein-protein interaction network, and weighted correlation network analyses. They conclude that in mild COVID-19 infection NK cell functionality is compromised and this is connected to cytokine interactions and Th1/Th2 cell differentiation pathways cross-talk, bridging the innate and the adaptive arms of the immune system.

The authors successfully recruited participants with both mild and severe COVID-19 between November 2020 to May 2021. The analyzed cohort is gender and acceptably age-matched and the results reported are promising. Signatures associated with NK cell cytotoxicity in mild and neutrophil functions in the severe group during acute infection are the chief findings reported in this manuscript.

Reviewer #3 (Public Review):

Summary:<br /> Medina and colleagues explored transcriptional kinetics during SARS-CoV-2 between non-hospitalized and hospitalized cohorts and identified that early NK signaling may be responsible for less severe disease.

Strengths:<br /> The paper includes extremely detailed analyses and makes an interesting attempt to link innate and adaptive responses. The analyses are appropriate for the data and described in clear language. The inclusion of late time points is interesting and potentially relevant to long COVID studies. Most findings were compatible with other detailed immune mapping during severe COVID-19.

Weaknesses:<br /> 1. The authors claim to be looking at the earliest stages of infection but this is not true as all patients enrolled are already symptomatic. The time points selected are unlikely to be useful clinically for biomarker selection as they are too late, and are likely beyond the point when the immune responses between severe and mild infection start to diverge.<br /> 2. The comparator timepoints between mild and severe cases do not match. The most comparison would be between day 7 of mild versus day 0 of severe which is already fairly late during infection.<br /> 3. The authors mention viral clearance but I see no evidence of viral loads measured in these individuals.<br /> 4. The cohort is quite small to draw definitive conclusions.<br /> 5. It is uncertain whether the results are applicable to current conditions as most infected people are immune experienced.<br /> 6. I found the discussion to be a bit too detailed and dense. I would suggest editing to make it more streamlined.

Reviewer #1 (Public Review):

Summary:<br /> The study entitled "Rifampicin tolerance and growth fitness among isoniazid-resistant clinical Mycobacterium tuberculosis isolates: an in-vitro longitudinal study" by Vijay et al. provides valuable insights into the association of rifampicin tolerance and growth fitness with isoniazid resistance among clinical isolates of M. tuberculosis. Antibiotic tolerance in M. tuberculosis is an important topic since it contributes to the lengthy and complicated treatment required to cure tuberculosis disease and may portend the emergence of antibiotic resistance. The authors found that rifampicin tolerance was correlated with bacterial growth, rifampicin minimum inhibitory concentrations, and isoniazid-resistance mutations.

Strengths:<br /> The large number of clinical isolates evaluated and their longitudinal nature during treatment for TB (including exposure to rifampin) are strengths of the study.

Weaknesses:<br /> Some of the methodologies are not well explained or justified and the association of antibiotic tolerance with growth rate is not a novel finding. In addition, the molecular mechanisms underlying rifampicin tolerance only in rapidly growing isoniazid-resistant isolates have not been elucidated and the potential implications of these findings for clinical management are not immediately apparent.

Reviewer #2 (Public Review):

Summary:<br /> This study by Vijay and colleagues addresses a clinically important, and often overlooked aspect of Tb treatment. Detecting for variations in the level of antibiotic tolerance amongst otherwise antibiotic-susceptible isolates is difficult to routinely screen for, and consequently not performed. The authors, present a convincing argument that indeed, there is significant variation in the susceptibility of isoniazid-resistant strains to killing by rifampicin, in some cases at the same tolerance levels as bona fide resistant strains. On the whole, the study is easy to follow and the results are justified. This work should be of interest to the wider TB community at both a clinical and basic level.

Weaknesses:<br /> The manuscript is long, repetitive in places, and the figures could use some amending to improve clarity (this could be a me-specific issue as they look ok on my screen, yet the colour is poor when printed).

It would have been great to have seen some correlation between increased rifampicin tolerance and treatment outcome, although I'm not sure if this data is available to the researchers. I agree with the researchers the use of a single media condition is a limitation. However, this is true of a lot of studies.

Reviewer #3 (Public Review):

Summary:<br /> The authors have initiated studies to understand the molecular mechanisms underlying the devolvement of multi-drug resistance in clinical Mtb strains. They demonstrate the association of isoniazid-resistant isolates by rifampicin treatment supporting the idea that selection of MDR is a microenvironment phenomenon and involves a group of isolates.

Strengths:<br /> The methods used in this study are robust and the results support the authors' claims to a major extent.

Weaknesses:<br /> The manuscript needs a thorough vetting of the language. At present, the language makes it very difficult to comprehend the methodology and results.

Reviewer #1 (Public Review):

The manuscript by Chen et al. investigated the interaction between CHI3L1, a chitinase-like protein in the 18 glycosyl hydrolase family, and gut bacteria in the mucosal layers. The authors provided evidence to document the direct interaction between CHI3L1 and peptidoglycan, a major component of bacterial cell walls. In doing so, Chi3l1 produced by gut epithelial cells regulates the balance of the gut microbiome and diminishes DSS-induced colitis, potentially through the colonization of protective gram-positive bacteria such as lactobacillus.

The study is the first to systemically document the interactions between Chi3L1 and microbiome. Convincing data were shown to characterize the imbalance of gram-positive bacteria in the newly generated gut epithelial-specific Chi3L1 deficient mice. Comprehensive FMT experiments were performed to demonstrate the contributions of gut microbiome using the mouse colitis model. However, the manuscript could've been strengthened by additional mechanistic studies concerning the binding between Chi3l1 and peptidoglycan, and how this interaction could facilitate the colonization of gram-positive bacteria. Additionally, the conclusion by the authors that disordered intestinal bacteria in gut epithelial-specific Chi3L1 deficient mice, rather than an effect by host cells, contributes to exacerbated colitis, needs further validation. In fact, the fact that FMT did not completely rescue the phenotype may point to the role of host cells in the processes. On the contrary, there is an existing body of literature demonstrating the detrimental roles of Chi3l1 in the mouse IBD model, conflicting with the current study. The differences in study design and approaches in these studies that lead to controversial findings will need to be discussed.

Specifically,<br /> 1) In Figure 1, it is curious that the authors only chose E.coli and staphytlococcus sciuri to test the induction of Chi3l1. What about other bacteria? Why does only E.coli but not staphytlococcus sciuri induce chi3l1 production? It does not prove that the gut microbiome induces the expression of Chi3l1. If it is the effect of LPS, does it trigger a cell death response or inflammatory responses that are known to induce chi3l1 production? What is the role of peptidoglycan in this experiment? Also, it is recommended to change WT to SPF in the figure and text, as no genetic manipulation was involved in this figure.

2) In Figure 2, the binding between Chi3l1 and PGN needs better characterization, regarding the affinity and how it compares with the binding between Chi3l1 and chitin. More importantly, it is unclear how this interaction could facilitate the colonization of gram-positive bacteria.

3) In Figure 3, the abundance of furmicutes and other gram-positive species is lower in the knockout mice. What is the rationale for choosing lactobacillus in the following transfer experiments?

4) FDAA-labeled E. faecalis colonization is decreased in the knockouts. Is it specific for E. faecalis, or it is generally true for all gram-positive bacteria? What about the colonization of gram-negative bacteria?

5) In Figure 5, the fact that FMT did not completely rescue the phenotype may point to the role of host cells in the processes. The reason that lactobacillus transfer did completely rescue the phenotypes could be due to the overwhelming protective role of lactobacillus itself, as the experiments were missing villin-cre mice transferred with lactobacillus.

6) Conflicting literature demonstrating the detrimental roles of Chi3l1 in mouse IBD model needs to be acknowledged and discussed.

Reviewer #2 (Public Review):

Chen et al. investigated the regulatory mechanism of bacterial colonization in the intestinal mucus layer in mice and its implications for intestinal diseases. They demonstrated that Chi3l1 is a protein produced and secreted by intestinal epithelial cells into the mucus layer upon response to the gut microbiota, which has a turnover effect on facilitating the colonization of gram-positive bacteria in the mucosa. The data also indicate that Chi3l1 interacts with the peptidoglycan of the bacteria cell wall, supporting the colonization of beneficial bacteria strains such as Lactobacillus, and that deficiency in Chi3l1 predisposes mice to colitis. The inclusion of a small but pertinent piece of human data added to solidify their findings in mice.

Overall, the experiments performed were appropriate and well executed, but the data analysis is incomplete and needs to be extended. Also, additional experiments are necessary for clarification and stronger support for their conclusions.

1) Images are of great quality but lack proper quantification and statistical analysis. Statements such as "substantial increase of Chi3l1 expression in SPF mice" (Fig.1A), "reduced levels of Firmicutes in the colon lumen of IECChil1" (Fig.3F), "Chil1-/- had much lower colonization of E.faecalis" (Fig.4G), or "deletion of Chi3l1 significantly reduced mucus layer thickness" (Supplemental Figure 3A-B) are subjective. Since many conclusions were based on imaging data, the authors must provide reliable measures for comparison between conditions, as long as possible, such as fluorescence intensity, area, density, etc, as well as plots and statistical analysis.

2) In the fecal/Lactobacillus transplantation experiments, oral gavage of Lactobacillus to IECChil1 mice ameliorated the colitis phenotype, by preventing colon length reduction, weight loss, and colon inflammation. These findings seem to go against the notion that Chi3l1 is necessary for the colonization of Lactobacillus in the intestinal mucosa. The authors could speculate on how Lactobacillus administration is still beneficial in the absence of Chi3l1. Perhaps, additional data showing the localization of the orally administered bacteria in the gut of Chi3l1 deficient mice would clarify whether Lactobacillus are more successfully colonizing other regions of the gut, but not the mucus layer. Alternatively, later time points of 2% DSS challenge, after Lactobacillus transplantation, would suggest whether the gut colonization by Lactobacillus and therefore the milder colitis phenotype, is sustained for longer periods in the absence of Chi3l1.

Reviewer #3 (Public Review):

Summary:<br /> Chen et al. are addressing a fundamental question in mammalian gut biology, namely how the host controls a mutualistic host-microbiota symbiosis. The authors focus on a protein called Chitinase 3-like protein 1 (Ch3l1) and its interaction with the protective colonic mucus layer. The rationale for the study comes from previous work showing that microbial-associated molecular patterns (MAMPs) can induce Ch3l1 in vitro, but its biological functions in the colon are unknown. In this study, the authors provide evidence supporting the claim that the gut microbiota induces the expression of Ch3l1 in vivo, mainly in mucus-producing goblet cells. Insightfully, the authors note that Ch3l1, although it lacks enzyme (chitinase) activity, still binds Chitin, a glycan that has structural similarity to bacterial cell wall peptidoglycan. This leads the authors to hypothesize that Ch3l1 binds microbial cell walls, particularly those of peptidoglycan-rich Gram-positive probiotic bacteria within the mucus, to promote their retention in the colon. Using a combination of in vivo work with mice conditionally lacking Ch3l1 in gut epithelium (IEC Ch3l1 KO); microbiota profiling; imaging of host-microbiota interactions with labeled microbes; and fecal transplants, the authors provide compelling evidence that Ch3l1 is secreted into the gut mucus layer and that the presence of Ch3l1 is associated with increased levels of beneficial Gram+ bacteria, including Lactobacillus spp. In turn, using a well-characterized colitis model, the authors show that Ch3l1 is associated with protection from intestinal injury caused by Dextran Sodium Sulfate. While these studies are novel and informative, there are several issues that undermine the authors' conclusions.

Strengths:<br /> The authors nicely link microbial induction of Ch3l1 to mucosal protection from intestinal injury. This is done through the use of germ-free and ex-germ-free studies and by comparing Ch3l1 expression in situ between them; microbial sequencing between Control and IEC Ch3l1 KO mice, and clinical and histological injury metrics between these strains. The authors convincingly demonstrate the presence of Ch3l1 in the gut mucus through imaging, and that the deletion of this protein in mice alters the microbiota by reducing the relative abundance of Gram-positive species.

The study employs a technically diverse set of analyses to address their hypothesis, including fluorescent labelling of microbial species for add-back studies, fecal transplants to distinguish the role(s) of the microbiota vs. host in the IEC Ch3l1 KO phenotypes in the intestinal challenge models.

Weaknesses:<br /> The claim that mucus-associated Ch3l1 controls colonization of beneficial Gram-positive species within the mucus is not conclusive. The study should take into account recent discoveries on the nature of mucus in the colon, namely its mobile fecal association and complex structure based on two distinct mucus barrier layers coming from proximal and distal parts of the colon (PMID: ). This impacts the interpretation of how and where Ch3l1 is expressed and gets into the mucus to promote colonization. It also impacts their conclusions because the authors compare fecal vs. tissue mucus, but most of the mucus would be attached to the feces. Of the mucus that was claimed to be isolated from the WT and IEC Ch3l1 KO, this was not biochemically verified. Such verification (e.g. through Western blot) would increase confidence in the data presented. Further, the study relies upon relative microbial profiling, which can mask absolute numbers, making the claim of reduced overall Gram-positive species in mice lacking Ch3l1 unproven. It would be beneficial to show more quantitative approaches (e.g. Quantitative Microbial Profiling, QMP) to provide more definitive conclusions on the impact of Ch3l1 loss on Gram+ microbes.

Other weaknesses lie in the execution of the aims, leaving many claims incompletely substantiated. For example, much of the imaging data is challenging for the reader to interpret due to it being unfocused, too low of magnification, not including the correct control, and not comparing the same regions of tissues among different in vivo study groups. Statistical rigor could be better demonstrated, particularly when making claims based on imaging data. These are often presented as single images without any statistics (i.e. analysis of multiple images and biological replicates). These images include the LTA signal differences, FISH images, Enterococcus colonization, and mucus thickness.

Joint Public Review:

Summary:<br /> Identifying dietary biomarkers, in particular, has become a main focus of nutrition research in the drive to develop personalized nutrition.

The aim of this study was to determine the accuracy of using food composition databases to assess the association between dietary intake and health outcomes. The authors found that using food composition data to assess dietary intake of specific bioactives and the impact consumption has on systolic blood pressure provided vastly different outcomes depending on the method used. These findings demonstrate the difficulty in elucidating the relationship between diet and health outcomes and the need for more stringent research in the development of dietary biomarkers.

Strengths:<br /> The primary strength of the study is the use of a large cohort in which dietary data and the measurement of three specific bioactives and blood pressure were collected on the same day. The bioactives selected have been extensively researched for their health effects. Another strength is that the authors controlled for as many variables as possible when running the simulations to get a more accurate account of how the variability in food composition can impact research findings that associate the intake of certain food components with health outcomes.

Weaknesses:<br /> The authors address the large variability when using food composition data, e.g. the range of tea and apple intake needed to meet recommendations depending on using the mean food composition data or using the lowest reported food content, however, there is no discussion on the intake needed if the biomarker is used. So how many cups of tea are needed to reach the suggested 200 mg/day of flavan-3-ols when using biomarker data instead of the food composition data? More information should be added on the effect of using biomarker data on dietary recommendations and risk assessment.

Reviewer #1 (Public Review):

Summary:<br /> Ye et al. identified a novel tumour microenvironment (TME) signature that can help to prognosticate DLBCL. They first interrogated a publicly available dataset to identify tumour purity-related genes (TPGs) and found these TPGs were associated with extracellular matrix organisation and immune response. Protein-protein interaction analysis identified hub genes that were associated with prognosis, and 3 genes (VCAN, CD3G, C1QB) were selected to construct a prognosis model. The authors attempted to validate the findings on immunohistochemistry (IHC) and showed prognostication using an IHC assay. Finally, they showed a possible prediction of drug sensitivity using the novel signature in DLBCL.

Strengths:<br /> This study investigated both immune and non-immune TME related to tumour purity. Tumour purity has not been thoroughly investigated in DLBCL. Hence, the prognostic significance of tumour purity demonstrated in this paper brought into light another potential area of research in DLBCL. Similarly, the investigation into non-immune TME was novel and thought-provoking, as most research in DLBCL TME has mostly been in the immune microenvironment.

The bioinformatics approach in identifying the key TPGs was well conducted, such as the GO and KEGG enrichment analysis which supported the role of these TPGs in the modulation of the microenvironment. The findings were also validated in another dataset, which increased the confidence in this model. However, it was not clear to me why the authors chose VCAN, CD3G, and C1QB out of the 9 intersection genes that they found. It would perhaps be useful to show the statistical justification in the Supplementary Results section.

The possible translation of these findings into clinical practice by immunohistochemistry (IHC) was a useful tool to make the findings applicable in the clinical setting. However, as stated by the authors, the real-life clinical application of these findings may be more challenging as these antigens seemed to be expressed in a continuum, rather than in a discrete manner. For example, in Figure 5A, even the low VCAN status still demonstrated strong cytoplasmic staining. Similarly, in Figure 5C, it seemed to be difficult to differentiate strong from background staining. This means pre-analytical variables may affect the staining and standardisation among different laboratories may be difficult to achieve without external controls.

Weaknesses:<br /> Though the rationale behind choosing the TPG genes and its correlation with non-immune TME was clear, the justification for investigating CD68+ macrophages, CD4+ T cells, and CD8+ T cells was not as strong. This was done in a subsection that was supposed to investigate the prognostic values of IHC staining in VCAN, CD3G, and C1QB. Hence, the analysis of the immune compartment of the TME was rather superficial. For example, it would be insufficient to correlate CD4+ and CD8T+ T cells without understanding their deeper phenotypes such as regulatory vs memory or exhausted vs activated. An attempt was made to subtype the macrophages by bioinformatics approach but it was not further investigated with IHC.

Similarly, the investigation into drug sensitivity was only done in-silico. This investigation was adequate for hypothesis generation. However, it was not enough to substantiate the claim that TPGs can be used to predict drug sensitivity. This claim requires functional in-vitro experiments to validate the bioinformatics approach, or even correlation with clinical data when the identified drugs were used in DLBCL, for example in the ReMODL-B cohort that used bortezomib.

Reviewer #2 (Public Review):

In this study, Zhenbang Ye and colleagues investigate the links between microenvironment signatures, gene expression profiles, and prognosis in diffuse large B-cell lymphoma (DLBCL). They show that increased tumor purity (ie, a higher proportion of tumor cells relative to surrounding stromal components) is associated with a worse prognosis. They then show that three genes associated with tumor purity (VCAN, CD3G, and C1QB) correlate with patterns of immune cell infiltration and can be used to create a risk-scoring system that predicts prognosis, which can be replicated by immunohistochemistry (IHC), and response to some therapies.

1. The two strengths of the study are its relatively large sample size (n = 190) and the strong prognostic significance of the risk-scoring system. It is worth noting that the validation of this scoring with IHC, a simple technique already routinely used for the diagnosis and classification of DLBCL, increases the potential for clinical translation. However, the correlative nature of the study limits the conclusions that can be drawn in regard to links between the risk scoring system, the tumor microenvironment, and the biology of DLBCL.

2. The tumor microenvironment has been extensively studied in DLBCL and a prognostic implication has already been established (for instance, Steen et al., Cancer Cell, 2021). In addition, associations have already been established in non-Hodgkin lymphoma between prognosis and expression of C1QB (Rapier-Sharman et al., Journal of Bioinformatics and Systems Biology, 2022), VCAN (S. Hu et al., Blood, 2013), and CD3G (Chen et al., Medical Oncology, 2022). Nevertheless, one of the strengths and novelty aspects of the study is the combination of these 3 genes into a risk score that is also valid by immunohistochemistry (IHC), which substantially facilitates a potential clinical translation.

3. Figures 1A-B: tumor purity is calculated using the ESTIMATE (Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data) algorithm (Yoshihara et al., Nature Communications, 2013). The ESTIMATE algorithm is based on two gene signatures ("stromal" and "immune"). It is therefore expected that tumor purity measured by the ESTIMATE algorithm will correlate with the expression of multiple genes. Importantly, C1QB is included in the stromal signature of the ESTIMATE algorithm meaning that, by definition, it will be correlated with tumor purity in that setting.

4. Figure 2A: as established in Figure 1C, high tumor purity is associated with worse prognosis. Later in the manuscript, it is also shown that C1QB expression is associated with a worse prognosis. However, Figure 2A shows that C1QB is associated with decreased tumor purity. It therefore makes it less likely that the prognostic role of C1QB expression is related to its impact on tumor purity. The prognostic impact could be related to different patterns of immune cell infiltration, as shown later. However, the evidence presented in the study is correlative and natural and not sufficient to draw this conclusion.

5. Figure 3G: although there is a strong prognostic implication of the risk score on prognosis, the correlation between the risk score and tumor purity is significant but not very strong (R = 0.376). It is therefore likely that other important biological factors explain the correlation between the risk score and prognosis.

6. Figure 6: the drug sensitivity analysis includes a wide range of established and investigational drugs with varied mechanisms of action. Although the difference in sensitivity between tumors with low and high-risk scores shows statistical significance for certain drugs, the absolute difference appears small in most cases and is of unclear biological significance. In addition, even though the risk score is statistically related to drug sensitivity, there is no direct evidence that the differences in drug sensitivity are directly related to tumor purity.

Reviewer #1 (Public Review):

The present study provides a phylogenetic analysis of the size prefrontal areas in primates, aiming to investigate whether relative size of the rostral prefrontal cortex (frontal pole) and dorsolateral prefrontal cortex volume vary according to known ecological or social variables.

I am very much in favor of the general approach taken in this study. Neuroimaging now allows us to obtain more detailed anatomical data in a much larger range of species than ever before and this study shows the questions that can be asked using these types of data. In general, the study is conducted with care, focusing on anatomical precision in definition of the cortical areas and using appropriate statistical techniques, such as PGLS.

I have read the revised version of the manuscript with interest. I agree with the authors that a focus on ecological vs laboratory variables is a good one, although it might have been useful to reflect that in the title.

I am happy to see that the authors included additional analyses using different definitions of FP and DLPFC in the supplementary material. As I said in my earlier review, the precise delineation of the areas will always be an issue of debate in studies like this, so showing the effects of different decisions in vital.

I am sorry the authors are so dismissive of the idea of looking the models where brain size and area size are directly compared in the model, rather preferring to run separate models on brain size and area size. This seems to me a sensible suggestion.

Similarly, the debate about whether area volume and number of neurons can be equated across the regions is an important one, of which they are a bit dismissive.

Nevertheless, I think this is an important study. I am happy that we are using imaging data to answer more wider phylogenetic questions. Combining detailed anatomy, big data, and phylogenetic statistical frameworks is a important approach.

Reviewer #2 (Public Review):

In the manuscript entitled "Linking the evolution of two prefrontal brain regions to social and foraging challenges in primates" the authors measure the volume of the frontal pole (FP, related to metacognition) and the dorsolateral prefrontal cortex (DLPFC, related to working memory) in 16 primate species to evaluate the influence of socio-ecological factors on the size of these cortical regions. The authors select 11 socio-ecological variables and use a phylogenetic generalized least squares (PGLS) approach to evaluate the joint influence of these socio-ecological variables on the neuro-anatomical variability of FP and DLPFC across the 16 selected primate species; in this way, the authors take into account the phylogenetic relations across primate species in their attempt to discover the the influence of socio-ecological variables on FP and DLPF evolution.

The authors run their studies on brains collected from 1920 to 1970 and preserved in formalin solution. Also, they obtained data from the Mussée National d´Histoire Naturelle in Paris and from the Allen Brain Institute in California. The main findings consist in showing that the volume of the FP, the DLPFC, and the Rest of the Brain (ROB) across the 16 selected primate species is related to three socio-ecological variables: body mass, daily traveled distance, and population density. The authors conclude that metacognition and working memory are critical for foraging in primates and that FP volume is more sensitive to social constraints than DLPFC volume.

The topic addressed in the present manuscript is relevant for understanding human brain evolution from the point of view of primate research, which, unfortunately, is a shrinking field in neuroscience. But the experimental design has two major weak points: the absence of lissencephalic primates among the selected species and the delimitation of FP and DLPFC. Also, a general theoretical and experimental frame linking evolution (phylogeny) and development (ontogeny) is lacking.

Reviewer #3 (Public Review):

This is an interesting manuscript that addresses a longstanding debate in evolutionary biology - whether social or ecological factors are primarily responsible for the evolution of the large human brain. To address this, the authors examine the relationship between the size of two prefrontal regions involved in metacognition and working memory (DLPFC and FP) and socioecological variables across 16 primate species. I recommend major revisions to this manuscript due to: 1) a lack of clarity surrounding model construction; and 2) an inappropriate treatment of the relative importance of different predictors (due to a lack of scaling/normalization of predictor variables prior to analysis).

Joint Public Review:

Summary:<br /> Desiderio and colleagues investigated the role of the TALE (three amino acid loop extension) homeodomain transcription factor Meis2 during maturation and target innervation of mechanoreceptors and their sensation to touch. They start with a series of careful in situ hybridizations and immunohistochemical analyses to examine Meis2 transcript expression and protein distribution in mouse and chick DRGs of different embryonic stages. By this approach, they identify Meis2+ neurons as slowly- and rapidly adapting A-beta LTMRs, respectively. Retrograde tracing experiments in newborn mice confirmed that Meis2-expressing sensory neurons project to the skin, while unilateral limb bud ablations in chick embryos in ovo showed that these neurons require target-derived signals for survival. The authors further generated a conditional knock-out (cKO) mouse model in which Meis2 is selectively lost in Islet1-expressing, postmitotic neurons in the DRG (IsletCre/+::Meis2flox/flox, abbreviated below as cKO). WT and Islet1Cre/+ littermates served as controls. cKO mice did not exhibit any obvious alteration in volume or cellular composition of the DRGs but showed significantly reduced sensitivity to touch stimuli and various innervation defects to different end-organ targets. RNA-sequencing experiments of E18.5 DRGs taken from WT, Islet1Cre/+ and cKO mice reveals extensive gene expression differences between cKO cells and the two controls, including synaptic proteins and components of GABAergic- and glutamatergic transmission. Histological analysis and electrophysiological recordings shed light on the physiological defects resulting from the loss of Meis2. By immunohistochemical approaches, the authors describe distinct innervation defects in glabrous and hairy skin (reduced innervation of Merkel cells by SA1-LTMRs in glabrous but not hairy skin, reduced complexity of A-beta RA1-LTMs innervating Meissner's corpuscles in glabrous skin, reduced branching and innervation of A-betA RA1-LTMRs in hairy skin). Electrophysiological recordings from ex vivo skin nerve preparations found that several, but not all of these histological defects are matched by altered responses to external stimuli, indicating that compensation may play a considerable role in this system. This study will be of interest to developmental biologists and neuroscientists, in particular those interested in the sensation of touch.

Strengths:<br /> This is a well-conducted study that combines different experimental approaches to convincingly show that the transcription factor Meis2 plays an important role in the perception of light touch. The authors describe a new mouse model for compromised touch sensation, characterize it by histology and electrophysiological recordings, and identify several genes whose expression depends on Meis2 in mouse DRGs.

Weaknesses:<br /> The authors use different experimental approaches to investigate the role of Meis2 in touch sensation, but the results obtained by these techniques could be better connected. For instance, the authors identify several genes involved in synapse formation, synaptic transmission, neuronal projections, or axon and dendrite maturation that are up- or downregulated upon targeted Meis2 deletion, but it remains to be resolved whether these chances explain the histological, electrophysiological, or behavioral deficits observed in cKO animals.

Reviewer #1 (Public Review):

Summary:<br /> In the current study, Papandreou et al. developed an iPSC-based midbrain dopaminergic neuronal cell model of Beta-Propeller Protein-Associated Neurodegeneration (BPAN), which is caused by mutations in the WDR45 gene and is known to impair autophagy. They also noted defective autophagy and abnormal BPAN-related gene expression signatures. Further, they performed a drug screening and identified five cardiac glycosides. Treatment with these drugs effectively in improved autophagy defects and restored gene expression.

Strengths:<br /> Seeing the autophagy defects and impaired expression of BPAN-related genes adds strength to this study. Importantly, this work shows the value of iPSC-based modeling in studying disease and finding therapeutic strategies for genetic disorders, including BPAN.

Weaknesses:<br /> It is unclear whether these cells show iron metabolism defects and whether treatment with these drugs can ameliorate the iron metabolism phenotypes.

Reviewer #2 (Public Review):

Summary:<br /> In this manuscript, the authors aim to demonstrate that cardiac glycosides restore autophagy flux in an iPSC-derived mDA neuronal model of WDR45 deficiency. They established a patient-derived induced pluripotent stem cell (iPSC)-based midbrain dopaminergic (mDA) neuronal model and performed a medium-throughput drug screen using high-content imaging-based IF analysis. Several compounds were identified to ameliorate disease-specific phenotypes in vitro.

Strengths:<br /> This manuscript engaged in an important topic and yielded some interesting data.

Weaknesses:<br /> This manuscript failed to provide solid evidence to support the conclusion.

Reviewer #1 (Public Review):

Summary:<br /> Zhang et al., investigated the relationship between monocular and binocular responses of V1 superficial-layer neurons using two-photon calcium imaging. They found a strong relationship in their data: neurons that exhibited a greater preference for one eye or the other (high ocular dominance) were more likely to be suppressed under binocular stimulation, whereas neurons that are more equivalently driven by each other (low ocular dominance) were more likely to be enhanced by binocular stimulation. This result chiefly demonstrates the relationship between ocular dominance and binocular responses in V1, corroborating what has been shown previously using electrophysiological techniques but now with greater spatial resolution (albeit less temporal resolution). The binocular responses were well-fitted by a model that institutes divisive normalization between the eyes that accounts for both the suppression and enhancement phenomena observed in the subpopulation of binocular neurons. In so doing, the authors reify the importance of incorporating ocular dominance in computational models of binocular combination.

The conclusions of this paper are mostly well supported by the data, but there are some limitations of the methodology that need to be clarified, and an expansion of how the results relate to previous work would better contextualize these important findings in the literature.

Strengths:<br /> The two-photon imaging technique used to resolve the activity of individual neurons within intact brain tissue grants a host of advantages. Foremost, two-photon imaging confers considerably high spatial resolution. As a result, the authors were able to sample and analyze the activity from thousands of verified superficial-layer V1 neurons. The animal model used, awake macaques, is also highly relevant for the study of binocular combination. Macaques, like humans, are binocular animals, meaning they have forward-facing eyes that confer overlapping visual fields. Importantly, macaque V1 is organized into cortical columns that process specific visual features from the separate eyes just like in humans. In combination with a powerful imaging technique, this allowed the authors to evaluate the monocular and binocular response profiles of V1 neurons that are situated within neighboring ocular dominance columns, a novel feat. To this aim, the approach was well-executed and should instill further confidence in the notion that V1 neurons combine monocular information in a manner that is dependent on the strength of their ocular dominance.

Weaknesses:<br /> While two-photon imaging provides excellent spatial resolution, its temporal resolution is often lower compared to some other techniques, such as electrophysiology. This limits the ability to study the fast dynamics of neuronal activity, a well-understood trade-off of the method. The issue is more so that the authors draw comparisons to electrophysiological studies without explicit appreciation of the temporal difference between these techniques. In a similar vein, two-photon imaging is limited spatially in terms of cortical depth, preferentially sampling from neurons in layers 2/3. This limitation does not invalidate any of the interpretations but should be considered by readers, especially when making comparisons to previous electrophysiological reports using microelectrode linear arrays that sample from all cortical layers. Indeed, it is likely that a complete picture of early cortical binocular processing will require high spatial resolution (i.e., sampling from neurons in neighboring ocular dominance columns, from pia mater to white matter) at the biophysically relevant timescales (1ms resolution, capturing response dynamics over the full duration of the stimulus presentation, including the transient onset and steady-state periods).

Reviewer #2 (Public Review):

Summary:<br /> This study examines the pattern of responses produced by the combination of left-eye and right-eye signals in V1. For this, they used calcium imaging of neurons in V1 of awake, fixating monkeys. They take advantage of calcium imaging, which yields large populations of neurons in each field of view. With their data set, they observe how response magnitude relates to ocular dominance across the entire population. They analyze carefully how the relationship changed as the visual stimulus switched from contra-eye only, ipsi-eye only, and binocular. As expected, the contra-eye-dominated neurons responded strongly with a contra-eye-only stimulus. The ipsi-eye-dominated neurons responded strongly with an ipsi-eye-only stimulus. The surprise was responses to a binocular stimulus. The responses were similarly weak across the entire population, regardless of each neuron's ocular dominance. They conclude that this pattern of responses could be explained by interocular divisive normalization, followed by binocular summation.

Strengths:<br /> A major strength of this work is that the model-fitting was done on a large population of simultaneously recorded neurons. This approach is an advancement over previous work, which did model-fitting on individual neurons. The fitted model in the manuscript represents the pattern observed across the large population in V1, and washes out any particular property of individual neurons. Given the large neuronal population from which the conclusion was drawn, the authors provide solid evidence supporting their conclusion. They also observed consistency across 5 fields of view.

The experiments were designed and executed appropriately to test their hypothesis. Their data support their conclusion.

Weaknesses:<br /> One weakness of their study is that calcium signals can exaggerate the nonlinear properties of neurons. Calcium imaging renders poor responses poorer and strong responses stronger, compared to single-unit recording. In particular, the dramatic change in the population response between monocular stimulation and binocular stimulation could actually be less pronounced when measured with single-unit recording methods. This means their choice of recording method could have accidentally exaggerated the evidence of their finding.

The implication of their finding is that strong ocular dominance is the result of release from interocular suppression by a monocular stimulus, rather than the lack of binocular combination as many traditional studies have assumed. This could significantly advance our understanding of the binocular combination circuitry of V1. The entire population of neurons could be part of a binocular combination circuitry present in V1.

Reviewer #3 (Public Review):

The authors have made simultaneous recordings of the responses of large numbers of neurons from the primary visual cortex using optical two-photon imaging of calcium signals from the superficial layers of the cortex. Recordings were made to compare the responses of the cortical neurons under normal binocular viewing of a flat screen with both eyes open and monocular viewing of the same screen with one eye's view blocked by a translucent filter. The screen displayed visual stimuli comprising small contrast patches of Gabor function distributions of luminance, a stimulus that is known to excite cortical neurons.

This is an important data set, given the large numbers of neurons recorded. The authors present a simple model to explain the binocular combination of neuronal signals from the right and left eyes.

The limitations of the paper as written are as follows. These points can be addressed with some additional analysis and rewriting of sections of the paper. No new experimental data need to be collected.

1) The authors should acknowledge the fact that these recordings arise from neurons in the superficial layers of the cortex. This limitation arises from the usual constraints on optical imaging in the macaque cortex. This means that the sample of neurons forming this data set is not fully representative of the population of binocular neurons within the visual cortex. This limitation is important in comparing the outcome of these experiments with the results from other studies of binocular combination, which have used single-electrode recording. Electrode recording will result in a sample of neurons that is drawn from many layers of the cortex, rather than just the superficial layers.

2) Single-neuron recording of binocular neurons in the primary visual cortex has shown that these neurons often have some spontaneous activity. Assessment of this spontaneous level of firing is important for accurate model fitting [1]. The paper here should discuss the level of spontaneous neuronal firing and its potential significance.

3) The arrangements for visual stimulation and comparison of binocular and monocular responses mean that the stereoscopic disparity of the binocular stimuli is always at zero or close to zero. The animal's fixation point is in the centre of a single display that is viewed binocularly. The fixation point is, by definition, at zero disparity. The other points on the flat display are also at zero disparity or very close to zero because they lie in the same depth plane. There will be some small deviations from exactly zero because the geometry of the viewing arrangements results in the extremities of the display being at a slightly different distance than the centre. Therefore, the visual stimulation used to test the binocular condition is always at zero disparity, with a slight deviation from zero at the edges of the display, and never changes. [There is a detail that can be ignored. The experimenters tested neurons with visual stimulation at different real distances from the eyes, but this is not relevant here. Provided the animals accurately converged their eyes on the provided binocular fixation point, then the disparity of the visual stimuli will always be at or close to zero, regardless of viewing distance in these circumstances.] However, we already know from earlier work that neurons in the visual cortex exhibit a range of selectivity for binocular disparity. Some neurons have their peak response at non-zero disparities, representing binocular depths nearer than the fixation depth or beyond it. The response of other neurons is maximally suppressed by disparities at the depth of the fixation point (so-called Tuned Inhibitory [TI] neurons). The simple model and analysis presented in the paper for the summation of monocular responses to predict binocular responses will perform adequately for neurons that are tuned to zero disparity, so-called tuned excitatory neurons [TE], but is necessarily compromised when applied to neurons that have other, different tuning profiles. Specifically, when neurons are stimulated binocularly with a non-preferred disparity, the binocular response may be lower than the monocular response[2, 3]. This more realistic view of binocular responses needs to be considered by the authors and integrated into their modelling.

4) The data in the paper show some features that have been reported before but are not captured by the model. Notably for neurons with extreme values of ocular dominance, the binocular response is typically less than the larger of the two monocular responses. This is apparent in the row of plots in Figure 2D from individual animals and in the pooled data in Figure 2E. Responses of this type are characteristic of tuned inhibitory [TI] neurons[2]. It is not immediately clear why this feature of the data does not appear in the summary and analysis in Figure 3. The paper text states that the responses were "first normalized by the median of the binocular responses". This will certainly get rid of this characteristic of the data, but this step needs better justification, or an amendment to the main analysis is needed. In the present form, the model and analysis do not appear to fit the data in Figure 2 as accurately as needed. The authors should address the discrepancy between the data as presented in Figures 2D, E, and Figure 3.

Citations<br /> 1. Prince, S.J.D., Pointon, A.D., Cumming, B.G., and Parker, A.J., (2002). Quantitative analysis of the responses of V1 neurons to horizontal disparity in dynamic random-dot stereograms. Journal of Neurophysiology, 87: 191-208.<br /> 2. Prince, S.J.D., Cumming, B.G., and Parker, A.J., (2002). Range and mechanism of encoding of horizontal disparity in macaque V1. Journal of Neurophysiology, 87: 209-221.<br /> 3. Poggio, G.F. and Fischer, B., (1977). Binocular interaction and depth sensitivity in striate and prestriate cortex of behaving rhesus monkey. Journal of Neurophysiology, 40: 1392-1405 doi 10.1152/jn.1977.40.6.1392.

Reviewer #1 (Public Review):

Summary:<br /> The authors ran an explorative analysis in order to describe how a "tri-partite" brain network model could describe the combination of resting fMRI data and individual characteristics. They utilized previously obtained fMRI data across four scanning runs in 144 individuals. At the end of each run, participants rated their patterns of thinking on 12 statements (short multi-dimensional experience sampling-MDES) using a 0-100% visual analog scale. Also, 71 personality traits were obtained on 21 questionnaires. The authors ran two separate principal component analyses (PCA) to obtain low dimensional summaries of the two individual characteristics (personality traits from questionnaires, and thought patterns from MDES). The dimensionality reduction of the fMRI data was done by means of gradient analysis, which was combined with Neurosynth decoding to visualize the functional axis of the gradients. To test the reliability of thought components across scanning time, intra-class correlation coefficients (ICC) were calculated for the thought patterns, and discriminability indices were calculated for whole gradients. The relationship between individual differences in traits, thoughts, and macro-scale gradients was tested with multivariate regression.

The authors found: a) reliability of thought components across the one hour of scanning, b) Gradient 1 differentiated between visual regions and DMN, Gradient 2 dissociated somatomotor from visual cortices, Gradient 3 differentiated the DMN from the fronto-parietal system, c) the associations between traits/thought patterns and brain gradients revealed significant effects of "introversion" and "specific internal" thought: "Introversion" was associated with variant parcels on the three gradients, with most of parcels belonging to the VAN and then to the DMN; and "Specific internal thought" was associated with variant parcels on the three gradients with most of parcels belonging to the DAN and then the visual. The authors conclude that interactions between attention systems and the DMN are important influences on ongoing thought at rest.

Strengths:<br /> The study's strength lies in its attempt to combine brain activity with individual characteristics using state-of-the-art methodologies.

Weaknesses:<br /> The study protocol in its current form restricts replicability. This is largely due to missing information on the MRI protocol and data preprocessing. The article refers the reader to the work of Mendes et al 2019 which is said to provide this information, but the paper should rather stand alone with all this crucial material mentioned here, as well. Also, effect sizes are provided only for the multiple multivariate regression of the inter-class correlations, which makes it difficult to appreciate the power of the other obtained results.

Reviewer #2 (Public Review):

The authors set out to draw further links between neural patterns observed at "rest" during fMRI, with their related thought content and personality traits. More specifically, they approached this with a "tri-partite network" view in mind, whereby the ventral attention network (VAN), the dorsal attention network (DAN), and the default mode network (DMN) are proposed to play a special role in ongoing conscious thought. They used a gradients approach to determine the low dimensional organisation of these networks. In concert, using PCA they reduced thought patterns captured at four time points during the scan, as well as traits captured from a large battery of questionnaires.

The main findings were that specific thought and trait components were related to variations in the organisation of the tri-partite networks, with respect to cortical gradients.

Strengths of the methods/results: Having a long (1 hr) resting state MRI session, which could be broken down into four separate scanning/sampling components is a strength. Importantly, the authors could show (via intra-class correlation coefficients) the similarity of thoughts and connectivity gradients across the entire session. Not only did this approach increase the richness of the data available to them, it speaks in an interesting way to the stability of these measures. The inclusion of both thought patterns during scanning along with trait-level dispositional factors is most certainly a strength, as many studies will often include either/or of these, rather than trying to reconcile across. Of the two main findings, the finding that detailed self-generated thought was associated with a decoupling of regions of DAN from regions in DMN was particularly compelling, in light of mounting literature from several fields that support this.

Weaknesses of the methods/results: Considering the richness of the thought and personality data, I was a little surprised that only two main findings emerged (i.e., a relationship with trait introversion, and a relationship with the "specific internal" thought pattern). I wondered whether, at least in part and in relation to traits, this might stem from the large and varied set of questionnaires used to discern the traits. These questionnaires mostly comprised personality/mood, but some sampled things that do not fall into that category (e.g., musicality, internet addition, sleep), and some related directly to spontaneous thought properties (e.g., mind wandering, musical imagery). It would be interesting to see what relationships would emerge by being more selective in the traits measured, and in the tools to measure them.

Taken together, the main findings are interesting enough. However, the real significance of this work, and its impact, lie in the richness of the approach: combing across fMRI, spontaneous thought, and trait-level factors. Triangulating these data has important potential for furthering our understanding of brain-behaviour relationship across different levels of organisation.

Reviewer #1 (Public Review):

Summary:<br /> This paper describes a reanalysis of data collected by Gagne et al. (2020), who investigated how human choice behaviour differs in response to changes in environmental volatility. Several studies to date have demonstrated that individuals appear to increase their learning rate in response to greater volatility and that this adjustment is reduced amongst individuals with anxiety and depression. The present authors challenge this view and instead describe a novel Mixture of Strategies (MOS) model, that attributes individual differences in choice behaviour to different weightings of three distinct decision-making strategies. They demonstrate that the MOS model provides a superior fit to the data and that the previously observed differences between patients and healthy controls may be explained by patients opting for a less cognitively demanding, but suboptimal, strategy.

Strengths:<br /> The authors compare several models (including the original winning model in Gagne et al., 2020) that could feasibly fit the data. These are clearly described and are evaluated using a range of model diagnostics. The proposed MOS model appears to provide a superior fit across several tests.

The MOS model output is easy to interpret and has good face validity. This allows for the generation of clear, testable, hypotheses, and the authors have suggested several lines of potential research based on this.

Weaknesses:<br /> The authors justify this reanalysis by arguing that learning rate adjustment (which has previously been used to explain choice behaviour on volatility tasks) is likely to be too computationally expensive and therefore unfeasible. It is unclear how to determine how "expensive" learning rate adjustment is, and how this compares to the proposed MOS model (which also includes learning rate parameters), which combines estimates across three distinct decision-making strategies.

As highlighted by the authors, the model is limited in its explanation of previously observed learning differences based on outcome value. It's currently unclear why there would be a change in learning across positive/negative outcome contexts, based on strategy choice alone.

Reviewer #2 (Public Review):

Summary:<br /> Previous research shows that humans tend to adjust learning in environments where stimulus-outcome contingencies become more volatile. This learning rate adaptation is impaired in some psychiatric disorders, such as depression and anxiety. In this study, the authors reanalyze previously published data on a reversal-learning task with two volatility levels. Through a new model, they provide some evidence for an alternative explanation whereby the learning rate adaptation is driven by different decision-making strategies and not learning deficits. In particular, they propose that adjusting learning can be explained by deviations from the optimal decision-making strategy (based on maximizing expected utility) due to response stickiness or focus on reward magnitude. Furthermore, a factor related to the general psychopathology of individuals with anxiety and depression negatively correlated with the weight on the optimal strategy and response stickiness, while it correlated positively with the magnitude strategy (a strategy that ignores the probability of outcome).

Strengths:<br /> The main strength of the study is a novel and interesting explanation of an otherwise well-established finding in human reinforcement learning. This proposal is supported by rigorously conducted parameter retrieval and the comparison of the novel model to a wide range of previously published models.

Weaknesses:<br /> My main concern is that the winning model (MOS6) does not have an error term (inverse temperature parameter beta is fixed to 8.804).

1) It is not clear why the beta is not estimated and how were the values presented here chosen. It is reported as being an average value but it is not clear from which parameter estimation. Furthermore, with an average value for participants that would have lower values of inverse temperature (more stochastic behaviour) the model is likely overfitting.

2) In the absence of a noise parameter, the model will have to classify behaviour that is not explained by the optimal strategy (where participants simply did not pay attention or were not motivated) as being due to one of the other two strategies.

3) A model comparison among models with inverse temperature and variable subsets of the three strategies (EU + MO, EU + HA) would be interesting to see. Similarly, comparison of the MOS6 model to other models where the inverse temperature parameter is fixed to 8.804).

This is an important limitation because the same simulation as with the MOS model in Figure 3b can be achieved by a more parsimonious (but less interesting) manipulation of the inverse temperature parameter.

Furthermore, the claim that the EU represents an optimal strategy is a bit overstated. The EU strategy is the only one of the three that assumes participants learn about the stimulus-outcomes contingencies. Higher EU strategy utilisation will include participants that are more optimal (in maximum utility maximisation terms), but also those that just learned better and completely ignored the reward magnitude.

Other minor issues that I have are the following:<br /> The mixture strategies model is an interesting proposal, but seems to be a very convoluted way to ask: to what degree are decisions of subjects affected by reward, what they've learned, and response stickiness? It seems to me that the same set of questions could be addressed with a simpler model that would define choice decisions through a softmax with a linear combination of the difference in rewards, the difference in probabilities, and a stickiness parameter.

Learning rate adaptation was also shown with tasks where decision-making strategies play a less important role, such as the Predictive Inference task (see for instance Nassar et al, 2010). When discussing the merit of the findings of this study on learning rate adaptation across volatility blocks, this work would be essential to mention.

Reviewer #3 (Public Review):

Summary:<br /> This paper presents a new formulation of a computational model of adaptive learning amid environmental volatility. Using a behavioral paradigm and data set made available by the authors of an earlier publication (Gagne et al., 2020), the new model is found to fit the data well. The model's structure consists of three weighted controllers that influence decisions on the basis of (1) expected utility, (2) potential outcome magnitude, and (3) habit. The model offers an interpretation of psychopathology-related individual differences in decision-making behavior in terms of differences in the relative weighting of the three controllers.

Strengths:<br /> The newly proposed "mixture of strategies" (MOS) model is evaluated relative to the model presented in the original paper by Gagne et al., 2020 (here called the "flexible learning rate" or FLR model) and two other models. Appropriate and sophisticated methods are used for developing, parameterizing, fitting, and assessing the MOS model, and the MOS model performs well on multiple goodness-of-fit indices. The parameters of the model show decent recoverability and offer a novel interpretation for psychopathology-related individual differences. Most remarkably, the model seems to be able to account for apparent differences in behavioral learning rates between high-volatility and low-volatility conditions even with no true condition-dependent change in the parameters of its learning/decision processes. This finding calls into question a class of existing models that attribute behavioral adaptation to adaptive learning rates.

Weaknesses:<br /> 1. Some aspects of the paper, especially in the methods section, lacked clarity or seemed to assume context that had not been presented. I found it necessary to set the paper down and read Gagne et al., 2020 in order to understand it properly.

2. There is little examination of why the MOS model does so well in terms of model fit indices. What features of the data is it doing a better job of capturing? One thing that makes this puzzling is that the MOS and FLR models seem to have most of the same qualitative components: the FLR model has parameters for additive weighting of magnitude relative to probability (akin to the MOS model's magnitude-only strategy weight) and for an autocorrelative choice kernel (akin to the MOS model's habit strategy weight). So it's not self-evident where the MOS model's advantage is coming from.

3. One of the paper's potentially most noteworthy findings (Figure 5) is that when the FLR model is fit to synthetic data generated by the expected utility (EU) controller with a fixed learning rate, it recovers a spurious difference in learning rate between the volatile and stable environments. Although this is potentially a significant finding, its interpretation seems uncertain for several reasons:

- According to the relevant methods text, the result is based on a simulation of only 5 task blocks for each strategy. It would be better to repeat the simulation and recovery multiple times so that a confidence interval or error bar can be estimated and added to the figure.

- It makes sense that learning rates recovered for the magnitude-oriented (MO) strategy are near zero, since behavior simulated by that strategy would have no reason to show any evidence of learning. But this makes it perplexing why the MO learning rate in the volatile condition is slightly positive and slightly greater than in the stable condition.

- The pure-EU and pure-MO strategies are interpreted as being analogous to the healthy control group and the patient group, respectively. However, the actual difference in estimated EU/MO weighting between the two participant groups was much more moderate. It's unclear whether the same result would be obtained for a more empirically plausible difference in EU/MO weighting.

- The fits of the FLR model to the simulated data "controlled all parameters except for the learning rate parameters across the two strategies" (line 522). If this means that no parameters except learning rate were allowed to differ between the fits to the pure-EU and pure-MO synthetic data sets, the models would have been prevented from fitting the difference in terms of the relative weighting of probability and magnitude, which better corresponds to the true difference between the two strategies. This could have interfered with the estimation of other parameters, such as learning rate.

- If, after addressing all of the above, the FLR model really does recover a spurious difference in learning rate between stable and volatile blocks, it would be worth more examination of why this is happening. For example, is it because there are more opportunities to observe learning in those blocks?

4. Figure 4C shows that the habit-only strategy is able to learn and adapt to changing contingencies, and some of the interpretive discussion emphasizes this. (For instance, line 651 says the habit strategy brings more rewards than the MO strategy.) However, the habit strategy doesn't seem to have any mechanism for learning from outcome feedback. It seems unlikely it would perform better than chance if it were the sole driver of behavior. Is it succeeding in this example because it is learning from previous decisions made by the EU strategy, or perhaps from decisions in the empirical data?

5. For the model recovery analysis (line 567), the stated purpose is to rule out the possibility that the MOS model always wins (line 552), but the only result presented is one in which the MOS model wins. To assess whether the MOS and FLR models can be differentiated, it seems necessary also to show model recovery results for synthetic data generated by the FLR model.

6. To the best of my understanding, the MOS model seems to implement valence-specific learning rates in a qualitatively different way from how they were implemented in Gagne et al., 2020, and other previous literature. Line 246 says there were separate learning rates for upward and downward updates to the outcome probability. That's different from using two learning rates for "better"- and "worse"-than-expected outcomes, which will depend on both the direction of the update and the valence of the outcome (reward or shock). Might this relate to why no evidence for valence-specific learning rates was found even though the original authors found such evidence in the same data set?

7. The discussion (line 649) foregrounds the finding of greater "magnitude-only" weights with greater "general factor" psychopathology scores, concluding it reflects a shift toward simplifying heuristics. However, the picture might not be so straightforward because "habit" weights, which also reflect a simplifying heuristic, correlated negatively with the psychopathology scores.

Reviewer #1 (Public Review):

Nitrogen metabolism is of fundamental importance to biology. However, the metabolism and biochemistry of guanidine and guanidine containing compounds, including arginine and homoarginine, have been understudied over the last few decades. Very few guanidine forming enzymes have been identified. Funck et al define a new type of guanidine forming enzyme. It was previously known that 2-oxogluturate oxygenase catalysis in bacteria can produce guanidine via oxidation of arginine. Interestingly, the same reported enzyme that produces guanidine from arginine also oxidises 2-oxogluturate to give the plant signalling molecule ethylene. Funck et al show that a mechanistically related oxygenase enzyme from plants can also produce guanidine, but instead of using arginine as a substrate, it uses homoarginine and does not produce ethylene. The work will stimulate interest in the cellular roles of homoarginine, a metabolite present in plants and other organisms including humans and, more generally, in the biochemistry and metabolism of guanidine derivatives.

1. Significance<br /> Studies on the metabolism and biochemistry of the small nitrogen rich molecule guanidine and related compounds including arginine have been largely ignored over the last few decades. Very few guanidine forming enzymes have been identified. Funck et al define a new guanidine forming enzyme that works by oxidation of homoarginine, a metabolite present in organisms ranging from plants to humans. The new enzyme requires oxygen and 2-oxogluturate as cosubstrates and is related, but distinct from a known enzyme that oxidises arginine to produce guanidine, but which can also oxidise 2-oxogluturate to produce the plant signalling molecule ethylene.

I thought this was an exceptionally well-written and interesting manuscript. Although a 2-oxogluturate dependent guanidine forming enzyme is known (EFE), the discovery that a related enzyme oxidises homoarginine is really interesting, especially given the presence of homoarginine in plant seeds. There is more work to be done in terms of functional assignment, but this can be the subject of future studies. I also fully endorse the authors' view that guanidine and related compounds have been massively understudied in recent times. Congratulations to the authors on a very nice study.

Overall, I thought this was a very interesting study, comprising biochemical, cellular, and in vivo studies. Of course, more could be done on each of these, and likely will be, but I think the assignment of biochemical function is very strong, across all three approaches. The one new experiment I requested was a demonstration of whether ethylene is produced by the new enzymes - this was clearly shown not to be the case.

Reviewer #2 (Public Review):

In this study, Dietmar Funck and colleagues have made a significant breakthrough by identifying three isoforms of plant 2-oxoglutarate-dependent dioxygenases (2-ODD-C23) as homo/arginine-6-hydroxylases, catalyzing the degradation of 6-hydroxyhomoarginine into 2-aminoadipate-6-semialdehyde (AASA) and guanidine. This discovery marks the very first confirmation of plant or eukaryotic enzymes capable of guanidine production.

The authors selected three plant 2-ODD-C23 enzymes with the highest sequence similarity to bacterial guanidine-producing (EFE) enzymes. They proceeded to clone and express the recombinant enzymes in E coli, demonstrating capacity of all three Arabidopsis isoforms to produce guanidine. Additionally, by precise biochemical experiments, the authors established these three 2-ODD-C23 enzymes as homoarginine-6-hydroxylases (and arginine-hydroxylase for one of them). Furthermore, the authors utilized transgenic plants expressing GFP fusion proteins to show the cytoplasmic localization of all three 2-ODD-C23 enzymes. Most notably, using T-DNA mutant lines and CRISPR/Cas9-generated lines, along with combinations of them, they demonstrate the guanidine-producing capacity of each enzyme isoform in planta. These results provide robust evidence that these three 2-ODD-C23 Arabidopsis isoforms are indeed homoarginine-6-hydroxylases responsible for guanidine generation.<br /> The findings presented in this manuscript are a significant contribution for our understanding of plant biology, particularly given that this work is the first demonstration of enzymatic guanidine production in eukaryotic cells. However, there are a couple of concerns and potential ways for further investigation that the authors should (consider) incorporate.

Firstly, the observation of cytoplasmic and nuclear GFP signals in the transgenic plants may also indicate cleaved GFP from the fusion proteins. Thus, the authors should perform a Western blot analysis to confirm the correct size of the 2-ODD-C23 fusion proteins in the transgenic protoplasts.

Secondly, it may be worth measuring pipecolate (and proline?) levels under biotic stress conditions (particularly those that induce transcript changes of these enzymes, Fig S8). Given the results suggesting a potential regulation of the pathway by biotic stress conditions (eg. meJA), these experiments could provide valuable insights into the physiological role of guanidine-producing enzymes in plants. This additional analysis may give a significance of these enzymes in plant defense mechanisms.

Reviewer #1 (Public Review):

Summary:<br /> "Phosphorylation, disorder, and phase separation govern the behavior of Frequency in the fungal circadian clock" is a convincing manuscript that delves into the structural and biochemical aspects of FRQ and the FFC under both LLPS and non-LLPS conditions. Circadian clocks serve as adaptations to the daily rhythms of sunlight, providing a reliable internal representation of local time.

All circadian clocks are composed of positive and negative components. The FFC contributes negative feedback to the Neurospora circadian oscillator. It consists of FRQ, CK1, and FRH. The FFC facilitates close interaction between CK1 and the WCC, with CK1-mediated phosphorylation disrupting WCC:c-box interactions necessary for restarting the circadian cycle.

Despite the significance of FRQ and the FFC, challenges associated with purifying and stabilizing FRQ have hindered in vitro studies. Here, researchers successfully developed a protocol for purifying recombinant FRQ expressed in E. coli.

Armed with full-length FRQ, they utilized spin-labeled FRQ, CK1, and FRH to gain structural insights into FRQ and the FFC using ESR. These studies revealed a somewhat ordered core and a disordered periphery in FRQ, consistent with prior investigations using limited proteolysis assays. Additionally, p-FRQ exhibited greater conformational flexibility than np-FRQ, and CK1 and FRH were found in close proximity within the FFC. The study further demonstrated that under LLPS conditions in vitro, FRQ undergoes phase separation, encapsulating FRH and CK1 within LLPS droplets, ultimately diminishing CK1 activity within the FFC. Intriguingly, higher temperatures enhanced LLPS formation, suggesting a potential role of LLPS in the fungal clock's temperature compensation mechanism.

Biological significance was supported by live imaging of Neurospora, revealing FRQ foci at the periphery of nuclei consistent with LLPS. The amino acid sequence of FRQ conferred LLPS properties, and a comparison of clock repressor protein sequences in other eukaryotes indicated that LLPS formation might be a conserved process within the negative arms of these circadian clocks.

In summary, this manuscript represents a valuable advancement with solid evidence in the understanding of a circadian clock system that has proven challenging to characterize structurally due to obstacles linked to FRQ purification and stability. The implications of LLPS formation in the negative arm of other eukaryotic clocks and its role in temperature compensation are highly intriguing.

Reviewer #2 (Public Review):

Summary:<br /> This study presents data from a broad range of methods (biochemical, EPR, SAXS, microscopy, etc.) on the large disordered protein FRQ relevant to circadian clocks and its interaction partners FRH and CK1, providing novel and fundamental insight into oligomerization state, local dynamics, and overall structure as a function of phosphorylation and association. Liquid-liquid phase separation is observed. These findings have bearings on the mechanistic understanding of circadian clocks, and on functional aspects of disordered proteins in general.

Strengths:<br /> This is a thorough work that is well presented. The data are of overall high quality given the difficulty of working with an intrinsically disordered protein, and the conclusions are sufficiently circumspect and qualitative to not overinterpret the mostly low-resolution data.

Weaknesses:<br /> None

Reviewer #3 (Public Review):

Summary:<br /> The manuscript from Tariq and Maurici et al. presents important biochemical and biophysical data linking protein phosphorylation to phase separation behavior in the repressive arm of the Neurospora circadian clock. This is an important topic that contributes to what is likely a conceptual shift in the field.

Joint Public Review:

The authors previously showed that expressing formate dehydrogenase, rubisco, carbonic anhydrase, and phosphoribulokinase in Escherichia coli, followed by experimental evolution, led to the generation of strains that can metabolise CO2. Using two rounds of experimental evolution, the authors identify mutations in three genes - pgi, rpoB, and crp - that allow cells to metabolise CO2 in their engineered strain background. The authors make a strong case that mutations in pgi are loss-of-function mutations that prevent metabolic efflux from the reductive pentose phosphate autocatalytic cycle. The authors also use proteomic analysis to probe the role of the mutations in crp and rpoB. While they do not reach strong conclusions about how these mutations promote autotrophic growth, they provide some clues, leading to valuable speculation.

Comments on revised version:<br /> The authors have thoroughly addressed the reviewers' comments. The major addition to the paper is the proteomic analysis of single and double mutants of crp and rpoB. These new data provide clues as to the role of the crp and rpoB mutations in promoting autotrophic growth, which the authors discuss. The authors acknowledge that it will require additional experiments to determine whether the speculated mechanisms are correct. Nonetheless, the new data provide valuable new insight into the role of the crp and rpoB mutations. The authors have also expanded their description of the crp and rpoB mutations, making it clearer that the effects of these mutations are likely to be distinct, albeit with potential for overlap in function.

Joint Public Review:

The manuscript highlights a mechanistic insight into meiotic initiation in budding yeast. In this study, the authors analyzed the genetic link between the mitotic cell cycle regulator SBF (the Swi4-Swi6 complex) and a meiosis inducing regulator Ime1 in the context of meiotic initiation. The authors' comprehensive analyses with cytology, imaging, RNA-seq using mutant strains lead to the conclusion that Swi4 levels regulates Ime1-Ume6 interaction to activate expression of early meiosis genes for meiotic initiation.

The authors first show a down regulation of Swi4 at the protein level upon meiosis entry and then investigate downstream consequences. This study reveals several regulations: 1) Mutations in CLN1 and 2, which are targets of Swi4, allow rescuing the delay in meiotic entry observed when Swi4 is overexpressed; 2) Ime1 activity is antigonized by Swi4, and more specifically its interaction with Ume6. 3) Expression of SWI4 is regulated by LUTI-based transcription at the SWI4 locus that impedes expression of canonical SWI4 transcripts 4) The expression of SWI4 LUTI is likely negatively regulated by the Ime1-Ume6 complex 5) Whi5 restrict SBF activity during meiotic entry, thereby ensuring Cyclin repression.

The important implication in this paper is that meiotic initiation is regulated by the balance of mitotic cell cycle regulator and meiosis-specific transcription factor.

Joint Public Review:

Summary<br /> Sender et al describe a model to estimate what fraction of DNA becomes cell-free DNA in plasma. This is of great interest to the community, as the amount of DNA from a certain tissue (for example, a tumor) that becomes available for detection in the blood has important implications for disease detection.

Strengths<br /> The question asked by the authors has potentially important implications for disease diagnosis. Understanding how genomic DNA degrades in the human circulation can guide towards ways to enrich for DNA of interest or may lead to unexpected methods of conserving cell-free DNA. Thus, the question "how much genomic DNA becomes cfDNA" is of great interest to the scientific and medical community. I believe this manuscript has the potential to be a widely used resource. As more data is collected on cell-free DNA yields and cellular turnover in the body, this work will only increase in importance.

Appraisal<br /> At this stage of the manuscript (second submission), I think the authors provide important evidence and analysis that aim to answer their research question. Previous concerns about methodology have been addressed.

Impact<br /> This manuscript will be highly impactful on the community. The field of liquid biopsies (non-invasive diagnostics) has the potential to revolutionize the medical field (and has already in certain areas, such as prenatal diagnostics). Yet, there is a lack of basic science questions in the field. This manuscript is an important step forward in asking more "basic science" questions that seek to answer a fundamental biological question.

Reviewer #3 (Public Review):

Summary<br /> Pham, Pahuja, Hagenbeek, et al. have conducted a comprehensive range of assays to biochemically and genetically determine TEAD degradation through RNF146 ubiquitination. Additionally, they designed a PROTAC protein degrader system to regulate the Hippo pathway through TEAD degradation. Overall, the data appears robust. However, the manuscript lacks detailed methodological descriptions, which should be addressed and improved. For instance, the methods used to analyze the K48 ubiquitination site on TEAD and the gene expression analysis of Hippo Signaling are unclear. Furthermore, the multiple proteomics, RNA-seq, and ATAC-seq data must be made publicly available upon publication to ensure reproducibility. Most of the main figures are of low resolution, which needs addressing.

Strengths:<br /> - A broad range of assays was used to robustly determine the role of RNF146 in TEAD degradation.<br /> - Development of novel PROTAC for degrading TEAD.

Weaknesses:<br /> - An orthogonal approach is needed (e.g., PARP1 inhibitor) to demonstrate PARP1's dependency in TEAD ubiquitination.

- The data from Table 2 is unclear in illustrating the association of identified K48 ubiquitination with TEAD4, especially since the experiments were presumably to be conducted on whole cell lysates with KGG enrichment. This raises the possibility that the K48 ubiquitination could originate from other proteins. Alternatively, if the authors performed immunoprecipitation on TEAD followed by mass spectrometry, this should be explicitly described in the text and materials and methods section.

- Figure 2D: The methodology for measuring the Hippo signature is unclear, as is the case for Figures 7E and F regarding the analysis of Hippo target genes.

- Figure S3F requires quantification with additional replicates for validation.

- There is a misleading claim in the discussion stating "TEAD PARylation by PAR-family members (Figure 3)"; however, the demonstration is only for PARP1, which should be corrected.

Reviewer #1 (Public Review):

Summary:<br /> In the first half of this study, Pham et al. investigate the regulation of TEAD via ubiquitination and PARylation, identifying an E3 ubiquitin ligase, RNF146, as a negative regulator of TEAD activity through an siRNA screen of ubiquitin-related genes in MCF7 cells. The study also finds that depletion of PARP1 reduced TEAD4 ubiquitination levels, suggesting a certain relationship between TEAD4 PARylation and ubiquitination which was also explored through an interesting D70A mutation. Pham et al. subsequently tested this regulation in D. melanogaster by introducing Hpo loss-of-function mutations and rescuing the overgrowth phenotype through RNF146 overexpression.

In the second half of this study, Pham et al. designed and assayed several potential TEAD degraders with a heterobifunctional design, which they term TEAD-CIDE. Compounds D and E were found to effectively degrade pan-TEAD, an effect which could be disrupted by treatment with TEAD lipid pocket binders, proteasome inhibitors, or E1 inhibitors, demonstrating that the TEAD-CIDEs operate in a proteasome-dependent manner. These TEAD-CIDEs could reduce cell proliferation in OVCAR-8, a YAP-deficient cell line, but not SK-N-FI, a Hippo pathway independent cell line. Finally, this study also utilizes ATAC-seq on Compound D to identify reductions in chromatin accessibility at the regions enriched for TEAD DNA binding motifs.

Strengths:<br /> The study provides compelling evidence that the E3 ubiquitin ligase RNF146 is a novel negative regulator of TEAD activity. The authors convincingly delineate the mechanism through multiple techniques and approaches. The authors also describe the development of heterobifunctional pan-degraders of TEAD, which could serve as valuable reagents to more deeply study TEAD biology.

Weaknesses:<br /> The scope of this study is extremely broad. The first half of the paper highlights the mechanisms underlying TEAD degradation; however, the connection to the second half of the paper on small molecule degraders of TEAD is jarring, and it seems as though two separate stories were combined into this single massive study. In my opinion, the study would be stronger if it chose to focus on only one of these topics and instead went deeper.

Additionally, the figure clarity needs to be substantially improved, as readability and interpretation were difficult in many panels. Lastly, there are numerous typos and poor grammar throughout the text that need to be addressed.

Reviewer #2 (Public Review):

The paper is made of two parts. One deals with RNF146, the other with the development of compounds that may cause TEAD degradation. The two parts are rather unrelated to each other.

The main limit of this work is the lack of evidence that TEAD factors are in fact regulated by the proteasome and ubiquitylation under endogenous conditions. Also lacking is the demonstration that TEADs are labile proteins to the extent that such quantitative regulation at the level of stability can impact on YAP-TAZ biology. Without these two elements, the relevance and physiological significance of all these data is lacking.

As for the development of new inhibitors of TEAD, this is potentially very interesting but underdeveloped in this manuscript. Irrespectively, if TEAD is stable, these molecules are likely lead compounds of interest. If TEAD is unstable, as entertained in the first part of the paper, then these molecules are likely marginal.

Here are a few other specific observations:

1 The effect of MG is shown in a convoluted way, by MS. What about endogenous TEAD protein stability?

2 The relevance of siRNF on YAP target genes of Fig.2D is not statistically significant.

3 All assays are with protein overexpression and Ub-laddering

4 An inconsistency exists on the only biological validation (only by overexpression) on the fly eye size. RNF gain in Fig4C is doing the opposite of what is expected from what is portrayed here as a YAP/TEAD inhibitor: RNF gain is shown to INCREASE eye size, phenocopying a Hippo loss of function phenotype. According to the model proposed, RNF addition should reduce eye size. The authors stated that " This is in contrast to the anti-growth effect of RNF-146 in the Hpo loss-of-function background and indicates RNF146 may regulate other genes/pathways controlling eye sizes besides its role as a negative regulator of Sd/yki activity". This raises questions on what the authors are really studying: why, according to the authors, these caveats should occur on the controls, and not when they study Hpo mutants?

5 The role of TEAD inactivation on YAP function is already well known. Disappointingly no prior literature is cited. In any case, this is a mere control.

6 The second part of the paper on the Development and Screening of pan-TEAD lipid pocket degraders is interesting but unconnected to the above. The degradation pathway it involves has nothing to do with the enzyme described in the first figures.

7 The role of CIDE on YAP accessibility to Chromatin is superficially executed. Key controls are missing along with the connection with mechanisms and prior knowledge, of TEAD, YAP, chromatin, and other TEAD inhibitors, just to mention a few.

8 The physiological relevance and the mechanistic interpretation of what should be in the ATAC seq in ovcar cells is missing.

Reviewer #1 (Public Review):

Summary:<br /> The current manuscript provides an extensive in vivo analysis of two guidance pathways identifying multiple mechanisms that shape the bifurcation of DRG axons when forming the dorsal funiculus in the DREZ.

Strengths:<br /> Multiple mouse mutant lines were used, together with complementary techniques; the results are very clear and compelling.<br /> The findings are very significant and clearly move forward our understanding of the regulation of axonal development at the DREZ.

Weaknesses:<br /> No major weaknesses were found. As it is I have no recommendations that would increase the clarity or quality of the manuscript.

Reviewer #2 (Public Review):

Summary:<br /> In this manuscript, the authors conduct a detailed analysis of the molecular cues that control the guidance of bifurcated dorsal root ganglion axons in a key region of the spinal cord called the dorsal funiculus. This is a specific case of axon guidance that occurs in a precise way. The authors knew that Slit was important but many axons still target correctly in Slit knockouts, suggesting a role for other guidance factors. Netrin1 is also expressed in this region, so they looked at netrin mutants. The authors found axons outside the DREZ in the Ntn1 mutants, and they show by single-neuron genetic labeling that many of these come from DRG neurons. Quantified axonal tracing studies in Slit1/2, Ntn1, or triple mutant embryos support the idea that Slit and Ntr1 have distinct functions in guidance and that the effect of their loss is additive. Interestingly none of these knockouts affect bifurcation itself but rather the guidance of one or both of the bifurcated axon terminals. Knockout of the Slit receptors (Robo1/2) or the Netrin 1 receptor (DCC) in embryos causes similar guidance defects to loss of the ligands, providing additional confirmation of the requirement for both guidance pathways.

Strengths:<br /> This study expands understanding of the role of the axon guidance factors Ntr1/DCC and Slit/Robo in a specific axon guidance decision. The strength of the study is the careful axonal labeling and quantification, which allows the authors to establish precise consequences of the loss of each guidance factor or receptor.

Weaknesses:<br /> There are some places in the text where the discussion of these data is compared with other studies and models, but additional details would help clarify the arguments.

Reviewer #3 (Public Review):

Summary:<br /> In this paper, Curran et al investigate the role of Ntn, Slit1, and Slit 2 in the axon patterning of DRG neurons. The paper uses mouse genetics to perturb each guidance molecule and its corresponding receptor. Cre-based approaches and immunostaining of DRG neurons are used to assess the phenotypes. Overall, the study uses the strength of mouse genetics and imaging to reveal new genetic modifiers of DRG axons. The conclusions of the experiments match the presented results. The paper is an important contribution to the field, as evidence that dorsal funiculus formation is impacted by Ntn and Slit signaling. However, there are some potential areas of the manuscript that should be edited to better match the results with the conclusions of the work.

Strengths:<br /> The manuscript uses the advantage of mouse genetics to investigate the axon patterning of DRG neurons. The work does a great job of assessing individual phenotypes in single and double mutants. This reveals an intriguing cooperative and independent function of Ntn, Slit1, and Slit2 in DRG axon patterning. The sophisticated triple mutant analysis is lauded and provides important insight.

Weaknesses:<br /> Overall, the manuscript is sound in technique and analysis. However, the majority of the manuscript is about the dorsal funiculus and not the bifurcation of the axons, as the title would make a reader believe. Further, the manuscript would provide a more scholarly discussion of the current knowledge of DRG axon patterning and how their work fits into that knowledge.

Reviewer #1 (Public Review):

Yu et al. investigated Fusarium oxysporum f. sp. lycopersici SIX effectors structure using experimental and computational approaches, and while doing so, the authors identified several SIX effectors as member of the FOLD family, and expanded the known diversity of the SIX effectors. A very interesting and novel finding is the presence of FOLD putative effectors in other Ascomycetes secretome, sharing structural similarities with SIX effectors Avr1, Avr3 and SIX6.

By performing technically sound predictions and experimental confirmation, the authors also confirmed co-operative interactions between Fol effectors, something that was previously known for different pairs of proteins, expanding this observation for new SIX effectors. In addition, the authors contributed to the understanding of the interaction Fol effectors, specifically FOLD and LARS effectors, - I receptors to suppress immunity by structurally similar effectors.

The conclusions of this paper are supported by data and I think it is a pioneer study analyzing the correspondence between AlphaFold predictions and experimentally derived structures, highlighting that models can answer the scientific questions in some cases but could not be enough in others.

Reviewer #2 (Public Review):

Yu et al. investigated the structural landscape of 'secreted in xylem' (SIX) effector (virulence and avirulence) proteins from the plant-pathogenic fungus, Fusarium oxysporum f. sp. lycopersici (Fol), with the goal of better understanding effector function and recognition by host (tomato) immune receptors. In recent years, several experimental and computational studies have shown that many effector proteins of plant-associated fungi can be assigned to one of a few major structural families. In the study by Yu et al., X-ray crystallography was used to show that two avirulence effectors of Fol, Avr1 (SIX4) and Avr3 (SIX1), which are recognized by the tomato immune receptors I and I-3, respectively, form part of a new structural family, the Fol dual-domain (FOLD) family, found across three fungal divisions. Using AlphaFold2, an ab initio structural prediction tool, the authors then predicted the structures of all proteins within the Fol SIX effector repertoire (and other effector candidates) and provided evidence that two other effectors, SIX6 and SIX13, also belong to this family.

In addition to identifying members of the FOLD family, structural prediction revealed that proteins of the Fol effector repertoire can largely be classified into a reduced set of structural families. Examples included four members of the ToxA-like family (including Avr2 (SIX3) and SIX8), as well as four members of a new family, Family 4 (including SIX5 and PSL1). Given previous studies had demonstrated that Avr2 (ToxA-like) and SIX5 (Family 4) interact and function together, and that the genes encoding these proteins are divergently transcribed, and because homologues of SIX8 (ToxA-like) and PSL1 (Family 4) from another Fusarium pathogen are functionally dependent on each other and, in the case of Fol, are encoded by genes that are next to each other in the genome, the authors hypothesized that SIX8 and PSL1 may also physically interact. In line with this, co-incubation of the SIX8 and PSL1 proteins, followed by size exclusion chromatography (SEC), gave elution and gel migration profiles consistent with interaction in the form of a heterodimer. AlphaFold2-Multimer modelling then suggested that this interaction was mediated through an intermolecular disulfide bond. Such a prediction was subsequently confirmed through mutational analysis of the relevant cysteine residue in each protein in conjunction with SEC.

Finally, using a variant (homologue) of Avr1 from another Fusarium pathogen, as well as chimeric forms of this protein that integrated regions of Avr1 from Fol, Yu et al. determined through co-expression assays in Nicotiana benthamiana with the I immune receptor, as well as subsequent ion leakage assays, that the C-domain of Avr1 is recognized by the I immune receptor. Furthermore, through these assays, the authors were also able to show that surface-exposed residues in the C-domain enable Avr1 to evade recognition by a variant of the I receptor in Moneymaker tomato that does not provide resistance to Fol.

Overall, the manuscript presents a large body of work that is well supported by the data. A key strength of the manuscript is the validation (benchmarking) of protein structures predicted using AlphaFold2, which is a first for large-scale effector structure prediction papers published to date. Another key strength is the use of large-scale effector structure predictions to make hypotheses about functional relationships or interactions that are then tested (i.e. the SIX8-PSL1 protein interaction and recognition of Avr1 by the I immune receptor). This testing again goes above and beyond the large-scale effector structure prediction papers published to date. Taken together, this showcases how experimental and computational experiments can be effectively combined to provide biologically relevant data for the plant protection and molecular plant-microbe interactions fields.

In terms of weaknesses, the manuscript could have validated the SIX8-PSL1 protein interaction with in planta experiments, such as co-immunoprecipitation assays or co-localization experiments in conjunction with confocal microscopy, to provide support for the interaction in a plant setting. However, given what is already known about the Avr2-SIX5 interaction, these additional experiments are not crucial and could instead form part of a follow-up study.

Reviewer #3 (Public Review):

In this work, the authors shed light onto the structures of Fusarium oxysporum f.sp. lycopersici proteins involved in the infection of tomato. They unravelled several new secreted effector protein structures and additionally used computational approaches to structurally classify the remaining effectors known from this pathogen. Through this they uncovered a new and unique structural class of proteins which they found to be present and widely distributed in fungal plant pathogens and plant symbiotic fungi. The authors further predicted structural models for the full SIX effector set revealing that genome-proximal effector pairs share similar structural classes. Building on their Avr1 structure, the authors also define the C-terminal domain and specific amino acid residues that are essential to Avr1 detection by its cognate immune receptor.

A major strength of this work is a portfolio of several (Avr1, Avr3, SIX6, SIX8) new structurally resolved proteins which led to the discovery that several of them fall into the same structural class. These findings are supported by strong results.

The experiments addressing the structure-function relationship of Avr1's avirulence activity are highly relevant to our understanding of disease resistance mechanisms against Fusarium. Additional controls would allow for better support of the conclusions to be drawn. An example is FonSIX4's cell death activity in N.benthamiana leaves and whether FonSIX4 cdll death is indeed dependent on the tomato I receptor. Complementary work in Fusarium mutants lacking Avr1 and expressing chimeric versions would document that the observations from transient expressions in Nicotiana benthamiana are relevant in the biological context of a Fusarium/tomato interaction.

The discovered solvent-exposed residues conditioning Avr1 recognition by the I receptor seem to be positioned in an area of the protein which had previously been highlighted as being highly variable in FOLD proteins of symbiotic fungi but it is not clear from the work whether this is indeed the case or whether Avr1 differs significantly in its structure from FOLD proteins found in other fungi.<br /> It remains to be tested whether the residues conditioning avirulence activity are also crucial for virulence activity in Fusarium.

This work uncovered a new structural class of proteins with critical roles in plant-pathogen interactions. Structure-based predictions and genome-wide comparisons have emerged as a new approach enabling the identification of similar proteins with divergent sequences. The work undertaken by the authors adds to a growing body of work in plant-microbe research, predominantly from pathogenic organisms, and more recently in symbiotic fungi.

Reviewer #1 (Public Review):

The author found the nifuroxazide has the potential to augment the efficacy of radiotherapy in HCC by reducing PD-L1 expression. This effect may be attributed to increased degradation of PD-L1 through the ubiquitination-proteasome pathway. These evidences support the future application of nifuroxazide in the treatment of HCC.

Reviewer #2 (Public Review):

Summary:<br /> Zhao et al. aimed to explore an important question-how to overcome resistance of hepatocellular carcinoma cells to radiotherapy. Given that immune-suppressive microenvironment is a major mechanism underlying resistance to radiotherapy, they reasoned that a drug that blocks PD-1/PD-L1 pathway could improve efficacy of radiation therapy and chose to investigate the effect of Nifuroxazide, an inhibitor of stat3 activation, on radiotherapy efficacy in treating hepatocellular carcinoma cells. From in vitro experiments, they find combination treatment (Nifuroxazide+ radiotherapy) increases apoptosis and reduces proliferation and migration, in comparison to radiotherapy alone. From in vivo experiments, they demonstrate that combined treatment reduces size and weight of tumors in vivo and enhances mice survival. These data indicate a better efficacy of combination therapy compared to radiotherapy alone. Moreover, they also determined the effect of combination therapy on tumor microenvironment as well as peripheral immune response. Specifically, they find that combination therapy increases infiltration of CD4+, CD8+ t cells and NK cells, activates CD8+ t cells, enhances polarization of M1 macrophages and decreases Treg cells in the tumor microenvironment. These changes in tumor microenvironment is consistent with reduced tumor growth by combination therapy. The most intriguing part of the study is the determination of effect of Nifuroxazide on PD-L1 expression in the context of radiotherapy. Considering Nifuroxazide is a stat3 activation inhibitor and stat3 inhibition leads to reduced expression of PD-L1, one would expect Nifuroxazide decreases PD-L1 expression through stat3. However, they find the effect of Nifuroxazide on PD-L1 is dependent on GSK3 mediated Proteasome pathways and independent of stat3, in the given experimental context. To determine the relevance to human hepatocellular carcinoma, they also measured the PD-L1 expression in human tumor tissues of HCC patients pre- and post-radiotherapy. The increased PD-L1 expression level in HCC after radiotherapy is impressive.<br /> Overall, the data are convincing and supportive to the conclusions.

Strengths:<br /> 1) Novel finding: Identified novel mechanism underlying effect of Nifuroxazide on PD-L1 expression in hepatocellular carcinoma cells in the context of radiotherapy.<br /> 2) Comprehensive experimental approaches: using different approaches to prove same finding. For example, Fig4, both IHC and WB were used. Fig5. Both IF and WB were used.<br /> 3) Human disease relevance: Compared observations in mice with human tumor samples.

Reviewer #3 (Public Review):

Summary:<br /> In this study, the authors investigated the potential of nifuroxazide to enhance responsiveness to radiotherapy, employing both an in vitro cell culture system and an in vivo syngeneic mouse tumor model.

Strengths:<br /> The researchers conducted a series of experiments to elucidate the role of nifuroxazide in facilitating the radiotherapy-induced reduction of proliferation, migration, and invasion of HepG2 cells.

Weaknesses:<br /> The evidence supporting the claim that nifuroxazide contributes to the degradation of radiotherapy-induced upregulation of PD-L1 via the ubiquitin-proteasome pathway is still relatively weak.

Reviewer #1 (Public Review):

Summary:

The authors developed a deep learning method called H3-OPT, which combines the strength of AF2 and PLM to reach better prediction accuracy of antibody CDR-H3 loops than AF2 and IgFold. These improvements will have an impact on antibody structure prediction and design.

Strengths:

The training data are carefully selected and clustered, the network design is simple and effective.

The improvements include smaller average Ca RMSD, backbone RMSD, side chain RMSD, more accurate surface residues and/or SASA, and more accurate H3 loop-antigen contacts.

The performance is validated from multiple angles.

The revised manuscript has cleared my previous concerns.

Reviewer #2 (Public Review):

This work provides a new tool (H3-Opt) for the prediction of antibody and nanobody structures, based on the combination of AlphaFold2 and a pre-trained protein language model, with a focus on predicting the challenging CDR-H3 loops with enhanced accuracy than previously developed approaches. This task is of high value for the development of new therapeutic antibodies. The paper provides an external validation consisting of 131 sequences, with further analysis of the results by segregating the test sets in three subsets of varying difficulty and comparison with other available methods. Furthermore, the approach was validated by comparing three experimentally solved 3D structures of anti-VEGF nanobodies with the H3-Opt predictions

Strengths:

The experimental design to train and validate the new approach has been clearly described, including the dataset compilation and its representative sampling into training, validation and test sets, and structure preparation. The results of the in silico validation are quite convincing and support the authors' conclusions.

The datasets used to train and validate the tool and the code are made available by the authors, which ensures transparency and reproducibiity, and allows future benchmarking exercises with incoming new tools.

Compared to AlphaFold2, the authors' optimization seems to produce better results for the most challenging subsets of the test set.

Weaknesses:

The comparison of affinity predictions derived from AlphaFold2 and H3-opt models, based on molecular dynamics simulations, should have been discussed in depth. In some cases, there are huge differences between the estimations from H3-opt models and those from experimental structures. It seems that the authors obtained average differences of the real delta, instead of average differences of the absolute value of the delta. This can be misleading, because high negative differences might be compensated by high positive differences when computing the mean value. Moreover, it would have been good for the authors to disclose the trajectories from the MD simulations.

Reviewer #3 (Public Review):

Summary:<br /> The manuscript introduces a new computational framework for choosing 'the best method' according to the case for getting the best possible structural prediction for the CDR-H3 loop. The authors show their strategy improves on average the accuracy of the predictions on datasets of increasingly difficulty in comparison to several state-of-the-art methods. They also show the benefits of improving the structural predictions of the CDR-H3 in the evaluation of different properties that may be relevant for drug discovery and therapeutics design.

Strengths:<br /> Authors introduce a novel framework, which can be easily adapted and improved. Authors use a well defined dataset to test their new method. A modest average accuracy gain is obtained in comparison to other state-of-the art methods for the same task, while avoiding for testing different prediction approaches. Although the accuracy gain is mainly ascribed to easy cases, the accuracy and precision for moderate to challenging cases is comparable to the best PLM methods (see Fig. 4b and Extended Data Fig. 2), reflecting the present methodological limit in the field.

Weaknesses:<br /> The proposed method lacks of a confidence score or a warning to help guiding the users in moderate to challenging cases.

Reviewer #1 (Public Review):

Summary:<br /> This study presents a valuable finding on the increased activity of two well-studied signal transduction pathways - STAT-3 and TGF-Beta in a specific subtype of pancreatic cancer. Specifically, SMAD4 deficient tumors (commonly observed in pancreatic cancer) are well differentiated in the presence of STAT3. Yet surprisingly, in the presence of SMAD4 in a STAT-3 deficient pancreatic cancer, the phenotype is poorly differentiated in the background of KRASGD12D. The evidence in the animal models supporting the authors' claims is solid, although including TCGA data and/or a larger number of patients would have strengthened the study. The work will be of interest to medical biologists working on pancreatic cancer and potentially the broader field.

Strengths:<br /> Strengths are the animal models and the lead author's expertise in STAT3 signaling.

Weaknesses:<br /> Weaknesses are the absence of correlation between the results from the animal studies and human pancreatic cancers.

Reviewer #2 (Public Review):

Summary:<br /> This manuscript explores mechanisms by which STAT3 may regulate KRAS mutant cancers.

In the first set of experiments, STAT3 GOF mutants diminished the transformation of p53-null mouse embryonic fibroblasts expressing endogenous mutant KRAS(G12D) (KP MEFs) and this was dependent on direct transcriptional activation induced by phosphorylated STAT3. It appears that this is mediated via a reduction in TGFb signaling such that knockout of either TGFBR2 or SMAD4 can phenocopy the effects of STAT3 GOF mutants in KP MEFs.

In the next part of the paper, the authors used murine pancreatic ductal adenocarcinoma (PDAC)-derived cell lines bearing endogenous KRAS(G12D) and TP53(R172H) mutations (KPC) to determine the extent to which STAT3 may regulate KRAS dependency. They determined that KRAS and STAT3 KO both induced mesenchymal-like phenotypes and that TGFBR2 and SMAD4 KO induced epithelial phenotypes. The loss of STAT3 appeared to correlate with a KRAS-independent signature, and SMAD4/TGFBR2 KO could not induce epithelial phenotypes when STAT 3 was also knocked out.

Strengths:<br /> Overall, this is an interesting paper that highlights the complicated interactions between KRAS, STAT3, and TGF beta signaling. The authors use multiple models and attempt to link data to patient cohorts.

Weaknesses:<br /> While correlations are strong, the study would benefit from additional cause-and-effect type experiments. It would also be beneficial to better tie together the first and second parts of the paper.

Reviewer #1 (Public Review):

Summary:<br /> The manuscript by Chen et al. presents a detailed metabolic characterization of male and female WT and CTRP10 knockout mice. The main finding is that female KO mice become obese on both low-fat and high-fat diets but without evidence of marked insulin resistance, hepatic steatosis, dyslipidemia, or increased inflammatory markers. The authors performed a detailed transcriptomic analysis and identified differentially expressed genes that distinguish high-fat diet-fed CTRP10 KO from WT control mice. They further show that this set of genes exhibits cross-correlation in human tissues, and that this is greater in females than in males. The data indicate that the CTRP10 KO model may be useful to understand how obesity and metabolic dysfunction are coupled to each other, and how this occurs by a sex-biased mechanism.

Strengths:<br /> The work presents a large amount of data, which has been carefully acquired and is convincing. The transcriptomic analysis will further help to define what pathways are associated with obesity, but not necessarily with metabolic dysfunction. The manuscript will be of interest to investigators studying metabolic diseases, and to those studying sex-specific differences in metabolic physiology. The limitations of the study are acknowledged, including that a whole-body knockout was used. The cause of the increased body weight is not entirely clear, despite the careful and detailed analysis that was performed. Notwithstanding these limitations, the phenotype is interesting, and this work will establish a basis for further work to understand the mechanisms that are involved.

Weaknesses:<br /> Genes identified as DEGs in the mouse RNAseq data set were used to identify a set of human orthologous transcripts and the abundances of these transcripts were correlated with each other in Figure 10. This identified a greater correlation ("connectivity") in subQ adipose compared to other tissues, and in females compared to males. The description of how this analysis was done could be clearer. In some cases, the text refers to the software that was used without describing the goal of the analysis. In other instances, specialized terminology was used (e.g. "biweight midcorrelation") without defining what this means.

Reviewer #2 (Public Review):

Summary:<br /> In the current study, the authors investigated the role of loss of CTRP10 results in female obesity with preserved metabolic health. The overall conclusion is supported by the experimental data that CTRP10 negatively regulates body weight in females and that loss of CTRP10 results in benign obesity with largely preserved insulin sensitivity and metabolic health. The authors have shown the role of sex differences in the metabolically healthy obese (MHO) phenotype, which may increase the scope for research in this area.

Strengths:<br /> The study provides a detailed idea of how genes are regulated in a sex-dependent manner.

Weaknesses:<br /> Mechanistic details are missing.

Reviewer #3 (Public Review):

Summary:<br /> This study examines the impact of CTRP10/C1QL2 absence on obesity and metabolic health in mice. Female mice lacking CTRP10 tend to develop obesity, particularly on a high-fat diet. Surprisingly, they do not display the typical metabolic traits associated with obesity, like fatty liver or glucose intolerance. This indicates a disconnection between weight gain and metabolic issues in these female mice. The research underscores the need to understand sex-specific factors in how obesity influences metabolic health.

Strengths:<br /> The study provides compelling evidence regarding Ctrp10's role in female-specific metabolic regulation in mice, shedding light on its potential significance in metabolically healthy obese (MHO) individuals.

Weaknesses:<br /> -The analysis and description of sex-specific human data require more details to highlight the relevance of Ctrp10 mouse data and the analysis of differentially expressed genes in humans.<br /> -There's a lack of analysis regarding secreted Ctrp10 under various dietary conditions.<br /> -The study didn't assess adipose tissue function to evaluate metabolic health.

Reviewer #1 (Public Review):

Summary:

Bartolome et al. report adaptation of proximity labeling using BirA and TurboID fusions to proteasome subunits to identify the proteasome-proximal proteome both in cultured cells and also in a newly developed mouse model. Using this approach, the authors demonstrate identification of many known proteasome-interacting proteins, as well as several new proteins, some of which are validated directly. The authors further evaluate the proteasome-proximal proteome in most mouse organs, and find substantial agreement with the proteome identified from cultured cells, as well as between tissues. This represents one of the first studies of the "proteasome-ome" in vivo, and sets the stage for addressing numerous important future questions regarding how the proteasome's environment changes over time, in response to different stimuli, and in distinct disease conditions.

Strengths:

Generally speaking, the approach provided is rigorous and supported by several complementary lines of evidence, such as demonstration that the interactome is enriched for known proteasome-binding proteins and co-purification or co-elution experiments. Similarly, the high agreement between the outcomes in cultured cells and in the mouse model developed by the authors provides further confidence in the results.

Weaknesses:

The major weakness of the work is arguably the choice of proteasome subunits for tagging with biotinylating enzymes. In most cases, the subunits and termini chosen for tagging are known to either protrude toward functionally important regions (such as the substrate-processing pore of the ATPase component), to have important functional roles likely to be disrupted via tagging, or are subunits known to be substituted by others in some conditions. Thus, the interactome reported may conflate those of normal proteasomes with those harboring tag-induced functional or structural defects. Although the authors made a commendable attempt to demonstrate minimal impacts of tagging, the conclusions would be greatly further strengthened by contrasting the impacts of tagging subunits less likely to cause perturbations and by more rigorously demonstrating normal proteolysis of a broader array of known proteasome substrates.

Reviewer #2 (Public Review):

Summary

In this work, Bartolome and colleagues develop a new approach to identify proteasome interacting proteins and substrates. The approach is based on fusing proteasome subunits with a biotin ligase that will label proteins that come in close physical distance of the ligase. These biotin-labeled proteins (or their resulting tryptic peptides) can be affinity purified using streptavidin and identified by mass spectrometry.

This elegant solution was able to identify a large proportion of known proteasome interactors, as well as multiple potential new interactors. Combining this approach with a proteasome inhibitor allowed also for the enrichment of substrates, due to increased contact time between substrates and the proteasome. Again, the authors were able to identify novel substrates. Finally, the authors implemented this strategy in vivo, providing the hints for potential tissue-specific proteasome interactors.

This novel strategy provides an additional approach to identify new proteasome substrates, which can be particularly powerful for low abundant proteins, e.g., transcription factors. The possibility to implement it in vivo in specific cell types opens the possibility for identifying proteasome interactors in small cell subpopulations or in subpopulations involved in disease.

Strengths:

The authors carefully characterized their genetically engineered proteasome-biotin ligase fusions to ensure that proteasome structure and activity was not altered. This is key to ensure that the proteins identified to interact with the proteasome reflect interactions that occur under physiological conditions.

The authors implemented an algorithm that controls the false positive rate of the identified interactors of the proteasome. This is an important aspect to avoid spending time on the characterization of potential interactors that are just an artifact of the experimental setup.

The addition of a proteasome inhibitor allowed the authors to identify substrates of the proteasome. Although there are other strategies to do this (e.g., affinity purification of Gly-Gly modified peptides, which is a marker for ubiquitination), this additional approach can highlight currently unknown substrates. One example are low abundance proteins, such as transcription factors.

The overall strategy developed by the authors can be implemented in vivo, which opens for the possibility of determining cell type-specific proteasome interactors (and perhaps substrates).

Weaknesses:

There is a small proportion of the PSMA4-biotin ligase fusion that remains unassembled (i.e., not part of the functional proteasome) and that can contribute to a small proportion of false positive interactions.

Reviewer #3 (Public Review):

Summary:

Bartolome et al. present ProteasomeID, a novel method to identify components, interactors, and (potentially) substrates of the proteasome in cell lines and mouse models. As a major protein degradation machine that is highly conserved across eukaryotes, the proteasome has historically been assumed to be relatively homogeneous across biological scales (with few notable exceptions, e.g., immunoproteasomes and thymoproteasomes). However, a growing body of evidence suggests that there is some degree of heterogeneity in the composition of proteasomes across cell tissues, and can be highly dynamic in response to physiologic and pathologic stimuli. This work provides a methodological framework for investigating such sources of variation. The authors start by adapting the increasingly popular biotin ligation strategy for labelling proteins coming into close proximity with one of three different subunits of the proteasome, before proceeding with PSMA4 for further development and analysis based on their preliminary labelling data. In a series of well-constructed and convincing validation experiments, the authors go on to show that the tagged PSMA4 construct can be incorporated into functional proteasomes, and is able to label a broad set of known proteasome components and interacting proteins in HEK293T cells. They also attempt to identify novel proteasomal degradation substrates with ProteasomeID; while this was convincing for known substrates with particularly short half-lives (exemplified by the transcription factor c-myc), follow-up validation experiments with other substrates were less clear. One of the most compelling results was from a similar experiment to confirm proteasomal degradation induced by a BRD-targeting PROTAC, which I think is likely to be of keen interest to the targeted degradation community. Finally, the authors establish a ProteasomeID mouse model, and demonstrate its utility across several tissues.

Strengths:

1) ProteasomeID itself is an important step forward for researchers with an interest in protein turnover across biological scales (e.g., in sub-cellular compartments, in cells, in tissues, and whole organisms). I especially see interest from two communities: those studying fundamental proteostasis in physiological and pathologic processes (e.g., ageing; tissue-specific protein aggregation diseases), and those developing targeted protein degradation modalities (e.g., PROTACs; molecular glues). All the datasets generated and deposited here are likely to provide a rich resource to both. The HEK293T cell line data are a valuable proof-of-concept to allow expansion into more biologically-relevant cell culture settings; however, I envision the greatest innovation here to be the mouse model. For example, in the targeted protein degradation space, two major hurdles in early-stage pre-clinical development are (i) evaluation of degradation efficacy across disease-relevant tissues, and (ii) toxicity and safety implications caused by off-target degradation, e.g., of newly-identified molecular glues and/or in particularly-sensitive tissues. The ProteasomeID mouse allows early in vivo assessment of both these questions. The results of the BRD PROTAC experiment in 293T cells provides an excellent in vitro proof-of-concept for this approach.

2) The mass spectrometry-based proteomics workflows used and presented throughout the manuscript are robust, rigorous, and convincing. For example, the algorithm the authors use for defining enrichment score cut-offs are logical and based on rational models, rather than on arbitrary cut-offs that are common for similar proteomics studies. The construction (and subsequent validation) of both BirA*- and miniTurbo- tagged PSMA4 variants also increases the utility of the method, allowing researchers to choose the variant with the labelling time-scale required for their particular research question.

3) The optimised BioID and TurboID protocol the authors develop (summarised in Fig. S2A) and validate (Fig. S2B-D) is likely to be of broad interest to cell and molecular biologists beyond the protein degradation field, given that proximity labelling is a current gold-standard in global protein:protein interaction profiling.

Limitations:

I think the authors do an excellent job in highlighting the limitations of ProteasomeID throughout the Results and Discussion. I do have some specific comments that might provide additional context for the reader.

1) The authors do a good job in showing that a substantial proportion of PSMA4-BirA* is incorporated into functional proteasome particles; however, it is not immediately clear to me how much background (false-positive IDs) might be contributed by the ~40 % of PSMA4-BirA* that is not incorporated into the mature core particle (based on the BirA* SEC-MS traces in Fig. 2b and S3b, i.e., the large peak ~ fraction 20). Are there any bands lower down in the native gel shown in Fig. 2c, i.e., corresponding to lower molecular weight complexes or monomeric PSMA4-BirA*? The enrichment of proteasome assembly factors in all the ProteasomeID experiments might suggest the presence of assembly intermediates, which might themselves become substrates for proteasomal degradation (as has been shown for other incompletely-assembled protein complexes, e.g., the ribosome, TRiC/CCT).

2) Although the authors attempt to show that BirA* tagging of PSMA4 does not interfere with proteasome activity (Fig. 2e-f), I think the experimental evidence for this is incomplete. They show that the overall chymotrypsin-like activity (attributable to PSMB5) in cells expressing PSMA4-BirA* is not markedly reduced compared with control BirA*-expressing cells. However, they do not show that the activity of the specific proteasome sub-population that contains PSMA4-BirA* is unaffected (e.g., by purifying this sub-population via the Flag tag). The proteasome activity of the sub-population of wild-type proteasome complexes that do not contain the PSMA4-BirA* (~50%, based on the earlier immunoblots) could account for the entire chymotrypsin-like activity-especially in the context of HEK293T cells, where steady-state proteasome levels are unlikely to be limiting. It would also be useful to assess any changes in tryspin- and caspase- like activities, especially as tagging of PSMA4 could conceivably interfere with the activity of some PSMB subunits, but not others.

3) I was left unsure of the general utility of ProteasomeID for identifying novel proteasomal substrates in homeostatic or stressed conditions. The immunoblots for the two candidates the authors follow up in Fig. 4g was not especially clear; the reduction in the bands are modest, at best. Furthermore, classifying candidates based on enrichment following proteasome inhibition with MG-132 have the potential to lead to a high number of false positives. ProteasomeID's utility in identifying potential substrates in more targeted settings (e.g., molecular glues, off-target PROTAC substrates) is far more apparent.