Reviewer #2 (Public Review):

Oxenford et. al., describe a novel open-source DBS visualization software package, Lead-OR that aims to fill a gap in the intraoperative visualization of DBS trajectories. While theirs is certainly not the first nor only attempt at achieving this, the described software is unique in combining an open-source approach with integration of multimodality data including integration with the most commonly used planning and microelectrode recording platforms. Their software has the potential to take intraoperative DBS visualization to the next level by combining patient-specific imaging with intraoperative electrophysiology and new normalization tools to incorporate external atlases. While some may find this approach unnecessary given the trend towards decreased reliance on MER in DBS for movement disorders, the tools described will still be useful for retrospective and research analyses. The true potential of LeadOR lies in its future potential as integration across platforms grows and other developers add to its capabilities over time.

Reviewer #1 (Public Review):

This paper sets out to address a conundrum in the literature on cognitive aging - that older participants tend to exhibit increased behavioural variability on cognitive tasks despite having decreased neural signal variability. Here, the authors tested the theory that this apparent discrepancy might reflect the influence of background slow oscillations that change with age but without necessarily being tied to the observed changes in behaviour. Data were collected from a group of older and younger participants who performed a Go/No-Go task in which the onset of the critical stimuli was foreshadowed by a warning cue. Older participants exhibited increased raw RT variability on the task but equivalent coefficients of variations. ERP analyses centered on the CNV signal which builds in anticipation of the critical stimulus. Older adults had reduced CNV amplitude variability and a relationship between ERP amplitude and RT was observed, although this was focussed over left frontal electrodes rather than the frontocentral electrodes where the CNV is focussed. EEG and pupillometry analyses showed that older subjects had lower slow oscillation amplitudes in both modalities. When these differences were controlled for, the group difference in CNV and pupil dilation amplitude variability disappeared and stronger relationships with RT was observed. Similarly, a significant relationship between pupil dilation and RT was only evidence after controlling for the slow oscillations. These results suggest that behaviourally-irrelevant slow oscillations can potentially confound comparisons of aging effects on ERP and pupil measurements as well as their relationships with behaviour.

This is a very interesting paper that addresses a long overlooked topic in the neurobiology of aging. Where there has been growing awareness of the impact of age-related vascular changes on BOLD responses in fMRI research, equivalent issues have rarely been considered in the EEG literature. This study therefore highlights an important issue that should be of broad interest and relevance to the field, with implications not only for studies of aging but any study comparing individuals or groups.

Some questions do remain. The contention that the ERP-RT relationships observed in this study pertain to the CNV specifically are questionable since the reported effects are focused over left frontal areas that may be more closely tied to preparation of the right handed button clicks participants were making.

Reviewer #2 (Public Review):

This paper is very well written and the analyses are very competently carried out. It reveals a very interesting aspect concerning the interplay between evoked responses, aperiodic activity and behavior. A strong aspect about the paper is that the results are quite convincing, and it is clear that aperiodic activity should be taken into account in future studies working on differences in the variability of evoked signals. A potential weakness is that this study feels more like a technical report, demonstrating an important "confound", rather than it teaching us something on its own (which might partly be the consequence of the focus of the paper).

Reviewer #3 (Public Review):

Significance:

The paper provides a rigorous analysis of the EEG and pupil data, and the results of this analysis sufficiently explain the apparently contradictory findings that the paper set out to investigate, namely that older adults exhibit increased behavioural variability but reduced neural variability. As such, I feel that these findings will be of great interest to researchers in the field of neuroscience and ageing, and more generally, the methods employed on the data might help EEG researchers in the resting-state activity field.

Strengths:

- The key to explaining the reduced neural variability in older compared to younger adults lies in the fact that their ongoing neural activity is fluctuating less. The authors do a very thorough analysis of this ongoing EEG and pupil signal, separating the aperiodic from the periodic component, checking the PSD, and computing the phase of both of these signals at cue onset. This in-depth analysis inspires confidence in their findings and could be of interest to other EEG researchers in the resting-state field.

Weaknesses:

- An important aim of the paper is to explain the increased behavioural variability in older adults. However, only a limited part of the behaviour, namely the reaction times on the employed go/nogo task, is being reported and analysed. It is easily imaginable that there are differences also between younger and older adults in terms of hit - misses - false alarms - correct rejections. It would be helpful if the paper provided a more complete picture of the behaviour.

- The paper considers two brain measures in younger and older adults, EEG and pupil size fluctuations. Although the relationship of both measures to the reaction time variability is described separately in great detail, the findings of both measures are not combined: for instance, it is not clear if and how their contributions to the behavioural variability interact, whether they explain different aspects of the behavioural variability, etc. In my view, the paper would improve from adding a coherent picture of how these two measures contribute to the behavioural variability together.

- The main component of the EEG signal that the authors look at is the amplitude of the Contingent Negative Variation (CNV). The main analysis window for the CNV amplitude is 1-1.5 sec post-cue onset (see for example the grey bar in Fig2A). A clear motivation for choosing this particular window is lacking, leaving open the possibility that the reported results are dependent on this particular analysis window that was chosen.

- The authors distinguish between two factors that contribute to variability in evoked responses: differences in brain state, or a simple summation of two independent signals (fluctuating baseline plus evoked response). They argue for the latter explanation for their data, for good reasons. However, I would like to point out that many studies on pupil size suggest that fluctuations in pupil size are caused by fluctuating brain states (e.g. Pais-Roldan et al, PNAS 2020; Reimer et al, Nat Comm 2016; Yuzgec et al, Curr Biol 2018). The authors could use the Discussion section of the paper to explain how they integrate these findings with their own results on simple summation of ongoing and evoked signals.

Reviewer #1 (Public Review):

I have confined my remarks purely to the cryo-EM, image analysis and three-dimensional reconstruction, and leave the evaluation of the significance of the work to other reviewers. I had no concerns about the technical aspects of the work, and the resolution statements appear very reasonable. At this resolution there should be no ambiguities in the atomic models that have been generated.

Reviewer #2 (Public Review):

P5CS is part of the proline and ornithine synthesis pathway, and catalyzes the reaction of L-glutamate to glutamate-gamma-semialdehyde in an ATP and NADPH dependent manner. Mutations in this enzyme lead to human disease and issues in agriculturally important plants. The authors present structures from three CryoEM analyses at moderate resolution (3.1-4.2 Agstrom) of P5CS showing filamentous structures in the presence of L-glutamate, L-glutamate and ATPgammaS, and L-glutamate, ATP, and NADPH in an effort to understand the enzyme mechanism and role of enzyme filamentation. Filamentation of enzymes is an important and newly appreciated mechanism of enzyme regulation, and this work provides important new information on how filamentation may enhance the enzymatic catalysis by P5CS. Large conformational changes are seen in the enzyme between the different structures, representing different stages of the enzymatic reaction. The enzyme forms tetramers which then assemble into left-handed helical filaments with 68 degrees a rise of 60 Angstrom (roughly the height of a tetramer) between adjacent tetramers. The authors suggest, base on the structure of P5CS with L-glutamine and a structure with G5P and ADP (the product of the first reaction between ATP and L-glutamine) that conformational changes upon ATP binding lead to a shift of reactants L-glutamate and ATP towards each other, creating an active state for the reaction of the first enzymatic step. While an interesting suggestion, it should be noted that the structure with ATP is not known, and this suggestion is conjecture based on a structure with no ATP and with ADP. It is possible that the structure with ATP is yet distinct. Binding of NADPH further induces a conformational change bringing the NADPH towards residue C598 (a residue apparently important for enzyme function, though a figure showing NADPH and C598 together is not given, and no details on what function C598 perform is discussed). The authors show that the filament accommodates all conformations, and suggest that the filament is dynamic, performing multiple rounds without depolymerization. This is an exciting possibility, but it should be noted that the authors do not have direct evidence that a depolymerization intermediate step is required (structures are of the final states, not the intermediate). The authors find in several of their new structures that an interface is formed by residues F642-P644 (which are distant from the active sites) in GPR domains of adjacent P5CS tetramers in the filament. They show that this interaction is responsible for the filamentation as a point mutation in the segment disrupts both filamentation and enzyme activity (which also shows the importance of filamentation to enzyme activity). They also show that a contact between adjacent GK domains forms a "hook" structure in some conformational states of the enzymes, which they suggest is formed upon ATP binding (though their structures show only ADP binding, not ATP). They find that mutations in this site do not disrupt filamentation in the apo and L-glutamate bound states, but found that addition of ATP results in depolymerization, and addition of NADPH induces the formation of filaments but that are much shorter than those of the wild type enzyme. The mutation in the hook region also strongly reduces enzyme activity. They conclude that ATP therefore initiates the reaction in the GK domain, and triggers the hook structure to stabilize the conformation necessary for the next step of the reaction. The authors speculate that the filament couples the reactions catalyzed at the two domains by a channeling effect - the intermediate of the two step reaction and product of the first step, G5P, is produced in an active site 60 Angstroms away from the active site of the second catalytic step. Both active sites face the interior of the filament, and therefore the filament may create a microenvironment to allow limited diffusion of G5P so that it may more efficiently diffuse from one active site to the other. In addition to showing new details of the enzymatic mechanism of P5CS, this work also contributes to our understanding of how filaments can facilitate enzymatic reactions (possibly via a caging effect). Finally, the authors do not discuss their structure in comparison to the known structure of human P5CS, which is an important omission.

Reviewer #3 (Public Review):

Jiale Zhong et al. investigated the structure of pyrroline-5 carboxylate synthase (P5CS) from Drosophila, a bifunctional enzyme composed of fused glutamate kinase (GK) and glutamyl phosphate reductase (GPR) domains. The crystal structure of human P5CS GPR domain was available in the Protein Data Bank and the structure of prokaryotic GKs had been previously reported, but there was no structure available for the full-length P5CS. Previously, the authors had shown that P5CS assembles into long filamentous structures both in vivo and in vitro. Now, they reported the detailed structural analysis of the full-length P5CS, showing that the protein folds into tetramers that assemble into a spiral filament.

The strength of the manuscript is the high-quality cryo-EM data, which allow the reconstruction of the protein filament in three different ligand-bound states at various resolutions: i) with glutamate in the GK domain and GPR free of ligands (4 Å); ii) with the product glutamate 5-phosphate in the GPR domain (it is unclear what is the content of GK in this structure) (4.2 Å); and iii) with glutamate 5-phosphate, ADP and Mg2+ in the GK and the GPR domain either free or bound to NADPH (3.6 Å). The study shows the structures of both enzymatic domains and provides some details of ligand binding and associated conformational changes.

Importantly, the structure reveals the contacts between P5CS tetramers along the filament axis. Based on this information, the authors designed point mutants that disrupt these contacts along the filament and showed that they also reduced severely the enzymatic activity. Thus, the authors conclude that filament formation is essential for P5CS activity. Given the distance between the GK and GPR active sites, they speculate that the filament grooves create a half-open chamber that accumulates the product of the GK reaction (glutamyl phosphate) and favors its diffusion to the GPR domains on the outer part of the filament. Overall, the data are of high-quality and the conclusions are of high interest to understand how the organization of proteins into supramolecular membraneless compartments regulate their activity.

A similar filamentous organization is expected for this enzyme in other higher eukaryotes, including humans. Defects in the human enzyme are the cause of rare congenital diseases. Based on the current data, the authors proposed a mechanistic effect for one pathogenic variant that would affect the interaction of the tetramers along the filament.

The study falls short in addressing the catalytic mechanisms as well as the possible communication/regulation between protein domains within the tetramer and along the filament. Also, the study does not speculate on how the formation of the P5CS filament could depend on the interaction of the enzyme with CTP synthase, as was reported by the authors in a previous article.

Reviewer #1 (Public Review):

Liu et al present findings that significantly extend the understanding of molecular and cellular pathways of mechanical nociception in Drosophila larvae. They present a detailed analysis of the mechanical response properties of the nociceptors that is performed using a newly developed preparation for optical recordings from these cells. Using mechanical probes of varying tip diameters they are able to investigate the responses as they relate to force and pressure. Mutants in the cut gene, which show reduced branching of the nociceptors, are used to interrogate how the dendritic morphology structure might relate to their physiological responses. As well, the response profiles of the neurons that are mutant for mechanosensory channels ppk-26 and piezo are investigated. The ppk-26 mutant shows a more strongly impaired deficit in comparison to piezo mutants and double mutants show a nearly abolished mechanical response. The mechanosensory neurons are also found to respond to mechanical forces that are outside of the main dendritic fields which suggests that they are able to detect forces that are viscoelatically coupled through the overlying epidermis. Finally, a voltage activated calcium channel is found to be important which is consistent with findings from prior studies (Terada et al 2016) that the dendrites of these nociceptive neurons may be "active" (as opposed to passive). Overall, the study significantly advances our understanding of the response properties of the mechanical nociceptors of the Drosophila larva.

Although the overall findings are quite interesting, the conclusions of the study could be strengthened according to the following points:

The duration of the calcium responses long outlasts the force application. It has been previously proposed that dendritic breakage could be a contributing factor in the transduction mechanism in the Drosophila nociceptive neurons (Tracey, 2017). What is the relationship of dendritic breakage occur with the various force stimuli and probe sizes in the imaging setup? Sharp probes (smaller probes) may be more likely to break dendrites. Similarly, do the sharp probes that trigger rolling in Figure 2J break dendrites?

A very nice paper from Joe Howards lab has found that axonal activation of the cIVda neurons can happen even in the absence of any dendritic Calcium signals. This finding argues that any active properties of the dendrites only occur with very strong activation and/or direct stimulation of the dendrites. The authors must cite this paper and incorporate it into their discussion: "Focal laser stimulation of fly nociceptors activates distinct axonal and dendritic Ca 2+ signals:, Rajshekhar Basak , Sabyasachi Sutradhar, Jonathon Howard 2, DOI: 10.1016/j.bpj.2021.06.001

Similarly, do forces applied outside the dendritic field activate calcium signals in dendrites or are these signals limited to the soma and axon?

Although prior studies of Terada et al have demonstrated the importance of Ca-alpha1D channels in the dendritic Ca transients, the distribution of the Ca-alpha-1D channels has not yet been demonstrated in these cells. It will be important to know in future studies if these channels distribute distally in the dendrites or if they have a more proximal localizations.

The results of experiments with cut are very interesting, and the authors are open and cautious in their interpretation when they state the caveats of unknown epistatic effects that may result from removal of a transcription factor. Even with the stated caveats, the unknown effects of cut removal causes the results to be very difficult to interpret. It cannot be strongly concluded that the deficits are a consequence of reduced branching. For example, perhaps calcium-alpha1d is a target of cut transcription?

Where are the values for the elastic modulus for larval cuticle coming from in line 543? Experimental measurements of the stiffness of larval cuticle come in at 0.39 +/- 0.01 MPa which is an order of magnitude lower than the values input to the model (10Mpa). Kohane M, Daugela A, Kutomi H, Charlson L, Wyrobek A, Wyrobek J. Nanoscale in vivo evaluation of the stiffness of Drosophila melanogaster integument during development. J Biomed Mater Res A. 2003 Sep 1;66(3):633-42. doi: 10.1002/jbm.a.10028. PMID: 12918047. The modeling should be repeated with more realistic values of larval elastic modulus.

Also in the model, the true value for instar larval cuticle is closer to 20 microns thick according to experimental measurements. (Christine E. Kaznowski, Howard A. Schneiderman, Peter J. Bryant, Cuticle secretion during larval growth in Drosophila melanogaster, Journal of Insect Physiology, Volume 31, Issue 10, 1985, Pages 801-813, https://doi.org/10.1016/0022-1910(85)90073-3).

The reference provided for elastic modulus of muscle comes from a study on human muscles and is therefore not valid for a study performed on Drosophila larvae.

Is the force probe is compressing the entire larval filet such that there is an indentation into the PDMS (as depicted in figure 1A)? If so, then isn't it true that the forces on the filet (dendrites) are not coming solely from the force probe itself? If the larva is being squished between the probe and the PDMS, then the larva is also being exposed to an opposing force that is coming from the PDMS.

Reviewer #2 (Public Review):

The present work by the authors characterizes the mechano-sensitive properties of nociceptive neurons in Drosophila larvae (so called c4da neurons) in a very precise way and aims to show how their morphology and specifically expressed channels contribute to their functional responses.

The authors developed a sophisticated piezo-driven probe to deliver precisely defined mechanical stimuli and combined it with functional imaging of Drosophila larval nociceptors in semi-intact preparations. Their clever setup allowed them to measure mechanical responses of these neurons to mN range stimuli. By using defined probes with different diameters, they showed that c4da neurons display almost uniform responses nearly independent of the stimulation site within their dendritic field. Moreover, the authors convincingly show that c4da neurons preferentially respond to small diameter probes (30 microns) and that their uniform dendritic coverage is also required for detection of distal mechanical stimuli. Stimulation of these neurons results in defensive rolling behavior, which has likely evolved in Drosophila larvae to defend against being stung by parasitoid wasps. The typical ovipositor of such wasps is 5-20um suggesting that c4da neuron responses are indeed optimized for detecting small diameter mechanical stimuli as shown by the authors. Elaborating on the broader biological significance of the authors' findings (and extrapolation to other species/higher organisms) might have been useful for a more general audience.

Using off-dendrite stimulation and theoretical modeling of perpendicular and lateral pressure distribution of their mechanical probe, the authors nicely show that sensory neuron dendrites are sensitive to lateral tension thus featuring an expanded force-receptive field. They continue by showing that two of the expressed mechanosensory channels, ppk26 and piezo, differentially contribute to c4da neuron responses. The authors argue that ppk26 is important for overall and piezo for localized mechanosensitivity, with both participating in sensing lateral tension. The interpretation of the data seems simplified for multiple reasons: a) piezo has been shown to be widely expressed in different tissues (e.g. Kim et al. Nature 2012), thus it might contribute in different ways to c4da mechanosensitivity. b) piezo loss of function does not strongly affect responses to the 30/60 micron probe when applied proximal to the neuron, but does so only in distal regions. c) loss of ppk26 results in disproportionally stronger loss of responses to the 30 micron probe (i.e. more local force), which suggests it could be particularly relevant for perpendicular pressure sensing. Overall, this might reflect that these channels are differentially involved in maintaining similar cellular responses throughout the dendritic field (similar to the analysis performed later on showing the role of Ca-alpha1D in signal propagation). The behavioral differences are less informative and not necessarily consistent, as it is impossible to deliver the probe accurately enough in this assay to distinguish proximal vs. distal responses with the 30 micron probe, which however seems relevant for piezo_ko.

Lastly, the authors investigate the contribution of the VGCC Ca-alpha1D, which has previously been implicated in c4da neuron function. They perform challenging dendritic calcium recordings and implicate Ca-alpha1D in signal propagation particularly for smaller diameter stimuli activating a smaller portion of the dendritic arbor. These data are of high quality and consistent with the authors' model. The authors could have considered if and how Ca-alpha1D might contribute to propagating piezo vs. ppk26 activation, which is particularly important for distal receptive field function in regard to small diameter stimuli.<br /> One limitation of the study is that it does not entirely represent the in vivo situation, as the animal had to be fileted to get access to the nociceptive neurons under the authors' experimental setup. Drosophila larvae are filled with hemolymph and display very different mechanical properties than the PDMS membrane used to pin the larval filet. This likely affects the absolute force/pressure needed to activate c4da neurons. In addition, neuronal responses depend on the physiological buffer used under these conditions, which do not necessarily mimic in vivo conditions (extracellular calcium concentration, pH, osmolality etc. will affect the evoked responses).

Overall, by establishing a cutting edge method to deliver precise mechanical stimulation the authors provide data showing that mechanical stimuli are sensed by the entire receptive field of these neurons, preferentially detecting small diameter stimuli resembling sharp objects. The study thus provides a so far unprecedented level of detail how noxious mechanical stimuli are sensed by a respective sensory neuron within a native tissue environment and contributes significantly to our understanding of mechanical nociception. This study is a big step forward in the field, as mechanosensation and the channels involved are notoriously difficult to study. Despite some limitations outlined above, this is a very exciting study providing interesting insight into how mechanical stimuli are sensed by respective sensory/auxiliary channels at the subcellular level and translated into robust neuronal responses and behavior.

Reviewer #3 (Public Review):

Liu et al. analyze the mechanosensory function of the nociceptive c4da neuron of Drosophila larvae by monitoring its calcium response while poking its dendritic tree. Using calibrated force probes, the authors find that the neuron is more sensitive to focal stimulation with sharp probes than to a more global stimulation with blunt probes, and that the receptive field of the neuron covers -and even extends beyond- its dendritic tree. Manipulating the complexity of this tree mainly affects the neuron's sensitivity to focal stimulation, especially when these stimuli are applied distally to the dendritic tree. A model is presented that suggests that neurons expand their receptive fields by monitoring both pressure and lateral tension. Moreover, mutant analysis suggests that whereas the global mechanosensitivity of the neuron requires PPK1/PPK26 channels, Piezo channels mainly mediate responses to focal pressure, with Ca-α1D, the pore-forming subunit of a voltage-gated channel, contributing to dendritic signal propagation/amplification. Relating dendritic tree morphology to mechanosensory function and mechanosensory ion channels, these findings are remarkable. There are, however, some issues that remain to be addressed.

1) Information is missing about the regions of interest in which calcium responses were measured. Judging from Fig. 1E, calcium signals were measured in the somata, and this should be specified. Also judging from this figure, calcium signals seem to be largely confined to the somata and virtually absent from dendritic arbors. Fig. 6a shows very faint signals in the dendrites, yet those signals seem to have been measured rather far from the point of force application (a scale bar is shown but undefined), and, for some unknown reason, not between soma and force application point). Should there be detectable calcium signals in the somata, respective image gains should be adjusted so that those signals can be appreciated by the reader. If there are no clear signals in the dendrites, this would affect interpretations concerning e.g. Ca-α1D.

2) Along this line, analyzing also the spacial distribution of dendritic calcium responses to the pokes would provide a much more detailed picture about how the dendritic tree responds to the various pokes. The beauty of the imaging approach chosen here is that it provides such information. Rather than ignoring this possibility, it should be exploited in this study, especially as respective data might provide much deeper insights into the relation between the mechanosensory function of the cell and its dendritic tree (and bolster the modelling results in Fig. 4 experimentally).

3) When showing response functions as in e.g. Figs. 2C,G,H, 3D, 5C-E, etc., the y-axis should have a logarithmic scaling; receptor potentials of receptor cells usually scale proportionally to the logarithm of the stimulus amplitude. Only then, the reader will be able to fully appreciate the sensitivity differences. This will also alter interpretation of response function slopes.

4) The knockdown and mutant data is interesting, yet important controls are missing. For the RNAi lines used, qPCR data on the knockdown-efficiency should be added. For the channel mutations, available genetic rescue lines should be used as controls. Data on protein localization is presented for the mechanosensitive channels, but not for voltage-gated calcium channel subunit. Should antibodies be available, respective stainings should be included. If not, the authors should at least check whether Ca-α1D is expressed in the cell using e.g. Mi{ET1}Ca-α1D[MB06807] that is available at Bloomington.

5) The statistics used is not entirely convincing. T-test are used throughout, though I do not feel that all the data is distributed normally. Moreover, some figures include multiple comparisons, apparently without statistical correction. The data should be re-analyzed using appropriate statistical procedures.

Reviewer #1 (Public Review):

In this paper, Liu, Kafri and colleagues seek to understand how human RPE1 cells maintain size homeostasis during cell cycle progression. This is a commonly discussed issue in cell biology that has not been resolved. Their primary experimental approach is to partially inhibit CDK2 to slow the cell cycle. Using advanced single cell measurement techniques, they show that this treatment slows the cell cycle during G1 progression, and that the cells first enlarge, but then later continue to divide whilst maintaining a constant size. By tracking cellular protein amounts in single cells, they show that cell growth rates and protein synthesis scale linearly with cell size, as expected, and that normalized growth rate is not different between control and CDK2-inhibited cells (Figs 1, 2). This part of the manuscript, which sets the stage, is robust and convincing. They go on to present data indicating that rates of protein degradation increase in cells that have become enlarged due to long term CDK2 inhibition (Fig 3, 4). This data is interesting, novel, and consistent with their conclusions that cell enlargement enhances protein degradation. However the presentation was unclear in certain aspects, and some obvious experiments are missing. Despite the use of several innovative tests and a number of interesting provocative results, due to the lack of control experiments the data on protein degradation are insufficient to support the author's conclusions to the degree I'd like to see. Another general issue is that, although the authors attribute the increased protein degradation to cell enlargement, they present very little data from experiments in which cells were enlarged using treatments other than CDK2 inhibition. Including examples of enhanced protein degradation after enlarging cells by alternative means is important to support the authors' conclusions (e.g. in Fig 5), which are very general. Providing examples using alternative modes of cell enlargement is also important to rule out the possibility that CDK2 inhibition directly affects protein turnover rates, for instance by altering degradation substrate phosphorylation. This issue is discussed, but not sufficiently resolved.

Reviewer #2 (Public Review):

In this manuscript, Kafri and colleagues assess the contribution of protein degradation to the cell size-dependent accumulation of total protein. This is an interesting line of research that has not previously been explored. Most of the focus on the size-dependence of protein accumulation has been on the synthesis part of the equation. As cells get too big, the efficiency of cell growth (mass accumulation per unit mass) decreases. It is argued that this is not due to the loss of the efficiency in protein synthesis, but rather is due to the increased protein degradation in larger cells. It is an interesting hypothesis, that might well be true, but there are some issues with key aspects of the data and other supporting data are quite indirect. More work needs to be done to support the central claims.

The major issue is that the data supporting the proportional increase in protein synthesis with cell size need to be strengthened. Protein synthesis is measured by the amount of a methionine analog that is incorporated in a fixed amount of time. Fig. 2 then plots this amount as a function of cell size, which is presumably measured using a total protein dye (this information is not included; incidentally the axis labels should note what the measurement is 'total protein' or 'forward scatter' rather than the more ambiguous 'cell size'). In any case, something is wrong with the cell size measurements in Figure 2 because many cells basically have almost negligible size (near 0) while others have sizes up to 5 or 6 arbitrary units. It makes no sense that there should be a 10-fold or even 100-fold range in cell sizes. For this reason, I can't interpret the data in Figure 2, which is unfortunate since that is a crucial figure for the authors' argument.

The data supporting higher rates of protein degradation per unit mass in large cells suffers from a similar problem as Figure 3E has the same issue as Figure 2 with too many tiny 'cells'. Moreover, the reliance on cycloheximide to treat cells and measure reduction in mass isn't ideal since shutting off all protein synthesis is a pretty drastic perturbation. It would have been better to shut off synthesis of a specific protein and measure its degradation in large and small cells while keeping the cells otherwise intact.

Reviewer #3 (Public Review):

The authors report a previously undocumented role for UPS-mediated protein turnover in size control in human cells. The study builds on previous observations made by the Kafri group that large cells undergo size compensation by reducing their rate of growth. In particular, recent published work by Ginzberg et al showed that CDK2 inhibition is accompanied by long term size compensation in the form of reduced cell growth whereas CDK6 inhibition is not. The authors investigate the basis for this effect and demonstrate in both unperturbed and perturbed growth/division contexts, using both fixed cells and time lapse microscopy, that the rate of protein synthesis increases proportionately in large cells that undergo size compensation even though mass accumulation is attenuated. The authors show that this effect appears to be mediated by increased proteasomal activity, as demonstrated by proteasome-dependent K48-ubiquitin chain turnover. Intriguingly, this degradation-mediated size compensation mechanism appears to be most active at the G1/S transition, the primary point at which size control operates. The experiments are well controlled, and the conclusions of the study are in general well supported by the data. The authors present an interesting set of discussion points that relate their observations to size control mechanisms in dividing and non-dividing cells. While specific mechanisms are not pursued, this study nevertheless adds an important new insight into the still unsolved problem of size control.

Reviewer #1 (Public Review):

This is well conducted and interesting study that uses a number of genetic approaches to show that in mice, Muller glia derived retinoic acid signaling favors cone photoreceptors' survival in a mouse model of RP caused by a mutation in a rod specific gene. The relevance of the findings is however over pushed. Survival of a considerable number of photoreceptors is still observed after inactivation of retinoic acid signaling in the retinal periphery, suggesting that other factors might be involved. This is also suggested by the RNA-seq comparison between central and peripheral retinal cells. Therefore, stating that RA signaling is sufficient for cone survival seems an overstatement. The significance of ALDH1A1 expression in the human retinal periphery is also unclear. In the large majority of human RP cases, rod degeneration starts in the retinal periphery and patients are left with tunnel vision, thus it is unclear whether the role of RA signaling in mouse could be of relevance for humans.

Reviewer #2 (Public Review):

This study aims at understanding cellular mechanisms which determine the selective survival of cone photoreceptors located at the periphery of the retina, using mouse models of Retinitis Pigmentosa, a genetic disease leading to photoreceptor death and progressive blindness. Data from this study shows that Retinoic Acid signaling is necessary and sufficient to promote cone survival and that an asymmetry in the expression pattern of its molecular machinery, which predominates at the retinal periphery, is present in the human retina as well.

Because retinoic acid acts through a general mechanism, independent from the mutation causing RP, the newly described pathway for cone protection can be exploited to promote cone survival in Retinitis Pigmentosa bypassing the high genetic heterogeneity of this disease. Retinoic Acid can also be used in many diseases leading to the degeneration of cones. These cells are fundamental to human vision, so that rescuing even a fraction of them would be therapeutically very relevant.

Reviewer #3 (Public Review):

The authors investigated a question that has wondered many vision/ophthalmology scientists, but still has remained unaddressed to date - What could explain long-term survival of peripheral cone photoreceptors (which is strongly biased to dorsal retina in mice) in rod degenerative diseases, mainly Retinitis Pigmentosa (RP)? This has a counterpart in clinical settings as isolated peripheral islands of cones are often resistant for degeneration in RP, even if photoreceptors die in other parts of the retina. The authors set out to address the issue by sorting cones and Muller glia cells in center vs. peripheral retina in a commonly used Rd1 mouse model of RP. Differential expression analysis reveals Aldh1a1, a crucial enzyme in retinoic acid (RA) synthesis, be distinctly upregulated in peripheral compared to central retina. Next, the authors use a RA response element (RARE) reporter mouse line to show that high RA activity and cone survival pattern correspond well during retina degeneration (RD). Next, the authors use a sophisticated set of genetic RA gain-of-function and loss-of-function experiments, by several complementary methods, to study if RA signaling is both sufficient and necessary for cone survival, respectively. These experiments proved the causal link between peripheral cone survival and colocalized RA activity in Rd1 mouse retinas. Finally, the authors compare ALDH1A1 expression level in peripheral vs. central retina in five post mortem human retinas and show it to be prominently higher in the periphery, suggesting that RA signaling may play a role in long-term peripheral cone survival in human RP patients.

The MS is of high interest and could potentially be clinically significant, as many clinical drugs (e.g. isotretinoin, disulfiram) can affect RA signaling. The findings could also lead to novel therapeutic strategies in the treatment of retinal degenerations. The experimental design is excellent and well addresses the questions in place. The MS is relatively concise, well-written and easy to understand. The main issue with MS relates to statistical analysis as authors use parametric analysis without justification. The authors do not state if normal distribution was tested, and if data is skewed, how this was considered in statistics. This issue, however, unlikely affects the main results and conclusions of the MS. Secondly, as the authors had access to precious post mortem human retinas, I am wondering why they settled for a simple quantitative PCR of one target gene. Lastly, the retina research community has collected a comprehensive set of open access retinal transcriptomic database (in GEO), including comparisons of human peripheral vs. central retina. I am wondering why authors did not choose to try correlate their findings with already published data by others.

Reviewer #1 (Public Review):

Using live imaging approaches, the authors first document that developing sense organ precursors (SOPs) - which develop in rows on the fly thorax - initiate divisions in a wave that starts in the middle and proceeds both anteriorly and posteriorly. They also observe that prior to mitosis, SOPs send out extensive filopodial extensions through which they contact neighbouring SOPs. These extensions are withdrawn during mitosis. Based on these observations, the authors develop a mathematical model that invokes an inhibitor and an activator of mitosis: Mitosis only occurs when the concentration of the activator reaches a certain threshold; prior to mitosis, the inhibitor keeps the activator below threshold through cell-cell interactions.

The authors test their model in two ways. First they abrogate cell extensions through mis-expression of dominant negative Rac and observe that the wave of divisions becomes more synchronous, thus demonstrating that cell-cell interactions are necessary for the wave. They next test the molecular mechanism of inhibition. They focus on Sca and D (part of the Notch pathway). A null mutant for sca, as well as double heterozygotes for sca and D also result in synchrony of divisions, suggesting that Sca (through D) is the inhibitor. They observe that Sca is expressed in the filopodial extensions, and that the extensions themselves are not affected in sca mutants. Finally, the authors investigate the implications of wave/synchronous divisions for axon targeting and resultant behaviour and observe that both are affected when the SOP divisions occur simultaneously.

Reviewer #2 (Public Review):

In this manuscript, Lacoste et al. closely examined division timing of sensory organ progenitors (SOP) in Drosophila notum, and found a wave-like propagation of mitoses within each proneural row. They modeled this mitotic wave on the assumption of two hypothetical components, pro-mitotic factor that is produced cell-intrinsically at a constant rate and anti-mitotic signal that is transmitted by the neighboring pre-mitotic SOPs in a cell contact-dependent manner. They showed that the mitotic wave becomes more synchronous when the dominant-negative form Rac1 is expressed in SOPs or when the expression of scabrous implicated in Notch signaling is down-regulated, possibly by reducing the anti-mitotic signal between SOPs. The authors furthermore showed that axon branching patterns from the sensory organ and the organ-mediated behavior in flies are impaired in the scabrous mutant, and hypothesize that these defects originate from changes in differentiation timing of the sensory organ caused by the flattened SOP mitotic wave.

Reviewer #3 (Public Review):

In this manuscript, Lacoste et al., investigate how neuronal diversity arises during the development of the peripheral nervous system. The authors use the Drosophila sensory organ precursors (SOPs), a pool of progenitor cells that give rise to the mechanosensory bristles of the adult fly to explore the spatiotemporal aspects of this process. By combining live imaging, mathematical modelling, genetics and behavioural assays the authors show that the timing of sensory neuron differentiation is controlled by spatially and temporally controlled entry of SOPs into mitosis. This timing is important for axonogenesis and proper spatial arborization, and its perturbation by interfering with the fibrinogen-like protein Scabrous leads to a defective response to tactile bristle stimulation. Overall, this paper provides interesting new insights into how spatial and temporal aspects of neuronal development can shape connectivity.

Reviewer #1 (Public Review):

Pituitary stem cells are a population of low proliferating and differentiating cells in the mature gland. In contrast, during the early post-natal period, they are much more active. In this manuscript "Decoding the activated stem cell phenotype of the vividly maturing neonatal pituitary", Laporte et al investigate the basis of this activation. To this end, they perform single cell transcriptomic analyses. In addition, the effect of manipulation of IL6 and WNT pathways are investigated in vivo and on pituitary organoids. Differential effects are observed between young and adult cells which may explain the proliferative difference between neonate and adult cells, however further investigations are required. Furthermore, based on the single cell transcriptomic analyses, cross-talks between Wnt-secreting and responding stem cells are suggested; these should be further substantiated. The consequences of acute endocrine cell ablation on stem cell regenerative potential are finally examined. These show that regeneration occurs efficiently in the neonate gland. Despite this, further activation of neonatal stem cells is not observed after acute cell ablation and the mechanisms underlaying regeneration remain unknown. In summary, this is the first report of a single cell transcriptomic analysis of the whole neonatal murine pituitary, allowing analysis of the activated stem cell compartment.

Reviewer #1 (Public Review):

The demonstration of single trial adaptation in speech is a very useful addition to the existing literature. It confirms, rather than challenge, existing theories - but it is an important finding nonetheless. The authors also provide a good estimate of the size of the effect. The relationship between the online compensation and adaptation cannot not decisively decide between feedback-command-based and prediction-error based models of adaptation given the somewhat mixed results (different relationship within- and across-subjects). In either case, the analysis does not provide a strong test of these hypothesis, as either outcome would be consistent with a prediction-error (or internal model) based explanation.

Reviewer #2 (Public Review):

This paper reports a re-analysis of data from six previous studies by the same authors, in which patterns of compensation are assessed in response to unexpected perturbations of auditory feedback in speech. The focus is on the relationship between the magnitude of the on-line vocal compensatory response and the characteristics of the acoustical change during the production of an immediately following unperturbed utterance. It is found that participants produce an on-line response which opposes the perturbation; if auditory feedback is perturbed upward in frequency, a relatively short latency shift in the frequency composition of the vocal output is observed in the opposite direction. On the following unperturbed trial, a shift in the frequency of the vocal output is observed that is likewise opposite to the direction of the original perturbation. The authors find that the across subjects there is a small but significant correlation in the magnitude of the initial online compensation response and the so-called one-shot adaptation on the subsequent trial; subjects that show larger compensatory responses also show greater adaptation on the following trial. The authors attribute this pattern to individual differences between subjects. When this same relationship is examined on a trial-to-trial basis no correlation is observed.

The basic idea of assessing whether adaptive responses in speech learning mirror those observed in upper limb movements is appealing. However, there are a number of concerns regarding the present paper. First, the perturbations which are used are unpredictable and hence unlearnable. From work on upper limb movement, it is known that when subjects are presented with unlearnable perturbations, their response is adaptive but different than that observed in response to learnable perturbations. With unpredictable perturbations subjects cocontract to resist limb displacement whereas a directional response is observed when the perturbation is predictable. Although compensation is present here in response to unpredictable perturbations, whether it matches that which occurs in learning is uncertain. It is hard to know whether responses to unpredictable speech perturbations can serve as a model to understand the adaptation that occurs during learning. This would seem important in the present context where the goal is to understand the structure of sequential dependencies in learning.

A further concern is the magnitude of the on-line compensation response and the adaptation response observed in the following movement. While there are statistical differences in the magnitudes of responses to upward and downward shifts in auditory feedback, neither response alone appears to be different than zero, nor are these specific tests reported. It is hard to draw any conclusion from non-zero responses.

The claim that on-line compensation responses and the frequency shifts associated with the subsequent utterance are based on separate mechanisms rests on the absence of a relationship between these variables. However, it is difficult to know what to conclude when a relationship is absent. One might suspect that part of the reason for the null relationship is that all perturbations in the present study were all more or less equal in magnitude. Accordingly, variations in both the compensatory response and the response on the subsequent trial may effectively be noise. A more convincing demonstration might involve the use of perturbations of different magnitudes. One would be more inclined to find the absence of a relationship between the variables of interest more informative if there was no relationship under these conditions.

Reviewer #1 (Public Review):

This manuscript presents foundational studies of voltage sensing of human KCNQ2 channels, a drug target for epilepsy, and how voltage sensing is altered by epilepsy-causing human mutations. The studies probe identify extracellular regions of the KCNQ2 voltage sensor that change conformation upon voltage activation, important information for drug design. The study develops a fluorescence method for measuring a KCNQ2 voltage sensor movement, although it is not clear which subset of voltage sensor movements produce the fluorescence changes. The fluorescence measurements reveal that the KCNQ2 channel operates distinctly from the more thoroughly characterized KCNQ1 channels. The fluorescence measurement techniques were able to establish that a human epilepsy mutation separates voltage sensor movements from pore opening. The study attempts to reconcile the KCNQ2 fluorescence and conductance measurements with a Markov-chain model, but the model is underdeveloped, limiting conclusions that can be drawn from the modeling. Overall the conclusions that a stretch of the S4 becomes exposed upon activation of KCNQ2 channels, that voltage dependence and kinetics of S4 movement and channel opening/closing correlate in wild-type channels , and that different human mutations distinctly alter voltage sensor to pore coupling are justified by the data. This study indeed provides insight into KCNQ2 channel function.

Reviewer #2 (Public Review):

Strengths:<br /> The study by Edmond and colleagues characterized voltage sensor (VSD) movements in KCNQ2 channels, which is an important component of the M-channel that controls neuronal excitability. Cysteine modification accessibility and voltage clamp fluorometry were used to measure VSD movements and the mechanism of how two epilepsy-associated mutations alter KCNQ2 voltage dependent activation. The authors report that the S4 transmembrane segment moved outward to expose a stretch of residues to the extracellular cysteine modifiers during activation, and the movement of VSD is followed closely by pore opening in kinetics and voltage dependence. A kinetic model is proposed to represent the experimental observations. The VSD movements and mechanism of channel gating were reported in KCNQ1 and other Kv channels. However, this is the first time similar studies are reported in KCNQ2. The optical measurements and chemical modification methods used in this study are known to be extremely difficult in the study of KCNQ2 channels. Therefore, this work establishes a detailed mechanism of voltage sensing in KCNQ2 channels for the first time based on a technical achievement.

Weaknesses:<br /> 1. The manuscript seems to claim that the study shows that S4 is the voltage sensor and S4 moves in KCNQ2. This has been repeated in Abstract, Introduction and Results. However, by this time S4 movements as a voltage sensor are well accepted mechanisms. The importance of the work is actually that it defines parameters of the VSD movement in KCNQ2 such as the stretch of S4 in and out of the membrane, and the relationship between VSD activation and pore opening. These points should be brought out as the rationale and significance of this work, rather than the well-known S4 function.<br /> 2. The closeness of fluorescence and current traces and FV and GV curves led to the conclusion that the movement of a single VSD could trigger channel opening. The rationale for connecting the experimental observations to this conclusion needs to be well explained when the conclusion is first made. References that have made similar arguments such as Osteen et al PNAS 2010; Westhoff et al PNAS 2019 should be cited. In addition, as the authors recognized in Discussion, the same observations can also lead to an alternative conclusion such that the movements of four VSDs highly cooperative to all activate and then open the pore. However, this alternative mechanism is not mentioned until at the end of the manuscript, while "the movement of a single VSD opening the pore" is firmly claimed in Abstract and Results. Some justifications need to be provided for this.<br /> 3. An explanation is needed for how same the covalent MTS modification of N190C at two voltages resulted in different GV relations (Fig 1E).<br /> 4. The model in Fig 6F raises several concerns including vertical transitions having the rates of VSD activation and detailed balance is violated.<br /> 5. Discussion. The argument of no intermediate open state based on K/Rb permeability ratio assumes that the pore properties such as ion selection and permeability of KCNQ2 are the same as that of KCNQ1. The evidence for this assumption is not provided or discussed. On the other hand, some evidence suggests that the VSD of KCNQ2 may activate in two steps. For instance, the time course of VSD activation can be fitted with two exponentials, and the fluorescence increases after a plateau at voltages > 0 mV in FV curves (Fig 2C). How these results affect the conclusion should be discussed.

Reviewer #3 (Public Review):

This study by Edmond and colleagues implements methods to characterize voltage sensor movements in neuronal KCNQ channels, test ideas about how these motions are linked to channel gating, and investigate how different epilepsy-linked mutations might alter channel function. The paper is an important step forward in terms of methodological development for measuring voltage sensor conformation in KCNQ2 channels using fluorescence spectroscopy - this has been very difficult and the progress here will help many other groups. The authors also succeed in demonstrating that the relationship between VSD conformation and gating may be altered differentially by different epilepsy-linked mutations. An aspect of the paper that could be improved is in the description of the details underlying generation of a kinetic model that describes both VSD movements (measured by fluorescence signals) and channel gating.

Strengths:

1. This manuscript provides a comprehensive set of observations of conformational changes that underlie voltage sensing in KCNQ2/Kv7.2 channels. The identification and reporting of an approach to measure KCNQ2/Kv7.2 conformation by VCF is a significant step forward and has been a major challenge to many groups studying biophysics of these channels.

2. The study provides concrete evidence for distinct mechanisms in which disease-linked mechanisms can alter KCNQ2/Kv7.2 function (for example, direct disruption of S4 movement in R198Q versus uncoupling of the pore from the voltage sensor in R214W). Ongoing detailed characterization of mutations may allow categorization into different subtypes based on mechanism, which may be relevant to pharmacotherapy. Mutations with certain properties may be sensitive to Kv7 activating drugs, whereas others may not.

Weaknesses:<br /> 1. I am convinced that the fluorescence signals reflect the voltage sensor conformation in the system. The authors focus quite a lot of attention on demonstrating that the fluorescence signals are not an experimental artifact, which is fine. However, I feel the authors could be more cautious in terms of describing how the mutations or dye conjugation may alter some of the gating properties. A place where this may be very important is in the description or characterization of activation kinetics as lacking sigmoidicity, which is part of the argument that these channels may open with only a fraction of voltage sensors activated. This may be correct in the modified (dye-conjugated) channel recordings, but many other recordings of unmodified channels (Figure 1) or WT KCNQ2 or 3 channels exhibit some sigmoidicity. I wonder if this difference may arise because the dye labeling may prevent complete VSD deactivation or interfere with gating in some other way. I would also add that this comment isn't meant to diminish the importance of the findings, I just think it would be wise to qualify some of the description of data with these possible caveats.

2. A brief aside on this point is that a lack of sigmoidicity does not necessarily imply a single transition required for opening - it can also arise if there is a rate-limiting step during a sequence of pre-open transitions.

3. The generation of a quantitative model is a useful application of the data. It was not clear to me whether there was a benefit to using multiple-exponential components to fit the fluorescence signals and generate a more complex model. This may add complexity where it may not be necessary, as it is not clear whether the fluorescence signals require multiple components for an adequate fit.

Reviewer #1 (Public Review):

In the present study, Bachmann and Morel et al., report a comprehensive survey of metabolic phenotypes and liver outcomes (gene expression, complex activities) in a unique subset of genetically diverse mouse strains. The authors focus on sex- and diet-dependent effects where notable differences are observed. The study is particularly appealing in that many gene x sex or gene x diet impacts are described; however, is also highly descriptive in nature.

Broadly, this reviewer's opinion is that the study was carefully designed and that potentially metabolically-relevant information is available, but is not apparent in the present manuscript. The authors successfully highlighted areas of diet- and sex-specific impacts and pointed out several important process while much remains to be described.

Reviewer #2 (Public Review):

The authors compared the 8 mouse strains of the collaborative cross plus DBA, which they have studied extensively as part of their work with the BXD recombinant inbred strain panel. This is a high-altitude view comprising whole-animal phenotypes (glucose tolerance, body weight, RER, exercise tolerance), liver mitochondrial function as assessed by citrate synthase activity and electron transfer complex activities, and liver transcriptomics. They observed strong sex effects, and somewhat weaker strain effects on the phenotypes. Interestingly, there were stronger sex effects on the liver transcriptome than on the physiological phenotypes.

Correlation analysis showed that some of the phenotypes were correlated with sex while others were correlated with strain. Complex I and V activities were negatively correlated with body fat and positively correlated with VO2 max and running distance.

This study, while providing valuable reference information for 9 mouse strains and their response to diet, did not make major mechanistic discoveries, but likely will be followed up with such studies. Many of the correlations have been observed in other mouse studies, including ones from the authors' laboratory.

The provision of the high-volume data in the form of a user-friendly web site is a very useful contribution to the community and may motivate laboratories studying metabolism in mice to make better informed choices of mouse strains for the study of particular phenotypes and genes.

Reviewer #3 (Public Review):

In an effort to disentangle the complexity of obesity in mammals this group has studied a range of metabolic phenotypes in 9 different inbred mouse strains. Importantly, 8 of the 9 strains are the founder strains that were used to construct the Collaborative Cross, an invaluable mouse panel that was constructed a number of years ago to study the genetics of complex phenotypes. This study involves males and females from each strain exposed to either a chow mouse diet or a western diet (WD) for about 13 weeks. The study shows that much like humans, mice respond to high-fat diet in a genetic- and gender dependent manner. For example, some animals put on a lot of weight in response to the WD while others do not. Some develop insulin resistance while others are seemingly protected. In an effort to get to the bottom of these discrepancies an extensive analysis of liver gene expression is undertaken. This shows that western diet feeding is associated with expression of genes involved in immunity and protein translation. Mitochondrial function was also assessed and this was also found to vary between strains. These studies shed light on the importance of genetic background and sex in determining metabolic outcomes.

The Shiny App described in the paper is likely to provide an invaluable resource for the community although it is a pity that all phenotypes were not housed within this resource. These studies add to the growing literature that shows that the metabolic response to diet in mammals is highly complex and they foreshadow how difficult it will be to study this in humans where it is so difficult to control environmental factors by comparison to mouse. While the studies are of high quality and the manuscript is well written, the manuscript lacks a clear and simple message or conclusion. Another major limitation is that the molecular analysis involved studies in liver, a major player in whole body metabolic homeostasis, yet there were no specific liver metabolic phenotypes that enabled a solid correlative analysis of these data.

Reviewer #1 (Public Review):

The authors use ribosome profiling (RiboSeq) and RNA sequencing (RNASeq) to characterise the transcriptome and translatome of two PRRSV species as well as the host in response to infection. One particularly exciting feature of the study is that the analysis is carried out at different times of infection, which shows how both the virus and the host regulate their gene expression. The authors identify several new regulatory mechanisms of virus gene expression. Unexpectedly, they also find that the frameshifting efficiency at the ORF1ab frameshifting site changes with time. This contradicts the dogma in the field, which states that frameshifting is constant and has evolved to be constant to produce the a particular ratio of the two protein isoforms. The strength of the paper is in its comprehensible analysis. The paper is extremely rich in data, with 12 main and 23 Supplemental Figs and 11 Supplemental Tables, all of them rather complex. The main weakness is that it is written in a technical language that will be hardly readable by a non-specialist readership. Unfortunately, the authors do not make a good job in guiding the reader through their findings and hardly identify the the most important findings, while leaving the details to the specialists. This is particularly exemplified in Fig. 12, which should present the summary of the findings and would be extremely helpful, but hardly provides any text at all. This is potentially a very interesting paper, but the impact on the field could be increased considerably by better presentation of the work.

Reviewer #2 (Public Review):

The authors used the ribosome profiling technique to study gene expression at transcriptional and translational levels in the cells infected with porcine reproductive and respiratory syndrome virus (PRRSV-1 and PRRSV-2) using ribosome profiling. The ribosome profiling was carried out on the cells at different time points within the first 12 hours of infection, thus providing information on gene expression changes during the time of infection.

The analysis of ribosome profiling data is exceptionally detailed and includes scrupulous characterization of footprint read lengths, de novo prediction of translated ORFs, characterisation of local pauses and differential gene expression of host and viral genes. The RNA-seq analysis is on par with that, the authors did a superb job at characterising the composition of the viral transcriptome that included identification of heteroclite RNAs and defective interfering RNAs. This provided the authors with reliable information for the interpretation of translational mechanisms responsible for the translation of ORFs discovered with ribosome profiling data.

A specific focus of the manuscript was placed on the characterisation of two instances of ribosomal frameshifting occurring in PRRSVs. In addition to "canonical" -1 frameshifting at a slippery sequence stimulated by downstream RNA secondary structure (common to many viruses), PRRSVs genome contains an additional frameshifting site whose efficiency is stimulated by a viral protein. The authors demonstrated that the efficiency of this frameshifting is increasing over time which is expected since the concentration of stimulating protein is increasing. Furthermore, the authors found that the efficiency of "canonical" frameshifting is also changed. The authors describe this as surprising since it directly contradicts the common description of its function as "setting the fixed ratio" between the synthesized products upstream and downstream of the frameshift site. Perhaps it is not so surprising in the hindsight, given that the frameshifting is dependent on so many different factors, folding states of RNA pseudoknots which are dynamic, ribosome density upstream, etc. it would be more surprising if the efficiency of frameshifting were indeed fixed. I think the "fixed ratio" was proposed mainly to draw a difference to ribosomal frameshifting occurring in cellular genes (like antizyme or bacterial release factor 2) where there seems to be only one functional product, but its synthesis level depends on the efficiency of frameshifting sensing certain conditions. It is great though that the authors observed such changes and I agree with the authors' speculations that this is unlikely to be unique to PRRSVs.

While I found the work to be largely descriptive, the authors did not shy away from speculating about potential mechanisms responsible for observed regulation. The manuscript is hard to get through simply due to its large length and a lot of data, but reading it is rewarding.

Reviewer #3 (Public Review):

The manuscript by Cook et al. describes the first comprehensive gene expression analysis of two species of PRRSV, an important agricultural pathogen. Using ribosome profiling and RNA-sequencing, the authors systematically analyze the transcriptome of the virus and its translation, and their temporal kinetics. The analysis revealed non-canonical RNA species that are suggested to contribute to translation of parts of ORF1ab, changing the stoichiometry between the NSPs. In addition, the authors use the ribosome profiling data to identify novel overlapping ORFs, including a conserved uORF in the 5' leader, and to analyze the efficiency of frame-shift in two sites in the viral genome, one of which is trans-regulated by the viral nsp1β. The frame-shift efficiency in both sites is presented to be increasing late in infection. The authors also present conservation analysis from hundreds of available genomes. Finally, analysis of host gene expression uncovers a pattern suggesting translation inhibition of induced transcripts, and by comparing a WT virus to a mutant virus lacking the nsp2 site frame-shift, the authors identify a gene (TXNIP) whose expression is affected by nsp2TF.

In this rigorous work, the authors uncover new insights on an important pathogen, which can be of value to the wider field of virology. However, due to technical issues a few of the authors claims may require reconsideration.

Reviewer #1 (Public Review):

In this paper, the authors examine the role of feedback from primary visual cortex (V1) to the dorsolateral geniculate nucleus of the thalamus (dLGN) under a variety of visual stimulus conditions. This is a well-defined circuit originating from a specific population of Layer 6 cells in the cortex, and the authors test the role of this projection by recording in dLGN during silencing of V1 via ChR2 expression in PV inhibitory cells. This is a well-established technique for strong silencing of cortex. However, because there are other disynaptic pathways from V1 to thalamus, they also perform a similar set of experiments using more targeted optogenetic inhibition of a genetically-defined class of Layer 6 (NTSR1) cells that make up most of the L6 corticothalamic projections. The fact that these experiments elicit similar results supports their interpretation that these direct projections are largely responsible for the observed results. While previous studies have manipulated corticothalamic projections pharmacologically, via V1 lesions, or via optogenetics, the authors rightly point out that most previous studies have focused on simple parametric stimuli and/or have been performed in anesthetized animals. The results of this study suggest feedback during natural visual stimuli and locomotion reveal effects that are distinct from these previous studies.

Overall, these are important and carefully-performed experiments that significantly advance our understanding of the role of corticothalamic feedback to the dLGN.

The authors suggestion that the different effects observed during simple and complex stimuli may be due to increased surround suppression during the full-field gratings seems reasonable, but I didn't understand how the analysis of blank periods during these two conditions supported this argument. It wasn't clear to me what mechanisms would be expected to support the alternative outcome, where suppressing feedback during the blank periods interleaved with the two different stimuli would have different effects - unless they are testing whether natural movies elicit some longer-lasting state change that would change the results observed during blank periods. This seems somewhat implausible, and unless the authors wish to expand the study to include different stimulus sizes, I think the interpretation regarding surround suppression is best left to the discussion, where it is already treated well.

The paper would benefit from more clearly highlighting results that agree or disagree with previous studies, with a brief mention of how the authors interpret these similarities or differences. For example the results of Olsen et al 2012 seem to be consistent with what the authors observe here with gratings but not with natural movies, and although Olsen et al performed some awake recordings, I think the LGN recordings were all under anesthesia. Specifically highlighting these differences (and suggesting an interpretation for them) would help emphasize the novelty of the study.

The authors should comment more on the spatial extent of V1 silencing and potential effects of the variability observed across mice, especially given that they appear to have made only a single injection of ChR2 to label PV cells. While silencing with this method extends beyond the injection site, it probably doesn't cover all of V1. Was any analysis done of variability across mice based on the size or location of the ChR2 expression measured post-hoc?

The decrease in reliability and sparseness during running is attributed partially to increased eye movements. In cortex this has been studied in awake animals with natural movies in a variety of studies where the opposite effects are observed including Froudarakis et al 2014 where there was a small increase in both metrics during running, and Reimer et al 2014 where reliability strongly increased during pupil dilation. If there is enough data to condition on running periods where eye movements are stable or dilation outside of running to measure the effects of feedback suppression during these periods, this would be useful information.

Reviewer #2 (Public Review):

Spacek et al. study the corticothalamic feedback of different visual stimuli on visual thalamus. With optogenetic suppression of visual cortex feedback and simultaneous multi-channel recordings in visual thalamus, the authors succeeded to acquire important data about this essential feedback loop in awake, behaving animals. The authors show in detail that the cortical feedback acts as a gain factor in thalamus for the transmission of signals from retina to cortex. They also show that naturalistic scenes result in robust feedback from cortex. As expected from anatomy, the authors find that modulatory feedback from cortex and modulatory input from brain stem act rather independently on thalamus. The paper is technically very impressive and the results are important for a wide range of readers.

It is advisable to revise the Introduction and Discussion to better integrate the new findings into the existing literature.

The authors distinguish between awake, resting state and running state. However, the awake, resting state in mice comprises a wide range of alertness levels. This range of alertness will most likely affect the bursting probability of thalamocortical neurons.

Reviewer #1 (Public Review):

This study establishes fundamental information on the mouse iris and its function. Using single nucleus RNA sequencing, the authors have characterized all major cell types in the mouse iris and ciliary body, defined two types of iris stromal cells and two types of iris sphincter cells, and shown cell-specific transcriptome responses in the resting, constricted, and dilated states. They have identified and validated antibody and in situ hybridization probes for visualization of the major iris cell types. They have quantified distortions in nuclear morphology associated with iris dilation and clarified the neural crest contribution to the iris by showing that Wnt1-Cre-expressing progenitors contribute to nearly all iris cell types, whereas Sox10-Cre expressing progenitors contribute only to stromal cells. This work will be a valuable reference for investigations of iris development, disease, and pharmacology, for the isolation and propagation of defined iris cell types, and for iris cell engineering and transplantation.

This paper was a pleasure to read. It is well written, thorough, and will provide tools to study the iris and ciliary body for the research community. I had no major concerns.

Reviewer #2 (Public Review):

Major strengths of the manuscript:

1) Using single nucleus RNA sequencing technology had several advantages over single cell RNA sequencing with minimum disturbance of the native transcriptional profiles.

2) This research revealed major cell types in the mouse iris and provided valuable and verifiable markers for each of the iris cell types. This research generated great resources for future studies on normal and diseased irises.

3) The study showed very interesting changes in the transcriptome and nuclear morphology associated with iris dilation, and the most upregulated genes identified could be great candidates for studying iris function and malfunction in diseases.

4) The study provided definitive experimental proof showing the neural crest contribution to the various iris cell types.

Overall, the study was well designed and precisely executed, the data analysis was clear and scientifically stringent, the results are comprehensive and revealing novel molecular correlates of cellular responses.

Reviewer #3 (Public Review):

This work defines the mouse iris transcriptomic atlas by single-nucleus RNA-seq (snRNA-seq), an approach that captures nuclear transcripts without enzymatic cell dissociation and processing. The major cell types defined/revealed are independently and rigorously validated by immunofluorescence and fluorescence in-situ hybridization. Immunofluorescence and fluorescence in-situ hybridization experiments further confirmed distinction between sphincter and dilator muscles and revealed distinct distribution of subtypes of sphincter and stromal cells. More importantly, the snRNA-seq approach they have undertaken, though only capturing the nuclear transcripts, is sufficient to profile the transcriptomic changes during constriction and dilation, and some of the expression changes were confirmed by immunofluorescence. The identification of transcription factors associated with defined cell types also allows tests of an unexplored question- does nuclear morphology change along with known changes in the the cell plasma during dilation? The authors assessed the nuclear morphology of each cell type by immunofluorescence of cell-type specific transcription factors they identified from snRNA-seq in this study, and found cell-type specific changes of nuclear morphology during dilation. Finally, the authors revisited a partially conflicting result on the neural crest cells contribution to iris cell types, with characterized transcription factors in this study to increase resolution. Overall, this is a rigorous study and could have broad interests. This version of manuscript could benefit from more details in statistics and methodology in some analyses. Despite the insufficient technical/statistical details in some figures, the authors' major claims and the identified sub-celltypes are justified by their data.

Reviewer #1 (Public Review):

The goal of this Tools and Resources article was to present a new method for optogenetic stimulation and optical imaging at the same time in two different cortical layers in vivo, and through 3 sets of experiments, highlight the promise and wide applicability of this method.

The method itself presents an elegant solution to several outstanding drawbacks among the many recent innovations in these lines of methodology, including high expense, lack of specificity and excessive brain tissue damage. The paper provides what I believe to be a fair account of the capabilities and limitations of existing methods and a clear description of how the new method builds on and overcomes these.

The three sets of experiments work well because they demonstrate reliability and feasibility in replicating previous findings from older techniques such as the phenonmenon of 'backpropagation-activated calcium spike firing' and net inhibitory influence of layer 2/3 cells on layer 5 cells, while also extending beyond those findings by verifying that some effects generalise to other areas than previous observations - the layer 2/3-5 interaction previously seen in primary somatosensory is here extended to motor cortex - and uncovering interesting phenomena that are relatively unexplored to date - the great variability in the degree of mirroring of activity in two layers receiving axonal input from the same thalamic area.

The method presents exciting possibilities for the fine-grained study of cortical microcircuits and how they enable perception and cognition and relate to behaviour. The simplicity and low cost of the solution opens it up to a wider range of laboratories globally, and its low-profile imprint on the cortex ensures that it most likely reflects activity of normal, intact, rather than damaged, cortical tissue.

Reviewer #1 (Public Review):

In this manuscript, the authors describe a generative model-based framework to better analyze stochastic growth data, including bacterial cell growth. They show how this framework can be applied to gain insight into the processes underlying these phenomena. More specifically, they start by showing how binning along different axes in the

This work is well-supported by simulations and data analysis and will likely be of interest to those trying to understand the processes governing bacterial growth, as well as those studying stochastic growth processes in biology more broadly.

Strengths:

- The choice and execution of the simulations were sensible and well-done, respectively, and they provided clarity as to the overall message of the manuscript.

- The conclusions are well-supported by the data.

- I found the writing to be clear throughout.

Weaknesses:

- It would be good to have a more extensive discussion about what is specifically new here. This is not my particular field, so having a bit more of an introduction about methods beyond binning (if any) that have emerged to understand these data.

Reviewer #2 (Public Review):

The manuscript by Kar et al. uses single-cell experiments, simulations, and theory to investigate a common method of determining whether cell size grows exponentially. Specifically, they show that a relationship that should adhere to y = x for exponential growth on average (where x is the product of the division time and mean growth rate, and y is the log of the ratio of the birth and division size), in fact deviates from y = x with noise, because x contains noise that y does not (growth rate noise). This makes exponential growth seem non-exponential. The resolution, they show, is to plot x vs. y instead. Additionally, they show that when plotting x vs. y for linear growth, the relationship is coincidentally very close to x = y. This makes linear growth seem exponential. The resolution, they show, is to plot the instantaneous growth rate vs. the normalized cell age, which will decrease for linear growth and will be constant for exponential growth. Applying this protocol to E. coli size data, they find that the growth rate actually increases weakly with age, indicating somewhat superexponential growth.

Strengths

The insights in this manuscript are highly important for the field to know. The fact that exponential growth can masquerade as non-exponential growth and vice versa means that much confusion likely exists in a field that is already surprisingly complex given how simple its questions are to state. The fact that the authors offer resolutions to these pitfalls means that this work should add clarity to the field and move it forward in a meaningful way.

The conclusions are well supported by the data. For example, in the case of seemingly non-exponential growth, the problem is presented by the experimental data, reproduced by the simulations, and explained by the theory, while the resolution is inspired by the theory, proven by the simulations, and demonstrated by the experimental data.

The manuscript is well written. While it is rooted in careful analysis, it remains understandable to a largely non-quantitative audience.

Weaknesses

The final result (Fig 4) is somewhat disconnected from the majority of the paper that precedes it. Specifically, the authors' procedure that resolves exponential vs. non-exponential growth results in E. coli in alanine being deemed exponential (Fig 2B) only to later be revealed as non-exponential (Fig 4A), albeit weakly. Furthermore, the procedure advertised as distinguishing exponential from linear growth (Fig 3B), when applied to the data, reveals neither (Fig 4). This makes the main point of the paper (the demonstration and resolution of pitfalls) feel disconnected from its application to a particular case, which is more nuanced and likely leaves many questions unanswered.

The title ("To bin or not to bin...") implies that binning is the main culprit behind potentially misleading analysis, but I would argue that in the end, it is linear regression. Each of the two main pitfalls and their resolution would be unchanged if the data were never binned, I believe. Binning affects the apparent curvature of the y vs x relationship, but this reads as a more minor point. Therefore, the title may be a bit misleading in service of its poeticism.

Reviewer #3 (Public Review):

Kar et al. examine an interesting and important question of how to make sense of large sets of observational data, specifically cell length data, which may or may not be consistent with various underlying biological mechanisms. As datasets improve in their technical quality (increasing spatiotemporal resolution, increasing numbers of observations), there is hope that the community will be able to resolve differences between underlying cell biological mechanisms of cell size homeostasis. As the authors point out, these interpretations and analyses require statistical analysis that can accurately perform the model selection or parameter estimation task of interest.

1) The authors succeed in bringing attention to the issue of appropriate binning when analyzing large datasets. The authors focus their figures and discussion on an important, and practical issue, as many researchers perform linear regression on binned data. The title and framing of the manuscript imply that it will provide a comparison with statistical methods that do not involve binning. The authors look at different choices of binning dimensions, but do not sufficiently explore the power of their generative model to perform (un)weighted regression or parameter estimation from the not-binned data. They do explore the unbinned data from an analytical statistics approach in section 5.4.1 and 5.5 but this not yet extensively explored in the figures and/or discussion.

2) The authors succeed in walking the reader through the power of examining a specific mechanism/model and the statistical properties of that model. In this case, the authors do this with both a model of cell length homeostasis that comes from exponential growth or linear growth with homeostatic feedback. This approach rests heavily on previous work from the same group including a reframing of the statistical correlations between different observables that was explored in the included references [13] and [16]. This reframing is complemented by additional experiments and reanalysis of various published experimental datasets.

Reviewer #1 (Public Review):

Kiparaki et al. extend the Baker lab's prior work, which showed induction Xrp1 in sub-optimal cells that are heterozygous for mutation in a ribosomal protein (Rp+/-). Rp+/- cells are eliminated by wild-type neighbors in the Drosophila wing imaginal disc epithelium. The Baker lab previously identified a mutation in rpS12 that renders cells resistant to Rp-dependent cell competition. In the current work, they show that Xrp1 mediates the reduced translation in and cell competition of Rp+/- cells. They demonstrate that Rp+/- have defects in ribosome biogenesis and have protein aggregation. A key advance to the field is their demonstration that Rp+/- cells display cell-autonomous phospho-eiF2aplha via PERK (but not Gcn2) and that eiF2alpha phosphorylation is downstream of induction of Xrp1. However, there appears to be a possible loop or circuit between phospho-eiF2alpha and Xrp1 because depletion of the phosphatase that dephosphorylates eiF2alpha causes cell autonomous induction of Xrp1. Another advance to the field is the observation that knockdown of numerous translation factors also leads Xrp1 induction, eiF2alpha phosphorylation, reduced translation and cell competition. They use genetics to try to separate reduced translation downstream of Xrp1 from cell competition translation downstream of Xrp1, but some additional experiments are needed to support these conclusions. The upregulation of Xrp1 when translation components were depleted dependent could be reduced in the competition resistant background rpS12G97D. They use molecular biology and cell culture to show that Xrp1 is a sequence specific transcription factor. They mutate three Xrp1 sequences in the commonly used anti-oxidant reporter GstD1-GFP and show that expression of this reporter is now greatly diminished. The latter result suggests that the anti-oxidant response observed in Rp/+ cells may result from Xrp1. The conclusions of this paper are moderately supported by data, and these results could be valuable to the field of cell competition.

Reviewer #2 (Public Review):

The authors make excellent use of Drosophila genetics tools in combination with molecular biology techniques to broadly implicate the transcription factor Xrp1 as the effector of loser cell status in cell competition. The authors rigorously demonstrate in multiple Rp (also known as minute in Drosophila) haplo-insufficiencies, that there are very little differences in number of ribosomal subunits large or small. They also painstakingly deplete a variety of different translation factors in mitotic clones to reduce new protein synthesis to show that low translation levels lead to Xrp1 induction. They also corroborate recently published results (Baumgartner et al NCB 2021, Recasens-Alvarez et al NCB 2021) showing that proteotoxic stress, as marked by phosphorylation of eIF2⍺ is elevated in loser cell populations that are heterozygous for Rp. The authors further examine the role of P- eIF2⍺ and convincingly show that while restoring P-eIF2⍺ levels does not eliminate cell death in loser cells, depletion of Xrp1 is sufficient to do so- this data and other support the author's central conclusion that Xrp1 is a common effector of loser status during cell competition.

Overall the study is solid and the data are strong but there remain some technical gaps that would need to be clarified:

1. The primary strength of their paper is in establishing a common 'loser' cell mechanism and but this is dampened by their incomplete analysis of Xrp1 in all the Rp mutants they test in Figure 1, leaving open the possibility that some minutes (such as RpL27A, which has slightly different effects from RpL14) might yet induce competition in a Xrp1-independent way.<br /> 2. The examination of whether Xrp1 localizes to the nucleolus by co-staining with Fibrillarin (Figure 2 supplement 2) is not done at sufficiently high magnification or resolution to support their conclusion that they do not observe nucleolar localization or displacement of Xrp1.<br /> 3. The activation of Ire1 measured by Xbp1-GFP (Figure 4 supplement 1) is not tested in clones, but rather in whole heterozygous discs, which leaves open the possibility that other UPR pathways do respond to minute-mediated competition. This is particularly relevant since the premise of cell competition is based on elimination due to differences in neighbors but survival in otherwise similar conditions.<br /> 4. The authors claim that depletion of Xrp1 blocks cell death in competition induced by all translation factors does not consider their data in Figure 7 supplement 1G which shows substantial Dcp1 staining in clonal populations where eIF5A and Xrp1 are both depleted.<br /> 5. Data in Figure 9F-N showing induction of Gstd1-GFP in unconvincing for reasons similar to point 3 above, in that they do not represent the context of cell competition using clonal analysis. Thus the authors' conclusion that Gstd1-GFP, which was found by Baumgartner et al, NCB 2021 to be elevated in Rp+/- cells, is a target of Xrp1 is unsupported.<br /> 6. This study shares a common weakness in many that examine phospho-eIF2⍺ outside the context of stress : the treatment of P-eIF2⍺ as a constant entity whereas others have demonstrated that it can vary with the circadian rhythm (Karki et al PNAS 2020), amongst other factors. While this is a technical limitation that is difficult to overcome, its acknowledgement is nonetheless warranted in the discussion.

Reviewer #3 (Public Review):

The authors begin by investigating the mechanism by which cells that are heterozygous for mutations in specific ribosomal proteins (rp) are eliminated by cell competition. They first show that ribosome subunit concentrations show modest changes and even these changes are different depending on which rp is mutated. There are hints that mutations in subunits of the large subunit may have different consequences to mutations that affect the small subunit. In these mutants, there is an increase in the levels of intermediates in the ribosomal assembly pathway. What seems to be common in all these cases is that the translation factor eIF2alpha is phosphorylated and this phosphorylation is dependent upon the transcription factor Xrp1 and the PERK kinase which is usually activated by the unfolded protein response. This also seems to result in the accumulation of cytoplasmic aggregates.

The authors then go on to show that eIF2alpha phosphorylation in turn induces Xrp1 and cell competition and that many different disruption of translation all result eventually in increased Xrp1 and cell competition. However, the key element seems to be the upregulation of Xrp1 because reducing eIF2alpha phosphorylation can still cause cell competition provided Xrp1 is upregulated.

The authors also show that Xrp1 functions as a positive regulator of gene expression by binding to a specific motif. They provide good evidence that a key reporter used in previous work that attributed cell competition to oxidative stress can be activated by Xrp1.

This paper significantly advances our knowledge of cell competition. The manuscript has a lot of information and is at times difficult to digest. However, the work is very thorough and all the appropriate controls have been included. It provides important insights into cell competition and the central role of Xrp1.

At one point, the authors interpret their experiments to conclude that "Interrupting the translation cycle activates Xrp1-dependent cell competition independently of diminished translation". I wonder if the experiments really show this. In every instance where competition is shown to occur, there is some degree of reduced translation and Xrp1 is elevated. So it is quite possible that both conditions need to be fulfilled for competition to occur. In some instances, when Xrp1 is reduced (and therefore competition does not occur), then translation levels are no longer diminished with the same perturbation. However, this does not tell us that diminished translation is not necessary when competition does occur (or am I misunderstanding something?). To support the conclusion they make, the authors would need to show a condition where Xrp1 is expressed, translation is not diminished and competition still occurs. (Maybe they showed this and I missed it). If they have not, they need to temper their conclusion.

Despite this issue, I think this is an excellent body of work that enhances our understanding of cell competition.

Reviewer #1 (Public Review):

Maji et al demonstrate co-storage of prolactin (PRL) and galanin (GAL) as functional amyloids in secretory granules of the female rat. In a series of detailed experiments, they show that both hormones promote their aggregation to amyloid. They show that PRL and GAL co-localize in the pituitary and that there is co-fibril formation, forming a new type of hybrid fibril. They further demonstrate that there is a unidirectional cross-seeding of GAL aggregation for PRL seeds, while cross seeding by mixed fibrils does not occur. Molecular dynamic studies show that co-aggregation of PRL and GAL induce the formation of a β-sheet at the protein surface. Overall, more efficient storage of the hormones in secretory granules is demonstrated, as well as faster release, as compared to the homotypic counterparts. Strengths include the rigorous techniques that were used, including biophysical techniques with transmission electron microscopy and the use of molecular dynamics to delineate the mechanism of PRL and GAL interactions at the atomic level. An additional strength is the novel observation of the unidirectional, heterotypic templating competency of PRL fibril seeds for GAL monomers, inducing GAL fibril formation.

Reviewer #2 (Public Review):

Research on peptide hormones released from the Pituitary, including Prolactin, has shown that the hormones are stored as functional amyloids. Furthermore, it is well established that Prolactin and Galanin are co-stored in secretory granules of the anterior pituitary until they are released into the bloodstream. However, the mechanism by which hormones are stored and released remains a mystery. This study describes the co-aggregation and functional heterotypic amyloid formation of Prolactin and neuro-peptide Galanin in secretory granules. This study suggests that the Prolactin and Galanin interact with each other at high specificity and form functional amyloids. These functional amyloids are heterotypic. Moreover, they demonstrated that Prolactin-Galanin amyloids can form surface-induced secondary fibrils on the surfaces of others. Galanin forms secondary fibrils on Prolactin seeds and Prolactin does not form secondary fibrils on Galanin seeds, indicating that this process occurs in a highly regulated manner. Additionally, they analysed the release of hormone monomers from amyloids in vitro. They found that Prolactin-Galanin functional amyloids are released faster than amyloids formed by Prolactin or Galanin homotypic fibrils. To understand the interactions between Prolactin and Galanin at the atomic level, they have also performed molecular dynamics simulations and docking studies.

A high point of the study is the identification of Prolactin's capability to cross-seed Galanin. This causes amyloid fibrils to be formed. However, in contrast, the Galanin failed in cross-seed the Prolactin. It emphasizes the specificity and regulation in functional amyloid formation. Additionally, the understanding of the interactions between Prolactin and Galanin at the atomic level from MD simulations strengthens the findings. The results of this study did not confirm the possibility of heteromeric fibril formation.

In this study, the authors succeeded in achieving their goals and their conclusions were backed up by their results.

Undoubtedly, this work will have a significant impact on the field of endocrinology and protein aggregation. By studying secretory granules of the pituitary gland, researchers have successfully stepped one step closer to understanding peptide hormone synthesis and release.

Reviewer #3 (Public Review):

Maji and coworkers present a tour de force study of the coaggregation of two hormones, prolactin and galanin. Protein aggregation in vivo is much more of a "messy" affair than in the tidy lab of an eppendorph tube and the authors demonstrate an intimate collaboration between these two hormones in the aggregation process. Their work ranges from IHC of tissue slices over experimental biophysics to computational studies, presented in a clear, user-friendly and illustrative manner and overall the conclusions are sound. I found the cartoon diagrams in various figures particularly helpful and of high quality.

Whether their conclusions can be extended to higher-order complexes between multiple hormones (even closer to real life) is the next question to address - but the mind boggles at the number of possibilities to explore.

Reviewer #1 (Public Review):

In this manuscript, the authors find CpGs within 500Kb of a gene that associate with transcript abundance (cis-eQTMs) in children from the HELIX study. There is much to admire about this work. With two notable exceptions, their work is solid and builds/improves on the work that came before it. Their catalogue of eQTMs could be useful to many other researchers that utilize methylation data from whole blood samples in children. Their annotation of eQTMs is well thought out and exhaustive. As this portion of the work is descriptive, most of their methods are appropriate.

Unfortunately, their use of results from a model that does not account for cell-type proportions across samples diminishes the utility and impact of their findings. I believe that their catalog of eQTMs contains a great deal of spurious results that primarily represent the differences in cell-type proportions across samples.

Lastly, the authors postulate that the eQTM gene associations found uniquely in their unadjusted model (in comparison to results from a model that does account for cell type proportion) represent cell-specific associations that are lost when a fully-adjusted model is assumed. To test this hypothesis, the authors appear to repurpose methods that were not intended for the purposes used in this manuscript. The manuscript lacks adequate statistical validation to support their repurposing of the method, as well as the methodological detail needed to peer review it. This section is a distraction from an otherwise worthy manuscript.

Major points<br /> 1. Line 414-475: In this section, the authors are suggesting that CpGs that are significant without adjusting for cell type are due to methylation-expression associations that are found only in one cell type, while association found in the fully adjusted model are associations that are shared across the cell types. I do not agree with this hypothesis, as I do not agree that the confounding that occurs when cell-type proportions are not accounted for would behave in this way. Although restricting their search for eQTMs to only those CpGs proximal to a gene will reduce the number of spurious associations, a great deal of the findings in the authors' unadjusted model likely reflect differences in cell-type proportions across samples alone. The Reinius manuscript, cited in this paper, indicates that gene-proximal CpGs can have methylation patterns that vary across cell types.

2. Line 476-488: Their evidence due to F-statistics is tenuous. The authors do not give enough methodological detail to explain how they're assessing their hypothesis in the results or methods (lines 932-946) sections. The methods they give are difficult to follow. The results in figure S19A are not compelling. The citation in the methods (by Reinius) do not make sense, because Reinius et al did not use F-statistics as a proxy for cell type specificity. The citation that the authors give for this method in the results does not appear to be appropriate for this analysis, either. Jaffe and Irizarry state that a CpG with a high F-statistic indicates that the methylation at that CpG varies across cell type. They suggest removing these CpGs from significant results, or estimating and correcting for cell type proportions, as their presence would be evidence of statistical confounding. The authors of this manuscript indicate that they find higher F-statistics among the eQTMs uniquely found in the unadjusted model, which seems to only strengthen the idea that the unadjusted model is suffering from statistical confounding.

3. The methods used to generate adjusted p-values in this manuscript are not appropriate as they are written. Further, they are nothing like the methods used in the paper cited by the authors. The Bonder paper used permutations to estimate an empirical FDR and cites a publication by Westra et al for their method (below). The Westra paper is a better one to cite, because the methods are more clear. Neither the Bonder nor the Westra paper uses the BH procedure for FDR.

Westra, H.-J. et al. Systematic identification of trans eQTLs as putative drivers of known disease associations. Nat. Genet. 45, 1238-1243 (2013).

Reviewer #3 (Public Review):

Although several DNA methylation-gene expression studies have been carried out in adults, this is the first in children. The importance of this is underlined by the finding that surprisingly few associations are observed in both adults and children. This is a timely study and certain to be important for the interpretation of future omic studies in blood samples obtained from children.

It is unfortunate that the authors chose to base their reporting on associations unadjusted for cell count heterogeneity. They incorrectly claim that associations linked to cell count variation are likely to be cell-type-specific. While possible, it is probably more likely that the association exists entirely due to cell type differences (which tend to be large) with little or no association within any of the cell types (which tend to be much smaller). In the interests of interpretability, it would be better to report only associations obtained after adjusting for cell count variation.

Several enrichments could be related to variation in probe quality across the DNA methylation arrays. For example, enrichment for eQTM CpG sites among those that change with age could simply be due to the fact age and eQTM effects are more likely to be observed for CpG sites with high quality probes than low quality probes. It is more informative to instead ask if eQTM CpG sites are more likely to have increasing rather than decreasing methylation with age. This avoids the probe quality bias since probes with positive associations with age would be expected to have roughly the same quality as those with negative associations with age. There are several other analyses prone to the probe quality bias.

Reviewer #1 (Public Review):

Renaud et al aims at developing a new computational method based on NMF relying on the difference in the fragment size profiles of cfDNA extracted from plasma of healthy individuals and metastatic castration-resistant prostate cancer patients. The manuscript is clear and the drawings very helpful.

Reviewer #2 (Public Review):

Renaud et al performed the fragment length signature analysis for circulating tumor DNA using non-negative factorization (NMF). The authors attempted to demonstrate how NFM accurately inferred the true tumor fragment length distribution based on an NMF component. The sample weights of this component correlated with cfDNA level (r = 0.75). This method could potentially remove the requirement of genetic variations (e.g. somatic mutations or copy number aberrations) for tumor DNA loading estimation. Nonetheless, cancer detection analysis in this study should be interpreted as a supervised algorithm even though NMF is an unsupervised method, because the authors used a support vector machine (SVM, a supervised algorithm) to evaluate the classification power between cancer cases and controls.

Reviewer #3 (Public Review):

The presented approach utilizes fragment size distribution to classify cancer patients. Although the observation that a large part of mutant fragment is smaller than the respective wild type fragments in not novel, it is certainly of value to apply novel methods to multiple cohorts. Biologically, the findings of this study seem to largely support previous insights on cfDNA fragment sizes, but com-pared to other studies leveraging fragmentomics, an important advance presented in this manu-script is the use of an unsupervised approach. Yet the cohorts used in this study design seem to limit the ability to gain a clear clinical statement. Using mCRPC samples two fragment length signa-tures (tumor, non-tumor) were extracted, that correlated with tumor fractions in plasma. Although the authors demonstrate that subsampling experiments revealed that NMF was more robust when less data is available than the ichorCNA algorithm, they lacked to provide convincing data that NMF is more sensitive with respect to tumor fraction. Moreover, when comparing targeted fragment sizes from targeted NGS data with the NMF, the authors did not correct for variants related to clon-al hematopoiesis. With respect to cancer detection their approach did not outperform previous approaches.

Reviewer #1 (Public Review):

In this paper, Fernandes et al. take advantage of synthetic constructs to test how Bicoid (Bcd) activates its downstream target Hunchback (Hb). They explore synthetic constructs containing only Bcd, Bcd and Hb, and Bcd and Zelda binding sites. They use these to develop theoretical models for how Bcd drives Hb in the early embryo. They show that Hb sites alone are insufficient to drive further Hb expression.

The paper's first half focuses on how well the synthetic constructs replicate the in vivo expression of hb. This approach is generally convincing, and the results are interesting. Consistent with previous work, they show that Bcd alone is sufficient to drive an expression profile that is similar to wild-type, but the addition of Hb and Zelda are needed to generate precise and rapid formation of the boundaries. The experimental results are supported by modelling. The model does a nice job of encapsulating the key conclusions and clearly adds value to the analysis.

In the second part of the paper, the authors use their synthetic approach to look at how the Hb boundary alters depending on Bcd dosage. This part asks whether the observed Bcd gradient is the same as the activity gradient of Bcd (i.e. the "active" part of Bcd is not a priori the same as the protein gradient). This is a very interesting problem and good the authors have tried to tackle this. However, the strength of their conclusions need to be substantially tempered as they rely on an overestimation of the Bcd gradient decay length.

Comments:

- My major concern regards the conclusions for the final section on the activity gradient. In the Introduction it is stated: "[the Bcd gradient has] an exponential AP gradient with a decay length of L ~ 20% egg‐length (EL)". While this was the initial estimate (Houchmandzadeh et al. Nature 2002), later measurements by the Gregor lab (see Supplementary Material of Liu et al. PNAS 2013) found that "The mean length constant was reduced to 16.5 {plus minus} 0.7%EL after corrections for EGFP maturation". The original measurements by Houchmandzadeh et al. had issues with background control, that also led to the longer measured decay length. In later work, Durrieu et al. MSB 2018, found a similar scale for the decay length to Liu et al. Looking at Figure 5, a value of 16.5%EL for the decay length is fully consistent with the activity and protein gradients for Bcd being similar. In short, the strength of the conclusions clearly do not match the known gradient and should be substantially toned down.

- All of the experiments are performed in a background with the hb gene present. Does this impact on the readout, as the synthetic lines are essentially competing with the wild-type genes. What controls were done to account for this?

- Further, the activity of the synthetic reporters depend on the location of insertion. Erceg et al. PLoS Genetics 2014 showed that the same synthetic enhancer can have different readout depending on its genomic location. I'm aware that the authors use a landing site that appears to replicate similar hb kinetics, but did they try random insertion or other landing site? In short, how robust are their results to the specific local genome site? This should have been tested, especially given the boldly written conclusions from the work.

- Related to the above, it's also not obvious that readout is linear - i.e. as more binding sites are added, there could be cooperativity between binding domains. This may have been accounted for in the model but it is not clear to me how.

- It would be good in the Introduction/Discussion to give a broader perspective on the advantages and disadvantages of the synthetic approach to study gene regulation. The intro only discusses Tran et al. Yet, there is a strong history of using this approach, which has also helped to reveal some of the approaches shortcoming. E.g. Gertz et al. Nature 2009 and Sharon et al. Nature Biotechnology 2012. Again, I may have missed, but from my reading I cannot see any critical analysis of the pros/cons of the synthetic approach in development. This is necessary to give readers a clearer context.

Reviewer #2 (Public Review):

It is known that Bicoid increases in concentration across the syncytial division cycles, the gradient length scale for Bicoid does not change, and hunchback also increases in concentration during the syncytial cycles but the sharp boundary of the hunchback gradient is constantly seen despite the change in concentration of Bicoid. This manuscript shows that by increasing the Bicoid concentration or by adding Zelda binding sites, the expression of hunchback can be recapitulated to that of a previously studied promoter for hunchback.

I have the following comments to understand the implications of the study in the context of increasing concentrations of Bicoid during the syncytial division cycles:

Bicoid itself is also increasing over the syncytial division cycles, how does this change in concentration of Bicoid affect the activation of the hunchback promoter given the cooperative binding of Bicoid and Bicoid and Zelda as documented by the study?

Does the change in concentration of Bicoid across the nuclear cycles shift the gradient similar to the change in numbers of Bicoid binding sites?

The intensity is a little higher for B9 and B12 at the anterior in 2B? Is this statistically different?, is this likely to change the amount of Bicoid expression at the locus and lead to more robust activation?

Are the fraction of active loci not changing across the syncytial cycles when the concentration of Bicoid also changes and consistent with the synthetic promoters?

How do the numbers of Hb BS change the expression of Hb? H6B6 has 6 Hb BS whereas the Hb-P2 has 1? Are more controls needed to compare these 2 contexts?

Does Zelda concentration change across the syncytial division cycles? How does the change in concentration in the natural context affect the promoter activation of Hb?

Changing the dose of Bicoid shifts the boundary of hunchback expression. It would be nice to model or test this in the context of varing doses of zelda or even reason this with respect to varying doses of zelda across the syncytial division cycles.

Reviewer #3 (Public Review):

I think the framing could be improved to better reflect the contribution of the work. From the abstract, for example, it's unclear to me what the authors think is the most meaningful conclusion. Is it the observations about the finer details of TF regulation (bursting dynamics), the fact that Bcd is probably the sole source of "positional information" for hb-p2, that Bcd exists in active/inactive form, or the fact that an equilibrium model probably suffices to explain what we observe? The first sentence itself seems to suggest this paper will discuss "dynamic positional information", in which case it's somewhat misleading to say this kind of work is "largely unexplored"; Johannes Jaeger in particular has been a strong proponent of this view since at least 2004. On that note some particularly relevant recent papers in the Drosophila early embryo include:<br /> 1) Jaeger and Verd (2020) Curr Topics Dev Biol<br /> 2) Verd et al. (2017) PLoS Comp Biol<br /> 3) Huang, Amourda, et al. and Saunders (2017) eLife<br /> 4) Yang, Zhu, et al. (2020) eLife [see also the second half of Perkins (2021) PLoS Comp Biol for further discussion of that model]<br /> Some reviews from James Briscoe also discuss this perspective.

I would also recommend modifying the title to reflect the biology found in the new results.

A major point that the authors should address is the design of the synthetic constructs. From table S1, the sites are often very closely linked (4-7 base pairs). From the footprint of these proteins, we know they can cover DNA across this size (see, https://pubmed.ncbi.nlm.nih.gov/8620846/). As such, there may be direct competition/steric hindrance (see https://pubmed.ncbi.nlm.nih.gov/28052257/). What impact does this have on their interpretations? Note also that the native enhancer has spaced sites with variable identities.

Reviewer #1 (Public Review):

This manuscript describes the identification of a group of Myf5-expressing cells which can give rise to not only myogenic cells but also non-myogenic connective tissue cells. Single-cell sequencing and trajectory analysis of mouse Mesp1-derived lineage at embryonic stage E10.5 identify a potential cell fate transition from myogenic to non-myogenic cells. This transition and the presence of bipotent Myf5+ cells are further confirmed by lineage tracing experiments in mouse demonstrating the presence of a significant number of Pdgfra-expressing cells in the Myf5 lineage in anterior muscles. Interestingly, these Myf5-derived cells are mainly restricted in anterior regions where the muscle connective tissue is not derived from neural crest but from the mesoderm. Analysis of the Myf5 lineage in extraocular muscle at different embryonic stages reveals a signaling network involving PDGFA and its receptor between myogenic and non-myogenic cell populations.

Overall, this is a very interesting study and the conclusions of the paper are consistent with their presented data.

Reviewer #2 (Public Review):

In this interesting and beautifully illustrated study, the authors are addressing the question of the emergence of craniofacial tissues by dissecting the interplay between skeletal muscle progenitors and associated connective tissue cells. By combining sophisticated lineage-tracing single cell RNA-seq experiments with potent computational analysis tools followed by in situ validations, the authors have identified a population of Myf5+ bipotent progenitors that give rise to both muscle and connective tissue. However, some conclusions are solely based on the RNA-Seq data that would require further experimental validations.

Reviewer #3 (Public Review):

In this manuscript, Grimaldi et al. present evidence for the existence of Myf5+ bipotent progenitors for myogenic and connective lineages in the dorsal regions of the mouse head, which is not populated by neural crest cell-derived connective tissue. The study relies heavily on scRNA-seq dataset obtained from cell populations sorted at defined time points, and refined computational analysis, including trajectory and gene network inference using the established tools RNA velocity and SCENIC, respectively. The proposed model is partially validated by in situ staining experiments, including genetic labeling, which identified Pdgfra+ non-myogenic cells within the Myf5+ lineages, notably in association with extraocular muscles (EOM). The authors propose a myogenic origin for the connective tissue, in regions devoid of neural crest cells, and show that loss of Myf5 function causes an increase in the proportion of Sox9+ cells among Myf5+ lineage cells, which is consistent with a binary fate choice from Myf5+ progenitors. The authors tentatively identify signaling molecules and transcription regulators underlying both fate decisions and cell-cell communications between myogenic and non-myogenic cell populations.

The general message of the study offers a potentially new paradigm to study neural crest cell-independent mesodermal fate decision in the vertebrate head, and is thus poised to augment our understanding of craniofacial development, and potential diseases.

Unfortunately, there are shortcomings that strongly reduce enthusiasm for this manuscript. Strictly speaking, there is no clear demonstration for the existence of bipotent progenitors in the absence of clonal analysis. The study relies excessively on computational analysis of descriptive scRNA-seq datasets, with a general paucity of secondary experimental validation. The manuscript would benefit from a refined focus on the key point, and addition of validation for the initial conclusions, at the expense of somewhat convoluted analyses (e.g. Figs. 6 and 7)

Reviewer #2 (Public Review):

The reported study includes an overall well-conducted and well-presented set of experiments. Ample data are reported and a clear and conclusive picture of the findings is portrayed.

1. The Introduction falls short of providing the background needed for fully appreciating the current findings and their importance. The authors don't present the current understanding regarding the role of 4-vinylanisole in locusts (mostly their own work). Nor do they present the accepted knowledge of the control of sexual maturation in locusts (mostly several decades-old work). Moreover, the importance of reproductive synchrony in the life history of gregarious locusts, including its tentative roles in maintenance of the homogeneity and integrity of the swarm, in ensuring high density conditions for the next generation, and more, is also not adequately presented.

2. Research on pheromonal signaling in locusts have traditionally focused on compounds with a putative role in density-dependent phase-specific behaviors. Hence, it is common to compare the response of crowd-reared vs. solitary locusts to applied chemicals. The challenge, however, is maintaining the density context, while attempting to conduct controlled similar experiments with locusts of the two phases (i.e. keeping the solitary phase locusts isolated, while the gregarious locusts must always be crowded). This is even more challenging when studying reproductive physiology. By the basic nature of the two phases, there can be a multitude of interacting factors (behavioral and/or physiological) affecting the much-desired reproductive synchronization in gregarious locusts, while such synchronization is not expected at all in solitary ones (it may even be claimed to have no fitness-related advantage).

3. In general, the authors of the current report have dealt well with these challenges, taking extra care to conduct multiple controls and making an effort to specifically test all the possible factors. However, there are several points that raise some uncertainties. For example:

o If I am not mistaken, females of both phases were included in the study only if already mated by day A+7 (LL355-357). While this is reasonable for gregarious locusts, it may not be suitable for the solitary locusts, imposing an undesired and unequal selection criterion.

o In the test of the effects of conspecifics interactions, 10 gregarious locusts provided stimulation to the tested gregarious female, while only one insect was the stimulating factor for the solitary female.

o It is not clear how were egg pods attributed to specific gregarious females (maintained in groups of 10)<br /> Overall, since the focus of this study is actually not on the comparison between the phases, it might have been beneficial to the readers if the focus was on the gregarious locusts only, with maybe a couple of experiments conducted on solitary insects and presented separately.

4. Assuming that within a locust group there is overall agreement in the age of males and females, there seem to be a not-fully-explained mismatch between the age of max 4-VA release by males (linearly increasing with age) and the age of max effect in females (critical period at A+3-4)

5. Similar to the introduction, the discussion section also does not present comprehensive arguments regarding the importance of reproductive synchronization in female locusts. Points that could have been discussed include: females' oviposition disrupting migration, synchronization affecting sexual selection, accelerating intra-sex competition over mates as well as oviposition sites, and more.

Reviewer #3 (Public Review):

Strengths: Grouping behavior for marching, sexual maturation, swarming, oviposition and egg hatching in gregarious locusts is complex and it's mediated by a combination of cues-olfactory, tactile, and visual cues to ensure synchronous behavior. The authors show that only olfactory cues released by gregarious adult males mediates maturation synchrony of females. This finding is a confirmatory result of a well-established phenomenon for maturation synchrony in both sexes of adult locusts, although in this study, the authors focused on only females. Further, the authors validated their findings using gene editing techniques to show that maturation synchrony was diffused in Or35-/- mutant adult females but not in wild type females exposed to adult male volatiles and the individual component identified as 4-vinylanisole among five male-abundant volatiles as promoting synchronous sexual maturation in only post adult eclosion females (PAE) 3-4 days old. Use of molecular and single sensillum recordings, followed by physiological experiments focused on the interaction between this specific adult pheromone and juvenile hormone to validate the behavioral results found for females add scientific value to the study.

Weaknesses: Firstly, synchronous and accelerated sexual maturation of young adults by older pheromone-producing ones, is a primer effect driven by males and this facilitates 'integration and cohesion' of both sexes of adults. In my view, the fact that this study focused on only females but not on both sexes, weakens the contribution of the study towards increased understanding of the biology/ecology of locusts. There are also weaknesses in the methods, such as focusing on only the five-abundant male volatiles based on heat maps. Basically, the decision as to which components in adult male volatiles may be contributing to sexual maturation should be made by antennae of different ages of PAE females and males to avoid selecting only abundant compounds based on artificial intelligence (AI). Since most studies in this subject area have demonstrated that there is no direct correlation between volatile abundance and detection at the periphery or central nervous systems of an insect, I believe that the authors will agree with me that often some of the minor volatile components tend to contribute more to the chemical ecology of an insect than the more abundant components. Without testing minor components identified in male volatiles as a blend or individually, as additional controls to increase the robustness of the study, I am not convinced that the authors have fully achieved their aim in identifying a male-produced volatile that promotes sexual maturation in females.

JH experiments - My main concern is the lack of proper controls to fully investigate the interactive effect of the male-produced pheromone promoting sexual maturation and juvenile hormone production. JH titers were not measured in females exposed to the other male-abundant compounds including PAN, guaiacol, veratrole and anisole or blend/individual minor components.<br /> Another notable weakness is the 'JH Rescue Experiment'. The authors did not inhibit JH synthesis in the corpora allata (allalectomized locusts) in treated locusts before injecting the JH-analog methoprene to accelerate maturation and reproduction in females.

Reviewer #1 (Public Review):

In their previous work, the authors had identified an aggregating pheromone produced by gregarious male locusts - 4VA. In this MS, they show that 4VA is sensed by females of all ages, but at a certain age, it induces the females to accelerate oogenesis and sexual maturity, leading to a synchronisation of egg laying. This synchrony constitutes part of the devastating swarming behavioural repertoire that make these insects a major agricultural pest.

Specifically, the manuscript shows that when a female of age 3-4 days (and not younger or older) senses 4VA, her CC-CA organ increases JH synthesis, her haemolymph JH titre increases, and her fat body ovary show increased levels of vitellogenins. The result of all this is the acceleration of sexual maturity and consequent synchrony in oviposition.

Reviewer #2 (Public Review):

The work by Ordabayev et al. details a Bayesian inference-based data analysis method for colocalization single molecule spectroscopy (CoSMoS) experiments used to investigate biochemical and biophysical mechanisms. By using this probabilistic framework, their method is able to quantify the colocalization probabilities for individual molecules while accounting for the uncertainty in individual binding events, and accounting for camera and optical noise and even non-specific binding. The software implementation of this method, called Tapqir, uses a Python-based probabilistic programming language (PPL) called pyro to automate and speed-up the optimization of a variational Bayes approximation to the posterior probability distribution. Overall, Tapqir is a powerful new way to analyze CoSMoS data.

Tapqir works by analyzing small regions (14x14 pixels) of fluorescence microscopy images surrounding previously identified areas of interest (AOI). The collection of images of these AOIs through time are then analyzed collectively using a probabilistic model that accounts for each time frame of each AOI and is able to determine whether up to K "binders" (K=2 here) are present and which of them is specifically bound. This approach of directly modeling the contents of the image data is relatively novel, and few other examples exist. The details of the probabilistic model used incorporate an impressive amount of physical insight (e.g., camera gain) without overparameterization.

The gamma-distributed noise model used in Tapqir captures quite a lot of physics and, given the analyses in Figs. 3-6, clearly works, but might be limited to certain types of cameras used in the fluorescence microscopy (e.g., EMCCDs). For instance, sCMOS cameras have pixel-dependent amplification and noise profiles, rather than a single gain parameter, and are sometimes approximately modeled as normal distributions with both mean and variance having an intensity-dependent and independent contribution that is different for each pixel on the camera. It is unclear how Tapqir performs on different cameras.

The variational Bayes solution used by Tapqir provides many computational benefits, such as numerical tractability using pyro and speed. It is possible that the exact posterior, e.g., as obtained using a Markov chain Monte Carlo method, would be insignificantly different with the amount of data typical for CoSMoS experiments; however, this difference is not explored in the current work.

The intrinsic use of prior probability distributions in any Bayesian inference algorithm is extremely powerful, and in Tapqir offers the opportunity to "chain together" subsequent analyses by using the marginalized posteriors from one experiment as the basis for the priors for subsequent experiments (e.g., in \sigma^{xy}) for extremely high accuracy inference. While the manuscript discusses setting and leveraging the power of priors, it does not explore the power of such "chaining" and the positive effects upon accuracy.

A significant number of CoSMoS experiments use multiple, distinct color fluorophores to probe the colocalization of different species to the target. The current work focuses only upon analyzing data with a single color-channel. Extensions to multiple independent wavelengths are computationally trivial, given the automated variational inference ability of PPLs such as pyro, and would increase the impact of the work in the field.

Tapqir analysis provides time series of the probability of a specific binding event, p(specific), for each target analyzed (c.f., Fig. 5B), and kinetic parameters are extracted from these time series using secondary analyses that are distinct from Tapqir itself.

The method reported here is well designed, sound, and its utility is well supported by the analyses of simulated and experimental data sets reported here. Tapqir is a cutting-edge image analysis approach, and its proper treatment of the uncertainty inherent to CoSMoS experiments will certainly make an impact upon the analysis of CoSMoS data. However, many of the (necessary) assumptions about the data (e.g., fluorescence microscopy) and desired information (e.g., off-target vs on-target binding) are quite specific to CoSMoS experiments and therefore limit the direct applicability of Tapqir for the analysis of other single-molecule microscopy techniques. With that in mind, the direct Bayesian inference-based analysis of image data, as opposed to integrated time series, as demonstrated here is very powerful, and may encourage and inspire related methods to be developed.

Reviewer #1 (Public Review):

"Bayesian machine learning analysis of single-molecule fluorescence colocalization images" by Ordabayev, et al. reports the development, benchmarking, and testing of a Bayesian machine learning-based method, which the authors name Tapqir, for analyzing single-molecule fluorescence colocalization data. Unlike currently available, more conventional analysis methods, Tapqir attempts to holistically model the microscopy images that are recorded during a colocalization experiment. Tapir uses a physics-based, global model with parameters describing all of the features of the experiment that are expected to contribute to the recorded microscopy images, including shot noise of the spots and background, camera noise, size and shape of the spots, and specific- and non-specific binders. Based on benchmarking on simulated data with widely varying properties (e.g., signal-to-noise; amounts, rates, and locations of specific and non-specific binders; etc.), Tapqir generally does as well and, in some cases, better than currently existing methods. The authors also test Tapqir on real microscopy images with similarly varying properties from studies that have been previously published by their research group and demonstrate that their Tapqir-based analysis is able to faithfully reproduce the previously published results, which were obtained using the more conventional analysis methods available at the time the data were originally published. This is a well-designed and executed study, Tapqir represents a conceptual and practical advance in the analysis of single-molecule fluorescence colocalization experiments, and its performance has been comprehensively and rigorously benchmarked on simulated data and tested on real data. The conclusions of this study are well supported by the data, but some of the limitations of the method need to be clarified and discussed in more depth, as outlined below.

1. Given that the AOI is centered at the target molecule and there is a strong prior for the binder also being located at the center of the AOI, the performance of Tapqir is dependent on several variables of the microscopy/optical system (e.g., the microscope point-spread function, magnification, accurate alignment of target and binder imaging channels, accurate drift correction, etc.). Although this caveat is mentioned and some of these factors are listed in the main text of the manuscript, the authors could have expanded this discussion in order to clarify the extent to which the performance of Tapqir depends on these factors.

2. The Tapqir model has many parameters, each with its own prior. The majority of these priors are designed to be uninformative and/or weak and the only very strong prior is the probability that a specific binder is located at or very near the center of the AOI. The authors could have tested and commented on how the strength of the prior on the location of a specific binder affects the performance of Tapqir.

3. Given the priors and variational parameters they report, the authors show that Tapqir performs robustly and seems to require no experiment-to-experiment optimization. This is expected to be the case for the simulated data, since they were simulated using the same model that Tapqir uses to perform the analysis. With regard to the real data, however, it is quite likely that this is due to the fact that the analyzed data all come from the same laboratory and, therefore, likely the same microscope(s). It would have therefore been very useful if the authors would have listed and discussed which microscope settings, experimental conditions, and/or other considerations, beyond those described in point 1 above, would result in a need for re-optimization of the priors and/or variational parameters.

4. Based on analysis of the simulated data shown in Figure 5, where the ground truth is known, the use of Tapqir to infer kinetics is less accurate that the use of Tapqir to infer equilibrium binding constants. The authors do a great job of discussing possible reasons for this. In the case of the real data analyzed in Figure 6 and in Figure 6 - Figure Supplements 1 and 2, the kinetic results obtained using Tapqir have different means and generally larger error bars than those obtained using Spot-Picker. To more comprehensively assess the performance of Tapqir versus Spot-Picker, the authors could have used the association and dissociation rates to calculate the corresponding equilibrium binding constants and then compared these kinetically calculated equilibrium binding constants to the population-calculated equilibrium binding constants that the authors calculate and report in the bottom plot in Panel D of Figure 6 and Figure 6 - Figure Supplements 1 and 2. This would provide some information on the accuracy of the kinetics in that the closer the kinetically and population-calculated equilibrium binding constants are to each other, the more accurately the kinetics have been estimated. Performing this type of analysis for the kinetics obtained using Tapqir and Spot-Picker would have allowed a more comprehensive comparison of the two methods.

Reviewer #3 (Public Review):

In this manuscript, the authors seek to improve the reproducibility and eliminate sources of bias in the analysis of single molecule colocalization fluorescence data. These types of data (i.e., CoSMoS data) have been obtained from a number of diverse biological systems and represent unique challenges for data analysis in comparison with smFRET. A key source of bias is what constitutes a binding event and if those events are colocalized or not with a surface-tethered molecule of interest. To solve these issues, the authors propose a Bayesian-based method in which each image is analyzed individually and locally around areas of interest (AOIs) identified from the surface tethered molecules. A strength of the research is that the approach eliminates many sources of bias (i.e., thresholding) in analysis, models realistic image features (noise), can be automated and carried out by novice users "hands-free", and returns a probability score for each event. The performance of the method is superb under a number of conditions and with varying levels of signal-to-noise. The analysis on a GPU is fairly quick-overnight-in comparison with by-hand analysis of the traces which can take days or longer. Tapqir has the potential to be the go-to software package for analysis of single molecule colocalization data.

The weaknesses of this work involve concerns about the approach and its usefulness to the single-molecule community at large as wells as a lack of information about how users implement and use the Tapqir software. For the first item, there are a number of common scenarios encountered in colocalization analysis that may exclude use of Tapqir including use of CMOS rather than EM-CCD cameras, significant numbers of tethered molecules on the surface that are dark/non-fluorescent, a high density/overlapping of AOIs, and cases where event intensity information is critical (i.e., FRET detection or sequential binding and simultaneous occupancy of multiple fluorescent molecules at the same AOI). In its current form, the use of Tapqir may be limited to only certain scenarios with data acquired by certain types of instruments.

Second, for adoption by non-expert users information is missing in the main text about practical aspects of using the Tapqir software including a description of inputs/outputs, the GUI (I believe Taqpir runs at the command line but the output is in a GUI), and if Tapqir integrates the kinetic modeling or not. Given that a competing approach has already been published by the Grunwald lab, it would be useful to compare these methods directly in both their accuracy, usefulness of the outputs, and calculation times. Along these lines, the utility of calculating event probability statistics (Fig. 6A) is not well fleshed-out. This is a key distinguishing feature between Tapqir and methods previously published by Grunwald et al. In the case of Tapqir, the probability outputs are not used to their fullest in the determination of kinetic parameters. Rather a subjective probability threshold is chosen for what events to include. This may introduce bias and degrade the objective Tapqir pipeline used to identify these same events.

Finally, the manuscript could be improved by clearly distinguishing between the fundamental approach of Bayesian image analysis from the Tapqir software that would be used to carry this out. A section devoted to describing the Tapqir interface and the inputs/outputs would be valuable. In the manuscript's current form, the lack of information on the interface along with the potential requirement for a GPU and need for the use of a relatively new programming language (Pyro) may hamper adoption and interest in colocalization methods by general audiences.

Reviewer #1 (Public Review):

In this paper, the authors explore the potential use of next generation CRISPR base editors in zebrafish and medaka embryos. They conduct extensive testing in F0 (injected) animals on existing loci and show impressive and overall precise gene editing that can be bi-allelic. The outcomes are provocative, and the summary figure makes a strong case on the overall potential for this approach to help understand the genetic basis underlying vertebrate genomic sequence changes such as human VUSes.

This well-presented work is balanced by some key technical questions.

First, base editors are well-described to cause double-stranded DNA breaks as an unintended consequence of their obligate 'nickase' enzymatic function. Prior work in the rapidly dividing zebrafish embryo has shown that single-stranded DNA nicks nearly immediately become DS DNA breaks during the rapid DNA replication phase. The authors do not address this issue at all, and instead suggest there are no DS DNA break off-target effects.

Second, in addition to the potential for DS DNA break off-target effects, there could be unanticipated other pathways being activated in F0 'editants' confounding their analyses. This is well-described for a number of genomic engineering tools, from RNAi, siRNA, shRNA, 'crispants' and 'morphants'. The presence of at least one such pathway was readily detected using differential transcriptomic work. I do not see any such approach in this manuscript.

Finally, the reason to ask any team to go through germline is to explore the area of 'what we do not know we do not know.' We've had false starts (i.e. from early 'crispant' work) where the promise that F1-incross data (F2 animals) simply do not reflect what was seen in the F0 work.

Reviewer #2 (Public Review):

The manuscript describe a novel and user friendly on live tool to design sgRNA oligos to use Base editors in zebrafish and Medaka and easily adaptable to other model organisms. The experimental part offers some examples of the validity of their approach using in both fish species various state-of -the-art base editors including some never tested before in fish. They demonstrate that high efficient and specific base conversions can be achieved in F0 injected embryos allowing the rapid assessment of the phenotype linked to the mutation. Examples with both Adenine and Citosine base editors are presented allowing to phenocopy known mutant phenotypes as well as to test missense mutations in novel candidate genes for congenital heart disease. Overall this work nicely illustrate the power of bese editors as highly efficient and specific tools to generate precise point mutations alleles in fish. It illustrates the power of this approach for human disease modelling in these animal models supporting their work flow approach with a set of nice examples that allow the authors to experimentally define the optimal editing window for the different base editors.

Reviewer #3 (Public Review):

This is an important study that comprehensively compares the activities of different base editors in both medaka and zebrafish. The authors also provide a web tool for experimental design allowing approximately 30% of known human disease associated nucleotide variants to be modeled in fish with validated editors within days following injection. While other studies have shown similar activities in zebrafish, the authors nicely demonstrate the ability to generate phenotypes using different base editors in both zebrafish and medaka that correlate with specific base changes. The manuscript is nicely put together, and the data presented support their conclusions.

Some of the most impressive data presented clearly demonstrates a high degree of precise mutagenesis in the F0 generation and the ability to generate specific phenotypes. This will greatly enhance the ability to test whether single nucleotide variants from human disease association studies have an impact on health. This coupled with the ability to design gRNAs efficiently with a web interface will likely have a lasting impact on the field.

Reviewer #1 (Public Review):

Strengths and Accomplishments:

1) This study tests an exciting potential intervention for sarcopenia, is well supported by prior literature investigating the effects of AdipoRon in age-related metabolic diseases, and now extends these data into aging.<br /> 2) The study uses a diversity of techniques and systems (in vitro, in vivo, and ex vivo, chronic and acute treatments, young and old mice) to investigate the effects and relevant mechanisms of AdipoRon from the level of the whole organism into the muscle fibers and further into cellular signaling pathways.<br /> 3) Similar cellular findings across species and cell types argues for strong conservation of the downstream effects of AdipoRon.<br /> 4) This study provides coherent and conserved downstream molecular mechanisms (e.g. PGC-1a) and physiological changes (fiber types, mitochondrial function, insulin sensitivity) that should be readily translatable into mechanistically-designed non-human primate and human clinical studies.<br /> 5) The presentation is well organized and logical, showing the effects of chronic AdipoRon treatment in old and then young male mice, followed by acute treatment in young mice and cells, moving from clinical to physiological to cellular/molecular findings.

Weaknesses and Limitations:

1) The key mouse studies are underpowered, resulting in inconclusive rotarod data in the aged group and no behavioral testing in the young group. There is no other whole-organism functional data to support the clinical relevance of the ex vivo and postmortem physiological and molecular findings.<br /> 2) The in vitro cellular use fibroblasts and immune cells, which supports an argument for broad conservation of AdipoRon mechanisms but does not directly support the primary muscle physiological findings.<br /> 3) Using different strains for young and old mice limits the interpretation of young vs old differences, which could be due to strain differences instead.

Reviewer #2 (Public Review):

This is a straightforward study, demonstrating utility of an agonist targeting energy metabolism pathways in aging mouse muscle. Rather than the treatment improving muscle function generally, it appears selective to muscles predominantly affected by age-related muscle loss (type II fibers). As the authors acknowledge, these results need to be replicated in females, as they only looked at male mice.

Reviewer #3 (Public Review):

In this manuscript the authors sought to investigate whether an adiponectin-receptor agonist could reduce the incidence of sarcopenia in aged mice. The authors provide compelling evidence that AdipoRon improves skeletal muscle function in aged mice, remodels muscle fibers, and appears to improve mitochondrial function at least in vitro.

The authors provide multiple lines of evidence for the effects of AdipoRon, from live measurements of muscle function in aged rodents, to ex-vivo muscle activity assays, to in vitro assessment of mitochondrial function and activation of pathways involved in mitochondrial remodeling.

The experiments complement one another very well, though it is unclear why two different strains were used for young and old mice, nor why the analysis was restricted to male animals only.

Overall, the study details a promising intervention to restore muscle function in elderly individuals and identifies a druggable pathway that can be exploited for this goal.

Reviewer #1 (Public Review):

The authors have taken a creative approach to addressing an important and controversial question, how much do the signaling pathways underlying spontaneous and evoked neurotransmission overlap? They have combined a number of approaches to examine small excitatory synapses from hippocampal neurons. In addition, this work focuses on the postsynaptic elements of synaptic transmission. The use of a fluorescent glutamate sensor is a major strength of the work as it allows localization of spontaneous and evoked release events to specific areas of the neuron unlike electrophysiological recordings where the synapse mediating a particular release event is usually unclear. Their experiments using fixed tissue and super resolution microscopy show that the fluorescent sensor accumulates near synapses. In the first half of the paper the authors validate the approach by examining how the likelihood of spontaneous and evoked release can be evaluated by this method and they compare the values with those obtained using electrophysiology under a range of conditions. A major finding is that the rate of recovery of fluorescence from the sensor, following photobleaching, appears to be very different for spontaneous and evoked events consistent with their thesis that the pathways reporting transmission are different for the two forms of release. However, their finding that stimulation did not impact the rate of photobleaching is puzzling and on first look appears inconsistent with the interpretation. Additional discussion of this point would enhance interpretation and increase its impact on the field. The work extends the study of synaptic function and allows localization of release to specific synapses or synaptic clusters.

Reviewer #2 (Public Review):

This is a very interesting and well executed study from an established and very well respected group of synaptic neuroscientists. They have used iGluSnFR based glutamate imaging to examine the fundamental properties of spontaneous vs evoked neurotransmitter release. There are a number of exciting and intriguing findings in this study. First, the authors report that GluSnFR is enriched in synapses in culture. Second, they report that there is a significant portion of GluSnFR that is immobile in neurons. Finally, they show that after photobleaching, the detection of evoked glutamate release is very slow to recover while the detection of spontaneous glutamate release recovers much more quickly. These findings are exciting and impactful as great efforts have been devoted to understanding the mechanisms that differentially regulate evoked and spontaneous release of neurotransmitters. The findings also have important implications on how to use and interpret glutamate imaging modalities, which are gaining significant popularity. Overall this is an exciting and thought provoking study. There are a number of technical and conceptual issues with the study that should be clarified and currently limit the ability to interpret the findings with complete confidence.

Strengths of the study:<br /> - Rigorous and quantitative analysis of spontaneous and evoked glutamate release.<br /> - Extensive validation of the imaging approach to answer the questions of interest.<br /> - Potentially very impactful finding of differential recovery of spontaneous and evoked glutamate release<br /> - Carefully analyzed and interpreted findings that shed new light on the spatial and temporal distinctions between spontaneous and evoked release.

Weaknesses of the study:<br /> - Additional clarity is required to understand exactly how spontaneous events were detected and quantified.<br /> - There is a difference, potentially of impact on the findings of the study, between the area that was photobleached and the area in which recovery from photobleaching was studied. This needs to be clarified and its potential implications on the finding considered.

Reviewer #3 (Public Review):

Wang CS et al investigate the localization of spontaneous and evoked vesicle fusion within a synapse to better understand a central question in neuronal communication. Given that spontaneous vesicle fusion is present in the brains of almost all organisms studied to date, far too little is understood about its role in the brain. The authors nicely document the use of a glutamate-sensitive fluorescent reporter (iGluSnFR to measure spontaneous and evoked vesicle fusion. Taking advantage of the iGluSnFR's rapid bleaching, the authors follow up previous molecular and physiological characterization of spontaneous vesicle fusion largely done in their labs with novel findings of unique spatial segregation of spontaneous vesicle fusion based on this bleaching characteristic. This manuscript has several high quality measurements and beautiful recordings made with excellent temporal resolution. Furthermore, the writing and presentation overall were very clear with interesting conclusions and discussion. While work from a number of labs has recently demonstrated restricted nano-domains for evoked vesicle fusion critical for our understanding of synaptic communication, unique sites for spontaneous release have not previously been reported postsynaptically. These findings suggest interesting future experiments to determine the unique receptors that localize (or not) at segregated sites of spontaneous vesicle fusion going forward. I was impressed by the use of the bleaching of a membrane probe to resolve the location of vesicle fusion. I have some questions and comments that I believe are important to be addressed to better understand the findings.

1. The iGluSnFR probe has a very high affinity to provide it with the sensitivity for detecting single vesicle fusion. A concern would be if the sensor is also detecting vesicle fusion as spillover from adjacent boutons of untransfected neurons not directly forming a synapse with dendrites expressing GluSnFR.

2. The difference in bleaching from glutamate perfusion compared to during rest or "no stimulation" for selectively impairing detection of evoked vs spontaneous release is striking and really interesting. That being said, it is very hard to understand the explanation the authors provide for the lack of difference between unstimulated and stimulated bleaching conditions if iGluSnFR bleaching requires glutamate release. Please provide more detail of the bleaching illumination intensity as well as changes in resting fluorescence of GluSnFR from the three bleaching conditions to better understand the interpretation.

Reviewer #1 (Public Review):

This study capitalizes on the crosstalk-free two-color STED developed by the authors (cf. Willg et al., Cell Rep 2021) to examine the dynamic changes in synapse structure in mouse visual cortex. Imaging the superficial dendrites of layer V pyramidal neurons, the authors report features of dendritic spine morphology and the dynamics of scaffolding protein contained within that are affected by rearing mice in an enriched environment (EE) compared to control housing. Curiously, EE mice show less variable spine head volumes and PSD95 areas compared to control mice, while the spine head volume is larger but not PSD95 area in EE group compared to the control group. Moreover, nano-organization of PSD95 displays more prominent changes in EE compared to controls. These findings may be of potential interest to neuroscientists studying synaptic network architecture.

Reviewer #2 (Public Review):

The authors have developed a new method that allows for two-color STED imaging. They have applied this method to measure spine head size and PSD95 changes following exposure to an enriched environment.

Strengths<br /> -The new method is well-described and seems to have considerably less crosstalk than previous attempts at in vivo two-color STED imaging. The analyses and controls of the method are compelling. I think that this method could be valuable for examining how different components of the synapse are changing in response to sensory or environmental changes.

-The method is appropriate for measuring the size of PSD95 and spine head size in the enriched environment paradigm they use here. They find that in the short-term spine head size and PSD95 size are not always correlated.

-They also find that there is less variability in the spine head size in animals in an enriched environment.

Weaknesses<br /> -The authors use an enriched environment plasticity paradigm to showcase the method and measure spine head and PSD95 size and how they change over short periods of time. This particular biological study is not well-motivated and there is not a stated reason for studying the short-term (30-120 minutes) dynamics of PSD95 and spine head size, and their correlations. They also show that the variability in spine head size is decreased with the enriched environment, but do not show what the implications of that change would be from a biological point of view for synaptic dynamics or synaptic function.

-The authors show that there are differences in the morphology of PSD95 between mice reared in enriched environments and those in control environments. While this quantification is done blindly by three different analysts, it is not done in a quantitative way. Also the authors do not show or explain the biological relevance of differences in the morphologies of PSD95, thus it is not clear what this measure means for synaptic plasticity or function.

-The authors use a cranial window preparation, which is commonly used in the literature. However, it is not clear how long they wait to image the mice after the cranial window. Previous work from Xu et al. (PMID: 17417634) suggests that there is in an increase in glial activation for a period of up to a month after surgery. The authors have not shown the degree of glial activation that follows after their surgeries and if they have not waited a month, there may be upregulation of microglia, which may alter synaptic stability (also demonstrated in the same paper). The authors have not discussed this point or the implications for their findings.

Reviewer #3 (Public Review):

Wegner et al. use two-color STED to follow spines and their PSDs in layer1 of mouse visual cortex over 2 hours under anesthesia. They compare mice that were kept in an enriched environment (EE) to control mice housed in standard laboratory cages. Spines in EE mice are larger and show larger fluctuations in size. PSDs in EE mice shrink during anesthesia and tend to change their nanostructure. Very importantly, changes in spine size were not driven by PSD size changes, or vice versa. Technologically, this is a landmark study, as tracking two different labeled structures in individual synapses at the nanoscale can obviously be applied to a large number of synaptic proteins and organelles, two at a time. Single-color superresolution microscopy is much less useful, as 'puncta in space', without cellular context, are difficult to interpret. This pioneering work is the first proof-of-concept of two-color in-vivo STED and of major importance for the community. Although stochastic processes seem to drive much of the synaptic dynamics under anesthesia, the environment shapes the spine size distribution and affects synaptic dynamics in a lasting fashion.<br /> One major comment:<br /> l.259: "These results suggest that Ctr housed mice undergo stronger morphological changes." This I find a bit misleading. What about: These results suggest that anesthesia induces stronger morphological changes in Ctr housed mice? Altogether, a discussion of the potential effects of anesthesia on spine/PSD dynamics is missing (see e.g. Yang et al., DOI: 10.1371/journal.pbio.3001146). The fact that there was weak correlation between spine head and PSD fluctuation could have something to do with the state of suppressed activity the system was in during imaging. Under conditions of intense processing of visual information, changes might have been more rapid and more tightly correlated. This could be mentioned as a perspective for the future - to visually stimulate the anesthetized animal.

Reviewer #1 (Public Review):

The authors provide quantitative-image data and stochastic modelling to address the detail of CLOCK nucleus mobility regulated by BMAL1, CLOCK-BMAL1-E-box binding time, BMAL1/CRY1 affinity, PER2-CRY1 mediated CLOCK-BMAL1 displacement from E-box and then visiting more new target sites. Based on their mathematic modelling data, the model is interesting and makes sense that they find CLOCK-BMAL1 move to new sites after removal by PER-CRY. Their quantitative biology and mathematic modelling method provide new insight, which are complementary with biochemistry and structure in the circadian field. The findings are consistent with current circadian model from the Sancar lab: CRY1 blocks CLOCK-BMAL1 activity independent of PER and PER-CRY-CK1 complex displace CLOCK-BMAL1 from E-box, and the authors go a step further from the Sancar lab regarding BMAL1 activator is inhibition by CRY1/CRY2 by "blocking type" repression or by CRY-PER complex by "displacement type" repression. This manuscript makes the important contribution that the displacement type repression frees CLOCK-BMAL1 to bind to other targets and activate several sets of genes. This is an important insight. However, some data need explaining and some statements need to change to improve the manuscript.

Reviewer #2 (Public Review):

The transcriptional negative feedback loop of the mammalian circadian clock is mainly regulated by interactions among BMAL1, CLOCK, PER1/2 and CRY1/2 in the nucleus. While the binding of CRY with BMAL1:CLOCK is known to block the transcriptional activity of BMAL1:CLOCK and the binding of PER:CRY dissociates BMAL1:CLOCK from DNA have been known, our understanding is limited in qualitative level. Koch et al. quantified the dynamic interactions among the core clock molecules such as their diffusion coefficients, binding affinity, and abundances in the nucleus. This will greatly improve our understanding of the mammalian circadian clock. Importantly, this dynamic information is incorporated via a mathematical model to understand BMAL1-CLOCK binding to the target site (e.g., circadian proteins operate within an optimal range to modulate E-box binding). Certainly, this work is novel and highly impactful. However, the description of how the quantified information can be incorporated into the mathematical modeling is unclear. There is also uncertainly in the identified parameters of the models.

Reviewer #1 (Public Review):

In this manuscript, the authors performed a two-sample Mendelian randomization (MR) study to investigate the effects of epigenetic age acceleration (i.e., HannumAge, Horvath Intrinsic Age, PhenoAge, and GrimAge) on the risk of multiple cancers (i.e., breast, prostate, colorectal, ovarian, and lung cancer). Genetic instruments were selected based on a genome-wide association study (GWAS) of epigenetic age acceleration including 34,710 participants of European ancestry. Genetic association data for cancer outcomes were obtained from the UK Biobank, FinnGen, and several international cancer genetic consortia. The analyses yielded several interesting findings, including the associations of genetically-determined GrimAge acceleration with increased risk of colorectal cancer and decreased risk of prostate cancer. The authors presented the three core assumptions required for MR. The F statistic for each genetic instrument was reported to assess the validity of the relevance assumption. Other sensitivity analyses, including MR-Egger, weighted median, weighted mode, and CAUSE methods, were used to detect and correct for potential horizontal pleiotropy.

Strengths:<br /> 1) The large-scale GWAS datasets used in this study had improved the power to detect the associations between epigenetic age acceleration and cancer risks.<br /> 2) This study represents a comprehensive effort to estimate the effect of genetic loci associated with epigenetic age acceleration on the risk of common cancers.

Limitations:<br /> 1) GrimAge includes data from 1,030 age-related CpGs associated with smoking pack-years and seven plasma proteins. However, only 4 SNPs were identified as genetic instruments of GrimAge acceleration, which explained 0.47% of the trait variance and thus limited the statistical power to detect the exposure-outcome associations.<br /> 2) Although the authors had stated the independence assumption, they did not evaluate whether this assumption hold. If the genetic instruments of GrimAge acceleration are associated with confounders, then the validity of the results is questionable.<br /> 3) Another concern is that the MR-Egger intercept test had low power to detect uncorrelated horizontal pleiotropy in the context of very few SNPs. The weighted mode method may also be misleading in this context. The evidence is not enough for making a claim that there is no horizontal pleiotropy.

Taken together, potential violations of the independence and exclusion restriction assumptions cannot be entirely ruled out, which may mislead the causal inference. Results from this study should be interpreted with caution.

Reviewer #2 (Public Review):

This article reports Mendelian Randomization (MR) analysis of associations between several DNA methylation (DNAm) clock biomarkers of aging and several cancers. The main positive finding is that GrimAge acceleration shows evidence of causal effect on development of colon cancer. Other associations were either null or proved inconsistent in sensitivity analysis. This result contrasts with past observational studies reporting correlations between multiple DNAm clocks and multiple types of cancer. It therefore has potential to change the way the field thinks about the integration of DNAm clocks into cancer epidemiology.

Connections between the biology of aging and cancer are many, motivating interest in whether biomarkers of aging can also inform assessments of cancer risk. However, the black-box nature of the DNAm clocks (they are developed from machine learning and the biology of their included CpG sites is mostly unknown) complicates their interpretation in clinical settings. Positive results in MR analysis would bolster confidence that clock associations with cancer are causal and motivate further inquiry to understand mechanism. This study therefore has the potential to contribute to interpretation of DNAm clocks in cancer risk assessment by informing causal interpretations of associations reported between older clock ages and risk of developing several cancers.

The main limitation of the study is that the genetics of DNAm clocks are not well established. GWAS sample sizes for clock analyses have been relatively small. In this study, the GWAS was based on just 30,000 participants and the SNPs identified explain only small fractions of the heritability of the clocks. The authors are transparent about statistical power for their analysis and report several sensitivity checks for their results. Nevertheless, a consequence of this limitation is that null results do not rule out causal effects of the clocks on cancer risk. Moreover, positive findings are observed only for the DNAm clock with the fewest identified SNPs (just 4 SNPs explaining <1% of variation in GrimAge).

Despite these limitations, it is my view that this study makes a substantive contribution to the emerging literature linking DNAm clocks and cancer.

Reviewer #3 (Public Review):

The authors' goal is to estimate the impact of DNA methylation aging (acceleration) on risk of various types of cancer using Mendelian randomization methods. This work reflects substantial efforts for data acquisition, formatting, and analysis. The authors use appropriate large scale data resources and a set of sophisticated analyses tools to address their hypotheses of interest. While the results consist of largely null and/or suggestive estimates of associations/effects, the hypothesis addressed is of substantial interest in the field, so the results presented are of significant interest to the community. Replication in future studies will be needed. The paper is well-written and easily understandable. I have no major concerns regarding the analysis approach, but additional details and data visualizations are needed for readers to have a complete picture of the results. There are limitations and biases associated with the MR method, but these are described and accounted for (to the extent possible) through sensitivity analyses.

Reviewer #1 (Public Review):

Droplet-based single-cell method development has stalled in the past years. The field has been overtaken by commercial solutions that optimized performance, but at much higher costs and without any possibility for customization. More recently, combinatorial indexing methods (e.g. SPLIT-seq) have gained popularity, requiring no specialized equipment and offering the possibility to massively increase output, but at the cost of reduced sensitivity.

De Rop et al. introduce a flexible microfluidics-based single-cell genomics technology that expands and improves over previously existing custom droplet-based scRNA-seq protocols (inDrops and Drop-seq) in several directions: better data quality, simplified workflow, high-cell recovery, and flexibility towards other single-cell applications, as exemplified by HyDrop-based single-cell ATAC-seq.

This is a much welcome development in the field and one that will hopefully stir the further development and optimization of custom droplet-based single-cell protocols (multi-ome, scChIP, and beyond).

Reviewer #2 (Public Review):

The authors present HyDrop as a flexible open-source and non-commercial microfluidic platform for scATAC and scRNA-seq using custom dissolvable hydrogel beads loaded with barcoded oligos. Optimisation, quality assurance and benchmarking experiments show that HyDrop performs well compared to inDrop and Drop-seq platforms. Moreover, the manuscript contains new datasets generated with this platform to show its sensitivity and use in studying cellular heterogeneity in a range of model organisms. Its flexibility should allow the research community to develop and implement new and custom workflows on this platform, including single-cell multi-omics technologies.

Strengths:<br /> 1) This manuscript describes the development of three protocols for an open-source droplet-microfluidics based single-cell genomics platform, HyDrop. The protocols are described in detail in the materials and methods and in the accompanying documentation on protocols.io.<br /> Small note to make here is that the source of some reagents is not explicitly mentioned. This is particularly important, for instance, for the source of the transposase used in the workflows. This should be remedied to enhance the ease of adoption and to avoid confusion.<br /> 2) The set of experiments designed to test the performance of the described methods was very well designed and included explanations for the rationale behind the choices made, and were convincingly executed. The conclusions on his performance are well supported by the presented evidence, which is clearly described and explained. Importantly, inclusion of these 'technical' data allows the readers to assess the quality for themselves in the context that is most relevant to them.<br /> 3) The applications of HyDrop for scATAC and scRNA-seq on human, mouse and drosophila samples support the notion of a robust platform that should be broadly applicable in other areas as well.<br /> 4) The use of a single microfluidic chip design for both scATAC and scRNA-seq is elegant. At the same time the 3-inlet chip (cells, beads and oil, akin to the 10X design) may limit some applications in the future. However, the open-source nature of the platform will allow users to also adapt the chip design if desired.

Weaknesses:<br /> 1) The only concern for the HyDrop platform is that it may not be easy ('plug-and-play') to implement for non-specialist labs. As such, its adoption will likely be linked to some sort of support for such users.<br /> However, this detracts nothing from the validity of the presented manuscript that will fulfill an urgent need for a high-performing non-commercial platform to make single-cell genomics more equitable.

Reviewer #2 (Public Review):

The paper by Howe et al investigates observational and genetic associations between taller height and cardiovascular disease (protective) as well as cancer (risk). They use within-sibship analyses to additionally control for familiar factors to confirm earlier findings. The strength of the study is the Mendelian Randomization models and family study designs used in two cohorts. However, they have limited power to explore more of the data, e.g., mediation, multivariate MR, to increase novelty. Hence, the impression is that they could have made some more analyses, and also presented the study with equal weights on observational/MR findings. Regardless, the conclusions from the results are fair.

Reviewer #1 (Public Review):

This study is examining the role of height in cancer, coronary heart disease and cardiovascular disease risk factors, using four different designs, i.e., an innovative Mendelian randomization design comparing siblings, a population based Mendelian randomization study and purely observational studies in siblings and the population. All the methods gave the same interpretation for cancer and coronary heart disease, i.e., that height increases risk of cancer and decreases risk of coronary heart disease, while the associations for the cardiovascular disease risk factors were largely null.

The study does an excellent job of providing estimates likely free from confounding, particularly by using the Mendelian randomization sibling design.

The other major source of bias in studies of cause and effect is selection (or collider) bias. The study also needs to consider possible selection bias from inevitably only selecting survivors to recruitment of their height (measured or genetically endowed), the disease of interest and any competing risk of the disease of interest. Whether, the sibling design, by requiring at least two siblings to survive to recruitment, is more open to selection bias than a population-based design could also be considered.

The study provides an interesting comparison of four different methods. Height increasing cancer risk is plausible because of the potential mechanism given, i.e., IGF1, the consistency with well accepted biological theories from evolutionary biology, and that cancer deaths tend to occur at younger ages than cardiovascular disease deaths. The results for coronary artery disease are possible, but could be overstated Greater consideration should be also be given to as to how "an individual-level causal effect" observed largely in Europeans generalizes.

Reviewer #1 (Public Review):

This study provides an evolutionary analysis of the diversity of landing maneuvers performed by bats. The authors describe the distribution of these maneuvers across a range of species in this group, and relate the diversity of landing styles (defined by the number of contact points used) to two other aspects of bat landing behaviours: (1) the impact forces experienced, and (2) the preferred landing substrate. A major strength of this study is the extent of sampling across a wide range of extant bat species, with careful quantification of key features of landing behaviours. The results provide support for two conclusions: (1) bats that make landings with lower impact forces are able to use landing styles with more tenuous contact (i.e., presumed based on there being fewer points of contact between the bat and the landing substrate), and (2) different landing styles exhibited by divergent bat species are associated with the use of different roosting substrates.

The authors also attempt to use these data to draw conclusions about the origins of bat flight from a gliding ancestor, however, a major weakness is that no alternatives or falsifiable predictions are presented to link origin hypotheses to distinguishing predictions. Without a clearly laid out logic of alternatives (i.e., that there are different predictions that would allow us to distinguish between competing origin models), the present data do not allow us to support or refute these models.

A second weakness of this article is that the putative causal pathway for the relationship between impact forces and landing style (# of points of contact) is unclear, and needs further setup and interpretation. One might predict that the kinematics of a bat's approach would dictate both the number of points of contact needed, as well as the impact forces experienced upon landing. It is unclear whether this relationship is driven by the fact that slow approaches require few contact points and have lower impact forces, on average. This would align with the authors' findings of the relationship (1) above.

A third weakness is that the analysis evaluating which substrate categorization best predicts landing style is not fully explained. To begin, the authors could provide a clearer explanation of the aggregation of the roost categories, which I believe is based on descriptions of landing structure geometry. It would strengthen this analysis to include a model with no substrate predictors at all. It was not clear from Table 2 whether the authors had indeed done that already. Including such a model would help establish whether a model with any substrate predictors is better supported than one with no substrate predictors at all. Finally, it was not clear from Table 2 why there are 6 AIC values, but only 2 models described in the Methods text.

Reviewer #2 (Public Review):

In this paper, the authors focus on an often overlooked, but important, aspect in the evolution of flight: the transition from moving in air to the standstill on the landing substrate and how landing strategies are related to the substrate's properties. They do so by studying the landing dynamics (substrate reaction forces) and landing strategies (use of the number of body-parts in landing) in bats in relation to the roosting ecology (substrate type, orientation, mechanical features), while taking account for the phylogenetic constraints. They reconstruct the ancestral state of the landing behaviour, and put the origin of bat flight in this perspective.

For this purpose, they collected and analysed 3D-high speed video recordings together with 3D substrate reaction forces on an impressive number of species, specimens and trials (665 landings, 96 bats, 35 species, 9 families). The landing strategy and maximal forces at landing were extracted from the raw data and combined with information from the literature on the roosting ecology and the physical substrate properties (categorized). A time-calibrated molecular phylogeny is pruned to the focal taxa.

The answers provided to the three main questions [i.e., i) are relationships between landing style and impact force consistent across the diverse sample of bats, ii) what is the evolutionary history of bat landing maneuvers, iii) is landing style linked to roosting ecology?] are convincing and conclusive.

It was suggested to the authors to guide the non-specialist reader a bit more by defining important key-terms and concepts somewhat better.

Reviewer #3 (Public Review):

This study examines bat landing across species and habitats. The data were collected at multiple, international field sites using wild bats which were trained to land at a testing device. An impressive 35 bat species across nine families were measured. Peak force during landing was recorded as the number of appendages that were used during landing (two to four "point" landings). The peak force data (corrected for body weight) and landing points data were analyzed in the context of the phylogenetic relationships of the bats. The roosting ecology was coded for phylogenetic analysis based on the published literature. The authors found that four-point landings had a higher peak force when compared to three-point and two-point landings. The latter had the most consistent and lowest peak forces. Based on phylogenetic reconstruction, they found that four-point landings were the ancestral condition, and two-point landings were more recently evolved. Based on the phylogenetic correlations and model comparisons between landing type and roost type, they found that the two-point landings were more likely to occur in horizontal roosts with stiff materials (e.g., caves), whereas three-point landings were more closely associated with small areas (e.g., leaf tents). They associate the number of points per landing with flight dynamics through the idea that two-point landings are more complex to navigate aerodynamically than four-point landings and three-point landings also require complex aerodynamics associated with navigating tightly constrained landing sites.

The major strengths of this study are in the rigorous comparative, experimental dataset, the integration with phylogenetic approaches, and the robust and thoughtful manuscript structure that both sets the stage for the complex, and highly integrative study and contextualizes it based on the current literature and the strengths/weaknesses of their analysis approach.

The major weakness of the study is in the use of peak force as a proxy for landing velocity or, more generally, for the bat's ability to land in a complex aerodynamically controlled way. A minor weakness of the study is the secondary connection of the study to flight aerodynamics.

Ultimately, the authors do achieve the primary aims of their study and the careful writing of the manuscript largely does appropriately interpret their findings.

The study is likely to be impactful for the field, given the impressive example of field-based data collection of live animals integrated with phylogeny-based analyses. These kinds of studies are extremely challenging and it is rare to find them integrated within a single study.

Reviewer #3 (Public Review):

The goal of the authors was to test how important local rat abundance is as a driver of Leptospira infection in humans. The authors approached this using a strong combination of datasets on human infection risk and rat abundance, across a spatial scale that is large enough to allow simultaneous assessment of multiple potentially important drivers of infection risk. This further enables the authors to develop infection prediction maps based on the fitted models.

This study design is a major advance towards understanding link between rat abundance and human infection risk.

Based on the top models tested in the study, the authors conclude that local rat abundance is indeed correlated with infection risk, and that this correlation is strongest at higher elevation.

This is an impactful finding, but in my opinion it is not yet clear how robust and important this is, because of two reasons:

(1) The infection risk data: while the actual infection risk data are not shown, the map shown in Figure 5B suggests that there is an infection hotspot that happens to be at high elevation. This raises the question of how strongly this single hotspot is driving the observed correlation between rat abundance and infection risk (which the authors find to be much stronger at high elevation than at lower elevations).

(2) The statistical models: if I understand correctly, all tested models of infection risk include the variable rat abundance, and while the individual effect estimates for rat abundance are statistically significant (Table 3), the more important question of how the fit of a model without the rat abundance variables compares with those of the other tested models (shown in Supplementary Table S2) has not been addressed.

Regardless of whether rat abundance is an important driver of human infection risk, this study is a major step in our understanding of the role of rats in the spread of leptospirosis, due to the strong combination of a unique combination of datasets and a spatial statistical modeling approach.

Reviewer #2 (Public Review):

Eyre et al. developed and applied a novel geostatistical framework for joint spatial modeling of multiple indices of pathogen (Leptospira) reservoir (rats) abundance and human infection risk. This framework enabled evaluation of infection risk at a fine spatial scale and accounted for uncertainty in the pathogen reservoir abundance estimates. The authors used data collected in two different field projects: (1) a rat ecology study in which three different approaches were used to detect rat presence "rattiness", and (2) a prospective community cohort study in which individuals were sampled during two different time periods to detect recent infections via seroconversion or a four-fold increase in anti-Leptospira antibody MAT titer. Univariable and then multivariable analyses were performed on these data to identify (1) the environmental variables that best predicted "rattiness", and (2) the demographic/social, environmental (household), occupational, and behavioral variables that best predicted human risk of infection. Once identified, the best predictors from (1) and (2) were included in a final, joint model to identify the significant predictors of both 'rattiness' and human infection risk. As a result of this study, the authors were able to detect spatial heterogeneity in leptospiral transmission to humans. They found that infection risk associated with increases in reservoir abundance differed by elevation, and that increases in reservoir abundance at high elevation were associated with a much higher odds ratio for infection than at low elevation. The authors suggest that this has to do with differences in how the infectious leptospires (shed by the rat reservoir) are dispersed in the environment. At high elevations, flooding is less frequent and thus rat shed leptospires are likely to stay where the rat deposited them. Whereas at lower elevations, flooding may play a large role in spreading leptospires more evenly across the landscape, reducing the importance of rat presence at smaller spatial scales. The final best model was then used to generate prediction maps of 'rattiness' as well as human infection risk at all locations within the study area (i.e. including those that lacked rat detection data and human infection data. This work represents an important advance in infection risk modeling as it explicitly incorporates estimates of reservoir abundance and the uncertainty surrounding these estimates into the infection risk assessment, and allows for modeling of infection risk at fine spatial scales. Findings from this study have important management implications at the authors' study site as it suggests that interventions directed at high elevations should be different from those designed to address infection risk at lower elevations. However these are broader implications, as this novel approach may be applied to other systems to enable identification of differences in infection risk for other pathogens at a fine spatial scale, predict infection risk more broadly, and facilitate intervention strategies targeted for the specific epidemiological and ecological conditions experienced by a population.

This was a well-designed study. The field sampling approach was well balanced, well described and appropriate. Broadly the modeling framework is appropriate for the questions being asked and for the data being used. The variable and model selection approaches were clearly described and appropriate. Evaluation of the more detailed mathematical approach is outside of my area of expertise, so I am unable to comment on the validity of the approach.

For the most part, the explanatory variables assessed in the different models were well described and justified, however there were some cases for which further explanation would have been helpful. For example, how did the authors determine which occupations to evaluate? Specifically, why traveling salesperson? What is the difference between open sewer within 10 m and unprotected from sewer?

I also had some concerns regarding the time-period of the rat ecology study used to determine abundance, potential fluctuations in rat abundance through time, and how this might align with sampling to detect infection in humans. Depending on the time scale of population fluctuation in rats as well as fluctuations in infection prevalence in rats, the abundances calculated from data from the ecology study may not be accurately reflecting true abundance (and therefore shedding and transmission risk) during the time period that a human may have been exposed. However, the authors do a nice job of addressing some of these issues in the discussion. They mention that infection prevalence in rats is consistently around 80% and that there don't appear to be seasonal fluctuations in human exposure risk in the study area.

Reviewer #1 (Public Review):

In their manuscript, these authors present a novel geostatistical framework for modelling the complex animal-environment-human interaction underlying Leptospira infections in a marginalised urban setting in Salvador, Brazil.

In their work, the authors combine human infection data and the rattiness framework of Eyre et al. (Journal of the Royal Society Interface, 2020) . They use seroconversion defined as an MAT titer increase from negative to over 1:50 or a four-fold increase in titer for either serovar between paired samples from cohort subjects. Whereas this is a commonly used measure of infection; the work would benefit from answering the question about how robust results are related to this definition of seroconversion.

The model framework relies on the concept of 'rattiness' previously defined by Eyre et al. (JRSI, 2020) and assumes conditional independence within its built up (equation (1)). Whereas this is a reasonable assumption, it would be good to discuss situations in which this assumption is questionable and what the implications are for applying the modelling framework to other settings.

The authors provide an extensive model building exercise and investigate, in different ways, whether the model captures the necessary complexity (GAM smoothers - testing linearity, spatial correlation, etc). I believe the work would benefit from (1) a formal diagnostic investigation, if feasible; (2) providing guidelines on how model building should be performed.

The authors are to be acknowledged for providing an extensive and thorough discussion of the different aspects of their work. Whereas the discussion is complete, I wonder whether the authors can give a brief example about how this model can be applied in a different setting.

Reviewer #1 (Public Review):

The ryanodine receptor type 1 (RyR1) shows a biphasic response to Ca2+ - a Ca2+-dependent increase in activity (open probability) at low to moderate levels of Ca2+, and a Ca2+-dependent inactivation (CDI) at high Ca2+ concentrations. This study compares cryoEM structures of RyR1 embedded in nanodiscs in different states - closed, Ca2+-bound open, and Ca2+-bound closed (inactivated) - to gain insights into the structural correlates of RyR1 inactivation. The open and inactivated state structures are distinguished from previously published RyR1 structures by being obtained solely in the presence of physiological activators, Ca2+ and ATP, without other non-physiological activators (e.g. caffeine or PCB95) present.

Features revealed by the structures include that: Ca2+ remains bound in the high-affinity binding site in both open and inactivated state structures, although with important changes in binding interactions between the two states; two intersubunit salt bridges that form between EF hands and S2-S3 loop are important for the inactivated state; Ca2+ binds to the ATP binding pocket and there are changes in the interaction network of this pocket between open and inactivated states; lipids bind to a hydrophobic crevice in the transmembrane domain to stabilize the inactivated state.

Overall, the work provides nice structural insights into the Ca2+-dependent inactivation process in RyR1, an important physiological phenomenon. The results nicely complement and rationalize previously published functional studies that show disease-causing mutations in RyR1 that alter Ca2+-dependent inactivation.

Reviewer #2 (Public Review):

The manuscript by Nayak et al. reported several cryo-EM structures of RyR1 with the aim to understand the inactivation mechanism of the channel. By comparing the structures from different functional states, they proposed a model how the rearrangement of RyR domains leads to a switch form the open to the inactivated state. The study is of great interest and the work is of fine quality.

Reviewer #1 (Public Review):

The recent development of AlphaFold2 has improved the ability to predict protein fold from sequence. However, this approach typically yields a defined structural fold, while it is known that proteins exhibit structural diversity through different conformations. In particular, membrane transport proteins and receptors are known to adopt distinct conformational states in order to allow for alternate access or signaling across the membrane. In this study, the authors demonstrate that by reducing the size of the input sequence alignment fed into AlphaFold2, conformational diversity in the structural predictions is increased, with some of these corresponding to known experimentally determined structures. They test this with a diverse set of transporters where the structures have been solved in both inward and outward facing conformations, as well as GPCRs in active and inactive states. Decreasing the size of the sequence alignment from 5120 to 32 leads to a general increase in conformational diversity with the predicted structures and that these structures are generally bounded by the experimental structures. The RMSF analysis of residues amongst the different models, corresponds to RMSD of residues in the experimental structures, and principal component analysis demonstrates that these models connect the two known conformations. Altogether, this analysis validates that the ability to predict alternate conformations of transporters and receptors is already present in the AlphaFold2.

This validation is important, but further analysis is necessary to move beyond a demonstration and towards a procedure for predicting relevant conformations. Along these lines, quantification of the robustness of the approach along different parameters is needed. Furthermore, the study stops short of defining how to statistically weed through the ensemble of models to predict meaningful conformations. AlphaFold2 may generate highly accurate models, but how does the user pick which ones are likely to be relevant? Therefore, this is an interesting study that is expected to be broadly impactful for the study of all proteins, not just membrane proteins tested here. However, limitations remain on the interpretation of the results and a clarification is needed to demonstrate how others may use this approach to predict new biologically relevant conformations.

Reviewer #2 (Public Review):

In "Sampling the conformational landscapes of transporters and receptors with AlphaFold2" the authors provide insight into the methods available for predicting varying conformations in dynamic membrane proteins. The authors noted that AlphaFold2, a recently reported breakthrough in structure prediction technology based on deep mining machine learning, tended to report outputs that are very homogeneous, even for proteins with dynamics as a primary feature, such as transporters or G-protein coupled receptors. The authors' goal was to produce a range of structural models more reflective of true conformations observed during function, by modifications to the input parameters, e.g. by providing templates or by reducing the number of input sequences.

Excitingly, the results indicated that, by reducing the number of "constraints" through limiting the number of provided sequences, a much greater variability of conformational space could be explored. Even more excitingly, these conformations reflected the major dynamics of the conformational changes, at least according to comparison with known structures.

A limitation of the reported work is the relatively small number of test cases (~10 different protein families), which is unavoidable given that AlphaFold2 was trained on almost the entirety of available structures in the Protein Databank. Indeed, for proteins in the training set, the strategies that the authors identified were of mixed effectiveness. Nevertheless, the authors provide a helpful strategy for researchers working with dynamic proteins, for whom AlphaFold2 results are currently rather limited. Moreover, their findings provide insights likely to contribute to the development of future machine learning tools.

Reviewer #3 (Public Review):

This manuscript describes a workflow for using AlphaFold2 (AF2) to model membrane proteins in different conformations. It then evaluates the models generated by this workflow on eight different membrane protein structures representing different structural classes and mechanisms. The authors conclude that AF2 can provide models with reasonable accuracy and conformational diversity of membrane proteins, but additional improvements are needed to be able to sample biologically relevant conformations.

In principle, the research presented in this study is timely and can be of general interest to the community. It attempts to address the question of whether AF2 can accurately predict membrane protein dynamics. As the authors state, they provide "a hack" for modeling membrane proteins with AF2. My main concern with this manuscript is that the adopted workflow needs to be optimized and assessed more rigorously, in order to support the conclusions regarding the usefulness of AF2 for modeling membrane proteins.

In addition to the importance of the topic, some strengths of the study include: focusing on proteins representing different folds and families, using different measures for structural evaluation, and presenting several examples in greater detail, particularly of important human proteins.

My specific comments can be found below:

A significant concern is that the Methods section of this manuscript is lacking. Additional details are needed in order to be able to evaluate the validity of the approach and reproduce these results. I list below some specific issues.

The alignments used to develop the models should be provided. Specific details on how the visual inspection of the alignments guided their refinement should also be included. I could imagine that the alignment quality may correlate with model accuracy. This is an important analysis to include.

For some of the targets, the template-based modeling clearly improved sampling of various conformations and for others it did not. The authors only vaguely discussed this observation without providing a detailed analysis. For example, how were the template selected for the template-based modeling? Was the performance of AF2 dependent on the sequence similarity between the template(s) and the target? These are critical points that are needed to understand the utility of the approach and how one can adopt the proposed workflow.

A key conclusion of this study is that there is no one-model fits-all approach with AF2 for accurately sampling the conformational space of membrane proteins. Although this conclusion sounds plausible, the authors do not provide significant evidence to support it: they tested the performance of the models for a very limited set of parameters. For example, they only used a few MSA depths, and they do not report performances for templates with different similarities to the target. Also, is it possible that a "one-model fits-all" exists for particular folds or families? For example, LAT1 and MCT1 each represent very large protein families and a clear workflow for each would represent an important advance in the field.

How were misfolded models were identified? Providing a reference is not sufficient here. It is also stated that "padding MSAs with additional sequences had the desirable effect of decreasing the proportion of these models, it also limited the extent to which alternative conformations were sampled. Thus, our results revealed a delicate balance that must be achieved to generate models that are both diverse and natively folded. No general pattern was readily apparent regarding the ideal MSA depth required to achieve this balance.". While this is interesting initial observation, finding a pattern in the ability to detect those misfolded structures (for at least some folds or protein families) could increase the impact of the work.

In general, the definition of the different conformations is nuanced for each structural class and a better explanation is needed for those proteins that are discussed in greater detail. For example, when discussing one of these proteins, MCT1, the authors state: "One target, MCT1, was exclusively modeled by AF2 in either IF or fully occluded conformations regardless of MSA depth. Notably, these results closely parallel those reported by DeepMind during their attempt to model multiple conformations of LmrP in CASP14.". Could the authors elaborate on this statement? Could they provide quantitative data defining how occluded and open conformations are defined? Many of the readers are unlikely to know the LmrP example from a previous publication.

The authors evaluate the models on structures that were not included in the AF2 training set. It would be useful to provide the list of the PDB ids that were included in the training of the AF2 version that was used in this study. This is important because the structures of some of these proteins were solved a few years ago with minor differences, even though they were classified as a "different conformation". As mentioned in the point above, the definition of "different conformation" can be highly nuanced depending on the protein family and the mechanism used by the protein.

In the section "Alternative conformations cannot be predicted for proteins with structures in the training set", the results should be described in a more quantitative way. Specifically, the following statement should be accompanied by quantitative data: "virtually every transporter model superimposed nearly perfectly with the training set conformation, and none resembled the alternative conformation".

Reviewer #1 (Public Review):

This manuscript leverages large scale cancer genomics and proteomics datasets from the Cancer Genome Atlas Project to evaluate the association between aneuploidy and protein structure / function in cancer. They perform a comprehensive multi-omics analysis to look at regulatory mechanisms.

This represents a comprehensive analysis across all different data modalities. They perform a comprehensive analysis of proteomics data, particularly novel for evaluation of protein structure and regulation and its association with aneuploidy.

Some of the other analyses, e.g., gene expression, DNA methylation, survival, could benefit from additional expansion.

Reviewer #2 (Public Review):

Aneuploidies, loss or gain of chromosomes of chromosome arms, are very common in cancer with up to 90% of tumors displaying these types of major chromosomal aberrations. Since certain types of aneuploidies are commonly recurrent in different types of cancers it's widely assumed that they give certain advantages to the cancer - e.g., by increasing the abundance of oncogenes or perhaps decreasing the abundance of tumor suppressor genes. However, these types of large-scale changes in gene dosage also cause issues for the cancer cell as all genes encoded on the aneuploid chromosomes become over or under-expressed.

The consequences of aneuploidy on gene expression have been studied, most commonly using cell lines or in yeast, and it has been found that RNA levels usually scale with the aneuploidy. For example, in yeast an extra chromosome in a monosomic strain leads to a 100% increase of mRNA encoded on the disomic chromosome. This 100% increase, mainly, holds true also at the protein level. However around 20% of proteins remain at baseline levels even though their mRNA levels are elevated, a phenomenon labeled attenuation. These attenuated proteins were found to be enriched for protein complex subunits, leading to the idea that stoichiometric assembly followed by degradation of orphaned (overexpressed) subunits causes the attenuation of the overexpressed subunits. At least in yeast, little translational feedback has been shown during aneuploidy, while increased degradation as well as aggregation of overexpressed and orphaned subunits have been directly measured, lending credence to this mechanism. Thus, the consequences of aneuploidy on the directly affected chromosomes are known to some extent. However, an important question that has yet to be answered is: What are the consequences of aneuploidy on gene expression of diploid chromosomes?

Here, Senger and colleagues leverage the massive transcriptomic and proteomic data sets generated by The Cancer Genome Atlas Project (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC) to not only shed light on the in vivo effects of aneuploidy on gene expression in primary tumors but also the consequences of aneuploidy on the diploid chromosomes. The authors find that once again protein complex subunits play a special role as the abundance of complex subunits encoded on diploid chromosomes tend to scale with their interaction partners on the aneuploid chromosomes. Further, the authors show that the proteins complex subunits encoded on diploid chromosomes that are most likely to be impacted by their interaction partners partake in relatively fewer complexes and are more aggregation prone. This the authors argue is to prevent the accumulation and aggregation of orphan subunits in cancer. Finally, they show that cancers that manage to correlate the expression of protein complex subunit genes between aneuploid and diploid chromosomes, have less upregulated proteasomes and importantly are related to lower survival probability in patients.

The manuscript is well written and argued and is a great example of how analyzing large public dataset can reveal important findings. They find a strong and believable link between differentially abundant proteins encoded on diploid chromosomes and differentially abundant proteins encoded on aneuploid chromosomes. E.g. around 40% of differentially expressed genes in COREAD are interaction partners with proteins differentially expressed genes on the amplified chromosome 7. In addition, the authors convincingly show that subunits that are less promiscuous and more aggregation prone drive the up regulation of interaction partners encoded on non-aneuploid chromosomes. Interestingly, the authors also show that cancers that effectively manage their aneuploidies by stoichiometrically upregulating interaction partners encoded on non-aneuploid chromosomes have a proliferation advantage and are associated with a worse outcome for patients. Some conclusions drawn in this manuscript I find a bit less well supported, although still interesting, such as the functional analysis in Figure 4 and the ubiquitin analysis in Figure 5. Overall, I think this manuscript is a great addition to the field.

Reviewer #3 (Public Review):

Aneuploidy, a state of whole- or arm-level chromosomal alterations, is detrimental in normal cells due to gene expression imbalance, but paradoxically it is a hallmark of cancer cells. A long-standing question has been how do cancer cells tolerate aneuploidy. In this study, the authors used elegant bioinformatic analyses integrating karyotypic, transcriptomic and proteomic data from hundreds of tumor samples to tackle this question. Extending the scope of previous findings suggesting post-transcriptional regulation as a dosage compensation mechanism in response to aneuploidy/CNAs, this study specifically focuses on the effects on non-aneuploid chromosomes, which have remained elusive at the proteome level. Proteins encoded by the non-aneuploid chromosomes found to be differentially abundant in aneuploid tumors, mainly complex with proteins encoded by the aneuploid chromosome. In amplifications, this dosage compensation primarily affects complex partners of aggregation-prone, non-promiscuous aneuploid proteins. In deletions, differentially abundant proteins encoded by the non-aneuploid chromosomes are mainly complex partners involved in essential cell function processes. Both the compensatory and functional maintenance mechanisms are shown to be regulated at the post-transcriptional level. Aneuploid tumors with higher post-transcriptional control of co-complex members of aneuploid proteins were found to associate with poor patient survival suggesting their improved fitness/adaptation to stoichiometric imbalance. These data add to the elucidation of the paradoxical role of aneuploidy in cancer. The conclusions of this paper are mostly well supported by the data, but interpretation of the results can be further improved.

Major comments:

1) The data in Fig. 1C interestingly shows that only 41-48% of the transcripts encoded by the aneuploid chromosomes are altered. This suggests that transcriptional control appears as a major determinant of gene dosage compensation for the aneuploid chromosome, whereas post-transcriptional regulation could predominate for the non-aneuploid chromosomes.

2) Still related to Fig. 1C, perhaps is misleading to show the transcriptome and proteome data in the same graphs. The transcriptome data appears to be from 32 cancer types, whereas the proteome data are from 3 cancer types as mentioned in page 6. The authors should provide in Fig. 1 the graphs of transcriptome and proteome comparative analysis in the COREAD, BRCA and OV cancer types only. This would be useful to address the questions below (see point 3).

3) Then, it would be important to crosstalk the transcriptome and proteome data to determine:<br /> - If the proteome changes primarily include genes altered at the transcriptome level or not;<br /> - If there is gene overlapping in the transcriptional and proteomic changes seen for the other chromosomes, which percentages are interestingly similar.

4) As mentioned in page 10, in the case of chromosomal deletions the aggregation propensity of downregulated proteins on the aneuploid chromosome should not affect the degree of correlation with complex partners. But did the authors consider investigating if the complex partners on other chromosomes, in the case of deletions, are aggregation-prone?<br /> This would mean that post-transcriptional regulation of partner co-abundance might operate in both amplifications and deletions, as well as functional selection apparently.

5) Regarding the phenotypic consequences of stoichiometric compensation proficiency, a correlation is shown in Fig. 6 between lower stoichiometry deviation score and poorer survival. Tumors with higher stoichiometry deviation score correlate with higher abundance of proteins involved in protein degradation. Do these correlations still apply if the tumors' samples are separated in amplification and deletion groups?

Joint Public Review:

A highly robust result when investigating how neural population activity is impacted by performance in a task is that the trial to trial correlations (noise correlations) between neurons is reduced as performance increases. However the theoretical and experimental literature so far has failed to account for this robust link since reduced noise correlations do not systematically contribute to improved availability or transmission of information (often measured using decoding of stimulus identity). This paper sets out to address this discrepancy by proposing that the key to linking noise correlations to decoding and thus bridging the gap with performance is to rethink the decoders we use : instead of decoders optimized to the specific task imposed on the animal on any given trial (A vs B / B vs C / A vs C), they hypothesize that we should favor a decoder optimized for a general readout of stimulus properties (A vs B vs C).

To test this hypothesis, the authors use a combination of quantitative data analysis and mechanistic network modeling. Data were recorded from neuronal populations in area V4 of two monkeys trained to perform an orientation change detection task, where the magnitude of orientation change could vary across trials, and the change could happen at cued (attended) or uncued (unattended) locations in the visual field. The model, which extends previous work by the authors, reproduces many basic features of the data, and both the model and data offer support for the hypothesis.

The reviewers agreed that this is a potentially important contribution, that addresses a widely observed, but puzzling, relation between perceptual performance and noise correlations. The clarity of the hypothesis, and the combination of data analysis and computational modelling are two essential strengths of the paper.

Overall this paper exhibits a new factor to be taken into account when analysing neural data : the choice of decoder and in particular how general or specific the decoder is. The fact that the generality of the decoder sheds light on the much debated question of noise correlations underscores its importance. The paper therefore opens multiple avenues for future research to probe this new idea, in particular for tasks with multiple stimuli dimensions.

Nonetheless, as detailed below, the reviewers believe the manuscript clarity could be further improved in several points, and some additional analysis of the data would provide more straightforward test of the hypothesis.

1. It would be important to verify that the model reproduces the correlation between noise and signal correlations since this is really a key argument leading to the author's hypothesis.

2. Testing the hypothesis of the general decoder:<br /> 2.1 In the data, the authors compare mainly the specific (stimulus) decoder and the monkey's choice decoder. The general stimulus decoder is only considered in fig. 3f, because data across multiple orientations are available only for the cued condition, and therefore the general and specific decoders cannot be compared for changes between cued and uncued. However, the hypothesized relation between mean correlations and performance should also be true within a fixed attention condition (cued), comparing sessions with larger vs. smaller correlation. In other words, if the hypothesis is correct, you should find that performance of the "most general" decoder (as in fig. 3f) correlates negatively with average noise correlations, across sessions, more so than the "most specific" decoder.

2.2 In figure 3f, a more straightforward and precise comparison is to use the stimulus decoders to predict the choice, and test whether the more specific or the more general can predict choices more accurately.

3. The main goal of the manuscript is to determine the impact of noise correlations on various decoding schemes. The figures however only show how decoding co-varies with correlations, but a direct, more causal analysis of the effect of correlations on decoding seems to be missing. Such an analysis can be obtained by comparing decoding on simultaneously recorded activity with decoding on trial-shuffled activity, in which noise-correlations are removed.

4. How different are the four different decoders (specific/monkey, cued/uncued)? It would be interesting to see how much they overlap. More generally, the authors should discuss the alternative that attention modulates also the readout/decoding weights, rather than or in addition to modulating V4 activity.

5. Quantifying the link between model and data :<br /> 5.1 the text providing motivation for the model could be improved. The motivation used in the manuscript is, essentially, that the model allows to extrapolate beyond the data (more stimuli, more repetitions, more neurons). The dangers of extrapolation beyond the range of the data are however well known. A model that extrapolates beyond existing data is useful to design new experiments and test predictions, but this is not done here. Because the manuscript is about information and decoding, a better motivation is the fact that this model takes an actual image as input, and produces tuning and covariance compatible with each other because they are constrained by an actual network that processes the input (as opposed to parametric models where tuning and covariance can be manipulated independently).

5.2 The ring structure, and the orientation of correlations (Fig 2b) seem to be key ingredients of the model, but are they based on data, or ad-hoc assumptions?

5.3 In the model, the specific decoder is quite strongly linked to correlated variability and the improvement of the general decoder is clear but incremental (0.66 vs 0.83) whereas in the data there really is no correlation at all (Fig 3c). This is a bit problematic because the author's begin by stating that specific decoders cannot explain the link between noise correlations and accuracy but their specific decoder clearly shows a link.

6. General decoder: Some parts of the text (eg. Line 60, Line 413) refer to a decoder that accounts for discrimination along different stimulus dimensions (eg. different values of orientation, or different color of the visual input). But the results of the manuscripts are about a general decoder for multiple values along a single stimulus dimension. The disconnect should be discussed, and the relation between these two scenarios explained.

7. Some statements in the discussion such as l 354 "the relationship between behavior and mean correlated variability is explained by the hypothesis that observers use a general strategy" should be qualified : the authors clearly show that the general decoder amplifies the relationship but in their own data the relationship exists already with a specific decoder.

8. Low-Dimensionality, beginning of Introduction and end of Discussion: experimentally, cortical activity is low-dimensional, and the proposed model captures that. But some of the reviewers did not understand the argument offered for why this matters, for the relation between average correlations and performance. It seems that the dimensionality of the population covariance is not relevant: The point instead is that a change in amplitude of fluctuations along the f'f' direction necessarily impact performance of a "specific" decoder, whereas changes in all other dimensions can be accounted for by the appropriate weights of the "specific" decoder. On the other hand, changes in fluctuation strength along multiple directions may impact the performance of the "general" decoder.

Reviewer #1 (Public Review):

Understanding the biology of MAITs is a challenging for immunologists even though this cell type represent interesting functions in a variety of diseases. In this manuscript, the authors present an exciting approach with compelling data to indicate the power of reprogramming on studying MAITs as well as the therapeutic potential on harnessing MAITs for anti-tumor responses. Overall, it is well performed and aims to address interesting and important questions with excellent approaches. Although I find this manuscript interesting, I do have some concerns on the reprogramming approaches the authors applied on the detailed cellular identity. Since transcriptomic and epigenetic events are affected by reprogramming and play important role in cell identity, it would be important to determine the transcriptome and chromatin accessibility in the reprogrammed MAITs and real MAITs from B6 mice. The comparisons will further strengthen the conclusion and allows the author more precisely interpret their results.

Reviewer #2 (Public Review):

The goal of this study was to establish a system that allows the collection of MAIT cells in large quantities in order to investigate their effector functions both in vitro and in vivo.

The employment of iPSC technology to generate re-differentiated MAIT cells from endogenously differentiated bona fide MAIT cells is an elegant approach as it allows interrogation of MAIT cell functions in disease models that may not be feasible with existing TCR transgenic mice due to genetic background or other associated tools. In addition, isolation of MAIT cells in large quantity is not practical due to their low abundance relative to other T cell subsets. Thus, reMAIT cells would be a great addition to the field as a new tool to unravel the previously understudied subset of T cells with significant physiological importance, as the study has illustrated. The authors successfully demonstrated that (1) it is possible to generate reMAIT cells from MAIT cell-derived iPSCs just as efficiently or better than previous iPSCs generated from mouse or human T cells by others, (2) the generated reMAIT cells are viable and functional in vivo after adoptively transferring into syngeneic recipient mice, and (3) the adoptive transfer of reMAIT cells significantly improved the overall survival of mice challenged with a transplantable model of lung cancer.

However, this approach still has its limitations. Although iPSCs were obtained from endogenous bona fide MAIT cells, their re-differentiation into MAIT cells (reMAIT) was carried out in vitro without receiving certain key cell-cell communications in the thymus that may not be recapitulated. While this was necessary in order to obtain enough reMAIT cells for in vivo study by transferring them into syngeneic recipient mice, the generated reMAIT cells show some characteristics that are different from endogenous MAIT cells, such as the acquisition of maturation and tissue-resident markers like CD44 and CD69, respectively, even after two weeks post-transfer. While in vitro activation with MR1 tetramer showed the production of key cytokines and chemokines characteristic of MAIT cells, the interpretation requires the same caution as for other studies utilizing transgenic MAIT TCR mice.

Reviewer #3 (Public Review):

In this work, Sugimoto et al. investigate the role of MAIT cells in tumor immunity, knowing the ambiguous literature describing both anti- and pro-tumoral effects of these cells in cancer. In order to overcome the paucity of these cells in mice - one of the main challenges to study them -, they use induced pluripotent stem cell technology to reprogram and redifferentiate MAIT cells. Upon adoptive transfer, the regenerated MAIT cells are capable of prolong survival in mice beared with LLC tumors through a NK-cell dependent cytolytic activity.

Overall, this is a straightforward, clearly written and well-constructed work. The rationale behind building a more relevant model to study murine MAIT cells in tumors is well introduced, especially in parallel with current studies mostly focused on Knock out or TCR transgenic models. The data and claims are supported by controlled experiments and interesting functional insight are added regarding in vitro and in vivo interactions with NK cells.

While the adoptive transfer technique appears to be an interesting tool, it is not clear how these data issued from in vitro differentiated MAIT cells reflect their role in anti-tumor immunity (both in mice and humans). In particular, insufficient data were provided comparing regenerated MAIT vs. endogenous cells, and the constrained distribution of these cells in different tissues could affect other cells involved in anti- or pro-tumor responses.

Along the same lines, the study is based on MAIT cells stimulated with 5-OP-RU and mMR1-tet in the absence of APC, which is not representative of the actual tumor microenvironment. Knowing the impact of microbiome in cancer and the involvement of microbiota-associated antigens in MAIT TCR recognition and activation, this work could benefit from assays involving such stimuli in order to validate their role as participants of the anti-tumor immunity.

Reviewer #1 (Public Review):

In this report, Mackay et al used Endothelial Cell (EC) specific knockout mice for Polycystin-1 and 2 (PC1 and PC2) proteins to study the function of these proteins in vascular function. PC-1 is a receptor like protein while PC-2 is a TRP family member of mostly non-selective cation channels. They report that either single knockout of PC-1 or PC-2 or double knockout of PC-1 and PC-2 similarly attenuates flow-mediated vasodilation, suggesting that these proteins work together to control vascular function. EC-specific PC-1 knockout mice have increased systemic blood pressure. Using pharmacological compounds, they propose that the vasodilatory function of PC-1 in ECs is mainly mediated by the Ca2+-dependent activation of eNOS, with a small contribution from SK channels. Using coimmunoprecipitations, FRET and SMLM microscopy, they show that PC-1 and PC-2 form close interactions in the plasma membrane and that knockout of either PC-1 or PC-2 inhibits surface clusters of both PC-1 and PC-2 in ECs. Non-selective cation currents activated by flow in ECs were inhibited by either PC-1 or PC-2 knockout or by C-terminal peptides of PC-1 or PC-2. The authors conclude that endothelial PC-1/PC-2 complexes control arterial contractility through Ca2+-dependent activation of eNOS and SK channels. However, neither increases in intracellular Ca2+ concentrations nor NO production were directly measured in the study. Further, there is a disconnect between current measurements under the physiological conditions of Fig 1 and those of Fig 6 that require clarifications.

Reviewer #2 (Public Review):

This work aimed to advance knowledge of the roles of polycystin-1 and polycystin-2 (PC-1, PC-2) in the vascular endothelium. For this, the authors developed tamoxifen-inducible Cre-lox models to delete PC-1, PC-2 or both specifically in endothelial cells of mice. Evidence is presented that flow or sheer stress activates PC-1-dependent current in endothelial cells, which is associated with NOS and KCa channel activation, smooth muscle hyperpolarization, and flow-dependent vasodilation. The Jaggar laboratory has recently reported that deletion of endothelial PC-2, a member of the TRP family, leads to loss of flow-induced Ca2+ influx, NOS and SK/IK activation, reduced vasodilation, and higher blood pressure. Thus, the novelty of the current work is the finding that PC-1 is similarly critical for activation of this pathway by flow, and that it is a physical interaction between membrane-localized PC-1 and PC-2 that underlies complex activation by flow.

Strengths of the current study include the use of powerful inducible knockout models in combination with a wide array of in vivo and ex vivo methods to test hypotheses. Thus, conclusions are based on multiple approaches and are mostly well supported. However, there are some concerns, specifically related to a lack of clarity on the interactions and purported interdependence between PC-1 and PC-2 that warrant further consideration.

1. The prospective impact of the current study is based on the suggestion that interactions between PC-1 and PC-2 via coiled-coil domains are required for activation of inward current by flow. However, the authors did not show evidence, via fluorescence imaging or otherwise (e.g., coIP), that peptides generated to disrupt this interaction actually do so. Does treatment with the coiled-coil domain peptides cause a shift in the PC-1-to-PC-2 distance (using TIRF-SMLM as in Fig 5)?

2. The use of immunoFRET to test for PC-1/PC-2 proximity is not ideal. At minimum, proper negative controls (e.g., use of cells from KO models) should be provided to demonstrate the specificity of this technique for PC-1/PC-2 interactions in endothelial cells.

3. The authors conclude that PC-1/PC-2 clusters in KO cells in SMLM experiments are likely due to non-specific antibody binding. While I agree with this, it raises a question as to the meaning of cluster size data. Considering that the approach relies on fluorophore-tagged antibodies, which cannot be assumed to be in 1:1 stoichiometry with proteins of interest, how relevant is cluster size?

4. Based on data shown in Figure 1, the authors conclude that there is a reduction in inward current with flow. Since the applied technique measures total current, couldn't this result also reflect an increase in outward current (e.g., K+) due to flow that depends on the presence of Ca2+? Also related to these data, the magnitude of initial flow-induced transient current was quite variable (~8 - ~45 pA). Was this due to differences in cell size? The authors should consider expressing data from current recordings in terms of density (pA/pF).

5. Endothelium-specific deletion of PC-1 increased blood pressure, implying that the proposed role for PC-1 is generally applicable to the resistance arterial network; yet here, only small mesenteric vessels were studied. Given the known heterogeneity in the regulation of vascular tone by sheer stress among different arterial beds, is the identified role of PC-1 observed outside of the mesenteric circulation?

Reviewer #3 (Public Review):

This study examined the role played by polycystin-1 (PC-1) in endothelium-dependent, flow-mediated vasodilation using a tomixifen-inducible knockout (KO) of endothelial cell PC-1 in mouse mesenteric resistance arteries. To that end, the authors show that a substantial portion of flow-induced: currents in isolated endothelial cells, hyperpolarization in pressurized arteries and vasodilation is attenuated by PC-1 KO, to a similar extent as with endothelial cell KO of polycystin-2 (PC-2). Immuniprecipitation and super-resolution immune localization provided compelling evidence of interactions of clusters of PC-1 and PC-2 that is required for their function in flow-mediated vasodilation.

Strengths of this study include the combination of approaches utilized including the use of endothelial specific KO's of PC-1 and PC-2, patch-clamp of isolated mesenteric endothelial cells, pressure myography, and sharp micro electrode measurement of membrane potential in pressurized arteries to the functional role of PC-1 and PC-2, along with immunoprecipitation, N-FRET, and super-resolution microscopy to show Interactions between PC-1 and PC-2. Finally, their use of competing peptides to provide additional evidence of physical interactions of PC-1 and PC-2 required for flow-mediated endothelial cell action currents was an additional strength.

Weaknesses that detracted from the strengths of this study include the following. First, the use of only mesenteric arteries in this study, such that the generality of their findings remain to be established. Second, while the authors clearly show roles for PC-1 and PC-2 in flow mediated vasodilation, how these proteins sense flow (shear stress) (is it direct or linked to some other "sensor") remains to be established and was not addressed by the authors. Finally, several other endothelial cell ion channels have been proposed to play roles in flow-mediated vasodilation, but their role and how they "fit" into an integrated scheme with PC-1 and PC-2 remains to be established.

Despite these weaknesses, this study moves the field forward and should provide ample impetus for further investigation.

Reviewer #1 (Public Review):

This paper examines EEG responses time-locked to (or "entrained" by) musical features and how these depend on tempo and feature identity. Results revealed stronger entrainment to "spectral flux" than to other, more commonly tested features such as amplitude envelope. Entrainment was also strongest for lowest rates tested (1-2 Hz).

The paper is well written, its structure is easy to follow and the research topic is explained in a way that makes it accessible to readers outside of the field. Results will advance the scientific field and give us further insights into neural processes underlying auditory and music perception. Nevertheless, there are a few points that I believe need to be clarified or discussed to rule out alternative explanations or to better understand the acquired data.

1) Results reveal spectral flux as the musical feature producing strongest entrainment. However, entrainment can only be compared across features in an unbiased way if these features are all equally present in the stimulus. I wonder whether entrainment to spectral flux is only most pronounced because the latter is the most prominent feature in music. Can the authors rule out such an explanation?

2) Spectral analyses of neural data often yield the strongest power at lowest frequencies. Measures of entrainment can be biased by the amount of power present, where entrainment increases with power. Can the authors rule out that the advantage for lower frequencies is a reflection of such an effect?

A related point, what was the dominant rate of spectral flux in the original set of stimuli, before tempo was manipulated? Could it be that the slow tempo was preferred because in this case participants listened to a most "natural" stimulus?

3) The authors have a clear hypothesis about the frequency of the entrained EEG response: The one that corresponds to the musical tempo (or harmonics). It seemed to me that analyses do not sufficiently take that hypothesis into account and often include all possible frequencies. Restricting the analysis pipeline to frequencies that are expected to be involved might reduce the number of comparisons needed and therefore increase statistical power.

Reviewer #2 (Public Review):

Kristin Weineck and coauthors investigated the neural entertainment to different features of music, specifically the amplitude envelope, its derivative, the beats and the spectral flux (which describes how fast are spectral changes) and its dependence on the tempo of the music and self-reports of enjoyment, familiarity and ease of beat perception.

They use and compare analysis approaches typically used when working with naturalistic stimuli: temporal response functions (TRFs) or reliable components analysis (RCA) to correlate the stimulus with its neural response (in this case, the EEG). The spectral flux seems the best music descriptor among the tested ones with both analyses. They find a stronger neural response to stimuli with slower beat rates and predictable stimuli, namely familiar music with an easy-to-perceive beat. Interestingly, the analysis does not show a statistically significant difference between musicians and non-musicians.

The authors provide an extensive analysis of the data, but some aspects need to be clarified and extended.

1. It would be helpful to clarify better the concepts of neural entertainment, synchronization and neural tracking and their meaning in this specific context. Those terms are often used interchangeably, and it can be hard for the reader to follow the rest of the paper if they are not explicitly defined and related to each other in the introduction. Note that this is fundamental to understanding the primary goal of the paper. The authors clarify this point only at the end of the discussion (lines 570-576). I suggest moving this part in the introduction. Still, it is unclear why the authors use the TRF model and then say they want to be agnostic about the physiological mechanisms underlying entertainment. The choice of the TRF (as well as the stimulus representation) automatically implies a hypothesis about a physiological mechanism, i.e., the EEG reflects convolution of the stimulus properties with an impulse response. Please could you clarify this point? I might have missed it.

2. Interestingly, the neural response to music seems stronger for familiar music. Can the authors clarify how this is not in contrast with previous works that show that violated expectations evoke stronger neural responses ([Di Liberto et al., 2020] using TRFs and [Kaneshiro et al., 2020] using RCA])? [Di Liberto et al., 2020] showed that the neural response of musicians is stronger than non-musicians as they have a stronger expectation (see point 2). However, in the present manuscript, the analysis does not show a statistically significant difference between musicians and non-musicians. The authors state that they had different degrees of musical training in their dataset, and therefore it is hard to see a clear difference. Still, in the "Materials and Methods" section, they divided the participants into these two groups, confusing the reader.

3. Musical expertise was also assessed using the Goldsmith Music Sophistication Index, which could be an alternative to the two-group comparison between musicians and non-musicians. Does this mean that in Figure 5, we should see a regression line (the higher the Gold-MSI, the higher should be the TRF correlation)? Since we do not see any significant effect, might this be due to the choice of the audio descriptor? The spectral flux is not a high-level descriptor; maybe it is worth testing some high-level descriptors such as entropy and surprise. The choice of the stimulus features defines linear models such as the TRF as they determine the hierarchical level of auditory processing, and for testing the musical expertise, we might need more than acoustic features. The authors should elaborate more on this point.

4. Regarding the stimulus representation, I have a few points. The authors say that the amplitude envelope is a too limited representation for music stimuli. However, before testing the spectral flux, why not test the spectrogram as in previous studies? Moreover, the authors tested the TRF on combining all features, but it was not clear how they combined the features.

Reviewer #3 (Public Review):

This study uses novel methodologies to study the neural tracking of music, and the results highlight the importance of accounting for spectral changes when quantifying neural tracking to music. However, more work needs to be done to validate that the results are not a consequence of their analyses or their choice of music before tempo manipulation.

One of the key strengths of this study is the use of novel methodologies. The authors in this study used natural and digitally manipulated music covering a wide range of tempi, which is unique to studies of musical beat tracking. They also included both measures of stimulus-response correlation and phase coherence along with a method of linear modeling (the temporal response function, or TRF) in order to quantify the strength of tracking, showing that they produce correlated results. Lastly, and perhaps most importantly, they also had subjects tap along with the music after listening to the full excerpt. While having a measure of tapping rate itself is not new, combined with their other measures they were able to demonstrate that neural data predicted the hierarchical level of tapping rate, opening up opportunities to study the relationship between neural tracking, musical features, and a subject's inferred metrical level of the musical beat.

Additionally, the finding that spectral flux produced the best correlations with the EEG data is an important one. Many studies have focused primarily on the envelope (amplitude fluctuations) when quantifying neural tracking of continuous sounds, but this study shows that, for music at least, spectral flux may add information that is tracked by the EEG. However, given that it is also highly correlated with the envelope, what additional features spectral flux contributes to measuring EEG tracking is not clear from the current results and worth further study.

All four of their main findings are important for research into the neural coding of musical rhythm. I have some concerns, however, that two of these findings could be a consequence of the methods used, and one could be explained by related correlations to acoustic features:

The authors found that their measures of neural tracking were highest for the lowest musical tempos. This is interesting, but it is also possible that this is a consequence of lower frequencies producing a large spread of correlations. Imagine two signals that are fluctuating in time with a similar pattern of fluctuation. When they are correctly-aligned they are correlated with each other, but if you shift one of the signals in time those fluctuations are mismatched and you can end up with zero or negative correlations. Now imagine making those fluctuations much slower. If you use the same time shifts as before, the signals will still be fairly correlated, because the rates of signal change are much longer. As a result, the span of null correlations also increases. This can be corrected by normalizing the true correlations and prediction accuracies with a null distribution at each tempo. But with this in mind, it is hard to conclude if the greater correlations found for lower musical tempos in their current form are a true effect.

If the strength of neural tracking at low tempos is a true effect, it is worth noting that the original tempi for the music clips span 1 - 2.5 Hz (Supplementary Table 1), roughly the range of tempi exhibiting the largest prediction accuracies and correlations. All tempos above this range are produced by digitally manipulating the music. It is possible that the neural tracking measures are higher for music without any digital manipulations rather than reflecting the strength of tracking at various tempi. This could also be related to the author's finding that neural tracking was better for more familiar excerpts. This alternative interpretation should be acknowledged and mentioned in the discussion.

Their last finding regarding predicting tapping rates is novel and important, and the model they use to make those predictions does well. But I am concerned by how well it performs (Figure 6), since it is not clear what features of the TRF are being used to produce this discrimination. Are the effects producing discriminable tapping rates and stimulation tempi apparent in the TRF? I noticed, though, that these results came from two stages of modeling: TRFs were first fit to groups of excerpts with different tapping rates or stimulation tempo separately, then a support vector machine (SVM) was used to discriminate between the two groups. So, another way to think about this pipeline is that two response models (TRFs) were generated for the separate groups, and the SVM finds a way of differentiating between them. There is no indication about what features of the TRFs the SVM is using, and it is possible this is overfitting. Firstly, I think it needs to be clearer how the TRFs are being computed from individual trials. Secondly, the authors construct surrogate data by shuffling labels (before training) but it is not clear at which training stage this is performed. They can correct for possible issues of overfitting by comparing to surrogate data where shuffling happens before the TRF computation, if this wasn't done already.

Lastly, they show that their measures of neural tracking are larger for music with high familiarity and high ease-of-tapping. I expect these qualitative ratings could be a consequence of acoustic features that produce better EEG correlations and prediction accuracies, especially ease-of-tapping. For example, music with acoustically-salient events are probably easier to tap to and would produce better EEG correlations and prediction accuracies, hence why ease-of-tapping is correlated with the measures of neural tracking. To understand this better, it would be useful to see how the stimulus features correlate with each of these behavioral ratings.

Reviewer #1 (Public Review):

The authors present the structure characterization of the unusual enzymatic Friedel-Crafts alkylation catalyzed by CylK in cylindrocyclophane biosynthesis. Based on the mutagenic screening, anomalous diffraction datasets with Bromide ions and molecular dynamic simulation, they identified the key residues for catalysis, and proposed an activation mechanism. The bioinformatics analysis of cylk homologues from other cyanobacteria indicates the preservation of key catalytic amino acids. The study is infomative for enzyme engineering, catalyst design, and natural product discovery.

Reviewer #2 (Public Review):

CylK is an enzyme with unusual Friedel-Crafts alkylation activity. Its 3D structure was determined by MR-SAD using the terbium(III) chloride soaking method and revealed a two-domain architecture. Although the substrate-bound structure was not feasible, the authors did not stop there and tried to locate the substrate chloride and hydroxyl group binding positions in a mechanistic sense by NaBr soaking. This effort resulted in identifying a putative substrate-binding site, which was further supported by mutant activity screens and the molecular dynamics simulation. Placing the active site at its N- and C-terminal domains interface is convincing and inspiring. Moreover, the CylK structure with mutagenesis study also identified the key residues Asp440 and Arg105 for the catalytic activity at the interface between Ca(II)-binding and beta-propeller domains.

Reviewer #1 (Public Review): 

In this manuscript, Goyal et al. address the important question of the taxonomic level that is the most informative ecologically for understanding microbial community dynamics. To do so, they isolate microbial communities from pitcher plants and "domesticate" them for 21 transfers until they reach some steady-state. Then they observe strain and species dynamics in these communities for >300 generations using 16S rRNA (species) and metagenomics (strains). They arrive at the conclusion that strain dynamics can diverge at a genetic distance of ~100 base pairs. I expect this conclusion, and the number value attached to it, to become an important and well-cited figure in microbial ecology. The impact of this study would therefore benefit from a more rigorous account of how the phylogenetic inference of strains and species was made.

Reviewer #2 (Public Review): 

General sentiment: This paper is, overall, excellent. There are a small number of points which could be clarified, some language which should be amended, and some additional analyses which could potentially strengthen the paper.

Reviewer #3 (Public Review): 

The authors cultivated natural microbial communities derived from pitcher plants (Sarracenia purpurea) under laboratory conditions over an extended period of time (i.e. > 300 generations). Throughout the experiment, community dynamics were quantified by sequencing metagenomes at different time intervals. By statistically analyzing correlations in the abundances of genotypic variants at different levels of phylogenetic differentiation revealed that (1) strain abundances tended to be more correlated than frequencies of species, (2) strains started to decouple when the genetic distance exceeded 100 SNPs, and (3) the correlation of the abundance trajectories was greater when interspecific strains were compared than between the respective species. The findings were recapitulated with consumer-resource models (with and without phenotypic differences between strains) to exclude the possibility of the observed dynamics being merely the consequence of stochastic effects. Finally, comparing the genomes of coupled versus uncoupled strains suggested SNPs in genes coding for transporters, regulators, and enzymes in central carbon metabolism mainly differentiated strains of both groups. Based on these results, the authors conclude that understanding the long-term evolution of microbial communities requires knowledge of the dynamics on the level of strains rather than species. 

The manuscript is well written and clearly structured. The rather complex data set is presented in a largely comprehensible way. 

However, the main conclusions of the paper rest entirely on the ability to detect ecological interactions between genotypic variants as their correlated changes. For example, strains can show positively or negatively coupled oscillations that may even be time-lagged. Thus, the question is: (i) How sensitive is the statistical approach used to also detecting such patterns? and (ii) How robust is the conclusion if these pattern remain undetected? These points should be clarified in the manuscript.

Reviewer #1 (Public Review): 

Jairaman and colleagues address the functional consequences of ablating the expression of TREM2, a myeloid protein that has been linked to Alzheimer's Disease. The study finds that TREM2 KO microglia derived from iPSC cells have exaggerated ADP/ATP evoked Ca signals, which is found to arise from increases in P2Y12 and P2Y13 receptor expression and enhanced receptor-evoked Ca signaling. Previous studies have shown that microglia migrate towards regions of brain damage or injury to clear the affected region of debris, infections, and plaques. Microglial migration is mediated in part by nucleotides released into the affected regions. Through detailed analysis of cell migratory patterns , the authors determine that the TREM2 KO microglia show alterations in cell migration that are manifested in several different ways. These changes include increase in the root mean square distance travelled, reduced turning, and markedly reduced directed migration in a chemotaxis assay. Directed migration is rescued by blocking P2Y12 receptors, confirming that the defects in ADP mediated cell migration is linked to enhanced P2Y12 receptor activity. 

The experiments and analysis are carefully performed using appropriate controls and the results are novel and add to our understanding of how loss-of-function TREM2 mutations impact microglial migration and the ensuing microglia-mediated clearance of plaques and damage seen in AD. That said there are some key issues that need to be addressed including whether increased displacement and track straightness in KO cells underlies the chemotaxis defect. Figure 7 I think shows a lot of promise of what could be, but there is no preliminary in vivo experiment for it to hold up which diminishes the significance of this in vitro study.

Reviewer #2 (Public Review): 

In this study, Jairaman et al used iPSC-derived microglia in which the AD-associated TREM2 gene has been knocked out to determine the impact of TREM2 loss of function on receptor-evoked Ca2+ signaling and chemotaxis. Cytoslic Ca2+ measurements were performed using the genetically-encoded ratiometric indicator Salsa6f previously developed by this laboratory. The authors made the critical discovery that loss of TREM2 leads to enhanced sensitivity and increased Ca2+ signaling of microglia to purinergic agonists, in particular to ADP. They showed that Store-operated Ca2+ entry in response to passive -maximal-store depletion by SERCA blockers was not altered in TREM2 KO cells. Rather, the enhanced sensitivity of the TREM2 KO cells was shown to originate from an upregulation of the purinergic receptors P2YR12 and P2YR13, leading to a left shift in EC50 of Ca2+ responses to ADP. The enhanced Ca2+ responses of TREM KO cells were associated with altered directional chemotaxis, whereby TREM2 KO cells showed enhanced displacement but reduced directionality. This phenotype was rescued with the application of P2YR antagonists in ADP-dependent chemotaxis assays. These results are novel, significant and of potentially broad impact to the pathology of AD. Although the molecular mechanisms of how lack of TREM2 leads to enhanced P2YRs is beyond the scope of this study, one moderate criticism of this manuscript pertains to lack of insights on how enhanced cytosolic Ca2+ leads to reduced directional chemotaxis and the potential effector proteins/pathways mediating this effect. Other relatively moderate issues and suggestions regarding controls have also been noted.

Reviewer #3 (Public Review): 

This study by Jairaman et al describes how iPSC-derived microglia exhibit exaggerated cytosolic Ca2+ responses to ADP stimulation in TREM2KO cells, and that this leads to a defect in turning behaviour and hence no directed migration to a chemotactic signal. 

The authors have used state-of-the-art molecular and cellular techniques to rigorously examine the inpact of TMEM2 KO on CA2_ signalling and astrocyte migration. Overall, the experiments are well conducted, carefully controlled and the findings are new and exciting. The authors nicely dissect out the underlying molecular basis for the larger Ca2+ responses to ADP and then extend their findings to cell movement and directed migration. Given the substantial body of evidence linking microglia to the pathogenesis of Alzheimer's disease, and the role for TREM2, the work by Jairaman et al. is of translational significance. As an aside, the introduction of the calcium-sensitive reporter Salsa6F is a welcome new tool in the arsenal for recording cytosolic calcium. The work will be of significant impact to the field because i) it identifies new roles for SOCE in the brain and ii) identifies CRAC channels as a target for altering microglia lol activity in Alzheimer's disease.

Reviewer #1 (Public Review):

In this study, Antonello et al. provide a detailed analysis of the evolution of effective connectivity along a 30-day maturation period of dissociated hippocampal networks in vitro. By using a rich repertoire of network measures and topological analyses, and linking them to neuronal activity-dependent mechanisms, the authors show that the networks gradually shift from a segregated configuration to an integrated one, with the emergence of a small-world organization, specific motifs, and modular traits that reflect the tendency of nearby neurons to interconnect. Altogether, this study substantially helps to understand in detail how neuronal circuits self-organize to shape non-random topological features that optimize the tradeoff between wiring cost and functional efficiency.

Strengths:<br /> - A robust and extensive analysis of experimental neuronal activity data using tools from complex networks, accompanied with substantial statistics.<br /> - The inclusion of numerical simulations to validate effective connectivity inference.<br /> - A Discussion section that analyses in detailed the obtained results in the context of the literature, bringing to light new concepts and ideas that help to understand the richness of self-organization and the emergence of complex topologies.

Minor weaknesses:<br /> - The Results are presented in a very concise manner and may be difficult to follow. Some details and small additional analysis could be incorporated here to help the reader to fully understand the results.<br /> - Some additional numerical simulations may be required to fully grasp some analyses and their implications.<br /> - Some parts of the Discussion could be extended to treat aspects that are not sufficiently clear, such as the role of inhibition or neuronal spatial distribution.

Reviewer #2 (Public Review):

By analysing signals from hippocampal neuron cultures, the manuscript describes how so-called effective neuronal connections change during the maturation process. The authors use a range of metrices such as small-worldness and modularity to characterize these changes with an aim of understanding the underlying self-organizing mechanisms.

Strengths. The manuscript's primary merit is that by weaving together different approaches from network neuroscience, it offers a systematic characterization of the changes of effective connections during the development process of neuron cultures.

Weakness. I see two main weaknesses in the manuscript:<br /> (1) Beyond the fact that the properties of effective connections change during the maturation process, the key mechanism of how and why the networks behave in the way as described in the manuscript is unclear. In my opinion, this lack of mechanistic account of the findings does not yield new insights into understanding the development of neural networks.<br /> (2) The general applicability of the findings based on neuron cultures to understanding neural development in vivo is limited. For instance, this study suggests that inserting new connections is crucial for the emergence of the complex coupling architecture, which contrasts with the established in vivo finding that synaptic pruning is a principled process during neural network development.

Because of these, it is unclear that the analysis presented in the manuscript significantly advances our understanding of developing neural networks.

Reviewer #3 (Public Review):

Neuronal activity in the adult brain is thought to be the result of genetic, chemical, mechanical, and stimulus specific factors acting on the connections between neurons. However, how this process takes place is not completely understood. To address this problem, Antonello et al. used primary cultures of dissociated rat hippocampal neurons. Neurons in culture spontaneously form networks. In order to follow neuronal network evolution over (~30 days) time, the authors let the cultures grow on a grid of electrodes (8x8, 200µm step distance). They were, thus, able to infer network structural features from the recorded activity (using transfer entropy), to follow network topology over time, and to study how neuronal assemblies influenced network evolution. The authors found that: (i) networks showed growth in edge density for the first 21 days, and saturated thereafter; (ii) networks progressed from segregated (high clustering coefficient) to integrated (low shortest path), with five 3-motifs occurring more often than chance (labels 5, 8, 11, 12, 13); (iii) neuronal populations formed non-overlapping sub-networks made of neighboring (not necessarily adjacent) neurons; (iv) in these communities, the most active neurons could be analyzed as 'hubs' according to a scoring system based on neuron degree, connections weights, betweenness, and closeness. High scoring neurons were found to be reliable participants (integrators) of distinct sub-networks.

The work addresses an important question for a broad audience. It adopts a clever experimental strategy. The results are clearly presented, consistently tackle the central question, and represent an advancement of our understanding. However, a number of points should be clarified or expanded (see details in the recommendations to authors).

Reviewer #1 (Public Review): 

The manuscript by Piccolo and colleagues employs an in vitro neuruloid system to investigate the role of Hippo/YAP signaling pathway in early ectodermal fate specification. The authors examine YAP expression in forming neuruloids and test how manipulation of Hippo/Yap signaling affects their cellular composition. They observe that YAP expression is dynamic and enriched in cells occupying periphery of the neuruloid. Overactivation of the YAP activity by the Lats-kinase inhibitor TRULI leads to an expansion of TFAP2A+ cells (NNE) at early stages and of KRT18+ cells (epidermal) at later stages of development. Accordingly, the authors propose that YAP acts as a lineage determinant that (i) promotes a NNE fate during early development and (ii) impacts the fate of NNE cells by promoting an epidermal instead of a neural crest fate. Finally, the authors report that neuruloids developed with cells harboring mutations characteristics of Huntington's disease display elevated Yap activity. 

The study takes advantage of the neuruloid system to examine the role of Hippo-Yap in early development and disease. A strength of the study is the use of the neuruloid as a proxy for the human embryo, which allows the authors to examine the control of spatial patterning in early development (in both wild type and altered cellular states). Yet, this model also presents significant limitations. Some of the results indicate a high degree of variability in YAP activity (and ectodermal patterning) in neuruloids obtained from different inductions. This raises the concern that the neuruloid system may interfere with Hippo/YAP. Furthermore, the model proposed by the authors is not consistent with the functional manipulations with pharmacological agents (e.g., pharmacological activation of YAP results in an increase of both neural and NNE cells; inhibition of YAP does not result in the expected phenotypes). 

Comments: 

The authors propose that YAP activation promotes a non-neural ectodermal (NNE) fate in early neuruloids, and subsequently drives NNE to differentiate into epidermis. However, manipulation of Hippo signaling with pharmacological inhibitors does not entirely support this, as treatment of neuruloids with agonist TRULI leads to expansion of both the PAX6 neural population and the NNE Tfap2a population. A prediction of the model is that treatment with verteporfin should neuralize the organoids, which is not the case (Fig 6A). This disconnect between the model presented in Figure 6D and the experimental results should be addressed by the authors. 

The study at times conflates YAP expression with activation of the Hippo-YAP pathway. While the images in figures 1,2, and 4 show changes in YAP expression, confirmation of Hippo-YAP pathway activity should include the use of a reporter (e.g., HOP-Flash) or at least high magnification images showing translocation of YAP to the nucleus. Overall, inclusion of better quantification of YAP-activity is crucial to support the manuscript's conclusions (the authors should also state the number of micropatterns used in each quantitative experiment). 

A limitation of the study is that it does not investigate the possibility that Hippo/Yap could be affecting cell proliferation in the different lineages, instead of acting as a cell fate determinant. This is particularly important since Hippo is affected by cell density, which varies from the center to the periphery of the neuruloid. Different rates of proliferation over several days could potentially lead to drastic changes in neuruloid cellular composition. 

The results of the study contradict a previous reports, and some of these contradictions are not sufficiently addressed. The authors state that the activation of YAP in culture leads to a "complete loss of NC-like SOX10+ colonies"; however, a number of studies in in vivo models support a role for YAP as a positive regulator of neural crest specification. Furthermore, the authors briefly speculate on the finding that Huntington's disease neuruloids have high YAP activity (whereas tissues from patients have low activity), but there is no real clear link to the pathophysiology of the disease. 

Experimental results presented in different figures are often inconsistent throughout the manuscript. This should be examined by the authors since it suggests a lack of reproducibility in the neuruloid protocol. For example, the expression of TFAP2A at D4 neuruloids is a sparse halo at D4 in Fig4D, but robust in Fig1E. The western blot in fig1D shows bands for tYAP and pYAP at D4, while in Fig2B the bands are not present (Fig1D also shows double bands for both markers while fig2B presents single bands). As Hippo responds very quickly to cell density, mechanical forces, etc., these inconsistencies could affect the proposed analyses.

Reviewer #2 (Public Review): 

This manuscript by Piccolo et al identifies YAP signalling as key player in lineage determination during development of early human ectoderm. Additionally, the authors show that neuroloids generated using cells engineered to express penetrant levels of CAG repeats in the HTT gene display aberrant YAP signalling during ectodermal specification and that this phenotype can be partially rescued by inhibition of this pathway. This is interesting study and the similarity of the YAP-activated neuroloids and the HD neuroloids is striking. The value of this work would be increased by providing experiments to definitively demonstrate the role of YAP signalling in NNE specification and in HD neuroloids. 

Specific comment: The authors describe the emergence of non-neuronal ectoderm (NNE) at the edges of the printed island cell colony and neuronal ectoderm (NE) within this circular colony. However, they do not show images of any lineage markers confirming that these regions are, in fact, NNE and NE. They also don't show that this YAP-GFP cell line recapitulates endogenous fix-and-stains of YAP in these colonies.

Reviewer #3 (Public Review): 

This study presents a human neuruloid model that has been engineered to report for expression and localisation of the transcription factor YAP, which is the downstream target of the Hippo pathway. The authors then use this model to investigate the role of the Hippo signalling cascade in the specification of neural and non-neural cell fates during human neurulation. The main technique used to manipulate YAP activity is the chemical inhibitor TRULI, which supresses YAP phosphorylation and therefore leads to its exclusion from the nucleus. This leads to the conclusion that YAP expression and dynamics are dynamically regulated during neurulation to progressively specify different cell fates. The authors also demonstrate that inhibition of YAP phosphorylation in WT neuruloids causes errors in neurulation that are similar to a Huntington's Disease neuruloid model. 

A key strength of this work is the use of the YAP reporter neuruloid model, which has enabled the authors to dissect an otherwise complicated regulatory relationship. They also combine their approach of inhibiting YAP phosphorylation with single cell genomics, therefore uncovering a global view of the effects on the transcriptional landscape. 

Overall, the conclusions of this work are supported by the data presented, but some aspects of the data acquisition and experimental logic will need to be clarified to fully support the conclusions.

Reviewer #1 (Public Review): 

The authors describe a fruit fly strain that combines constructs that establish both repressible female-lethality and genetic incompatibility based on CRISPR transactivation. They show that this strain has high penetrance for these two traits and that it can suppress wild-type flies when released into cycling cage populations. The paper is thus a neat technology-demonstrator for a genetic control strategy possibly applicable to other insects such as pests or vectors.

Reviewer #2 (Public Review): 

Ambuj Upadhyay et al. developed a novel genetic control strategy named as sex-sorting incompatible male system (SSIMS) for insect pests and demonstrated it in the model insect Drosophila melanogaster. This study is based on the previous works from the same group which developed the genetic incompatibility based on the lethal overexpression of a target gene and conditional female lethality based on the Tetracycline(Tet)-off system. The authors successfully generated viable SSIMS flies that contain transgenes for both genetic incompatibility and female lethality, and used them for female lethality, male competitiveness and laboratory cage trials. The design of SSIMS system is highly complex but well executed by the authors, and the results from the cage trials were promising which suggest this could be an alternative method to species-specific pest control approaches such as insect sterile technique (SIT). 

Strengths: 

During the last seven decades, SIT has been used to battle insect pests worldwide. However, there are some factors may limit its application in certain species. Specifically, two major concerns are the fitness penalty that induced by radiation and inefficient control effect caused by bi-sex releasing. To compensate these two aspects, very often a large number of sterile insects are needed for releasing which considerably increased the running costs of SIT program. In some cases, bi-sex release may not be allowed since the harmful effects from released females cannot be tolerated. This study developed insect strains that could potentially address these two problems simultaneously: radiation is no longer needed due to the engineered genetic incompatibility (although the fitness of such insects needs to be further evaluated towards field application) and male-only releasing is possible due to the conditional female lethality. While female lethal strains have been generated in some insect species, this is one of the first studies that incorporating male sterility (mimicked by incompatibility) and female lethality into the same insect strain. 

Due to the binary design for lethality, SSIMS created a redundant system that kill insects in two different mechanisms. This could be particular important to slow the resistance to such strain in mass-rearing or field scenarios. 

The results from the cage trials supported some major claims of the authors. Specifically, releasing SSIMS caused collapse of wildtype (WT) population, and SSIMS females died out after the releasing stopped, suggesting that SSIMS can be an effective but also self-limited strategy which might be favoured by regulation. 

Weaknesses: 

The competition assay used in this study may not truly reflect the competitiveness of SSIMS males. The mating assay used 20 virgin WT females and 4 males (including both WT and SSIMS), resulting 5:1 sex ratio so the males are not really competing for females. A more competitive ratio (such as WT females: WT males: SSIMA males at 1:1:1) should be designed to address this. Also, the sperm competition assay mixed the mated WT females with SSIMS males for 12 days, allowing plenty of time for the females to remate with these males. Therefore, it's more like a sperm replacement assay rather than competition assay. The authors should either repeat it with a strict time control, or soften their statements for sperm competitiveness. 

Some necessary information or statistics are not shown or mis-presented. For example, the alternative splicing diagram in Figure 1c likely was taken from the original transformer gene, but here it's the tTA gene so the male intron should be removed since it's not in the construct; the panels of Figure 2 were not consistent to the legend and confusing; the statistics for different tetracycline concentration tests were not shown in Figure 2 or text to answer their hypothesis "(to) optimize rearing of SSIMS stock, .....we titrated Tet in the food"; Figure 3b shows 5-8 day old females were used but in the text it's 5-6 day, and it didn't mention the duration of the first crossing and time lag until the second crossing which are critical in such experiments; the conclusion and statistics for Figure 3c among tests with mixed males should also be mentioned. 

The discussion is largely towards the merits of SSIMS but missing some key points that might decide how it can be translated into applications or transferred to other species. First, the actual basis for tTA lethality that employed in this study is still unknown which is subject to suppression by a pre-existing inherent variation in the targeted field population. The very phenomenon may also be true for any gene-overexpression-based lethality including EGI lines generated here. Second, the complete penetrance observed from the relatively small sample size here can be hardly used to predict field or mass-rearing condition. Previous study showed that mutations in such lethal construct could occur at a one out of 10,000 frequency, and typical SIT program release millions of sterile insects every week. Third, while the authors claimed SSIMS is "one of the most complex engineered systems in insects", they also proposed that "the genetic design is likely to be portable to other species" without mention any potential obstacles along the way. Therefore, efforts should be made to give full picture of SSIMS including rain and sunshine.

Reviewer #2 (Public Review): 

This is a fascinating study that adds great resolution to the mechanisms of water flow in the mouth of fish during suction feeding. Using high-speed x-ray video (XROMM) to track food items and particles in the water, the authors show convincingly that fish have an intriguing ability to generate flows that center the food at the esophagus, and that intraoral flow differs between species. The video is impressive, showing all the particles flow into the mouth, and separation to direct food to the gullet and water to the outflow exit (gill arches). 

The methods of XROMM and particle tracking are quite well known – there is nothing new in either approach, nor in combining them to track the prey item. However, the authors created a new kind of marker to enable tracking water flow patterns; a bead surrounded by foam, to create neutral buoyancy, that worked really well. Overall a fascinating study that adds to our understanding of suction feeding in fish.

Reviewer #1 (Public Review): 

Suction feeding is recognized as a nearly ubiquitous prey capture mode in fishes, and the hydrodynamics of these flows are reasonably understood. Provoni et al. deal with a role that is as important but much less understood, i.e. the role of these flows in intra-oral prey transport. Specifically, they ask how the flows within the buccal cavity can help transport the prey into the mouth. The major obstacle to understand these flows is that they are internal, so it was difficult to quantify them. 

Here, the authors developed and used a technique that enabled tracking of tracer particles that are smaller and less dense than the prey, thereby improving our ability to quantify the flows. They show that the suction flows can be directed towards the esophagus at least in one of the species they used, and that repeated bidirectional flows can be used to redirect particles trapped at the branchial basket towards the esophagus. In doing so, they highlight the role of the suction flows in transport of food, providing a possible explanation to the ubiquity of suction flows even among fish that don't rely on the external flows to capture their prey. 

Quantifying internal flows is a demanding task, and the paper presents new and exciting data. As is typical to new techniques, there are important limitations to its current use. The tracers are larger than those used for particle imaging velocimetry, and are heavier than the water. Therefore, they don't track the water precisely. It is difficult to predict the error generated due to this limitation, because it depends on the velocity gradients in the flow (for example accelerations). Additionally, the number of tracers is limited, so they provide a partial representation of the flows within the mouth. It stands to reason that particles drawn from different locations will have different trajectories, however this is not quantitatively analyzed. 

Particle tracking can lend itself to a quantitative analysis of the transport flows, but unfortunately the paper does not take full advantage of these capacities. The intake flow patterns are qualitatively described, and a quantitative estimate of the volume of water that passes near the esophagus are examples for such potential. Other important parameters such as efficiency can be potentially derived directly. 

The most important message of the results presented is that the flow of water inside the mouth has a functional role in moving the prey towards the esophagus, and that it can differ between species. These results teach us that the suction flows are important not only to prey capture but also to prey transport.

Reviewer #1 (Public Review):

Summary:

The authors have discovered and characterized a novel genetic pathway responsive to hypoxia, which acts in parallel to the canonical response through activation of Hypoxia-Inducible Factor (HIF). Specifically, the authors discovered that the Caenorhabditis elegans nuclear hormone receptor NHR-49, ortholog to mammalian PPAR-alpha, is essential for survival under hypoxic conditions and regulates target gene expression that is hif-1-independent; identifying an essential role of autophagy. Further the authors discover both positive and negative regulators of NHR-49 and a putative feedback loop.

Overall analysis:

The genetic analysis conducted by the authors is outstanding. However, the study is lacking in a few key areas and the authors may have over-interpreted results in a few places, which diminishes my overall enthusiasm. These concerns are addressable and doing so would greatly strengthen the manuscript. I highlight individual major concerns below, and save minor concerns and specific suggestions for private recommendations for the authors.

Major concerns:

1. The authors have provided strong genetic evidence for a parallel mechanism to canonical HIF-1 activity in response to hypoxia. The authors should more rigorously test whether there is evidence for cross-talk between the two mechanisms. In the discussion the authors' highlight findings in mammals that support this possibility. For example, does loss of one lead to hyperactivation of the other in an attempt to compensate for hypoxia? Specific examples:<br /> • In regards to lines 425-426, does loss of hpk-1 stabilize HIF-1 (or does hpk-1(oe) repress hif-1)?<br /> • Does loss of hif-1 or nhr-49 alter the expression, stability, or activity of the other (either under normoxic or hypoxic conditions)?<br /> • Can overexpression of either hif-1 or nhr-49 rescue the developmental defects caused by loss of the other (i.e. overexpress hif-1 in nhr-49 mutant animals, and vice versa).<br /> • Does NHR-67 negatively regulate hif-1 (specificity to NHR-49)?

2. The role of autophagy in hypoxia should be explored in greater detail. While the evidence presented by the authors clearly demonstrates autophagy is essential for hypoxic survival, autophagy is an important component of many biological processes. Thus, it's critical to distinguish whether autophagy is merely required (perhaps for very indirect reasons) or whether autophagy is a part of an adaptive response to hypoxia. The authors (Miller lab) previously failed to find a role for autophagy in hypoxia (Fawcett et al. 2015 Aging Cell), which should be addressed. Has autophagy been previously linked to hypoxia in C. elegans? The novelty of this discovery should be discussed in greater detail.

3. The authors have possibly over-interpreted their results in Figure 4B and the possibility that NHR-49 acts cell non-autonomously. The authors speculate that tissue specific genetic rescue by NHR-49 over-expression could indicate the existence of a signaling molecule (line 499). Ectopic over-expression of a transcription factor within one tissue is always tricky to interpret, as it may not be physiologically relevant, which I fear may be the case as rescue is achieved when NHR-49 is over-expressed within any tissue (i.e. there is no specificity). An alternative explanation, which is a more indirect model, is that NHR-49 over-expression shifts metabolism within a tissue to generate metabolites that are released throughout the organism to sustain it during hypoxia.

4. As an extension of MC#3, the authors demonstrate that NHR-49 is induced throughout the animal after hypoxia (Figure 5A). Presumably sites of NHR-49 induction (tissues) equates to the sites where nhr-49 is necessary. However, the images within 5A cannot be resolved to identify individual tissues, higher resolution images are necessary and quantification of GFP expression within individual tissues could lend biological insight.

5. The gene expression analysis is lacking details. For example, the RNA-seq data shown in Figure 3A&B is confusing. The numbers in the text do not match the figure and it is unclear whether the intersection in the Venn Diagram represent inverse relationships (i.e. the proportion of genes that are upregulated in wild-type that are either hif-1 or nhr-49 dependent). Greater detail and explanation is needed, as presented little biological insight can be discerned from the Figure 3A&B. Next, qRT-PCR validation of autophagy gene expression found in Figure 3C should be provided with that result. Lastly, are there existing datasets for changes in gene expression of C. elegans exposed to hypoxia? If so, how do the datasets compare?

6. The authors identify a putative negative feedback loop between NHR-67 and NHR-49, and suggest this regulation is at the protein level (Figure 5F,G) based on a translational reporter and not transcriptional regulation based on qRT-PCR results and similar results previously found with hpk-1 (Figures S5A, 7a, and a previous study). However, the authors should more rigorously rule out dynamic changes in expression between tissues that cannot be ascertained by qRT-PCR (i.e. test whether nhr-49p::GFP expression is altered after nhr-67(RNAi) +/- hypoxia.

Reviewer #2 (Public Review):

The data provided in the manuscript is mostly of good quality and the interpretations are sound. However, since the central message of this paper is characterization of this HIF-1-independent hypoxia response pathway, some more mechanistic detail needs to be provided. How the kinase HPK-1 activates NHR-49 specifically in hypoxic conditions needs some further investigation using biochemical approaches. In addition, the reciprocal inhibitory relationship between nhr-49 and nhr-67 during hypoxia should be explored a bit further because both the NHRs seem to promote survival in hypoxic conditions, but it is not clear whether they do so via the same or parallel pathways.

Reviewer #3 (Public Review):

Here, the authors identify a HIF-independent pathway controlled by NHR-49 in the C. elegans system. Authors hypothesized that nhr-49 has a role in hypoxia and regulates fmo-2 and genes involved with autophagy. Genetic analysis indicates that nhr-49 is required for hypoxia survival. Transcriptomic studies show that NHR-49 regulates a set of genes in hypoxia, that are HIF-1 independent. Authors provide evidence that NHR-67 is a negative regulator and HPK-1 is a positive regulator of the NHR-49 pathway. Authors found that autophagy gene dysfunction compromized hypoxia survival. It is unclear if the NHR-67 and HPK-1 regulators of NHR-49 impact the genes involved with autophagy as most transcriptional readout assays were done with the fmo-2 and acs-2 reporters. However, authors convincingly show that NHR-49 is important for hypoxia responses, and functions in a HIF-1 independent/parallel pathway. It will be of interest to further investigate the role of NHR-49 and autophagy regulation in hypoxia survival.

Reviewer #1 (Public Review):

The manuscript by Gaubitz et al. reports structures of the yeast clamp loader (RFC)-sliding clamp (PCNA) complex in 6 different states during the clamp loading cycle. Although structures of yeast, human, E. coli and T4 clamp-loader-clamp complexes have been determined previously in various states, a major advance of the authors' work is to obtain structures of distinct intermediates in a single system. These structures provide a detailed description of the conformational changes in both RFC and PCNA during clamp loading, explaining ordered PCNA and primer/template DNA binding by RFC, the mechanism of clamp opening and closing, and the regulation of RFC's ATPase activity. In addition, the structures reveal differences in the mode of primer/template recognition between yeast and T4/E. coli clamp loaders. RFC melts the final base pair of the primer/template duplex using a separation pin in RFC-A, which is not seen in T4 or E coli clamp loaders. The authors confirm this interesting and unexpected observation biochemically. Although the authors speculate this mechanism could be used for distinguishing primer/template substrates from other DNA structures, the physiological significance of DNA melting and base flipping by RFC remains unclear. Overall, the findings reveal new nuances of the clamp loading cycle but the manuscript could be strengthened by solidifying the importance of base flipping for substrate recognition and RFC function.

Reviewer #2 (Public Review):

In this study, Gausman et al. use cryo-electron microscopy to elucidate structures of complexes between the eukaryotic clamp loader (RFC) and its ligands, the DNA polymerase processivity clamp (PCNA) and DNA. Clamp loaders and clamps are required for DNA replication and repair in all domains of life. Understanding of the molecular mechanisms of clamp loading is not only important for DNA replication and repair, but also because clamp loaders are members of a larger group of motor proteins which are critical to many aspects of cellular metabolism. To date, our structural understanding of clamp loader mechanisms is based on comparison of structures for different clamp loader-ligand intermediate complexes from a variety of organisms including E. coli, yeast, bacteriophage, and humans. This paper presents the first structural data for multiple clamp loader-ligand intermediate complexes from a single organism, Saccharomyces cerevisiae, and sheds new light on protein-ligand interactions. Importantly, this work highlights structural features of the clamp loader that give rise to the order of ligand binding where the clamp loader binds and opens the clamp before binding DNA.

To capture clamp loader ligand complexes, RFC was bound to the slowly hydrolyzable ATP analog, ATPγS, and intermediate complexes were further stabilized by protein crosslinking, predominantly intramolecular crosslinking of RFC subunits. Two types of RFC-PCNA complexes were observed, one in which the PCNA ring closed and a second where it is open. A family of closed complexes was observed in which three of the five RFC subunits contact the surface of the PCNA ring. Rigid body modeling suggests that this closed complex is dynamic such that the plane of the ring 'swings' relative to the clamp loader to potentially allow all five clamp loader subunits to engage the clamp to open the ring. In the open complex, the diameter of the complex expands and the opening in the PCNA ring is large enough to allow ds DNA to enter the ring and the chamber formed by the RFC subunits. A large hinge-like conformational change in the RFC-A subunit on going from closed to open complexes creates a channel for the ssDNA template to bind and exit the chamber. These remarkable structures show that the clamp loader is not in a suitable conformation to bind DNA prior to forming an open clamp complex which favors clamp binding before DNA binding.

This manuscript provides remarkable insight into intermediate complexes that exist in the clamp loading reaction pathway. Having a family of structures for a single clamp loader and clamp provides a clearer picture of and highlights differences in clamp loading mechanisms from different organisms. Overall, this work well done, but perhaps some of the mechanistic conclusions drawn from static structures should be viewed with caution in the absence of rigorous dynamic or kinetic approaches.

1) Crosslinking the proteins to stabilize intermediates could potentially bias the pool of conformations that are observed.

2) A statistical analysis of the differences in the ATPase activities of wild-type and mutant clamp loaders would be helpful to determine whether the mutations have an effect on the activity. Moreover, steady-state ATPase activity was measured in this experiment and these rates may not reveal differences in rates of intermediate steps in the clamp loading reactions. For the mutations to affect the ATPase activity, they would have to either change the rate of the rate-limiting step in the pathway or change the identity of the rate-limiting step. Thus, the decrease in ATPase activity for W638G mutant could be interesting if statistically significant.

3) Given that Phe-582 and Trp-638 seem to be important for binding DNA at the 3' end, an analysis of the effects of mutations to these residues on DNA binding activity would be informative.

4) Kinetic data in the literature support a mechanism in which the clamp loader hydrolyzes ATP prior to clamp closing. In the absence of supporting kinetic data, it may be overinterpreting structural data to assert that the clamp loader need not hydrolyze ATP prior to closing the clamp.

Reviewer #1 (Public Review): 

The ability of exercise to mitigate the disease-associated phenotypes for the inherited ataxias is an area under investigation for several years. Previous studies explore the ability of endurance exercise to promote neuronal plasticity and memory as well as diminish neurodegeneration. Here the investigators examine the therapeutic ability of endurance exercise to disease in drosophila models of the spinocerebellar ataxias types (SCAs) 2, 3, and 6. Chronic exercise was found to strongly impact motor performance of SCA2 flies, moderately impact disease in SCA6 flies and have no effect on SCA3 fly disease. Interestingly, the exercise-inducible protein, Sestrin (Sesn) reduced mutant Atxn2 and suppressed disease in unexercised SCA2 flies. This study adds important new insight into the link between exercise and its potential to mitigate SCA disease.

Reviewer #2 (Public Review): 

This is important work trying to decipher some of the potential benefits and pathways from exercise. Although it is interesting that the strongest effects were seen with SCA2, but less SCA3 and not SCA6 at all, the authors did an adequate job in not overly interpreting and extrapolating to vertebrate models or humans. SCA2 was most impacted by exercise, and this correlated with Sestrin increases that proportionally led to decreases in the disease causing SCA2 protein (but not SCA3 so much). Thus, the remainder of the mechanistic studies focus on SCA2. Importantly, Sestrin alone was able to affect the disease severity in SCA2 flies, and the authors used specific point mutants to address specificity of this activity via the interaction with mTOR and the autophagy pathway, though more convincing evidence of the role of autophagy is necessary to make this claim. 

One concern for almost every figure legend is a lack of information on number of flies or samples per group and the exact statistical comparisons and post-hoc tests used. It is far more useful to have this information directly in the figure legend, especially for readers who go straight to the figures and do not focus on the text as much. For example, Fig 2 states N>100, n>8 vials of 20 flies, but do the data points represent the mean of each vial's average, or N>100 as an entire group? What statistical test is used? The same is true for Fig 3-6, and additionally these figures lack information on how many vials were in each group (i.e. are there any batch effects from vials that could skew the results?). 

The data for Figure 7 is the key supporting evidence of the claim that this is operating via autophagy. However, given the variability in the data and the very small sample sizes N=3-4/group, this is not currently supported. For example, the effects of the dSEN WT expression on ATXN2 protein levels is only significant because the grouping is tighter but there is a decrease in the other dSesn mutants. Same is true for AtgIa/IIa ratios where there is some effect of the dSesn mutant expression but just more variability. Either more samples are needed, or these data and claims should be removed from the manuscript.

Reviewer #3 (Public Review): 

The manuscript entitled "Endurance exercise ameliorates phenotypes 1 in Drosophila models of 2 Spinocerebellar Ataxias" by Sujowski et al., presents an interesting role of endurance exercise in rescuing the Spinocerebellar Ataxias Type 2 (SCA2). The manuscript highlights the role of endurance exercise, with widespread benefits, using the fly model of SCA2, 3 and 6. In Drosophila model of SCA2,3 and 6, they found marked protection of speed and endurance in exercised SCA2 flies and modest protection in exercised SCA6 and no benefit is observed in SCA3 flies. In the SCA2 model only, the causative protein SCA2 levels were reduced through induction of exercise-inducible protein Sestrin (Sesn). Furthermore, they found that high levels of Sesn can decrease levels of disease protein SCA2 even without exercise, and through increased autophagic flux. Sesn protein includes domains that reduce oxidative damage and modulate mTOR activity. This study demonstrates differential responses of polyQ SCAs to exercise, highlighting the potential for more extensive application of exercise-based therapies in the prevention of polyQ neurodegeneration. This study defines the mechanisms by which endurance exercise suppresses polyQ SCAs will open the door for more effective treatment for these diseases.

Reviewer #1 (Public Review): 

In the manuscript, "Nanoscale architecture and coordination of actin cores within the sealing zone of human osteoclasts" by Portes and colleagues several types of static and dynamic microscopy are used to develop a better molecular picture of the podosomes and actin rings of osteoclasts. 

A summary of what the authors were trying to achieve:

Describe the overall structure of actin rings of osteoclasts. Examine the dynamics of the actin and associated proteisn in the actin ring. Provide support for a model in which force is applied by the podosomes through actin polymerization and this is countered by attachments through integrins and associated proteins 

An account of the major strengths and weaknesses of the methods and results:

Strengths, new and state of the art microscopy techniques are brought to bear. The anaysis is quantitative. Primary osteoclasts are studied. A quantitative data based model is presented. <br /> Weaknesses, data is presented sometimes in a less than fully transparent manner in that certain deatils are omitted or not presented clearly. The underlying problem, that the podosome are undergoing rapid directed polymerization and depolymerization is not well described or integrated into models presented. 

An appraisal of whether the authors achieved their aims, and whether the results support their conclusions:

The authors achieve their goals in describing the actin rings using different techniques which gives a valuable different picture. Their data do support the protrusion/traction model they have presented previously. I think the model could better integrate the source of the protrusion, regulated directed actin polymerization pushing against the membrane. 

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

This impacts the community of actin biologists ad osteoclast biologists by providing further data supporting the protrusion/traction idea. There are various predictions that come from this model. 

Additional context:

Actin rings are a special adaptation of machinery cells use to invade matrix. With mineralized matrix, the osteoclasts has to take the basic structure of the podosome, and form a higher order structure, in order to form the sealing zone and segragated extracellular resorption compartment. Understanding of podosomes of osteoclasts likely translates to understanding matrix invasion in general.

Reviewer #2 (Public Review): 

Portes et al. investigated the nanoscale architecture and dynamics of the osteoclast sealing zone using high-end microscopy techniques. They first use DONALD 3D single molecule localization microscopy on osteoclasts seeded on glass to study the lateral and axial localization of key components of the sealing zone. They show that for some components (vinculin, talin C-terminus), the axial localization was higher when molecules were in close proximity to the actin core while for other components (cortactin, actinin, filamin, paxillin), there was no difference in height as a function of distance from the actin core. They next show that random illumination microscopy (RIM) is a suited microscopy technique to study the sealing zone of osteoclasts on a bone mimetic substrate. They continue to use RIM to show that the dynamics of neighbouring podosomes correlate up to a distance of about 1.5um. They next show that within the sealing zone, groups of podosomes are surrounded by the classical adhesion adaptor proteins such as vinculin, talin and paxillin while actinin is present at the periphery of all single cores. This suggests that the sealing zone has an "intermediate" level of organization and that groups of podosomes form a functional unit within the sealing zone. The authors lastly demonstrate that the fluorescence intensity of the cores within these groups correlate with the intensity of the adaptor proteins that surrounds the group and that also the fluorescence intensity of the cores within one group correlates with each other. 

Strengths: 

The authors use bone slices to evaluate the nanoscale organization of cytoskeletal components in the sealing zone. Podosome conformations in osteoclasts strongly depend on the substrate type and the usage of bone slices accurately mimics the physiological environment in which osteoclasts reside in vivo. 

The authors use state-of-the-art imaging approaches to evaluation the nanoscale organization and dynamics of multiple podosome components in the sealing zone. 

The identification of groups of podosomes that demonstrate correlated dynamics within the sealing zone is a novel finding that is convincingly demonstrated. 

Weaknesses: 

The rationale for the analysis performed on the DONALD super-resolution images (explained in Figure S1) is unclear. The analysis is also not properly explained and it is unclear how the data should be interpreted or put into context. Specific comments related to this analysis: 

- The authors make a distinction between towards the internal or external part of the cell when it comes to the height of the investigated proteins but it is unclear why this is done. Also, while the authors make this distinction, no conclusions are derived from this distinction and only the height values from towards the internal part of the cell are mentioned in the text. 

- It is very much unclear how the distance of the investigated proteins towards the actin core is calculated. From Figure S1, it seems like a rectangle is taken that is centered around a podosome but the rectangle in the example contains more than one core. It seems like this would influence a proper interpretation of the data presented in the figures than contain the height values. The authors should better explain how the analysis was performed and how the analysis deals with the presence of multiple podosome cores in the rectangle of interest. 

- In the text, the distance of the proteins with respect to the actin core is given (350nm-710nm depending on the specific protein and localization towards the external or internal part of the cell). It is mentioned that the measurements are not shown but it should be better explained how these numbers were derived from the data and the measurements (average, SD/SEM) should be shown. 

- Related to the previous comment. While it is mentioned that vinculin for example is located at ~500nm from the actin core, the height values (Figure 1E) are binned within 50nm of the core. This does not seem to match. It would be very helpful if the authors would add how many localizations are found so close to the core. Since this is expected to be low it would also be valuable it the authors would discuss what this means for difference in height between the molecules found close by and away from the core. 

- For cortactin, filamin A and actinin it is found that they reside on average at a height of approximately 150nm, even up to a large distance from the podosome core. It is unclear how these values should be interpreted. 150nm is way above the location where actin is expected to be (and also way above the average actin height that is found by the authors, with approximately 80nm more distant from the cores). The authors should add a discussion of what type of structures cortactin, filamin A and actinin would associate to at this position or how this height can be explained. This should also be included in the final model of Figure 6. In the current cartoon, filamin A for example seems to be associated with the integrins but this does not match with the height position observed by the authors. 

The authors mention that the RIM resolution is 100nm and 300nm in the lateral and axial direction, respectively. This should also be confirmed on the bone slices with beads. It is well conceivable that the optical properties of bone have an effect on the optimal RIM resolution. 

The authors find three specific fluctuation periods (100s/25s/7s) but it is unclear what these periods mean. The authors only very briefly mention that these periods correlate with similar observations in macrophages but they should also add the implications of this finding and suggested a possible molecular mechanism that underlies these different fluctuations. 

The authors find that actinin-1 is localized around the podosome cores while filamin and vinculin surround groups of podosomes. The current representative images, though, that are chosen to support this difference display a very different density in podosome cores. The filamin and vinculin images seems to have a much denser podosome content compare to the actinin and cortactin images. I would encourage the authors to select images that are more comparable to fully appreciate the difference in localization of the associated proteins. 

In Figure 4 and 5, the authors show that the sealing zone is subdivided in groups of podosomes and it is implied that these for functional units within the sealing zone. Yet, it is unclear how persistent these groups are. Considering the dynamic nature of podosomes in other cell types (and as also demonstrated in the supplementary movies) it is well conceivable that these groups continuously fuse and remodel. To better define the nature of these groups of podosomes, the authors should add an analysis on these podosome groups and measure parameters such as group stability, podosome number per group, group size etc. This would very much enhance the novel aspects of the findings in this paper. 

The authors mention in the discussion that their finding about the groups of podosomes is very different from the "double circle" distribution found in previous publications. Yet, it is unclear what explains these different observations. While the authors use RIM super-resolution in this paper to assess the localization of the adaptor proteins, it is very unlikely that this is the source of this difference since the groups of podosomes would have been easily identified by conventional or confocal microscopy as well. The authors should add an extended discussion on how these differences could be explained and what this means for bone resorption properties.

Reviewer #1 (Public Review):

The manuscript by Sim and colleagues explores HLA C1 and C2 defining polymorphism and its impact on TCR recognition as opposed to the more documented impacts on KIR recognition. The manuscript is well written but requires some relatively minor changes. The overall findings that subtle changes in peptide repertoire and peptide binding dictate TCR recognition is perhaps not surprising. The context of the study looking at KRAS G12D derived peptides provide additional interest to this manuscript. The work has been performed to a high level and includes reports of novel ternary complexes of HLA-C with a G12D heteroclitic peptide analogue along with associated biophysical characterization of the TCR interaction with these pHLA complexes.

Reviewer #2 (Public Review):

This manuscript by Sim et al. describes the impact of different HLA-C1 and -C2 allotypes on T cell receptor (TCR) recognition. The study demonstrates that dimorphic position 77 in the HLA-C heavy chain affected amino acid preferences at the C-terminus of the bound peptide, resulting in a weaker TCR affinity for HLA-C2 allotypes. The manuscript is clearly written, the data is sound and the figures are of high quality. The study is interesting and original; however the overall biological relevance remains unclear. It is uncertain how generalizable the findings will be to TCR recognition of HLA-C1 vs -C2 alleles in general, or whether the findings are perhaps more limited to this particular system. Moreover, the link/relevance to KIR recognition (if any) was not explained.

Reviewer #3 (Public Review):

Sim et al. investigate the structural and functional differences between group C1 and group C2 HLA-C molecules, which differ only in two amino acids (position 77 and 80 of HLA-C) that line the peptide binding group. Nevertheless, the KRAS-D12D specific TCR can discriminate both HLA-C groups as demonstrated in this study using cellular immune assays, X-ray crystallography and immunopeptidomics. As a result, the manuscripts provides an important insight into the functional differences of the C1/C2 dimorphism, especially in the context of cancer immunotherapy using T cell based therapeutics.

The authors study two HLA-C*08:02 (group C1) and HLA-C*05:01 (group C2) restricted TCRs that recognize the KRAS mutant peptide G12D. Using cell based assays the authors establish that both TCRs (one specific for the KRAs 9mer peptide and the other for the KRAS decamer peptide) recognize HLA-C*08:02 presenting the mutant peptide but not the wildtype peptide, while HLA-C*05:01 expressing cells only stimulate the jurkat T cells very weakly and also only with the mutant peptide. Further, the authors identify using peptide elution studies that C*08:02 can also present peptides with terminal alanine anchor residues, albeit at a frequency of only 1.5%, while C*05:01 does not. They authors hypothesize that the amino acid differences at position 77 and 80 of HLA-C, which are close to the F pocket are influencing the amino acid preference at the C-terminus of the presented peptide. Using Surface Plasmon Resonance studies (SPR), the authors assessed the binding of both KRAS-specific TCRs to different synthetic KRAS peptides with different anchor residue at P(omega) and reveal that HLA-C*05:01 is bound with high affinity when the KRAS G12 peptide is modified with a preferred leucine anchor residue at P(omega), instead of the natural alanine. Therefore, the authors argue that the inability of T cell stimulation using C*05:01 is a result of the inability of this allele to stably present the KRAS G12D peptide. Finally, using Xray crystallography the authors further identified that the C1-C2 dimorphism has only a minimal impact on the overall TCR binding mode when C*05:01 is loaded with the well-binding KRAS G12D peptide that contains the terminal Leucine anchor residue. In addition, the dimorphic residues at HLA-C position 77 and 80 are not directly contacted by the TCR. By swapping out the dimorphic residues, the authors are further able to switch T cell responses from C*08:02 toward C*05:01 using the KRAS G12D peptide, suggesting that the dimorphic residues are directly involved in shaping the immunopeptidome for each allele and directly influencing the binding/presentation of the peptide that gives rise to the observed T cell response. Furthermore, the authors identify different amino acids usage in P(omega)-1 position, which is suggested to be a result of slight differences in the peptide binding orientation at the C-terminal end. Specifically, large residues at P(omega)-1 diminished T cell recognition for group C1 HLA-C.

The experiments are well planned and executed and support the conclusion. The proper controls are included. The findings are discussed appropriately and the references contain many of the original studies in the field.

This is an important study that investigates the amino acid differences of the HLA-C C1 and C2 groups and how they affect peptide binding, presentation, and recognition. This knowledge is fundamental in order to design HLA allele-specific TCR based therapeutics.

Reviewer #1 (Public Review):

The genome-editing strategies presented here represent a fantastic technology pipeline, comprehensively tested and precious to the cell biology field. While I am positive about the value of this contribution, I have three major requests that require some experimental work to make the study truly convincing and comprehensible.

1. The DExCon system allows re-expression of N-terminal tagged proteins from the endogenous locus and, in theory, should allow re-expression of all protein-coding splicing isoforms. This provides an advantage over generation of a KO cell line and subsequent tet-inducible rescue from a viral vector containing cDNA. This is undeniably an important technical advantage because it can potentially recapitulate the spectrum of functions of the locus. However, the authors do not provide direct evidence that the DExCon system does allow for re-expression of multiple splicing isoforms. One suggestion would be to identify the Rab11 splice variants expressed in A2780 cells and demonstrate that the relative abundance of these splice variants is not altered upon fluorescent-tagging and CMV-promotor-driven overexpression of Rab11 from the endogenous locus. This seems to me to be a crucial result to demonstrate the effectiveness of the method.

2. The authors use a CMV-promotor to rescue of Rab11/25 gene expression. They convincingly show that it is possible to tune expression levels by FACS sorting. However, for most experiments, the authors use expression levels of Rab11a/b/Rab25 that are much higher than endogenous levels. Since high expression levels of Rab11a/b can affect its localization (transient expression Fig 2G), they should show that the Rab11a/b/Rab25 expression levels used do not alter localization and function. This could be tested simply by a transferrin recycling assay. To ensure that DExCon-Rab11/25 expression levels do not affect localization, the authors could use cells containing a knock-in of mCH-Rab11a on one allele and DExCon-mNG-Rab11a on the other allele and compare their localization.

3. In Fig 6F, the effect of Rab11 on migration is tested using DExogron-mCH-R11b in a wound healing assay. Loss of R11b expression by DExogron-mCH-R11b reduced migration and this effect could be rescued by dox-induced expression of DExogron-mCH-R11b. However, IAA treatment failed to prevent this rescue as would have been expected. The authors hypothesize that this results from incomplete protein degradation under +dox +IAA conditions. In Fig 6K the authors solve this problem by removing dox when treating with IAA. The authors should repeat the experiment 6F under -dox +IAA conditions.

Reviewer #2 (Public Review):

This is a very interesting, quite dense study that reports several new techniques of controlling cellular protein levels, as well as performing spatiotemporal image analysis. The strength of this study is the combination of various previously known approaches (like CRISPR knock-in, Degron, knock-sideways) to allow quite precise control of protein levels (by controlling degradation or expression), as well as imaging of endogenously tagged proteins. The ability to inactivate/reactivate proteins of interest is a huge achievement that will be very useful in many studies by this and other laboratories. Another big strength of this study is the fact that the authors took time to optimize and streamline these approaches making them much more user-friendly as compared to earlier versions of many of these approaches. The only very minor drawback of this manuscript is the fact that authors have chosen to perform proof-of-principal studies using Rab11 family of proteins (which is great) but in rather boring cell types. Rab11 family members are presumably involved in differentially regulating various aspects of cell polarity and recycling. Thus, it's not too surprising that authors did not see that many differences in rab11a, rab11b and rab25 functions since they used a single cargo (transferrin) and non-polarized cancer cells. However, I do realize that the main goal of this study is not to investigate Rab11 but rather to develop new techniques.

Reviewer #1 (Public Review):

Dias et al proposed a new method for genotype imputation and evaluated its performance using a variety of metrics. Their method consistently produces better imputation accuracies across different allele frequency spectrums and ancestries. Surprisingly, this is achieved with superior computational speed, which is very impressive since competing imputation software's had decades of experience in optimizing software performance.

The main weakness in my opinion is the lack of software/pipeline descriptions, as detailed in my main points 3-6 below.

1. In the neural network training workflow, I am worried it will be difficult to compute the n by n correlation matrix if n is large. If n=10^5, the matrix would be ~80GB in double precision, and if n=10^6, the matrix is ~2TB. I wonder what is n for HRC chromosome 1? Would this change for TOPMed (Taliun 2021 Nature) panel which has ~10x more variants? I hope the authors can either state that typical n is manageable even for dense sequencing data, or discuss a strategy for dealing with large n. Also, Figure 1 is a bit confusing, since steps E1-E2 supposedly precede A-D.

2. I have a number of questions/comments regarding equations 2-4. (a) There seems to be no discussion on how the main autoencoder weight parameters were optimized? Intuitively, I would think optimizing the autoencoder weights are conceptually much more important than tuning hyper-parameters, for which there are plenty of discussions. (b) I suppose t must index over each allele in a segment, but this was not explicit. (c) Please use standard notations for L1 and L2 norms (e.g. ||Z||_1 for L1 norm of Z). I also wonder if the authors meant ||Z||_1 or ||vec(Z)||_1 (vectorized Z)? (d) It would be great if the authors can more explicitly describe the auto-encoder matrices (e.g. their dimensions, sparsity patterns if any...etc).

3. It is not obvious if the authors intend to provide a downloadable software package that is user-friendly and scalable to large data (e.g. HRC). For the present paper to be useful to others, I imagine either (a) the authors provide software or example scripts so users can train their own neural network, or (b) the authors provide pre-trained networks that are downloaded and can be easily combined with target genotype data for imputation. From the discussion, it seems like (b) would be the ultimate goal, but is only part dream and part reality. It would be helpful if the authors can clarify how current users can benefit from their work.

4. Along the same lines, I also found the description of the software/pipeline to be lacking (unless these information are available on the online GitHub page, which is currently inaccessible). For instance, I would like to know which of the major data imputation formats (VCF/BGEN..etc) are supported? Which operating systems (window/linux/mac) are supported? I also would like to know if it is possible to train the network or run imputation given pre-trained networks, if I don't have a GPU?

5. Typically, imputation software supplies a per-SNP imputation quality score for use in downstream analysis. This is important for interpretability as it helps users decide which variants are confidently imputed and which ones are not. For example, such a quality score can be estimated from the posterior distribution of an HMM process (e.g. Browning 2009 AJHG). Would the proposed method be able to supply something similar? Alternatively, how would the users know which imputed variants to trust?

6. I think the authors should clarify whether input genotypes must be prephased. That is, given a trained neural network and a genotype data that one wishes to impute, does the genotype data have to be phased? The discussion reads "our current encoding approach lacks phasing information..." which can be understood both ways. On a related note, I hope the authors can also clarify if the validation and testing data (page 7 lines 14-23) were phased data, or if they were originally unphased but computationally phased via software like Eagle 2 or Beagle 5.

7. It is unclear if the reported run times (Figure 6) includes model training time, or if they are simply imputing the missing genotypes given a pre-trained autoencoder? For the later, I think the comparison may still be fair if users never have to train models themselves. However, if users currently have to train their own network, I feel it is imperative to also report the model training time, even if in another figure/table.

Reviewer #2 (Public Review):

In this manuscript the authors introduce a segment based autoencoder (AE) to perform genotype imputation. The authors compare performance of their AE to more traditional HMM-based methods (e.g. IMPUTE) and show that there is a slight but significant improvement on these methods using the AE strategy.

In general the paper is clearly presently and the work in timely, but I have some concerns with respect to the framing of the advances presented here along with the performance comparisons.

Specific Points:

1. The authors aren't doing a good enough job presenting the work of others in using deep neural networks for imputation or using autoencoders for closely related tasks in population genetics. For instance, the authors say that the RNN method of Kojima et al 2020. is not applicable to real world scenarios, however they seem to have missed that in that paper the authors are imputing based on omni 2.5 at 97% masking, right in line with what is presented here. It strikes me that the RNNIMP method is a crucial comparison here, and the authors should expand their scholarship in the paper to cover work that has already been done on autoencoders for popgen.<br /> 2. With respect to additional comparisons-Kenneth Lange's group recently released a new method for imputation which is not based on HMM but is extremely fast. The authors would be well served to extend their comparisons to include this method (MendelImpute)-it should be favorable for the authors as ModelImpute is less accurate than HMMs but much faster.<br /> 3. The description of HMM based methods in lines 19-21 isn't quite correct. Moreover-what is an "HMM parameter function?"<br /> 4. Using tiled AEs across the genome makes sense given the limitations of AEs generally, but this means that tiling choices may affect downstream accuracy. In particular-how does the choice of the LD threshold determine accuracy of the method? e.g. if the snp correlation threshold were 0.3 rather than 0.45, how would performance be changed?<br /> 5. How large is the set of trained AEs for chromosome 22? In particular, how much disk space does the complete description of all AEs (model + weights) take up? How does this compare to a reference panel for chr22? The authors claim that one advance is that this is a "reference-free" method - it's not - and that as such there are savings in that a reference panel doesn't have to be used along with the genome to be imputed. While the later claim is true, instead a reference panel is swapped out for a set of trained AEs, which might take up a lot of disk space themselves. This comparison should be given and perhaps extrapolated to the whole genome.<br /> 6. The results around runtime performance (Figure 6) are misleading. Specifically HMM training and decoding is being performed here, whereas for the AE only prediction (equivalent to decoding) is being done. To their credit, the authors do mention a bit of this in the discussion, however a real comparison should be done in Figure 6. There are two ways to proceed in my estimation - 1) separate training and decoding for the HMM methods (Beagle doesn't allow this, I'm not sure of the other software packages) 2) report the training times for the AE method. I would certainly like to see what the training times look like given that the results as present require 1) a separate AE for each genomic chunk, 2) a course grid search, 3) training XGBoost on the results from the course grid search, and 4) retraining of the individual AEs given the XGBoost predictions, and 5) finally prediction. This is a HUGE training effort. Showing prediction runtimes and comparing those to the HMMs is inappropriate.<br /> 7. One well known problem for DNN based methods including AEs is out-of-sample prediction. While Figure 5 (missing a label by the way) sort of gets to this, I would have the authors compare prediction in genotypes from populations which are absent from the training set and compare that performance to HMMs. Both methods should suffer, but I'm curious as to whether the AEs are more robust than the HMMs to this sort of pathology.

Reviewer #3 (Public Review):

Over the last 15 years or so genotype imputation has been an important and widely-used tool in genetic studies, with methods based on Hidden Markov Models (HMMs) and reference panels emerging as the dominant approach. This paper suggests a new approach to genotype imputation based on denoising autoencoders (DAE), a type of neural network. This approach has two nice advantages over existing methods based on Hidden Markov Models (HMMs): i) once the DAE is trained on a reference panel the reference panel can be discarded, and users do not need access to the reference panel to use the DAE; ii) imputation using a DAE is very fast (training is slow, but this step is done upfront so users do not need to worry about it). The paper also presents data showing that the tuned DAE is competitive in accuracy with HMM methods.

I have two main concerns.

First, it is unclear to me whether the accuracy presented for the tuned DAE (eg Figure 3, Table 4) is a reliable reflection of expected future accuracy. This is because the tuning process was quite extensive and complex, and involved at least some of the datasets used in these assessments. While the paper correctly attempts to guard against overfitting and related issues by using separate Training, Validation and Testing data (p7), it seems that the Testing data were used in at least some of the development of the methods and tuning (eg p14, "A preliminary comparison of the best performing autoencoder..."; Figure 2 and Table 2, all involve the Testing data). Because of the complexity of the process by which the final DAE was arrived at it is unclear to me whether there is a genuine concern here, but it would seem safest and most convincing at this point<br /> to do an entirely independent test of the methods on genotype data sets that were not used at all up to this point.

Second, there is a potentially tricky issue of to what extent distributing a black box DAE trained on a reference sample is consistent with data sharing policies. Standards of data sharing have evolved over the last decade. Generally there currently seems to be little hesitation to publicly share "single-SNP summary data" such as allele frequency information from large reference panels, whereas sharing of individual-level genotype data is usually explicitly forbidden. It is not quite clear to me where sharing the fit of a DAE falls here, or how much information on individual genotypes the trained DAE contains. The current manuscript does not adequately address this issue.

Reviewer #1 (Public Review):

The concept of Intrinsic neural timescale (INT) has recently emerged as an important dimension and organizing principle for cortical hierarchy, but how it is reflected and measured by the functional MRI has not been thoroughly tested and compared to the INT based on the single neuron activity in the same species. Manea and colleagues measured INT from anesthetized rhesus monkeys using the resting-state fMRI in a high-field (10.5T), and found that they show patterns consistent with the previous electrophysiological measurements and are correlated with the anatomical gradient in the functional connectivity within and between different cortical areas. These results provide robust empirical foundation for broader applications of INT to probe variability and heterogeneity of cortical functions. The analytical methods and statistical models used to measure INT have some weaknesses, and the authors should discuss the effects of anesthesia on the main conclusions.

Reviewer #2 (Public Review):

Manea and colleagues present an analysis of autocorrelation in BOLD timeseries in anesthetized monkeys collected with high-field fMRI. Using a measure (INT) related to autocorrelation timescale (but see concern below), they demonstrate that inter-regional differences in INT follow patterns observed in prior studies measuring autocorrelation (intrinsic) timescales using single-neuron spike train recordings. They demonstrate in frontal and parietal lobes that INT follows topographies of functional connectivity variation. In addition to comparing cortical regions, they observe topography of INT variation within striatum.

This study will be of broad interest to systems neuroscientists and neuroimagers. Prior studies have characterized intrinsic timescales of resting-state BOLD in human cortex and observed topography related to hierarchy. Establishing this in non-human primate allows closer linking to prior observations in neuronal recordings, and potentially opens up research directions to probe the origins of intrinsic timescales (e.g., through causal perturbation).

I have two methodological concerns which could be addressed, one related to the INT measure, and the other related to the functional connectivity gradients.

1. INT measure: They authors put forth the INT measure as related to intrinsic timescale. INT is defined as the integrated area under the autocorrelation function (ACF) up until the first point where the ACF goes below zero. This is different than how intrinsic timescale has been measured in single-neuron spike trains or in prior fMRI studies. While a longer timescale would be expected to increase INT, the problem is that INT (as an integrated area) combines effects of autocorrelation timescale and autocorrelation amplitude.

- It would be insightful to visualize INT properties at the whole-brain or whole-cortex level (instead of only a single lobe), including (i) INT values themselves, (ii) the lag-one autocorrelation value (reflecting autocorrelation amplitude, and (iii) the zero-crossing lag time used to compute INT.

- A highly relevant paper (which is not currently cited) is Ito et al. (2020) NeuroImage, "A cortical hierarchy of localized and distributed processes revealed via dissociation of task activations, connectivity changes, and intrinsic timescales". Fig. 5 of Ito shows a cortex-wide map of intrinsic timescale as defined in single-neuron studies (i.e. fitting time constant of decay). Fig. 6 then shows this is related to cortical hierarchy as reflected in the T1w/T2w map (which in principle could be tested by the authors here too). Ito's analysis was performed on the parcellated timeseries, not the voxel level as in the present study, which is a notable methodological difference.

- That INT combines effects of timescale and amplitude would not be a problem if the autocorrelation amplitude does not vary across brain regions. However, it appears that it does for whatever reasons (neural and/or in fMRI measurement such as SNR). A relevant preprint is by Shinn et al. (2021) bioRxiv, "Spatial and temporal autocorrelation weave human brain networks". In human cortex, again using parcellated timeseries, Fig. 1F there shows systematic variation across cortical parcels of the lag-1 autocorrelation value. In the present study, it is currently unknown whether INT is reflecting regional differences in autocorrelation timescale (as interpreted), amplitude (not considered), or both.

- Contribution of autocorrelation amplitude to INT may potentially explain why a cortex-wide map of INT does not follow an expected hierarchy as much the more restricted views within one lobe as the current manuscript focuses. For instance, Fig. 1 shows that INT values for somatosensory cortex (Fig. 1A) are larger than association regions (Fig. 1B). Is this potentially due to autocorrelation amplitude being larger in somatosensory cortex?

- Perhaps some smoothing or parcellation would be required to better tease apart autocorrelation timescale from autocorrelation amplitude.

2. Functional connectivity gradients: Figures 3 and 4 rely on functional connectivity gradients calculated within a single lobe, against which INT topography is correlated. My concern here is that on such a restricted geometry as a single lobe, a functional connectivity gradient may be reflecting a simpler property, namely the geometry of the restricted cortical sheet. In other words, given the sheet geometry of the frontal lobe, does an anterior-posterior topography fall out naturally as the first gradient (e.g. with distance-dependent falloff) and medial-lateral as second gradient? If so, it is difficult to strongly interpret these results as linking INT to functional connectivity when the gradient is a generic consequence of the sheet geometry. In human neuroimaging such functional gradients are typically calculated at the whole-cortex level which reveals less trivial topographies (e.g. Margulies et al., 2016, PNAS). These results and interpretations should be considered in light of this concern.

Reviewer #3 (Public Review):

This is an influential paper that establishes the utility of fMRI for studying the hierarchy of temporal dynamics across the macaque brain. The authors demonstrate that the time constants of BOLD responses in different cortical regions have the same ranking as those previously discovered with electrophysiological measurements of spiking activity. This paper extends previous studies by providing whole-brain maps of temporal hierarchy, showing a close correspondence with the hierarchies inferred from a variety of functional connectivity as well as anatomical measurements. Overall, this is a strong paper with deep technical and scientific implications for the field. However, there are interpretational and technical concerns that I would like to see addressed.

Does the calculation of fMRI-based neuronal time constants obscure the unit of time? True comparison with ephys data is not possible without clarifying the relationship of the two quantities compared. In the ephys measurements time constants are in units of seconds and often below 1s. In contrast, BOLD response has a sluggish time course (tens of seconds) due to the properties of the hemodynamic response function. The smoothing of spiking and field-potential activity with the HRF introduces substantial auto-correlation in BOLD and is expected to reduce our ability to distinguish small differences of time constants discovered with ephys. Because the analyses in this paper do not explore the complications caused by the slow and noisy BOLD measurements, it is impossible to know if the observed temporal hierarchy has the same nature and origin as those reported with ephys. I would love to see additional analyses and modeling that clarifies this missing link. If that is not possible, at the very least I would recommend explicit reporting of the units of time constants based on BOLD in the figures, and discussing if the differences of BOLD time constants across regions match the differences of spiking activity time constants in previous publications.

Reviewer #1 (Public Review):

Through elegant experimentation (heterologous NOX expression), the authors show that maintaining redox balance is essential for virulence of the pathogen. The experiments are well controlled, and I have only one suggestion regarding the conclusions. Overall, the study represents an important contribution to understanding pathogen metabolism during infection.

Reviewer #2 (Public Review):

The study by Rivera-Luogo et al. focuses on the role of respiration in Listeria monocytogenes energetic metabolism and multiplication. In a previous work, the authors had identified two respiratory pathways in L. monocytogenes and here they aim at assessing the contribution of each of them in various phenotypes, both in lab culture and host tissues.

Respiration provides organisms with two benefits: regeneration of NAD and production of proton motive force (pmf). The former is necessary for oxido-reductases activity and the later powers ATP synthase as well as numerous other processes (transport, secretion, motility ...), as listed by the authors (line 78). Here the authors aim at sorting out which one on the two, redox balance control or pmf production, matters the most for Listeria to multiply. This is both an original and interesting objective, which the authors answer to by using a heterologous NOX system allowing to regenerate NAD, without producing pmf. The provocative outcome is that NAD regeneration, and not pmf production, is what Listeria makes the most out of respiration. This is clearly a new way of perceiving respiration but such a claim needs additional support and reinforced experimental evidences.Experiments are well carried out. Results are convincing. Effects of mutations are analyzed using an impressive battery of tests (phenotype, growth, metabolic products analysis, plaque, macrophage and mouse experiments).

Reviewer #3 (Public Review):

The authors show that poor growth, defective intracellular survival, and the reduced ability to expand in tissue of a Listeria strain deficient in aerobic and ferric respiration is partially restored by the heterologous expression of an NADH oxidase (NOX) from Lactobacillus. The investigators conclude that the main role of respiration in Listeria pathogenesis is related to its capacity to balance redox. This is particularly true in spleen tissue. However, the recovery of virulence-associated with the heterologous expression of NOX is more modest in liver, demonstrating that the critical role played by aerobic and ferric respiration in maintaining redox balance is probably tissue specific. Thus, I recommend the claims about the main role of respiration being associated with redox balance be softened in the abstract and elsewhere in the paper. The authors should consider roles for respiration other than redox balance. If the role of the ETC were mainly to maintain redox balance, then the expression of NDH-II, which is uncoupled for proton translocation, would be enough. However, Listeria, as many other organisms, seems to express a proton-couple NDH-I as well. In addition to using O2 and Fe3+, Listeria may utilize other terminal electron acceptors such as nitrate, DMSO or TMAO. This might be especially important in liver parenchyma that receives oxygen-depleted blood from the intestine.

Reviewer #1 (Public Review):

In the present manuscript, Strieter and coworkers reveal a cryptic, K48-linkage specific ubiquitin binding site on the backside of the proteasome-associated deubiquitinase UCH37, providing important new insights into the debranching activity of UCH37 and its role in proteasomal degradation of substrates marked with branched ubiquitin chains. Other groups had previously postulated the existence of an additional binding site based on functional assays, yet experimental evidence had been missing. Here, the authors use a variety of well-suited biochemical and biophysical approaches, including hydrogen-deuterium exchange combined with mass spectrometry (HDX-MS), chemical crosslinking, NMR, small angle X-ray scattering (SAXS), molecular dynamics simulations, and site-specific mutagenesis, to identify and characterize this new site regarding linkage specificity, ubiquitin binding modes and critical motifs, as well as its effects on substrate turnover by the proteasome. Revealing the involved regions and residues in UCH37 enabled the authors to place specific mutations that disrupt ubiquitin binding to this site and inhibit UCH37's debranching activity, allowing the identification of cellular substrates that depend on this activity for degradation by the proteasome.

Although the study does not uncover the molecular mechanism of UCH37's backside-mediated ubiquitin cleavage and the details of why certain substrates depend on it for proteasomal turnover, it represents an important advance to our understanding of ubiquitin-chain cleavage and editing at the 26S proteasome, and it sets the stage for future mechanistic investigations.

Reviewer #2 (Public Review):

Du et al apply an arsenal of biophysical methods to evaluate the mechanistic origin of UCH37 preference for K48-linked Ub chains. They find a new Ub binding site formed by UCH37 residues in helix5-6 and that this region is essential for K48 chain binding and debranching as well as for UCH37-dependent substrate degradation by the proteasome. This new site for Ub binding is distinct from and in addition to that established by previous crystal structures of UCH37 with monoubiquitin. The original site is found to be the most germane for Ub-AMC cleavage, but not important for K48 chain activity. They further find proteins that are dependent on UCH37 for degradation following H2O2 treatment and thereby link UCH37 activity to cellular pathways.

Reviewer #3 (Public Review):

In this manuscript the Strieter lab studies branched ubiquitin chain cleavage by UCH37/RPN13, using a series of biochemical and structural approaches. Interestingly they find that branched chains bind to a completely different site in the enzyme than the canonical mono-ubiquitin binding site. This allows them to identify a set of very convincing separation of function mutations, and initiate the analysis of the specific roles of UCH37 in chain debranching and mono-ubiquitin clean up. This is highly exciting development that elucidates an unexpected and beautiful biological phenomenon. There is no doubt that this manuscript will have profound impact in the future.

The experiments are creative, very well done and overall well presented. However, there are very many of those, and the following suggestions may help to explain the findings to researchers that do not have a background in UCH catalysis to clarify the big picture<br /> A) The introduction could use some clarification of concepts<br /> a. The existing structure of UCHL37:RPN13:Ub (4WLR) is the base for the definition of the "canonical" S1 site. Yet, no figure exists that shows that structure, and there is only an incidental description of how the ubiquitin positioning in this structure relates to the message of the paper and to the backside (lines 168-169). Please show the structure as a figure, and define clearly where the S1 site is.<br /> b. The cross-over loop (abbreviated as CL, although the abbreviation is never made explicit in the text) is clearly important in (1) determining substrate specificity and size of UCHL DUBs and (2) in the current model of RPN13-mediated regulation of UCHL37. Please add a paragraph in the introduction to discuss the CL.<br /> c. A reminder of what is the definition of proximal and distal ubiquitin (donating the C-terminus) in this particular context should also be in the text.

B) It would be very helpful if the main finding of the paper, the fact that branched chains use a novel S1 binding site, could be illustrated in a schematic figure. Ideally such a figure compares the previous S1 and the new one (could be useful to find a consistent name for it: e.g. S1*? ) for branched chains side-by-side. Of course if such a figure could reflect the various interaction sites mentioned, as well as the location of the various named parts of the ub-substrate (distal, proximal) and linkages.

There are some minor points in the writing that could be strengthened.

Reviewer #3 (Public Review):

The authors developed an image processing platform to quantify the 3D network of keratin filaments. The concept of this approach is based on 3D visualization of fluorescent labeled proteins using confocal scanning microscopy. The major advantage of this approach is that after the initial segmentation of the network, the filaments are divided into pieces (in silico). This approach allows for quantify the segmented and compute some of the network properties of the keratin networks in cells. Additionally, this approach allows nice visualization of the keratin network in 3D.

I find this development interesting and believe that substantial characterization of the keratin network would be conducted, in particular during physiologically important cellular processes.

Reviewer #1 (Public Review):

The authors adapted and developed tools for the three-dimensional visualization and systematic analysis of the entire keratin filament network in three different types of cells. The resulting contribution is highly original, provides insight at both a methodological and biological levels, and nicely complements and extends emerging information about the high resolution structure of intermediate filaments in situ (by cryoelectron tomography). The manuscript is well-written, well-illustrated, and the authors are thorough in their recognition of previous studies of relevance to their own. This article will be foundational in the specialized field of intermediate filament biology and will have a significant impact in the broad field of cell biology.

Reviewer #2 (Public Review):

This paper presents an intriguing pipeline that can be applied to understand and predict the mechanical and biophysical properties of intermediate filaments in a given cell type. The work is very well documented as a continuum from obtaining the imaging data to the analyses in Matlab and Fiji to the translation into virtual reality. The descriptions are concisely written so anyone can understand the essence of different parameters. The strategy forms a rather pioneering multi-dimensional visualization approach that revealed hallmark features of different keratin filaments networks in various cell types.

• There is a good selection of cell lines to accommodate the varied presentations of keratin filaments in cell lines with different properties. The morphological representations of the figures in 3D very well illustrate the nature and organization of the cells in vitro and in vivo which is further examined in the measurements of different parameters such as curvature and orientation.<br /> • This pipeline introduces a fresh strategy to analyze, compare and interpret network organization of cellular filaments. The comparison of the filament orientation between MDCK and HaCaT B9 cells was intriguing, as it highlights the nature of their arrangement in in vitro monolayers and draws a parallel between how cell shape influences network arrangement aside from the assumed polarity.<br /> • It would be interesting to compare how these parameters differ in MDCK cells with cuboid or cylindrical geometries.<br /> • With regards to segmentation of images, there seems to be a difficulty in segmentation of denser areas and some dim segments in light to medium intensity areas as noticeable in Fig .1. Any remedy for this?<br /> • It would be informative if an expert panel would manually segment some images to compare with automatically segmented ones so that a false positive/negative ratio could be established.<br /> • In the transformation of 3D fluorescence recordings of keratin filaments into digital networks, other than whole-cell networks, it will be interesting to show a few examples of keratin structures at representative subcellular domains, such as the nucleus.<br /> • The authors pointed out that in MDCK cells, the basal domain has thicker bundles compared to the apical domain, while the lateral keratin network is more heterogeneous. Is it possible to statistically present this feature of keratin filaments? And what would be the case in HaCaT and REP cells?

Reviewer #2 (Public Review):

The authors show that ACD-5 channel is a homomeric proton-sensing channel by performing electrophysiology experiments in ACD-5 injected Xenopus oocytes. The results are solid and strongly support the idea that ACD-5 is an acid-sensing cation channel. They show ACD-5 localizes to the apical membrane of intestine by fluorescent imaging of translational reporters. Co-localization with different markers strongly supports that ACD-5 is concentrated on the apical membrane of the intestine. They further show that ACD-5 controls proton concentrations in the lumen through Maximum Anterior Transition (MAT) experiments. The disrupted intestinal lumen pH was rescued by expressing ACD-5 cDNA in the intestine, further supporting ACD-5's function as a pH regulator of the intestinal lumen. The authors show that ACD-5 may have a very minor role in in regulating intestinal calcium oscillations and defecation behavior, because null mutants were normal for cycle length and only exhibited small changes in calcium dynamics. On the other hand, larger (albeit subtle) differences in some parameters were seen in the dominant acd-5(ok2657) mutant, suggesting that this unusual allele is a dominant negative with some additional (neomorphic?) characteristics, raising questions about how useful this allele is in attributing functions to acd-5 .

In the second part of paper, the authors show that FLR-1 forms a pH-sensitive channel with ACD-3 and/or DEL-5. They further show a strong link between FLR-1/ACD-3/DEL-5 channels with calcium oscillation in the intestine and defecation behavior. The loss of function phenotype of flr-1 has been described previously and this study extends that characterization by showing that flr-1 regulates intestinal calcium oscillations. However, the study does not establish a clear subcellular site-of-action for these channels, nor does it provide a mechanistic link between intestinal acid sensing and release of calcium from internal stores. This would require a more complete exploration of how the FLR-1/ACD-3/DEL-5 channels interact with the other players known to regulate calcium signaling in the intestine.

Reviewer #1 (Public Review):

Kaulich and colleagues investigated the role DEG/ENaCs - ASICs channel family members in pH homeostasis underlying the rhythmic defecation cycle of C. elegans, using a combination of in vitro electrophysiology, in vivo imaging and behavioural genetics. They show evidence, from heterologous expression in oocytes, that ACD-5 is sensitive as a homomer to a specific range of pH, while FLR-1 requires co-expression of either ACD-3 or DEL-5 to be sensitive to pH, suggesting a heteromeric complex being inhibited by protons. acd-5 mutants have a strong effect on pH dynamics / proton concentration oscillations in the intestinal lumen, while showing rather subtle effects on rhythmic intestinal Ca++ oscillations and the rhythmic defecation pattern. flr-1 single mutants or RNAi and acd-3/del-5 double mutants show stronger effects on Ca++ oscillations and behaviour. Together with localization studies, the authors propose a model in which ACD-5 acts on the apical membrane facing the intestinal lumen controlling luminal pH, while acid sensitivity of a FLR-1/ACD-3/DEL-5 complex from the basolateral membrane controls intracellular Intestinal Ca++ oscillations. Finally, the overall effect of channel mutants on growth and fat-metabolisms is assessed documenting a functional implication of the degree by which the defecation cycle is impaired. Altogether, the work is interesting, and experiments are carefully performed and controlled. The model could be substantiated by tissue specific rescue experiments of flr-1, acd-3, del-5 mutants where possible.

Reviewer #3 (Public Review):

The authors have identified two acid-sensing DEG/ENaC channels that act in C. elegans intestine to regulate the defecation motor program (DMP). The first channel ACD-5 is a homomeric channel that localizes to the apical membrane, is inhibited by high and low pH, and is important for maintaining the pH oscillations in the lumen but not for Ca2+ oscillations in the intestine. The other channel is a heteromeric channel formed by FLR-1 with ACD-3 and/or DEL-5. This channel localizes to the basolateral membrane, is inhibited by acidic pH, controls Ca2+ oscillations, and is important for worm development and lipid metabolism. The authors have proposed a model to explain the differential roles of the two ASIC channels in regulating DMP.

Overall, the work is interesting, revealing two ASIC channels with distinct biophysical properties and physiological functions. The experiments were well designed and executed.

Reviewer #3 (Public Review):

This work follows on a large body of work on efficient coding in sensory processing, but adds a novel angle: How do non-uniform receptor densities and non-uniform stimulus statistics affect the optimal sensory representation?

The authors start with the motivating example of fingers and tactile receptors, which is well chosen, as it is not overstudied in the efficient coding literature. However, the connection between their model and the example seems to break down after a few lines when the authors state that they treat individual regions as independent, and set the covariance terms to zero. For finger, e.g. that would seem highly implausible, because we typically grasp objects with more than one finger, so that they will be frequently coactivated.

The bottleneck model posited by the authors requires global connectivity as they implement the bottleneck simply by limiting the number of eigenvectors that are used. Thus, in their model, every receptor potentially needs to be connected with every bottleneck neuron. One could also imagine more localized connectivity schemes that would seem more physiologically plausible given the observed connectivity patterns between receptors and relay neurons (e.g. in LGN in the visual system). It would be very interesting to know how this affects the predictions of the theory.

The representation of the results in the figures is very dense and due to the complex interplay between various factors not easy to digest. This paper would benefit tremendously from an interactive component, where parameters of the model can be changed, and the resulting surfaces and curves are updated.

For parts of the manuscript, not all conclusions made by the authors seem to follow directly from the figures: For example, the authors interpret Fig. 3 as showing that activation ratio determines more strongly whether a sensory representation expands or contracts than density ratio. This is true for small bottlenecks, but for relatively generous ones it seems the other way around. The interpretation by the authors, however, fits better the next paragraph, where they argue that the sensory resources should be relatively constant across the lifespan of an animal, and only stimulus statistics adapt. However, there are notable exceptions - for example, in a drastic example zebrafish change their sensory layout of the retina completely between larvae and adult.

In the final part of the manuscript, the authors apply their framework to the star nosed mole model system, which has some interesting properties; in particular, relevant parameters seem to be known. Fitting to their interpretation of the modeling outcomes, they conclude that a model that only captures stimulus statistics suffices to model the observed cortical allocations. However, additional work is necessary to make this point convincingly.

Reviewer #1 (Public Review):

Edmondson et al. develop an efficient coding approach to study resource allocation in resource constrained sensory systems, with a particular focus on somatosensory representations. Their approach is based on a simple, yet novel insight. Namely - to achieve output decorrelation when encoding stimuli from regions with different input statistics, neurons in the sensory bottleneck should be allocated to these regions according to jointly sorted eigenvalues of the input covariance matrix. The authors demonstrate that, even in a simple scenario, this allocation scheme leads to a complex, non-monotonic relationship between the number of neurons representing each region, receptor density and input statistics. To demonstrate the utility of their approach, the authors generate predictions about cortical representations in the star-nosed mole, and observe a close match between theory and data.

Strengths:

These results are certainly interesting and address an issue which to my knowledge has not been studied in-depth before. Touch is a sensory modality rarely mentioned in theoretical studies of sensory coding, and this work contributes to this direction of research.

A clear strength of the paper is that it demonstrates the existence of non-trivial dependence between resource allocation, bottleneck size and input statistics. Discussion of this relationship highlights the importance of nuance and subtlety in theoretical predictions in neuroscience.

The proposed theory can be applied to interpret experimental observations - as demonstrated with the example of the star-nosed mole. The prediction of cortical resource allocation is a close match to experimental data.

Weaknesses:

The central weakness of this work are the strong assumptions which are not clearly stated. In result, the consequences of these assumptions are not discussed in sufficient depth which may limit the generality of the proposed approach. In particular:

1. The paper focuses on a setting with vanishing input noise, where the efficient coding strategy is to reduce the redundancy of the output (for example through decorrelation). This is fine, however, it is not a general efficient coding solution as indicated in the introduction - it is a specific scenario with concrete assumptions, which should be clearly discussed from the beginning.

2. The model assumes that the goal of the system is to generate outputs, whose covariance structure is an identity matrix (Eq. 1). This corresponds to three assumptions: a) variances of output neurons are equalized, b) the total amount of output variance is equal to M (i.e. the number of of output neurons), c) the activity of output neurons is decorrelated. The paper focuses only on the assumption c), and does not discuss consequences or biological plausibility of assumptions a) and b).

Reviewer #2 (Public Review):

The authors propose a new way of looking at the amount of cortical resources (neurons, synapses, and surface area) allocated to process information coming from *multiple* sensory areas. This is the first theoretical treatment of attempting to answer this question with the framework of efficient coding that states that information should be preserved as much as possible throughout the early sensory stages. This is especially important when there is an explicit bottleneck such that some information has to be discarded. In this current paper, the bottleneck is quantified as the number of dimensions in a continuous space. Using only the second-order statistics of the stimulus, and assuming only the second-order statistics carrying information, the authors use variance instead of Shannon's information. The result is a non-trivial analysis of ordering in the eigenvalues of the corresponding representations. Using clever mathematical approximations, the authors arrive at an analytical expression -- advantageous since numerical evaluation of this problem is tricky due to the long thin tails of the eigenvalues of the chosen covariance function (common in decaying translation-invariant covariances). By changing the relative stimulus power (activity ratio), receptor density (effectively the width of the covariance function), and the truncation of dimensions (bottleneck width), they show that the cortical allocation ratio, surprisingly, is a non-trivial function of such variables. There are a number of weaknesses in this approach, however, it produced valuable insights that have a potential to start a new field of studying such resource allocation problems all across different sensory systems in different animals.

## Strengths

* A new application of the efficient coding framework to a neural resource allocation problem given a common bottleneck for multiple independent input regions. It's an innovation (initial results presented at NeurIPS 2019) that brings normative theory with qualitative predictions that may shed new light to seemingly disproportionate cortical allocations. This problem did not have a normative treatment prior to this paper.

* New insights into allocation of encoding resources as a function of bottleneck, stimulus distribution, and receptor density. The cortical allocation ratios have nontrivial relations that were not shown before.

* An analytical method for approximating ordered eigenvalues for a specific stimulus distribution.

## Weaknesses

The analysis is limited to noiseless systems. This may be a good approximation in the high signal-to-noise ratio regime. However, since the analysis of allocation *ratio* is very sensitive to the tail of eigenvalue distribution (and their relative rank order), not all conclusions from the current analysis may be **robust**. Supplemental figure S5 perhaps paints a better picture since it defines the bottleneck as a function of total variance explained instead of number of dimensions. The non-monotonic nonlinear effects are indeed mostly in the last 10% or so of the total variance.

In case where the stimulus distribution is Guassian, the proposed covariance implies that the stimulus distribution is limited to spatial Gaussian processes with Ornstein-Uhlenbeck prior with two parameters: (inverse) length-scale and variance. While this special case allowed the authors to approach the problem analytically, it is not a widely used natural stimuli distribution as far as I know. This assumed covariance in the stimulus space is quite *rough*, i.e., each realization of the stimulus is spatially continuous isn't differentiable. In terms of texture, this corresponds to rough surfaces. Of course, if the stimulus distribution is not Gaussian, this may not be the case. However, the authors only described the distribution in terms of the covariance function, and lacks additional detail to fill in this gap.

The neural response model is unrealistic: Neuronal responses are assumed to be continuous with arbitrary variance. Since the signal is carried by the variance in this manuscript, the resource allocation counts the linear dimensions that this arbitrary variance can be encoded in. Suppose there are 100 neurons that encode a single external variable, for example, a uniform pressure plate stimulus that matches the full range of each sensory receptor. For this stimulus statistics, the variance of all neurons can be combined to a single cortical neuron with 100 times the variance of a single receptor neuron. In this contrived example, the problem is that the cortical neuron can't physiologically have 100 times the variance of the sensory neuron. This study is lacking power constraint that most efficient coding frameworks have (e.g. Atick & Redlich 1990).

The star-nosed mole shows that the usage statistics (translated to activity ratio) better explains the cortical allocation than the receptor density. However, the evidence presented for the full model being better than either factor is weak.

Reviewer #1 (Public Review):

Cui and colleagues have performed a longitudinal analysis of blood cell counts in a cohort of ALS patients. The major findings include increases in neutrophils and monocytes that negatively correlated with ALSFRS-R score, but not disease progression rate. Increases in NK and central memory TH2 T cells correlated with a lower risk of death, while increased CD4 CD45RA effector memory and CD8 T cells were correlated with a higher risk of death.

Strengths of the study include the sample size and effort to broadly include data.

Limitations of the study include indication bias, as the authors acknowledge, because the timing of the blood draws is not predefined. The specific review for possibility of infection does not, in this reviewer's opinion, sufficiently address this potential for bias. Also concerning is the fact that half the subjects have only a single measurement, and how well the findings generalize to more or late measurements is not clear. Similarly, the number of later measurements driving some of the main findings is much lower, further raising concern about the potential bias. Given these issues, one really would want to see disease controls, and how the different cell counts change in another disease. Finally, there is not discussion about how or whether treatments, or changes in treatment, could influence observed counts.

Reviewer #2 (Public Review):

Cui et al. investigated the correlation of immune profiles in ALS patients to functional status (by ALSFRS-R score), disease progression (rate of ALSFRS-R decline) and/or risk of death (or invasive ventilation use). The study longitudinally assessed basic immune profiles from a large cohort of ALS patients (n=288). Additionally, they deeply immunophenotyped a subset of ALS patients (n=92) to examine immune cell subtypes on ALS status, progression rate, and survival. The longitudinal design, deep immunophenotyping, and large cohort are significant strengths. Using various statistical models, the authors found leukocyte, neutrophil, and monocyte counts increased gradually over time as ALSFRS-R score declined. Within lymphocyte subpopulations, increasing natural killer cells and Th2-diffrentiated CD4+ central memory T cell counts correlated with a lower risk of death. Increasing CD4+ effector memory cells re-expressing CD45RA T cell and CD8+ T cell levels associated with a higher risk of death. These findings have broad implications for ALS pathogenesis and the development of immune-based ALS therapies tailored to specific immune cell populations.

Reviewer #1 (Public Review):

The authors describe the partial articulated skeleton of a new armoured dinosaur from an osteological and taxonomical point of view. The finds are of particular importance both for the anatomical understanding of early armoured dinosaurs and for the paleogeographical aspects of the group. The authors achieved their aims, and their results support their conclusions. A detailed comparative study confirms the existence of a new taxon. Using the data matrix of Maidment et al. (2020) and Norman (2021), they determine the phylogenetic position of the new taxon, which clearly shows its basal position among the thyreophrans.

Reviewer #2 (Public Review):

This is a well-constructed anatomical description of an exciting specimen of a new armored thyreophoran dinosaur species from the Early Jurassic of Yunnan, China, which the authors name Yuxisaurus kopchicki. For years, the presence of Early Jurassic thyreophorans in China has been inferred from isolated fragments ultimately deemed undiagnostic (the two previously named taxa "Bienosaurus" and "Tatisaurus" are now widely considered nomina dubia). Yuxisaurus kopchicki is therefore the first valid thyreophoran taxon from the Early Jurassic not only of China but also of Asia. Yuxisaurus is the easternmost occurrence of a thyreophoran from Lower Jurassic Laurasian strata, confirming the rapid spread and diversification of armored dinosaurs throughout the northern hemisphere early in their evolution.

The authors added Yuxisaurus to two recent datasets to evaluate its phylogenetic relationships, both of which support its referral to Thyreophora; however, both datasets heavily emphasize thyreophoran taxa and are limited in taxon sampling overall, so I think adding Yuxisaurus to a dataset emphasizing basal taxa in other ornithischian clades would have strengthened the study given the apparently basal position of Yuxisaurus within Thyreophora. That being said, I think the included analyses are sufficient for what the authors were trying to achieve.

The Early Jurassic record of thyreophorans is limited to only a handful of taxa, mostly from Europe and the USA, so this new study is especially valuable for understanding the early evolution of armored dinosaurs and will surely be referenced heavily by dinosaur researchers in the future.

Reviewer #1 (Public Review):

In this manuscript, the authors tackle an interesting puzzle: why do cancer cells secrete most of their glucose as lactate? The authors propose that acid export is sufficient to enhance glycolysis and provide a selective advantage to cancer cells growing in vivo. To this end, the authors show that clonal lines expressing CA-IX or PMA1, each of which will facilitate proton export, have elevated capacity to acidify extracellular medium and can drive increased migration/invasion and tumor growth or metastases. In support of the model that extracellular pH is a key driver of metastases, the effect of CA-IX expression on lung metastases is reversed following bicarbonate treatment. While many of the individual conclusions of the manuscript are not novel-for example, pH has been reported to control glycolysis and it is established that CA-IX expression modulates migration/metastases-providing a comprehensive assessment of the ability of proton export to drive the Warburg effect, and assessing the significance of metabolic rewiring driven by acid export on tumor growth, would represent an important resource for researchers intrigued by the pervasive observation that cancer cells secrete lactate despite potential bioenergetic disadvantages of discarding biomass.

The strength of the manuscript lies therefore in tying these disparate observations together in a coherent model and testing the role of acid export per se on glycolytic flux. The technical weaknesses of the paper prevent such coherent model building. A major concern is that all cell lines appear to be generated by transient transfection followed by clonal selection, giving rise to cells with notable variability and inconsistent phenotypes. More traditional approaches to manipulate enzyme expression will provide more robust model systems to test the proposed model. Similarly, direct measures of glycolytic flux are required to make conclusions about the role of acid export in promoting glycolysis. Another strength is the use of heterologous enzyme systems to alter proton export in cancer cells, but alternative explanations for these results are not fully considered. Ultimately, to what extent acid export per se, as opposed to altered metabolism driven by acid export, drives enhanced tumor metastases is not addressed.

Reviewer #2 (Public Review):

The work by Xu et al proposes that the Warburg effect - the increase of glycolytic metabolism usually displayed by tumor cells, is driven by increased proton excretion rather than by oncogenic dysregulation of glycolytic enzyme levels. As a proof-of-principle, they engineered tumor cells to increase proton excretion. They observed an increase in glycolytic rate, pH, and malignancy in their engineered cells.

1. My main issue with this work is that I do not agree with the authors when they say that the "canonical view" is that oncolytic mutations are thought to drive the Warburg effect. What I understand the consensus to be, is that it is fast proliferating cells - rather than malignant cells - the ones who display this form of metabolism. The rationale is that glycolytic metabolism allows keeping biomass by redirecting lactate and from the phosphate pentose pathway. In contrast, the end product of oxidative phosphorylation is CO2 that cannot be further utilized in cell metabolism.

They claim that they Vander Heiden et al., 2009 shows that "fermentation under aerobic conditions is energetically unfavorable and does not confer any clear evolutionary benefits." This is incorrect. While that review states that the Warburg effect has little effect on the ATP/ADP ratio, they do show this form of metabolism has significant benefits for fast proliferating cells. In fact, the whole review is about how the Warburg effect is a necessary metabolic adaptation for fast proliferation rather than a unique feature of malignant cells.

2. Their main observation is not surprising. From a biochemical standpoint, protons are final product of glycolysis (from the production of lactic acid). Thus, by mass action, any mechanism to remove protons from the cell will result in accelerated glycolytic rate. Similarly, reducing intracellular pH will necessarily slow down LDHA's activity, which in turn will slow down pyruvate kinase and so on.

3. Their experiments are conducted on transformed cells - that by definition - have oncogenic driver mutations. They should test the effect of proton exporter using primary non-transformed cells (fresh MEFs, immune cells, etc). I would expect that they will still see the increase in glycolysis in this case. And yet, I would still have my concerns I expressed in my previous point.

4. The fact that they can accelerate the Warburg effect by increasing proton export does not mean is the mechanism used by tumor cells in patients or "the driver" of this effect. As I mentioned, their observation is expected by mass action but tumors that do not overexpress proton transporter may still drive their Warburg effect via oncogenic mutations. The biochemical need here is to increase the sources of biomass and redox potential and evolution will select for more glycolytic phenotypes.

Reviewer #3 (Public Review):

The authors claim that "proton export drives the Warburg effect". For this, they expressed proton-exporting proteins in cells and measured the intracellular proton concentration and the Warburg effect. Based on their data, however, I do not see elevated Warburg effect in these cells and thus conclude that the claim is not supported.

The authors concluded that the CA-IX or PMA1 expressing cells had increased Warburg effect. I don't think this conclusion can be made based on the data presented. For the MCF-7 cells, the glucose consumption is ~18 pmol/cell/24hr (Fig. 5E) and lactate production is ~0.6 pmol/cell/24hr (Fig. 5F), indicating that 0.6/18/2 = 1.7% of the glucose is excreted as lactate. This low percentage remains true for the PMA1 expressing cells. For example, for the PMA1-C5 cells, the percentage of glucose going to lactate is about 1.8/38/2 = 2.4% (Fig. 5EF). While indeed there was an increase of both the glucose and lactate fluxes in the PMA1 expressing cells, the vast majority of the glucose flux ends up elsewhere likely the TCA cycle. This is a very different phenotype from cancer cells that have Warburg effect. The same calculation can be done for the CA-IX cells but the data on the glucose and lactate concentration there are inconsistent and expressed in confusing units (which I will elaborate in the next paragraph). Nevertheless, as there were at most a few folds of increase in lactate production flux in the M1 and M6 cells, the glucose flux going to lactate production is likely also a few percent of the total glucose uptake flux. Again, these cells do not really have Warburg effect.

The glucose and lactate concentration data are key to the study. The data however appear to lack consistency. The lactate concentration data in Fig. 1F shows a ~5-fold increase in the M1 and M6 cells than the controls but the same data in S. Fig. 2 shows a mere ~50% increase. The meaning of the units on these figures is not clear. While "1 ng/ug protein" means 1ng of lactate is produced by 1 ug protein of cells over a 24 hour period, I do not understand what "ng/ul/ug protein" means (Fig. 1F). Also, "g/L/cell" must be a typo (S. Fig. 2). Furthermore, regarding the important glucose consumption flux, it is not clear why the authors did not directly measure it as they did for the PMA1 cells (Fig. 5E). Instead, they showed two indirect measurements which are not consistent with each other (Fig. 1E and S. Fig. 1).

Reviewer #1 (Public Review):

This is an interesting study looking at the evolution of ageing in social insects using ants as a model. As I haven't seen the initial submission, I have looked at the manuscript and the response to reviewers and I base my suggestions on both documents.

Evolution of ageing remains only partially understood and this field seems to be experiencing a sort of renaissance in recent years with a surge of theoretical advances and new empirical findings. Queens of social insects, and ant queens in particular, have remarkable lifespans and understanding the biology of their long life can help in understanding the biology of ageing in a more general sense.

In this study, the authors focus on following quite a large number of ant (C. obscurior) colonies and provide intriguing data in relation to age-specific mortality and reproduction. The gist of their argument is that the mortality is decreasing with age while reproduction (production of sexuals) is increasing with age, such that there is little evidence of ageing in this species.

Overall I think this is an interesting dataset that provides important information that will advance the field. However, I think the manuscript currently lacks clarity, structure and suffers from poor formulation of ideas in places, and is rather difficult to follow even for an expert in the field. I think that it requires quite a bit of work to sort this out. However, I also have a methodological question (#15) which could be key for the interpretation of the results.

My understanding is that queens live for 40-50 weeks max (Fig. S3). Fig. 4 suggests that from week 30 onwards the production of eggs, worker pupae and queen pupae decline. This suggests that while queen mortality declines in late life, so does queen reproduction. So, do queens of this species show reproductive senescence?

The data do suggest that relative investment into reproduction (queen worker ratio) increases with age, but the absolute number of queens declines with age. This suggests an interesting result from the life-history theory perspective - increased investment in reproduction with reduced residual reproductive value, but not necessarily the absence of reproductive senescence. Please clarify.

Reviewer #2 (Public Review):

The authors investigated the evolutionary drivers of delayed senescence in ant queens by carefully observing the survival and productivity of C. obscurior colonies that were maintained at 10, 20, or 30 workers. They show that the 10 worker treatment produces fewer new queens, and lower quality workers, indicating low colony efficiency under a reduced workforce. The authors focused their conclusions on the observation of a hump-shaped relative mortality curve, with queens having a higher than average mortality around 30 weeks and then a lower than expected mortality around 40 weeks. The colonies produced more queens at the end of their lifespan, so the authors conclude high fitness gains at the end of life selects for minimal senescence in ant queens, thus generating the drop in mortality they observed at 40 weeks.

There is a large body of research focused on the early life stage and establishment of ant colonies, but relatively little that follows their worker and reproductive trajectory to the end of life. Partially, this is because many commonly studied ant species have a lifespan too long to feasibly track, and partially because most ant species do not readily produce sexual queens or males in the lab setting. For this alone, the study provides valuable insight into the ant lifecycle and demonstrates that C. obscurior is an ideal species for future study. The experimental design and analyses are sound, and I must acknowledge the incredible amount of work that must have gone into the data collection. However, I have some serious concerns about how the results are interpreted, and what is left out of the discussion on ant colony structure and limitations that are crucial to reaching accurate conclusions.

One issue is that the conclusions hinge on the observation that relative queen mortality decreases at the latest observational period, around 40 weeks. The authors raise this as evidence that queens are under selection for reduced senescence, as they also conclude that fitness gains (queen production) are highest late in life. The problem is that according to figure S3, only a handful of queens survive past week 40, and they all manage to hang on for another month or two before dying out. I cannot be sure how many colonies survive to this period from how the data is presented, but I worry that the authors are resting their conclusion on a low number of particularly tenacious queens. These colony numbers should be provided, and the authors should demonstrate that the drop in mortality is observable even if these outliers are excluded.

It also appears that the queen pupae production drops off precipitously during the end of the observational period, according to figure 4A, which runs counter to the argument that selection is reducing senescence in these older queens because they have high reproductive output at this stage. The authors put a lot of emphasis on the queen/worker ratio being highest at the end of the observational period, but this doesn't necessarily mean queens are receiving the highest fitness during this period. A queen would have a high queen to worker production ratio if she lays one worker and one queen, but she would have higher fitness if she lays 100 workers and 10 queens. Figure 2A indicates that the highest overall queen pupae laying occurs around 30 weeks, which actually corresponds with the highest level of relative queen mortality. The question of fitness gains at advanced queen age would be better answered by just analyzing which stage in their life they produced the most queen pupae. Does the queen laying rate reach a maximum and remain stable for the rest of a queen's life, or does it decrease along with worker production as they reach end of life? Figure 4A makes it appear that it decreases towards end of life, but I'm not sure if that is only because so few colonies lasted until the end of the observational period.

Another factor that should be discussed is sperm depletion. The authors state that each queen mated with a single male when they set up the colonies, so sperm depletion may be more important than senescence for determining the reproductive lifespan of these queens. I'm not sure if this species is normally single mated in the wild, or the length of their natural colony lifespan, but this is important information to provide in order to dismiss issues of sperm depletion in this study. Without this information it is impossible to determine if the decrease in egg laying towards the end of the study is due to senescence or sperm depletion.

Taken together, it could be argued that these data better support selection on an optimal lifespan, around 30 weeks, as opposed to selection for directional extended lifespan and reduced senescence. If the reproductive benefits of an extended lifespan are capped by sperm depletion, the alternative strategy would be to produce a robust workforce as quickly and efficiently as possible, and then produce as many sexual offspring as possible with the remaining sperm. Perhaps selection has determined that the optimal length of this cycle is around 30 weeks, with variation dependent on the amount of sperm transferred during mating and the condition of the queen. This possibility should be addressed, and if possible additional data should be provided on sperm depletion in C. obscurior, and the colonies that survived to the end of the observation period. Without these additions, the conclusions on senescence and lifespan remain tenuous.

Reviewer #1 (Public Review):

With the ever increasing interest in single-molecule imaging techniques, accompanied by ever increasing experimental schemes and analytical frameworks it becomes difficult to report such experimental results in a manner that is universal, one which can adapt to any type of experiment and analysis, one which assists in reproducibility, and one which will keep the data as compact as possible, yet its usage as efficient as possible. This work ("Mars, a molecule archive suite for reproducible analysis and reporting of single molecule properties from bioimages", by Huisjes, Retzer et al. ) introduces a novel platform for the reproducible archiving of camera-based single-molecule imaging experiments. This work will appeal to practitioners of single-molecule imaging experiments, both experienced and newbies. The readers of this work would benefit from understanding how to employ a rational data archiving process using Mars, following three examples the authors provide, which exhibit the generality of the platform and its ease of use. Readers who might want to employ Mars for their own single-molecule imaging measurements can also experience an intuitive guide on Mars GitHub or in Jupyter Notebooks the authors provide.

I judge that it would even be better if several items that will assist audiences who are not experts in single-molecule imaging (such as a guidance chapter in the manuscript itself, in addition and before sending the readers to the guide online) could be provided in the text. Additionally, adding a layer of data validation can enhance the archiving procedure even more. Aside from this and a few other FRET-related comments and suggestions, I conclude that this work can be quite useful and presents a major step forward towards open science in single-molecule imaging.

Reviewer #2 (Public Review):

The manuscript by Huisjes et al presented an open-source platform for the storage and processing of imaging data, particularly for single-molecule imaging experiments. Compared to sequencing data, which have a more standardized format for data storage, imaging data have more diverse formats due to the fact that different research labs tend to use different instruments and software (either commercial or home-built) for data collection and analysis. Manual input is almost always necessary at certain steps of data analysis. All these create difficulties in data storage and reproducibility. The authors provide a practical solution to this problem by the molecular archive suite, "Mars". This platform is integrated into imageJ/Fiji, and can be used for storing detailed description of experimental settings, performing standard imaging processing steps, and recording manual input information during data analysis. I judge this platform, if fully functional and generalizable, will be very useful to many labs who are using single-molecule imaging methods in the research.

Strength:

1. The work presented a fairly user friendly interface (using Fiji directly), and fairly detailed protocol and other documentations in a very nicely designed website. I was able to download and use it based on the tutorial.<br> 2. It is integrated very well with Fiji, and some analysis modules are directly from existing Fiji analysis/plugins.

Weakness:

I invited one of my students to co-test the suite. We tried on both Mac and Windows systems, using the example FRET data set described in the manuscript and one of our own single-molecule images. We encountered some technical issues.

Joint Public Review:

This manuscript by Giwercman, et al., pursues the identification of protein biomarkers of androgen activity in humans. These investigators carried out a proteomic analysis in blood from 30 healthy males treated with a GnRH antagonist at baseline, after medical castration and at a third time point after testosterone replacement. Proteins that were most significantly associated with testosterone changes were tested further in a separate cohort of 75 hypo- and eugonadal men with infertility. Associations between these proteins and cardio-metabolic parameters, as well as androgen receptor (AR) CAG repeat length were assessed. The major findings include the observation that 4-hydroxyphenylpyruvate dioxygenase (4HPPD), insulin-like growth factor-binding protein 6 (IGFBP6) and fructose-bisphosphate aldolase (ALDOB) are biomarkers that follow testosterone level and presumably AR activity. Overall, this is a very interesting and novel body of work.

Reviewer #1 (Public Review):

The study tests the validity of using in vitro models to recapitulate (and thus potentially predict) the outcome of human phage therapy. To do this they sample a human volunteer undergoing nasal decolonisation by phages, in vivo isolated bacteria and phages and characterised phenotypically and genetically, and also used to establish in vitro experimental treatments which are then similarly characterised.

The key strength and novelty of the study is the ability to directly compare the evolutionary response of the same bacteria to phage therapy both in vivo and in vitro. To my knowledge this has not been achieved before. An understandable weakness is that there is only one human subject, but this is compensated for by the ability to perform well replicated in vitro studies which also allow testing of alternative treatment regimens.

The aims of the study are achieved. The results are compelling, showing that resistance readily evolves both in vivo and in vitro, and results in similar alterations in the ability of phage resistant bacteria to establish acute infections, reduced growth rate, and changes in biofilm formation. In hindsight and in light of the genomic data there are several additional phenotypes that it would be interesting to test (in particular motility) but these additional phenotypes would be unlikely to alter the overall message of the study. The results support the conclusions.

The work is likely to have an impact on the field. There are still relatively few studies of resistance evolution against cocktails, and even fewer of this process in vivo. The validation of using in vitro systems could dramatically improve the ability to test phage cocktails for robustness to resistance prior to clinical use, thus leading to translational clinical impacts of the work.

Reviewer #2 (Public Review):

This work by Castledine et al. addresses the important question of whether results from in vitro (laboratory-based) evolution studies may be useful for predicting evolution during phage therapy in a clinical setting. In order to explore this question, the authors cultured a set of bacterial isolates from a patient pre- and during phage therapy, as well as phages from several time points during therapy. They then experimentally evolved (in vitro) a mixture of the bacterial isolates from the patient in the absence of phage, or in the presence of phage using two different treatments (phage added once or added repeatedly). Overall, they observed similarities between the evolutionary outcomes (genomic and phenotypic) in vitro and in the patient. Resistance evolved rapidly in the patient and in vitro under phage selection, and similar genomic changes were observed in both environments. The approach of using bacterial isolates directly from the patient (as well as the phages used for therapy) in vitro is clever, and the observed similarities are compelling. However, I think there are some limitations with the study that should be addressed in the text.

In particular,<br> (1) While the similarities in vitro and in the patient are quite interesting, there are some differences that were dismissed as being minor without justification. Calling the results "highly parallel" is a bit subjective - in vitro in the repeated phage treatment (which is suggested to be most similar to the clinical context), there did appear to be phage coevolution that was not observed in vivo. The tradeoffs/relationships between traits (as shown in Fig. 3) also differed to some extent. Additionally, for the genomic results only a subset of variants were plotted (those in genes of known function), but there were far more significant variants in genes of unknown function that were not included. It is difficult to assess whether the genomic findings are truly similar across environments if only a fraction of those results were presented in the manuscript.

(2) Much of the text is framed around whether in vitro outcomes are predictive of those in vivo, but this study only included results from a single patient. Thus, it is impossible to know whether these findings are by chance or representative of a more general relationship between in vitro and in vivo evolution.

(3) Although the evolutionary outcomes appear to be similar, the pathogen was successfully cleared from the patient but persisted throughout experimental evolution. Whether the pathogen is successfully eliminated or not is presumably the most important clinical outcome, and while this difference is not surprising, it is an important one to point out to the reader. Essentially, evolution was similar to some extent but the consequences of evolution for bacterial persistence in each environment were quite different.

Reviewer #1 (Public Review):

As we lack empirical data of the response of most species to environmental changes, developing predictive tools based on traits that are easier to access or infer may help us developing better management tools. This is the case even for terrestrial mammals, a rather well studied group but with a large study bias towards temperate Europe and North America. This study uses maximum longevity, litter size and body mass to predict the sign and size of the relationships between annual temperature and precipitation anomalies and population growth rates, using the Living Planet database for times series of abundance and Chelsa for weather anomalies. The authors use a Bayesian framework to relate the size and absolute magnitude of the relationships between detrended population growth rates and weather anomalies, the framework accounting for the uncertainty in estimates as well as phylogenetic dependencies. They did not find any systematic effects -- on average the slopes of the relationships were close to 0 -- but the magnitude of the coefficients decreases for species with high maximum longevity and low litter size. Therefore, this study points to possible predictions of the magnitude of the response to weather variability using simple demographic indices such as longevity and litter size. The study has clear limitations that are common to similar "meta-regressions" using publicly available databases, but they are not ignored when discussing the results. One would hope that such limitations would lead to improving the quality of such databases, both in terms of taxonomic and geographic coverage as well as quality of data.

I would like to challenge the authors in terms of why one would expect relationships of a given sign or magnitude. First with respect to sign of relationships, even for the same species and the same weather parameters, one could expect different signs depending on where the study is done with regards to the climatic niche. If one is close to the warm (or wet) edge, any positive temperature (or precipitation) anomalies would probably have a negative effect, but the reverse would happen when close to the cold or dry edge. There are studies showing such demographic and growth rate variability differences. I find therefore hard to interpret the sign of such weather anomalies and what it tells us about the "effect" of weather variability. Second with regards to the magnitude, it is clear that the maximum growth rate is strongly linked to maximum longevity and litter size -- slow species have a much lower maximum rate of growth than fast species. So, one would expect that variability of population growth rates is larger in fast species than slow species, and therefore the magnitude of their response to environmental variability. Now the question might also be whether weather variability explains a smaller or larger proportion of the variability in population growth rates -- that is, does weather have a relatively larger influence in fast species than slow species? You might have the answer but with the multiple standardizations of the response and predictor variables it is not obvious (that is, when you standardize the response and predictor variables, coefficients are correlations, but this is across species, not for a given population).

Your analyses remove trends -- that is, climate or other systematic change as opposed to weather anomalies (yearly differences) -- and trends might be the main concerns in terms of conservation. This is made clear in the discussion but perhaps not as much in the introduction where you seem to focus on climate change (the title reflects this well, however, as you mention weather, not climate). This confusion between weather and climate is often made in the literature, when reference is made to climate effects rather than weather effects.

Finally, I would like to see a measure of how good is the prediction you can make using traits. You may have "significant effects" but not helping much in terms of prediction (see PB Adler et al. 2011 in Science, for an example with species richness and productivity).

Reviewer #2 (Public Review):

Jackson et al. present a global analysis of the effects of life history on the response of terrestrial mammal populations to weather, showing that litter size and longevity significantly alter how populations respond to anomalies in temperature and rainfall. The topic is highly interesting, as it has implications for what data we should monitor to make more reliable predictions about species' responses to climatic change, and how we should prioritise which species to conserve by identifying those which might be at greatest risk.

The authors comprehensively validate their results with substantial secondary analyses, and I believe that their assertions are supported by the results presented here. Whilst global scale analyses such as this provide useful generalities, they should be taken as that: an investigation of the general trends observed across large spatial scales, and caution should be taken extrapolating too far away from the species which have been analysed for this study.

Reviewer #3 (Public Review):

In this study, the authors aim to investigate how mammalian species are likely to respond to climate change. To this end, they investigate the effects of weather anomalies on the growth rates of mammalian populations. They use long-term population records for 157 terrestrial mammals from the Living Planet database. They explore three different questions using a two-step modelling approach: (1) whether temperature and precipitation anomalies have significant effects on population growth rates across species; (2) whether responses differ among species and biomes; and (3) whether life-history traits explain species responses to weather anomalies.

The work undertaken in this manuscript is of broad appeal in the field and has the potential to inform conservation. Overall, the methodology is sound and the modelling framework robust; the authors took care to test the robustness of their models by fitting alternative sets of models. The two-step design of this study is interesting and the choice of the study system is relevant for the questions the authors aim to tackle. The authors also paid attention to some important points that are at times overlooked such as resolving taxonomy before running their analyses. I also appreciated the fact that the authors made their code available.

I nevertheless think that, in its present form, the main weakness of this manuscript is the clarity of the writing, the framing of the study and the overall flow. I found the manuscript at times a bit difficult to follow. That said, I think there is much scope for the authors to improve it. First, I think the work would benefit from better explanation of the underlying hypotheses. Second, in some places I think the authors go into a lot of details at the expense of clarity. As such, I think the authors should strive to better balance clarity with detailed information (notably in the results and methods; adding summary sentences, for example, could help clarify these sections). Third, I think there is room for improvement in the narrative and the flow of the introduction and the discussion. Finally, I think stronger justifications are sometimes required regarding specific points of the analysis.

I believe that the conclusions of this work are supported by the data and the analyses, and think they are of interest and relevant to the field. However, I think the discussion should highlight the main limitations of the study. In particular, I think the biases in the data should be discussed, and notably whether these biases are expected to affect the results (and if so, in what way).

To conclude, I think that beyond the aforementioned weaknesses of this study, the results and the methods are of interest for the field. I think the modelling framework is applicable to other study systems and relevant to the field as well.

Reviewer #1 (Public Review):

In this manuscript, the authors investigate the circuit and molecular basis of sexual receptivity in Drosophila. They focus on the role of the neuropeptide Drosulfakinin (Dsk) in this process. By genetically manipulating the peptide and its receptor, the authors demonstrate that Dsk, via its receptor, CCKLR-17D3, modulates sexual receptivity in female flies. In terms of circuitry, they use a combination of intersectional genetics, trans-synaptic labelling, electrophysiology, and circuit dissection to show that the neurons within the R71G01-Gal4 expression domain modulate this receptivity via the Dsk-m neurons. The data in this study are clear and generally well presented.

Reviewer #2 (Public Review):

Innate drives in animals are integrated with external environmental cues to modify behavioral outputs. The manuscript by Wang et al. investigated the role of neuropeptide Drosulfakinin (Dsk) in promoting female sexual receptivity. Using a neurogenetic approach the authors demonstrate that Dsk gene product as well as neuronal activity is required for female copulation rate. Genetic approaches were used to identify R71G10-GAL4 neurons as downstream to Dsk neurons and electrophysiology was used to show functional connection between the two populations. A medial subset of Dsk neurons was identified to modulate female copulation rate. It was further shown that female mating is modulated by its receptor CCKLR-17D3.

While the findings reported seem interesting for a broad neuroscience audience, the advance made appears incremental. Several of the claims in the manuscript will require further experiments to support the conclusions drawn. The manuscript adds further data to recent findings reporting Dsk function in Drosophila, however, several of these earlier studies were either omitted altogether or not appropriately cited in the manuscript, some of these include- Nichols and Lim, 1996, Cell Tissue Res; Soderberg, et al. 2012, Front. Endocrinology; Williams et al., 2014, Asahina et al., 2014, Cell; Genetics; Wu et al. 2019, Nat. Comm.; Agrawal et al. 2020, JEB.

One of the major concerns about the study comes from omission of using appropriate tools to dissect Dsk function. The Dsk-GAL4 and Dsk antibodies reported in this manuscript and an earlier study from the same lab (Wu et al., eLife, 2020) do not cover all the Dsk neurons. Earlier studies have shown that the neurons from pars intercerebralis region contain Dsk producing neurons and these Dsk neurons are crucial for regulating hunger and aggressive behaviors. Therefore, it will be important to identify what is the role of these Dsk producing neurons in regulating female mating behaviors by using these earlier published drivers. This will be important given the conclusions drawn are for involvement of Dsk and Dsk neurons overall, which were further sub-divided to elucidate the role of Dsk-M neurons.

Several of the genetic tools used in this study were published from these authors earlier, however these are not referred to appropriately in the main text which leads to confusion as to which tools are created in the current study vs. earlier study.

Copulation rate and latency was used as a proxy for overall female sexual behavior, and it is extended further in the discussion section and even speculated that Dsk neurons might integrate various sensory stimuli including courtship song and pheromonal cues. However, to conclude the effects of Dsk signaling on female sexual behavior it would be more appropriate to look at other behavioral parameters during mating through video analysis and provide rigorous statistical analysis.

Reviewer #3 (Public Review):

The authors present an extensive set of experiments, spanning from behavior, trans-synaptic mapping to electrophysiology, to show that the Drosulfakinin (DSK) signaling pathway regulates sexual receptivity in Drosophila. They report that DSK neurons act downstream of 71G01-Gal4 neurons to promote female sexual receptivity. The authors describe a subset of DSK neurons in the brain as essential mediators of this behavior. They further identify the receptor through which DSK exerts its function, paving the way to investigate the downstream circuitry involved in female sexual receptivity. More generally, this work, combined with a previous report by another group focused on male courtship, suggests that the DSK circuitry modulates sex-specific behaviors essential for reproductive success in Drosophila.

Overall, Wang et al., address an interesting question and provide insights into the neural circuit mechanisms of female sexual behaviors in flies. In addition, the study introduces new transgenic tools that will be of interest to the scientific community. The authors use alternative approaches to validate their main findings, which strengthen the study. However, there are a few issues regarding data analysis and presentation that require attention.

It is not clear whether the authors have used proper statistical tests. The authors should assess the nature of the data and use rigorous statistical analyses. Also, they should clearly report what tests are used to analyze the data and what comparisons are made in each data set.

I believe a substantial improvement in the writing style would help readers fully judge and appreciate the findings of this study. The authors could enrich the Introduction by adding information about the courtship ritual in flies and explaining its relevance for mate selection. Moreover, they could describe how female flies signal sexual receptivity and accept a male for copulation. This would help them highlight the importance of their work and make it more attractive for non-specialist readers. In addition, more elaboration on the rationale of the experiments, details of the techniques used in the study, and a better description of the results would help readers better grasp the findings and implications of this study. Finally, the findings of the study could be better discussed in the context of what it is known regarding female sexual receptivity and other key neural players. The authors could speculate how DSK neurons control sexual receptivity. They could elaborate on the interesting finding that the same peptidergic pathway shapes key male and female behaviors during courtship. How do the findings in the female compare to what it's known in the male?

Reviewer #1 (Public Review):

The study presents one of many recent studies trying to find potential functional meaning of epigenetic clock. For that, different measurements were performed and correlated with DNA methylation biomarkers. Further the changes in the epigenetic age were assessed depending on diet, therefore metabolic status of the animal. Two major observation has been made 1. the diet is affecting the epigenetic clock and 2. Specific QTLs were uncovered highlighting the importance of metabolism and cell cycle in aging. Interestingly, same loci were previously associated with epigenetic age acceleration in human further confirming the relevance of the association and study itself.

It is of highest importance to understand the correlation of epigenetic changes with biology and physiology. Recent study of Levine lab has shown the similar work on large sample of human samples from UK Biobank (Kuo et al 2020). Here, the number of samples is significantly smaller but the defined genetic, phenotypic, and full genome sequence of mice used in the study is an advantage in distilling the correlations.

This reviewer finds the observation that metabolic state is inducing changes in epigenetic age very interesting and worth studying. Series of correlations with acceleration of epigenetic age is well presented and quantified. However, some observations, although in lower numbers, were previously presented by the laboratory in another paper.

Description of QTLs is one of the most interesting parts of the study. Correlations of metabolically relevant loci with age acceleration is highly suggestive of molecular mechanism, probably based on negative feedback loop, that moderates DNAm. This idea is, however, not discussed in the paper.

Reviewer #2 (Public Review):

The manuscript titled "Genetic Analyses of Epigenetic Predictors that Estimate Aging, Metabolic Traits, and Lifespan" by Mozhui et al. extended the application of the DNA methylation microarray technology to the liver of the BXD mouse cohort. This study examines on the relationship between the epigenome aging parameters such as epigenetic age and phenotypic parameters such as diet and metabolic traits. It also explored potential genetic mechanisms responsible for epigenetic age acceleration by identifying QTLs on chromosomes 11 and 19. Overall, this is an informative and well-organized study of importance to aging research, and it is inspiring as it explores mechanisms of epigenetic age acceleration.

Reviewer #3 (Public Review):

The manuscript by Mozhui et al. investigates genetic and environmental modifiers of DNAm based biomarkers of ageing. The authors use a number of interesting measures to evaluate these effects, from epigenetic age, to maximum predicted lifespan, to methylome entropy. The manuscript then presents and discusses the effects of genetics and environment on these measures.

Methylome entropy: The study shows that "Entropy was also significantly higher in the HFD group" - the difference between the two groups is very small (Table 2). Is this difference possibly meaningful? And assuming it is, are any of the enzymes relevant for maintaining or changing DNAm (DNMTs, TETs, ...) differentially expressed between the groups which could explain the difference? This question would also be interesting in the context of the finding that "entropy had an inverse correlation with body weight ", which somewhat is in conflict with the HFD results.

The finding of "lower age acceleration with higher glucose" may be due to the focus on liver tissue? Are there maybe any expression data pointing to healthier samples or other types of effects which might affect ageing, e.g. lower basal metabolic rate (-> less glucose drop during fasting)?

A very interesting part represents the "Genetic analysis of epigenetic age acceleration and predicted-maxLS". Both genomic loci on Chr 11 and Chr 19 harbour various interesting genes. In extension of the current analysis, the authors could have looked at existing HiC datasets to identify if several of the found SNPs are within long-ranging genome - interactions and may also play a regulatory role towards more distant genes.

Also, the finding of HFD, BW, glucose levels, etc affecting several of the measures used prompts the question if any of the genes present in the Eaaq11 or 19 have been implicated with these metabolic phenotypes? Are mouse models available of genes found in these regions to ask whether OVX or KD would have an effect on ageing? Also, given that the readout is primarily DNA methylation and not physiology, would any of genes in these loci affect DNA methylation itself? This may be a potential confounder.

Reviewer #1 (Public Review):

In this paper, Burugupalli et al studied the lipid metabolism in pregnancy, and from birth to four years. They performed lipid profiling in 1074 samples from mothers and their offsprings in the BIS, which is a population based pre-birth cohort. The authors measured 776 distinct lipid species across 42 lipid classes using UHPLC. The study included measurement of lipids in 1032 maternal serum at 28 weeks' gestation, 893 cord serum at birth, and 793, 735, and 511 plasma samples at 6, 12 months, and four years, respectively. They showed that lipidome was different between the mother and their newborns, and the change increased with age. These studies also demonstrated that cord serum contained higher levels of long chain poly-unsaturated fatty acids (LCPUFAs), and cholesteryl esters compared to the maternal samples. Phosphatidylethanolamine containing LC-PUFAs also increased with postnatal age, while lysophospholipids and triglycerides decreased. Regression analyses to investigate the associations of cord serum lipid species with birth factors, including gestational age, birth weight, mode of birth and duration of labor showed that the majority of the cord serum lipids associated with gestational age and birth weight, with most lipids having opposing associations. The authors concluded that there "were marked changes in the plasma lipidome over the first four years of life. This study sheds light on lipid metabolism in infancy and early childhood and provide a framework to define the relationship between lipid metabolism and health outcomes in early childhood."<br> The paper is well written and the experimental approach is sound. 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 authors need to better define the significance of their findings. They try to link the levels of certain lipids with gestational age, birth weight, mode of birth and duration of labor. However, there are several lipids that have been shown to have toxicity in certain cells. For example, ceramides have been shown to cause cardiotoxicity in adults. The authors need to assess the levels of these potential toxic lipids in the serum and whether they are linked to any changes in the overall or system physiology. 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.<br> The paper will likely have a high impact on the field, as it identifies changes in plasma lipids at the early stages after birth and its link with factors related to pregnancy.

Reviewer #2 (Public Review):

In this manuscript, Burugupalli et al. perform longitudinal lipid profiling on a key demographic that fills a gap in the field: mother-child pairs. The strengths of the paper include comprehensive lipid profiling of over 700 lipid species and an impressive longitudinal cohort of over 1000 mother-child dyads.

The authors validate their measurements using ultra high-performance liquid chromatography, with quality control samples measured every 20 samples. They perform principal component analysis to show how the circulating lipidome is different among mothers, newborns, and infants. Additionally, they perform longitudinal analyses using adjusted linear regression models to show associations between circulating lipids with birth weight and BMI at 4 years old.

The work is likely to be impactful in the field of lipid biology. More broadly, information will be useful for exploratory hypothesis-generating ideas in the field of lipidology, particularly in pediatric subjects.

Reviewer #3 (Public Review):

Strengths:<br> The present manuscript is impressive in scope. I appreciate the deep effort put in by the team to identify clinical associations between a wide range of maternal and child parameters, and the measured lipid species. In particular, as the authors explicitly stated that they wanted to correlate lipid signatures to known child parameters that portend the metabolic syndrome, their analyses rightly focused on those variables, such as birth weight and BMI. The scientific descriptions are meticulous and detailed, a testament to the strength of the team's analytical chemistry capabilities, that render this work an immensely useful lipidomic atlas for the research community. It is also to the authors' credit that they cross-validated some of their findings in a distinct, ethnically more diverse cohort, namely GUSTO, which enhances the robustness of their results.

Weaknesses:<br> Although the goal of the authors was to find out whether specific lipid signatures correlate with factors predisposing to poor health outcomes in later life, several other interesting and impactful clinical questions could also have been answered with the present dataset.

For example, in the section on birth mode and labor duration, the authors assert that there are specific cord serum lipid signatures that associate with birth mode and labor duration. The authors should determine whether any maternal lipid signature during pregnancy can predict labor duration. Further, details on the use of epidurals and labor inducers, if any, do not seem to be given. This is particularly important since some of these chemicals are lipophilic in nature and may interact or shape maternal and cord lipidomes.

Complications during labor are not uncommon. From the manuscript, it seemed that mothers who had experienced labor complications were not excluded from the analysis. If so, it would seem appropriate for the authors to discuss how various complications e.g., breech births, meconium aspiration, might associate with gestational lipid signatures, with immediate implications for triage and emergency planning.

The child's lipidome can be influenced by nutrition. In the first year of life, it is therefore heavily shaped by the milk (breast and/or formula) consumed. However, the authors have not discussed milk as a source of lipids that can influence the ontogeny of the child's lipidome.

In sum, while the design and execution of the investigation have been generally done well, the analyses presented in the manuscript have limited clinical impact. To attain the team's long-term goal of understanding how early life events impact health outcomes in later life, more decades of research is still needed. The interim data gathered and presented in this work can be better employed in understanding events proximal to labor, delivery and the first year of life to generate timely and impactful clinical insights.

Public Review:

In the manuscript by Sun et al the authors examine the roles played by the gasdermin-interleukin axis in the fracture healing process. Gasdermins (GSDMs) form plasma membrane pores and thus enable secretion of interleukins 1β and 18. These proinflammatory interleukins then initiate fracture healing. The authors utilize a variety of models to demonstrate the importance of interleukin 1β in this process including: (1) GSDM knockout mice; (2) tibia fracture model and (3) interleukin 1 knockout mice. The use of various in vivo and in vitro testing is a strength of the manuscript convincingly demonstrating a role for interleukin 1β in fracture healing. A weakness of the manuscript is that a potential role for interleukin 18 is underdeveloped and not as convincing as that for interleukin 1β. The authors themselves point this out in the Discussion section. The authors raise the possibility that their findings are translationally relevant with the development of GSDM inhibitors for clinical use.

Reviewer #3 (Public Review):

In this manuscript, the authors present data to suggest that transcriptional activation of the Slp1/2 temporal factors in the medulla neuroblasts of the developing Drosophila optic lobe is dependent on two enhancer elements. The authors concluded that these two enhancers were able to be activated by Ey and Scro, two other factors identified to be involved in the temporal cascade of the medulla NB. The authors show that cell cycle progression is necessary for Notch signaling, and that Notch signaling activates and sustains the temporal transcription factor cascade. The authors use GFP reporter assays to correlate the enhancer activity to Slp1/2 expression and used DamID to show in-vivo binding of Su(H) and Ey to the enhancer fragments.

I agree with the authors that it is important to define the mechanisms by which Notch, cell cycle control and these temporal transcription factors function through their cis-regulatory elements to establish this self-propagating cascade to generate diverse cell types during neurogenesis. Further work will be needed to offer new insights toward reaching this goal.

First, studies in invertebrate and vertebrate neurogenesis have agreed on the conceptual framework that transcriptional control plays a key role in regulating the generation of diverse cell types. The data showing the patterns of slp1/2 transcript reaffirm the proposed model as well as recently published single-cell transcriptomic analyses of fly optic lobe neuroblasts.

Second, it remains unclear how physiologically relevant the enhancer analyses presented in this study are to the regulation of Slp1/2 expression, as the data can only suggest that they act redundantly to each other. It is also concerning to see that mutating binding sites of a single transcription factor appears to completely abolish enhancer activity while Slp1/2 protein expression is delayed in mutant clonal analyses.

Third, the authors do not explain how Notch signaling contributes to the timing of Slp1/2 expression, considering that Notch signaling should be active during the entire life of the neuroblast based on canonical Notch target gene expression. What action do Ey and Scro play in this timely enhancer activation as both appear to be necessary to activate the enhancers along with Notch?

Fourth, many studies including the Okamoto et al., 2016 study cited in this study have contributed to our appreciation of the role of proper cell cycle control in promoting generation of diverse neurons in vertebrate neurogenesis. It is unclear to me how the findings from the current study contribute to significant advancement on this regulatory link.

Reviewer #1 (Public Review):

This manuscript has great potential. The study is well designed, performed, and written, with good statistical analyses. On the other hand, it does not have a sufficient experimental basis. The authors investigated whole body immunoglobulin diversity in killifish and found that it decreases with age. This decrease is mostly driven by larger clones, in other words, by the expansion of B cell clones. They further analyzed immunoglobulin diversity in the intestine and found that its decrease is much more pronounced than in the whole body. It was also observed that the transfer of the young gut flora to old fish does not rejuvenate the B cell repertoire. The major novelty of this work is the model organism, killifish. Also, while this study is solid, it is descriptive, without many mechanistic insights.

Some of the following experiments, or other experiments, may help explore mechanisms and make the study more compelling: 1) whole genome sequencing of lymphoid tissues and brain as a control, from the same old fish to determine whether there are clonal somatic mutations. If confirmed, it may be an important finding, as it would mean that clonal expansions emerge as fast as the killifish lifespan, and it would be a great model to study mechanisms of mutation accumulation and clonal selection with age. This WGS data may be further used to reconstruct immunoglobulin repertoires to understand if the whole-body decrease is driven solely by intestine B cells, or it initiates in lymphoid tissues. 2) RNA sequencing of intestine samples or spleen from young versus old killifish to obtain insights into possible molecular mechanisms clonal expansion and diversity loss. Spleen RNA sequencing may be used to reconstruct the immunoglobulin repertoire. The authors used 750 ng of total RNA in the current study, so there should be enough material for RNA sequencing. As an alternative, single cell RNA sequencing may be performed.

Reviewer #2 (Public Review):

This study introduces the killifish as a short-lived vertebrate model for immune aging and immunosenescence and characterizes the changes in the immune-repertoire during aging. The authors convincingly show a decrease in diversity of the large expanded B-cell clones that is greater than small clones and a more pronounced change in the intestinal antibody repertoire with age. A limitation of the current study is its descriptive nature and lack of strong evidence that these animals truly experience functional immunosenescence. The impact of this work could be strengthened by functional data showing a decline in adaptive immunity that goes along with the loss of diversity in the antibody repertoire or citation and discussion of prior literature supporting this relationship. As it is, it is difficult to know the extent to which the observed changes are strongly correlated with changes in immune function, and the manuscript currently somewhat overstates the importance of the observations. It should be explicitly noted that further research is needed to determine whether the changes in immune-repertoire actually reflect immune senescence or simply changes with little or no consequence.