Reviewer #3 (Public Review):

The tRNA ligase complex participates in protein (endonuclease & ligase)-mediated RNA splicing, which contrasts with the better known RNA-mediated splicing in the context of the spliceosome. Protein-mediated RNA splicing may date back to the ancient RNA-protein world where it might have served to defend RNA against invading introns. RTCB-like RNA ligases such as found in the human tRNA ligase complex act in numerous cellular pathways including bacterial RNA repair, and they follow a highly interesting and complex but structurally poorly understood mechanism that joins RNA 5'-hydroxyl ends with 3'-ends that carry a 2'-3' cyclic phosphate. Although the components of the human tRNA ligase complex are known and although there is crystal structural information on archaeal RTCB homologs, the function of the non-enzymatic components and the structural organization of the complex are unknown.

The quality of the data in Kroupova et al. and their presentation in the manuscript is outstanding, which is easy to follow even for general readers. The authors carefully avoid to over-interpret their cross-linking data, although some remarks on this as outlined in my detailed remarks may benefit readers who are less familiar with this method. The methods are described with exceptional detail and information and will be extremely useful to scientists who plan similar approaches for their own protein complexes of choice and especially if these are still too undefined and/or flexible to be amenable for direct structural analysis by X-ray crystallography or single-particle cryo-electron microscopy.

This is a prime example for an analytic biochemical approach with modern methodology to a challenging problem in structural biology.

Reviewer #1 (Public Review):

The manuscript addresses the important question about the inner mechanics that makes photoreactions of vertebrate rhodopsins non-cyclic and include the release of the isomerized retinal whereas invertebrate rhodopsins undergo cyclic reactions or may switch forward and backward by light ( bistable rhodopsins).

Experiments are well done and convincing, clearly documented and presented by the figures.

The discussion of the interpretation why G188C mutant prevents retinal release during the Meta II state and its thermal or photochemical anti/syn isomerization could be done much better. Many invertebrate rhodopsins do not release the retinal during the Meta II state and do not have any Cys residue at this location. Moreover, some of them deprotonate during the meta state and others do not. The authors are experts in invertebrate rhodopsins as well and privileged to give a better interpretations.

Next, the suggestion that Cys causes transient thioadduct formation during the photocycle is not justified by anything and should be done with more care. For example a Cys at a similar position has been found in microbial Channelrhodopsins (ChRs) and has been shown to be critical for the photocycle kinetics and for anti/syn isomerization. The sulfur of the Cys seems to act as a nucleophile for retinal polyene chain, forming a thioadduct as in OPN5L but not in other rhodopsin. The Cys obviously influences the charge distribution in darkness and during the excited state. Moreover, residues in the active site including E113, E188 and other residues could act as steric constrains that influence the isomerization specificity as well.

Reviewer #2 (Public Review):

The studies of the non-visual pigment Opn5L1, that preceded this study, suggested a very interesting mechanism and it indicated bistability of the pigment. Starting from these insights, the authors attempted successfully to transfer key properties of Opn5L1 to rhodopsin. Their data clearly indicates that a mutation of a glycine residue at position 188 of rhodopsin to a cysteine residue, which is well conserved in Opn5L1 related photo pigments, makes the retinal protein photo cyclic and photo reversible. This indicates that residue 188 contributes to the diversification of photoreactions in opsins. The spectroscopic data in combination with signalling assays and the determination of retinal isomers fully support the claims of the authors. The photo reversibility is shown by using UV light illumination that is clearly increased in the mutated protein over the wild type protein. In addition, the slow thermal reversion can be unambiguously derived from the provided data. To achieve these experimental results, it was essential to use a stabilization strategy of the opsins. Similar strategies have been previously used to study retinal uptake in retinitis pigmentosa mutations and rhodopsin structures successfully. Stabilization is introduced by the double mutation N2C/D282C which crosslinks the extracellular N terminal domain to the receptor. It is known that this modification does not result in significant thermos stabilisation and no interference with spectroscopic properties and light activation. The presented work really proofs that the equivalent residue in Opn5L1 to residue 188 in rhodopsin is very important for the bistable nature of this retinal pigment. The study highlights a very important link between invertebrate bistable pigments and our vertebrate visual pigment and it makes it likely that our low light sensitive optimized GPCRs have evolved from the more ancient invertebrate-like bistable pigments. This is an outstanding scientific achievement for our understanding of the visual pigments. The study also has implications for our understanding of bistable pigments, and for the engineering of retinal proteins for optogenetic applications. The work is very well executed and the data is justifying the scientific conclusions.

Reviewer #1 (Public Review):

The current manuscript investigates the mechanisms of DTT toxicity in C. elegans. In a veritable detective story, the authors show that developmental DTT toxicity is determined by the bacterial food source. They realize that the toxicity might be linked to vitamin B12 content of the food and can indeed show that low B12 levels in OP50 bacteria lead to the strongest DTT toxicity while their data suggest that wild-type worms on high B12 bacteria are protected against DTT toxicity. Indeed, B12 supplementation suppresses DTT toxicity on OP50 bacteria and this is dependent on a functional methionine synthase gene. The authors then perform a forward genetic mutagenesis screen to identify DTT resistance loci and hone in on a particular locus encoding a SAM-dependent methyltransferase they name drm-1. drm-1 loss of function protects against DTT toxicity providing support to the idea that it is the depletion of SAM that leads to DTT toxicity in worm development. This is further supported by methionine and choline supplementation experiments. Finally, the authors address the relative contribution of ER stress and SAM depletion in the DTT developmental resistance. Interestingly, they find that UPR signaling mutants affect become DTT hypersensitive only at high but not at low DTT levels. This suggests that SAM depletion is responsible for DTT toxicity at lower concentrations while only at high DTT levels, its effect on the ER becomes toxic.

In all, this is a well-executed paper that is clear and well written. The finding is relevant as it sheds new light on the DTT mechanism, which is broadly considered an ER stressor acting on disulfide bond formation, which needs to be reconsidered now. The DTT effect on SAM is surprising and important.

Reviewer #2 (Public Review):

Gokol et al. use C. elegans as a model to explore links between stress caused by the compound DTT, diet and growth. They show that effects of DTT toxicity are dependent on diet and link vitamin b12 and the met/SAM cycle through dietary rescue and use of Met/SAM cycle mutants. We do not find that the authors results support their claims. Since DTT is a compound used in labs to induce ER stress and is not naturally present, the general impact is lessened.

Strengths:<br> 1) C. elegans is a good model for investigating links between stress and diet.<br> 2) This work includes a mutant screen for animals that regain viability on DTT

Weaknesses:<br> 1) DTT can affect protein folding in general. While it clearly induces ER stress by disrupting protein folding, it could be affecting a myriad of other processes. Although the authors have a figure to show that DTT toxicity appear to correlate with acdh-1 expression, acdh-1 is part of a pathway that detoxifies propionate (multiple papers from the Walhout lab). Thus, the idea that there is a specific link between the Met/SAM cycle is difficult to sustain. The authors also show that both ER and mito stress reporters are activated, showing the non-specificity of the stress response. An alternate possibility is that SAM is necessary for this histone modifications to activate the stress response to DTT, this is not explored experimentally.

Also, Figure 1F is mostly data that has been published previously by the Walhout lab (Watson et al. Cell 2014). Although paper is cited early that section of the results, this figure simply re-presents the previously published data which is not cited in the figure legend or when the data is directly discussed.

As the Apfeld lab (Schiffer, et al. 2020, eLife) have also recently shown, different bacteria can produce metabolites that affect oxidative stress phenotypes, thus conclusions based on dietary effects are complex.

2) Although the authors isolate a mutant encoding a putative methyltransferase that is resistant to DTT toxicity, this is of limited use as there is no data showing what this methyl transferase does. Figure 3 also shows the development of drm-1 only on DTT, no wild type is shown.

3) The authors show that methionine rescues DTT effect in wt, and metr-1 backgrounds, but not sams-1. This could also be due to multiple effects. sams-1 animals have defects in membranes, that have not been reported in metr-1. Thus, DTT could simply be more toxic to these animals.

4) The partial choline rescue was done at 80mM, this is much higher than the previously published amounts (30mM, Brendza, et al. 2007). Even at this high level of choline, the rescue is partial, which brings the rescue in question.

Reviewer #3 (Public Review):

This manuscript studies mechanisms of DTT toxicity in C. elegans, using larval development as readout. The authors find that DTT is not toxic to C. elegans when exogenous vitamin B12 is provided i.e. animals successfully develop. This depends one only one of the two B12 dependent enzymes, methionine synthase metr-1 but not on MMCoA dismutase mmcm-1. A forward genetic screen for mutations that suppress DTT toxicity identified 12 alleles in drm-1 (R08E5.3). An independently generated mutation in drm-1 also showed resistance to DTT, and this was blocked by expression of drm-1 from its own promoters. mRNA of drm-1 and of its homologs R08E5.1, R08F11.4, but not K12D9.1, are induced by DTT. Using metabolite supplementation and mutant analysis, the authors pinpoint SAM deficiency as the key consequence of DTT exposure; in part, this is rescued by choline, suggesting PC deficiency as a key issue. Because ER stress is linked to the 1-carbon cycle, the authors next studied the UPR and found that its activation by DTT is reduced by B12, Met, or choline. Functionally, ire-1 and xbp-1 mutation, but none of the other UPR genes tested, rescued the developmental delay, but only at intermediate (5mM) concentration of DTT, not at a high concentration. The authors propose a model whereby DTT activated drm-1 expression causes SAM depletion, which contributes to DTT toxicity and results in larval arrest.

The mechanisms identified here is to my knowledge novel and appears very interesting. The experiments in this manuscript are well done and well controlled, and the authors' conclusions are (mostly) well justified by the data. The study provides new insights into the action of DTT toxicity, and pinpoints drm-1 as a new gene implicated in thiol resistance; identifying 12 alleles is extremely compelling as to the key role of this gene (but see below on other methylases). The paper is also well written and explains well the rationale and the reasoning behind the experiments.

However, I think the authors need to measure SAM levels in the various contexts to actually support the main conclusion drawn her. They also should examine more broadly both the role of thiol agents as well as of methylases related to drm-1, to better define what the specificity of the discovery pathway is, as well as probe more deeply into the role of drm-1 function.

Reviewer #1 (Public Review):

Cohesin complex is involved in both sister chromatid cohesion (SCC) and intra-chromatid loop formation. Combined of molecular genetic and cytological tools with genome-wide calibrated ChIP and HiC analyses, the authors elegantly showed that Eco1, thus, Smc3 acetylation, promotes the boundary formation of the chromatin loop by the cohesin, which is critical for both meiotic recombination in prophase I and sister chromatid segregation in meiosis II. This role in the boundary formation is independent of its role in SCC. However, it still remains to be solved how Eco1-mediated Smc3 acetylation stabilizes the cohesin to convergent transcription sites for the boundary formation at a molecular level, due to the lack of biochemical analysis of the acetylated cohesion complex in loop-extrusion activity.

This paper discloses new findings on regulation/functions of meiotic cohesion complex at two distinct chromosome regions in meiosis: the centromere and chromosome arm (peri-centromeric borders). The experiments are of good quality and the results are very much convincing.

Reviewer #2 (Public Review):

This manuscript by Barton and colleagues explores the roles of the conserved Eco1 transacetylase in modulating cohesin function in meiosis in budding yeast. Numerous studies in mitotically dividing cells have shown that the Eco1 family of transacetylases acetylate the Smc3 subunit of cohesin and that this acetylation renders cohesin on chromosomes resistant to removal by the Wapl (Wpl1 in budding yeast) family of proteins. Cohesins play critical roles in both sister chromatid cohesion and chromatin organization (through the formation of intrachromosomal loops). How cohesins are regulated by Eco1 in meiosis to accommodate meiotic chromosome structures such as the synaptonemal complex, chromatin domains around centromeres, repair of programmed meiotic double strand DNA breaks in prophase, and sequential removal of cohesins - first at arms in meiosis I and centromeres at meiosis II - is largely unexplored. Thus, this manuscript is exploring important new areas.

The authors show that Eco1 persists thru prophase I (longer than it does in vegetative cell cycles), that it is not necessary for cohesin loading at centromeres but is needed to counteract Wpl1 to protect centromeric cohesion, that it is critical for the establishment of chromatin loops on meiotic chromosome arms and that it is critical for protection of the arm cohesin from removal by Wpl1. The authors also provide evidence that, in meiosis, Wpl1 exhibits underappreciated functions in cohesin loading or cohesion establishment in addition to its recognized role in cohesin removal.

The experiments demonstrate that Eco1 is necessary for sharp cohesin boundaries that flank the centromeres and suggest this might be a replication-independent function of Eco1 (the boundaries form in clb5, clb6 cells with no DNA replication phase) but it is unclear if the detectable, but diminished, boundaries in clb5,clb6 cells were formed in the replication-free meiosis or presist from the S-phase associated loading and cohesion establishment from the preceding mitotic cycle.

Immunofluorescence imaging assays are used to observe the behavior of sister chromatids in meiosis I and meiosis II as a function of Eco1 activity. In wild-type cells sister chromatids co-orient in meiosis I and move to the same pole of the spindle. In mammalian cells and fission yeast this co-orientation requires cohesin while studies in budding yeast have suggested the co-orientation is cohesin-independent. Here, the authors show that when Eco1 is depleted, the sisters often move to opposite poles at meiosis I, and suggest that cohesin (and Eco1) is indeed required for sister co-orientation. An alternate possibility is that the sisters have lost their association in meiotic prophase (due to cohesin failures) before attaching to microtubles and segregating randomly - often to opposite poles.

In summary the authors show that Eco1 has distinct roles on chromosome arms and centromeres and probably in both replication-linked and replication-independent events, acts to modulate cohesin location and function in meiosis.

Reviewer #3 (Public Review):

This paper investigates the meiotic roles of two regulators of cohesin, the cohesin destabilizer Wpl1 and the cohesin acetyltransferase Eco1. The authors provide evidence that Eco1 antagonizes Wpl1 to allow stabilization of centromeric cohesin, which is important to establish meiotic chromosome segregation patterns. In addition, Eco1 regulates the stable anchoring of cohesin at boundaries to promote defined chromosome loop formation in meiotic prophase.

The study uses a combination of calibrated ChIP-seq analysis, and chromosome conformation capture techniques to convincingly show that loop formation is altered in wpl1 depletion and eco1 depletion mutants. Well-established cytological techniques are used to demonstrate different effects on chromosome cohesion along arms and at centromeres, and to show that Eco1 is important for establishing the meiotic segregation pattern. The paper is well written and the data largely support the conclusions. As such, this paper is expected to be of substantial interest to the field.

One notable weakness is the poor definition of the eco1 anchor-away allele (eco1-aa), on which much of the eco1 phenotypic analysis is based. The presented data indicate that addition of the FRB-GFP tag alone causes most of the phenotypes, regardless of nuclear depletion. It is well possible that the tag creates a meiosis-specific loss-of-function allele, although it is surprising that the tag does not have mitotic defects even though Eco1 presumably has the same substrate (the cohesin subunit Smc3) in both situations. Encouragingly, some of the phenotypes could be confirmed using a non-acetylatable smc3 mutant. However, the tag may also create neomorphic effects that may contribute to the Wpl1-independent effects and the apparent stronger defects of the eco1-aa allele compared to the non-acetylatable smc3 mutant.

Reviewer #1 (Public Review): 

The paper consists of two parts. 

The first part deals with "the three pathways to division of labour". It builds on a mathematical model showing that division of labour can evolve when (1) there are accelerating returns for individuals from specialisation, (2) there are pre-existing differences between individuals such that some individuals are predisposed for one task or the other, and (3) there is reciprocal specialisation leading to synergistic efficiency benefits at the group level. This model recovers the findings by Rueffler et al. 2012. This part is very well written and reads more like a review, bringing specialised and non-specialised readers onto the same page. 

The second part focusses on pathways (2) and (3), where individual returns can be diminishing, and thus the evolution of division of labour is more difficult to explain. This part is motivated by a recent paper by Yanni et al. 2020 in eLife showing that topological constraints (e.g. small network of individuals, limited number of neighbours) are essential to favour division of labour with diminishing returns. The authors challenge this view and provide an in-depth analysis on topological constraints. They show that such constraints in combination with pre-existing differences between individuals and reciprocal specialisation are indeed conducive for the evolution of division of labour, but not essential. They show that when the benefit of cooperation is larger for neighbours than for the cooperator than division of labour can evolve with diminishing returns even in the absence of topological constraints. This is a key new insight. But perhaps even more important, the authors highlight that pathways (2) and (3) rely on the assumption that individuals have access to information from neighbours to coordinate their actions at the group level. The authors show that division of labour cannot evolve with diminishing returns when such information is absent. And because mechanisms of information collection and coordination are likely to only evolve once division of labour is already in place, the authors argue that topological constraints might play a minor role in driving the initial evolutionary steps towards division of labour. 

In brief, this is a very insightful paper and significantly advances our theoretical and conceptual understanding of division of labour. It will spur future theoretical and empirical work in the field, and for the latter, the authors present guidelines of how to test the theory.

Reviewer #2 (Public Review): 

This is a very interesting paper on the evolution of division of labor. In particular, the authors explore the impact of topology, the convexity and concavity of fitness returns on investment, and different biological 'modalities' through which division of labor may arise. This is a difficult topic to study as, in most lineages, division of labor evolved long ago, and thus cannot be directly studied in the lab. Further, many theoretical predictions have proven difficult to directly test. This manuscript furthers our understanding of the underlying theory of the evolution of division of labor, and presents a means to test which modality is responsible for the emergence of division of labor in different cases. 

However, there are a few caveats worth mentioning. Comparisons to previous works are not always clear. Different models built with different assumptions can produce different predictions; however, that does not mean they disagree, only that they describe different scenarios. Further, the model used here allows an entity to give away all of its 'viability,' making it unclear how it continues to live and function. Finally, the order in which division of labor and 'patterning' evolve is presented as definitive, when it is ultimately a postulate.

Reviewer #1 (Public Review):

The authors have set out to define the role of integrins in macrophage function using in vitro and in vivo analysis and conditional/inducible knockout mice. The authors use in vitro systems to investigate chemotaxis in a 3D environment, an in vitro model for efferocytosis based on phosphatidylserine coated beads, and in vivo investigation of efficient efferocytosis in lymph nodes. The authors have demonstrate that macrophages can chemotax in a 3D collagen gel without integrins, although the WT and KO cells are slower than dendritic cells or neutrophils. Integrins were similar not required for macrophages to move towards find me signals generated by laser injury. However, profound morphological changes and defects in efferocytosis are noted in vitro and in vivo. The in vivo system shows that defect in haptokinesis based largely on beta1 integrin function are responsible for the efferocytosis defect in vitro. As macrophages are tissue resident cells that often display a "mesenchymal" like morphology and play an important homeostatic role in clearance of apoptotic cells the demonstration of a clear and selective role of integrins in these processes is biologically important.

Reviewer #2 (Public Review):

In this study, the authors use live imaging and both in vitro and in vivo approaches to investigate how a lack of integrin function affects macrophage motility and how these highly adaptive cells can compensate for a loss of adhesiveness within a complex three dimensional environment. They also investigate how the shape and mode of motility used by an individual cell can influence the efferocytic ability of a macrophage. This is a fascinating question and builds on recent evidence that macrophages have evolved mechanisms to ensure efficient efferocytosis within the many different environments in which a they find themselves in vivo. The imaging is excellent and the data of high quality. However, not all the conclusions are supported by the experimental results in their current format and alternative interpretations cannot be ruled out.

Reviewer #3 (Public Review):

In this study, Paterson and Lämmermann examine the molecular basis underlying the scanning and efferocytic behavior of tissue macrophages. In fact, while efferocytosis is central for tissue homeostasis, there has been thus far a surprising lack of information on the strategies employed by macrophages during this process. Using an elegant combination of in vitro/in vitro imaging, as well as genetic and pharmacological manipulation of macrophages, the authors uncover a central role for the beta1 - talin signaling axis to promote haptotactic motility and clearance of apoptotic bodies. This is in contrast to the largely integrin-independent motility shown by dendritic cells and other leukocytes. The authors further report that macrophages display some flexibility in their efferocytosis strategies, by regulating either baseline motility or via extension of long protrusions.

Altogether, the work performed by the authors is comprehensive, complete and elegantly presented, and the data interpretation is consistent with the results.

Reviewer #2 (Public Review):

Tendler et al present carefully planned and well-executed resource offering the field access to a set of unique datasets. Highlights include high-resolution post-mortem diffusion data and PLI in humans along with images from a diverse range of primates and other species including the extinct thylacine. For samples where post-mortem data are available, the imaging data has been co-registered to facilitate cross-modal comparisons. The tailor-made online Tview tool enables easy initial visual inspection of the various datasets, including transparent layering of the various modalities. The Digital Pathologist theme is a particular asset as open human pathology datasets are still rare. Here the theme is currently limited to a single pathology - ALS - with no description of future planned studies expanding this to other pathologies. Hopefully the publication of the platform and resource will help to inspire the curation of similar datasets for other neurological/psychiatric diseases. There are additional data releases planned in the near future to include a multimodal macaque dataset and neonatal diffusion data.

Overall the Digital Brain Bank's online platform and openly shared datasets represent a valuable resource to the neuroscientific community.

I have a few minor comments that I believe would strengthen the manuscript:

- There is a strong focus on diffusion-based imaging and corresponding PLI microscopy, which reflects the expertise and interests of the authors as well as addressing a gap in what other post-mortem datasets are currently available. It might be useful to place the Digital Brain Bank in the context of a few of these resources and platforms, such as the Allen Institute, which focuses on transcriptomic data, and Human Brain Project/BigBrain, which is predominantly histological.<br> - It's a major strength that the MRI and histology have been coregistered and the online tool to view them is intuitive to use. The authors have chosen here to present the MRI data co-registered to the 2D histology and not vice versa. Sectioning can introduce morphological shifts in the tissue that might alter neuroanatomical findings relative to the original brain structure. Could the authors add a note to explain why they chose to present the registration this way round?

Reviewer #1 (Public Review):

In this paper, the authors describe a MRI-based functional connectivity mode for the striatum and attempt to show that it is related to dopaminergic input from the midbrain. Currently, dopaminergic input can only be assessed in humans with radionuclide imaging modalities (PET and SPECT), which have poor spatial resolution, relatively long acquisition times, and require radioactive tracers. The MRI-based method would provide higher resolution and greater accessibility, and moreover, can be applied retrospectively to data that has already been collected. The authors use multiple lines of study to build the case: comparison to DaT SPECT, which shows the distribution of dopamine transporters; alteration in Parkinson's Disease, where dopaminergic input is known to be reduced; and relation to alcohol and tobacco use in healthy volunteers, where dopamine signalling in the brain's reward processing pathway is altered.

The combination of clinical, behavioral and imaging experiments to validate the MRI biomarker of dopamine input is the major strength of this study. Not only is the biomarker altered as expected in each case, but the alterations also exhibit regional specificity that is consistent with prior reports often obtained with invasive measurements. A direct validation of the biomarker would require invasive histology that is clearly impossible in healthy humans, but while any single finding from one modality would be less convincing, taken together, they provide sufficient circumstantial evidence to motivate further use and investigation of the biomarker. The authors use quantitative techniques to characterize the change in the functional connectivity mode and find truly impressive correspondence with the SPECT measurements of DaT at the group level. As expected, the correspondence is weaker at the individual level, but still respectable. The authors show substantial individual data throughout the manuscript in addition to the group data, which increases confidence in both their results and the potential utility of the biomarker in the clinic. For example, the relationship between symptom severity after L-DOPA and changes in the biomarker at the individual level is very encouraging.<br> The least convincing aspect of the manuscript was the relationship between the connectivity mode and the amount of tobacco use (Fig 6, top) where the line fit looks as if it may have been driven by two very high use points. Given the strength of the other findings, even if the relationship with tobacco use does not completely hold up, it detracts very little from the overall study. The lack of a difference in the biomarker between the left-dominant Parkinson's group and the control group is also a bit surprising. Given the discussion about flooring effects, it may be a power effect, but it definitely warrants more investigation in the future.

Reviewer #2 (Public Review):

This is an excellent paper with an excellent outstanding methodology and sequence of steps which contains many strengths:

- First, they apply a novel fMRI resting state functional connectivity method, connectopic mapping (CM). This is validated in a large standard data set, the HCP fMRI, in around 800 healthy subjects.<br> - Secondly, they use the measurement of a striatal DA transporter, DaT SPECT, in a large number of subjects (around 200) to establish spatial correlation with fMRI connectopic mapping.<br> - Thirdly, they measure subjects where striatal dysfunction is known to be altered. Parkinson disease (PD) with L-Dopa therapy; this serves the purpose to the direct impact of dopamine deficiency (D2-receptors) and dopamine replacement therapy (L-Dopa) on striatal connectopic mapping<br> - Fourthly, they further support that by scanning people with daily alcohol or nicotine consumption whose degree of substance use corelates with the striatal connectopic mapping.

Some weaknesses shall be mentioned:

- I was wondering how their striatal DA connectopic marker stands in relation to others like melatonin-sensitive MRI (Cassidy et al.PNAS and others). This should at least be discussed. Ideally, they do melatonin scanning in their sample and correlate it with their striatal connectopic marker. This would provide the opportunity to more directly validate their marker.<br> - Another issue is the biochemical specificity. The striatum contains also many glutamatergic (medium spiny) and gaba-ergic neurons which are key in mediating DA effects as the latter (as far as I know) terminate on the former. Moreover, it is known that rsFC is related to excitation-inhibition balance and thus to glutamate-GABA. How can the authors make sure that their cortical conenctopic maps are really related specifically to DA rather than glutamate and/or GABA? This is even more urgent given that we know glutamate changes in alcohol and/or smoking and also in PD to be prevalent.<br> - It would be good if this issue of specificity could be addressed. Like in people who receive ketamine (anti-NMDA): if the authors' connectopic marker is specific for striatal DA, it should not be changed under NMDA treatment.<br> - Another way is to conduct computational modelling: modulation of glutamate/GABA should ideally not affect the striatal cortical connectopic marker....<br> - Some key literature should be cited and discussed: Conio et al. 2020 establishes a model of DA projects and their implications for psychiatric disorders<br> - Yet another issue is the question for serotonin. Various papers by Marinto/Magioncalda in especially bipolar disorder recently established modulation of nigral D2 by raphe-based serotonin. This should be discussed at least: Could the connectopic marker be related to such modulation? How could they make sure that their marker is related exclusively to cortical D2 projections rather than cortical serotonine effects? I am aware that these are tough questions but they should at least be addressed in the discussion...<br> - Moreover, the striatum is a complex region with subdivisions like dorsal and ventral which again can be featured by different dopamine systems (D2 vs D1/5) - this should be probed in their data to enhance specificity for nigral-based D2 of their connectopic marker....

Reviewer #3 (Public Review):

The study provides an impressive breadth of analyses, including comparisons to SPECT imaging, Parkinson's patients, drug manipulation and behavior, which build to form a compelling case that the identified patterns of functional connectivity. The surface modeling approach employed provides an interesting alternative to more standard parcellation approaches, which highlights the possibility that organization with the striatum occurs along gradients, rather than within functionally or anatomically circumscribed regions. Importantly, the findings have potentially wide-ranging implications and applications, since striatal dopamine (DA) and cortico-striatal connectivity are of great interest across a wide variety of fields, including their variation across the lifespan, disruption in various clinical populations, and contribution to normative behaviors.

While the surface modeling approach has some appealing features, it is a rather complex approach that is hard to understand intuitively. The difficultly to grasp its nuances limited my ability to follow some of the interpretations provided. For example, an important aspect of the results is that only the second order mode of the functional connectivity profile (and not the 0th or 1st order modes) are associated with dopamine measures and manipulations, but I found it difficult to assess what these different modes are capturing. Are these overlapping modes of distinct aspects of connectivity (each of which is expressed to a different extend), or different characterizations of the same pattern? Do the modes represent the extent to which different striatal regions exhibit the same pattern of cortical connectivity, or is the connectivity pattern also shifting? Some additional clarity on these patterns would have greatly helped me understand the subsequent results. Similarly, in the results of PD patients, it is stated "we can interpret the observed alteration in the connection topography as a decrease in dopaminergic projections to striatum." (l. 242). A decrease in the quadratic term of the TSM would seem to indicate less spatial variability, but not obviously an overall decrease, which would seem instead to be reflected by the 0th order term (if I understand these modes correctly). Some clarification on this interpretation, and more description of the modes in general, would be helpful.

Several common confounds for rsFC analyses, especially head motion, are not sufficiently well addressed as to ensure that they do not contribute to the spatial patterns reported. Specifically, the second-order fit would seem to capture some sense of the "sharpness" of the spatial connectivity profile in the striatum. This seems like it could be driven either by neurophysiological features regarding the functional segregation of these regions, or data quality features regarding the smoothness of the data. Since one effect of head motion (in both resting state fMRI and other domains such as PET/SPECT) can be to change the spatial smoothness of data, it would be important to characterize how much of the variance in this measure can be accounted for by head motion (or other confounds). This is especially true since such confounds are known to be greater in, e.g., patient populations, which could affect the analyses performed later.

Finally, the findings are at various points referred to as a potential biomarker for dopamine (dys)function. While this term has been used in a wide range of contexts, such claims generally require a greater burden of proof than the presence of statistically significant associations, e.g., including classification and/or sensitivity/specificity analyses. These assertions do not yet seem well supported by the included statistics, and may need clarification.

Reviewer #1 (Public Review):

In this tour-de-force analysis of transcriptional regulation and cell fate specification in C. elegans, Vidal et al. explore the role of the Sine Oculis/Six1/Six2 ortholog ceh-34 in the pharyngeal nervous system. Previous work demonstrated that ceh-34 is exclusively expressed in all pharyngeal neuron types. The current work data shows that ceh-34 function is required for the diverse differentiated features of all pharyngeal neuron subtypes, as well as their interconnectivity, but, interestingly, not for their basal "pan-neuronal" features. ceh-34 is also required to maintain these features, at least into larval stages. Convincing evidence is presented indicating that subtype-specificity emerges through the cooperation of ceh-34 with various individual homeodomain factors, consistent with the homeodomain "code" model that has emerged from this group's earlier work. One of the most fascinating aspects of this study is the association of ceh-34 with circuit formation, as it marks an entire set of interconnected neurons, and appears to be required for at least the gross features of this connectivity. The findings of Vidal et al. raise interesting questions about whether ceh-34 expression would be sufficient to endow non-pharyngeal neurons with pharyngeal characteristics, or whether it would instruct adjacent neurons and/or processes to form synaptic connections; these issues are not addressed in this work. There is also the potential for some confusion around nomenclature: the authors refer to the pharyngeal nervous system as the "enteric nervous system," which is not standard terminology in C. elegans. Previous work in the field has used "enteric" to describe muscles and neurons that regulate intestinal contraction and defecation; these are associated with the posterior intestine, not the pharynx. Nevertheless, the idea that the pharyngeal nervous system might share molecular similarities, and perhaps ancestry, with enteric circuits in other organisms is an interesting proposition. This speculation places the authors' findings into an evolutionary context that suggests a key role for homeodomain transcription factors in the specification of the enteric nervous system, proposing that more complex nervous systems may have evolved simple structures like the C. elegans pharyngeal nervous system.

Reviewer #3 (Public Review):

This study addresses the development of the enteric nervous system in C. elegans. The authors find that the homeobox gene ceh-34 is specifically expressed in all 14 classes of pharyngeal neurons, but no other neurons in the animal. Mutant analysis reveals that ceh-34 is necessary for the differentiation of all 14 neuron classes. ceh-34 acts with several other homeodomain TFs to specify the different neuron classes. The authors speculate that the selective role for ceh-34 in specifying the entire enteric nervous system supports the notion of this system as perhaps the primordial origin of the nervous system.

The study is overall of high quality and the conclusions balanced. While the combinatorial coding of neuronal subtype cell fate by TFs dates back at least two decades, this concept has not hitherto been probed in the enteric nervous system, which makes the study particularly interesting.

Reviewer #2 (Public Review):

Vidal et al. describe the role of the SIX-family transcription factor CEH-34 in the development of the pharyngeal nervous system. The manuscript is strong in several ways. The study is comprehensive and elegantly presented. The authors' conclusions are, with only a few minor exceptions, clearly supported by the presented data. And the study uses sophisticated genetic approaches to determine gene expression in vivo and manipulate gene function.

The manuscript has some weaknesses. First, some of the manuscript is redundant with published data. The authors could better consolidate and synthesize what is known about the pharyngeal nervous system and CEH-34. The authors could also relegate data that support a point already made to supplementary figures. Second, the study does not address functional consequences of disrupting CEH-34 function. The pharynx is an excellent model for the genetic study of neural circuits and how they are assembled not only because of its defined anatomy but also because its function is readily assessed. Finally, the overarching premise of the study is that the pharyngeal nervous system is a model for the enteric nervous system, and that this model can be used to illuminate developmental mechanisms that have heretofore been poorly understood. This premise causes some confusion. The pharynx is not gut, and calling the pharyngeal nervous system the 'enteric' nervous system might raise an eyebrow. Also, the authors do not compellingly demonstrate that the development of the pharyngeal nervous system follows rules that differ from those that govern development of the rest of the nervous system. Any conceptual distinction that the authors attempt to make in their introduction is not relevant to their major conclusions.

Reviewer #1 (Public Review):

Three of nature's life-sustaining processes, respiration, photosynthesis, and nitrogen fixation, all rely on proteins (Fe-S protein) that contain simple inorganic cofactors constructed of Fe and S (Fe-S clusters). Fe-S proteins also participate in a huge and diverse array of metabolic processes. As such there has been considerable interest over the past two decades towards understanding how Fe-S clusters are formed and distributed to their cognate proteins. A related issue, the topic of the present work, is: why is it that many Fe-S proteins from diverse microbial species cannot be heterologously produced in Escherichia coli in active forms. This issue is of considerable interest not only from the perspective of microbial Fe-S proteins but also for heterologous expression of active eukaryotic Fe-S proteins. The study described in the manuscript provides considerable insight into the question. It is shown that for the case of at least one model Fe-S protein (NadA) heterologous expression of an active species sometimes also requires the heterologous co-expression of the native Fe-S cluster assembly machinery. Whether or not compatible co-expression of the heterologous Fe-S cluster machinery for expression of active NadA in E. coli is required is correlated with phylogenic distance. This same phylogenic correlation, however, could not be established for the active/inactive heterologous expression in E. coli of a different Fe-S protein (IspG). Instead, in some cases, the production of active heterologously expressed IspG required the heterologous co-expression of electron transfer proteins necessary for IspG turnover. The important conclusion of the work is that heterologous production of Fe-S proteins in E. coli is complicated and could require either heterologous co-expression of Fe-S cluster machinery or heterologous expression of compatible electron transfer partners, and perhaps, in certain cases, heterologous expression of both assembly and electron transfer systems. This reviewer found the work to be thorough and compelling. The conclusions should be of considerable interest to a very wide readership because there are so many biochemical processes that rely on Fe-S protein and their experimental analysis is very often stymied by an inability to produce them in active forms using heterologous gene expression. There is also great interest on this topic from the biotechnological perspective because many industrial processes are limited by an inability to heterologously produce active Fe-S proteins that are currently rate-limiting. Along these lines the authors provide an interesting proof of concept by demonstrating an ability to boost production of 2-methyl tryptophan by circumventing the Fe-S protein dependent bottle neck.

Reviewer #3 (Public Review):

Summary:

In this study, the authors use synthetic biology to study the challenges with programming cells using non-native proteins containing iron-sulfur cluster cofactors. The study focuses on two different challenges in synthetic biology. The first challenge is focused on understanding what controls the activity of iron-sulfur cluster dependent enzymes. The authors find that functional expression can depend upon evolutionary distance from the host being programmed and expression of iron-sulfur cluster machinery. The second challenge focuses on understanding what controls the activity of iron-sulfur cluster containing protein electron carriers within synthetic electron transfer pathways. The first aspect of this study screened large numbers of iron sulfur enzymes for function in the cellular assay, while the second aspect assessed the function of three ferredoxins in a synthetic electron transfer pathway.

Major strengths:

The use of cellular assays to study iron-sulfur cluster enzymes and electron transfer proteins is critical to developing synthetic biology rules for programming cells that are useful for green chemistry, bioelectronics, and synthetic biology. A great deal of effort in synthetic biology has focused on developing design rules for protein expression, such as developing models to predict transcription and translation initiation. However, models are lacking for adding in considerations of iron-sulfur cluster biogenesis (or other cofactors). This study seeks to better understand what governs expression of functional iron-sulfur enzymes and electron carriers. By expressing large numbers of enzyme homologs from across the tree of life within cellular selections, this study uses a high-throughput approach to assess metallocluster biogenesis. This aspect of the study is a major strength within the context of the iron-sulfur enzyme focus.

Major weaknesses:

As one considers using cellular assays to study challenges with iron-sulfur cluster biogenesis, there are several challenges to consider beyond cofactor biogenesis. All of the proteins being studied are expressed from a promoter and ribosomal binding site (RBS), which can lead to variability in protein accumulation prior to iron-sulfur cluster biogenesis. In fact, gene sequences proximal to the RBS can affect translation initiation as illustrated in the many applications of the RBS calculator. Note that the context of an RBS site placed next to divergent iron-sulfur proteins is expected to depend upon evolutionary distance of those proteins so suitable expression controls in cellular assays should be used to benchmark protein expression.

Additional challenges with cellular assays include the need for a protein to fold within the cell, such as melting temperatures and interactions with chaperones. Recent studies of iron-sulfur cluster containing electron transfer proteins have revealed that changing the temperature of a cellular assay can affect the observed function of iron-sulfur cluster containing proteins. This can arise because those proteins evolved to function at temperature below the standard growth temperature of E. coli, or because they evolved to function with chaperones in addition to iron-sulfur cluster assembly proteins as noted. This study notes the diverse organisms used as sources of enzymes, but it is not clear how the optimal growth temperatures of those organisms relates to the conditions used for cellular assays.

Oxygen has been important to the evolution of iron-sulfur proteins and cluster biogenesis machinery. The complementation observed could depend upon oxygen and iron-sulfur cluster biogenesis, especially if the enzymes being tested are orthologs or paralogs from organisms that grow under anaerobic conditions. Understanding more characteristics about the sources of those enzymes such as growth conditions, iron-sulfur cluster assembly systems, and their divergence seems critical to interpreting cellular assay data. Finally, it isn't clear if expression of non-native iron-sulfur biogenesis systems rescues complementation because of specificity of that system versus the lack of regulation over the expression of the system.

Conclusions supported by results:

In the abstract, the authors conclude" Our results clarify how incompatibilities with foreign Fe-S and electron transfer networks each impede functional heterologous expression and provide insight into how these barriers can be overcome for engineering Fe-S enzyme-dependent biosynthetic pathways." The results assessing complementation of non-native enzymes in E. coli show that a subset can be rescued by expressing a non-native iron-sulfur cluster biogenesis system. However, it is not clear how much mechanistic insight is provided, since the study does not consider different mechanisms that could influence the observed trends, such as variation in translation initiation rates, thermostabilities of enzymes in the context of the cellular assay, oxygen sensitivity, and the lack of regulated expression of the iron-sulfur biogenesis systems. From the results, it isn't clear how a synthetic biologist should choose different iron-sulfur cluster assembly systems to include in their cellular programming.

Reviewer #2 (Public Review):

D'Angelo et al. carry out a comprehensive screen to determine which members of two enzyme families can be heterologously expressed in E. coli in an active form. The two enzymes used for the study, NadA (quinolinate synthase) and IspG (4-hydroxy-3-methylbut-2-enyl-diphosphate synthase) both require an iron-sulfur (Fe-S) cluster cofactor for activity and Fe-S enzymes are notoriously difficult to express in non-native organisms. The authors are attempting to determine which Fe-S cluster biosynthesis (maturation) pathways are necessary to help produce fully activated NadA and IspG. They utilize an elegant genetic complementation assay for the initial activity screen, followed by more comprehensive proteomic analysis, and then iterative addition of alternative Fe-S cluster biosynthesis pathways and partner proteins to help activate recalcitrant NadA or IspG orthologues. NadA demonstrated a relatively straightforward behavior. It was matured to the active form by phylogenetically related Fe-S cluster biosynthesis pathways. NadA orthologues more distant from E. coli required co-expression of an Fe-S cluster biosynthesis pathway from organisms more closely related to their native species.

The results with IspG were more complex. Only a 8 of 47 IspG proteins tested were active in the complementation assay. There did not seem to be a clear relationship between successful expression of active IspG in E. coli and the phylogenetic relatedness of the native organism to E. coli. Furthermore, co-expression of heterologous Fe-S cluster biosynthetic pathways did not significantly improve IspG activation. Subsequently, the authors also co-expressed an electron transfer partner protein with each IspG in order to help reduce the oxidized IspG Fe-S cluster formed during its reaction cycle, thereby assisting IspG with turnover in vivo. Significantly, they found that co-expression of a heterologous electron transfer partner protein resulted in a marked improvement in activation of many IspG proteins in E. coli. They interpreted these results to indicate that IspG proteins were likely maturing properly to the Fe-S form but required the appropriate electron transfer partner for multiple turnovers (and full activity). They further probed the question of what defines an "appropriate" electron transfer protein using a somewhat tangential approach. They analyzed which specific ferredoxin proteins, when co-expressed heterologously, could allow turnover of the cobalamin-dependent rSAM methyltransferase TsrM, from Streptomyces laurentii. They discovered that an appropriate electron transfer protein for this enzyme must have the necessary reduction potential to catalyze the reduction of Co(II) to Co(I) (-730 mV vs Ag/AgCl). From this, one could extrapolate that IspG may face a similar electrochemical/thermodynamic hurdle if it is expressed in the absence of its appropriate redox partner.

The conclusions of the paper are supported by the data presented. This work also provides one of the few systematic analyses of factors that influence heterologous Fe-S protein expression. However, there were several experiments that could have improved the manuscript and helped tease more information out of the studies performed.

Joint Public Review:

In this work, Carignano et al. built a library of 24 yeast strains for signal sensing, synthesis, and depletion as an attempt to implement modular composition of multicellular circuits to demonstrate defined consortia dynamics that can be predicted from the models of the constituent parts. They systematically characterized all the dose-response curves for each strain experimentally and fitted model 1 and model 2 based on ordinary differential equations with eight parameters. Then they proved that these strains can be modularly combined to realize several functions, including two- and three-strain cascades. They set a factor K as the output gain in the model and as the fold-change with respect to the standard initial cell concentration in the experiment to easily tune the response dynamics. In addition to monotonic, quasi-linear dynamic systems with a single equilibrium point, they used a positive feedback loop to generate non-linear responses to the inputs to extend the range of observable behaviors. Furthermore, they constructed a bistable switch strain circuit that requires enhanced nonlinearity to accomplish through induced signal degradation. They used model 3 to capture this circuit and tuned the indicated gains of the circuit to maximize the distance between equilibria in the phase diagram. The switching behavior to the respective external signal was clearly demonstrated. To expand the target behaviors, the authors developed an automated approach to compose the 24 engineered yeast strains to generate logic gates such as AND, NOR, NAND, and OR gates and verified the predictions in the experiments. Finally, they exploited their automated design strategy to identify the circuit designs for time pulses and bandpass filters from a very large design and test space that are out of reach by using an experimental approach. The mutuality of both the strains and mathematical model was maintained amazingly well in the multistrain systems. Overall, this paper is very interesting and useful given that circuit modularity can be easily lost due to the resource competition in a single strain. The manuscript is generally easy to read, and the figures are easy to understand.

Strengths:<br> - Characterization of modular components for engineering multicellular signaling circuits in the experiments and data fitting in the models paves a solid foundation for later development in this work.<br> - Their automated design strategy that comprises topology search, optimal circuit identification, and stoichiometry optimization largely increases the success rate in experimental realization from a large design space of the intended behavior in consortia.

Weakness:<br> - Small difference in strain growth rate could explain the stable-to-unstable shift in a bistable switch circuit (Figure 3F and SI Figure 9). This suggested that uniform or approximately the same growth rate of all strains in multicellular consortia imposes a constraint to ensure the intended consortia dynamics.

Reviewer #1 (Public Review):

The paper clearly indicates that by using parallel fMRI and ECoG experiments, the authors are able to detail the hierarchy of predictive coding in the cortical and higher subcortical areas of the auditory pathway. The methodology is well detailed and I didn't spot any major concerns.

The scientific methodology detailed in this paper appears to be sound. Further, the main conclusions appear to be well argued.

The statistical analysis, however, is not reported clearly in the main text. For instance, I'm unsure how multiple comparison correction was addressed. A more detailed primer on the statistical methods used in the results section is warranted.

My largest concerns are to do with communication, and language overreach. At one point the term "lower auditory pathways" is used, but the lowest portion investigated in this study is the IC, and this usage was in reference to the thalamus. There's a lot of brain between the IC and cochlea, to say nothing of the thalamus. There are also concerns about both the temporal and spatial resolution of fMRI and ECoG - the text at times implies that the resolution for these techniques is far greater than it is. However, these are communication issues that should be easily addressed.

Reviewer #2 (Public Review):

In this study, Jiang et al. combined whole-brain 9.4 T functional magnetic resonance imaging and large-scale electrocorticography to study brain wide activation patterns in response to different pattern violations in marmosets. The authors confirm previous results of a cortical hierarchy for auditory predictive processing and expand on these results by quantifying subcortical responses in MGB and IC as well as using omission to confirm previous results obtained with mismatches. The results highlight the existence of the two levels of auditory prediction signals in the marmoset brain that can be interpreted in a hierarchical predictive processing framework.

The paradigm used to assess the hierarchical depth of predictive auditory sequences for processing predictions errors and prediction updates at two distinct timescales is well designed, and presumably based on one of the authors earlier studies (Chao et al., 2018). Unfortunately, the current study fails to highlight the novelty of this work (as far as we can tell, mainly the omission responses) and give adequate credit to previous work on the topic. However, this can be easily fixed by rephrasing the relevant passages of the manuscript.

Main concerns:

1) It would be good to clarify what the novelty of the present manuscript is (omission responses) in comparison to the previous work (Chao et al., (2018)). The authors do argue that their higher resolution fMRI, allows them to also study subcortical response - which is correct - but the authors make no use of them in any meaningful way in the manuscript. The emphasis on novelty is likely better placed on the omission responses.

2) Figure 3C (and all similar figures). We fear this figure is not interpretable without a substantially improved explanation. Both what the arrows mean (i.e. how they are computed), and what the values indicate that are listed next to the arrows is not explained (arrows appear randomly bi- or unidirectional and the legend at the bottom of the figure is not very helpful).

Reviewer #2 (Public Review):

This is a correlative study with the main result that microsaccades do not alter attention-related modulations of neuronal activity. This is an important question, speaking to the origin of one of the mind's most fundamental processes. The experimental manipulations and analyses are well chosen, carefully conducted and visualized. They include critical controls for alternative explanations. To ascertain their claims, however, it is important that the authors cover their ground. In pursuit of that, a few important analyses are required.

1. Did the manipulation of attention work?<br> In the present version of the manuscript, the authors do not report behavioral results, which is necessary to confirm that the cue was successful in manipulating attention. That is, the observed modulation in firing (in RF vs outside of RF) should be related to a behavioral advantage in sensitivity to changes at the cued location. To confirm the link of the neural results to attention (rather than, say, just the cue), the behavioral results provide opportunities for critical tests. One way to do this would be to analyze neural firing rates as a function of response rather than cue location (provided subjects made enough errors). Note: A detailed discussion of why the cue cannot be equated to attention can be found in Laubrock et al. (2010, Atten Percept Psychophys; https://doi.org/10.3758/app.72.3.683).

2. Were all microsaccades detected?<br> One of the main results of the study is that attention-related modulations were observed even in the absence of microsaccades. These results hinge on successful detection of *all* microsaccades, even at a very small scale. Given the video-based eye tracking the authors will have missed a (possibly large) number of smaller microsaccades (Poletti & Rucci, Vision Res, 2016; https://doi.org/10.1016/j.visres.2015.01.018). This concern is exacerbated by the fact that eye tracking was monocular, such that a validation of detected microsaccades based on the signal in the other eye could not be performed.

3. Relation to previous claims of causality<br> Hafed (2013, Neuron) reported perceptual changes in attentional cueing that covaried with the occurrence of microsaccades. Hafed (2013) argued that microsaccades might be underlying the performance changes commonly attributed to covert shifts of attention. This point seems central to the current paper's line of argument and should thus be discussed in detail with respect to the current findings. At present, the paper by Hafed (2013) is not cited in the current manuscript when its conclusions may need reconsideration based on the current results.

Reviewer #1 (Public Review):

This is a clearly written manuscript describing an elegant study that demonstrates how microsaccades are not the triggers of attentional effects, and that attentional modulations can be observed in the absence of microsaccades. This is a very much needed work, especially in the light of the recent debate regarding whether or not microsaccades are the cause of peripheral attentional effects. By explicitly comparing and quantifying the effects of attention on neuronal responses in the presence and in the absence of microsaccades, this work provides important insights on this debate. I think the work is well conducted and the results are solid. I only have few comments/suggestions:

1. Lines 125-126, the authors report that monkeys generated frequent microsaccades but their overall direction was not systematically biased towards the cue location. This seems to be in contrast with what previously reported in the literature in humans and monkeys. I think this discrepancy should be discussed in the discussion. Is this simply the result of different experimental paradigms (maybe exogenous vs endogenous attention, or the presence of the cue for the entire duration of the trial, ect)?

2. It is very interesting that microsaccades modulate neural responses for stimuli that are much further away from their landing location. However, the stimulus used in these cueing tasks is also unnatural. Normally we are not fixating on a meaningless dot while all the interesting stimuli are presented in the periphery. In normal conditions the foveal input is rich in detail and it is generally relevant (that's why we are foveating certain stimuli in the first place). I wonder if the authors can comment on whether the modulations reported here would also occur in more natural conditions when an interesting and maybe salient/relevant stimulus is presented at the center of gaze, while subjects are also attending to a peripheral target. Will the neural response be modulated selectively for neurons for which the receptive field is on the peripheral target or will it also affect neurons where the receptive field aligns with the microsaccade target location in the fovea?

3. The authors do not report behavioral performance. Presumably the task is very easy, but I wonder if reaction times and performance correct was related with the attentional effects and how did it change with respect to microsaccade direction, e.g., were subjects' reaction times shorter at the cued location also when microsaccades were directed at the opposite location? I think this information would be very valuable.

4. Another important difference in the paradigm used in Lowet et al vs the one described in this manuscript is that in Lowet et al monkeys were instructed to saccade toward the target position at some point during the trial after the cue and the target presentation. Hence, monkeys presumably prepared the saccade and held off its execution during the time the cue and the target were presented. This was not the case in the current paradigm, where the monkey is instructed to maintain fixation as in a standard spatial cueing paradigm. I wonder if this difference may explain some discrepancies in the results.

Reviewer #3 (Public Review):

Villa et. al. conduct a genetic screen in C. elegans to identify mutants which activate the oxidative stress response transcription factor, skn-1, and its target gene gst-4 selectively in muscle during aging. From the screen, they identified 96 mutants showing activation of skn-1, and all of the mutants map to a single gene, alh-6. The alh-6 gene encodes the worm ortholog of ALDH4A1, which is required for the degradation of the amino acid proline. When ALDH4A1 is mutated in worms, the toxic metabolite 1-pyrroline-5-carboxylate accumulates from proline degradation and leads to oxidative stress and mitochondrial dysfunction in the muscle. The identification of so many alleles of a single gene from a genetic screen suggests that alh-6 and proline degradation plays an essential role in preventing oxidative stress in muscle during aging.

To build upon these findings, the authors use data from the Health and Retirement Study to determine if variants in the human ALDH4A1 gene are associated with aspects of muscle function during aging. They study the association of 53 single nucleotide polymorphisms in the ALDH4A1 region with a variety of phenotypes including muscle strength, declines in muscle strength over time, gait speed, and a number of functional measures. They find that specific polymorphisms are associated with some of the phenotypes but not others.

Based on the human data showing age-related declines in muscle function, Villa et. al. return to using the nematode model, and show that the majority of the alh-6 alleles are associated with declines in worm mobility in aging adult worms (day 3 adults) but not in L4 larval worms. These findings suggest that alh-6 has a selective impact on muscle function during aging, but not during larval life.

Strengths of the work include the combined use of C. elegans experiments and human data to study the role of the ALDH4A1 gene in muscle function during aging. The C. elegans experiments particularly use an extensive genetic screen, and then complementation studies followed by gene sequencing with 96 distinct strains to demonstrate that all of the identified mutations map to a single gene. These point to a critical role for alh-6 in preventing oxidative stress in aging worm muscle.

Weaknesses of the work focus on the human studies. A wide range of strength, gait speed, and functional phenotypes are explored by the authors. However, some of the phenotypes, such as IADL1 which involves the ability to take medication, use the telephone, and manage money, have little connection with muscle function. Instead these IADL abilities are more closely tied to memory and cognitive abilities. Alternately, other phenotypes like arm lifting, are influenced greatly by health factors separate from muscle function, like shoulder arthritis or rotator cuff injuries, which are common in older individuals. Additionally, the ability to walk across a room, walk 1 block, walk several blocks, and then jog 1 mile represent progressive stages in community mobility. Finding that a gene is associated with difficulty walking across a room but not mobility of greater distances is surprising. Hence, the broad focus of the selected phenotypes leads to the data seemingly being equally likely to be due to chance than a muscle aging effect. This concern could be potentially addressed by utilizing other available datasets to see if the initial findings can be replicated in a separate population.

Beyond the phenotypes selected for study, there are assumptions made by the authors that could be better discussed. While in C. elegans alh-6 plays a critical role for proline metabolism in muscle, in humans ALDH4A1 is expressed more highly in liver and kidney than in muscle, and ALDH4A1 is also expressed fairly broadly at a lower level. Seeing changes in muscle strength, gait speed, or other phenotypes could be due to changes in muscle function, or could be secondary to diseases like diabetes or chronic kidney disease in another organ system. Acknowledging these limitations does not minimize their findings but reflects the greater complexity of human aging.

Reviewer #2 (Public Review):

Declining muscle function characterizes aging across animals. In this study, Villa and Stuhr et al. investigate a potentially conserved role for ALDH4A1, the human homolog of C. elegans alh-6, in age-related muscle decline. Previous work from the same group showed that alh-6 was necessary to avoid shortening of lifespan in C. elegans fed certain diets through a mechanism that involves improved mitochondrial function in the muscle. In the current paper, the authors establish that alh-6 controls muscle function in aging C. elegans. Further, the authors search for evidence for a potentially conserved role for ALDH4A1 in human muscle health. Promisingly, the authors succeed in identifying multiple SNPs in the ALDH4A1 locus. However, in the current state, some important C. elegans data are missing, and the human genetics analyses fall short of conclusively advancing the notion that ALDH4A1 modulates muscle aging in humans.

Reviewer #1 (Public Review):

In this manuscript entitled "Genetic variation in ALDH4A1 predicts muscle health over the lifespan and across species", the authors showed that many loss-of-function mutations in alh-6, which encodes evolutionarily conserved mitochondrial proline dehydrogenase, contributed to age-dependent declines in muscle functions in C. elegans. They also performed gene-wide association study (GeneWAS) in human cohorts and showed that genetic variants in ALDH4A1, a human homolog of C. elegans alh-6, correlated with age-dependent changes in muscle function. This paper's approach is very elegant and provides valuable information regarding how genetic variants in a single gene conserved in C. elegans and humans can affect muscle aging.

Reviewer #1 (Public Review):

The study presents an impressive array of biophysical experiments showing that NBI-921352 potently inhibits inactivated Nav1.6 channels from both humans and rodents. The degree of selectivity for Nav1.6 over 6 other channel subtypes (Nav1.1-Nav1.5 and Nav1.7) is excellent. However, the compound was not tested against Nav1.8 and Nav1.9 channels. It may be unlikely that NBI-921352 would inhibit either of these as they are more divergent in the likely binding region than the other isoforms test, this is not a given and is a slight weakness. While the electrophysiological data clearly show that NBI-921352 shows voltage-dependence and likely state dependence, the study lacks an assay of use-dependence using repetitive higher frequency depolarizations. In several places it is stated that NBI-921352 preferentially inhibits activated (inactivated or open) channels. There is no direct assay looking at inhibition of open but not-inactivated channels, so this conclusion is potentially misleading. The data on persistent and resurgent currents adds to the strength of the gating analysis, but the statement that a preference for persistent currents is a feature of all the compounds in the Nav inhibitor class is not justified by either data presented in the study or by an appropriate reference. The three seizure models employed in mice (2) and rats (1) provide compelling evidence that NBI-921352 can substantially reduce seizure activity. It is stated that NBI-921352 is rapidly cleared in mice, but it is unclear if this is different in humans, which could limit the translational potential. The manuscript reports that NBI-921352 reduces seizure activity at substantially lower plasma concentrations than the concentration that is observed to cause behavioral signs of adverse effects in rats. Unfortunately, it is not clear what behaviors were monitored and therefore these data are not easily evaluated. As Nav1.6 is the major isoform found at nodes of Ranvier in myelinated fibers, including motor neurons axons, NBI-921352 might be expected to motor activity at some concentrations.

Reviewer #2 (Public Review):

In this manuscript, Johnson Jr, et al. investigated the potency and selectivity of NBI-921352, a novel Nav1.6 blocker, on different voltage-gated sodium channel (VGSC) isoforms as well as on epileptic Nav1.6 variants. NBI-921352 exhibited exquisite selectivity against Nav1.6 channels, preferentially acting on activated channels, and inhibited tested Nav1.6 variants at similar potency except for the R1617Q, a variant that is proximal to the predicted binding site of NBI-921352. Brain slice recordings revealed that NBI-921352 effectively attenuated AP firing in excitatory pyramidal neurons, but not in inhibitory interneurons. Seizure assays in three rodent models demonstrated the protective effect of NBI-921352 on electrically induced seizures in all three models.

Nav1.6-selective blockers have been reported before, but their relative selectivity between Nav1.6 and Nav1.2 are not great; NBI-921352 is the first blocker that shows a high Nav1.6 selectivity over Nav1.2, making it a promising candidate for the development of therapeutics of Nav1.6-related disorders including early onset encephalopathies and mental disabilities. The study on epileptic variants of Nav1.6 further supports its potential use for the treatment on SCN8a-related diseases, which was confirmed by the seizure assays. NBI-921352 will also be a valuable pharmacological tool in VGSC-related basic research.

Despite all the wonderful work the authors have completed, there are some issues should be addressed.

First, different protocols were adopted to examine the selectivity of NBI-921352 on different VGSC isoforms. NBI-921352 is a state-dependent inhibitor, holding potential may alter the potency of NBI-921352 by changing channel activation/inactivation state, and therefore, difference in voltage-clamp protocols could introduce bias in the comparison of selectivity among VGSCs.

Second, a depolarized holding potential (-45 mV) was used in the study to determine IC50 of NBI-921352 on most VGSCs, which is uncommon under physiological conditions. The selectivity of NBI-921352 on Nav1.6 vs other VGSCs under physiological conditions could be different compared to the values reported here. It is better to hold cells at physiologically-relevant membrane potentials or using action potential waveforms derived from real AP recordings in neurons. The authors should discuss these limitations, and possible impact on their assessment of selectivity against other VGSCs in their native cellular backgrounds.

Third, Nav1.6 is highly expressed in Purkinje neurons and motor neurons, and plays important roles in motor system. Did the authors observe any motor impairment in the behavior studies? It would be informative to examine the effect of NBI-921352 on AP firing and resurgent currents in Purkinje neurons.

Fourth, wrong statistical test was used in the current-clamp study, and there is no description of statistical methods used for seizure assays. Please add a section of statistical analysis in Materials and Methods, and list the statistical analysis method used in each experiment.

Reviewer #2 (Public Review):

This article provides a detailed account of both in vitro and in vivo experiments that:

• Establish the role of fibrinogen (Fg) in the etiology of catheter-associated urinary tract infections (CA-UTI)

• Investigate the prevention of CA-UTI with the use of LIS catheters, containing anti-fouling modifications (liquid infused silicone) to prevent the interaction between Fg and common uropathogens.

The study follows up on previous (by the investigators) research on the role of Fg on the attachment of uropathogens and the formation of biofilms. It is a comprehensive article that contains a detailed description of the following experiments:

1. In vivo experiments demonstrate the interaction between Fg and uropathogens in the bladder and the catheter lumen.<br> 2. The manuscript provides in vitro evidence that Fg-coated silicone catheters enhances the binding of uropathogens, compared to uncoated or bovine serum albumin coated catheters.<br> 3. The manuscript describes the development of the LIS catheter, in which a catheter is drained in silicone gel. It demonstrates the effects of this process on the catheter weight, length and inner and outer membrane diameter.<br> 4. The manuscript provides in vitro evidence that the use of a LIS-catheter reduces Fg deposition and uropathogens binding.<br> 5. Using in vivo mouse experiments, the study provides evidence that when introducing a variety of uropathogens and thereby inducing CA-UTI, the use of LIS-catheters reduces<br> o Fg deposition and uropathogens binding on the catheter<br> o uropathogens colonization of the kidneys, spleen and heart<br> 6. Finally, the manuscript demonstrates in mice that the LIS catheter reduces protein deposition on catheters in case of CA-UTI

The study has a clear structure and there is little to criticize about the study methods. For steps 4 to 6, they used a control group of uncoated catheters, which they compared with a Mann-Whitney U test. The results, although not all statistical significant, provide convincing evidence for the efficacy of LIS catheters within this study. Another strength of the study is the simplicity of the development and (probably) the limited costs of a LIS catheter, so that it can also be applied in the future in less wealthy countries.

I identified two potential weaknesses of this study. Addressing these would improve the replication of these findings, the set-up of follow-up studies, also outside your study group, and it would help in the translation and implementation of the LIS catheter in humans.

First, it is insufficiently clear from the methods how the LIS catheter was developed exactly, and specifically the LIS-catheter that was used for the mice experiments. This complicates the understanding and replication of these study findings. It is not exactly clear for me if these catheters were drained in liquid infused silicone or whether liquid infused silicone was infused into the catheter tuber before insertion? For how long were the LIS-catheters that were finally used for the mice experiments incubated in silicone oil?

Second, the article demonstrates that the drainage of a catheter in silicone gel increases the weight, length, inner and outer diameter of the mouse catheter. These results seem to stand alone and are not addressed in the discussion. What influence this could have on the urinary flow and the introduction/ascent of uropathogens? Could it be that the effect of the silicone gel diminishes over time, which necessitates a catheter change? Do you have evidence on the stability of this polymer? Would it be possible to infuse silicone oil when the catheter is in situ?

Overall the study is of excellent quality. While there are still barriers to overcome before the LIS catheter can be applied in humans, as also acknowledged in the discussion, it offers a hopeful new strategy in preventing CA-UTIs.

Reviewer #1 (Public Review):

In a set of in vitro and in vivo experiments the investigators demonstrated that coating of urinary tract catheters with fibrinogen-degrading substances reduced adhesion and colonization with a broad range of bacteria relevant in the pathogenesis of CAUTI. This approach might, therefore, be interesting for prevention of CAUTI as an alternative to catheters coated with antibiotics.

The major strengths are a clear hypothesis and the consecutive description of a set of experiments, each time demonstrating the next step in the pathogenetic pathway.

The weakness is that the experiments stop where the clinical relevance would start. Are the in vitro and in vivo animal experiments representative of the in-human situation? Moreover, it does not become clear from the discussion whether this approach of coating is technically feasible. This step towards in-human testing will determine the impact and significance of the work.

Reviewer #1 (Public Review):

In this report, Shekhar et al, have profiled developing retinal ganglion cells from embryonic and postnatal mouse retina to explore the diversification of this class of neurons into specific subtypes. In mature retina, scRNAseq and other methods have defined approximately 45 different subtypes of RGCs, and the authors ask whether these arise from a common postmitotic precursor, or many ditinct subtypes of precursors. The overall message, is that subtype diversification arises as a "gradual, asynchronus fate restriction of postmitotic multipotential precursors. The authors find that over time, clusters of cells become "decoupled" as they split into subclusters. This process of fate decoupling is associated with changes in the expression of specific transcription factors. This allows them to both predict lineage relationships among RGC subtypes and the time during development when these specification events occur. Although this conclusion based almost entirely on a computational analysis of the relationships among cells sampled at discrete times, the evidence presented supports the overall conclusion. Future experimental validation of the proposed lineage relationships of RGC subtypes will be needed, but this report clearly outlines the overall pattern of diversification in this cell class.

Reviewer #2 (Public Review):

The manuscript "Diversification of multipotential postmitotic mouse retinal ganglion cell precursors into discrete types" by Shekhar and colleagues represents an in-depth analysis of an additional transcriptomic datasets of retinal single-cells. It explores the progression of retinal ganglion cells diversity during development and describes some of aspects of fate acquisition in these postmitotic neurons. Altogether the findings provide another resource on which the neural development community will be able to generate new hypotheses in the field of retinal ganglion cell differentiation. A key point that is made by the authors regards the progression of the number of ganglion cell types in the mouse retina, i.e., how, and when neuronal "classes diversify into subclasses and types" (also p. 125). In particular, the authors would like to address whether postmitotic neurons follow either a predetermination or a stepwise progression (Fig. 2a). This is indeed a fascinating question, and the analysis, including the one based on the Waddington-OT method is conceptually interesting.

Comments and questions:

>Is the transcriptomic diversity, based on highly variable genes (the number of which is not detailed in the study) a robust proxy to assess cell types? One could argue that early on predetermined cell types are specified by a small set of determinants, both at the proteomic and transcriptomic level, and that it takes several days or week to generate the cascade that allows the detection of transcriptional diversity at the level of >100 gene expression levels.

> Since there are many RGC subsets (45) that share a great number of their gene expression, is it possible that a given RGC could transition from one subset to another between P5 and P56? Or even responding to a state linked to sustained activity? Was this possibility tested in the model?

> The authors state that early during development there is less diversity than later. This statement seems obvious but how much. Can this be due to differential differentiation stage? At E16 RGC are a mix of cells born from E11 to E16, with the latter barely located in the GCL. Does this tend to show a continuum that is may be probably lost when the analysis is performed on cells isolated a long time after they were born (postnatal stages)? Alternatively, would it be possible to compare RGC that have been label with birth dating methods?

> Comparing data produced by different methods can be challenging. Here the authors compared transcriptomic diversity between embryonic dataset produced with 10X genomics (E13 to P0) and, on the other hand, postnatal P5 that were produced using a different drop-seq procedure). Is it possible to control that the differences observed are not due to the different methods?

> It might be important to control the conclusion that diversity is lower at E13 vs P5 when we see that thrice less cells (5900 vs 180000) were analyzed at early stage (BrdU, EdU, CFSE...)? A simple downsampling prior to the analysis may help.

> Ipsilateral RGC: It is striking that the DEG between C-RGC and I-RGC reflect a strong bias with cells scored as" ipsi" are immature RGC while the other ("contra") are much more mature. This bias comes from the way ipsilateral RGC were "inferred" using non-specific markers. Can the author try again the analysis by identifying RGC using more robust markers? (eg. EphB1). Would it be possible to select I-RGC and C-RGC that share same level of differentiation? Previous studies already identified I-RGC signature using more specific set-up (Wang et al., 2016 from retrogradely labelled RGC; Lo Giudice et al., 2019 with I-RGC specific transgenic mouse).

> It would be important to discuss how their findings differs from the others (including Rheaume et al., 2018). To make a strong point, I-RGC shall be isolated at a stage of final maturation (P5?) and using retrograde labelling, which is a robust method to ensure the ipsilateral identity of postnatal RGCs.

> It is unclear how good Zic1 and Igf1 can be used as I-RGC marker. Can the author specify how specific to I-RGC they are? Have they been confirmed as marker using retrograde labelling experiments?

> The enrichment procedure may deplete the RGC subpopulation that express low levels of Thy1 or L1CAM. A comparison on that point could be done with the other datasets analysed in the study.

> In supplemental Fig. S1e: why are cells embedded from "Clark" datasets only clusters on the right side of the UMAP while the others are more evenly distributed?

> What could explain that CD90 and L1CAM population are intermingled at E14, distinct at E16, and then more mixed at P0?

> On Fig. 6: the E13 RGC seems to be segregated in early born RGC expressing Eomes and later born expressing neurod2. Thus, fare coupling with P5 seems to suggest that Eomes population at P5 may have been generated first, and Neurod2 generated later. Is that possible?

> Methods:<br> The Methods section is extensive, and yet it is presented in a rather complex manner so that it is difficult to understand for a broad audience. It would be valuable if the authors could simplify or better explain some parts (the WOT section in particular).<br> *Highly variable genes (HVG) used for clustering and dimensionality reduction: how many of them and what are they? Are they the same used for each stage?<br> *In the methods p9: "The common features G = GR ∩ GT are used to train a third classifier ClassR on the reference atlas AR. This ensures that inferred transcriptomic correspondences are based on "core" gene expression programs that underlie cell type identity rather than maturation-associated genes." Could the authors explain the relevance of using a third model and, more importantly, is there any genes that eliminated through the procedure that could be important to drive the diversification process? If so, would it be possible to estimate their number and the relative impact?<br> *Methods page 15: Inference of ipsilaterally-projecting RGC types. Wouldn't it be more valuable to consider more markers to distinguish RGC precursors?

> Discussion:<br> *Is there somewhat a plasticity that allow the RGC subgroups to switch over time? (IF we were to record the transcriptome of the same cell over time, will one observe that the cell belong to another cluster / subgroup?<br> *While the data appears technically rigorous, and the number of cells sequenced very high, the results seem redundant with several prior studies and the discrepancies are not sufficiently discussed.

Reviewer #1 (Public Review): 

In this paper, Mansell et al investigated the relationship between infection and plasma metabolomic and lipidomic profiles at 12 months of age, and link between of these associations and inflammation. The authors generated matched infection, metabolomics and lipidomics data from 555 infants in a pre-birth longitudinal cohort. their data demonstrate that frequent infant infection is associated with adverse metabolomic profile, characterized by elevated inflammation markers, triglycerides, phenylalanine, and lower HDL, apolipoprotein A1, and omega-3 fatty acids, and lipidomic profiles, characterized by elevated phosphatidylethanolamines and lower hexosylceramides, trihexosylceramides, and cholesteryl esters. Similar profiles were noted with higher GlycA, but not hsCRP. They concluded that "Infants with a greater infection burden from birth to 12 months had pro-inflammatory and pro-atherogenic plasma metabolomic/lipid profiles, indicative of heightened risk of cardiovascular disease, obesity, and type 2 diabetes in adults. These findings suggest potentially modifiable pathways linking early life infection and inflammation with subsequent cardiometabolic risk." 

The paper is interesting and the data is based on a very large cohort. The paper contains a large amount of data and addresses an important and an understudied area of science. However, there are a couple of areas that need to be improved. First, the paper contains a number of errors that make the paper hard to follow. The authors are strongly encouraged to review the paper carefully prior to the next submission. Second, the authors need to better define the significance of their findings. For example, although the link between inflammation and metabolome change is known, the authors need to provide a better explanation on why their specific findings are important for infection in infants. Additionally, it is hard to read and understand some of the graphs that are provided in the paper. Finally, the authors need to study whether environmental factors during early infancy (such as exposure to second hand smoke or body weight, etc) also correlate with a change in serum lipids and metabolome. 

The paper will likely have a high impact on the field, as it identifies changes in lipids and metabolome with infection.

Reviewer #2 (Public Review): 

Mansell et. al. report on a highly unique cohort (Barwon Infant Study) of 555 mother-child dyads with extensive phenotyping data including parental report of infant infections from birth to 12 months, metabolomic, and lipidomic profiles from cord blood at brith and at 12 months of age, and markers of inflammation at 12 months (GlycA and hsCRP). 

Major strengths of the study include the study of a population-based pre-birth longitudinal cohort with detailed phenotyping maternal and child information. Data from birth records were abstracted including infant gestational age and birth weight, which are major predictors of child outcomes, including cardiovascular disease risk. 

Weaknesses of the study include the potential for residual confounding, particularly due to shared upstream risk factors that may confer increased risk for both infant infections and adverse metabolic/lipid profiles, such as adverse intrauterine environment (e.g., gestational diabetes) and preterm delivery between 32-37 weeks gestational age. 

The results support the conclusions of associations and do not confer causation. The mediation analysis proposes a biologic pathway that is hypothesis generating given the cross-sectional nature of the mediator and outcome at the same time point. This work will have significant impact on the field due to the deep phenotyping performed in children age 12 months in the context of a pre-brith cohort.

Reviewer #3 (Public Review): 

Mansell et al. examined the link between early-life infections (during the first year of life) and markers indicative of poor cardiometabolic health when the infants are 1 year old. They drew on an impressive dataset of 555 infants that were part of a longitudinal cohort, which included parent-reported infection data from birth to 12 months, and paired this with peripheral inflammatory markers and metabolomic and lipidomic profiles at 12 months. They found that more frequent infections were associated with adverse metabolomic and lipidomic profiles at 12 months of age, which might indicate heightened risk for cardiometabolic diseases later in life. They also thoughtfully compared the predictive ability of two inflammatory markers, hsCRP and GlycA, and found that the less-commonly-used marker, GlycA, was more predictive of 'omics profiles at 12 months of age as well as more strongly associated with parent-reported infection burden. This is a useful methodological advancement/validation that suggests that GlycA is a better marker of cumulative infection burden/inflammation, while CRP primarily reflects acute inflammatory responses. Together, the findings provide novel insights into how early-life exposures, like infections, might set individuals on different health trajectories that have long-lasting effects. These data might provide an opportunity to identify the most at-risk individuals using single markers (like GlycA) in order to intervene and potentially monitor the success of the intervention.

The paper is thoughtfully written and the conclusions mostly justified by the data, but a bit of clarification is needed-especially with regard to the mediation analysis/conclusion: 

1) One technical concern is the variation in the amount of time the blood samples spent between post-processing and storage at -80C (and how they were held during that interval). The authors should be commended for their sensitivity analysis looking at just those samples that were stored for < 4 hours. However, the justification for using only those < 4 hours is not clear. It is also unclear and a bit difficult for a reader to look at Supplementary files (1A and 1B) and draw the authors' conclusion that "this had little difference on the estimated effect sizes observed in analyses with the full cohort". Could the authors make more direct comparisons between the effect sizes to hammer this point home? Given that the sample size is smaller for this analysis, the p-values should be higher (less power), but the effect sizes should be unbiased and relatively similar. Perhaps calculating the correlation between effect sizes would help improve this sensitivity analysis and be good to report in the main text.

2) The authors show that while the correlation between GlycA (or CRP) and number of infections is relatively low (albeit a bit higher for GlycA), the effect sizes of GlycA and infections on the metabolome and lipidome are strongly correlated (and to a lesser extent between CRP and infections). A third comparison here that would be very useful, would be between GlycA and CRP effects on the metabolome and lipidome. 

3) The mediation analysis is the least convincing part of the manuscript. It is appreciated that the authors are trying to identify how infections might be translating to adverse metabolomic and lipidomic profiles. However, the justification for GlycA or hsCRP being the mediating mechanisms is not convincing. The authors are implying (statistically through their mediation models) that the effect of the number of infections on many metabolites and lipids is due to the changes in GlycA (or hsCRP). Since both GlycA and number of infections are cumulative measures at 12mo, it is unlikely that one (e.g., GlycA) precedes the other. Rather, as the authors state, GlycA is a marker of cumulative infections. This would preclude it from being a mediator unless the authors have data from earlier timepoints (like previous months) that would provide more support for a mediation effect.

Reviewer #1 (Public Review):

The investigators' goals were to describe the epidemiology and kinetics of post-acute covid lung sequalae and to determine the risk factors predictive of persistent lung impairment. A major strength of the study is the longitudinal observation through 6 months with protocolized clinical assessments that included patient-reported outcomes, lung function tests, inflammatory marker testing, and computed tomography of the chest, in a reasonably sized cohort that reflects the spectrum of disease severity in the pre-vaccination era. We learn a great deal about the different patterns of recovery in this group of COVID-19 survivors. The primary epidemiologic finding is that 52% of survivors continued to have symptoms at 6 months, while up to 72% of those with severe COVID requiring ICU level care continued to have lung abnormalities by chest imaging. This confirms general observations of "long covid" which also encompasses non-lung effects. While lung disease is less common in those with milder disease, the proportion of patients who were never hospitalized but experienced persistent symptoms is striking (50%), with lung function impairment in 17% at 6 months. As expected, the patients who had the most severe disease-those who needed the ICU-had the highest degree of chest imaging abnormalities. The kinetics of recovery is a significant observation: Figure 3 shows that most of the post-acute recovery in structural lung abnormalities occurs in the first 3 months and slows down thereafter, particularly for the hospitalized non-ICU patients. The investigators then embarked on a sophisticated analysis to determine how to predict persistent lung abnormalities (as detected by chest CT) at 6 months. When analyzed individually, among 50 clinical characteristics or lab values, the strongest unfavorable risk factors were elevated IL-6 (an inflammatory cytokine that is the target of tocilizumab) and CRP (c-reactive protein). Other variables that were strongly associated with CT abnormalities included immunosuppressive therapy, ICU stay as well as pre-existing conditions. When machine learning techniques were applied, risk factors that correlated with each other could be grouped together, and the patients could be categorized as low, intermediate, and high risk for delayed pulmonary recovery. As expected, known factors for COVID19 infection (age, male sex, medical comorbidities) and disease severity (need for oxygen therapy, ICU care and antibiotics) were more frequent in the intermediate and high risk groups. These predictive factors at acute COVID and day 60 follow-up mostly held up when tested against part of the cohort that was not used for analysis. Interestingly lung function impairment as measured by pulmonary function tests were only weakly correlated with persistent and severe chest imaging abnormalities.

The novelty of this study lies in taking the epidemiology a step further with a machine learning analysis to determine which clinical characteristics and chest imaging features at the onset of acute COVID-19 are predictive of later persistent disease. One limitation of this study, however, is that it was conducted on patients in the early part of the pandemic, prior to the widespread use of remdesivir and corticosteroids/anti-cytokine therapies, that are now considered standard of care. Based on these findings, we can now hypothesize that current treatments are likely to reduce the impact of long-covid.

Machine learning (artificial intelligence, AI) is now being increasingly used to answer clinical questions on limited cohorts; the application of machine learning in this study contributes to our conceptual understanding of how clinical characteristics and biological factors cluster together to contribute to long-term COVID outcomes. Namely, the profound inflammation that characterizes severe acute COVID-19 pneumonia and poor early outcomes also contributes to chronic lung damage in survivors. In addition, a robust antiviral immune response (as seen with elevated anti-viral antibodies) without elevated systemic inflammatory markers were associated with less severe chest imaging patterns, also supporting the notion that an individual's immune response to the virus is responsible for the trajectory of disease. As noted, a significant proportion of non-hospitalized patients also suffered from chronic lung impairments. Taken together, the impact of prolonged convalescence on the workforce, healthcare, and individual lives should not be underestimated. These results underscore the paramount need for continued public health measures and vaccinations to prevent COVID-19, particularly for the most vulnerable individuals (older, immunocompromised, and with preexisting health problems). These observations provide additional biologic justification for the use of agents directed at reducing lung inflammation early in the course of disease, and potentially at an early post-recovery time point (i.e 2 months). Machine learning algorithms may one day help clinicians decide which patients should be targeted for additional therapies after the acute phase. With further study, implementation of AI to real world medicine may be on the horizon.

Reviewer #2 (Public Review):

This is a potentially valuable manuscript which links early markers of inflammation with residual abnormalities on chest CT following SARS-CoV-2 infection. Surprisingly, early surveyed symptoms do not predict long term radiologic outcomes (6 months after infection) while inflammatory markers have stronger predictive value. The cohort is well designed and the selected tools for analysis are appropriate.

While this finding is potentially of high importance for clinical practice, the endpoints are inconsistently defined, and certain components of the machine learning and clustering analyses are difficult to interpret as presented. It is therefore challenging to understand whether the conclusions are justified by the analysis.

Several components of the analysis are confusing and would benefit from further elucidation:

1) The authors do not clearly define "delayed pulmonary recovery". My sense is that they are using several radiologic based definitions rather than their functional definition (defined by FEV1, FEV:FVC & DLCO) of lung function but this is never explicitly stated. Are the functional outcomes and symptomatic recovery considered in any of the analyses other than correlations with radiologic findings in S1?

2) To this end, I was surprised that the functional definition and symptomatic recovery were not used as the primary endpoints. The functional definition and resolution of symptoms seem most important for the recovering patient so seems like the more important outcome. However, in Figures 5-7, it is often not clear whether the functional outcome is being considered at all.

3) For the clustering in figure 5, I am uncertain how CT severity score >5 & CT abnormalities cluster separately, when these 2 outcomes appear to logically overlap. Specifically, does the CT abnormalities outcome include patients with the high severity score outcome? In other words, are patients in the "high severity" group a subset of patients with "CT abnormality"? If not a subset, then the CT abnormality should be labeled "non-severe CT abnormality". This could all be clarified by listing the number of patients in each group and showing with a Venn diagram whether there is any overlap.

4) For the same reason, figure 4 is hard to interpret. Are CT severity >5 being compared to those with normal CTs only or those with normal or mild / moderate CTs? Please provide more specific definitions of normal, "CT abnormality" and "severe CT abnormality" and provide the number of people in each category and specify the comparator groups in all analyses.

5) Similarly, how can GGO @V3 be used a potential explanatory variable for the outcome CT abnormalities @V3 when these 2 variables are clearly non-independent. Inclusion of highly related and likely correlated variables may throw off the overall conclusions of the clustering analysis

6) In Figure 6, the criteria for the low, medium, and high-risk subsets are unclear. Is this high risk for persistent functional abnormality, radiologic abnormality, or both? Why were 3 sub populations selected? Was this done subjectively based on the clustering algorithm?

7) The accuracy and sensitivity of the machine learning approaches shown in S5 & S6 are somewhat limited. Please comment on why such highly granular data can only provide limited prediction about degree of lung damage post infection. Are there missing data types that might make the algorithm more predictive?

8) The authors state that "the sole application of a lung function measurement at screening for subjects at risk of delayed lung recovery may bear insufficient sensitivity". I am not sure that I agree with this assessment. From the perspective of a patient, full recovery of lung function with limited or no residual symptoms, even in the presence of residual chest CT abnormalities, seems like a favorable outcome. I would suggest either changing this statement or providing citations that associate residual chest CT abnormalities (in the absence of residual functional lung dysfunction) with adverse long-term outcomes. Do the authors hypothesize that persistent radiologic abnormalities may predate organizing pneumonia which will ultimately become symptomatic?

9) The authors note selection bias against ordering CT and perhaps inflammatory markers early during infection as a limitation. I would suggest a sensitivity analysis to understand whether this misclassification will impact the model's predictions.

Reviewer #1 (Public Review):

Kinetic proofreading is the canonical mechanism that is posited to enable biological systems to discriminate far more finely between inputs than equilibrium considerations alone would suggest. It has also long been studied in the context of ligand discrimination by T cells. A few years ago, Weiner and co-workers published a paper in which they described the development of the light-gated receptor system employed in this paper (LOV2) that enabled studying signaling with controlled receptor-ligand half-lives. However, they had some puzzling results therein, such as the level of proofreading observed at ZAP70. Now, the authors have improved their experimental system using adhesion molecules to stabilize the synaptic junction and obtained the results described in this paper. Their main results are: 1) they can now observe how proofreading steps increase as signaling progresses down the signaling pathway; 2) their results suggest that the signaling pathway resets more slowly as you traverse down the pathway. The paper is clearly written and easy to understand.

Reviewer #2 (Public Review):

The manuscript from Britain & Weiner revisit previous experiments from the Weiner group, using optogenetics to gauge the kinetic proofreading (KP) capability of the T cell antigen receptor (TCR) at different parts of the signaling network. By using ICAM1-functionalised lipid bilayers, they are now able to observe some form of proofreading/time delay in the recruitment of ZAP70 to the TCR, which was not the conclusion from their previous work. They then proceed to investigate proofreading at the level of LAT clustering and DAG production by PLCγ1, finding this is indeed the case. A simple mathematical model of KP is then used to fit their datasets to extract an estimate of the number of steps (n) in the KP pathway.

Strengths:

The use of ICAM1-functionalised bilayers has clearly significantly improved the power of the authors' experiments to study more proximal signaling events in TCR triggering. The result that the LFA-1/ICAM1 interaction is important for efficient T cell engagement to bilayers has been known for some time, though. By essentially redoing many of the same experiments from the previous paper with this new strategy, some form of proofreading can now be observed at the very proximal events of ZAP70 recruitment and corrects the technical deficiency in the previous work. This is a helpful result, and it is good that the authors have revisited this question, as the opposite conclusion drawn from their previous work was rather counter-intuitive.

The data presented also provides good evidence that there are readily measurable time delays between receptor triggering and different downstream outputs within the TCR signal transduction network, which has been hard to measure by other approaches.

Weaknesses:

In the KP concept originally proposed by Hopfield (oddly not cited), it was important that the output response was intimately linked to the bound state of the receptor, in this case the TCR, with ligand unbinding rapidly reversing all proofreading steps. This means that dissociation of a single TCR should disrupt signaling, and implicitly assumes a direct physical connection between the bound receptor and the KP modifications. However, this mechanism becomes much harder to argue when the KP steps are physically uncoupled from bound TCR, such as in LAT microclusters or DAG production. This is because it is possible (and likely) that multiple bound TCRs contribute to the LAT phosphorylation or PLCγ1 activation to produce DAG. The data clearly demonstrate a time delay between receptor binding and the measured outputs, but it is not so surprising that this lag would exist in propagating the signal through the intracellular network. If the authors had measured ERK activation as another readout of TCR engagement, for instance, they almost certainly would have found evidence for proofreading at this point in the network with n>11, but it would be very difficult to reconcile this step as KP proper. Signalling from multiple TCRs can and does lead to ERK activation, which would invalidate proofreading at this step; the same logic surely applies to PLCγ1 activity.

The authors use a simple equation for KP to fit their datasets in Figure 4, equivalently to their previous work. However, no goodness-of-fit metric is provided for these fits, and by manual inspection it is hard to see the defining curves of their KP model in the datasets, especially not for LAT and DAG, where the datasets look much more like vertical bars. The estimated values of steps (n) may well be the best fit to the data, but they are not necessarily a 'good' fit. The values of n are also very high, which would imply that the kp rate constant might be very fast to compensate; no estimates of this value are presented. Recent data from the Dushek lab (Pettmann et al, eLife 2021) measured n to be ~3, which seems much more physically realistic. Furthermore, in their previous published work, Tischer & Weiner measured n to be 2.7 for DAG production but in the present study it is now n=11.3, using the same equation. If the fitted value of n provides no realistic insight into the KP mechanism, it should not be discussed as though it does.

While it is good to confirm it, the result that downstream signaling complexes reset more slowly than distal ones is surely to be expected, given the increased number of steps over which ligand unbinding must traverse, as in their Erlang distribution. You would not expect ERK phosphorylation to decrease at the same rate as LAT cluster dissociation for this same reason. However, the fact that the lifetime of LAT clustering (14.2s) or ZAP70 (9.6s) is so different to LOV2 (3.3s) provides good evidence that it is not proofreading, as by definition the measured outputs should rapidly return to the 'unbound' state in line with ligand unbinding. At least for LAT, there must be a 'memory' from previous signalling lasting several seconds, which means the system has not reset, as required for true KP.

Reviewer #1 (Public Review): 

The study aims at phylogenetically identifying determinants of HIV transmission dynamics in an HIV prevention trial in Botswana. The study found that most HIV transmissions occurred between similarly aged partners within the same trial community or between trial communities in close proximity. Interestingly, there was a greater flow of HIV transmissions into intervention communities from control communities than vice versa - which could potentially explain worth-than-expected outcomes of such prevention trials. 

Strengths: 

1. This research question is of public health relevance. <br> 2. The sample size was large for a phylogenetic study in this setting. This is due to the prospective planning of the study, which enabled sequencing proviral DNA of the large fraction of participants with suppressed viral load. <br> 3. Overall precise analysis: Efforts to adjust the analysis for sampling density, weight the mean age gap etc. <br> 4. A large part of the limitations of this study was discussed. <br> 5. The figures are an added value and nicely made. <br> 6. A large amount of supplementary information is available for the interested reader. <br> 7. An R package with all code will be made available. 

Weaknesses: 

For me, most of the weaknesses of this manuscript are related to the cluster detection: 

8. There is no consensus on the definition of transmission clusters in the field. However, the rational of taking the union (rather than the intersection) of two different methods (HIV-TRACE and cluster picker) did not become clear to me. <br> 9. HIV-TRACE defines clusters based on pairwise genetic distances and cluster picker identifies clusters using pairwise genetic distance with the guidance of a phylogenetic tree (and node support / bootstrap values). Given the underlying sample size and that the phylogeny was constructed already, the rationale for the purely distance related criterion of HIV-TRACE did not become clear. <br> 10. For a phylogeny of this size it is feasible to calculate real bootstrap values instead of using (in my experience more liberal) Shimodaira-Hasegawa support values. <br> 11. In Supplementary Note 2.5 it is described how the linkage and direction of transmission score threshold of 57% was chosen. However, the finding that almost half of the accordingly selected probable source-recipient pairs were same-sex and had to be excluded from the analysis questions the reliability of the threshold. 

Conclusions are justified by the data: 

If the authors can justify their cluster definitions, I believe that their conclusions are justified. 

Discussion: 

This study might help to understand results of past HIV prevention trials and impact the design of future HIV prevention trials. Further, it provides methodological tools for HIV phylogenetic studies in similar settings.

Reviewer #2 (Public Review): 

Using a case control trial design, Magosi et al., describe an impressively large study that makes use of deep viral sequencing to investigate the contributions location, gender, age and ready access to HIV care have in the transmission of HIV between people in Botswana. They identified that most transmission occurs within close geographic proximity, that increased access to HIV care can significantly reduce transmission, and that men and women contributed similarly to the spread of infection in phylogenetically linked pairs. They also introduce the program 'bumblebee' which provides methods for estimating transmission flows between populations. This study is highly relevant for the interpretation of other universal test-and-treat HIV prevention trials and in the design of HIV prevention programmes more broadly. 

Overall, the data were well analysed. However, some methods require further explanation and motivation.

Reviewer #1 (Public Review): 

Wang et al., investigated the role of RNA m6A modification in intestinal epithelial cells (IECs) in the context of rotavirus infection. The authors found that the mice which specifically lacks METTL3 in IECs show resistance to rotavirus infection. They attributed this effect to increased IFN and ISG expression presumably via IRF7 upregulation. Further genetic IRF7 ablation in IECs led to the sensitivity rotavirus infection. They also found that ALKBH5 is suppressed by a rotaviral protein, although the knockout of ALKBH5 in IECs did not influence viral infection. 

Overall, although the resistance of IEC-specific METTL3-deficient mice upon rotavirus infection via the control of IRF7 is a novel and interesting finding, the proposed model is not fully supported by the findings here. Especially, the following points need to be addressed: 

1) The m6A dot blot used in Figure 1 is not a good measurement system of total m6A modification levels, because the antibody used here also detects other RNA modification, m6Am (PMID: 31676230). Therefore, it is unclear if the increase of m6A dot blot intensity is due to the increase of m6A in RNAs mediated by METTL3 in IECs. The authors should investigate the m6A levels in IECs, not BMDMs, under METTL3 deficiency. Ideally, this analysis should be done using mass spectrometry. 

2) The authors show that Alkbh5 expression is increased when the mice grow up to 3 weeks old. However, the Alkbh5 protein expression changes are missing. 

3) The authors claim that m6A declined from 2 to 2 weeks post birth is caused by increased Alkbh5 (Line 110). However, it is not clear if the subtle increase in Alkbh5 mRNA leads to the change in global m6A levels. The author can use ALKBH5-deficient mouse cells to confirm this point. 

4) The authors should describe the overall phenotype of IEC-specific METTL3-deficient mice at the steady state. It is important to clarify if the augmented expression of ISG upon METTL3 deficiency is dependent on rotavirus infection. Also, the authors should describe any detectable abnormalities or changes without stimulation. 

5) The finding that IRF7 is targeted by METTL3 is not convincing. First, the authors performed MeRIP-seq and -qPCR experiments only using RNAs from wild-type IECs not from METTL3-deficient cells. It is necessary to show that the modification levels on IRF7 mRNA is indeed reduced upon METTL3 deficiency. Second, it is unclear if MeRIP-seq is properly performed or not, because there is no quality checking figure shown. For instance, the authors can generate metagene plots or gene logos of m6A modified sites to see if there is any consistency with previous reports. Third, in Figure 2h, the authors should show that the change in luciferase activity between wild-type and mutant Irf7-3'UTR reporters is dependent on METTL3 activity by performing METTL3 knockdown or knockout. Also, the authors should describe how they mutagenize the sequences for clarification. Fourth, in Figures 2F and 3C, they showed that IRF7 is upregulated in METTL3-deficient IECs while in Figure 3F, IRF7 is conversely downregulated in METTL3-deficient IECs. This is apparently contradictory to each other. 

6) It is unclear if the augmented expression of IRF7 per se upregulates IFN and ISG expression. Since IRF7 exerts its transcriptional activity upon phosphorylation, the authors should examine IRF7 phosphorylation and total protein levels in METTL3-deficient IECs. Also, it is interesting to see if the phosphorylation of TBK1 is augmented or not. 

7) In Figure 3, the authors utilized METTL3 and IRF7 deficient mice to show the contribution of METTL3-mediated IRF7 regulation in rotavirus infection. However, if IRF7 is totally abrogated, IFN production should be greatly impaired as shown in Figure 3A. Thus, it is not surprising to see that the IFN response is diminished. The authors can use heterozygous IRF7 deficient mice instead to check if upregulation of IRF7 under METTL3 deficiency is critical to control rotavirus infection. 

8) Given no effect of ALKBH5 knockout on rotavirus infection as shown in Figure 4, it is questionable if ALKBH5 has a profound role in the regulation of m6A in IECs. The authors should determine if m6A modification levels are increased in IECs under ALKBH5 deficiency.

Reviewer #1 (Public Review):

In this manuscript, Ketkar et al. describe the role of Lamina L1, L2 and L3 neurons in processing contrast and luminance information in the Drosophila visual system. To this end, they combine calcium imaging to quantify the sensory responses of the three neuron types with neurogenetic silencing to unravel their role in modulating the fly's behavioral responses to moving OFF and ON edges of varying luminance. The main conclusions of the study are that L1, L2, and L3 each encode different aspects of luminance and contrast information, and that they each make complex contributions to visual processing in both the ON and OFF pathway. Together, the three neuron types enable the fly to respond equally well to high-contrast stimuli over a large luminance range. Overall, this is an important study complementing and significantly expanding earlier work on luminance and contrast processing in the (fly) visual system. Given how well-suited and established the Drosophila visual system is as a model for visual processing in general, this paper should be highly relevant to a broad neuroscience readership.

The strengths of the paper are that the authors combine calcium imaging of individual neuron types with behavioral experiments in which they silence these same neuron types individually and in combinations, using comparable visual stimuli in all experiments. The authors test for behavioral necessity and sufficiency of L1, L2, and L3 neurons by using straight-forward silencing of individual neuron types as well as broad silencing of multiple neuron types combined with rescuing individual types. Hence, the authors either silence single neurons, or all neurons other than the target for that particular experiment. This approach allows them to identify the behavioral contributions of L1, L2, and L3 neurons both individually and in combinations. The calcium imaging experiments allow the authors to define which aspects of the visual stimuli each neuron type is encoding. Thus, we get a clear picture of the contributions L1, L2, and L3 make to processing high contrast stimuli in different luminance regimes.

The quality of the data and analysis is high, the data presentation is clear, and the conclusions are convincing. The main area of this manuscript that would benefit from improvements is the writing and presentation.

Reviewer #2 (Public Review):

The visual system must extract two basic features of visual stimuli: luminance, which we perceive as brightness, and contrast, the change in luminance over space or time (this paper focuses on changes over time). Contrast is separately processed by ON and OFF pathways, which encode luminance increments or decrements, respectively. Contrast must be robustly detected even if the overall luminance changes rapidly, as might occur if an animal is moving in and out of shadows. This paper addresses how such a luminance correction occurs in the fly.

In the fly, three types of first-order interneurons - L1, L2, and L3 - transmit information from photoreceptors to the medulla, where ON and OFF encoding emerges. Previous work suggested that all three interneurons primarily encode contrast signals and that they project to distinct pathways: L1 to the ON pathway and L2 and L3 to the OFF pathway. Ketkar et al. show that, contrary to this model, these interneurons encode both contrast and luminance in specific ways and are not cleanly segregated into ON versus OFF inputs.

This study reveals several new insights into early visual processing that are interesting and well-supported by the data:

1) The authors show that behavioral responses to ON stimuli can compensate for rapid changes in luminance. However, the purported sole input to the ON pathway, L1, shows activity that is highly dependent on luminance. This suggests that a luminance correction must arise downstream of L1. These results are analogous to findings previously made by the same group regarding the OFF pathway (Ketkar et al., 2020). The previous paper showed that L2 provides contrast information to the OFF pathway, and L3 provides luminance information to allow for a luminance correction in downstream contrast encoding. But unlike the multiple inputs to the OFF pathway, the ON pathway was thought to only receive input from L1, provoking the question of whether L1 is able to provide both contrast and luminance information.

2) Using well-designed calcium imaging studies, the authors surveyed the responses of the three interneurons and found that they encode different stimulus features: L1 encodes both contrast and luminance, L2 purely encodes contrast, and L3 purely encodes luminance (with a different dependence than L1). These are interesting and important findings revealing how both contrast and luminance encoding are distributed across the three interneurons.

3) Using neuronal manipulations, the authors dissected the contributions of the three interneurons to ON and OFF behavior under changing luminance. These experiments showed that L1 and L3 are required for the luminance correction in the behavior. Moreover, the finding that all three interneurons contribute to both ON and OFF behavior contrasts with the existing model of segregated pathways. Thus, this paper could change the way we think about early visual processing in the fly: rather than relaying similar information to distinct downstream pathways, first-order interneurons relay distinct information to common pathways.

Overall, the major claims of this paper are important and supported by the experiments. There are just a few concerns that I would note:

1) The authors state that they have shown luminance invariance in ON behavior (e.g. line 376-377 of the Discussion), but this is not entirely accurate: the ON behavior decreases as luminance increases. This is still an interesting effect since it's the opposite of what L1 activity does, so it's clear that the circuit is implementing a luminance correction, but it is not "luminance invariance".

2) The visual stimuli presented for most imaging experiments (full-field) are not the same as those presented for behavior (moving edges). It is possible neuronal responses and their encoding of luminance and contrast may differ if tested with the moving edge stimuli (if so, this would be concerning). The authors did image L1 with both types of stimuli and could compare these responses. Also, testing behavior at 34º and imaging at 20º presents a possible discrepancy in comparing these data.

3) I find it puzzling that silencing L1 has little effect on ON behavior at 100% contrast and varying luminance (Figure 3A), but severely affects ON behavior to 100% contrast (and lower values) when different contrasts are interleaved (Figure S1). The authors note this but do not provide a clear explanation of why this might be the case. Aside from mechanism, it is not clear whether the difference is due to varying luminance in the first experiment or varying contrast in the second one (e.g. they could test 100% contrast without varying luminance).

4) I do not entirely agree with the authors' interpretation of the L1 ort rescue experiment for OFF behavior. They state that rescue flies "responded similarly to positive controls". However, the graph shows that the rescue flies generally fall in between the mutant and heterozygote control flies; they resemble the controls at low luminance but resemble the mutants at high luminance. One may conclude that L1 is sufficient to enhance OFF behavior at low luminance, but it is a stretch to say it's a complete rescue.

5) The authors typically use t-tests to analyze experiments with 2 variables (genotype and luminance) and 3 or more conditions per variable. This is not the most appropriate statistical test; typically one would use a two-way ANOVA. At the least, it should be clear whether they are performing corrections for multiple comparisons if performing many t-tests on the same dataset.

Reviewer #3 (Public Review):

Ketkar et al combine calcium imaging and behavioral experiments to investigate the encoding of luminance and contrast in 3 first-order interneurons in the Drosophila lamina: L1, L2, and L3, as well as the role of these signals in moving ON edge behavior across luminance. The behavioral experiments are well performed. The rescue experiments are particularly interesting. Together with silencing they support and nicely extend previous work showing that L1/2/3 are not simply segregated between ON and OFF pathways. My main issue is the link that the authors make between the cellular responses and the behaviors performed and therefore the overall conclusions and claims of the paper about the roles of contrast vs luminance encoding of each neuron type (particularly L1) in the behaviors.

Major concerns:

1. The authors state that the main behavior they study, namely optomotor response to moving light edges at 100% contrast, is "luminance invariant". A strict definition of this would be that behavioral responses are constant with increasing luminance. However, there are very few plots in this paper where this is the case. In almost all examples, the response is decreasing with respect to increasing luminance. The authors do qualify a "nearly" invariant behavior, but this does not change the fact that interpretation of the data in the context of the framing of the paper is often problematic.

2. The manuscript would benefit from clear definitions of luminance and contrast, as well as an explanation of how contrast and luminance sensitivity can be inferred from experiments. In particular, the authors use transient vs. sustained response properties in L1, L2, and L3 as indicators of contrast and luminance sensitivity, but this is not stated clearly. It would be important to explain this to the reader early on.

3. In the manuscript, it is often stated that "calcium imaging experiments reveal that each first order interneuron is unique in its contrast and luminance encoding properties" (line 110). This was shown clearly for L2 and L3 in their previous work in Ketkar et al. 2020, with a well-designed two-step stimulus that was able to tease apart contrast vs. luminance invariance. Unfortunately it does not seem that this level of experimental detail and analysis is applied to L1 here. In particular, the authors state " L1 encodes both contrast and luminance in distinct response components." Line 112, in the summary of their findings. I would not agree that the authors have actually shown this properly in this manuscript.

4. The results as they are stated, are at times not well supported by the data. The manuscript would benefit from a careful assessment of the accuracy and precision of the language used to interpret the data. Sometime just moving some conclusions to the discussion and explaining the assumptions made to reach a particular conclusion would be enough. A few of examples:

o Figure 2: "Lamina neuron types L1-L3 are differently sensitive to contrast and luminance". It is overall true that from the raw traces, the response are different. However the quantification in C-E only pertains to luminance.<br> o Figure 3: "L1 is not required but sufficient for ON behavior across luminance". The data convincingly shows this. I would however point out that the statement "this data [..] highlights its behavioral relevant role of its luminance component" line 231 is an overstatement.<br> o Figure 6: "L1 luminance signal is required and sufficient for OFF behavior" the data presented shows convincingly that when L1 is inactive the behavior becomes (more) intensity variant. However, it does not show that it is the "luminance signal" in L1 that is required for this effect. In general, because L1 has a sustained and a transient response, it is difficult to strictly implicate one or the other in supporting any behavior, short of manipulating L1 to make it fully transient or fully sustained.<br> o It is often not clear which conclusions stem from this work and which from their previous work Ketkar et al. 2020, or even other previous work on contrast sensitivity in particular. Clarifying this might help with my concern about statements not well supported by the data in this paper, and also justify their overall novelty. In general the manuscript assumes familiarity with this previous work, which is not always helpful for the reader.

Reviewer #1 (Public Review):

S-palmitoylation is one of the most common post-translational modifications in the brain. scRNAseq datasets provide crucial data about the diversity of neurons and non-neuronal cells and their regional expression patterns. A recent increase in the publication of these datasets necessitates tools that make a comparison between these possible and easily accessible. This need is addressed in the current work where the authors developed a web tool that curates datasets from several previous publications allowing direct comparison, the evaluation of developmental patterns of expression, and exploration of cell-type diversity.

The authors use several examples demonstrating the utility of this tool and how it can lead to hypotheses development regarding the role of palmitoylating and depalmitoylating enzymes in various neuronal functions and dysfunctions. Through these examples, the authors make a convincing case for the utility of their web tool. The web tool depicted in this manuscript is user-friendly and provides easy access to a large amount of data. The benefit to the readers is the universal visualization and evaluation tool that will be useful for any lab that studies palmitoylating and depalmitoylating enzymes and their accessory proteins

Reviewer #2 (Public Review):

Using available RNAseq data the S. Bamji team builds a web resource that permits assessing patterns and formulating hypothesis regarding the expression of (de)palimitoylating enzymes in the mouse nervous systems. The authors further showcase predictions and validation methodologies.

Strengths:

- BrainPalmSeq. The established database provides a curated and easy-to-work platform which will enable researchers to survey the expression patterns of S-acylation enzymes and their potential co-expressed substrates within the mouse nervous system. This constitutes a novel resourceful tool that will be useful not only for research concerning neurobiology-related fields but also to understand the fundamental aspects of S-acylation cascades.

- This study describes the first RNA expression patterns of ZDHHC and APT enzymes within the nervous system. It highlights a remarkable transcriptional diversity, which shows that ZDHHC enzymes have regional- and cell-specific expression patterns which can be correlated with abundantly co-expressed substrates and tissue and cell specific functions. Furthermore, within the first 4 figures the authors describe an interesting roadmap which led them to uncover novel hypotheses regarding the functional associations between particular ZDHHC groups, the palmitoylated substrates enriched in specific brain regions and associated cell types and finally the cellular functions attributed to different brain compartments and cell types.

- In figure 5 the authors demonstrate that the expression patterns of ZDHHC 8,9 and PPT1 correlate robustly with the anatomic brain regions and specific cell types affected by loss-of-function mutations in the same enzymes. This analysis further confirms the scope and potential of the BrainPalmSeq database.

Weaknesses:

- It is surprising that certain enzymes with established depalmitoylation activity were excluded from BrainPalmSeq data-base (e.g. ABHD4, ABHD11, ABHD12, ABHD6)

- Albeit not essential it will be of great interest to include in the established database enzymes necessary for synthesis of ACYL-CoA (e.g. ACSL enzymes). One improvement may include the ability of future researchers to add such curated analysis to the platform within future research studies.

- The experimental validation presented in figure 6 relies on over-expression of substrates and ZDHHC enzymes. This setup is known to often provide unspecific S-acylation events which result from excess enzyme or substrate availability. Hence, such validation would be greatly strengthened by loss of function experiments.

- The authors relevantly use in-situ hybridization images from the Allen Brain atlas to validate their predictions. Although it is understandable that an extensive experimental validation of the predictions here established would be out of the scope of the current study, this work could be improved by validating the RNA expression at the protein level of certain abundant ZDHHC enzymes in available neuro-associated cell types.

- It would be interesting if the authors would further compare the predicted association clusters (e.g. figure 1), substrates (figures 1 and 2), and S-acylation pairs (figure 4) here determine, with previous determined ZDHHC enzyme associations described in different cell types and biological systems. Alternatively, further relevant validation could include testing whether further established ZDHHC-ZDHHC cascades (e.g. ZDHHC3-7) can be also detected with specific cells or regions of the CNS.

- Figure 3B: it is not clear why the cluster of zdhhcs with high layer specific expression displayed at the top of the graph does not follow the low-to-high expression scale of the table.

- Figure 4D: the more relevant potential cooperative pairs (ZDHHCs-APTs) could be highlighted in more contrasted colours.

Conclusions are justified by their data:<br> This study demonstrates that cellular and tissue localization of ZDHHCs and APTs enzymes is controlled by different transcriptional networks, may underly their tissue and cellular functions, and be used to predict and determine enzyme-substrate associations The future use of the database will further explore this potential.

Reviewer #3 (Public Review):

Wild and co-workers present a compendium of expression data for enzymes regulating protein palmitoylation based on single cell RNA seq data from different brain regions. The compendium has been developed into an online tool to facilitate user engagement and access. Using the tool, the authors have visualised expression of the palmitoylating zDHHC enzymes throughout the mouse nervous system and provide detailed snapshots of these enzymes expression and co-expression with palmitoylated proteins in the hippocampus and pyramidal neurons. Regional zDHHC enzyme expression data is used to explain pathologies associated with mutations in the zDHHCs and to predict enzyme / substrate relationships. As the authors themselves acknowledge that their analysis of this dataset is merely the tip of the iceberg, and much of the value of this investigation lies in the opportunities it will afford others in the field to explore expression patterns of palmitoylating and depalmitoylating enzymes in the brain.

Strengths:<br> The visual representation of the data is strong and emphasises the eye-catching dominance of certain zDHHCs throughout the nervous system. The correlation between the expression of particular zDHHCs with particular neurotransmitters is highly likely to seed future investigations. Correlations (and lack of correlations) between the expression of particular zDHHCs and accessory proteins will clearly be of interest to the field and suggest multiple future lines of research.

The detailed cell-type specific maps of zDHHC expression in the hippocampus and somatosensory cortex highlight the heterogeneity of zDHHC expression and provide evidence about the processes controlled by these enzymes to which palmitoylation likely contributes. Again, this will be an incredibly useful resource for the field in the future.

A particular highlight of the investigation is the insight offered by the analysis of phenotypes associated with loss of function mutations of some zDHHC-PATs. By highlighting the brain region(s) most 'dependent' on a particular zDHHC-PAT, the authors offer insight into the likely driving forces behind disease pathogenesis.

Weaknesses:

There is a vast amount of data available and the description and discussion of this could be endless, but there are a few points that could be brought out in more detail. For example, the correlation (or lack of correlation) of expression of the proposed zDHHC-PAT accessory proteins with their cognate zDHHCs. The dominance of a relatively small number of zDHHC enzymes (20, 2, 17, 3, 21, 8) in the CNS also merits some discussion. Is the combination of a high-capacity, low-specificity enzyme (zDHHC3) with others that are regarded as more 'specific'? I believe none of these are ER-resident - they represent Golgi and PM?

Reviewer #1 (Public Review):

The authors report that neurons expressing tachykinin-1 in the parasubthalamic nucleus (PSTNTac1) play significant roles in anorexic feeding behavior in response to anorexigenic hormones such as amylin, cholecystokinin, and peptide YY. They found that the injections of these peptides dramatically increased neuronal activation in PSTNTac1 neurons, and optogenetic/chemogenetic activation of PSTNTac1 neurons reduced food intake. In addition, they found that chemogenetic inhibition of PSTNTac1 neurons partially reversed the anorexic effects of the peptides. Further, they determined the key downstream sites of PSTNTac1 neurons using optogenetics. The authors also addressed another neuronal population, corticotropin-releasing hormone-expressing neurons in PSTN, but these neurons are unlikely to be involved in food intake behavior. In sum, the experiments are well-designed, the manuscript is well-written, and the results support their conclusion.

Reviewer #2 (Public Review):

In this manuscript the authors examine a relatively understudied brain area, the parasubthalamic nucleus (PSTN) to determine the molecular signatures of resident neurons and how they contribute to regulating ingestive behavior. They use a combination of cre-expressing mice and site-specific injection of cre-dependent AAVs to modulate the PSTN neurons, and specific subsets within this region. This study arises from their finding that the PSTN provides projections to the parabrachial area, a region now well-established to modulate feeding. They then demonstrate that the PSTN contains two separate sets of neurons that are marked by their expression of Tac1 and Crh respectively. Both of these PSTN populations are activated by feeding stimuli and the anorectic hormones amylin, CCK and PYY, as assessed via cFos and more temporally specific fiber photometry measurement. Moreover, both PSTN subpopulations project to roughly the same brain areas. Intriguingly, only experimental inhibition or activation of the PSTNTac1 subset modulates feeding, while neither activation nor inhibition of the PSTNCrh subset budges feeding.

The major strength of the manuscript is its use of a wide variety of approaches to map the connections of the PSTN neurons, their endogenous regulation and employing both chemogenetic and optogenetic approaches to describe the roles of the PSTN neurons in ingestive behavior. What results is a comprehensive survey of the PSTN neurons, and a mostly complete comparison of the PSTNTac1 vs. PSTNCrh subpopulations. A weakness is that not all of the manipulations provide a 1:1 comparison of the subpopulations, which would be valuable. Given the wealth of methods herein, the data are presented logically, the text is economical, and there is a parsimonious interpretation of what the respective populations do and do not do. This work is exemplary for those studying ingestive behavior of how to combine the current neuroscience tools to characterize the molecular signature, pathways and function of neural populations. It will therefore be of broad interest to the neuroscience community using these tools, and more specifically, to the ingestive behavior field seeking to define which neurons coordinate feeding.

Reviewer #3 (Public Review):

Two cell types in the parasubthalamic nucleus (a region of the posterior hypothalamus) are activated following food intake. The authors determine that the Tac1 expressing population is sufficient to suppress food intake and the Crh population does not influence food intake. Further, the authors demonstrate that only the Tac1 population projects to the PBN. The Tac1 neurons are transiently activated following food presentation or satiation hormones (for about 1 minute). This transient change in activity is interesting and fits into a lot of other recently published work showing transient neural activity changes that are involved in longer term behavior. Longer term activation of these neurons reduces food intake and the authors begin to explore the circuits/networks that these neurons influence. Overall, the work is well done and the experiments support the conclusions. Some minor clarifications could enhance the manuscript and could be addressed through further analysis or adding in text.

1. What % of the overall PSTN neurons are tac1/crh (ie, how many other cell types are there?). Or what % of the vglut2 neurons do they make. This just requires further analysis of the current dataset. And, are there any GABAergic cells (like are the PV GABAergic)?

2. The 60 second increase in tac1 neuron activity is interesting. In the discussion, the authors present some plausible arguments for how that may affect feeding for hours. Additionally, it would be nice to point out that this is a recurring theme. This occurs in other neuron populations that influence food intake. Although this is seemingly counterintuitive, I think it is good to mention as these short-term neural activity changes are clearly having large effects on behavior and it is important for everyone to realize this.

3. Something a little strange with the meal frequency. I thought CCK reduced meal size not frequency. Why does the rescue then increase frequency? Could it be that the rescue to the CCK is by a different means than just blocking the effect of CCK? Adding some language to the discussion about how to interpret the satiation peptide data would be useful.

4. The axonal stimulation data needs qualification - as axons could project to multiple target regions (like the projections to the PVT could also have a collateral to the CEA). For this type of experiment, I prefer to use the phrase "neurons with a projection to region X do behavior Y". Otherwise, the implication in reading the results is that the particular projection is mediating the behavior. Also, the collateral issue, which is qualified in the discussion, should be mentioned here.

Reviewer #1 (Public Review):

This study provides data suggesting that tonic presynaptic a7 nicotinic receptor activity enhances corticostriatal input-mediated excitation of striatal medium spiny neurons; the data also suggest that tonic a4b2 nicotinic receptor activity on PV-fast spiking GABA interneurons inhibits striatal medium spiny neurons. These data advance our understanding about the complex cholinergic regulation of striatal neuronal circuits.

The presented data are generally clean and high quality; but there are some problems that require the authors' attention.

1. In this study, ADP is a key parameter manipulated by several pharmacological treatments. But it is not clearly defined. The authors indicate EPSP and ADP are distinct by stating "LED pulse of increasing intensity generates excitatory postsynaptic potentials (EPSPs), or an AP followed by an after depolarization (ADP)." But the data (e.g. Fig. 1B) indicates that much of the ADP is probably EPSP. Please clarify.

If much of the ADP is indeed EPSP, how are the data interpretation and the overall conclusion affected?

In Fig. 1F, ADP is absent. Why? Please clarify.

If ADP is distinct from EPSP here in MSNs, has it been reported in the literature, and how is it generated?

2. In Fig. 1F, the holding potential for mecamylamine is a few mV more negative than the control, but the spike latency is shorter under mecamylamine. This is hard to understand because membrane potential (current-injection-induced depolarization + EPSP) determines spike firing and latency. If the holding potential is the same, then it's easy to understand (larger EPSP under mycamylamine).

3. Data in Fig. 2D, E are weak. The spiking ability of whole-cell recorded neurons often declines over time (evidence: the AP duration for the red trace is longer); recovery/partial recovery from MLA is needed for the data to be reliable.<br> Fig. 2E shows 8 cells: 6 had no response, 2 increased. Sample size needs to increase.

4. Fig. 7: the data on DhbE increasing AP duration is not convincing: no effect in 4 neurons, increase in 4 other neurons, and decrease in other neurons. Data ismore important than p<0.05. How do you interpret DhbE increasing AP duration?

Fig. 7F shows AP duration for PV-FSI is around 1.75 ms (some are over 2 ms, recorded at 35 C). This is unusually long. Also, the AP rise time is around 1.4 ms, very long. 1.75 ms total rise time vs. 1.4 ms for just rise: they do not add up?

Reviewer #2 (Public Review):

This manuscript examines one aspect of how acetylcholine influences striatal microcircuit function. While striatal cholinergic interneurons are known to be engaged in key events and tasks related to the basal ganglia in vivo, and pharmacological studies indicate cholinergic signaling is complex and critical to striatal function, the mechanistic details by which acetylcholine regulates individual cell types within the striatum, as well as how these integrate to shape striatal output, remain largely unknown. This work thus addresses an important problem in the basal ganglia field, with likely relevance to both normal function and disease-related dysfunction. The authors used a brain slice preparation in which a large number of excitatory cortical inputs to the striatum are activated, and they could measure the resulting activation of striatal projection neurons (SPNs). Their primary finding was that in this preparation, blocking nicotinic acetylcholine signaling resulted in more rapid activation of SPNs. They then explored some of the potential mechanisms for this phenomenon, and conclude that in their preparation, cholinergic interneurons are engaged both tonically and phasically, resulting in recruitment of local GABAergic interneurons that provide feedforward inhibition onto SPNs. They show that one striatal GABAergic interneuron subclass, PV-FSI, are modestly excited by tonic nicotinic signaling, and suggest this may be one contributor to their primary finding.

Strengths of the study include the focus on cholinergic signaling across multiple striatal cell types, careful and clearly displayed slice electrophysiology, good writing, and a methodical approach to pharmacology.

Weaknesses include reliance on the Thy1-ChR2 line to activate excitatory cortical inputs to the striatum (this line may be less specific to cortical pyramidal neurons than a specific Cre recombinase mouse line used with Cre-dependent ChR2, and thus have unintended influences on the results), and despite a strong start, a fairly weak mechanistic exploration of what GABAergic neuron subclasses might contribute to their original phenomenon.

Reviewer #3 (Public Review):

The manuscript by Matityahu et al., investigated the role of tonic activation of AChRs on the spike timing of striatal spiny projection neurons (SPNs) in acute striatal slices. By selectively activation of corticostrialal projections using optogenetic tools (ChR2), they find that pharmacological blockade of presynaptic α7 nAChRs delays SPN spikes, whereas blockade of α4β2 nAChRs on GABAergic interneurons advances SPN spikes. The work is carefully done with proper control experiments, and the main conclusions are mostly well supported by data.

Although they only constitute ~1% of the total striatal neurons in rodents and humans, cholinergic interneurons (ChINs) are gatekeepers of striatal circuitry because of their extensively arborized axons and varicosities which tonically release ACh. Whereas the role of muscarinic AChRs (mAChRs) in modulating striatal output has been well established, the role of nAChRs (especially the tonic activation) remains to be elucidated. The study is solid and the results are new and convincing. The data suggest that tonic activation of nAChRs may place a "brake" on SPN activity, and the lift of this brake during pauses of ChIN firing in response to salient stimuli may be critical for striatal information processing and learning. The findings from this study will enhance our understanding of the role of tonic nAChR activation in controlling SPNs and striatal output.

Unjustified Conclusions and Suggestions:<br> 1) The change of the SPN spike timing by AChR modulation is on a few milliseconds time scale. To make the current study more significant, the authors should design and perform additional experiments to demonstrate the functional consequence in controlling striatal output and learning. For example, will activation or blockade of nAChRs have effects on striatal STDP?<br> 2) Modulation of striatal circuitry is complex. The addition of a diagram illustrating the hypothesis and key results would help.

Reviewer #3 (Public Review):

In this manuscript, Bitzenhofer and colleagues report multiple findings regarding processing of odors in the lateral entorhinal cortex (LEC). Specifically, they show:

1. L2 neurons preferentially respond to odors in phase with sniffing and do so relatively early relative to sniff onset.

2. They use optogenetic manipulations during and odor-guided 2AFC task to investigate the impact of silencing LEC on odor discrimination, finding that this silencing reduces the ability of mice to discriminate odor identify (one set of experiments) and odor intensity (a second set of experiments) to chance level.

3. They perform fine-grained analysis on odor coding in LEC, investigating spike rate vs. sniff phase timing and find that while rate conveys information regarding odor identity, timing represents odor intensity.

4. They suggest a physiologically plausible mechanism for temporal coding of odor input in the LEC supported by relative spike timing between cell types.

5. Finally, they compare odor coding in the LEC to that in the CA1 region of the hippocampus and find that odor intensity in CA1 is only encoded by spike timing rather than spike rate.

Strengths:

The neural population recordings and encoding analyses are informative and will allow direct comparison to other work done in the olfactory bulb and cortex of awake mice. The behavior has been conducted with appropriate controls for off-target optogenetic effects. The authors' effort to connect the encoding they demonstrate with plausible mechanisms through both recording of cell populations in awake mice as well as in vitro examination of LEC circuitry enhance the impact of this work.

Weaknesses:

Some additional information could be included to further enable direct comparison to previous work. The optogenetic behavioral studies, although conducted with appropriate controls, suffer from the usual issues with interpretation, since this method suddenly disrupts major cortical circuits during behavior, which can often have non-specific effects and could significantly decrease mouse performance on the task independent of its impact on odor encoding in the LEC.

Impact:

The olfactory system, as the most "shallow" of the sensory systems, conveys information broadly to numerous cortical structures directly from the first olfactory relay in the brain, the olfactory bulb. This work will substantially contribute to an understanding of the broad range of encoding that occurs across these cortical structures by adding significant information regarding odor encoding in the LEC.

Reviewer #1 (Public Review):

The lateral entorhinal cortex (LEC) receives direct inputs from the olfactory bulb (OB) but their odor response properties have not been well characterized despite a recent increase in interests in the role of LEC in olfactory behaviors. In this study, Bitzenhofer and colleagues provide unprecedented details of odor response properties of layer 2 cells in LEC. The authors first show that LEC neurons respond to odors with a rapid burst of activity time-locked to inhalation onset, similarly to the piriform cortex (PCx), but distinct from the OB. Firing rates of LEC ensembles conveyed information about odor identify whereas timing of spikes odor intensity. The authors then examined the difference between two major cell types in LEC layer 2 - fan cells and pyramidal neurons, and found that, on average, fan cells responded earlier than pyramidal neurons, and pyramidal neurons, but not fan cells, changed their peak timing in response to changes in concentrations, providing a basis for temporal coding of odor concentrations. Additionally, the authors show that inactivation of LEC impairs odor discrimination based on either identify or intensity, and demonstrate different cellular properties of fan cells and pyramidal neurons. Finally, the authors also examined the odor response properties of hippocampal CA1 neurons, and showed that odor identify can be decoded by firing rate responses, while decoding of odor concentration depended on spike timing.

The authors performed a large amount of experiments, and provide an impressive set of data regarding odor response properties of LEC layer 2 neurons in a cell type specific manner. The results reported are very interesting, and will be a point of reference for future studies on odor coding and processing in the LEC. The manuscript is clearly written, and data are well analyzed and presented clearly. I have only relatively minor concerns or suggestions.

1. The authors infer the time at which "mice could discriminate odors" from the time at which d-prime becomes significantly different between baseline and odor stimulation conditions (line 111 and line 121). However, the statistical test applied to these data does not guarantee that an observer can accurately discriminate odors. For example, a small p-value can be obtained even when discrimination accuracy is only slightly above chance if there are many trials. The statement such as "mice could discriminate two odors by as early as 225 ms after inhalation onset" (line 111) can be misleading because this might sound as if mice can accurately discriminate odors at this timepoint, while this is not necessarily the case (as indicated by the d-prime value).

2. Optogenetic identification can be a little tricky when identifying excitatory neurons as in this study. Please discuss some rational or difficulty regarding how to distinguish those that are activated directly by light from those activated indirectly (i.e. synaptically). Do the results hold if the authors use only those that the authors are more confident about identification?

3. The authors sort odor response profiles by peak timing, and indicate that odor responses peak at different timing that tiles respiration cycles. However, this analysis does not indicate the reliability of peak timing. Sorting random activity by "peak timing" could generate similar figure. One way to show the reliability or significance of peaks is to cross-validate. For instance, one can use a half of the trials to sort, and plot the rest of the trials. If the peak timing is reliable, the original pattern will be replicated by the other half, and those neurons that are not reliable will lose their peaks. Please use such a method so that we can evaluate the reliability of peaks.

Reviewer #2 (Public Review):

In this study, Bitzenhofer et al recorded odor-evoked activity in the LEC and examined the coding of odor identity and intensity using extracellular recordings in head-fixed mice, and used the standard suite of quantitative tools to interpret these data (decoding analyses, dimensionality reduction, etc). In addition, they performed behavioral experiments to show the necessity of LEC in odor identity and intensity discrimination, and deploy some elegant and straightforward 'circuit-busting' slice physiology experiments to characterize this circuit. Importantly, they performed some of their experiments in Ntng1-cre and Calb-cre mice, which allowed them to differentiate between the two major classes of LEC principal neurons, fan cells and pyramidal cells, respectively. Many of their results are contrasted with what has previously been observed in the piriform cortex (PCx), where odor coding has been studied much more extensively.

Their major conclusions are:

Cells in the LEC respond rapidly to odor stimuli. Within the first 300 ms after inhalation, odor identity is encoded by the ensemble of active neurons, while odor intensity (more specifically, responses to different concentrations) is encoded by the timing of the LEC response; specifically, the synchrony of the response. These coding strategies have been described in the PCx by Bolding & Franks. Bolding also found two populations of responses to different concentrations: one population of responses was rapid and barely changed with concentration and the second population of responses had onset latencies that decreased with increasing concentration. Roland et al also found two populations of responses using calcium imaging in anesthetized mice: one population of responses was concentration-dependent and another population was 'concentration-invariant'. However, neither Bolding nor Roland were able to determine whether these populations of responses emerged from distinct populations of cells. Here, the authors elegantly register these two response types in LEC to different cell types: fan cells respond early and stably, and pyramidal cells response latencies decrease with concentration. This is a novel and important finding. They also showed that, unlike PCx or LEC where concentration primarily affects timing rather than rate/number, odor concentration in CA1 is only reflected in the timing of responses.

Using optogenetic suppression of LEC in a 2AFC task, the authors purport to show that LEC is required for both the discrimination of odor identity and odor intensity. If true, this is an important result, but see below.

In slice experiments, the authors characterize the differential connectivity of fan and pyramidal cells to direct olfactory bulb input, input from PCx, and inhibitory inputs from SOM and PV cells. This work is elegant, novel, and important, although it is a little out of place in this manuscript. As such, their findings are irrelevant/orthogonal to the rest of the results in this study. But fine.

The simultaneous recordings from three different stations along the olfactory pathway are impressive.

Major concern<br> My major concern with this manuscript regards the behavioral experiments. The authors show that blue light over the LEC in GAD2-Cre/Ai32 mice completely abolishes (i.e. to chance) the mouse's ability to perform a 2AFC task discriminating between either two different odorants or one odorant at different concentrations. Their interpretation is that LEC is required for rapid odor-driven behavior. The sensory component of the task is so easy, and the effect is so striking that I find this result surprising and almost too good to be true. The authors do control for a blue-light distraction effect by repeating the experiments in mice that don't express ChR2, but do not control for the effect of rapidly shutting down a large part of the sensory/limbic system. If they did this experiment in the bulb I would be impressed with how clean the result was but not conceptually surprised by the outcome. I think a different negative control is needed here to convince me that the LEC is necessary for this simple sensory discrimination task. For example, the authors could activate all the interneurons (i.e. use this protocol) in another part of the brain, ideally in the olfactory pathway not immediately upstream of the LEC, and show that the behavior is not affected.

More specifically, both the presentation and the interpretation of the data are confusing. First, there is a lack of detail about the behavioral task. I was not sure exactly when the light comes on and goes off, when the cue was presented, and when the reward was presented. In the manuscript they say (line 108) "...used to suppress activity during odor delivery on a random subset...". There is nothing more about this in the figure legend or Methods. The only clue to this is the dotted line in the 'LED On' example at the bottom of Fig. 2a. The authors also say that (line 660) "Trials were initiated with a 50 ms tone." When exactly was the tone presented? In the absence of any other information, I assume it was presented at odor onset. When was the reward presented? Lines 106-7 say "Mice were free to report their choice (left or right lick) at any time within 2 s of odor onset." Presumably this means the reward was presented to one of the ports for 2 seconds, starting at odor onset.

These details matter because the authors want to claim that "LEC is essential for rapid odor-driven behavior." The data presented in support of this claim are (1) that mice perform this task at chance levels in LED On trials, presumably based on which port the mouse licked first (this is the 'essential' part), and (2) that in control in LED Off trials, d' becomes statistically different from baseline after ~200 ms (this is the 'rapid' part).

On first reading, these suggested that shutting off LEC makes odor discrimination worse and/or slower. However, the supplementary data clarifies several things. First, the mice never Miss (Fig.2S.2a & c), meaning then they always lick. Second, in LED Off trials (F2S2 & e), the mice make few mistakes, and these only occur immediately after inhalation, presumably meaning the mice occasionally guess, possibly in response to the auditory cue. Thus, the mean time to lick is much shorter for Error trials than Correct trials. To state the obvious, the mice often wait >300 ms before they lick, and when they do wait, they never make mistakes. Now, in the LED On trials, the mice almost always lick within the first 300 ms and perform at chance levels, with the distribution of lick times for Correct and Error trials almost overlapping. In fact, although the authors claim LEC is required for rapid odor discrimination, the mean time to lick on Correct trials appears to decrease in LED On trials. This makes me think that the mice are making ballistic guesses in response to the tone in LED On cases, which doesn't necessarily implicate a dependence on LEC for odor discrimination.

The authors' interpretation of their data would be more solid if, for example, there were a delay between the auditory cue and odor delivery and/or if the reward was only available with some delay after the odor offset. Here, however, it seems just as likely as not that the mice are making ballistic guesses in response to the tone in LED On cases, which doesn't necessarily involve dependence on LEC for odor discrimination. Here, the divergence of d' from baseline in the control (i.e LED Off) condition seems mostly because mice take longer to correctly discriminate under control conditions. While this is not formally contradictory to LEC is essential for rapid odor-driven behavior", it is nevertheless a bit contrived and misleading. An interesting (thought) experiment is what would happen if the authors presented a tone but no odor. I would guess that the mice would continue licking randomly in Light On trials.

Reviewer #1 (Public Review):

This manuscript elegantly demonstrates that the degradation of PTPN14 by human papillomavirus (HPV) 16 and 18 E7 proteins previously reported by the authors is essential for E7-mediated YAP1 activation. This is important for E7-mediated maintenance of basal cell state and presumably persistence of HPV infection. The authors use a series of innovative tissue models combined with validation in clinical samples to demonstrate the importance of YAP1 activation in high-risk HPV pathogenesis.

The data are of high quality with excellent controls. The manuscript is well-written and the rationale of each experiment easy to follow. In general the results support the authors conclusions. I have the following suggestion to improve the manuscript:

The enhanced nuclear expression of YAP in the basal cells of epithelia expressing HPV16/18 E7 is difficult to see in the low resolution IF images shown. The magnified images do show enhanced expression compared to HFK cultures, but to remove any bias in selection of enhanced areas, could the authors include quantification of the distribution of IF signal in the basal cells, compared to the suprabasal cells, of the epithelia shown with statistical analysis?

Figure 2 would also benefit from quantification as described above.

Reviewer #2 (Public Review):

Strengths:

A major strength of this report is the use of several different technical approaches, the results from which converge to provide several types of data supporting their conclusions. These various techniques include genetic knockdown/overexpression in primary keratinocytes, organotypic raft cultures, laser-capture microdissection, cell fate monitoring assays, and analysis of publicly available datasets. The manuscript is well-written and the figures are well-made.

Weaknesses:

Overall, there are only a few minor weaknesses related to figure quality and presentation.

Are claims/conclusions justified by data?<br> Overall, the authors' conclusions are adequately justified by the data. However, there were a few interpretations I felt were somewhat overstated given the experiments performed and data provided.

1. The first issue relates to the interpretation/conclusion of the results from experiments analyzing basal cell number. In Figure 2, the basal cell number was indeed reduced in R84S compared to WT E7. However, it was not reduced to parental HFK levels, suggesting other E7 activities are involved in increasing basal cell number. A similar observation is presented in Figure 7 (E-F), where the R84S E7 mutant still had significantly higher basal cell retention than the empty vector control, albeit lower than WT E7. While their data certainly indicates that the binding and subsequent degradation of PTPN14 is an E7 function important to increasing basal cell number and retention, there are clearly other E7 functions involved. While the authors don't necessarily over-interpret these findings, the possibility that other E7 functions are involved is not explicitly acknowledged or explored in the Discussion.

2. The second issue pertains to the findings related to the effect on differentiation upon modulation of key Hippo pathway components (Figure 4). It does not appear that the authors performed these studies in the presence of any well-known stimuli that induce the differentiation process in keratinocytes grown in 2D culture (high calcium, high serum, etc) nor did they use these cells in organotypic rafts wherein differentiation occurs during the raft stratification process. This is particularly true in the studies exploring PTPN14 plus LATS1/2 silencing and the effect on repression of keratinocyte differentiation. Whereas it seems PTPN14 itself was serving as the differentiation stimuli in earlier experiments (Figure 4C/D), it does not appear any differentiation stimuli were provided in the experiments shown in Figures 4E-I. For these reasons, the interpretation drawn by the authors that "...inactivation of three different YAP1 inhibitors dampens differentiation gene expression" (Line 220-221) and "inactivation of LATS1 or LATS2...also repressed differentiation genes" (Lines 349-350) seems specific to endogenous levels of differentiation genes. It seems difficult to conclude that inactivation of the Hippo pathway is actively repressing the induction of differentiation if the cells are not being treated with stimuli to induce differentiation.

Reviewer #2 (Public Review):

The topic of actin driven cell motility will be of general interest. The authors provide new ideas for the field of research, the modeling methods and model design seem valid and appropriate, and the paper is well written. My main concern is whether the fluctuation spectrum derived from the model corresponds to that of the experimental images.

Visually (and perhaps mistakenly on my part), the experimental analysis of Fig. 1b seems to show a nearly periodic red-blue curvature pattern with a scale of order 4 microns that persists over 10-15 sec, a time over which the cell advances by a distance of order the size of the lamellipodium. While such a nearly periodic pattern would be expected to lead to peaks at the corresponding periods and wavelength in Fig. 1e and 1g, no clear peaks are observed in those figures.

However, the autocorrelation functions in Fig. 1e are not plotted over times comparable to 10-15 sec. Further, the analysis of the leading edge contour is done with a background subtraction method that removes fluctuations over 7 microns, a length scale that may be dampening a real peak at ~4 microns in Fig. 1g.

The feature I am pointing out could be occurring at a length scale in between the shortest length scales (a pixel) and the longest ones (cell size) in the system. Instabilities, a main theme of the paper, frequently get amplified at a characteristic length scale. Here there may be a length scale that is selected by the system that may not be picked up by the analysis or the proposed model.

Reviewer #1 (Public Review):

Here, Garner and Theriot investigate the question of leading edge maintenance in migrating cells. They analyze small and dynamic fluctuations of the membrane at the cell front in order to understand how membrane stability emerges from these seemingly random and uncoordinated events. Experimental data enable description of fluctuations at different length scales and their relaxation in a visco-elastic manner.

To gain knowledge about this system, a stochastic model of branched actin network growth against a membrane is developed, taking into account a number of molecular reactions at play. This model recapitulates correctly the cellular observations, with correct orientation of the filaments and similar membrane fluctuations. Also, addition of Latrunculin B which leads in vivo to increased amplitude of the fluctuations with decreased fluctuation rates is described in the model when nucleation and elongation rates are decreased.

Changing the different parameters of the model reveals that two features are critically important (2): a branching reaction occurring solely at proximity of the membrane, and the possibility for filaments to spread laterally. Other important parameter includes the Arp2/3 complex branching angle, where a 70-80{degree sign} geometry is found to be optimal for minimizing actin density fluctuations and leading edge fluctuation amplitudes.

This work is of excellent quality and its conclusions seem justified. However, it would be important to have more details on the limit of detection of membrane shape fluctuations and network growth by phase contrast microscopy.

Reviewer #3 (Public Review):

In their manuscript, Lee and colleagues explore the dynamics of the functional community structure of the brain (as measured with fMRI) during sustained experimental pain and provide several potentially highly valuable insights into, and evaluate the predictive capacity of, the underlying dynamic processes.

The applied methodology is novel but, at the same time, straightforward and has solid foundations. The findings are very interesting and, potentially, of high scientific impact as they may significantly push the boundaries of our understanding of the dynamic neural processes during sustained pain, with a (somewhat limited) potential for clinical translation.

However (Major Issue 1), after reading the current manuscript version, not all of my doubts have been dissolved regrading the specificity of the results to pain.<br> Moreover (Major Issue 2), some of the results (specifically, those related to the group level analysis of community differences) do not seem to be underpinned with a proper statistical inference in the current version of the manuscript and, therefore, their presentation and discussion may not be proportional to the degree of evidence.

Despite these issues, this is, in general, a high quality work with a high level of novelty and - after addressing the issues - it has a very high potential for becoming an important contribution (and a very interesting read) to the pain-research community and beyond.

Major Issue 1:

The main issue with the manuscript is that it remains somewhat unclear, how specific the results are to pain.

Differences between the control resting state and the capsaicin trials might be - at least partially - driven by other factors, like:<br> - motion artifacts<br> - saliency, attention, axiety, etc.<br> Differences between stages over the time-course might, additionally, be driven by scanner drifts (to which the applied approach might be less sensitive, but the possibility is still there ) or other gradual processes, e.g. shifts in arousal, attention shifts, alertness, etc.

All the above factors might emerge as confounding bias in both of the predictive models.

This problem should be thoroughly discussed, and at least the following extra analyses are recommended, in order to attenuate concerns related to the overall specificity and neurobiological validity of the results:<br> - reporting of, and testing for motion estimates (mean, max, median framewise displacement or anything similar)<br> - examining whether these factors might, at least partially, drive the predictive models.<br> - e.g. applying the PCR model on the resting state data and verifying of the predicted timecourse is flat (no inverse U-shape, that is characteristic to all capsaicin trials).

Not using the additional sessions (bitter taste, aversive odor, phasic heat) feels like a missed opportunity, as they could also be very helpful in addressing this issue.

Major Issue 2:

Another important issue with the manuscript is the (apparent) lack of statistical inference when analyzing the differences in the group-level consensus community structures (both when comparing capsaicin to control and when analysing changes over the time-course of the capsaicin-challenge).

Although I agree that the observed changes seem biologically plausible and fit very well to previous results, without proper statistical inference we can't determine, how likely such differences are to emerge just by chance.

This makes all results on Figs. 2 and 3, and points 1, 4 and 5 in the discussion partially or fully speculative or weakly underpinned, comprising a large proportion of the current version of the manuscript.

Let me note, that this issue only affects part of the results and the remaining - more solid - results may already provide a substantial scientific contribution.

Therefore I see two main ways of handling Major Issue 2:<br> - enhancing (or clarifying potential misunderstandings regarding) the methodology (see my concrete, and hopefully feasible, suggestions in the "private part" of the review),<br> - de-weighting the presentation and the discussion of the related results.

I believe there are many ways to test the significance of these differences. I highlight two possible, permutation testing-based ideas.

Idea 1: permuting the labels ctr-capsaicin, or early-mid-late, repeating the analysis, constructing the proper null distribution of e.g. the community size changes and obtain the p-values.

Idea 2: "trace back" communities to the individual level and do (nonparametric) statistical inference there.

Reviewer #1 (Public Review):

This study investigates the dynamics of brain network connectivity during sustained experimental pain in healthy human participants. To this end, capsaicin was applied to the tongues of two cohorts of participants (discovery cohort, N=48; replication cohort, N=74). This procedure resulted in pain for several minutes. During sustained pain, pain avoidance/intensity ratings and fMRI scans were obtained. The analyses (i) compare the pain state with a resting state, (ii) assess the dynamics of brain networks during sustained pain, and (iii) aim to predict pain based on the dynamics of brain networks. To this end, the analyses focus on community structures of time-evolving networks. The results show that sustained pain is associated with the emergence of a brain network including somatomotor, frontoparietal, basal ganglia and thalamic brain areas. The somatomotor area of the tongue is particularly involved in that network while this area is decoupled from other parts of the somatomotor cortex. Moreover, the network configuration changes over time with the frontoparietal network decoupling from the somatomotor network. Frontoparietal-cerebellar connections were predictive of decreases of pain. Together, the findings provide novel and convincing insights into the dynamics of brain network during sustained pain.

Strengths<br> • The brain mechanisms of sustained pain is a timely and relevant topic with potential clinical implications.<br> • Assessing the dynamics of sustained pain and relating it to the dynamics of brain networks is a timely and promising approach to further the understanding of the brain mechansims of pain.<br> • The study includes discovery and replication cohorts and pursues a cutting-edge analysis strategy.<br> • The manuscript is very well-written and the results are visualized in an exemplary manner including a graphical outline and summary of the findings.

Weaknesses<br> • It remains unclear whether the changes of brain networks over time simply reflect the duration of sustained pain or whether they essentially reflect different levels of pain intensity/avoidance.<br> • Although the manuscript is very well-written it might benefit from an even clearer and simpler explanation of what the consensus community structure and the underlying module allegiance measure assesses.<br> • The added value of the assessment of the dynamics of brain networks remains unclear. Specifically, it is unclear whether the current analysis of brain networks dynamics allows for a clearer distinction between and prediction of pain and no-pain states than other measures of static or dynamic brain activity or static measures of brain connectivity.

Reviewer #2 (Public Review):

The Authors J-J Lee et al., investigated cortical and subcortical brain networks and their organization in communities over time during evoked tonic pain. The paper is well-written, and the findings are interesting and relevant for the field. Interestingly, other than confirming well known phenomena (e.g., segregation within the primary somatomotor cortex) the Authors identified an emerging "pain supersystem" during the initial increase of pain, in which subcortical and frontoparietal regions, usually more segregated, showed more interactions with the primary somatomotor cortex. Decrease of pain was instead associated to a reconfiguration of the networks that sees subcortical and frontoparietal regions connected with areas of the cerebellum. The main novelty of the proposed analysis, lies in the resulting high performances of the classifier, that shows how this interesting link between frontoparietal network and subcortical regions with the cerebellum, is predictive of pain decrease. In summary, the main strengths of the present manuscript are:<br> • Inclusion of subcortical regions: most of the recent papers using the Shaefer parcellation in ~200 brain areas1, do not consider subcortical areas, ignoring possible relevant responses and behaviors of those regions. Not only the Authors smartly addressed this issue, but most of their results showed how subcortical regions played a key role in the networks reconfiguration over time during evoked sustained pain.<br> • Robust classification results: high accuracy obtained on training dataset (internal validation), using a leave-one-out approach, and on the available independent test dataset (external validation) of relatively large sample size (N=74).<br> • Clarity in the description of aim and sub-aims and exhaustive presentation of the obtained results helped by appropriate illustrations and figures (I suggest less wording in some of them).<br> • Availability of continuous behavioral outcome (track ball).

Even though the results are mostly cohesive with previous literature, some of the results need to be discussed in relationship to recently published papers on the same topic as well as justifying some of the non-standard methodological procedures adding appropriate citations (or more detailed comments). The Authors do not touch upon the concept of temporal summation of pain, historically associated with tonic pain, especially when the study is finalized to better understanding brain mechanisms in chronic pain populations (chronic pain patients often exhibit increased temporal summation of pain2). I would suggest starting from the paper recently published by Cheng et al. that also shares most of the methodological pipeline3 to highlight similarities and novelties and deepen the comparison with the associated literature. Here the main significant weaknesses of the study:<br> • The data analysis is entirely conducted on young healthy subjects. This is not a limitation per se, but the conclusion about offering new insights into understanding mechanisms at the basis of chronic pain is too far from the results. Centralization of pain is very different from summation and habituation, especially if all the subjects in the study consistently rated increased and decreased pain in the same way (it never happens in chronic pain patients). A similar pipeline has been actually applied to chronic pain patients (fibromyalgia and chronic back pain)3,4. Discussing the results of the present paper in relationship to those, could offer a more robust way to connect the Authors' results to networks behavior in pathological brains. Vice versa, the behavioral measure used to assess evoked pain perception (avoidance ratings), has been developed for chronic pain patients and never validated on healthy controls5. It might not be an appropriate measure considering the total absence of pain variability in the reported responses over forty-eight subjects6,7.<br> • The dynamic measure employed by the Authors is better described from the term "windowed functional connectivity". It is often considered a measure of dynamic functional connectivity and it gives information about fluctuations of the connectivity patterns over time. Nevertheless, the entire focus of the paper, including the title, is on dynamic networks, which inaccurately leads one to think of time-varying measures with higher temporal resolution (either updating for every acquired time point, as the Authors did in their previous publication on the same dataset4, or sliding windows involving weighting or tapering8,9). This allows one to follow network reorganization over time without averaging 2-min intervals in which several different brain mechanisms might play an important role3,10,11. In summary, the assumption of constant response throughout 2-min periods of tonic pain and the use of Pearson correlations do not mirror the idea of dynamic analysis expressed by the Authors in title and introduction. I would suggest removing "dynamic" from the title, reduce the emphasis on this concept, address possible confounds introduced by the choice of long windows and rephrase the aim of the study in terms of brain network reconfiguration over the main phases of tonic pain experience.<br> • Procedure chosen for evoking sustained pain. To the best of my knowledge, capsaicin sauce on the tongue is not a validated tonic pain procedure. In favor of this argument is the absence of inter-subject variability in the behavioral results showed in the paper, very unusual for response to painful stimulations. The procedure is well described by the Authors, and some precautions like letting the liquid drying before the start of the scan, have helped reducing confounds. Despite this, the measures in figure 1B suggest that the intensity of the painful stimulation is not constant as expected for sustained pain (probably the effect washes out with the saliva). In this case, the first six-minute interval requires particular attention because it encapsulates the real tonic pain phase, and the following ones require more appropriate labels. Ideally the Author should cite previous studies showing that tongue evoked pain elicits a very specific behavioral response (summation, habituation/decrease of pain, absence of pain perception). If those works are missing, this response need to be treated as a funding rather than an obvious point.

References<br> 1. Schaefer, A. et al. Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI. Cereb. Cortex N. Y. N 1991 28, 3095-3114 (2018).<br> 2. Price, D. D. et al. Enhanced temporal summation of second pain and its central modulation in fibromyalgia patients. Pain 99, 49-59 (2002).<br> 3. Cheng, J. C. et al. Dynamic functional brain connectivity underlying temporal summation of pain in fibromyalgia. Arthritis Rheumatol. Hoboken NJ (2021) doi:10.1002/art.42013.<br> 4. Lee, J.-J. et al. A neuroimaging biomarker for sustained experimental and clinical pain. Nat. Med. 27, 174-182 (2021).<br> 5. Vlaeyen, J. W. S. & Linton, S. J. Fear-avoidance model of chronic musculoskeletal pain: 12 years on. Pain 153, 1144-1147 (2012).<br> 6. Asmundson, G. J., Norton, P. J. & Norton, G. R. Beyond pain: the role of fear and avoidance in chronicity. Clin. Psychol. Rev. 19, 97-119 (1999).<br> 7. Beebe, J. A. et al. Gait Variability and Relationships With Fear, Avoidance, and Pain in Adolescents With Chronic Pain. Phys. Ther. 101, pzab012 (2021).<br> 8. Hindriks, R. et al. Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI? NeuroImage 127, 242-256 (2016).<br> 9. Lurie, D. J. et al. Questions and controversies in the study of time-varying functional connectivity in resting fMRI. Netw. Neurosci. 4, 30-69 (2020).<br> 10. Allen, E. A. et al. Tracking Whole-Brain Connectivity Dynamics in the Resting State. Cereb. Cortex N. Y. NY 24, 663-676 (2014).<br> 11. Hutchison, R. M. et al. Dynamic functional connectivity: promise, issues, and interpretations. NeuroImage 80, 360-378 (2013).

Reviewer #1 (Public Review):

This article by H. Izgi et al. describes interesting work measuring transcriptional changes through development and later aging. The authors broadly conclude that these tissue transcriptomes diverge during development, but re-converge during aging. They name this expression pattern divergence convergence, or DiCo.

After drawing this conclusion from tissue samples drawn from 16 mice of their own, they look at published mouse and human transcriptomic data and observe similar patterns of change.

Overall the authors emphasize that both highly mitotic and less mitotic tissues show examples of the DiCo transcriptional pattern, supporting the possibility that this may be a general phenomenon.

In addition, the authors ask whether the tissue-specific changes they observe might depend on changes in cell composition with tissues, or cell autonomous transcriptional changes within cells, using published single-cell data. They conclude here that both play a role.

Some of the more specific findings are not surprising and in this support the soundness of parts of the methodology, e.g. that shared developmentally down-regulated genes were enriched in functions such as cell cycle and cell division.

My largest suggestion centers around an alternative hypothesis that may occur to readers; namely that the convergence or Co part of DiCo could be just regression to a mean due to heteroscedasticity with respect to time (age) caused by increased noise in expression. As the divergence could be imagined to be largely due to tissue differentiation during development, which has been studied extensively previously, the overall novelty of these findings relies much more on the later convergence that the authors have observed. The authors note: "Interestingly, we found no overlap between gene sets with the reversal pattern (up-down or down-up genes) across tissues, relative to random expectation". They also note "Intriguingly, we found that similar cell types (i.e. those with the highest correlations) among tissues become less similar with age (36/54 [67%] of pairwise comparisons, Figure 5-source data 1). On the contrary, the most distinct cell types (i.e. those with the lowest correlations) among tissues become more similar with age (45/54 [83%], Figure 5-source data 1).", which is at first glance consistent with this alternative hypothesis. The authors do directly address previous observations of increased noise with age in their Discussion (Bahar et al. 2006; Martinez-Jimenez et al. 2017; Angelidis et al. 2019; Somel et al. 2006), although I might also suggest perhaps PMID: 20832724 PMID: 8604994, and PMID: 28965763. Their acknowledgment refers to the disagreement of their own findings of inter-tissue correlation distributions being modest and comparable between aging and development in Figure 1c. Their CoV trajectory data in Figure 2, perhaps most relevant here in Figure 2c, may also speak to this issue. Nevertheless, in my opinion it would strengthen the manuscript greatly for many readers if this alternative hypothesis were more explicitly and clearly spelled out, and then perhaps more explicitly ruled out, in the manuscript.

Reviewer #2 (Public Review):

In this manuscript, Izgi et al investigated age-dependent gene expression pattern changes in male mice by analyzing a new bulk RNA-seq data from four different tissues collected at different ages covering post-natal development and aging. Gene expression patterns observed before and after sexual maturation seem to suggest inter-tissue divergence and convergence of gene expression profiles, respectively. The authors name that phenomenon Divergence-Convergence or "DiCo". Analysis of publicly available single cell RNA-seq [scRNAseq] datasets (from the Tabula Muris Senis consortium) suggests that such gene expression pattern changes may be explained by both alterations in tissue cell type composition, as well as by cell-autonomous expression changes. These observations may suggest that aging results in at least a partial loss of tissue identity acquired developmentally.

Although the authors report an intriguing finding, there are major issues in the manuscript as it stands, notably concerning the clarity and rigor of the data analysis and manuscript. Notably, the authors compare expression levels across samples using the FPKM normalization method, which has been shown to be a problematic metric. There are also inconsistencies in statistical and methodological choices for which there is not a clear rationale explained in the manuscript. Finally, the authors use only male animals, which may not reflect age-related trajectories in female animals, but draw broad cross-species conclusions without raising sex as a caveat to the generalization of the conclusions.

Reviewer #3 (Public Review):

In this manuscript Izgi et al. analyzed gene expression time-course data in four tissues during postnatal development and ageing in mice. Authors show that the expression levels of genes often reverse with ageing compared to development. Authors further show that the expression pattern diverge among the tissues during postnatal development and converge among tissues with ageing. This divergence and convergence pattern (called DiCo) is analyzes at both individual gene and genome-wide levels using multiple statistical approaches. Both cellular composition changes and cell autonomous expression changes contribute to the reversal of gene expression pattern during ageing. This study connects expression pattern during postnatal development with ageing, extending previous work on a single tissue.

Strengths:

- The expression convergence with age is consistently seen across multiple datasets and species indicating it can be widespread.<br> - The datasets generated are unique and would be useful resource for ageing genomic community.<br> - Authors go beyond bulk RNA-seq and also analyze available single cell RNA-seq datasets in mice to asses the contribution of cell composition changes and cell intrinsic expression changes to DiCo.

Weaknesses:

- Many aspects of expression convergence and DiCo pattern have low effect size and some are not significant. It also appears that this pattern is best seen at the genome-wide level.<br> - Although there is statistical support for DiCo, there are no consistent functional associations discovered in Gene Ontology enrichment.<br> - The mechanism for DiCo and the extent to which the same genes or pathways underlie this across species is unclear.

Reviewer #1 (Public Review):

Dicks et al. in this study characterized electrophysiological properties of mutant and wild-type hiPSC-chondrocytes and the expression of chondrocyte-associated markers during chondrogenic differentiation of the cells, and analyzed the differential expression of global transcriptome between the different chondrocyte groups. They demonstrated TRPV4 mutation-induced changes in calcium signaling, mechanical property of matrix, and transcriptome of hiPSC-chondrocytes and concluded that the V620I and T89I mutations of TRPV4 in chondrocytes delay or inhibit hypertrophy, which may be a potential cause of skeletal dysplasias.

This study applied a gene-editing tool to creating mutant hiPSCs as a human cell model of the disease in culture to study TRPV4 mutation-induced alteration in cellular activities and molecular regulation. Establishing such an hiPSC model for disease study is novel and considered a major strength. Other strengths of this report include adequate background information, solid data analysis, and well-referenced discussions. The iPSC model established in this study could potentially be used to study pathogenic mechanisms of the diseases and identify molecular targets involved in regulating the mechanisms for the development of disease treatments.

However, there are two weaknesses identified in this current report, which are described below.

1. Through comparison, differences in biological response and activities between mutant and wild-type hiPSC-chondrocytes were shown, and molecules and mechanisms of interest were identified as potential regulators involved in the mutation-induced changes. However, critical experiments such as gain- and loss-of-function assays to determine whether and how some or all of the identified molecules or mechanisms (HOXs, TGFB, biomineralization genes ...) are regulated by the mutations to alter chondrocyte activities are missing. These experiments are needed to strengthen their conclusions. The discussions about the identified molecules and mechanisms with cited references are inadequate as a support for the conclusions.<br> 2. The data currently presented in Figures 1, 5 and 6 are insufficient to justify the claims regarding mutation-induced changes of TRPV4, chondrocyte hypertrophy, and expression levels of the identified molecules.

Reviewer #2 (Public Review):

In this manuscript, Dicks et al. generated two human iPSC lines with TRPV4 mutations (mild V620I or lethal T89I) using a CRISPR-Cas9 approach and examined their channel function and differentiation abilities into chondrocytes. While their initial goal is to elucidate the detailed molecular mechanisms underlying how these two mutations lead to strikingly distinct severities of skeletal dysplasias, most of their data found that these two mutations behave in a similar manner. The minor differences they found are: 1) increased basal currents in V620I cells; 2) reduced mechanical properties of cartilage matrix in V620I chondrocytes; 3) some differences in DEGs of RNA-seq data. They also stated that "The severe T89I mutation inhibits chondrocyte hypertrophy more than moderate V620I 298 mutation" (page 16). However, no substantiated data were provided to support this conclusion. While a serial of RNA-seq experiments were performed to explore the underlying mechanism, they were not followed by validation experiments to pinpoint the exact pathways or molecular mechanisms. Thus, although using CRISPR-Cas9 and iPSCs are novel and potentially important, this manuscript is overall descriptive with limited mechanistic information.

Reviewer #3 (Public Review):

TRPV4 is an ion channel protein and mutations in TRPV4 gene resulted in different types of skeletal defects. In this study, the authors created two types of TRPV4 mutations (mild V620I and lethal T89I mutations) in human iPS cells through CRISPR-Cas9 gene editing. They found that mutations of TRPV4 accelerated calcium signaling and reduced response of calcium signaling to TRPV4 agonist. In addition, the V620I mutation led to decreased mechanical properties of cartilage matrix later in chondrogenesis. TRPV4 mutations also upregulated HOX genes and downregulated antioxidant genes, such as CAT and GSTA1, through entire chondrogenesis process. BMP4-induced chondrocyte hypertrophy was also inhibited in TRPV4 mutant cells. This study provided novel information about the functions of TRPV4 in chondrogenesis and help us understand the skeletal dysplasia observed in patients with TRPV4 mutations.

Reviewer #1 (Public Review):

In this study, using whole-exome sequencing of 10 members of a family (4 generation) suffering from hereditary gingival fibromatosis revealed ZNF862 as a genetic cause for this disease. While in the 10 tested family members also mutations in 2 other genes co-segregated with the phenotype, namely ATP7B and CDADC1, these could not be validated in other family members by conventional PCR.

Transcriptional analysis of fibroblasts from patients and healthy controls revealed an upregulation of genes associated with fibrosis like COL1A1, TGFB1/2 and SMAD1 in HGF. Furthermore, shRNA knock-down of ZNF862 in gingival fibroblasts isolated from healthy controls lead to an up-regulation of pro-fibrotic genes including TGFB/SMAD1 as well as ACTA2 and COL1A1. These assays suggest a pathophysiological role for ZNF862 in HGF, however, a detailed mechanistic analysis remains to be done.

The authors speculate that the top 100 up- or down-regulated genes lie downstream of ZNF862. Furthermore, pathway enrichment analysis revealed an association to several infection-related pathways. The authors conclude that this is due to an association with IL-6 signaling. However, they do not provide any evidence for this conclusion.

Since surgery is currently the only treatment for HGF patients with a high recurrence rate due to the genetic predisposition, the identification of ZNF862 as a genetic cause for the disease is of high importance for the development of a therapy.

Reviewer #2 (Public Review):

By WES sequencing of a four-generation HGF family and co-segregation analysis, the authors identified a novel heterozygous missense mutation (c.2812G>A) in the ZNF862 gene. At the level of functional studies, through ZNF862 knockdown experiments, the authors found that the ZNF862 gene was associated with gingival tissue fibrosis to some extent. Furthermore, by comparing transcriptomic analysis of gingival fibroblasts from HGF patients and normal subjects, this study shows genes that are aberrantly expressed at the genome-wide level and the corresponding bioinformatic analysis.

Strengths:

WES sequencing methods are well established, the screening process for mutated genes is rigorous and reliable, and co-segregation analysis is certainly necessary.<br> On the other hand, as a relatively independent experiment from the pathogenic gene screen, transcriptomic analysis of gingival fibroblasts from HGF patients is valuable, allowing the reader to take a global perspective and analyze the genes with abnormal changes in expression in HGF gingival tissue.

Weaknesses:

It is unreliable to claim that ZNF862 is the causative gene of HGF just by WES sequencing and co-segregation analysis; even if ZNF862 knockdown experiments were performed, this would be of little help. It is clearly insufficient evidence to exclude the presence of the c.2812G>A variant of the ZNF862 gene in normal individuals simply by searching in the relevant databases.

Reviewer #3 (Public Review):

Wu and colleagues addressed a knowledge gap in the genetics of hereditary gingival fibromatosis, the most common form of gingival fibromatosis. The Authors investigated the genetic causes leading to HFG in a four-generation Chinese family with high incidence of the disease among its family members and found that mutation of ZNF862 was present in all affected family members and in none of the non-affected members.

Next, the authors investigated the transcriptional landscape of explant culture derived from two members of the family with HGF and from four control individuals, confirming the hypothesis that an increase in profibrotic genes could contribute to HGF pathology. Although the sample size of the examined cohort was small and included only two affected individuals, the relevance of the results were validated in the subsequent experiment. In this experiment authors suppressed expression of ZNF862 in cells derived from a control individual using shRNAi technology and showed that indeed this led to upregulation of profibrotic genes, mimicking changes seen in HGF patients of the family.

Overall, we commend the clear experimental design and advancing the knowledge of the genetic origins of HGF. The study presents novel findings and interesting results despite the small sample size of the experimental cohorts. However, a few aspects of data visualization could be improved such as the graphical representation of differentially expressed genes. A couple of conclusions should be backed up by further experimental data or referencing previously published studies. These conclusions are:

- Suggestion that top down-regulated and up-regulated genes from RNA-seq lie downstream of ZNF862 and play a role in HGF pathophysiology. This claim would have to be supported by experimental data showing that ZNF862 indeed directly regulates the expression of those genes. It is possible that these genes do not contribute to the pathology of HGF.

- The suggestion that REST and ZNF862 may execute similar transcriptional functions requires further experimental evidence or referencing previously published data showing which genes are regulated by REST, what the overlap with ZNF862 target genes is, or experimental data of ZNF862 DNA binding sites.

Authors should also acknowledge a previous body of work on ZNF862 in their manuscript.

Reviewer #1 (Public Review):

The manuscript by Ghareh et al examines the role of the anterior insula cortex (aIC) in nicotine seeking, punishment-induced abstinence of nicotine seeking and in context induced relapse. The paper used a variety of methods including immediate early gene imaging as a marker of neuronal activity, fibre photometry, inhibition using DREADDs. The paper shows that activity in ipsilateral and contralateral aIC and ipsilateral Bla is elevated in context-induced relapse of punished nicotine-seeking. Population calcium imaging using fibre photometry showed modulated aIC neural activity across nicotine infusion, punishment and relapse test. Although differences in neural activity were not seen during relapse tests across different nose poke options. Silencing the aIC during relapse test reduced relapse after punishment or extinction.

Strengths:

Overall the manuscript is of broad interest to the addiction field and researchers interested in insula function. It uses a strong behavioural model to study abstinence and shows clear evidence of relapse following punishment-induced abstinence. It is a model that fits the existing literature on the effects of punishment on drug-seeking.

The paper uses a variety of methods aimed at providing a thorough picture of aIL neural profile in nicotine-seeking, punishment-induced abstinence of nicotine seeking and context-induced reinstatement of nicotine seeking. There are strong behavioural comparisons for the neural signal including active and inactive nose pokes, nicotine vs nicotine+shock reinforcement, as well as strong neural comparisons including bootstrapping the neural signal, permutation tests and GFP vs hM4Di.

The data provide clear evidence using diverse methods for the role of the aIL in context-induced relapse of nicotine-seeking.

The authors provide important evidence that the aIC regulate relapse of nicotine-seeking similarly whether abstinence was punished- or extinction-induced.

The discussion is excellent.

Weaknesses:

Although this is not critical to the paper, having vehicle and GFP controls for clozapine and hm4di would have been preferable. The authors provide a justification for that, which is reasonable, but CNO, which converts to CLZ, does have off-target effects that can only be detected when compared again to the vehicle conditions.

It was unclear why the neural signal was modulated during context-induced tests when there were no differences in the neural signal between active nosepokes (followed by nicotine cue or nothing) and inactive nose pokes during the relapse tests. The behaviour clearly shows evidence of relapse. The authors discuss this in terms of targeting different populations of cells. But it is unclear why one would use photometry if the imaging signal could not be used to inform the neural manipulations.

Activity is aligned to nosepoke, but it would be of value to see activity aligned to nicotine alone and nicotine+shock delivery.

Statistics need to be added to the photometry data. Currently the photometry data are purely descriptive.

The punishment photometry data are quite interesting as the neural signal seems to be similar between the two active nose poke irrespective of whether they lead to nicotine or nothing (active NP>nicotine vs activeNP>nothing). The authors suggest that this is because the nicotine-reinforced active nose poke is modulated, but the data are not so clear. There is a change in the signal (it is no longer biphasic) but the overall increase (assuming identical scale, which I think is reasonable given the scale provided) in the signal seems to change for the active nosepoke that is not reinforced. How punishment affects behaviour on the active nose poke on trials when those nosepoke are not punished is fundamental to understanding the signal and the role of the aIC in this task.

Reviewer #2 (Public Review):

In their study Ghareh et al. used retrograde tracer with the neuronal activity marker c-fos, fibre-photometry and chemogenetic to functionally characterize the role of the anterior insular cortex (aIC) in a new model of context-induced relapse to nicotine seeking after punishment. They demonstrated an increase of neuronal activity in aIC, and inputs from contralateral aIC and ipsilateral BLA during context-induced relapse to nicotine seeking. Then, they showed an increase of aIC neuronal activity, measured by fibre photometry, during self-administration, punishment, and context-induced relapse. Finally, they demonstrated that chemogenetic inhibition of aIC glutamatergic neurons decreases context-induced nicotine-seeking. The strengths of the manuscript include the longitudinal study of the coding properties of aIC neuronal population during self-administration, punishment and relapse reinforced by the demonstration of the causal role of the aIC in context-induced nicotine relapse. The experiments were carefully designed and implemented. The manuscript is well written and includes interesting and novel findings on the role of aIC neurons in nicotine relapse. However, there are a few methodological and analysis issues. First, a clearer rationale for focusing on contralateral aIC and ipsilateral BLA inputs is needed. Further analyses and clarifications are also needed to fully benefit from the fibre photometry approach.

Reviewer #3 (Public Review):

Ghareh et al. investigated the role of the anterior insular cortex in context-induced relapse to nicotine seeking after punishment. Notably, the authors extend their previous work on context-induced relapse after punishment to the widely used addictive drug nicotine. The authors use complementary approaches, including Fos immunohistochemistry combined with retrograde tracing, fibre photometry, and chemogenetic inhibition to assess the role of the anterior insular cortex in relapse to nicotine seeking with several different levels of analysis. They show that context-induced relapse to nicotine seeking is associated with increased neuronal activity in the anterior but not middle or posterior insular cortex and with increased activity of ipsilateral anterior insular cortex neurons and contralateral basolateral amygdala neurons that project to the anterior insular cortex. Fiber photometry data show that anterior insular cortex activity increases after nose-pokes that lead to nicotine infusion and punished nose-pokes. Lastly, chemogenetic inhibition of the anterior insular cortex decreases context-induced relapse to nicotine seeking after punishment and extinction.

Strengths:

The experiments are well-designed and support the main conclusions of the paper. The authors very nicely show generalization of the context-induced relapse after punishment model to nicotine.

On the neurobiological level, it is particularly interesting and informative to juxtapose post-mortem readouts of neuronal activity (Fos immunohistochemistry) with in vivo real-time readouts of neuronal activity (fibre photometry) in awake-behaving rats in the same behavioral procedures. The authors also analyze Fos and CTb expression along the anterior-posterior axis of the anterior insular cortex and basolateral amygdala. An additional strength of the paper is that the authors used chemogenetic inhibition to test the causal role of the anterior insular cortex in context-induced relapse to nicotine seeking after both punishment and extinction.

Lastly, the authors do an excellent job of pointing out the limitations of their study in the discussion section, which include potential differences in neurobiological substrates depending on route of nicotine administration, exclusion of a vehicle-control group in the chemogenetic experiments, and use of different viral promoters between the fibre photometry and chemogenetic experiments.

Weaknesses:

There are two main weaknesses which limit interpretation of the data presented. First, during the punishment phase of the fibre photometry experiment, it is difficult to know which outcome the changes in calcium identified with fibre photometry are due to (e.g., nicotine infusion or footshock). Ideally, appropriate acknowledgement of these limitations in interpretation or inclusion of a yoked control or separate sessions with nicotine infusions or footshock exposure would help address this interpretation issue because this would allow for an analysis that disentangles the complex outcomes.

Second, with the chemogenetic experiment, the authors observe a decrease in nose pokes in the hM4Di group in Context A (when responding is normally high) but not Context B (when responding is normally low). It is possible that a non-specific effect on responding (e.g. motor or motivational impairment) could be masked in Context B due to a floor effect. Therefore, while the test in Context B is informative, chemogenetic inhibition in another situation where responding is high (e.g. nicotine or food self-administration) would be helpful in the ability to interpret the specificity of hM4Di inhibition of the anterior insular cortex in context-induced relapse to nicotine seeking after punishment or extinction.

Reviewer #1 (Public Review):

This manuscript describes three decision biases, and attempts to relate them to neural recordings from OFC. They analyze neural recordings from a previous experiment, in which animals were either presented with simultaneous or sequential juice offers. They describe three behavioral observations: (1) choices are noisier for sequential offers; (2) for sequential offers, monkeys exhibit an order bias, in which they are more likely to choose the second offer; (3) for sequential offers, monkeys exhibit a preference bias, in which they are more likely to choose the preferred juice. They argue that the first bias (decision noise) occurs at the valuation stage, as they observe reduced value signals in the sequential task. They argue that the order bias occurs at the "value comparison" stage, because neural correlates are reflected in chosen value cells, not offer value cells, shortly preceding and after the second offer. Finally, they argue that the preference bias is a post-decision bias. Understanding the neural correlates of various decision biases is a goal of the fields of decision-making and neuroeconomics. I think the data are of high quality and the findings are interesting. However, I think there are questions about the methodology used to classify neurons as participating in these different cognitive processes (valuation, value comparison, post-decision). This methodology arbitrarily forces neurons to only participate in one process, when in reality individual neurons could support or encode several or all of these processes. In my view, this is the major weakness of the study, but is also central to the interpretation of the results.

Reviewer #2 (Public Review):

This manuscript from Shi, Ballesta, and Padoa-Schioppa examines the relationship between neural activity in the monkey orbitofrontal cortex (OFC) and various choice patterns that arise in sequential (versus simultaneous) choice. This approach addresses a central question in the study of decision-making: how can one identify value-dependent versus value-independent effects on choice behavior when value is defined from that behavior itself? Here, the authors document three behavioral differences in sequential choice: choosers are nosier, show an order bias, and show a preference bias. Leveraging a conceptual computational framework for OFC activity that the authors have developed over many years, the authors link reduced accuracy to changes in neural valuation in the OFC, order effects to post-valuation decision activity in the OFC, and preference effects to extra-OFC processes. For decision neuroscientists, these findings show specific differences between sequential and simultaneous choice, and suggest the integration of multiple stages (valuation, decision, and post-decision) in the selection process. More broadly, this work shows how an examination of neural activity can shed light on aspects of the decision process that cannot be distinguished by an examination of behavior alone.

Strengths:

Overall, this paper presents a novel and thoughtful task design that allows comparison of neural and behavioral value and choice effects. In concert with an established circuit-based framework for parsing different types of OFC response patterns, the authors test and validate a number of hypotheses on the link between neural activity and choice.

(1) Comparing sequential and simultaneous choice tasks in an interleaved manner is a clever approach to separate valuation and comparison processes in time. While not entirely novel (e.g. see work from the Hayden group), the combination of this approach with the OFC response pattern (offer value, chosen value, chosen juice) framework allows a distinction between valuation and comparison-related effects.

(2) This paper is the latest in a significant series of related papers on orbitofrontal activity from this group, and cleverly utilizes their expertise in characterizing, analyzing, and conceptualizing different patterns of OFC activity. In addition to the long-established offer value/chosen value/chosen juice categorization, recent papers from this group have established the causal contribution of OFC offer value activity to economic choice and established similar OFC neural contributions to sequential and simultaneous choice tasks.

(3) Apart from a causal test (e.g. cell type specific stimulation) of the contribution of different neural responses to different choice effects, the next strongest evidence is a demonstration of a consistent relationship across sessions. The authors show such a relationship between offer value coding strength and choice accuracy, between chosen value sequence effects and behavioral order bias, and between chosen juice inhibition and order bias. At the least, these relatively strong effects show a strong correlation between different OFC responses and behavior.

Weaknesses:

While the experimental approach and rigor of the analyses are strengths, there are issues of interpretation and generality of analytical approaches that should be clarified.

(1) The abstract, introduction, and discussion touch on canonical behavioral economic choice effects as a prelude to the behavioral effects documented here, but it's not clear they are so closely related. Many of the effects in the cited literature (framing effects in risky choice, preference reversals, etc.) are robust across different task paradigms, whereas the effects shown here arise specifically from a comparison of choice across different task paradigms (sequential vs. simultaneous). Furthermore, it's not clear that the term "bias" adequately captures the array of effects in the behavioral economic literature (for that matter, one of the main effects in this paper is reduced choice accuracy rather than a bias). The paper would benefit from a clearer conceptual linkage between documented behavioral biases (particularly in humans) and the effects shown here.

(2) The analyses rely on a particular quantification of choice behavior (probit regression), which interprets choice effects (e.g. relative valuation of the two juices, sigmoid steepness) via specific parameter combinations and relies on specific assumptions about the construction of choice (e.g. cumulative normal distribution, constant sigmoid slope across order effects). This method of quantifying choice behavior is well-documented in previous studies, allowing a comparison to past work. However, given the importance of this approach to both quantifying choice effects and comparing choice to OFC responses, the paper would benefit from directly addressing two issues: (1) how well does probit regression actually capture stochastic choice behavior (in both Task 1 and Task 2), and (2) do the findings rely on specific choice modeling assumptions? The second issue is most important for the order bias effects, which assume a constant sigmoid across conditions - do the authors reach similar conclusions if this assumption is relaxed?

(3) There are some issues with the strength and interpretation of the preference bias that need to be addressed. Re: strength and significance of the preference bias, the text seems to overemphasize the dependence of the effect on relative value (rotation of the rho-2 vs rho-1 ellipse) at the cost of the simple task difference (shift in the ellipse above the identity line). Conceptually, a preference bias (an shift in relative value towards the favored item) requires only the task difference, not the dependence on relative value. It would be clearer for example if the main text (pg. 6) presented the statistics (t-test, Wilcoxon) supporting the difference in relative values (rhos) between Tasks 1 and 2. Furthermore, the rotation does not seem as robust: the text states that the result is significant in both animals (p<0.04) but the ANCOVA results (Fig 3C and 3F) suggests that the effect is only significant in Monkey J. Is the preference effect significant only in one animal, and if so, is the effect significant across the combined data?

(4) On a related note, the authors present and view the effects as detrimental for the animals, but I think they have to more explicitly state how they are defining outcomes. For example, the abstract states "By neuronal measures, each phenomenon reduced the value obtained on average in each trial and was thus costly to the monkey". Does this mean that outcomes are less valuable, with value defined by (offer value cell) firing rates? A clarification is particularly important for the preference bias, where animals show a stronger bias for the preferred option compared to simultaneous choice. At the behavioral level, this effect seems to only be a poorer outcome if one assumes that simultaneous choice demonstrates true values - can it not be assumed that sequential choice demonstrates true preference, and the preference bias reduces performance in simultaneous choice? The authors may have an explanation in mind based on OFC value coding, and it would be helpful to be explicit here.

(5) Finally, at a broad level, the authors rigorously define and test hypotheses about how the different behavioral effects relate to OFC activity within the context of their neurocomputational framework (offer value, chosen value, chosen juice cells arranged in a competitive inhibition network; Fig. 1). However, it should be acknowledged that the primary conclusions - about how the different behavioral effects arise during valuation, comparison, or post-comparison - relies on the assumption that the different OFC response patterns reflect these specific circuit functions, and that OFC is causally related to choice. It would be more balanced if the authors could acknowledge this point in the discussion, and discuss any relevant potential alternative explanations for their findings.

Reviewer #3 (Public Review):

This manuscript by Shi and colleagues describes possible neuronal correlates of behavioral biases observed in monkey, when options are presented sequential versus simultaneously. These behavioral effects are that for sequential presentations, the monkeys show: (1) less accurate choices, (2) a preference for the second (more recently) presented option, (3) a preference for the more preferred juice type, independent of amount. The paper builds on a long series of work from the Padoa-Schioppa lab that has identified 3 different functional signals in OFC: (1) option value, (2) chosen value, (3) chosen juice type. Important for the logic of the analysis in this paper, the option value cells can be interpreted as input signals that drive the decision process (a comparison of the value of each option), while the chosen value and juice type signals reflect the outcome of the decision process. The fact that these different cell types represent different stages of the decision process is used here to investigate at which functional stage neuronal activity correlates with behavioral biases. Using this general approach, the authors find that: (1) diminished accuracy is due specifically to lower value sensitivity of option value cells, (2) order bias emerges at the comparison stage, but is not driven by input signals, (3) preference bias is not reflected during the comparison stage (immediately following the presentation of the second option), but seems to emerge later in the trial during a waiting period, before the choice is indicated. The paper is written very clearly and the underlying logic for the different analysis is very well described. The results are for the most part convincing. It is particularly noteworthy that the authors were able to identify very specific mappings between neuronal signals and behavioral effects by showing systematically that one particular class of neurons is correlated with the effect, but others are not. That is very impressive. (Of course, the authors were also lucky, because in principle, an effect could have easily arise from multiple stages of the decision process.) Overall the paper is great. This is one of the first examples, in which suboptimal choices can be convincingly related to fluctuations in the underlying circuit for value estimation and comparison.

Reviewer #1 (Public Review):

In this paper, gene deletions, complementations, and careful phenotype analyses define roles of GPC1 in secretion and binding to the autocrine cell surface. GPC1 which is GPI-anchored to the cell surface, facilitates export of FGF2, acts as a highly specific surface receptor for FGF2. GPC1 is specific in these activities, i.e. it is much more active than other heparan sulfate-containing GAGs. A chimeric protein with the GPC1 N-terminal and the transmembrane segment of another GAG can substitute for GPI-anchored GPC1. GPC1 is dispensable for FGF2 downstream signaling. Thus, this work describes specific molecular roles for the GPC1 extracellular region in the lab's long-standing investigation of the unconventional secretion mechanisms for FGF2. A disaccharide analysis leads to the proposal that the specificity and affinity of GPC1 for FGF2 is dependent on the increased concentration of ligand specific to GPC1. However, the data supporting this idea is not is not convincing. Nevertheless, the further elucidation of the mechanism of non-conventional secretion is of interest to a broad readership in cancer, and glycobiology, and cell biology. The work is carefully described in appropriate detail.

The authors propose that the increased presence of a specific disaccharide sequence in GPC1 explains the high specificity and affinity relative to other heparan sulfate GAGs. However, the modest 1.2-fold increase of this disaccharide is insufficient to explain the difference in FGF2 affinity. Therefore, the manuscript needs to be revised to accommodate other possible explanations for the great specificity and affinity of GPC1 as the receptor and export facilitator.

Reviewer #2 (Public Review):

This is a rigorously designed and carefully controlled study that is well-presented. I have only the following issues.

1. The authors raise the issue of how BirA might be able to label GP1 given the ATP requirement. Is this simply that FGF2-BirA is bound to the activated biotin as it transits the membrane? Are the kinetics of FGF2 transfer consistent with the stability of this intermediate of BirA?<br> 2. Fig 2 shows a strong reduction of FGF2 secretion upon GPC1 KO using the surface biotinylation assay (approx. 75% with little error). This is the critical evidence that GPC1 is required, yet in Fig4A and D the result looks much less convincing (50% with large error). For Fig4D the WT data are present and it seems questionable whether there is a significant difference. This needs to be explained and/or corrected. Statistical significance of WT vs KO should be reported in Fig1 and the result should be reproducible throughout, if using the same assay.<br> 3. The authors imply that FGF2 binding to proteoglycans is critical for it to initiate signaling and their data argue that this does not require GPC1. The role of proteoglycans here was confusing to me and may be to other readers. The authors should tell us how proteoglycan binding relates to RTK binding, and whether there is a reason that lower affinity interactions with proteoglycan sites (relative to the higher affinity interaction with GPC1) would be sufficient in this role.

Reviewer #3 (Public Review):

The authors claim that GPC1, a conventionally secreted GPI anchored glycoprotein, binds unconventionally secreted FGF2 at the cell surface. Loss of GPC1 affects FGF2 secretion and over expression of GPC1 stimulates FGF2 secretion. The authors also identify the specific sugars with N-linked sulphate groups in GPC1 that appear to function in FGF2 secretion.The data are of high quality. Overall, the paper provides a new component in the pathway of unconventional secretion of FGF2. Additional data are necessary to rule out the trivial possibility that loss of GPC1 affects membrane properties that indirectly affect the translocation of FGF2.

Reviewer #1 (Public Review): 

This manuscript describes an elegant system of electro-fused cells where multiple simultaneous actin waves can be visualized and analyzed. For the study of electrotaxis, the advantage of this system is to decouple reorganization of actin networks under application of electric fields from other cellular events that can complicate this analysis. The authors use electro-fused cells to study propagation of actin waves in 2D on flat surfaces, or in 1D on nanotopographic surfaces. This study is mostly descriptive, but this compelling experimental system opens up possibilities for the field to analyze the molecular subtleties involved in these cytoskeletal reorganizations. Authors achieved their aims, conclusions seems to be supported by the results, although few additional controls are probably required.

Reviewer #2 (Public Review): 

In this manuscript, Yang and coworkers investigate the impact of electric fields on the dynamics of cortical actin waves. Their experiments were performed with giant Dictyostelium discoideum cells that were produced by electric pulse-induced fusion and expressed a fluorescence marker for filamentous actin. Due to the large sizes of these fusion products, unconfined waves dynamics could be observed in the giant cells by fluorescence microscopy. Besides freely moving waves, the authors also focused on small wave-like patches that exhibit shorter lifetimes and emerge if cells are placed on nanopatterned substrates. The recordings show that electric fields increase the area, duration, and speed of the cortical waves. In particular, their results convincingly demonstrate that the direction of wave propagation is guided to preferentially align with the direction of the electric field and follows switches in the orientation of the field, with fast switches in direction in the case of the small patches on nanoridges and slower u-turns in the case of freely moving waves. An inhomogeneous intracellular distribution of waves in the presence of an electric field furthermore reveals subcellular polarization with different time scales of wave initiation and inhibition at the front and back of the cell. 

Taken together, the results are a timely contribution to current research on the dynamics and functional role of cortical actin waves in motile cells.

Reviewer #3 (Public Review): 

Summary of what the authors were trying to achieve:

The authors set out to relate electric field stimulation to cortical actin wave dynamics, which they do largely by fusing small together into large cells that make the spatiotemporal elements of these elusive waves much more tractable to analyze. This is quite clever and clearly effective. They try to further relate wave dynamics to specific nanostructured surfaces and to an excitable systems framework called STEN-CEN. Despite some correctable flaws, this paper was a pleasure to consider and explore. 

Major strengths and weaknesses:

This is a detailed study with some real technical accomplishments and experimental finesse. There is no doubt that electric fields do alter cortical actin wave dynamics. The imaging and data are novel in this regard. However, there were also a number of choices in how the data were presented and discussed that made it difficult for this reviewer to grasp the larger picture. The need for the nano topography is lost until the end, the statistics are a bit hard to follow, the STEN-CEN concept and cartoons are interesting but don't quite come together as currently written, and some key methodology and statistical metrics need to be addressed in the figures and methods section. 

Did the authors achieve their aims and support their conclusions:

All of that said, the authors presented clear and novel data. Their core conclusion is irrefutable-field stimulation caused changes to cortical actin waves, with field direction biasing the wave direction and dynamics. Some of the subsidiary claims related to STEN-CEN, signaling pathways, and the specific role of the field could be better supported, but this is certainly feasible. 

A discussion of the likely impact of the work on the field, and the utility of the methods and data to the community:

These data are particularly exciting because the field of bioelectric stimulation (non-neural) is in dire need of live imaging of key downstream processes related to the actual physical mechanisms and machinery in cells that respond to such stimulation, and this paper beautifully illustrates that. Very few studies in this field have any sort of live imaging of sub cellular processes, and these data were a pleasure to go through.

Reviewer #1 (Public Review):

The reduced amplitude of the mismatched negativity (MMN) in Schizophrenic patients has been associated with NMDA receptor malfunction. Weber and colleagues adjusted the systemic levels of two neurotransmitters (acetylcholine and dopamine), that are known to modulate NMDA receptor function, and examined the effects on mismatch related ERPs. They examined mismatch related ERPs elicited during a novel passive auditory oddball paradigm where the probability of hearing a particular tone was either constant for at least 100 trials (stable phases) or changed every 25-60 trials (volatile phases). Using impressive statistical testing the authors find that mismatch responses are selectively affected by reduced cholingeric function particularly during stable phases of the paradigm, but not by reduced dopamine function. Interestingly neither enhanced cholingeric or dopamine function affected MM responses at all. While the presented data support the main conclusions mentioned above, there are some claims in the abstract and text that are not supported by the results.

1) The authors state in the abstract that "biperiden reduced and/or delayed mismatch responses......", while the results (Figure 2) support the statement that biperiden delayed mismatch responses, the claim that biperiden reduced mismatch responses is misleading as on P13 the authors actually report that "mismatch signals were stronger in the biperiden group compared to the placebo group at right central and centro-parietal sensors" around 200ms. This is close both in time and spatially to the traditional temporal and spatial locations of the MMN component. If one were to only read the abstract they would take away the result that the muscarinic acetylcholine receptor antagonist biperiden has an attenuative effect on MMN which is not what the results show.

2) The conclusion that biperiden reduced mismatch responses may be due to the finding that at pre-frontal sensors mismatch responses were significantly smaller in the biperiden group than in the amisulpride (a dopaminergic receptor antagonist) group (P9) around 164ms. However, it is difficult to interpret if this is a meaningful result as amisulpride was found not to significantly alter mismatch responses in any way compared to placebo. It would be more convincing if the significant difference here were between biperiden and placebo groups. Or are we to think of amisulpride as being comparable to a placebo?

3) The authors use the words mismatch negativity (MMN) and mismatch responses interchangeably however in some cases it is clearly mismatch responses being described and not the classical MMN ERP component. This occurs especially in the Introduction where the authors describe the study and that they plan to focus on the MMN but in the results section, since the initial analysis focuses on all sensors, other mismatch responses are consistently discussed. These differences in wording need to be precisely defined and used consistently in the text.

4) A weakness of the paper would be that the authors offer no prediction in the Introduction about what the expected effects of these specific neurotransmitter modulations would be on mismatch responses.

5) A nice aspect of this paper is that the authors re-analyzed their data using pre-processing settings identical to those used in comparable research papers examining the effect of cholinergic modulation on MMN. The main findings did not differ following this re-analysis.

Reviewer #2 (Public Review):

The authors found that Biperiden (M1 antagonist) delayed and altered the topography of MMN responses, particularly in the stable condition. Amisulpride did not do so, and neither did Galantamine or L-DOPA. The analysis using an ideal Bayesian observer (the HGF) detailed in the Appendix showed that Biperiden reduced the representation of lower-level prediction errors and increased that of higher-level prediction errors (about volatility).

The methods were rigorous (including obtaining drug plasma levels and detailing alternative preprocessing techniques) and I have no suggestions for improvement from that point of view.

I only have one main comment that I think could be discussed. I'm not an expert on this but as I understand it, Olanzapine is most selective for M2 receptors rather than M1 (https://www.nature.com/articles/1395486), although Clozapine metabolites do have some M1 selectivity (https://www.pnas.org/content/100/23/13674) - I'm not sure about Clozapine itself. So Biperiden (very M1 selective) might not be the ideal drug to use to explore a treatment allocation paradigm, at least for Olanzapine? I suspect the options are quite limited but it would probably be worth commenting on this.

Reviewer #1 (Public Review):

In this paper, the authors present a set of studies that examine the assembly reaction and function of Perfringolysin O (PFO) with POPC/cholesterol membranes. Using single-molecule TIRF microscopy, they are able to visualize PFO labelled with dye AF647 binding to vesicles that are loaded with AF488. With this co-localization experiment, they are able to examine the molecular steps in PFO assembly that couple to functional membrane leakage. This approach offers an increase in resolution that allows a direct observation of the reaction. With this, the behavior of monomers and dimers in the membrane can measured, as well as the higher-order oligomeric reaction and transition to a functional lysis pore. One interesting observation is that lysis occurs at lower oligomer numbers than would be expected for a full ring pore, and that oligomerization continues to occur after membrane leakage, supporting a model that an incomplete arc pore is functional for membrane lysis. In addition, this approach allows for a full investigation of the kinetics of each step of the reaction, as well as the dependency on concentration and pH. While further technical details would be useful in this paper, the experiments appear to have been carried out carefully and rigorously, providing quantitative access to this complex reaction. Thus, this study will be of interest to many working on the mechanisms of cytolysin, as well as those studying protein binding to membranes by single-molecule microscopy approaches.

Reviewer #2 (Public Review):

This work investigates the mechanism of pore formation for large nanopore. It is known that water soluble monomers are excreted by cells and then oligomerize on targeted membranes. However, the molecular details of this process, especially for large pores, is still largely debated.

In particular, it is not clear how nucleation and growth happen. What is the minimal amount of protomer that can sustain nucleation? Is the pore-forming protein growing one protomer at the time or by adding dimer, trimer, tetramers? It has also been observed that arc-pores can be formed. However, what is not clear is how such arc pores can close to form full pores.

Here the authors are attempting to answer much of these questions. The experiments are well performed. The authors use microscopy to image the formation of labelled promoters. They used a clever use of fluorescent labels to image both the association with the membranes and the punctuation mechanism. Lipids are labeled with a dye that remains transiently bound, which allows the localization of the liposome. Then the interaction of fluorescently tagged protomers is investigate. Finally, the mechanism of pore formation is observed by measuring the efflux quenched dyes.

This work is clear, well written, and the interpretation is at large convincing. The authors conclude that monomeric PFO interact with the membranes only transiently (for less than a second. However, when they encounter a second monomer, they oligomerize and remain (almost) permanently bound to the membrane. It is the dimer that is then the nucleation for the pore and individual protomers are then added sequentially.

Reviewer #3 (Public Review):

They demonstrate that PFO binds the membrane as a monomer with a short binding half-life and that its dimerization nucleates the growth of a stable oligomer. They show that PFO oligomerizes by addition of monomers from the solution in an irreversible process and that small oligomers can insert into the membrane leading to perforation. Their thorough analysis quantifies binding rates, oligomerization rates, insertion rates and the relationships between them, thereby providing new understanding how these processes interrelate to define the stochastic process of pore formation.

The study is rigorous and well-presented. The authors' conclusions are largely supported by the results and enforced by mathematical models on the PFO binding and pore-formation kinetics. One of the main strengths of the manuscript lies on the nature of the new method, that provides high throughput analysis of thousands of pore events in individual liposomes in parallel with the single molecule analysis of pore forming protein self-assembly. Furthermore, the new insight provided into the mechanism of PFO is of high interest and will impact our understanding of pore forming proteins in general.

Reviewer #2 (Public Review):

The manuscript addresses an important question concerning the mechanisms regulating temporal transitions in Drosophila neural progenitors called neuroblasts. Here, they concentrate on a specific transition between the transcription factors Ey and Slp1/2 that are sequentially expressed within a cascade involving at least 6 temporal transcription factors. Using a combination of new transgenes, bioinformatics and genome-wide profiling of transcription factor biding sites (Dam-ID), they functionally characterize two enhancers of the Slp1/2 genes that are active during this transition. This led to the identification of the Notch pathway as an important facilitator of the transition. They also show that Notch signaling requires cell cycle progression and that Slp1/2 is a direct target of Ey, validating the importance of transcriptional cross-regulatory interactions among the temporal transcription factors to trigger progression.

In my opinion, the study is very interesting, representing the first careful analysis of enhancers involved in temporal transitions in neural progenitors, and leading to new insights into the mechanisms promoting temporal progression.

Reviewer #1 (Public Review):

Muktupavela and colleagues extended a model based on two-dimensional partial differential equations to infer the allele frequency trajectories of recent beneficial mutations across space and time. Specifically, the approach fit the model to the allele frequencies computed from a series of ancient DNA samples associated to their corresponding radiocarbon dates to infer the dispersal-related parameters as well as the selection strength under which the allele has been evolving. The authors test the performance of their statistical approach using deterministic and spatially explicit simulations and conclude that the method provides accurate estimates of selection coefficients and relatively acceptable estimates of diffusion and advection parameters. When applied to cases of mutations for which there is currently strong evidence of recent positive selection (such as the case of the lactose-persistence allele and one of the alleles associated with skin pigmentation - LCT/MCM6 and TYR, respectively), the statistical method developed by Muktupavela et al appears to provide estimates for the selection coefficients of the two mutations generally in agreement with previous estimations.

Strengths:

1) Bringing in genotype information from ancient DNA collections to fit the model is definitely the major improvement of the method proposed by Muktupavela et al in comparison with its previous version. It is an invaluable source of information to built maps of allele frequency trajectories over the recent past history of the European populations. Such maps can thus be used together with archaeological and historical data to pinpoint the events underlying some of the inferred changes in allele frequencies.

2) Another important aspect of their new statistical approach is the additional layer of realism resulting from including into the model the advection parameters, which account for the effect of population movements in changing the location of highest allele frequency, and allowing the parameters to be fit to different time periods. Such different time periods may represent, for instance, differences in population mobility (such as the ones the authors explain) or differences in the strength of selection.

3) The approach appears to perform relatively well with respect to inferring selection coefficients and diffusion parameters for time periods for which there is considerable ancient DNA information.

4) The method appears to be very promising as it can be further extended to accommodate novel features (as discussed by the authors) and thus increase its scope.

Limitations:

1) While the method might become a powerful tool to study the evolution of allele frequencies over time and space, its current version is tested under the best case scenario. The authors select alleles that are currently deemed to be among the strongest cases of positive selection in humans, which implicitly also means using ancient DNA collections from the most studied region - Western Eurasia. We now know that cases of positive selection such as the ones studied by the authors are rare (Hernandez RD, et al 2011 Science). Therefore, it remains unclear the extent to which the current version of the method can be applied to other alleles, to other regions and even to other species.

2) Modelling the spread of beneficial alleles continuously across time and space is clearly an advantage in relation to other available methods. However, spatially explicit approaches are sensitive to sampling heterogeneity. While it is true that ancient DNA is accumulating at a very fast speed and temporal/spatial sampling gaps will be less of an issue, that not the case for a large part of the globe. Yet the authors do not assess the impact of such data limitations in the accuracy of the parameter estimates.

3) Finally, although parameter estimates such as selection coefficients, diffusion parameters and the geographical origin of the allele are in general well estimated, it is important to keep in mind they might not be robust to the misspecification of the allele age or incorrect inference of the geographical origin of the allele.

Reviewer #2 (Public Review):

Dr Muktupavela et al. present a novel likelihood-based method for inferring the strength of natural selection and basic demographic parameters, such as mobility rates, from time-stamped ancient DNA data in a spatially explicit framework. This is an elegant method that is, in many ways, a natural extension of previous work in the field that has focussed mainly on inferring natural selection from temporal data to a spatial setting. In addition to the simplest scenarios of isotropic dispersal the authors also consider models with different dispersal rate in longitudinal and latitudinal directions, as well as biased dispersal. Selection strength, dispersal rates and bias are assumed to be constant across space and piecewise constant in time (but it would be very straightforward to relax these assumptions). The bias component of the model is an interesting addition that, in principle, allows to broadly account for the effect of long-range dispersals such as the spread of agriculture across Europe from the fertile crescent and Bronze age migrations from the Asian steppes on the spatiotemporal pattern of allele frequencies.

Although the main idea is clearly communicated, there is room for improvement of the manuscript regarding investigating the properties of the model and presenting the results. Notably, the authors assume that the age of mutation is known and correct in their assessment of the performance of the model on simulated data (which may inflate the reported accuracy of the reconstructions) and use estimates from the literature when the method is applied to empirical data. Although it is necessary to specify the age of the allele, and this could easily have been treated as a free parameter in the framework. I would like to see a discussion of why the method may not be suitable for this, and a more systematic test for the sensitivity of the method to misspecification of the age (which could be very substantial, especially if the population history has been complex). In the cases where the model is run for different allele age estimates in the manuscript, such as for the lactase persistence scenario, the authors should present the (approximate maximum) likelihoods for the different scenarios in the text.

A further weakness of the method is that it uses the Fisher information matrix to estimate uncertainty. While this works well if the posterior distribution is narrow, it can severely underestimate the uncertainty if this is not case, in particular if the distribution is non-gaussian in the tails. It would be better, but perhaps computationally prohibitively expensive, to report Bayesian posterior distributions for the parameters as well as Bayes factors that could be used to formally compare the fit of different models to the data.

Finally, although the rationale behind the model is clearly described, the detailed descriptions of the model and the numerical implementation have some shortcomings. First, there are typos in the appendix where the continuous model is derived from a discrete approximation (the right-hand side of Eq. (8) should not contain the term p(x,y,t) for it to be consistent with Eqs. (9) and (10)). Second, any differential equation model is incomplete without specifying the boundary conditions. This is especially important here as the assumption of uniform diffusion and advection on the grid is violated by the constraints imposed by the land mask, where the population is assumed to vanish on water areas (suggesting an absorbing boundary condition). Further down in the methods, details are also missing on how Eq. (10) was solved numerically, merely that it was discretized at a certain resolution.

In summary, this is an elegant framework that accounts for spatially and temporal patterns and is a welcome addition to the existing range of tools for evolutionary inference from ancient DNA data. Although the manuscript in its current incarnation has some shortcomings, I am sure these can be easily overcome, and I look forward to the next version of the manuscript.

Reviewer #1 (Public Review):

In this manuscript, Bishop et al. aim to quantify the ventilatory response to hypoxia and hypercapnia in the common marmoset, an increasingly more common primate research model. They also present an unsupervised analysis tool to quantify ventilatory behavior, which is a potentially major contribution to the respiratory field.

Strengths of this manuscript include the inclusion of male and female animals and the development of an analysis toolkit that may be less impacted by biases that are introduced when hand analyzing respiratory behavior, as is commonly done in the field. This tool could be of tremendous value to the respiratory community. Identification of sniffs, sighs, and apneas are often plagued by the qualitative nature of the analysis.

Limitations of the study relate to the measure of the hypoxic and hypercapnia ventilatory drive. Tidal volume in whole body plethysmography is not accurate unless the plethysmograph and body temperature are taken into account. (See, https://pubmed.ncbi.nlm.nih.gov/25080926/). This is particularly important when the animal's core body temperature changes during hypoxia because of a fall in metabolic rate. The decrease in VCO2 shown here suggests that this is occurring here.

It is worth pointing out that the fall in VCO2 is not typically observed in humans. So, while the authors conclude that minute ventilation does not increase in the marmoset, it is not necessarily a valid conclusion that that hypoxia ventilatory drive is low because VE should be expressed as a function of VCO2. If VCO2 falls but VE is constantly, ventilation per unit metabolism will actually have increased. Ventilation may also be under-estimated here because of the fall in core body temp that likely coincides with a lower VCO2.

It is also worth noting that the hypoxic ventilatory response is not necessarily linear and the full range of the response is not characterized. For example, 15% O2 in the rat elicits very little response but there is a robust response with 9% O2. It is also worth noting, relevant to the previous points, that this is not an isocapnic ventilatory response, so the hypoxic response is certainly confounded by the changing CO2 which may not mimic situations like sleep apnea.

Reviewer #2 (Public Review):

I do not see any fundamental flaws in this paper as such. However, what really compromises the paper, it the lack of a "punch line". It is highly descriptive rather than analytical, it reads like a list of mostly predictable outcomes, but what is the question, what is the novelty, why is it important... This does not come out at all. On one hand it is important to have such basic information about marmosets but is it best placed into a non-specialist journal? In addition, the whole point of getting involved with monkeys is because they are closer to humans than rodents, but authors did not fully explore these similarities/differences or focus on them or try to explain them. One would want to have a clear conclusion in the end, how closely they resemble humans, for what type of experiments they are better than rodents, because of what... But this is not evident.

Neither is it clear what is the value of the novel protocol for data analysis which seems to have been a major effort. In the end we are left with the impression that the results you get with it are the same as with the old protocols... What is its value then? Something needs to be done to make this paper attract readers others but only specifically interested in this topic.

Reviewer #1 (Public Review):

Conway et al. use high quality light microscopy to observe microtubule (MT) arrangements and dynamics in live HeLa cells at metaphase. Local photoactivation of fluorescence produces a bar of labeled tubulin that migrates poleward, as shown previously by several groups on several types of cells. In this system, the intensity of the bar (corrected for photobleaching) decreases at a rate best fit by two exponentials, apparently corresponding to the fast turnover of non-kinetochore MTs and the slower turnover of the kinetochore MTs (KMTs) themselves. Moreover, the bar spreads significantly as it migrates poleward. The speed of the bar's peak along the spindle axis slows as it approaches the pole and is less for bars generated near the spindle pole. These results are interpreted to mean that KMTs flux poleward at different rates in different parts of the spindle. This slowing is used to evaluate models for KMT turnover in the HeLa spindle, whose structure is modeled with the concept of an active liquid crystal. The fraction of MT fluorescence that turns over slowly corresponds nicely with the fraction of MTs near the spindle equator that ends on kinetochores, as determined in a companion paper that uses electron tomography, so slowly fading fluorescence is interpreted as a metric for KMTs. This leads the authors to conclude that there are no slowly turning over MTs other than KMTs. The authors then compare spindle structure as seen by high quality polarization microscopy and by electron tomography with a computational model.

Reviewer #2 (Public Review):

In their manuscript, "Self-organization of kinetochore-fibers in human mitotic spindles," Conway and colleagues use a combination of photomarking, electron tomography, polarized light microscopy and theory to test models for how they kinetochore-fibers are born in the mammalian spindle. Indeed, how KMTs in mammalian spindles self-organize to build a k-fiber remains poorly understood. This work addresses this gap and contributes two major advances. First, while it is known that microtubule flux can vary regionally in spindles of different architectures in other species, the authors report on spatial flux differences in human spindles, which has implications for how the mammalian spindle builds and maintains itself. Second, the authors develop a model that describes KMT self-organization in human spindles and combine it with their experimental data to test models for how k-fibers are born. This model supports nucleation of KMTs predominantly at kinetochores as opposed to a search-and-capture model.

We appreciate the pertinence of investigating mammalian k-fibers, the new and careful experimental data, as well as the model the authors have developed to describe k-fiber self-organization. This work will be an important contribution to the field, both for the framework it provides and for the conclusion it makes on how k-fibers are born, though it could be improved for clarity and accessibility.

Reviewer #3 (Public Review):

In the present work, Conway et al. investigate the origin of kinetochore microtubules in human HeLa cells by combining electron tomography data showing that about 50% of kinetochore microtubule minus ends do not end at the kinetochore, with measurements of microtubule dynamic properties in living cells showing that kinetochore microtubules grow away from kinetochores along defined trajectories and slowing down near the poles, as well as theoretical modeling to test models of kinetochore microtubule origin for comparison with experimental data from cells. The authors find that the fraction of kinetochore microtubules directly determined by their electron-tomographic reconstructions matches to perfection the slow turnover component of a double exponential fit obtained from photoconversion of fluorescent tubulin in living cells. To my knowledge, this constitutes the most definitive demonstration of a long-proposed concept that this slow turnover population corresponds essentially to the entire kinetochore microtubules in the mitotic spindle. This is timely, as a recent work from the Gorbsky lab suggested the existence of more than two microtubule populations in the spindle. Availability of the present data, will allow the field to properly evaluate the significance of the findings and respective interpretations. Another interesting aspect provided by the present manuscript is the finding that flux in human cells decreases speed near the spindle poles, but kinetochore (and non-kinetochore) microtubule turnover was uniform along the spindle. Pol-scope analysis (and theory) revealed that both microtubule populations were highly aligned. Last, the authors use biophysical modeling to confront two possible models of kinetochore microtubule origin based on many of the parameters measured experimentally. The results show that a "kinetochore nucleation" model best explains the experimental results obtained from living cells. Overall, the authors propose an integrated model that predicts the lengths, orientations, and dynamics of kinetochore microtubules in mitotic spindles of human HeLa cells. Although many of the proposed concepts are not novel per se and have been demonstrated in other systems, including metazoans, the validation in human cell models is really the highest value of the present work. The writing of the manuscript is rigorous and the interpretations carefully discussed and well supported by the data and theory. This work will certainly change the way we conceive spindle assembly and kinetochore-microtubule formation in mammalian cells, and will motivate a revision of current textbooks.

Reviewer #1 (Public Review):

This paper is a technical tour de force, as demonstrated best in the many videos associated with the text. They reveal the huge amount of microtubule tracking that has been achieved and show HeLa spindles with more clarity and detail than has previously been accomplished. Thus, the paper is a landmark in spindle study. In its current form, however, the paper contains some technical errors and some issues with interpretations, and their remedy would make this paper an important classic in structural cell biology. These issues include: taking account of the collapse in section thickness that is brought about by the electron beam; recognizing that the great number of non-KMTs near the pole will bias the probability of MT-MT interactions in ways that should be taken into account; and re-examining the data to see if additional issues, such as the opened or closed status of KMTs at their polar ends can be determined. With these and other improvements, the paper will become a classic in the field.

Reviewer #2 (Public Review):

In their manuscript, "Three-dimensional structure of kinetochore-fibers in human mitotic spindles," Kiewisz and colleagues performed sophisticated reconstructions of human kinetochore-fibers using electron tomography, and then analyzed the ultrastructure and organization of their kinetochore-microtubules. Previous work has been done to analyze k-fiber structures by EM in other cell types and species, but this manuscript represents the most comprehensive reconstruction of k-fibers to date across three human spindles. Here, the authors determine the number, length and morphology of KMTs, as well as their positioning and interactions relative to the MT network, revealing key differences in the makeup and organization of KMTs versus non-KMTs.

We appreciate the rigorous experimental design and analysis, and the pertinence of choosing to work on k-fiber ultrastructure. The experimental logic is clear, the analysis is methodical, and the data are presented transparently and clearly. Overall, this work will be a great asset to the scientific community, though the main point of the paper and of certain figures requires more motivation and context.

Reviewer #3 (Public Review):

Kiewisz et al present analysis of (traced) microtubules in the spindles generated from 3D tomography data for 3 human cells. As a full complement of MTs in human spindles this is a phenomenally rich data set and a fantastic reference resource. The authors' analysis provides crucial quantification of the human spindle and new insight into spindle architecture, and essential numbers for modellers. A key result is that bundles of KMTs starting at the kinetochore deteriorate through KMT termination and splay in going to the pole. They have made available a visualisation tool so readers can examine the data themselves - this is particularly welcome and aids understanding of their results.

The authors discriminate kinetochore MTs (ending at KTs) and non-KMTs, counting both sets. They restrict analysis predominantly to the KMTs, quantifying number per KT, length, tortuosity (a proxi for curvature), 'bundle' deterioration from KT to pole, minus end proximity to other MTs and length-wise associations. The data appears consistent with previous studies eg O'Toole et al, 2020 but based on substantially higher samples of kinetochores/K-fibers (complete cell MT complement); one key new result is the heterogeneity and stochasticity in bundles of KMTs. Only 50% of KMTs reach the spindle pole zone and the (KMT) bundles are sometimes compact along their length or are only compact at the KT with significant splay (and KMT curvature) moving towards the pole. The KMTs in the periphery of the metaphase plate are less likely to reach the pole compared to those near the centre of the metaphase plate. There are thus substantial numbers of minus ends in the spindle, in fact from both KMTs and non-KMTs. An analysis of KMT proximity to other MTs is then presented - this suggests that KMT interactions increase in the pole, probably unsurprising as MT density increases near the poles (although allowance for this effect is not discussed).

Reviewer #1 (Public Review): 

This study investigates the role of enhancer activity in the regulation of stable random monoallelic expression (RME) using the Ly49 and Nkg2 receptor gene families expressed in natural killer (NK) cells, as models of RME genes. The authors show that, unlike promoters of RME genes, enhancer are accessible on both alleles and display histone marks of active enhancers. Moreover, they show that weakening enhancer activity, via CRISPR-mediated deletion, can lower the frequency of gene expression or lead to variegated expression patterns, that are reminiscent of RME. The manuscript is clearly written and the data presented are compelling. This study takes advantage of previously-characterised allele-specific antibodies for various genes expressed in NK cells, a powerful tool allowing the analysis of random monoallelic expression (RME) at the protein and single-cell level within a population. The use of these antibodies allows the investigation of in vivo cell population and circumvents the analysis at the RNA level, which is limited by expression bursts and transcript levels. The authors also substantiate their model using examples of receptor genes expressed in other cell types from the hematopoietic lineage. One question that remains is whether this model applies to other developmentally regulated stable RME genes, that are 1-not expressed at the cell surface (such as transcription factors) and 2- expressed in other cell lineages? It is also unclear what defines the strength of an enhancer upstream of the RME genes studied, e.g. what is the difference between a weak enhancer for Ly49 genes and strong enhancer. These points should be of broad interest for the readers and could be discussed further in the discussion part of the manuscript.

Reviewer #2 (Public Review): 

In this manuscript, Kissiov et al. investigate the role of enhancers in the control of stable random monoallelic expression (RME). In order to do so, they initially focus on a limited set of natural killer (NK) receptor genes that are subject to RME, which they investigate using several in vivo genetic models. Once they convincingly showed that the expression of these NK receptor genes is controlled by bona fide enhancers, they perform a number of allele specific analyses to convincingly show that enhancer deletions can lead to a clear increase in the number of cells showing mitotically stable RME. Lastly, the authors also show that RME seems to be considerably more prevalent than previously estimated, which is in agreement with the proposed importance of enhancers in RME and the variability of enhancer landscapes (strength and number) among different genes. 

At a mechanistic level, the authors show that enhancers remain active in both cells expressing or not expressing the target gene alleles, which, nevertheless differ in their promoter state (active vs inactive). Since in the investigated loci the enhancers are quite proximal to their target genes, it is rather intriguing and still an open question why the enhancers fail to activate the gene alleles subject to RME. Therefore, the molecular mechanisms behind RME and of how the interplay between genes and enhancers control this process remain unknown. 

Overall, the presented work, which is based on an impressive battery of in vivo mouse models, has major implications for the gene regulation field. Briefly, the presented work provides novel insights into how RME can be regulated and extends previous observations that indicate that enhancers work in a binary manner controlling the probability rather than the level of gene expression.

Reviewer #3 (Public Review): 

In this article, the authors investigate in detail several regulatory elements of the NKR locus, located on mouse Chr6 and that include several Ly49 and NKg2 genes. More specifically they perform a series of analyses and genetic experiments to test the idea that regulatory elements potentially regulating gene expression of the NKR genes are random mono-allelic enhancers. Based on their results, they propose that the analyzed genes obey a binary model of activation in which the enhancers control gene expression through a probabilistic rather than through an increase of gene expression. This article is interesting and provides a rich set of data useful for the community. It will also feed the debate about the role and mode of action of enhancers in random mono-allelic gene expression (RME) and provides clues about some possible roles of enhancer redundancy. One general criticism is that the work is not simple to read for a general audience that is not familiar with immunology and the RME questions. Some of the statements in the beginning of the manuscript are also too strong and need to be moderated. The presentation of the genomic datasets used in the study could also be improved.

Joint Public Review:

Davis et al. parameterize a published, coarse-grained classical density functional theory (DFT) model to describe the free energy landscape of the FG-NTR system. They leverage their previously published experimental data (Zahn et al. eLife, 2016) to develop the model of inter-molecular cohesion calculations, which were tuned to reproduce their previous experimental results. The authors investigate NTR binding behavior to the planar film of FG-nups, first for single NTRs and then by combinations of NTRs. They confirm that the higher concentration of NTRs in the FG-nup films decreases their affinity to the film, which provides one rationale to explain the "transport paradox" of NTRs, which bind specifically to FG-nups but transit the NPC extremely rapidly and at high density. The second result is that increasing the concentration of one of the transport receptors in the film (by increasing its bulk concentration) reduces the adsorbed amount of the other transport receptor (whose concentrations is fixed). Last, the authors thus suggest that within some NTR concentration regimes there emerges a phase separation of the two NTRs such that NTF2 (small NTRs) locate near the surface while importin beta (large NTRs) go to the film/solution interface, implying the existence of separate transport pathways inside the NPC, which has been reported previously in experimental findings.

There was broad enthusiasm for the model, which was found to be interesting, relevant, and to have successfully delivered testable insights. In general, the conclusions were found to be supported by the model outcomes. The segregation of small and large NTRs to different regions of the film was found to be an interesting result. Some results were found to be less exciting, for example the effect of competition between NTRs as they possess only repulsive interactions in the model.

While there was some disagreement about the quality of the writing, there was a consensus that the explanation of the motivation, methodology, and impact of the conclusions was not sufficient. In particular, the reviewers felt there was a lack of sufficient context related to prior work in the field in the introduction and discussion and the need to better articulate the impact of the findings in the study. Thus, although the work was found by some to be a meaningful contribution addressing two important questions in the NPC field: how different NTRs are organized within the permeability barrier and if NTR organization and dynamics contribute to the efficient rates of nucleocytoplasmic transport through the crowded environment of the NPC, this point needs to be made clearer. Moreover, more attention is needed to previous theoretical works related to protein adsorption in polymer brushes.

There was a consensus that the authors could have increased the impact of the work by broadening the study to investigate (or at a minimum discuss) 1) how the combination of NTRs with inert molecules behave (i.e. does the addition of NTRs influence the exclusion of inert cargo?); and 2) how cargo bound to the NTRs (particularly NTF2, which has a single cargo - Ran) influences the results (e.g. would the importin-beta effect be exacerbated by its coupling to an "inert" cargo?). A related theme was concern over the potential impact that the geometry of the NPC in vivo would have on the model outcomes, which speaks to the biological relevance. While the authors mention this issue in the Discussion, more directly addressing whether they can speculate on how their results will change for a cylindrical geometry and how the calculations would compare in a system with opposing surfaces (i.e., two surfaces modified by polymer brushes) was warranted. The latter system was felt to be a good proxy to understand how the effects of nanoconfinement in a cylindrical geometry may affect the results.

Reviewer #1 (Public Review):

Understanding neuronal mechanisms underlying social disorders is an important question in neuroscience. Using tetrode recording, Kim et al. revealed that the firing rates of excitatory mPFC neurons are increased but the dynamic range of firing and the proportion of burst activities were decreased in the presence of social targets in IRSp53-KO mice. This study provides a possible mechanistic explanation of why autistic mice have difficulties in responding to social targets and will be of interest.

The biggest criticism is that in the identified single-units in the mPFC of either WT or IRSp53 KO, only 10% were responsive to social and/or object targets, while 90% of the recorded units are not responsive to either target (Figure 7E). Consistently, the mean firing rate in E-E, fS-O and sS-O are similar in both WT and KO mice (Figure 3-supplement 1E), indicating that the presence of social targets had little effect in regulating most of the recorded mPFC neurons. Therefore, it is hard to believe that the recorded mPFC neurons play an essential role in discriminating social targets. Although the authors found that the dynamic range of firing rates (Figure 3E), burst proportion/spike variability (Figure 4) were different between WT and KO mice, such differences may be due to general suppression of firing in the KO mice. The authors should ideally further examine whether the FR range and burst firing proportion are different between WT and KO in resting and non-social conditions.

The authors found that the average resting firing rates of excitatory mPFC neurons in awake IRSp53-KO mice are larger than that in WT mice (Figure 1C and 1D). These results oppose their previous findings obtained from anaesthetized mice (Chung et al., 2015, Figure 8b and 8c). However, no such difference was observed between awake and anaesthetized WT animals. The authors should comment on the differences between these findings.

Reviewer #2 (Public Review):

The manuscript by Kim et al. studies activity of prefrontal neurons during simple social tasks in IRSp53-KO mice. These mice are proposed as a model of autism and have deficits in social interaction. The major findings are that in KO mice: 1) activity levels of putative excitatory neurons are elevated, 2) baseline firing rates are elevated but peak firing rates and variability of firing during social exploration are reduced resulting in a reduced dynamic range, 3) bursting in putative excitatory neurons during the social exploration task is reduced, 4) the normalized difference in firing rates near objects vs. social targets is smaller, and 5) a decreased proportion of neurons are classified as selective for social targets vs. objects.

Major strengths are the approach which records from many individual neurons in the prefrontal cortex of behaving mice, the findings of effects that are specific to putative excitatory neurons, and the link between changes in the firing rates of neurons and changes in how information about social behavior is encoded. Current weaknesses include: the discrimination index employed by the authors does not account for the possibility that changes in neural variability may compensate for the reduced dynamic range of individual neurons or measure information encoding at a population level; the relevance of this gene to autism is only supported by a case-control study; the relationship of the changes in neural activity to specific mechanisms is unclear; it is unclear whether these changes in neural simply co-occur with behavioral changes vs. whether there is a tight correlation or even causal relationship between them; and the magnitude of many of the effects is small.

Together the current findings do indicate a specific pattern of changes in firing rate which produce altered encoding of social information by prefrontal neurons in this mutant mouse.

Reviewer #3 (Public Review):

Here Kim et al. record in the medial prefrontal cortex (mPFC) of IRSp53 knockout (KO) mice. They focused on evaluating activity patterns recorded with tetrodes in prelimbic (PrL), infralimbic (IL), and cingulate (Cg1) cortical regions of male adult wild-types and KOs, as animals interacted with other males or objects in a linear chamber. KO mice sniffed less and spent less time in the interaction zone on the social side vs object side during the first session, but sniff durations and dwell times were comparable for the second session. Recordings indicated that putative excitatory single-units from KO animals had a dampened dynamic range, statistically lower variability, and less bursting in the linear chamber compared to neurons from wild-types, with higher basal firing rates and smaller instantaneous changes in the KOs during S-O sessions. These are interesting experiments and a really rich dataset, but I have some questions about both the robustness of the behavioral and neuronal results, as well as concerns that the analysis is at present just barely scratching the surface and thus not quite as enlightening as it potentially could be.

The authors state based on the results of Figure 1 that KO animals 'display social impairments', but I wonder if this is the only interpretation of their data. In Figure 1E, time spent sniffing the objects during the first S-O session is not significantly different between wild-types and KOs, although is much more variable in the KOs; similarly, social sniff times are also quite variable. These times are measured in tens to hundreds of seconds, likely enough time for animals to obtain a considerable amount of sensory information from their sniffing- perhaps the KO animals have somewhat stronger sniffs, or are faster at processing the olfactory information somewhere in the brain, requiring them to spend less time (around ~25% less time compared to wild-types) sniffing and remaining in the in-zone. I'm not sure this presents as 'social impairments' (plural).

There may be missed opportunities to say much more of potential interest about the unit activity. For one, I'm not sure why the classification in Figure 7 occurs at the end, rather than earlier in the manuscript, and the analysis of firing rate statistics done separately for 'social' vs 'non-social' neurons. I also wonder how a unit classification (i.e., of 'social' or not) holds up across sessions or episodes of engagement within a session, for example. If these really are 'social' units, presumably that aspect should be reliable across interactions with different mice. More importantly, the authors take the trouble to use machine learning methods to classify mouse part position, but they do not seem to do much with these data beyond marking where the mouse is in the track. The unit activity over time might be much more interesting if correlated with DeepLabCut-based analysis of what the animal was doing (or perhaps what the social interaction partner was doing) during the sessions. The analyses of Figures 5 and 6 are a step in this direction, but the authors really could have gone much further in terms of determining how the behavior relates to the activity, both of individual units and of simultaneously-recorded populations. Two more minor critiques are that the subregions of mPFC seem lumped together (neglecting any individual differences between PrL, IL, and Cg1), and that the reduction in variability in Figure 3, although technically statistically significant, seems marginal. Again, perhaps this would be improved by a cleaner analysis of relating the unit spike trains to moment-to-moment features of social interaction or other behaviors occurring during the S-O sessions.

What does the discrimination look like in Figure 6 if only 'social' units from wild-types and KOs were considered? A related question: do these changes in unit (and network) responses somehow directly result from the cellular function(s) of loss of IRSp53 (specifically in mPFC and/or elsewhere in the central nervous system)? Any chance that these results are not local to mPFC computations, but that the differences in firing in KOs is inherited from earlier regions providing, e.g., olfactory information eventually to mPFC?

Reviewer #1 (Public Review):

In their manuscript "CompoundRay: An open-source tool for high-speed and high-fidelity rendering of compound eyes", the authors describe their software package to simulate vision in 3D environments as perceived through a compound eye of arbitrary geometry. The software uses hardware accelerated ray casting using NVIDIA Optix to generate simulations at very high framerates of ~5000 FPS on recent NVIDIA graphics hardware. The software is released under the permissive MIT license, publicly available at https://github.com/ManganLab/eye-renderer, and well documented. CompoundRay can be extraordinarily useful for computational neuroscience experiments exploring insect vision and robotics with insect like vision devices.

The manuscript describes the target of the work: realistic simulating vision as perceived by compound eyes in arthropods and thoroughly reviews the state of the art. The software CompoundRay is then presented to address the shortcomings of existing solutions which are either oversimplifying the geometry of compound eyes (e.g. assuming shared focal points), using an unrealistic rendering model (e.g. local geometry projection) or being slower than real-time.

The manuscript then details implementation choices and the conceptual design and components of the software. The effect of compound eye geometries is discussed using some examples. The speed of the simulator depending on SNR is assessed and shown for three physiological compound eye geometries.

Opinion:

I find the described open source compound eye vision simulation software extraordinarily useful and important. The manuscript reviews the state of the art well. The figures are well made and easy to understand. The description of the method and software, in my opinion, needs work to make it more succinct and easier to understand (details below). In general, I found relevant concepts and ideas buried in overly complicated meandering descriptions, and important details missing. Some editorial work could help a lot here.

Major:

1. The transfer of the scene seen by an arbitrary geometry compound eye into a display image lacks information and discussion about the focal center/ choice of projection. I believe that only the orientation of ommatidia is used to generate this projection which leads to the overlap/ non-coverage in Fig. 5c. Correct? It would be great if, for such scenarios, a semi-orthogonal+cylindrical projection could be added? Also, please explain better.

2. It is clear that CompoundRay is fast and addresses complex compound eye geometries. It remains unclear, why global illumination models are discussed while the implementation uses ray casting to sample textures without illumination which is equivalent to projection rendering which runs fast on much simpler hardware. If the argument is speed and simplicity of writing the code, that's great, write it so. If it is an intrinsic advantage of the ray-casting method, then comparison with the 'many-cameras' approach sketched below should be done:

In your model, each ommatidium is an independent pin-hole camera. Instead of sampling this camera by ray-casting, you could use projection rendering to generate a small image per ommatidium-camera, then average over the intensities with an appropriate foveation function (Gaussian in your scenario, but could be other kernels). The resolution of the per-camera image defines the number of samples for anti-aliasing, randomizing will be harder than with ray-casting ;). What else is better when using ray-casting? Fewer samples? Hardware support? Possible to increase recursion depth and do more global things than local illumination and shadows? Easier to parallelize on specific hardware and with specific software libraries? Don't you think it would make sense to explain the entire procedure like that? That would make the choice to use ray-casting much easier to understand for naive readers like me.

3. CompoundRay, as far as I understand, currently renders RGB images at 8-bit precision. This may not be sufficient to simulate the vision of arthropod eyes that are sensitive to other wavelengths and at variable sensitivity.

Reviewer #2 (Public Review):

In this paper, the authors describe a new software tool which simulates the spatial geometry of insect compound eyes. This new tool improves on existing tools by taking advantage of recent advances in computer graphics hardware which supports high performance real-time ray tracing to enable simulation of insect eyes with greater fidelity than previously. For example, this tool allows the simulation of eyes in which the optical axes of the ommatidia do not converge to a single point and takes advantage of ray tracing as a rendering modality to directly sample the scene with simulated light rays. The paper states these aims clearly and convincingly demonstrates that the software meets these aims. I think the availability of a high-quality, open-source software tool to simulate the geometry of compound eyes will be generally useful to researchers studying vision and visual behavior in insects and roboticists working on bio-inspired visual systems, and I am optimistic that the describe tool could fill that role well.

As far as weaknesses of the paper, the most major issue for me is that I could not find any example of why the additional modeling fidelity or speed is useful in understanding a biological phenomenon. While the work is technically impressive, I think such a demonstration would increase its impact substantially. I can identify a few more, relatively minor, weaknesses: the software tool is not particularly easy to install but I think this is due primarily to the usage of advanced graphics hardware and software libraries and hence not something the authors can easily correct. In fact, the authors provide substantial documentation to help with installation. Another weakness of the tool, which the authors might like to address in the paper, is that there are some aspects of insect vision and optics which are not directly addressed. For example, the wavelength and polarization properties of light rays are hardly addressed despite extensive research into the sensation of these properties. Furthermore, the optical model employed here is purely ray based and would not allow investigating the wave nature of light which is important for propagation from the corneal surface to the photoreceptors in many species.

Reviewer #3 (Public Review):

Millward et al. introduce their software CompoundRay that aims at realistic, high-performance, real-time modeling of compound eye function, and pose it as a tool to explore compound eye-mediated behaviors. The design and operation of the software, and its critical advantages over competing software, are spelled out, and some illustrations are offered.

Overall, the paper figures, both in presentation and content, could be strengthened significantly. In particular, specific compound eye configurations with heterogeneous ommatidia, which the software supports, are largely lacking. One such comparison could have been done with the eyes of rober flies, which present a fovea of high visual acuity ommatidia.

Reviewer #2 (Public Review):

This is an excellent study that introduces new players for the regulation of energy expenditure with sophisticated approaches to neuronal and peripheral metabolism. The experiments are well executed with appropriate controls and careful interpretation. The paper is clearly written and relays the conceptual advance that TIGAR controls acetyl choline levels to fuel changes in thermogenesis.

Reviewer #1 (Public Review):

My main concern relates to the title, which does not appear to be supported by the data. One can't conclude that the reported effects are strictly due to altered glycolysis in cholinergic neurons without directly assessing glucose metabolism in these neurons. Moreover,TIGAR functions by blocking glycolysis and directing the pathway into the pentose phosphate shunt. Therefore, the resulting effect of deleting TIGAR in a neuronal population might be multiple.

The authors show convincingly that deleting TIGAR from ChAT-expressing neurons, but not adipose or muscle cells, protects mice from cold-induced hypothermia. It is however unclear whether this leads to alteration in energy expenditure per se. This it important considering the first argument of the discussion highlighting how approaches to increase energy expenditure through the development/activation of brown/beige adipose tissue thermogenesis have failed. Moreover, it is unclear if TIGAR also affects heat dissipation considering the impact of its deletion from ChAT-expressing neurons on blood pressure and heart rate, two parameters that will likely influence the tail vasoactivity. Evaluating energy expenditure and heat loss appears to be necessary to support the conclusion that the resistance to hypothermia is exclusively dependent on shivering thermogenesis.

One key aspect that may deserve discussion is a potential contribution of the sympathetic nervous system to the observed phenotype. The focus of the manuscript is on acetylcholine but one can't disqualify that sympathetic compensations may happen following the deletion of TIGAR in ChAT-expressing neurons.

There are many data that are not shown but that would worth be included (lines 99, 113, 119, 159, 168, 181, 221,

Reviewer #3 (Public Review):

Strengths:<br> The study is nicely written and presented. The investigation of whole-body TIGAR knockout (TKO) clearly demonstrates resistance to cold exposure, and the authors logically follow potential sources through the obvious tissue candidates.

Both skeletal muscle and adipose specific TIGAR knockouts were generated, neither of which recapitulated the effect of the TKO. Other obvious candidates, such as UCP1 content in adipose and basal oxidative capacity and contractility of skeletal muscle were ruled out using ex vivo techniques.

Nevertheless, pharmacological interventions indicated that muscle contraction was necessary for protection from cold exposure and that the loss of TIGAR overcame competitive antagonism of the nicotinic acetylcholine receptor. These data were supportive of a role for skeletal muscle contraction, particularly at the level of cholinergic signaling.

A cholinergic neuron specific TIGAR knockout was produced. Loss of TIGAR was molecularly confirmed, and this mouse recapitulated the whole-body knockout's resistance to cold exposure.

Tracer studies are largely compelling and confirm that loss of TIGAR increases substrate dependence on glucose oxidation in a cell model.

Weaknesses:<br> The TKO mice were not characterized for body weight, body composition or energy expenditure, leaving some room for alternative or additive mechanisms.

Although the tracer data demonstrate that loss of TIGAR causes the cell model to increase reliance on glycolysis compared to other unlabeled substrates, the data do not necessarily demonstrate an increase in the absolute rate of glycolysis or total acetyl-CoA production as intimated in the discussion. It is also unclear why media glutamate is examined for tracer incorporation rather than tissue glutamate.

There are some minor weaknesses related to the description of the methods. For example, the 18O studies need clarification. It will be unclear to most readers how this method works.

Reviewer #1 (Public Review):

In this interesting paper by Dady and colleagues the nature of human neural progenitor differentiation is evaluated via transplantation studies. The first part of the paper establishes the timing of neural differentiation in human IPSC model systems and in human embryonic spinal cord, showing that the relative timing of neurogenesis and gliogenesis is maintained. In the second part of the paper these human IPSC neural rosettes are transplanted into the chick spinal cord during neurogenic stages (i.e. Isochronic transplantation) and they find that neurons are generated by these transplanted populations. Analysis of transplants at later stages reveals that neurogenesis has "stalled" and is relatively reduced within the transplanted population.

Overall, this is an interesting paper that uses classic approaches to answer potentially interesting questions, however there are some issues that limit it's potential impact. The first two figures are recursive and show that the authors can implement an existing protocol. The transplantation studies are intriguing but do not offer sufficient new insights. The key finding seems to be that at later stages post-transplantation neuronal differential is "stalled". There are many other reasons (besides "stalling") that could explain their results. Suppose that stalling was indeed occurring, the authors offer no cellular or molecular insights into what regulates these intrinsic differences across species. At the end, it is not sufficiently clear what we have learned about the mechanisms that control the timing (pace seems to be another term for timing) of differentiation in human neural stem cells.

Reviewer #2 (Public Review):

In general, this manuscript provides new significant knowledge by comparing between neural differentiation rate within the same species (human) in vivo and in vitro and between species (human and chick). The quality of the data is excellent, and the combining of the in vivo chick model to compare between grafted and host cells is a fantastic idea, that can only be done in this experimental model. Yet, some controls and more in-depth analysis are missing and are required in my opinion before publication.

1. In the grating experiment, non-manipulated embryos serve as controls. Yet, the grafted rosettes are inserted near an injured area where a piece of the neural tube was moved. A better control would be to graft homologous cells from a donor chick embryo (GFP+ chick line is available in the UK) or quail embryo (which has a similar growing rate as chick at E2) and examining whether the injured area doesn't affect the grated cells to differentiate in a different pace as compared to the human grafts. This control is necessary to rule out the possibility that the human graft did not accelerate their differentiation rate and later stopped differentiating due to extrinsic signals/lack of signals form the manipulated environment.

2. When examining the entire results of the manuscript some important points need to be addressed: On the one hand, the rosettes correspond to their in vitro growth conditions/extrinsic cues and display an accelerated differentiation pace, when compared to their in vivo counterpart human cells. On the other hand, the rosettes do not correspond initially to the chick environment and maintain their own intrinsic tempo. Later, they do change their developmental program and attenuate their differentiation. Therefore, the conclusion that the cells mostly obey to intrinsic regulation is confusing. It would be great if the authors could provide better experimental data to confirm their conclusion. Some ideas that the authors may consider are to determine whether there is a time window that sets the tempo of the rosettes that cannot be influenced later by extrinsic cues. Will the grafted cells correspond differently whether they would be grafted at a more/less advanced stages and domains? Is there an initial mechanistic elucidation to the different behavior of spinal cord progenitors in the three contexts? Is there a possibility somehow to obtain human spinal cord progenitors and grow them in the same in vitro conditions as the rosettes to compare their differentiation rate? I am aware that some of these experiments are very hard to perform and not expecting the authors to perform all the suggested ones, yet, some more in-depth analysis would enable this article to explain better the presented observations.

Reviewer #3 (Public Review):

The authors have developed dorsal spinal cord rosette assays from human pluripotent stem cells (hPSCs) and also from human induced pluripotent stem cells (hiPSCs) in a minimal culture medium containing retinoic acid. They define the dorsal spinal cord identity of these cells based on the presence of SOX2, PAX6, SNAI2 and PAX7, and absence of OLIG2 (characteristic of more ventral neural tube). Assessment of markers for migrating neural crest-like cells (HNK1, SOX10 and TFAP2alpha), immature neurons (DCX) and glial progenitors (NFIA) at different time points was used to show that the in vitro model recapitulates sequential differentiation observed in the spinal cord of avian and mouse embryos. Next, by comparing these results with neural differentiation in the human embryo, the authors show that neural differentiation occurs faster in vitro than in vivo. The authors then asked how these hiPSC-derived neural rosettes would respond to the more rapidly developing chicken embryonic environment, by grafting the rosettes into the developing chick neural tube. By assessing expression of various neural markers in the graft-derived cells, authors conclude that after two days of culture, human cells continued differentiation at the rate of the in vitro hiPSCs rather than at the rate of the host chicken cells. After longer culture (5 days), authors say that neurogenesis rate among graft-derived human cells attenuates and that the cells stall in the neural progenitor phase. Authors conclude that while initially an intrinsic differentiation programme is followed by the human cells, appropriate extrinsic inputs are required to maintain the neural differentiation trajectory of human cells.

However, it is difficult to assess whether all conclusions by the authors for the human-into-chicken graft experiments are supported by their data, as some details of analysis are unclear (1) or experimental design was not conducive to the questions being asked (2). Some aspects of data analysis therefore need to be clarified and extended.

1. Position of graft derived cells within the chicken host is very important when analysing presence/absence of a marker, but it is not always clear whether this has been taken into account by the authors. It appears that authors are assessing expression of markers in graft derived cells that are present outside OR inside domains in the chick host that would normally express that marker, and are not separating out such analysis. This will confuse interpretation of results and affect conclusions.

One example where this would affect major conclusions of the manuscript is in the case of Islet-1 expression in human graft derived cells in the chicken host. Authors say that no Islet-1 was found in single graft derived cells in the chick embryo after two days of culture and use this to support their conclusion that the "pace of neural differentiation in the grafted human rosettes is unaltered in a more rapidly differentiating environment". However, Islet-1 expression in the chick is restricted to specific domains, therefore it would be important to know whether the graft-derived cells that the authors were analysing were within these Islet-1 positive host domains. Lack of Islet-1 in graft derived cells within such Islet-1 positive domains in chick would suggest that the graft derived cells have not responded to the host's timing of differentiation, and would support the authors' conclusions. However, lack of Islet-1 in graft derived cells outside of such Islet-1 positive domains could not be used to conclude the same thing as cells would be receiving different signals from the host. It appears that the graft used by the authors to show absence of Islet-1 in Fig 4G is outside of chick Islet-1 positive domains. Therefore, lack of Islet-1 in graft derived cells cannot be used to suggest that pace of human neural differentiation is initially directed by cell intrinsic factors, unless the location of the human cells in the chick is clearly shown to be within Islet-1 expressing domains in the chick.

2. Size of the graft used when transplanting human iPSCs into the chick will also affect the interpretation of results, as human cells will be exposed to varying levels of host signal depending on how much of their surface is exposed to host cells. Since the authors are using this experiment to test the effects of the chicken environment on human cells, this is a crucial point. After grafting hiPSC derived neural rosettes into the chick and culturing the chick embryo, authors assess expression of various markers in the graft-derived cells and separate out their analysis of marker expression across three different categories; cells found in 'cell rosettes', 'cell groups' or as 'single cells'. However, it remains unknown for how long these groupings were true during the culture time. For example, while it is known that at the time of grafting the cells were in a rosette structure, it is unknown at what time cells detached to incorporate as single cells (it could have been directly after grafting, or just prior to analysis) and is therefore not consistent across cells being analysed.

One way to go around this would be not to graft the entire rosettes, but rather to dissociate the rosette and graft single cells/small groups of cells into the chick. With single cells the community effect (Gurdon 1988) would be avoided and the experiment would be testing the influence of only the host environment on this cell (rather than a combined influence of host environment and environment created by neighbouring graft derived cells as is the case in the current manuscript). This is particularly important as the data presented in the manuscript appear to show a difference between marker expression in single cells versus groups of cells and rosettes (plots in Fig 4 and 5).

Reviewer #1 (Public Review):

Belville et al. investigated the epigenetic regulation of inflammation in the zone of altered morphology (ZAM) and zone of intact morphology (ZIM) of the fetal membranes before labor at term. The authors first utilized human fetal membrane samples to undertake methylomic and transcriptomic surveys and identify the biological processes enriched in hyper- and hypo-methylated genes in the ZAM/ZIM of the chorion and amnion. The combined analysis of these surveys identified several key processes, all of which involved TLR4. The presence of TLR4 in human fetal membranes was confirmed using immunofluorescence and in vitro LPS stimulation. The authors then demonstrated hypermethylation of TLR4 in the amnion, but not the chorion, together with increased expression of the inhibitory mi-RNAs miR-125b-1 and let-7a-2. Based on their findings, the authors propose a mechanism whereby TLR4 expression is epigenetically regulated in the amnion and chorion, providing evidence of tissue-specific control of this receptor as part of the inflammatory process required for labor.

Overall, the authors' findings provide novel insight into the mechanisms controlling the expression of TLR4 in the fetal membranes prior to term labor. The confirmation that TLR4 plays an important role in the inflammatory processes associated with term labor further highlights that TLR4 may contribute to "sterile" inflammatory processes rather than only those involving microbes and their products. The combined use of methylomic and transcriptomic surveys to cross-validate the authors' findings is a notable strength of the study. However, there are some aspects of the study that require further consideration.

1. A notable shortcoming of the authors' interpretation is the generalization of their findings to preterm premature rupture of membranes (PPROM). As noted by the authors, term labor is considered a "sterile" process, which is particularly important in terms of the authors' findings since TLR4 in the fetal membranes may be responding to endogenous signals such as danger signals. However, a large proportion of PPROM cases are associated with microbial invasion of the amniotic cavity, and thus in this context TLR4 would be responding to bacterial products.

2. It is a well-known concept that TLR4 is expressed by the fetal membranes and is responsive to LPS stimulation, and thus the confirmatory set of experiments performed by the authors do not seem to be as novel. Indeed, given that this study was focused on the "sterile" process of term labor, perhaps the utilization of danger signals that can interact with TLR4 would be more appropriate.

3. The distinction between the ZAM and ZIM seems to have been lost among the TLR4-focused experiments, and thus it is unclear how these fetal membrane zones fit into the conceptual model proposed by the authors in the final figure.

4. The study is largely descriptive and would benefit from the addition of fetal membrane tissues from pregnancy complications such as PPROM and/or animal models in which premature rupture of the membranes has been induced.

5. The study focuses on the mechanisms of rupture of membranes, but does not provide an explanation as to how the regulation of TLR4 mediates the process of membrane rupture.

Reviewer #2 (Public Review):

This is a well-conceived and executed paper that adds novel data to improve our understanding of rupture of the human fetal membranes. The new information presented not only addresses gaps in our understanding of normal parturition mechanisms but also the significant issue of preterm birth.<br> The authors highlight the need to understand the understudied human fetal membranes to be able to understand its role in normal parturition but also to lower the rates of preterm birth. They not only establish the need to study this tissue but also to improve our appreciation for regional differences within it, using a comprehensive genetic approach. The authors provide data from a genome wide methylation study and cross reference this with transcriptome data. Using this new knowledge, they then zero in on a specific gene of interest TLR4. This receptor is already established as an extremely important receptor for preterm birth but little is known about its role in normal parturition. Strengths of this paper stem from the comprehensive data set provided, answering both the questions pertaining to the specific aims of this paper but also potentially future questions and providing potential focused targets of study. One example of this may be the common methylated genes that are found in both the ZIM and ZAM, illustrating not regional changes but gestational programming of this tissue.

Reviewer #3 (Public Review):

Manuscript by Belville et al describes the significance of epigenetic and transcription associated changes to TLR4 as a mechanistic event for sterile inflammation associated with fetal membrane weakening, specifically in the zone of altered morphology. This manuscript is timely in an understudied area of research.

The authors have taken an extensive set of experiments to derive their conclusions.

However, it is unclear why the focus is on TLR4. Although LPS is a ligand for TLR4, gram negative infections are rare in PPROM but mostly genital Mycoplasmas. The methylome and transcriptome analysis does not necessarily warrant examination of a single marker. A clear rationale would need to be included.

Reviewer #1 (Public Review): 

In this manuscript, an interesting strategy is presented for directing simultaneous induction of both mesoderm-derived cardiac and endoderm-derived lung epithelial lineages from human induced pluripotent stem cells (hiPSC). This follows from published observations by others showing mutual beneficial cross-talk between the developing heart and lung during embryogenesis. The culture model is partly based on such observations in e.g. mice as well as on comparing protocols for cardiac and pulmonary differentiation from hiPSC, and may be of value for studying such interactions in the developing human heart and lung. The availability of such a human model is important in view of frequent failures in translating findings from rodents to humans. The authors characterized the obtained alveolar and cardiac cells based on a limited number of markers, electron microscopic analysis of lamellar bodies, and by showing contractility of the cardiac tissue obtained. A drawback is that the efficiency of the differentiation process (including % of differentiated cells in the final cultures) was not fully elucidated. Furthermore, since the experiments presented are based on analysis of a single hiPSC cell line, and only part of the differentiation was repeated in another cell line, the broader applicability of the presented protocol remains to be established. However, the interesting data support the conclusions presented. It is likely that the presented methods will be very useful for researchers focusing on heart and lung development, and may inspire others to take similar approaches for studying development of other organs.

Reviewer #2 (Public Review): 

In this manuscript, Ng et al., report on a system where cardiac mesoderm and pulmonary endoderm co-develop from pluripotent stem cells. This is of potential interest, as it could provide an integrated model for the study of human cardiopulmonary development. 

The main weakness lies in the lack of thorough characterization of the resulting cells and tissues. The characterization relies almost entirely on reporter gene expression and PCR for a limited set of markers. The only indication that ATII cells are generated is expression of a SPC-dTomato reporter and SFTPC mRNA. No evidence is given of function, of expression of other markers or direct staining for SPC, or of ultrastructure. No data are provided whether the lung component contains other lung cells. Another outstanding question for the lung component is whether any pulmonary mesenchyme was generated. 

The same is true for the cardiac component. Which types of cardiac cells are generated: ventricular, atrial, endocardium, epicardium, conducting tissue? No benchmarking was done compared to either human tissues or similar cells generated using more focused differentiation protocols, and functional studies are very limited. 

Another weakness is that there is no characterization of early intermediate developmental stages: primitive streak, mesendoderm, definitive endoderm, cardiac mesoderm, first or second heart field. This type of analysis would be required to validate this complex model as an approach to study human cardiopulmonary development. 

There is also no quantification of differentiation efficiency and yield, and neither are data shown to document absence or presence of other endodermal or mesodermal lineages. NKX2.1, for example is also expressed in the forebrain and in the thyroid. 

A final limitation is that multiple pluripotent line should be used. 

This type of model could be very useful, but it not clear that the goal of integrated cardiopulmonary development was achieved.

Reviewer #3 (Public Review): 

Ng and Johnston et al. reported the successful multilineage co-differentiation of mesoderm-derived cardiac and endoderm-derived lung progenitors from human pluripotent stem cells (hPSCs). The authors achieved their goals through a stepwise strategy built on the knowledge from published cardiac and lung differentiation protocols. The authors first employed WNT activation using GSK3 inhibitor CHIR, an established WNT signaling agonist, at relatively high dosage to induce primitive streak formation from hPSCs maintained in pluripotent medium (days 1-2). This is supported by knowledge from vertebrate development that both mesodermal and endodermal germ layers are patterned by primitive streak. This is also consistent with recent findings by Martyn et al. (PMID 29795348, https://doi.org/10.1038/s41586-018-0150-y) that activation of WNT signaling is sufficient to induce primitive streak from hPSCs. In the subsequent step (days 2-4), the newly formed primitive streak provides a gradient of endogenous WNT, BMP and Nodal/Activin signaling, which allows the co-induction of both mesoderm and definitive endoderm (DE) from the remaining hPSCs in culture in a serum and morphogen free differentiation medium. Consistently, high Nodal (by exogenous Activin A) favors endodermal induction at the expense of mesodermal specification, and medium-high exogenous BPM4 is detrimental to lung endodermal specification but enhances cardiac mesodermal differentiation. The authors then demonstrated that dual TGF and WNT inhibition is efficient to pattern the mesoderm and endoderm simultaneously for future cardiac and lung induction (days 4-8). This agrees with the existing knowledge that lungs derive from anterior foregut endoderm, and cardiac progenitors, the major substance of heart, derive from cranial lateral mesoderm. Mesoderm and DE patterning was followed by lung and heart specification through the activation of WNT and RA signaling exogenously, in the presence of endogenous BMP4 signaling (days 8-15). 

The differentiation strategy developed by the authors follows the lung and cardiac developmental paradigm overall, the protocol yields efficient lung and heart progenitor specification on the tested hiPSC line. The work provides a new insight into cardiac and lung directed differentiation, and offers a valuable platform to study human heart and lung development in vitro. For cardiac and pulmonary progenitor differentiation (days 4-15), the protocol described in this manuscript relies mainly on the exogenous application of common key developmental signal events shared by heart and lung specification from meso- and endo- derms, respectively. For progenitor maturation (post day 15), the data shows expedite alveolar maturation process in cardio-pulmonary co-differentiation culture, suggesting paracrine signal(s) from cardiac cells positively regulate alveolar maturation. The authors did not report any data on whether/how paracrine signal(s) from lung lineages may influence cardiac maturation. The authors achieved their goals, and the results support the conclusion of the paper overall. 

The weaknesses of manuscript are: 1) Lack of evidence/characterization of primitive streak formation at 48 hours of differentiation. 2) Lack of a thorough characterization of the composition of the entire differentiation culture at progenitor stage (day 15): it is very likely that there are pulmonary mesenchymal/mesodermal cells generated in the differentiation culture, besides cardiac mesoderm. The pulmonary mesenchyme may not be abundant in quantity but it plays critical roles in promoting alveolar maturation that the authors observed at day 18 of co-differentiation culture. Before drawing a conclusion, the authors must examine rigorously whether alveolar maturation was promoted by cardiac mesoderm or pulmonary mesoderm. 3) the paper can benefit from providing mechanistic insights into whether/how alveolar maturation medium (CDCIK, days 15-18, and KDCI days 18-25) influenced the downstream cardiac lineage fate specification from the cardiac progenitors. Besides contracting/beating cardia cells, are there any other type(s) of cardiac lineages present in d25 culture? Do the cardiac progenitors generated by this protocol mainly represent cells from primary heart field? Is there any second heart field potential?

Reviewer #1 (Public Review):

The authors use electrical recordings and calcium imaging of adult neurons in cortical slices from mice to study how calcium transients in mitochondria respond to patterns of synaptic input and action potential firing that elicit long-term plasticity. Prior work has shown that mitochondria are required for cortical plasticity, making the question of how their calcium fluxes vary with the neurons inputs and outputs an important one. The authors show convincingly that features of the mitochondrial calcium transients can explain several previously identified features of "spike timing-dependent plasticity," such as its dependence on firing frequency and on repetition. The work opens up a series of further interesting questions about the molecular mechanisms involved in these mitochondrial calcium transients and about whether their subsequent influence on plasticity depends only on metabolism or on some additional function of mitochondria.

This is a carefully carried out study that addresses an interesting and timely question: how do neuronal mitochondria "decode" coincident action potential firing and synaptic input? It has long been known that mitochondria have their own calcium transients, and that these are critical to their metabolic function. Recent work in cultured neurons showed that locally inhibiting dendritic mitochondria could impair long-term synaptic plasticity. The authors use viruses target the calcium indicator mitoGCaMP6m to neuronal mitochondria and simultaneously image their calcium transients and cytosolic calcium transients at two different wavelengths using two-photon imaging while also recording electrical activity and stimulating presynaptic axons.

The experiments, carried out in layer 5 pyramidal neurons from ex vivo slices made from adult mice convincingly document highly nonlinear summation of mitochondrial calcium transients that are sensitive to the relative timing of synaptic input and neuronal firing in much the same way that long-term change in synaptic strength depends on these parameters. In addition, like long-term plasticity in these neurons, the summation is frequency dependent, showing a threshold above which it increases rapidly. The two also shows a similar dependence on the number of pre- and postsynaptic pairings. Together with prior work showing that mitochondrial function is required for long-term plasticity and that mitochondrial function is dependent on calcium fluxes, the present study suggests that the observed properties of mitochondrial calcium fluxes may be a defining feature of how correlated pre- and postsynaptic firing lead to long-term synaptic change.

Although there are many remaining questions, such as exactly how these transients respond to synaptic input and how they subsequently influence synaptic strength, these are appropriately left for future studies.

Reviewer #2 (Public Review):

This work describes how mitochondrial calcium in different regions of pyramidal neurons is controlled by action potentials and EPSPs. The authors show that calcium is controlled in a highly non-linear manner by calcium entry into cells (through voltage-dependent calcium channels) during sequences of action potentials. A particularly interesting finding is the high degree of localization of calcium rises in individual mitochondria in dendrites, and the requirement for both EPSPs and back-propagating action potentials to produce prominent rises of calcium in dendritic mitochondria. The work provides fundamental new information about how calcium entry during action potentials and EPSPs controls mitochondrial function.

This paper complements very nicely a collection of recent papers that have described some key mechanisms and consequences of calcium entry into neuronal mitochondria, including the demonstration by Rangaraju et al (2019) that mitochondria serve as highly-localized energy reserves for morphological synaptic plasticity, the paper by Ashrafi et al (2020) showing that MICU3 allows neuronal mitochondria to achieve calcium entry with much smaller increases in cytoplasmic calcium than non-neuronal cells, the paper by Garg et al (2021) showing how MICU proteins regulate the uniporter channels, and the paper by Diaz-Garcia et al (2021) showing that Ca2+ uptake into the mitochondria is responsible for controlling buildup of mitochondrial NADH, probably through Ca2+ activation of dehydrogenases in the TCA cycle. The current manuscript explores how the time course of mitochondrial calcium is controlled in a highly non-linear manner by calcium entry into cells during sequences of action potentials. A particularly interesting finding is the high degree of localization of calcium rises in individual mitochondria in dendrites, and the requirement for both EPSPs and back-propagating action potentials to produce prominent rises of calcium in dendritic mitochondria. This observation complements the 2019 Rangaraju Cell paper very nicely in giving a picture of a mechanism with a central role for dendritic mitochondria in spike-timing dependent plasticity.

The work in the paper is done to a very high technical standard, and all the results were convincing. The work is clearly presented and the paper is clearly and concisely written.

Reviewer #3 (Public Review):

In this study the authors combine brain slice electrophysiology with two-photon calcium imaging of fluorescent dye and genetically encoded calcium reporters (in cytoplasm and mitochondria, respectively) to further investigate, the relationship between neuronal activity and mitochondrial calcium handling. They find that, from a certain calcium threshold, neuronal activity levels closely correlate with both mitochondrial calcium handling and energetic capacity (determined using NAD(P)H imaging). The authors also demonstrate that in proximal dendrites mitochondrial calcium transients upon single synapse stimulation require a co-incident back-propagating action potential. They go on to hypothesise this could be important for some forms of synaptic plasticity.

By combining brain slice electrophysiology with two-photon calcium imaging of fluorescent dye and GECI reporters (in cytoplasm and mitochondria, respectively)the authors aimed to further investigate, the relationship between neuronal activity, mitochondrial calcium handling and metabolic rate.

Quite a lot has been done to look at the impact of neuronal activity (and frequency dependency thereof) on mitochondrial calcium handling in axons using combinations of electrophysiological and imaging techniques, (e.g. Kwon et al., 2016; Gazit et al., 2016; Vaccaro et al ., 2017; Lewis et al., 2018; Devaraju et al., 2017; Styr et al., 2019; Ashrafi et al., 2020 - several of these key studies not cited). Several of these earlier studies also go on to address the physiological relevance of mitochondrial calcium handling for neuronal function, synaptic properties and plasticity.

In contrast less is know about these relationships in somato-dendrites (but see also Divakaruni et al., 2018). In the current work the authors aim to address this gap in the knowledge. Importantly a strength of the study is that the work is performed in acute brain slices using an elegant combination of electrophysiology and two-photon imaging. The work is interesting and appears well performed throughout. However, a weakness of the current study as it stands is that it is mostly correlative and descriptive in nature. No attempt is made to better understand mechanisms of some of the reported observations (e.g. the spatial variation in activity-dependent mitochondrial calcium handling), or to causally test (genetically or pharmacologically) the importance of the observed phenomena on neuronal function or plasticity. Thus while the title of the study implies a relationship between frequency dependent mitochondrial calcium handling and metabolic rate this is not robustly tested. Similarly the final sentence of the abstract is a significant over-interpretation of the presented results. No evidence is provided that the proposed co-incidence detection by mitochondria can be read out as metabolic or plasticity changes. As such the mostly observational nature of the findings currently dampens their potential impact.

Joint Public Review:

The human lipid flippase ATP8B1 transports and enriches phospholipids to the cytosolic leaflet of plasma membrane.

Diedonnne et al purified and determined the cryo-electron microscopic structure of human lipid flippase ATP8B1 in complex with CDC50A (and obligatory targeting subunit) at 3.1 Angstrom resolution. The cryoEM structure presents the architecture of ATP8B1 and the arrangement of its three cytosolic domains (A, N and P) with respect to each other.

The authors found that the conserved C-terminal motif locks ATP8B1 in an autoinhibited resting state, and that full activation of the enzyme requires release of both C- and N-termini as well as phosphoinositides binding. Impressively, they restored the inhibition of a truncated thus active protein by adding synthetic peptides and found that Ser-1223 phosphorylation reduced inhibition by C-terminal peptide, revealing a unique role of the C-terminus of ATP8B1. These findings of the human enzyme are largely consistent with the well-studied yeast homolog Drs2p flippase and are deemed significant and likely appealing to a broad audience, given the fact that ATP8B1 is associated with the liver disease intrahepatic cholestasis. The study is well designed; however, there are several suggestions for including control experiments, improving the presentation, and discussing the results and interpretations.

Reviewer #1 (Public Review)

Authors, for the first time, demonstrated that Nephronectin (Npnt) was abundantly expressed in the migrating periocular neural crest cells during early corneal stromal development in chick. It has been shown that Npnt bound to the integrin a8 (Itga8) for cell migration during kidney development. In this study, authors attempted to test whether Npnt-Itga8 signaling was indispensable for corneal stroma development as well. Interestingly, authors clearly demonstrated that knockdown of Npnt and Itga8 attenuated corneal migration into corneal stroma leading to thinner cornea. In contrast, overexpression of Npnt facilitated cell migration and increase corneal thickness. It is interesting to know that one can control corneal thickness by manipulating Npnt-Itaga8 interaction. In summary, authors concluded that Npnt-Itga8 signaling was required for proper migration of periocular neural crest cells during chick corneal development.

Reviewer #2 (Public Review)

Early vertebrate eye development is a complex and fascinating system driven by complex 3D morphogenetic events including cells of the neuroectodermal, ectodermal and neural crest origin. The transparent cornea is the most outer part of the eye and is formed from three different cellular layers, the corneal epithelium formed first from the surface ectoderm, followed by sequential migration of two waves of the neural crest cells/periocular mesenchyme to generate the corneal endothelium followed by corneal stroma. These processes require precise activities of multiple signaling pathways and extracellular matrix proteins (ECMs). Although earlier studies in kidney examined synergistic functions of two proteins, nephronectin (gene name Nptm) and alpha8-integrin (Itga8), in kidney; the findings on their roles in the cornea are entirely novel and shed very much needed light into the coordination of multiple embryonic processes that occur in parallel in the anterior portion of the developing eye. The data are driven by gene loss- and gain-of-function experiments taking advantages of the chick as experimental model and generation of both wild type and mutated proteins using RCAS virus. Control experiments, biological and technical replicates are at place. Taken together, the data are novel for understudied genes/proteins and their critical roles in the vertebrate anterior segment morphogenesis.

Reviewer #1 (Public Review):

Ford et al. investigated protein import into the mitochondrial matrix via the presequence pathway using an innovative NanoLuc translocation assay. In this assay, a model precursor tagged C-terminally with a small fragment of the NanoLuc enzyme is imported into purified mitochondria. The mitochondria were prepared from a yeast strain overexpressing a NanoLuc enzyme lacking the small fragment that contains a mitochondrial presequence that directs the protein into the matrix. Upon import of the model precursor, the active NanoLuc enzyme is formed and produces a luminescense signal in the present of a dye. The authors used this assay to study the effect of ATP and loss of the membrane potential on the kinetics of protein import. The kinetic profiles indicate the presence of two rate-limiting steps. The authors propose that the first step corresponds to binding of the precursor protein to the TOM complex. The second step could represent the initiation of transport across the inner membrane. They further found that precursor properties such as net charge and size have an impact on these steps. Based on the findings the authors proposed a kinetic model including two rate-limiting steps. The used assay could be an interesting to study the dynamics and import kinetics of different types of mitochondrial precursor proteins.

Reviewer #2 (Public Review):

In this study, the authors use a luminescence-based method (NanoLuc) to investigate the kinetics of protein import by the main mitochondrial protein translocases, the TOM and the TIM23 complexes. In a recently published paper, the same group had described the NanoLuc approach to dissect the mechanisms of protein transport across biological membranes, including import into mitochondria. Compared to other methods that have been traditionally used to study protein import into mitochondria, the NanoLuc approach offers elevated time resolution and rapid data quantification, providing a powerful means to dissect the mechanisms that drive the protein transport reactions.

In this new paper, the authors exploit the NanoLuc approach to obtain precise and time-resolved information on the transport of mitochondrial, matrix-targeted presequence-containing precursors (PCPs). By dissecting the import of a relatively small PCP, they observe an import kinetics characterized by two rate limiting steps. Taking into account the dependency of the import reaction on the main energy sources that drive transport by TIM23, the inner membrane electrochemical potential (delta psi) and the hydrolysis of ATP, they attribute the slowest rate-limiting step to transport by the TOM complex. The authors also suggest that PCPs are fully transported across the OM prior to engaging with the TIM23 complex. This result is somewhat in discordance with a mechanistic model based on the transport of larger PCPs, which generate two-membrane spanning translocation intermediates tethering the TOM and TIM23 complexes. Importantly the authors also investigate how charges in the amino acid sequence of the mature protein, i.e. the portion of the PCPs C-terminal to their MTS, influence the import reaction. This aspect of the study is particularly intriguing as the role of PCP mature segments in determining import efficiency is only marginally understood. The authors conclude that positively charged precursors are imported with a very fast kinetics and cause rapid depletion of the membrane potential, which limits the final import amplitude. Instead, negatively charged precursors "consume" less delta psi but reach higher import amplitudes.

The conclusions of this study are well supported by the experimental data. However, I am not fully convinced about once specific claim related to the fact that the import reaction may be largely single turnover.

Taken together, the findings presented in this manuscript advance our mechanistic understanding of mitochondrial protein import by the TOM and TIM23 complexes. Most notably, this study also sets an important benchmark for the investigation of other mechanistic aspects of the mitochondrial import reactions. Furthermore, this approach can be useful to screen for (and characterize) drugs targeting the mitochondrial import apparatus. In summary, this study is of high relevance for the broad scientific community.

Reviewer #3 (Public Review):

The import of soluble precursor proteins into the mitochondrial matrix is a complex process that involves two membranes, multiple protein interactions with the translocating substrate, and distinct forms of energetic input. The traditional approaches for in vitro measurement of protein translocation across membranes typically involve radiography or immunodetection-based assays. These end-point approaches, however, often lack optimal resolution to analyze the sequential processes of protein transport. Therefore, the development of techniques to dissect the kinetic steps of this process will be of great interest to the field of protein trafficking.

This study by Ford et al. employs a novel bioluminescence-based technique to analyze the import of presequence-containing precursors (PCPs) into the mitochondrial matrix in real time. As a follow-up study to previous work from the Collinson group (Pereira et al. 2019), this approach makes use of the split NanoLuc luciferase enzyme strategy, whereby mitochondria are isolated from yeast expressing matrix localized 'LgBiT' (encoded by the mt-S11 gene) and used for import experiments with purified PCPs containing 'SmBiT' (the 11-residue pep86 sequence). The light intensity that results from the high-affinity interaction of pep86 with mt-S11 is convincingly shown in this study to be a reliable reporter of protein import into the matrix space. Therefore, from a technical stance, this appears to be a very promising approach for making high-resolution measurements of the different kinetic steps of protein translocation.

The authors leverage this technology to seek insights into several features of mitochondrial protein import, with some observations challenging key long-standing paradigms in the field. Using series of PCP constructs differing in length and placement of the pep86 peptide, the authors perform luminescence-based import tests with varying protein concentration, energetic input, and presequence charge distribution. Fits to the time course data suggest two main kinetic steps that govern matrix-directed import: transit of the PCP across the TOM complex into the IMS and association of the PCP with the TIM23 motor complex. The results support some very interesting insights into TIM23-mediated protein import, including: that precursor accumulation is strongly dependent on length; that the kinetically limiting step of IM transport is engagement with the TIM23 complex, not transmembrane transport itself; and that presequence charge distribution differently affects import rate and matrix accumulation. The results of this study appear repeatable among samples and the mathematical fits to time courses are well explained. However, there remain some questions about the nature of the experimental approach and the interpretation of the kinetics data in terms of the underlying biological processes. These questions are as follows:

Major points

Overall system characterization and mathematical analysis

1) The Western-based characterization of the amount of matrix-localized 11S (shown in Figure 1 - figure supplement 1) shows that the concentration of 11S varies significantly (> twofold concentration difference, quantified as a ratio to Tom40) among yeast/mitochondria preps. Is there a particular reason for this large variability? Perhaps more significantly, the import efficiency (judged by luminescence amplitude) shows high batch variability as well (> twofold efficiency difference). While this series of experiments makes the case that the luminescence readout of import is not limited by matrix-localized 11S, it does raise a potential concern of batch-to-batch variation in import competence. Could this have any implications for the reproducibility of results by this assay, particularly regarding the kinetic parameters reported?

2) My understanding from the Pereira 2019 JMB paper is that the yeast expressing the matrix-targeted 11S were engineered so that the 11S construct contained a 35 residue presequence from ATP1. In Figure 1 - figure supplement 1, panel A, it looks like the mitochondria-derived 11S constructs are significantly larger than the purified 11S constructs used to calibrate concentration. If the added residues on the mitochondrial 11S constitute a presequence, then they should be cleaved up on import to yield the mature sized protein. Why are the mitochondrial 11S constructs so much larger than the purified ones? Explicit labeling of MW markers would be useful here.

3) From Figure 1D, given that the amplitude linearly increases with added Acp1-pep86 up to ~45 nM, this suggests that matrix-localized 11S is in stoichiometric excess of imported peptide within this range of added substrate. Given a matrix [11S] of 2.8 uM, a stoichiometrically equivalent amount of Acp1-pep86 would be equivalent to an import of <0.5% of added substrate, and it is suggested that import efficiency is actually much lower than that. How can this very low import efficiency be explained?

4) Apropos of point #3 above: Given the low efficiency of import observed for the purified PCP substrates in this study, one wonders if this due to the formation of off-pathway (translocation incompetent) precursors established during the import reaction, before substrates have a chance to engage OM receptors (e.g., due to aggregation, etc.) In this case, the interpretation of single-turnover conditions may instead be caused by a vast majority of PCP losing translocation competence, rather than the requirement for energetic resetting that is suggested. Might this be a possibility?

5) Import time courses in many cases show a progressive drop in luminescence at later time points after a maximum value has been reached. This reduction in signal cannot be accounted for by the two rate constants in the equation used in two-step kinetic model. How were such luminescence deviations accounted for when fitting data to obtain these kinetics parameters? What might be the reason for this downward drift in signal once maximum amplitude has been reached?

Import kinetics: dependence on total protein size

6) In Figure 3 - figure supplement 1, some of the kinetic parameters from the PCP concentration-dependent responses are quite noisy. For instance, responses for the shortest constructs (L and DL) show a lot of variability in the k1 and k2 parameters. Is this (partly) due to difficulty in resolving these two parameters during the nonlinear least-squares fitting protocol for these particular constructs?

7) The data in Figure 3, panels E and F (derived from Figure 3 - figure supplement 1) in some cases show non-linear dependence of kinetic parameters on the 'N to pep86 distance' for the length (panel E) and position (panel F) variants. For instance, from the length series, the k1 mean goes from 132 to 385 to 237 nM for the DL, DDL, and DDDL constructs, respectively. The variances suggest that these differences are real. Is there a reason that kinetic parameters would have such non-monotonic dependence on length?

Import kinetics: dependence on energetic input

8) The data of Figure 4A show the results of partial dissipation of the membrane potential by 10 nM valinomycin. Most studies designed to cause a gradual dissipation of membrane potential do so by protonophore (e.g., CCCP) titration. Given that matrix-directed import is completely blocked by low micromolar amounts of this potent ionophore, it would be useful to have an independent readout (e.g., TMRM measurements) of the residual membrane potential that exists upon treatment with the lower concentrations of valinomycin used here.

9) The step associated with k1, designated as transport across the TOM complex, is suggested to go to completion before starting the step associated with k2, engagement of the TIM23 complex. The k1 step shows a strong dependence on membrane potential (Fig. 4A, middle), particularly for the length series. Why would this be, given that no part of translocation across the OM should be associated with a valinomycin-sensitive electric potential?

Working model

10) One of the most surprising outcomes of this study is that passive transport of substrates across the TOM complex and energy-coupled transport via the TIM23 complex are kinetically separable and independent events. As the authors note in the Discussion, the current paradigm of the field is that matrix-targeted substrates concurrently traverse the OM and IM via the TIM-TIM23 supercomplex, and this model is supported by quite a bit of experimental evidence. Even in this study, the fact that the PCP-pep86-DHFR construct exposes the pep86 sequence to the matrix in the presence of MTX (Figure 2) is evidence of a two membrane-spanning intermediate. Key mechanistic questions arise regarding the model proposed in this study. For example, if PCPs traverse the TOM complex as a stand-alone step, what is the driving force (e.g., a simple pathway of protein interactions with increasing affinity)? And would soluble, matrix-directed substrates be expected to accumulate in the very restricted space of the IMS? If so, how would TIM23-directed membrane proteins keep from aggregating in the aqueous IMS? These questions would be worth addressing in the discussion of the model.

Import kinetics: dependence on MTS charge distribution

11) The fact that import rates are increased with a more electropositive presequence makes sense in terms of the electrophoretic pull exerted on the PCP (matrix, negative). However, the greater accumulation of precursors containing more electronegative presequences remains puzzling. In the manuscript, this is explained based on the concept that accumulation of positive charges will cause partial collapse the membrane potential. However, I am still uncertain about this explanation for a few reasons. First, for each PCP, the presequence will constitute just a small fraction of the total length of the precursor, and therefore contribute a small fraction of the total charge density of imported protein. Would such a small change in total PCP charge be expected to have the dramatic effect observed among samples? Second, given the small amount of protein imported under these conditions, would the total charge of imported PCPs be expected to affect transmembrane ion distribution so significantly? For instance, as I recall, it takes up to micromolar amounts of mitochondria-targeted lipophilic cations (e.g., TPP+) to cause a major change in the TMRM-detected membrane potential. Finally, I would expect isolated mitochondria to be capable of respiratory control. It is well known, for example, that isolated mitochondria can respond to temporary draw-down of the membrane potential (e.g., by ADP/Pi addition) by going into state 3 respiration and restoring membrane gradients. Why would that not be the case here (Figure 5D)?

General

12) Although the spectral approach in this study is developed as an alternative to the more traditional import assays, it would be useful to have some control import tests (done with Westerns or autoradiography) as complements to the luminescence-based imports. For example, control tests to accompany Figure 1 that show import efficiency or tests that accompany Figure 3 to show import of the different length and position series constructs. Perhaps this could be done with immunodetection of Acp1 or the pep86 epitope, showing protease-protected, processed import substrates that appear in a membrane potential/ATP-dependent manner. Even if the results from the more traditional techniques ran contrary to the results using the NanoLuc system, this would still allow the authors to compare which effects are consistent and which are dissimilar between different approaches.

13) The authors might also consider conducting imports with mitoplasts as a way to test the kinetic model that includes the TIM23-mediated step alone.

14) It is difficult to follow the logic in the Discussion regarding the number of TIM23 sites limiting the number of 11S imported into mitochondria in live cells (page 15, lines 23-27). Are the authors suggesting that in vivo, one TIM23 complex serves to transport a single protein? This needs to be clarified.

Reviewer #1 (Public Review):

The paper by Thom et al investigates genetic associations between adiposity and human blood traits. They use the Mendelian Randomization framework and conclude that BMI may be causally linked to low hemoglobin levels. Strengths of the study are the different methods used that put higher confidence to the results and several exposures/outcomes. Limitations may be that the paper is better for a more specialized audience as it is a bit difficult to follow all the exposure/outcome variables used and why and how some analyses were done.

Reviewer #2 (Public Review):

The study investigates obesity and adipose distribution on hematopoiesis. It shows that genetically determined adiposity plays a previously underappreciated role in determining blood cell formation and function.

The authors performed all the relevant and available MR analyses in the "toolbox". The results support the conclusions. The study will help understand the pathogenesis for clonal hematopoiesis.

Reviewer #3 (Public Review):

The authors, using Mendelian Randomisation, showed that obesity (overall weight) and truncal adiposity (weight distribution) have opposite effects on blood traits. They show that the effect of BMI is not restricted to the erythroid lineage, but it also has an effect on other blood cell types. These findings explain some observation previously made in obese individuals. Moreover, using their conditional analysis they were able to show that there are loci that lose their association with blood traits and other that do become associated to a particular trait.

While the analyses are convincing, the manuscript would benefit from a better introduction of the key concepts, which in the current version are just considered a given, especially for a journal with a wide audience such as this.

Reviewer #1 (Public Review):

Karasawa and colleagues report in this manuscript the aggregation of NLRP3 mutants associated with a group of auto-inflammatory diseases called the cryopyrin-associated periodic syndromes (CAPS). Gain of function mutations in NLRP3 is associated with the systemic inflammatory characteristics in these diseases. This manuscript reports that CAPS-associated NLRP3 mutants (L353P and D303N) form cryo-sensitive aggregates, which function as scaffolds for NLRP3 inflammasome assembly in a NEK7- and potassium efflux-independent manner. Another key finding of this paper is the sensitivity of NLRP3 mutant aggregation to calcium. The strength of the manuscript is elegant immunofluorescence studies demonstrating the cold-sensitive aggregation of NLRP3 mutants. However, the role of calcium in NLRP3 CAPS mutant aggregation and inflammasome assembly needs clarification.

Reviewer #2 (Public Review):

In this manuscript aggregation and activation of mutant NLRP3 at normal or low temperature is examined in several ways, which is a strength of the manuscript. In particular the imaging studies are performed in two cell lines, and appropriate quantification is usually provided. However, when considering the effect of temperature on the number of foci, some quantification on the area of the foci should also be considered, as the total amount of NLRP3 appears unchanged. Temperature could also have effects on pore formation and phagosomal rupture, so additional mechanisms of NLRP3 activation as control should also be considered.

This manuscript also suggests that the effect of the two mutations in NLRP3 that are studied is independent of K+ efflux, MCC950 inhibition and NEK7, but dependent on Calcium influx. This appears reasonable but may require further controls. However I remain confused as to the importance for this as a feed-forward mechanism regulated by caspase-1 activation and this appears to contradict earlier data in the manuscript where the NLRP3 mutants formed foci independent of ASC.

Reviewer #3 (Public Review):

In their manuscript, "Cryo-sensitive aggregation triggers NLRP3 inflammasome assembly in cryopyrin-associated periodic syndrome,", Karasawa et al. use in vitro models to investigate the mechanism of CAPS associated mutations. They use confocal microscopy of several cell lines transfected with fluorescently tagged constructs, western blot, ELISA, qPCR, calcium and pyroptosis imaging, and inducible systems or inhibitors of NLRP3, caspase 1 and calcium signaling to investigate the mechanism of cold induction of NLRP3.

The strengths of this paper are:<br> 1. Very carefully performed studies with novel findings using different cell lines, constructs, and techniques<br> 2. Data and interpretation that come together for an understandable story and a clear model

The primary weaknesses are:<br> 1. Mechanistic data that does not necessarily agree with clinical findings

Overall the authors achieved their aims of elucidating the mechanisms of CAPS mutation induced inflammation

This work has impact on the treatment of patients and our understanding of NLRP3 inflammasome mechanisms

Reviewer #1 (Public Review):

In this study, the authors used a high throughput mutational scanning approach to profile antibody epitopes in the SARS-CoV-2 Spike protein using serum from two cohorts of vaccinated and/or infected subjects. Key findings of the study are that antibody binding to the major epitope regions differed between severe COVID-19 and vaccinated individuals compared to individuals with mild COVID-19. The authors also identified potential viral escape pathways in these epitope regions, some of which differed between vaccination and infection or drifted over time. The authors acknowledged that this approach is limited to the detection of linear epitopes and did not include neutralization epitopes like those found in the receptor binding domain. However, the study provides new insight into the major epitope regions targeted by polyclonal antibodies elicited by vaccination vs. infection, as well as potential pathways that could be used by the virus to escape recognition.

Reviewer #2 (Public Review):

Garrett and Galloway et al. present a study that analyzed the SARS-CoV-2 Spike linear epitopes that are targeted by antibodies following infection or vaccination with the SARS-CoV-2 mRNA vaccine mRNA-1273. The authors use a phage display profiling method termed Phage deep mutational scanning (PhageDMS) where T7 phage display libraries are generated to display overlapping 31 aa peptides that span the entire SARS-CoV-2 spike protein. Serum samples from naïve, SARS-CoV-2 infected hospitalized and non-hospitalized individuals, or individuals vaccinated with two different doses of the Moderna mRNA vaccine mRNA-1273 with and without prior infection are incubated with the PhageDMS library. Antibody-bound phage are then isolated and sequenced to identify the spike peptide targeted by antibodies.

The authors find both common and unique immunodominant spike protein epitopes between infected hospitalized and non-hospitalized individuals and vaccinated individuals. Infection-induced antibodies targeted the fusion protein (FP) and the stem helix region upstream of heptad repeat 2 (SH-H), whereas vaccination-elicited antibodies also targeted epitopes within the NTD and CTD in addition to the FP and SH-H regions. The authors include a subset of vaccinated individuals who were infected prior to vaccination, yet do not find any significant differences across the epitope binding profiles compared to vaccinated individuals without prior infection.

The authors performed additional experiments to determine whether mutations within the immunodominant spike epitopes could escape antibody binding. Within the vaccine targeted NTP and CTD regions, a uniform escape profile was observed for the NTD for vaccinated individuals whereas the CTD escape profile was variable. Within the immunodominant epitopes elicited by infection, FP escape at specific sites could be observed, whereas SH-H was more variable. Interestingly, for infected individuals who were subsequently vaccinated, the SH-H mutations became more uniform, and converge on a site known to be targeted by cross-coronavirus monoclonal antibodies.

Overall, the study is well-designed and presented. The authors data provides insight into the potential mechanisms of escape that could impact long-term vaccine and infection induced humoral immunity. A major limitation of the study that the authors acknowledge is that the PhageDMS method only identifies linear epitopes, and thus antibodies that bind to conformational epitopes including the receptor binding domain, which is a neutralizing antibody target are not captured. Similarly, the mutational scan used to identify potential pathways to escape is also limited to linear epitopes, potentially missing loss of antibody binding that may occur from conformational epitopes that are disrupted upon mutation. That being said, the fact that specific mutations do lead to loss of antibody binding provide strong evidence that mutation at that site would clearly be detrimental to antibody efficacy. The authors note that these mutations have not yet emerged in viral variants, but it is unclear from the current study whether the possible escape mutations would negatively impact viral fitness and thus may not ever emerge on a population level. Regardless, the more information we have regarding potential loss of antibody binding and efficacy, the better prepared we will be to understand the importance of a given mutation should it arise in the next variant.

Reviewer #1 (Public Review):

This paper documents the immune profiles of solid tumors using a novel JAMMIT algorithm applied to data from TCGA. This work is important because it could help inform cancer immunotherapy which shows great promise for treatment. The paper is well-written with interesting results.

Reviewer #2 (Public Review):

In this manuscript, the authors have utilized a novel algorithm, JAMMIT for the mRNA data analysis of different cancers obtained from TCGA. They demonstrated that the main source of variation in the tumor microenvironment is inflicted through alterations in T and B cells. In addition, they have classified tumors based on the abundance of Tc1, Tc17, and immune inert immunophenotypes and used a 3-gene signature to evaluate the infiltration of monocytes. The strengths of this study include the use of IPA, Cibersort, and published patient data for the validation of computational findings. This study would help in the development of immune signatures as prognostic indicators and also in clinical decision-making.

Joint Public Review:

Strengths: The main strength of this study is the methods that were used. These techniques are at the forefront of the field. The authors combined two-photon calcium imaging with a genetically encoded sensor in astrocytes in the Tg-ArcSwe AD mouse line. Data was recorded from awake animals that were sitting on a running disk, so information was gathered from various behaviour states: quiet wakefulness, spontaneous running, and when startling the mouse with an air puff to the face. The sophisticated analytical tools developed by the authors were used to identify regions of astrocyte calcium activation in AD mice and controls for comparisons. This is the first study to examine astrocytes in awake, behaving Alzheimer's mice.

Weaknesses: Clearly lay out the goals of the study, providing sufficient description of all the various aspects-role of NA signaling in AD, NA induction of astrocytic Ca2+ signals, behavior-induced (NA-dependent) Ca2+ signals, novel technique for real-time monitoring of astrocytic Ca2+ signals, etc-for the reader to follow along. Provide a better description of the imaging processing and analysis procedures (including event-based detection aspect) in Methods so that the reader doesn't have to go to a previous publication to understand the basic workflow. Include better (or at least some) description of what panels illustrate in figure legends (see comments in Public Review). Provide statistics on regression analyses; a supplemental figure might be helpful in this regard. Consider using another read-out of NA activity that provides more reliable data than pupil responses (if available) or improve experimental procedures as necessary to reduce noise in pupil dilation data.

Reviewer #1 (Public Review):

In this manuscript, the authors explore mechanisms in Pseudomonas aeruginosa involved in defending cells from T6SS-dependent attacks by other bacteria. Using a genome-wide Tnseq approach, the authors identify three gene clusters involved in this defensive response. They also report that these gene clusters are activated by the GacS/GacA/Rsm pathway. The authors also convincingly show that each of the three gene clusters encode proteins involved in the defence against specific toxins. Finally, one of the defence proteins is analyzed in more detail and found to prevent the accumulation of lysophospholipids generated by the Tle3 phospholipase toxin.

I did not identify any weaknesses except that the manuscript is incredibly densely written, making it difficult to read.

Reviewer #2 (Public Review):

In this work, Ting and colleagues performed genome-wide screens to identify genes affecting P. aeruginosa (Pa) ability to cope with B. thailandensis (B.thai) type 6 secretion (T6S) antagonistic action. The group had previously shown that during Pa-B.thai competition the Pa Gac/Rsm regulatory cascade is activated in response of kin cell lysis, leading to an increase of the H1-T6S synthesis as part of the PARA (P. aeruginosa response to antagonism) regulon. Here, three additional gene clusters (named arc1-3) regulated by the Gac/Rsm cascade are identified and shown to be important to counteract the damage caused by B.thai T6S. Interestingly, arc2 and arc3 genes provide a fitness advantage towards specific B.thai T6S effectors: ColA and Tle3, respectively. Since the arc1-arc3 genes are encoding for hypothetical proteins which function is unknown, the authors then focused on the characterization of the molecular action of Arc3A and Arc3B, showing its absence leads to an accumulation of lysophopholipids that is dependent on Tle3 activity (and independently from Aas recycling).

Overall, the study is remarkable, exciting and well-conceived, as it reveals new molecular pathways affecting bacterial susceptibility to T6S effectors. The novelty resides on the fact that the arc1-3 "protective genes" are not classical toxin immunity genes, but are widespread genes probably involved in housekeeping or damage repair cellular functions, like the Arc3 genes and phospholipids homeostasis.

Reviewer #1 (Public Review):

In an experimental human study with pharmacological functional magnetic resonance imaging (fMRI), Jepma and colleagues aimed to dissect neural brain circuits and neurochemical underpinnings of two mechanisms of pain avoidance learning, namely learning from unexpected pain and learning from unexpected pain relief. By combining behavioral data from a probabilistic pain avoidance learning task with computational reinforcement learning modelling the authors demonstrated that unexpected pain has a stronger influence on participants' decision-making and learning than unexpected pain relief. Interestingly, the difference in learning rates from unexpected pain and pain relief disappeared with pharmacologically transiently increasing phasic dopamine using a single dose (100mg, p.o.) of levodopa as well as with blocking mu-opioid receptors using a single dose (50mg, p.o.) of naltrexone. Both interventions specifically increased learning from unexpected pain relief with no effect on learning from unexpected pain. In contrast, these pharmacological manipulations did not show any effects on neural data recorded during task performance using fMRI. Nevertheless, distinct patterns of brain responses were found related to pain prediction errors (ie. pain expected but not received) and pain relief prediction errors (ie. no pain expected but pain received).

Despite the known relevance of pain avoidance in the development and maintenance of chronic pain, the mechanisms of pain avoidance learning are still not well understood. A better understanding could contribute to an improvement of therapeutic approaches targeting pain avoidance in chronic pain. Accordingly, the provided results by Jepma and colleagues offer important and novel insights. Data and results largely support the authors' conclusions.

The combination of a validated behavioral task with pharmacological interventions, fMRI, and computational modelling is a strength of the present manuscript. This approach allows a comprehensive investigation of underlying mechanisms, enabling high-quality conclusions. Nevertheless, potential insights are somewhat hinder by the small sample size in combination with a between subject experimental design. Particularly for such research questions, a within-design has many advantages and allow better conclusions. For example, a within comparison on drug effects would have been of high interest in this context and might have reduced error variance in the present data.

The use of computational modelling approaches has many advantages in the context of investigations of learning processes. For example, reinforcement models, as used in the present study, are well validated and enable to draw conclusions on underlying mechanisms. As such they provide a much deeper level of mechanistic understanding compared to direct standard group comparisons of behavioral outcomes. This is obvious in the present study as well, because the authors demonstrate a convincing and reasonable differentiation of learning rates based on unexpectedly received pain or pain relief, which cannot be observed in the simple outcome measures of the task. This is also compellingly resembled in the fMRI results, outlining two distinct brain systems mediating learning from unexpected pain and learning from unexpected pain relief.

Although the fMRI data well supports the idea of two brain systems mediating different learning mechanisms, the pharmacological manipulation does not show any effects on brain responses. While such a dissociations has been reported before in similar contexts, the present study lacks a manipulation check for the pharmacological manipulation or a check of individual differences in the responses to the drugs. Many factors such as height/weight, genetic markup etc. can influence how levodopa and naltrexone is metabolized and thus utilized on individual levels. The doses used in this study resemble standard dosages in similar experiments and positive effects have been often reported, but as well strong individual differences in the response to the drugs have been reported as well. The effects on learning from unexpected pain relief suggest that the pharmacological interventions were effective, albeit they are in part in contrast to the hypotheses. Nevertheless, without a manipulation check (e.g. other independent behavioral results, autonomic measures known to be influence by dopamine/opioids, blood samples, prolactin for dopamine, etc.) the negative results on all other behavioral outcomes and the fMRI data have to be viewed with caution.

Reviewer #2 (Public Review):

Jepma et al. report an interesting manuscript studying how we learn from pain and its avoidance. The authors use an instrumental pain avoidance task where participants are required to choose between two stimuli, one of which is followed by painful thermal stimulation to the leg and the other is not. The probabilities of receiving pain drifted across trials using random walks. The authors combined this with pharmacological manipulation of the dopamine (via oral levodopa) or opioid (via oral naltrexone) systems and also with computational modelling of Q-learning rules and neuroimaging via fMRI. So, this is an ambitious and well conceived manuscript.

There are real strengths here. The manuscript is theoretically motivated, addresses a fundamental question about how we learn, and is generally well executed. The task is well controlled, the modelling choices seem appropriate, the imaging and its analyses are broad but well defended and choices in analysis strategies are well defined. The manuscript is well written. I did enjoy reading the manuscript.

The results have some interest. The modelling and neuroimaging data suggest important dissociations between learning about pain and learning about its absence - the modelling suggests faster learning rates for learning from pain than its avoidance. The imaging suggests that these two forms of learning are associated with different networks, with a known network linked to learning about pain but a novel network linked to learning about avoided pain.

These are worthwhile knowledge gains. The idea that different rate parameters govern learning about events that are present versus those that are absent is an old one. It is built into most error-correcting learning rules since Rescorla-Wagner and it makes sense. However, it was useful to see it supported here. The finding that different networks of brain regions were associated with the learning from pain versus avoided of pain was also interesting. The networks linked to the former made sense based on the literature. The networks linked to the latter were more novel and notably did not include classic 'relief' brain regions.

However, there were also important weaknesses here, at least on my readings.

I struggled as a reader to understand how the modelling actually related to the behavior and imaging. That is, there is a real disconnect in the manuscript for me between what is observed (behavior) what is inferred (modelling as well as it basis for correlations with fMRI data).

There were no differences in behavior reported between the two kinds of trials (learning from received pain versus avoided pain) effects, no effects of the drugs on behavioral performance, and no differential effect on learning from received pain versus avoided pain. I have no problems with reporting null effects, but here the reader is left wondering: if there are no behavioral differences reported, then why does the modelling predict that there should be? How accurate is the model given that it clearly predicts slower learning from avoided than received pain in the controls and faster learning from avoided pain under naltrexone and levodopa compared to control? In other words, what is it about the modelling that yields differences in learning rates between the two behavioral conditions and between the vehicle, levodopa, and naltrexone conditions when the behavioral data shown do not? Of course, it could be that the task was too easy - the modelling may be prescient and perhaps possible learning rate differences would be picked up under more difficult (more cues) and weaker probabilistic conditions. Perhaps there are behavioral data (reaction times?) not reported that do actually show differences in learning rate between learning from received pain versus avoided pain or show differences between the drug conditions?

I may have misunderstood all of this and am happy to be corrected. If not, think this issue needs to be addressed and would need new data that is hopefully already in hand to do convincingly (such as choice reaction times) to show some difference in behavior between learning from received pain versus avoided pain and/or some effects of the pharmacological manipulations on these.

In the absence of the data the manuscript seems to have three parts:

1. A more compelling set of findings reporting imaging differences between learning from received pain versus avoided pain that are interesting because they suggest a novel network of brain regions for the latter compared to the literature.<br> 2. A set of null findings that neither pharmacological manipulation affected behavior or these imaging findings.<br> 3. A less compelling set of findings that link the above to possible underlying differences in learning rate parameters.

The first could be of interest but the latter two need to be strengthened, in my opinion.

I had other minor points (e.g., consider the literature on opioid and dopamine receptor manipulations in the ventral striatum on aversive prediction errors because this suggests the opposite to the literature cited for the midbrain; is the word 'appetitive' in the title really appropriate given the findings in the manuscript), but these are less important than the above.

Reviewer #1 (Public Review):

This new work from Chen et al. reports on a critical question that is whether astrocytes can be converted in situ into dopaminergic neurons in response to the targeting of specific factors using, for example, gene therapy. This concept is enormously significant because, if correct, it may open powerful new repair strategies for disorders of the nervous system, such as Parkinson disease (PD). Relevant to this view is the remarkable demonstration by two groups that repressing the expression of the RNA-binding protein PTBP1 promotes the conversation of astrocytes into dopaminergic neurons in rodents associated with behavioral and neuropathological improvements in a model of PD. However, soon after the publication of this amazing data, independent research teams were unable to confirm these findings and rather attributed them to reported apparent conversion of technical issues, including leakage of viral vector. The lack of evidence of astrocyte conversion into dopaminergic neurons upon repression of PTB1 was done in a context where astrocytes were quiescent, a point that caught the attention of Chen and collaborators, who sought in the present study to revisit the same question of astrocyte conversion to dopaminergic neurons in a context where astrocytes are reactive. This difference is highly significant because studying reactive astrocytes would not only model PD more closely but would also examine whether particular intrinsic properties of reactive vs quiescent astrocytes may lead to very different responses to PTB1 repression. Therefore, to address this important point of cell biology, Chen and collaborators embarked on a very elegant and straightforward study using the 6-OHDA model of PD in mice to show that despite the presence of reactive astrocytes, there was no evidence of astrocytes conversion into neurons, including into dopaminergic neurons, neither in the striatum, nor in the substantia nigra upon repression of PTB1. This study is extremely solid and elegant and while perhaps beyond the scope of the study, it would have been valuable to also obtain behavioral and neuropathological data in these mice. Lastly, it would also be nice to test the authors' hypothesis in a more chronic model of PD, as produced by gene mutations which may cause a more protracted response of astrocytes. Having said this, it is true, however, that genetic models of PD are problematic and overt, and reproducible neurodegeneration is often questionable, shown in initial reports where conversion was done in 6-OHDA models.

Reviewer #2 (Public Review):

This brief manuscript by Chen et al. revisits the important question whether knock-down of the RNA binding protein Ptbp1 can convert quiescent or reactive astrocytes residing in the striatum or the substantia nigra into induced dopaminergic neurons. Previous work in the literature suggests that this might occur and could contribute to functional repair in a mouse model of Parkinsonism. However, in these earlier studies there was no unambiguous proof of the astrocyte origin of the allegedly induced dopaminergic neurons.

Thus, in the present study, the authors subjected this notion to scrutiny by combining genetic fate mapping to ascertain astrocyte origin with or without 6-hyrodxydopamine induced death of endogenous dopamine neurons, followed by adeno-associated virus-mediated Ptbp1 knockdown. The authors illustrate effective Ptbp1 knockdown in astrocytes by an AAV encoding a short-hairpin RNA against Ptbp1. A (possibly minor) weakness is that hGFAP regulatory sequences are used to drive transgene expression, known to be less specific than other approaches using AAVs. In any case, the authors report then that they do indeed observe a progressive increase in virus-labelled neurons over the time course of 3 months, most prominently in the substantia nigra, and less so in the striatum. No increase is observed when using an AAV encoding a scramble shRNA for control. However, in sharp contrast to expectation, they then find that none of the virus-labelled neurons could be traced back to fate-mapped astrocytes. To test whether Ptbp1 knockdown would be more effective in reactive astrocytes, they induce a 6-OHDA lesion which results in massive loss of TH immunoreactivity. The authors state that Ptbp1 knockdown does not improve TH levels, and none of the TH-positive neurons remaining in the substantia nigra can be traced back to astrocytes.

This study strongly suggested that knock-down of Ptbp1 is not sufficient to induce the conversion of quiescent (shown for striatum or substantia nigra) or reactive astrocytes (substantia nigra) into dopaminergic neurons.

Reviewer #3 (Public Review):

This paper re-evaluates whether repression of the expression of the RNA binding protein PTBP1 in astrocytes results in the trans-differentiation of astrocytes into mesencephalic dopamine neurons which are lost in Parkinson's Disease. The authors conclude that repression of PTBP1 in astrocytes does not induce the formation of dopamine neurons in contrast to the findings published earlier by two independent groups. By extension, the authors speculate that previous observations mis-identified neurogenesis due to compromised tissue specificity of an astrocyte specific promoter when expressed from an AAV vector. Clarifying the effects of PTBP1 repression on dopaminergic neurogenesis is of significance in order to guide efforts to develop strategies for neuronal replacement from endogenous sources in Parkinson's Disease.

Strengths:<br> The authors applied a stringent in vivo cell fate tracing technique to determine whether astrocytes can contribute to dopaminergic neuron replacement. This strategy revealed the significance of an underappreciated confound in previous published work, namely tissue specificity of the utilized promotors in the context of AAV.

Weaknesses:<br> The authors observed a qualitative downregulation of PTBP1 expression by immunohistochemistry assessment. They also show that AAV infected astrocytes reveal altered morphology. It is not clear whether the observed down-regulation of PTBP1 is due to the expression of the silencer RNA or secondary to AAV induced physiological stress in infected astrocytes. Thus, ruling out attenuation of PTBP1 as a technique of inducing dopaminergic neurons appears premature in absence of AAV independent techniques to modulate PTBP1 selectively in astrocytes.

Reviewer #1 (Public Review):

The authors previously performed an I-DIRT experiment in irradiated and activated splenocytes to define the RIF1 interactome (Delgado-Benito et al., 2018). Here data from this assay are used to extract RIF1 post-translational modifications, focusing on SQ/TQ consensus sites for ATM/ATR-dependent phosphorylation. Some of these RIF1 sites seem to be conserved not only between mouse and human, but also from yeast to humans (Table S2).

Figure 1 describes the RIF1 interactome from the previously reported I-DIRT assay performed in the irradiated, activated B cells. It would have been interesting if some of the interactions relevant to replication/repair could be validated in this setting, as B cells represent a special cellular model, being activated to undergo CSR and showing an interval of high proliferation that could make them intrinsically susceptible to replication stress.

The manuscript is focused on three SQ/TQ sites (S1387Q, S1416Q and S1528Q) localized within RIF1 intrinsically disordered region (IDR). These should be highlighted in Table S2. The reason for choosing these sites is not entirely clear (other than their proximity, meaning they could form a cluster), as most of the sites listed in Table S2 are conserved between mouse and human. It is not clear either whether these sites correspond to 7 clustered SQ/TQ sites identified in yeast (referred to in Discussion).

Reviewer #2 (Public Review):

This manuscript uncovers a novel regulatory mechanism that modulates RIF1 function during the DNA replication stress response. The authors identify a cluster of three phosphorylation sites within the intrinsically disordered region of mouse RIF1 using a mass spectrometry-based approach. They show that phosphorylation of these three sites is dispensable for the ability of RIF1 to limit double-strand break resection, but is required to counteract the degradation of stalled replication intermediates mediated by the DNA2 nuclease. Collectively, the authors' findings would be of interest for the DNA replication and repair fields. However, the study is very preliminary and the authors need to include new experiments to strengthen their conclusions and support their model. Specifically, additional data are necessary to define mechanism by which blocking RIF1 phosphorylation regulates DNA2-dependent degradation of stalled replication intermediates. Moreover, the model that RIF1 phosphorylation is dispensable for the ability of RIF1 to inhibit DSB resection is not fully supported by the data.

Reviewer #1 (Public Review):

This study attempts to identify predictors of human-infective RNA virus discovery and predict high risk areas in a recent period in the United States, China and Africa using a ecological modelling framework.

According to findings from their previous study published in 2020, the main predictors for virus discovery at the global scale were GDP-related i.e. and they concluded that this may largely have driven by research effort rather than the underlying biology. In the current study, they have attempted to focus on more restricted and homogenous regions where they suspect research effort is less heterogeneous to an attempt to identify predictors more associated with virus biology. The study is relevant in the current context and identification of areas at threat of emerging viral pathogens. However I'm not certain that the design and data (and inherent biases in virus discovery) may impact these findings/predictions and also whether the more distal covariates/predictors used truly capture viral biology and emergence in space-time.

Reviewer #2 (Public Review):

The last two decades have seen considerable research efforts in identifying global hotspots and drivers of RNA virus emergence for guiding surveillance and control efforts. A recent study by the same authors (Zhang et al. 2020) used machine learning methods and a well-curated list of discovery sites of human RNA viruses to show that previously learnt patterns of virus discoveries at a global scale may be driven by socio-economic (GDP/research effort-related) rather than underlying biology. In this manuscript, the authors extend this work through a separate analysis of three relatively homogeneous regions (US, China and Africa) to identity variation in virus discovery rates between regions, but there was consistency in variables (land-use and socio-economics) in all three regions. They also identify potential new discovery hotspots in 2010-2019. This paper is in line with a series of data-driven studies that aimed to identify variables that can be useful for improving surveillance and control against emerging viruses.

I have no particular concerns with the data, analysis and results presented in this manuscript. It appears to follow their recent work performed on a global scale.

Reviewer #1 (Public Review):

The authors use single-cell sequencing to identify non-muscle cell populations in the regenerating zebrafish heart and based on this they provide evidence that the transcription factor tal1 is required in endothelial cells and the matrix metalloproteinase mmp14 in macrophages for heart regeneration. While the sequencing data represent a nice resource for future studies and surely will be of interest to a large community of researchers studying heart regeneration and regeneration in general, some of the conclusions, in particular those on the function of tal1 and mmp14, are not yet sufficiently supported by data.

Reviewer #2 (Public Review):

Rolland et al. analysed the composition and relative quantity of different cardiac cell types, 3, 7 and 14 days after ventricle resection injury in zebrafish and showed mechanistic contributions of these cell types to regeneration. They focused on changes in interstitial cells and characterized cardiac cell populations including macrophages epicardial/epicardial derived, neural crest and endothelial cells. Alongside the identification and marker assignment to different cell types, the authors also provide some novel mechanistic insights to regeneration. First, the lack-of-upregulation of the myofibroblast injury associated gene acta2, which upon inhibition of matrix metalloproteinases (MMPs) and subsequent inhibition of regeneration is upregulated in a similar way to mammals. Second, tal1+ endothelium, which was previously shown to be important to development was suggested to have dynamic interactions with binding partners as the tal1+ cells express different levels of lmo2/cgnl1 on different days following injury. Tal1+ endothelium was shown to be crucial for proper regeneration as expressing its dominant negative form resulted in fibrosis following injury. Third, mmp14b, which was expressed by macrophages and epicardial derived cells, is crucial for zebrafish cardiac regeneration, possibly by affecting macrophage numbers in the tissue, highlighting the importance of specific MMPs to the regeneration process.

The findings of this paper could be broadly separated to the characterization of the interstitial cells by scRNA-seq and transgenic reporter lines, and the regeneration specific processes facilitated by these cells. For the first part the conclusions of this paper are largely supported by data and will undoubtedly be used as a very valuable reference dataset for future single cell studies. Regarding the mechanistic insights, some questions need to be clarified.

Reviewer #3 (Public Review):

The zebrafish is an excellent model to study cardiac regeneration as this process is very efficient when compared to injury induced scaring of the mammalian heart. The scRNA-seq data may be a very valuable resource for the research community. The first part of the manuscript largely deals with the description of the different cell types identified by the scRNAseq analysis. Cardiomyocytes seems to be under-represented, which could be due to their large size and shape. All other major cell types were identified based their expression patterns and some cell types (endothelium, fibroblast, macrophages) could be subclustered. This resulted in the identification of a sox10+ cell type within the fibroblast cluster. While interesting, it remains unclear what these cells are and whether these are true neural crest derived cells as claimed by the authors. In the second part of the manuscript the authors investigate Tal1+ endothelial cells and generate a transgenic line with an endothelial specific and inducible expression of a dominant negative (dn)Tal1 protein. Expression of the dnTal1 in endothelial cells results in impaired regeneration, albeit the role of Tal1 in endothelial cells during regeneration was not studied in more detail. In the last part of this study, the authors focus on the macrophage sub clusters that they identified from the scRNAseq data. The authors claim that these sub clusters represent resident and recruited macrophages, but these claims need experimental validation. Macrophages that appear after injury express mmp14 and an Mmp14 inhibitor is used to reveal a possible role for Mmp14. Treatment of fish with the mmp14 inhibitor resulted in a reduced number of macrophages in the wound area, differences in their transcriptome and impaired heart regeneration. Whether macrophage numbers decline due to reduced proliferation or migration was not addressed nor how Mmp14 inhibition affects heart regeneration.

Overall, this work describes a very interesting scRNAseq dataset of the interstitial cells in the regenerating zebrafish heart. The second part of the manuscript contains new hypothesis which need careful validation to support the authors' conclusions.

Reviewer #1 (Public Review):

The work nicely demonstrates that neurons tend to assume the specialized computational roles of either transmitters, receivers or mediators of information flow, depending on burst position, i.e., early, middle and late bursters behave respectively as transmitters, mediators and receivers. A main strength of the work is the tool used for the analysis, i.e. a continuous-time estimator of the transfer entropy (TE) which was demonstrated in a recent work by the same authors to be far superior than the traditional discrete-time approach to TE estimation on neural data. The main weakness identified relies on a limited reference to previous literature analyzing the same publicly available data.

Reviewer #2 (Public Review):

This work analyzes, for the first time, changes in information flow in developing dissociated neuronal cultures using their recently developed continuous-time transfer entropy (TE) estimator. This is a timely study, since the field of network and systems neuroscience critically needs better estimators for structural, effective and / or functional connectivity. Recent technical developments allow us to track hundreds, and even thousands of neurons during development (both in vitro and in vivo) in several organisms. However, current tools to assess changes in connectivity across time are severely lacking, and this study directly tackles this problem. Their method is the state of the art and appears to be extremely well suited to this task since it is able to deal with information flow across multiple time-scales and deals with the sparsity / multiple comparisons problem with strict statistical testing.

The authors apply their transfer entropy estimator to a publicly available dataset (Wagenaar et al, 2006) consisting of multielectrode array (MEA) recordings from dissociated cortical cultures during development. The original dataset consists of over 50 cultures from 8 different batches (with different plating densities) recorded between DIV (day in vitro) 3 to 35. For this study the authors selected 4 cultures at 3-4 time points to claim that 1) Information flow undergoes a dramatic increase during development. 2) The spatial structure of information flow is "locked-in" early. 3) During bursting activity, nodes (neurons/electrodes) play a specialized role that is also "locked-in" early.

The activity of dissociated cultures is highly heterogeneous, and an appropriate sample size is needed to assess the significance of any observed features or patterns. This is well described in the original work that provided the dataset used in this study "An extremely rich repertoire of bursting patterns during the development of cortical cultures", (Wagenaar D.A., et al, 2006, BMC Neurosci). For example, in the discussion section they state "[...] that cross-plating variability was larger than variability between sister cultures implies that it is crucial to use cultures from several different platings to obtain unbiased results." The current study uses only 4 cultures (from 2 different batches) recorded at 4 time points (sampled around 1 week apart on average) that might belong to the original categories of "fixed-bursting" and "superbursts". In this work, results from these 4 cultures are often reported on a case-by-case basis, and sometimes without any statistical significance assessment and with unclear summary statistics. Given that, the validity and significance of the results is difficult to assess in their current form.

A major strength of the TE estimator framework developed by the authors is that it can account for the statistical significance of any TE estimate. However, it is unclear how this significance test is used throughout most of the results. Figures 1a and 3 to 8 appear to consistently include points with 0 TE that have an impact on the measured quantities, like means, quartiles and correlations. Additionally, the correlation plots across days (figures 3 to 7) include least-squares fits that are often dominated by what appear to be outliers in the data (or possibly non-significant TE values). The estimates of Spearman correlation might also suffer of a similar issue due to "ties".

Regarding the "locked-in" information flow, evidence is always presented through Spearman correlations across TE scores at different days. These values are often not significant or show a weak correlation (Figures 3 and 4). An early "lock-in" of information flow would imply not only pairwise correlations, but also a long temporal correlation of a node (or edge) TE score across several days.

The study of information flow within bursts is really interesting. As the authors point out, TE appears to be well poised to measure this contribution, and their burst-local TE measure appears to be equivalent to other methods that condition TE estimates on population-wide activity levels, e.g., Stetter et al, PLOS Comp Biol, 2012. In here, they analyze the correlations between the burst-local TE measures and the burst position (in time) of a node. For the existence of time ordering the authors mention "[...] cultures often follow an ordered burst propagation [23, 36]". But that does not appear to be a universal property of developing cultures. It is unclear whether the cultures used in this study show consistent temporally ordered bursting patterns. From the 2 cited references, in Maeda et al, the bursting pattern and temporal ordering changes from burst to burst (see Fig. 3). It is uncertain that an average "burst position" can be defined for any given node. Similarly, in Schroeter et al, there are several characteristic MUA patterns (Fig 4C), and even there, it might not be possible to define a consistent temporal ordering.

Reviewer #1 (Public Review): 

This manuscript entitled "A ppGpp-mediated brake on photosynthesis is required for acclimation to nitrogen limitation in Arabidopsis " by Romand et al. described ppGpp accumulation is necessary for acclimation to nitrogen starvation in a model plant Arabidopsis. The authors revealed that ppGpp accumulation leads to remodeling the photosynthetic electron transport chain to downregulate photosynthetic activity. It seems to be reasonable that the ppGpp signal works for protecting plant cells from oxidative stress. Further, the authors also clarified that ppGpp accumulation affects patterns of chloroplast gene expression during nitrogen starvation. The findings will be highly appreciated.

Reviewer #2 (Public Review): 

Romand et al investigates the role of hyperphosphorylated guanosine nucleotides (ppGpp) in acclimation of plant chloroplasts to nitrogen limitation. The signaling role of ppGpp as alarmone is well established in the stringent response of bacteria. The stringent response allows bacteria to adapt to amino acid or carbon starvation and other acute abiotic stress conditions by downregulation of resource-consuming cell processes. A series of studies, including the current one, have demonstrated the retention of the bacterial-type ppGpp-mediated signaling response in plant and algal chloroplasts. The current study convincingly demonstrates the involvement of ppGpp in remodeling of photosynthetic machinery under nitrogen limitation. Using three Arabidopsis RSH lines (two underaccumulators and one overaccumulator of ppGpp), the authors show that the ppGpp is required for preventing excess ROS accumulation, oxidative stress and death of cotyledons under nitrogen limiting condition. The authors show a transient accumulation in ppGpp upon nitrogen limitation, which is followed by a sustained increase in the ratio of ppGpp to GTP. There is a prompt decline in maximum photochemical efficiency of photosystem II (PSII) and linear electron transport under nitrogen deficiency in wild type and ppGpp overaccumulator plants. However, mutants with low amount of ppGpp have a delayed decrease in these photosynthetic parameters. PpGpp is further shown to decrease (or degrade) photosynthetic proteins, and a remodeling of PSII that involves uncoupling of LHC II from the reaction center core has been suggested to occur under nitrogen starvation. The authors also show a ppGpp-mediated downregulation of chloroplast gene transcription and a coordinated plastid-nuclear gene expression under nitrogen deficiency. 

Strengths: 

1) The conclusions of this paper are mostly well supported by data. With three different RSH lines, there is a convincing demonstration of the specific involvement of ppGpp in nutrient acclimation. The line carrying conditional overexpression of Drosophila ppGpp hydrolase (MESH) nicely complements the RSH lines and strengthens many of the conclusions. This is a detailed analysis of ppGpp function in a plant species. The data supplement accompanying each main figure is extensive and helpful. 

2) The genomic analysis in nitrogen replete and deplete wild type uncovers an interesting regulation of RSH enzymes at the transcriptional level. This is likely to be part of a signaling response that works in conjunction with allosteric modulation of RSH activity under nitrogen limitation. 

3) The large-scale analysis of plastid and nuclear gene transcripts supports the involvement of ppGpp in coordinated repression of plastid and nuclear gene transcription. 

4) By the inclusion of mitochondrial genes and proteins in their analysis, the authors clearly show that the ppGpp action is limited to plastids and does not extend to mitochondria, which like chloroplasts, have a bacterial ancestry. 

5) The thorough demonstration of the involvement of ppGpp in low nitrogen acclimation of photosynthetic metabolism adds greatly to the understanding of plant abiotic stress tolerance mechanisms and ppGpp function in both plants and bacteria. 

Weaknesses: 

1) With two earlier reports from a different laboratory (Maekawa et al 2015 and Honoki et al 2018) showing the involvement of ppGpp in acclimation to nitrogen deficiency, the novelty of the current study is diminished. The authors mention that the double mutant (rsh2 rsh3) used by Honoki et al does not show a clear phenotype other than a delay in Rubisco degradation. It is not clear to me why the lack of two major RSH isoforms, involved in synthesis of ppGpp under light, would not produce any phenotype. This discrepancy should be discussed further in the manuscript. 

2) The authors at times show a tendency to overinterpret their results. A ppGpp-mediated repression of chloroplast transcription and translation is sufficient to explain most of the observations in this study. However, the authors seem to go beyond this simple explanatory framework by invoking specific roles for ppGpp in remodeling of PSII antenna-core interaction and in blocking of PSII reaction center repair. There is no data in the manuscript in support of these two propositions. A coordinated decrease in synthesis of most chloroplast proteins, including the D1 reaction center protein of PSII, is sufficient to explain the decrease in Fv/Fm. There is no evidence in the manuscript for "photoinactivation gaining an upper hand via ppGpp-mediated signaling". The circuit breaker analogy of PSII photoinhibition that the authors discuss in support is just an interpretation. The remodeling of PSII antenna-core interaction, likewise, could be a simple consequence of the ppGpp-mediated decrease in D1 protein synthesis. The high antenna-core ratio under nitrogen starvation likely reflects the lag in the decrease of LHCB1 (which eventually decreases significantly by day 16). Since ppGpp-signaling primarily affects plastid transcription and translation, there is a rapid decrease in plastid psbA gene product (D1) relative to the nuclear-encoded LHCB1. The unconnected LHCII might simply be a result of the mismatch in antenna-core stoichiometry rather than an active regulation of PSII functional assembly by ppGpp. 

3) The work is mostly descriptive of the involvement of ppGpp in low nitrogen tolerance without any data on how the nitrogen deficiency is sensed by the RSH enzymes and how ppGpp orchestrates the multi-faceted acclimatory response. Perhaps, these aspects are beyond scope of the current manuscript, but they could be discussed more.

Reviewer #3 (Public Review): 

The manuscript by Romand et al. explores the role of guanosine penta- and tetraphosphate, ppGpp, in the acclimation of plants to nitrogen limitation. It shows that an early and transient ppGpp accumulation - and a controlled ppGpp/GTP ratio - is necessary for a proper acclimation of plants to such stress. The pathway is shown to act on remodeling the photosynthetic machinery and downregulating photosynthesis during stress, thus limiting ROS damage to the plants. This regulation most likely takes place by affecting chloroplast transcription, maintaining the balance between nucleus- and chloroplast-encoded proteins. 

The manuscript proposes a thorough analysis of the ppGpp-induced response including extensive wild type and mutant analyses at the gene and protein expression level as well as at the physiological level under nitrogen limitation together with heterologous expression of ppGpp hydrolase from Drosophila. The conclusions are carefully backed by the data (but for the lack of gene expression analysis in the high ppGpp line, rsh1-1), the figures and text clear, well-written and easy to follow. Altogether it represents a solid new step in improving the comprehension of plant response to nitrogen limitation, as well as on the role of ppGpp in plants and possibly throughout the green lineage. An alternative hypothesis to ppGpp photoprotective role could be discussed in that photoprotection may be an indirect effect due to photosynthetic protein degradation enabled by ppGpp, possibly through modulation of ppGpp/GTP ratio affecting chloroplast protease activity.

Reviewer #3 (Public Review):

This is a well-written and clearly presented paper exploring an important effect that is somewhat overlooked by the literature. Links between neuronal signals and peripheral body parts such as the pupils a growing and critical area of modern cognitive neuroscience. This manuscript represents an exploratory investigation which will be of interest to researchers interested in brain-body interactions, attention, arousal and neuronal oscillations.

The analyses are of a generally of a high standard and the conclusions are justified by the results. The authors acknowledge and handle points where conclusions are unclear very well. Particular strengths are in sample size, the consideration of a wide range of possible interactions, strong links to literature and balanced discussion. I have three specific concerns in which the strength of analyses could be improved.

1 - Two approaches for relating pupil dynamics to time-frequency MEG data appear to be used (a Morlet wavelet and a sliding window Welch's periodogram), it is not clear why multiple methods are used and why these could not be matched?

Further, I have some concerns with the choice of a Morlet wavelet analysis in this application. Each individual frequency band will have a different temporal resolution and all (or at least most) will have a very different time resolution to the relatively slow pupil data. In other words the autocorrelation between successive time-points will dramatically vary between frequencies and are all likely to be fast compared to any pupil dynamics. This may introduce between frequency differences in noise or sensitivity which are tricky to account for. Moreover, conclusions are draw about the relative timings of different frequency bands though each of these bands have different temporal resolutions in the wavelet transform.

2 The spatial maps in figures 4, 5 and 6 are well described but it is not clear whether these represent distinct effects or simply follow the topography of power in those frequency bands. Specifically, a quadratic effect in 10Hz power might be more detectable in a brain region exhibiting high 10Hz power - to what extent are the interesting correlation maps distinct from the simpler power distributions? If these are strongly linearly associated, then the conclusions about of spatial specificity in the pupil-brain interactions are challenging.

3 The present results use a different 3-40Hz range for assessing spectral slope, citing that Gao et al 2017 to link this parameter to E:I balance. However Gao et al specifically conclude that the 30-70Hz range reflects E:I balance and make no claims about other frequencies. The reason for this difference is unclear and undermines the otherwise compelling discussion of E:I balance and arousal.

Reviewer #1 (Public Review):

Pfeffer, Keitel et al. collected pupil dilation as a non-invasive proxy of cholinergic and noradrenergic neuromodulation. In a large sample of healthy human participants, they related spontaneous fluctuations in pupil-indexed neuromodulation to concurrently recorded changes in magnetoencephalographic activity.

First, they show that pupil size co-varies with power fluctuations, especially in the alpha-beta band at posterior sensors. Next, in subsequent cross-correlation analyses, they show frequency-specific associations of pupil dilation and band-limited power. Decreases in low (2-4 Hz) as well increases in high (64-128 Hz) frequencies preceded pupil dilations by > 500 ms. For intermediate frequencies (8-16 Hz), both positive and negative associations were found with a closer temporal proximity to peak pupil dilation. Analyses were repeated for the first derivative of pupil dilation, which may be more closely linked to noradrenergic (relative to cholinergic) neuromodulation.

The authors additionally performed pupil-MEG correlations in source-space to reveal the spatial profiles of pupil-linked power fluctuations (findings largely consistent with the sensor-level). For this, they shifted the pupil data in time with respect to the MEG data to account for the sluggishness of pupil response. The temporal lag was determined based on previous research (see below). In a second set of source-space analyses, they additionally tested for quadratic associations of pupil dilation and band-limited cortical activity, which were observed for the alpha-beta band mainly at posterior sites.

Finally, the authors linked pupil dilation to the aperiodic component of the power spectrum. They found that larger pupil dilations were associated with a shallower slope, suggesting a higher excitation to inhibition ratio. Notably, pupil associations with band-limited power remained reliable after removing the aperiodic component (not so for the first derivative).<br> Recently, pupil dilation was linked to cholinergic and noradrenergic neuromodulation as well as cortical state dynamics in animal research. This work adds substantially to this growing research field by revealing the temporal and spatial dynamics of pupil-linked changes in cortical state in a large sample of human participants.

The analyses are thorough and well conducted, but some questions remain, especially concerning unbiased ways to account for the temporal lag between neural and pupil changes. Moreover, it should be stressed that the provided evidence is of indirect nature (i.e., resting state pupil dilation as proxy of neuromodulation, with multiple neuromodulatory systems influencing the measure), and the behavioral relevance of the findings cannot be shown in the current study.

1. Concerning the temporal lag: The authors' uniformly shift pupil data (but not pupil derivative) in time for their source-space analyses (see above). However, the evidence for the chosen temporal lags (930 ms and 0 ms) is not that firm. For instance, in the cited study by Reimer and colleagues [1] , cholinergic activation shows a temporal lag of ~ 0.5 s with regard to pupil dilation - and the authors would like to relate pupil time series primarily to acetylcholine. Moreover, Joshi and colleagues [2] demonstrated that locus coeruleus spikes precede changes in the first derivative of pupil dilation by about 300 ms (and not 0 ms). Finally, in a recent study recording intracranial EEG activity in humans [3], pupil dilation lagged behind neural events with a delay between ~0.5-1.7s. Together, this questions the chosen temporal lags.

More importantly, Figures 3 and S3 demonstrate variable lags for different frequency bands (also evident for the pupil derivative), which are disregarded in the current source-space analyses. This biases the subsequent analyses. For instance, Figure S3 B shows the strongest correlation effect (Z~5), a negative association between pupil and the alpha-beta band. However, this effect is not evident in the corresponding source analyses (Figure S5), presumably due to the chosen zero-time-lag (the negative association peaked at ~900 ms)).

As the conducted cross-correlations provided direct evidence for the lags for each frequency band, using these for subsequent analyses seems less biased.

Related to this aspect: For some parts of the analyses, the pupil time series was shifted with regard to the MEG data (e.g., Figure 4). However, for subsequent analyses pupil and MEG data were analyzed in concurrent 2 s time windows (e.g., Figure 5 and 6), without a preceding shift in time. This complicates comparisons of the results across analyses and the reasoning behind this should be discussed.

2. The authors refer to simultaneous fMRI-pupil studies in their background section. However, throughout the manuscript, they do not mention recent work linking (task-related) changes in pupil dilation and neural oscillations (e.g., [4-6]) which does seem relevant here, too. This seems especially warranted, as these findings in part appear to disagree with the here-reported observations. For instance, these studies consistently show negative pupil-alpha associations (while the authors mostly show positive associations). Moreover, one of these studies tested for links between pupil dilation and aperiodic EEG activity but did not find a reliable association (again conflicting with the here-reported data). Discussing potential differences between studies could strengthen the manuscript.

Related to this aspect: The authors frequently relate their findings to recent work in rodents. For this it would be good to consider species differences when comparing frequency bands across rodents and primates (cf. [7,8]).

3. Figure 1 highlights direct neuromodulatory effects in the cortex. However, seminal [9-11] and more recent work [12,13] demonstrates that noradrenaline and acetylcholine also act in the thalamus which seems relevant concerning the interpretation of low frequency effects observed here. Moreover, neural oscillations also influence neuromodulatory activity, thus the one-headed arrows do not seem warranted (panel C) [3,14].

4. In their discussion, the authors propose a pupil-linked temporal cascade of cognitive processes and accompanying power changes. This argument could be strengthened by showing that earlier events in the cascade can predict subsequent ones (e.g., are the earlier low and high frequency effects predictive of the subsequent alpha-beta synchronization?)-

Cited references<br> 1 Reimer, J. et al. (2016) Pupil fluctuations track rapid changes in adrenergic and cholinergic activity in cortex. Nat. Commun. 7, 13289<br> 2 Joshi, S. et al. (2016) Relationships between pupil diameter and neuronal activity in the locus coeruleus, colliculi, and cingulate cortex. Neuron 89, 221-234<br> 3 Kucyi, A. and Parvizi, J. (2020) Pupillary dynamics link spontaneous and task-evoked activations recorded directly from human insula. J. Neurosci. 40, 6207-6218<br> 4 Dahl, M.J. et al. (2020) Noradrenergic responsiveness supports selective attention across the adult lifespan. J. Neurosci. 40, 4372-4390<br> 5 Kosciessa, J.Q. et al. (2021) Thalamocortical excitability modulation guides human perception under uncertainty. Nat. Commun. 12, 1-15<br> 6 Whitmarsh, S. et al. (2021) Neuronal correlates of the subjective experience of attention. Eur. J. Neurosci. DOI: 10.1111/ejn.15395<br> 7 Nestvogel, D.B. and Mccormick, D.A. (2021) Visual Thalamocortical Mechanisms of Waking State Dependent Activity and Alpha Oscillations. bioRxiv DOI: 10.1101/2021.04.14.439865<br> 8 Senzai, Y. et al. (2019) Layer-Specific Physiological Features and Interlaminar Interactions in the Primary Visual Cortex of the Mouse. Neuron 101, 500-513.e5<br> 9 Buzsáki, G. et al. (1991) Noradrenergic control of thalamic oscillation: The role of α‐2 receptors. Eur. J. Neurosci. 3, 222-229<br> 10 Buzsáki, G. et al. (1988) Nucleus basalis and thalamic control of neocortical activity in the freely moving rat. J. Neurosci. 8, 4007-26<br> 11 McCormick, D.A. (1989) Cholinergic and noradrenergic modulation of thalamocortical processing. Trends Neurosci. 12, 215-221<br> 12 Goard, M. and Dan, Y. (2009) Basal forebrain activation enhances cortical coding of natural scenes. Nat. Neurosci. 12, 1444-1449<br> 13 Rodenkirch, C. et al. (2019) Locus coeruleus activation enhances thalamic feature selectivity via norepinephrine regulation of intrathalamic circuit dynamics. Nat. Neurosci. 22, 120-133<br> 14 Totah, N.K. et al. (2021) Synchronous spiking associated with prefrontal high gamma oscillations evokes a 5 Hz-rhythmic modulation of spiking in locus coeruleus. J. Neurophysiol. DOI: 10.1152/jn.00677.2020

Reviewer #2 (Public Review):

This work examines the impact of biological arousal (pupil diameter) throughout the cortex with high spatio-temporal resolution. The analysis of neuromagnetic signals, extending beyond the state-of-the art, supports the overall conclusion that power fluctuations of neuronal oscillations are closely linked to arousal. Independent from stimulus input, it is the peak timing of pupil dilation that coincides and initiates power modulation of low and high frequency activity throughout the cortex.<br> The central aim of the work, the non-invasive characterization of the link between fluctuations of arousal and cortex wide neuronal population activity, is achieved by analyzing a large dateset across 3 different laboratories, combining eye-tracking and magnetoencephalographic data.

Both, the analytic approach and the conclusions, are substantial new advance and of broad interest bridging across basic and clinical neuroscience. The access to deep brain structures via combination of eye-tracking and biomagnetism sets a new benchmark and a challenge to the often cortico-centric view of contemporary neuroscience.

Reviewer #1 (Public Review):

This work raises the question of how in plane forces generated at the apical surface of an epithelial cell sheet cause out of plane motion, an important morphogenetic motif. To address this question, a new ontogenetic dominant negative rho1 tool, based on the cry2-CIBN system is presented. The authors use this tool to analyze the well studied biophysical process of ventral furrow formation, and dissect the spatiotemporal requirement of rho1 signaling to modulate myosin accumulation. They separate the effect on morphogenesis into an early phase that becomes significantly slowed down by myosin inhibition, and a late phase where the kinetics is comparable to wild type despite treatment. For interpretation of the data, an older model of cell mechanics treating tissue as a purely elastic material is presented. It fails to reproduce the observations. As a modification, in analogy to buckling of a thin beam under load, a compressive stress exerted by the adjacent ectoderm is introduced. Further analysis of cell behaviors in response to various laser mediated tissue manipulations is presented as support of the proposed mechanism.

Overall, the manuscript addresses an important aspect of morphogenesis. In particular the use of optogenetic tools promises new insights that might be more challenging to achieve with traditional mutant analysis. However, reservations remain with respect to (1) rigor of the analysis, and (2) interpretation and quality of the data in support of the proposed mechanism; this applies in particular to presentation of biophysical observations, including experiment and simulations.

The manuscript adds valuable quantitative data, in particular the findings described in Fig 2ab. However, insufficient analysis are performed to fully support the claims of the manuscript by the data presented.

(I) The manuscript proposes an elasticity based model of tissue mechanics, but provides no experimental evidence in support of this assumption. Many rheology studies performed in a wide range of specimen (including the Drosophila embryo) found a separation of time scales, that shows elasticity is a good approximation of tissue mechanics only for time scales short compared to the process studied here.<br> (II) The manuscript uses a method of micro-dissection to soften cells, but does not provide a clear definition of the concept softening, provides no rational for the methods functioning, and does not provide independent validation. The described treatment might affect cells in many alternative ways to the offered interpretation. This data is the central experimental evidence given in support of the proposed ectoderm compression mechanism, and therefore it is essential to provide a precise physical explanation of the method, and validation of measurements that bolster the conclusion.<br> (III) Mechanical isolation of the mesoderm is a very exciting approach to test the possible involvement of adjacent tissues in folding. Indeed, the authors report a delay of ventral furrow formation. However, there is no evidence provided that (a) the mesoderm is mechanically uncoupled, and (b) that the treatment did not have undesired side effects. For example, a similar procedure (so-called cauterization, see Rauzi 2015) has been used to immobilize cells in the Drosophila embryo. Such an effect could account for the observed delay in furrow formation.<br> (IV) Some panels show two distinct molecules tagged with the same or spectrally overlapping flurophores, that unfortunately localize in similar spatial patterns. This encumbers data validation.<br> (V) The physical model is a central part for data interpretation. In its current form it is very challenging to follow. It is also critical the system be studied with proper cell aspect ratio, as the elasticity of thin sheets has a well established non-linear thickness dependence.

Reviewer #2 (Public Review):

Guo and colleagues aim to unravel the mechanisms driving the fast process of mesoderm invagination in the Drosophila early developing embryo. While cell apical constriction is known to drive ventral furrowing (1st phase), it is still not clear if apical constriction is necessary/sufficient to drive mesoderm internalization (2nd phase) and weather other mechanisms cooperate during this process. By using 1ph optogenetics, the authors cannot test specifically the role of apical constriction but can systematically affect the overall actomyosin network in ventral cells in a time specific fashion (1-minute resolution). In this way, they come to the conclusion that actomyosin contractility is necessary for the 1st phase but not for the 2nd phase of mesoderm invagination. Interestingly, they conclude that the system is bistable. In the second part of this study, the authors test the role of the coupling between mesoderm and ectoderm by using 2D computational modelling and infrared pulsed laser dissection. They propose that the ectoderm can generate compressive forces on the mesoderm facilitating mesoderm internalization (2nd phase).

This project is of interest since it tackles a key morphogenetic process that is necessary for the development of the embryo. The conclusion of 'bistability' resulting from the RhoDN optogenetic experiments (1st part of this study) are well supported and quite interesting. The IR laser experiments used to tackle the coupling between ectoderm and mesoderm (2nd part of the study) are key to support main conclusions, nevertheless their experimental design and results are puzzling. It is not clear what the authors are actually doing to the tissues. The experiments performed in the 2nd part of this study need to be revisited and conclusions eventually softened.

Major comments:

1) The 920 nm laser ablation of ectoderm cells is a key experiment in this study to support the ectoderm compression hypothesis. Nevertheless, this experiment is puzzling: the rationale of the experimental design, the effect of the laser on cells and the interpretation of the results are unclear.

2) The authors propose to use again 920 nm laser ablation but this time to "physically separate" the two ectoderms from the ventral tissue. This is again a key experiment, but it raises some concerns:

a. "Physical separation" would need to be demonstrated (e.g., EM after laser ablation). From Fig. 6b it is clear that IR laser ablation results in prominent auto-fluorescent zones. This has been already reported in previous work (De Medeiros G. et al. Scientifc Reports 2020) showing that high power and sustained IR fs laser targeting produces auto-fluorescence and highly electron-dense structures in the early developing Drosophila embryo. This process is referred to laser cauterization that does not induce separation between tissues. This structures eventually displace together with the lateral tissue (also shown in Fig.6 b).<br> b. This strong laser "treatment", that should be ectoderm specific, results in perturbation of other non-ectoderm related processes (e.g., mesoderm apical constriction as shown by the authors). This can support the idea that many other processes are affected and that in general this laser heating "treatment" has global effects. These results might invalidate the conclusion proposed by the authors.

Reviewer #3 (Public Review):

The authors address how contractile forces near the apical surface of a cell sheet drive out-of-plane bending of the sheet. To determine whether actomyosin contractility is required throughout the folding process and to identify potential actomyosin independent contributions for invagination, they develop an optogenetic-mediated inhibition of myosin and show that myosin contractility is critical to prevent tissue relaxation during the early stage of folding but is dispensable for the deepening of the invagination. Their results support the idea that the mesoderm is mechanically bistable during gastrulation. They propose that this mechanical bistability arises from an in-plane compression from the surrounding ectoderm and that mesoderm invagination is achieved through the combination of apical constriction and tissue compression.

Regarding global message of the manuscript, I have two main critics. The authors consider their work as the first to prove that there is a additional mechanism to apical constriction leading to invagination. This is not true.

First, the fact that the ectoderm could exert an compressive force on the invaginating mesoderm is not new and has been not only proposed, but tested previously (Rauzi and Leptin, 2015). Second, several recent publications demonstrated that on top of apical constriction, lateral forces were also required for the invagination and the authors ignore these data (Gracia et al, 2019 ; John et al, 2021).

They generated an optogenetic tool, "Opto-Rho1DN", to inhibit Rho1 through light-dependent plasma membrane recruitment of a dominant negative form of Rho1 (Rho1DN).

The specificity of local inactivation of Myosin was tested on apical myosin before and during invagination. They observed a strong reduction of Myosin II recruitment and a phenotype that mimicks Rok inhibition. They found that acute loss of myosin contractility during most of the lengthening phase results in immediate relaxation of the constricted tissue, but similar treatment near or after the lengthening-shortening transition does not impede invagination.

They conclude that the second part of furrow invagination is not due to myosin activities at the apical or lateral cortices of the mesodermal cells and that actomyosin contractility is required in the early but not the late phase of furrow formation.<br> This part regarding the temporal requirement of Myosin during invagination brings novelty in the field since it has never been tested before.

They observe that ectodermal cells shorten their apico-basal axis prior to Ttrans, and that compression from the ectoderm is independent of ventral furrow formation since it still occurs even if invagination is inhibited.

They further develop two types of simulations to test theoretically the importance of compressive stress in the invagination process.

The theoretical part would need to be further developed and discussed. They would need to integrate all the different components that have been shown to be essential for the invagination (not only apical constriction) and the dynamic aspect of the vertex model has to be clearly explained.

Reviewer #1 (Public Review):

In this paper the authors use a conditional knockout strategy to assess the effects of deletion of the dominant oxygen-sensing hypoxia-inducible factor (HIF) hydroxylase enzyme, prolyl hydroxylase 2 (Phd2) restricted to the regulatory T cell (Treg) lineage. They use a well-established Foxp3-driven Cre recombinase allele. Phd2 is thus silenced in cells that have expressed or continue to express Foxp3 from the time this transcription factor, which is essential for Treg development and function, first occurs. They show that this approach leads to a change in Treg behaviour resulting in loss of some aspects of regulatory function and development of a Th1-like phenotype by the Foxp3 expressing cells. Effects are in general reversed when HIF-2 is silenced alongside Phd2, and may be amplified by simultaneous silencing of the HIF-1 isoform.

The findings overlap with those reported following generalised silencing of Phd2 and following adoptive transfer of Treg in which Phd2-silencing is induced (Yamamoto et al., 2019) and are broadly compatible with those reported following a similarly Treg-restricted knockout of the von Hippel-Lindau gene (the recognition component of the E2-ubiquitin ligase that targets HIF-alpha chains that have been modified by Phd2) (Lee et al., 2015) but the results reported also differ significantly from these earlier reports in a number of intriguing respects which I feel warrant further discussion and ultimately investigation.

The Introduction is in general informative and well written but it is a shame that it does not contain more discussion of the current state of knowledge of the interplay between HIF signalling and Treg function. This would provide a platform for a more detailed and scholarly discussion of the similarities and differences between this work and existing literature in the Discussion, where existing papers are currently described rather briefly. The introduction contains the statement 'Further complexity in this pathway has been provided by the identification of additional, non-HIF-related, PHD substrates, suggesting a role of proline hydroxylation in other settings requiring oxygen-dependent regulation', citing a single reference. This does not really represent the complex balance of arguments across the literature about non-HIF substrates for the HIF hydroxylase enzymes.

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

A central issue for any conditional knock-out strategy is whether the intended tissue restriction is successfully achieved. The authors acknowledge that some issues have been reported with the Cre-recombinase allele they use. They, however, show the expected restriction to cells of the Treg lineage in two of the lymphoid tissues under investigation (spleen and mesenteric lymph node - Supplementary figure 1b) but do not show similar results for other tissues. Some concerns arise because in Figure 8b YFP (which is expressed alongside the Cre-recombinase) is visible in what appears to be the endothelium of the spleen. Additionally, the spleen sections illustrated show convincing splenomegaly in the Phd2-deficient Treg mice but expansion of the red pulp appears to be at least as prominent as any changes that might have occurred in the white pulp. Furthermore, the gross changes in abdominal appearances described as a 'hemorrhagic abdomen' (Figure 1c) include a more plethoric abdominal wall, prominent intestinal blood vessels and a much darker, and perhaps enlarged, liver compared with the control animal. These appearances might result from increased angiogenesis and / or erythropoiesis, neither of which would be expected to result from Treg lineage restricted Phd2 knockout but are known to occur with Phd2 ablation in other tissues. If there is convincing evidence of haemorrhage it would be nice to see this more obviously displayed macro- or, perhaps better still, microscopically.

Given that the Cre-recombinase allele used is expressed through the endogenous Foxp3 locus which is located on the X-chromosome and thus subject to random inactivation in the cells of females it is important that the sex of animals used in the experiments is specified.

Experiments show alterations in Phd2-deficient Treg mice compared with control mice in homeostatic proliferation in a lymphopenic environment (Figure 3), the induction of colitis by DSS colitis (Figure 4) and the response to Toxoplasma gondii infection (Figure 4). Given the time courses these effects are likely to be real but interpretation is complicated by the spontaneous effects on the colon of Phd2-deficient Treg mice reported in Figure 1d and e. Given the wide general importance of interferon-gamma in immune / inflammatory responses I am not sure how much weight to place on the observation that concurrent interferon-gamma knockout results in loss of the Phd2-deficient Treg mice pro-inflammatory phenotype (Figure S3). No differences are seen in an in vivo model in which inflammation is induced by injection of anti-CD3 antibodies (Figure S2).

An important conceptual difference between the interpretation of results reported here and those reported by Yamamoto et al. is that the 'Phd2-deficient Treg' purified here do not show a change in regulatory function in vitro whereas those used by Yamamoto et al. failed to act normally as regulatory cells. It is unclear whether this is due to differences in the way proliferation was stimulated, the cell purification strategies used (YFP+ in the current work; CD4+;CD25+ in Yamamoto et al.), the silencing of Phd2 (by knockout throughout development here versus through an inducible-shRNA only in mature cells in Yamamoto et al.), some other feature of the experiments (e.g. the use of feeder cells) or whether a difference would be revealed by more extensive titration. The result reported here is somewhat surprising given the presence of a Th1-like immunophenotype in the cells used in these in vitro suppression assays, which at face value might mean that this immunophenotype is not responsible for changes in their regulatory capacity seen in vivo. This may be true, but it is at odds with Bayesian argumentation.

It may be a coincidence, but both models in which control Treg and Phd2-deficient Treg behave similarly involve treatment with anti-CD3 antibodies, raising the possibility that these antibodies in some way nullify differences reported with other stimuli, rather than this necessarily being related to the hypothesised difference between Th1 and Th17 responses in the in vivo model.<br> Data showing reversal of the Phd2-deficient Treg in vivo phenotype by knockout of HIF-2alpha, but not HIF-1alpha are convincing and support the data of Yamamoto et al. The observation that Treg-specific PHD2-HIF1α double knockout mice were born at sub-mendelian ratios, displayed a marked weight loss during adult life and reduced viability, indicative of a more pronounced pro-inflammatory status is reported but data is not shown. This is certainly of interest and will no doubt receive further attention. The data that Treg-selective HIF1α or HIF2α deficiency does not affect immune homeostasis in naive mice shown in Figure S4 is relevant and compelling. These results are discussed in the context of recent work published by Hsu et al., 2020 which is interesting. Taken together these data highlight the fact that results reported throughout this manuscript arise from a combination of developmental differences with those occurring in the adult animal.

The transcriptomic data presented has not, to date, been made available to reviewers or the public. Importantly, it is reported to show a disconnection between changes in glycolytic gene expression pattern and the immune phenotype. Specifically, whilst loss of Phd2 expression in Treg is associated with alterations in their regulatory function and with induction of glycolytic genes, the change in function, but not the change in glycolytic gene expression, is reversed by simultaneous knockout of HIF-2alpha and conversely the gene expression pattern, but not the change in function, is reversed by simultaneous knockout of HIF-1alpha. This will be of great interest to those working on the hypothesis that the switch between oxidative phosphorylation and glycolysis underlies functional changes in T cells, particularly if the changes in glycolytic gene expression actually convert into changes in glycolytic flux (as observed following HIF-induction in other cell types).

The authors propose that a change in CXCR3 expression resulting from a change in STAT1 phosphorylation (but not absolute levels of STAT1) consequent on Phd2- inactivation leads to mal-distribution of Treg (at least in the spleen), and that given the broadly paracrine action of Treg this feature alone might explain the loss of regulatory activity in vivo. This is an intriguing hypothesis based at least in part on associative data rather than a formal proof of causality. Changes in STAT1 phosphorylation following interferon-gamma stimulation are far from 'all-or-nothing' (at the timepoint illustrated many cells have normal pSTAT1 levels even though the mean fluorescence intensity is reduced). Results in Figure 7b show that changes in STAT1 phosphorylation are seen in conventional Foxp3 negative T cells; since Phd2 knockout is restricted to the Treg lineage this change is presumably indirect, raising the possibility that the change seen in Treg is also indirect, rather than truly cell autonomous. Changes in pSTAT1 are acknowledged to affect a huge number of genes / processes so picking any one as the total explanation for any change in behaviour may be an over simplification. The analysis of changes in Treg localisation in the spleen is potentially interesting and may reach the correct conclusion but the methodology used is not clearly explained and in particular it is not clear how splenomegaly / changes in gross splenic architecture have been taken into account.

Overall, this work contains many interesting datasets which need to be taken into account as we build our understanding of the intersection between HIF-signalling and regulatory T cell function, particularly as pharmacological manipulation of HIF signalling may provide a route to immunomodulation through alterations in regulatory T cell function.

Reviewer #2 (Public Review):

Despite not being the first study reporting the role of PHD2 in TREG function, Ajouaou and colleagues put together an extensive characterization of a FOXP3 conditional PHD2 KO in mice. By using 4 additional genetically modified mouse models, the authors of this study established a link between the PHD2 driven dysregulation of TREGs and another oxygen sensor, HIF2a. Both in vitro and in vivo data are compelling, and most of the hypothesis discussed in the manuscript are supported by experimental evidence (of note, the formulated hypothesis as to why the suppressive function of PHD2 KO TREGs is impaired only in vivo and appears unaffected in vitro, which might be linked with a defective co-localization between TREG and other T cells). The weakest part of this study is when authors highlight its relevance to the clinical use of oxygen sensor inhibitors, namely against PHDs, but fail to provide real evidence that these inhibitors might lead to TREG disfunction similar to that observed with the PHD2 deletion. Nevertheless, the exhaustive characterization of the PHD2-HIF2-STAT1 axis in TREGs contributes to the knowledge in the immunology field with new questions which justifies the publication of this story without further experiments.

Reviewer #3 (Public Review):

To understand how the oxygen sensor PHD2 works in the immune system, particularly in Tregs, is of importance in understanding how the immune cells adapt to oxygen tensions in the tissue microenvironments and thereby exert their biological functions. Previous studies have shown that PHD2-HIF2a axis is critical in controlling Treg function using a systemic in vivo reversible knockdown system (Yamamoto, JCI, 2019). The current work utilized conditional PHD2 gene deletion by employing Foxp3-cre system to selectively ablate gene expression in Tregs and they found similar phenotypes like spontaneous systemic autoinflammation, activation of conventional T cells to become effector/Th1-like cells, failure to inhibit inflammatory responses in vivo using adoptive transfer or chemical/pathogen-challenged models. The authors further showed that HIF2a, but not HIF1a, is the critical mediator for this function, again consistent with the previous observation. They further attempted to find the mechanisms by performing transcriptome analysis and suggested that the expression of several chemokine receptors (and others) may be responsible for the altered localization of PHD2-deficient Tregs.

Strengths (major findings):

1, in contrast to the previous systemic knockdown system, the current study employed a conditional knockout of PHD2 gene in Tregs using Foxp3-cre mice, which would give a clean system to study Tregs.

2, consistent with the previous report, the effects of PHD2 knockout in Tregs were profound, with systemic autoinflammation phenotypes. The loss of PHD2 in Tregs caused the activation of conventional CD4 T cells, which were converted into effector-Th1-like cells, the loss of in vivo suppressive activity, and the failure to control chemical-induced colitis or pathogen-driven inflammation.

3, further genetic deletion experiments showed that the effect of PHD2 is mediated by HIF2a, but not HIF1a, and the PHD2-HIF2a double deletion in Tregs reversed the above-mentioned phenotype, in general agreement with the previous publication.

4, Transcriptome analysis showed the alteration of gene expression profiles by the deletion of PHD2 alone or in different combination with HIFs, with anti-inflammatory, chemokine, or cell survival pathways as potential downstream targets. Further studies showed that Stat1 phosphorylation was affected by PHD deficiency, which was reversed by HIF2a deletion, and this may cause mis-positioning of Tregs in the spleen.

Weaknesses:

1, the major findings in this paper are largely confirmatory, with only incremental progress over the previous findings.

2, the approaches for the analysis of Tregs are not rigorous, particularly the lack of analysis of thymic Tregs, which may affect the programming of Tregs at the early stage. In addition, the extrinsic or intrinsic roles of PHD2 in Tregs in relation to the autoimmune phenotype were not strictly analyzed.

3, the claim of the reduced in vivo suppressive capacity by PHD2-deficient Tregs seems to be misleading, if the authors believe that mislocation (not really a suppression issue) is the reason for the defect. In addition, the observed Th1 phenotype in PHD2-deficient mice may not be convincingly explained by the altered location of PHD2-/- Tregs.

4, the mechanistic analyses are superficial and murky at this stage, and it does not clarify a casual vs. causal relationship between PHD2 and HIFs, or between PHD2-HIF2a axis and the downstream effectors.

Reviewer #3 (Public Review):

The manuscript by Wang et al. conclusively demonstrates that the cyanobacterium Synechocystis sp. PCC6803 prefers to use the ferredoxin-reducing enzyme PFOR over the NAD+-reducing PDH-pathway when grown under photomixotrophic conditions while the PDH-route is favored under photoautotrophic conditions. Both the potential physiological meaning of this switch and implications for the evolutionary history of the role of the respective enzymes and their pathways are discussed.

The main hypothesis of this work considers that PFOR-mediated decarboxylation of pyruvate replaces the PDH-based one when cells shift from photoautotrophic to photomixotrophic growth conditions. This hypothesis is assessed via the comparison of growth curves measured on a host of deletion mutants and via direct detection of expression levels of certain enzymes. The authors' hypothesis is robustly supported by the majority of the reported experiments and the reviewer is fully convinced by these data. However, I would hold that the data shown with respect to phosphorylation of PDH (Fig. S4) are unconvincing. I can't see a clear difference in growth-curves for the incriminated mutants deltaspkB and L which would convincingly exceed the variation observed for the entire dataset.

While I consider the results part as describing (largely) well-performed and pertinent experiments, I have a few issues with the discussion and conclusion sections. To my mind these sections contain a few unwarranted conclusions and misconceptions which need straightening out prior to publication.

Reviewer #1 (Public Review):

In Wang et al., the authors investigate issues related to the relative proportion of flux for the enzymatic decarboxylation of pyruvate between PDH (pyruvate dehydrogenase) and PFOR (pyruvate-ferredoxin oxoreductase) in the model organism Synechococystis. The manuscript provides evidence that PDH becomes increasingly inactivated by a high ratio of NADH:NAD+ as well as evidence to suggest that PFOR is transcribed and remains intact under aerobic conditions. The authors put forward the theory that both PDH and PFOR are functionally active routes for pyruvate decarboxylation under aerobic conditions, whereas PFOR has previously been assumed to be inactive under growth conditions containing oxygen. This distinction is particularly highlighted by conditions where Synechocystis is grown photomixotrophically - and where the NADH:NAD+ pool may be relatively over-reduced because of two parallel inputs of reductant (water-splitting at PSII and catabolism of glucose). The authors examine growth under photoautotrophic and photomixotrophic conditions for a number of relevant mutants including members of the ferredoxin/flavodoxin family, PFOR, and NDH-1 complex subunits.

The theory put forward in this manuscript is of general interest regarding electron flux through the combined electron transport chain (photosynthetic + respiratory) of cyanobacteria. The authors further broaden the potential audience for the manuscript by elaborating on the potential significance of these results in the context of a switch from PFOR (ancestral) to PDH (oxygenic/modern).

Comments:<br> Generally, theories put forward in this manuscript are intriguing and have a number of potential implications for understanding electron flux and regulation of central metabolic processes in photosynthetic microorganisms. If these theories are supported and become more generally adopted, they would have significant impact on the understanding of the regulation of central carbon metabolism in cyanobacteria. That said (due in no small part to the complexity of some of these pathways), the evidence provided to support the hypotheses is indirect in many instances. In some cases, there is a pairing of indirect data with broad statements that can come across as over-reach. These problems can be somewhat exacerbated by an unclear organization at parts of the Discussion, a lack of succinctly defined claims, and numerous typographical considerations.

Major considerations:<br> A major component of the proposed theories in this manuscript rest upon the assumption that PFOR is an active enzyme under highly aerobic conditions: this claim is never directly demonstrated. Indirect evidence of altered growth of pfor mutants, increased repression of PDH, and the higher NADH:NAD+ ratio under photomixotrophic conditions is in general alignment with this theory. However, while deletion of pfor does indeed result in altered growth dynamics in Synechocystis under periods of photomixotrophy, the alterations do not entirely align with the idea that this pathway is critical for rapid growth under aerobic conditions. For instance, pfor and most of the highlighted mutants (fdx 3, fdx 9, isiB) presented in Figure 3 show the greatest defects in their OD after reaching stationary phase (more rapid decline in OD on/after Day 6) relative to WT. This doesn't align as nicely with the highest NADH:NAD+ seen in Days 3-5 (which is also specifically called out: e.g., Line 146, Supplemental Figure S8). In this context, the deletion of F-GOGAT is much more convincing in it's severity and timing, yet for this mutation to have a more severe phenotype is unexpected if PFOR is one of the primary/sole electron donors to the ferrodoxin pool from glucose utilization as proposed (i.e., stated differently, F-GOGAT is only one of the enzymes downstream of ferrodoxin and might be expected to have a more subtle phenotype in comparison to the KO of PFOR if that is a primary source for electrons to ferredoxin under photoheterotrophic conditions).

A central tenant of the argument put forward on the evolutionary importance of using either PFOR vs. PDH is the conservation of extra free energy by the former reaction. However, additional information on the ferredoxin paralog(s) that accept electrons from PFOR is necessary to evaluate these claims. Based on the data within these manuscripts, Fdx3, Fdx9, and IsiB have the strongest links to PFOR: though the authors do take care to never state directly that they have evidence that these are the acceptors in vivo. Given the variability in the midpoint potentials of different ferredoxins, some ferredoxin acceptors may better conserve the free energy in pyruvate, while others may actually be more 'wasteful' than NAD+ as the acceptor through PDH. Unfortunately, the midpoint potentials for Fdx3, Fdx9, and IsiB are unknown or not stated in this manuscript. It is therefore unclear what ferredoxin is being used as the reference point for conservation of Gibbs free energy in Figure 4C and referenced multiple times in the text.

Finally, the measurements of NADH:NAD+ (most prominently used for measurements in Fig 1B) utilized kits that require multiple, long centrifugation steps in the dark prior to assaying this rapidly exchanging pool. While it appears that the authors were able to get reproducible results with these kits, it is difficult to interpret what the increase in relative NADH levels in glucose-fed cells means given that 10+ minutes of incubation in the dark and/or changing temperatures elapsed after the cyanobacteria were removed from the incubator before the NADH:NAD ratio was assessed. While it superficially makes logical sense that the cytosol would be over-reduced when illuminated and under glucose feeding relative to illumination alone, it shouldn't be assumed that these measurements are representative of this rapidly-exchanging pool under the steady-state growth conditions.

Reviewer #2 (Public Review):

The observation that cyanobacteria can use two alternative pyruvate decarboxylating enzymes using either NAD+ or ferredoxin is an interesting and the work is useful contribution. The authors very nicely characterize the enzymatic properties of the two pyruvate metabolizing enzymes and also are able to connect the ideas of redox balance with a set of ferredoxins. Even though they are not able to definitively characterized the specific ferredoxin which interacts with the enzyme, the analysis is nicely conducted and it's clear that the suggestion they're making regarding the involvement of the minor ferredoxins is compelling. However, the work could be written in a way that might be more useful.

Specific comments:

Overall this is an interesting study, but the arguments could be sharpened and better connected with the literature. The introduction needs to be considerably revised in my opinion. It is not obvious whether it is even appropriate to discuss the enzymes as an aerobic enzymes or aerobic enzymes, since this concept is simplistic and perhaps, archaic. Indeed, placing the results of the present study in the context of "aerobic enzymes versus aerobic enzymes" is a bit of a 'strawman' argument. For example, the counter examples of O2-tolerant enzymes cited seem to suggest that PFORs have been capable of evolving into O2-tolerant enzymes quite readily and that two types of decarboxylase have evolved for quite different reasons than simple replacement for a new environment. Instead, I think a more current and general perspective relates more to the interpretation that the authors are already putting forth. Namely, the enzymes are utilized according to redox balance considerations rather than sensitivity to oxygen. Therefore I think the very long and pedantic introduction is useful for review, but only if it is shortened and also includes the alternative interpretation regarding adaptations to redox potential in the cytoplasm. My guess is that there are plenty of examples of redox balance function arguments in the literature to refer to in contrast to the evolutionary replacement argument used. Certainly there are good examples regarding glucose toxicity in mutants of Synechocystis that can be considered.

Given the interpretation that the alternative forms of the enzyme help cells adjust their redox balance to different conditions, such as photomixotrophic growth, the very nice enzymatic analysis and growth studies of the mutants work would be significantly strengthened by more direct physiological measurements that report intracellular redox states.

Minor comments:<br> line 211: Perhaps, "..the deleted alleles failed to segregate, keeping some wild type copies."

It would be interesting to characterize whether the observed distribution of PFOR correlates with specific physiological features. In other words, PFOR seems to become important upon the addition of an external carbon source in way that must integrate with autotrophic metabolism (i.e. mixotrophic growth) altering the balance of the oxidized and reduced form of redox cofactors--does the observed distribution correlate at least with the the metabolic characteristics of the handful that have been studied in the lab?

A more detailed set of calculations that help explain panel C in figure 4 need to be included to support the quoted values for redox potential in free energy. I assume these are standard values and and the specific superscripts and subscription associate with the ΔG nomenclature needs to be defined.

Reviewer #1 (Public Review): 

The manuscript addresses the role of different lipids on diabetic kidney disease (DKD). It is known that lipids can have a negative impact on DKD, but little is known about different lipids and potential mechanisms triggered. This open question is addressed by the authors and their provide new insights by showing that different lipids affect the ER-stress response differentially. A limitation of the study is that mechanistic studies are limited to in vitro work, leaving the question to which extend the identified mechanisms would be relevant in vivo open. A strength is the combined transriptomics and lipidomics analyses. More details regarding membrane fluidity - using different techniques - will be required in the future.

Reviewer #2 (Public Review): 

This study examined the potential role of unsaturated fatty acids (MUFA) vs. saturated fatty acids (SFA) in mediation of kidney proximal tubule injury in a model of diabetes. Utilizing cultured proximal tubule cells, they find that MUFA led to increased tubule lipid droplets by stimulating triacylglycerol formation and prevented injury by decreasing ER stress compared to SFA. Of note simultaneous administration of MUFA still inhibited the effect of SFA. 

This is a very complete study utilizing a combination of animal models, transcriptomic analysis and lipidomics. In general, the results appear consistent and the studies appear to be well performed. The authors have attempted to investigate underlying mechanisms relating to the observed phenomena. They demonstrate and increase in tubule injury and interstitial fibrosis in the mouse model and that SFA induce ER stress. There remains some uncertainty about the mechanism by which MUFA inhibit injury by simultaneous addition of SFA. although the authors postulate it is due in part to increased TGA formation, the protective effect remained even with dacylglycerol transferase inhibition. There are also some other addressable issues.

Reviewer #3 (Public Review): 

Strengths: 

The work presents the interesting and novel hypothesis that like fat cells, kidney cells have the capacity to accumulate and store surplus lipids without incurring damage common to most non-adipose cells overloaded with excess lipids. 

Using state of the art methodologies, the study makes the compelling case that the pathways for the formation and function of the storage lipid droplets depends on the type of lipid. Unsaturated oleic acid found in olive oil promotes more lipid storage in kidney cells associated with less cellular toxicity while saturated palmitic acid commonly found in butter-rich diets causes less lipid accumulation but more toxicity. The concept is supported by studies showing concurrently added oleic acid can protect cells against palmitic acid-induced cell damage. 

The work is timely as there is increasing recognition that lipotoxicity transcends documentation of elevated levels by traditional lipid panels, e.g., total cholesterol, lipoproteins, and triglycerides. Moreover, since lipotoxicity occurs in most, if not all kidney diseases, understanding the pathways for channeling different lipids into cellular structures, mitochondrial -oxidation, or lipid droplets may become useful in developing therapeutic approaches to treating these conditions. 

Weaknesses: 

Although both butter and olive oil enriched diets increased kidney lipids animals with diabetic nephropathy, lipid accumulation was stronger in olive oil-fed mice compared to butter-fed mice, yet tubular damage was less. This observation forms the basis for the conclusion that monounsaturated olive oil diet has a protective role by increasing lipid storage in lipid droplets. The cause and effect of this critical conclusion should have additional support clearly demonstrating and quantitating lipid droplets within the kidney tissue along with evidence of damage in the lipid-loaded kidney cells. 

The design of the studies does not adequately link the in vivo observations in the animal model of diabetic nephropathy with in vitro studies delving into the cellular response to palmitic and oleic acid driven injuries. For example, the possible effects of high glucose levels prevailing in diabetes is not included in lipid metabolic studies in cultured kidney cells. 

Lipid accumulation within the kidney tissue is clearly not uniform. However, the heterogeneous handling of palmitate and oleate among kidney cells is not accounted for in the experimental design. 

Lipid droplet formation follows a predictable path for handling excess fatty acids in a variety of cells and is well described in the existing literature. More direct support that lipid droplets formation are indeed protective in kidney cells is needed together with discussion of how this is different in kidney cells.

Reviewer #1 (Public Review):

Pramod and colleagues find that inferred physical stability of visual scenes is represented in parietal-frontal areas designated as the "Physics Network", but not in the ventral visual pathway. Furthermore, unlike in previous studies, they report that physical stability cannot be determined from representations in standard CNNs trained on object classification. These novel findings are the result of studying inferred stability not in one type of image only, but by generalizing between different types of scenes (object- and people-dominated scenes, respectively). The authors combine a number of sophisticated measurement and analysis techniques to substantiate their claims: CCNs, fMRI in combination with MVPA, eye-tracking, and clever control conditions.

The authors speculate that this "Physics Network" contains a generative model, running forward simulations of dynamic physical scenes. This is an intriguing hypothesis, that of course requires much further testing. For instance, how does this Physics Network relate to the dorsal attention network? This, together with the fact that unstable scenes evoked more activity than stable ones, could make one wonder whether we're simply looking at stronger vs. weaker attention (or engagement) rather than something specific to the physics of the scene. However, eye-movement analyses, as well as the perilous-vs-non-perilous condition as a control for arousal and attention, help take care of these concerns.

It will also be important in future to dissociate effects of physical stability from effects of implied motion. In the current study, a higher response to unstable vs stable scenes in reported in motion area MT, which the authors describe in terms of implied motion. It therefore seems possible to describe the results in the "Physics Network" in terms of implied motion, rather than physical stability, as well.

In short, much future work remains to be done to ascertain what computations exactly take place in this Physics Network during inference on physical stability, but the current study represents an intriguing step forward in this domain.

Reviewer #2 (Public Review):

This paper combines neuroimaging, behavioral experiments, and computational modeling to argue that (a) there is a network of brain areas that represent physical stability, (b) these areas do so in a way that generalizes across many kinds of instability (e.g., not only a tower of blocks about to fall over, but also a person about to fall off a ladder), and (c) that this supports a simulation account of physical reasoning, rather than one based on feedforward processing; this last claim arises through a comparison of humans to CNNs, which do an OK job classifying physical instability but not in a way that transfers across these different stability classes. In my opinion, this is a lovely contribution to the literatures on both intuitive physical reasoning and (un)humanlike machine vision. At the same time, I wasn't sure that the broader conclusions followed from the data in the way the authors preferred, and I also had some concerns about some of the methodological choices made here.

1. The following framing puzzled me a bit, and even seemed to raise an unaddressed confound in the paper: "Here we investigate how the brain makes the most basic prediction about the physical world: whether the situation in front of us is stable, and hence likely to stay the same, or unstable, and hence likely to change in the immediate future".

Consider the following minor worry, which sets up a more major one: This framing, which connects 'stability' to 'change' and which continues throughout the paper, seems to equivocate on the notion of 'stability'. One meaning of 'stable' is, roughly, 'unchanging'. Another meaning is 'unlikely to fall over'. The above quotation, along with others like it, makes it seem like the authors are investigating the former, since that's the only meaning that makes this quotation make sense. But in fact the experiments are about the latter -- towers falling down, people falling off ladders, etc. But these aren't the same thing! So there's a bit of wordplay happening here, it seemed to me.

This sets up the more serious worry. As this framing reveals, unstable scenes (in the likely-to-fall-over sense) are, by their nature, scenes where something is likely to change. In that case, how do we know that the brain areas this project has identified aren't representing 'likeliness to change', rather than physical stability? There are, of course, many objects and scenes that might be highly likely to change without being at all physically unstable. Even the first example in the paper ("a dog about to give chase") is about likely changes without any physical instability. But isn't this a confound? All of the examples of physical instability explored here also involve likeliness to change! So these could be 'likely to change' brain areas, not 'physically unstable' brain areas. Right? Or if not, what am I missing?

The caption of Figure 1 seems to get at this a bit, but in a way I admit I just found a bit confusing. If authors do after all intend "physically unstable" to mean "likely to change", then many classes of scenarios that are unexplored here seem like they would be relevant: a line of sprinters about to dash off in a race, someone about to turn off all the lights in a home, a spectacular chemical reaction about to start, etc. But the authors don't intend those scenarios to fall under the current project, right?

(Also: Is stability really 'the most basic prediction' we make about the world? Who is to say that stable vs. unstable is a more basic judgment than, say, present vs. absent, or expected vs. unexpected, or safe vs. unsafe, etc? I know this is mostly just trying to get the reader excited about the results, but I stumbled there.)