Reviewer #1 (Public Review):

The paper by Paganos and collaborators is an interesting one. It deals with the characterization of cell types (using single cell sequencing) in the sea urchin larvae. The paper combines different sets of data, from the transcriptome data to detailed analysis of gene expression domains. The advantage, and the strength of the paper, and the biological system, the California purple urchin, is that the cell transcriptome can be consistently related to specifically located cells in the larvae. The well characterized genome, transcriptomes, gene expression domains and gene regulatory networks, allows a clear mapping of cells and activities to specific areas of the embryo. This possibility is only amenable in a very few systems and, among them, the larvae of Strongylocentrotus purpuratus.

Though it is a bit peripheral to the main intention of the paper it is particularly interesting that the authors show that given the extensive knowledge of territorial and cell type markers known in S. purpuratus, the clusters and their expression domains are strictly correlated. In fact, a very nice validation of the technology that others (working with other animal systems) can take as an example.

On the down side (a very minor "down") is the lack of information for other developmental time points. This would have allowed the authors to follow the lineage of many of the cell types characterized, with a more thorough description of developmental trajectories.

Reviewer #2 (Public Review):

Development of the sea urchin larva offers a particularly useful model to unravel the gene regulatory interactions that drive the developmental process, and has served in the past to unravel basic principles of how gene regulatory networks control the specification of cell types and how these networks evolve. Paganos et. al. take full advantage of the model system to generate a novel and highly valuable data set with important evolutionary implications. The authors employ single-cell mRNA-sequencing to thoroughly characterize the cell types of the sea urchin larva at a critical developmental stage. By exploiting the rich previous knowledge in the model system, Paganos et. al. carefully validate the transcriptomics data, and convincingly establish a high level of reliability of the data set. In this manner, the study provides a complete and very valuable map of cell types and transcriptional profiles, and is able to: i) add potentially important regulators to well established gene regulatory networks that control the developmental process in the sea urchin; and ii) unravel previously undescribed neuronal and immune cell types. At the technical level, the authors convincingly establish a higher sensitivity of the single-cell mRNA-sequencing data in comparison to in situ hybridization experiments.

One of the major strengths of the paper is the detailed characterization of a previously described population of neurons, which shares a large number of transcription factors with vertebrate pancreatic cells. The transcriptional profile of this enigmatic neuronal population is likely to reflect an interesting evolutionary event with broad implications. Although the hypothesis of a pancreatic transcriptional profile in this population of neurons in the sea urchin was already proposed in a previous paper by the same group, the current study provides important novelty at two levels: i) it adds strong support to the hypothesis; and ii) it unravels the expression in these neurons of a set of very relevant genes, allowing the authors to propose new evolutionary scenarios as well as to contribute to a more general and currently very active debate about how cell types evolve.

Reviewer #3 (Public Review):

This manuscript is very well written and with beautiful high quality images to represent the data. The SC-seq and cluster analysis is done well with six samples and four biological replicates and reasonable parameter choice, cuttoffs, and controls. The extensive GRN and previously published expression data in the sea urchin allow the authors to convincingly provide multiple examples of "proof of principle" that their single cell data analysis is working well. Their ability to identify 12 neuronal subclasses further demonstrates the quality of the data. The authors provide extensive whole mount insitu images to verify their SC clustering. The analysis reveals the cell type complexity of this simple deuterostome larvae, and provides a robust dataset for those working with the sea urchin model system and also for those studying cell type evolution. The most significant new finding is in the last section where they identify the putative GRN of the neuroendocrine cell in the sea urchin and to show the extraordinary similarity in gene expression profiles between this cell and vertebrate pancreatic cell. This suggests an ancient origin on neuroendocrine cell types and is a great example of using this approach to understand cell type evolution.

Reviewer #1 (Public Review):

This paper examines the ionic currents underlying the pacemaker firing in midbrain dopamine cells, neurons that have a key role in motivation, reinforcement and locomotion, and have been implicated in multiple neuropsychiatric disorders. The authors first demonstrate that blocking TRPC3 receptors or all TRPC channels prevents repetitive firing; however, surprisingly, the TRPC3 KO mouse has normal pacemaking in the DA cells. The authors then provide good evidence supporting the idea that in the TRPC3 KO mouse there is upregulation of NALCN leak channels, and that these substitute for the normal pacemaking contribution by TRPC3. Although it was previously demonstrated that TRPC channel family blockers such as 2-APB inhibit pacemaker firing, these compounds have many off-target effects as well. The present paper adds strongly to the argument that TRPC3 channels make an important contribution to pacemaking in these cells, and the suggestion that TRPC3 channels and NALCN channels together are the workhorses of this feature in dopamine cells is well-supported. In my opinion, the authors' conclusions are justified, and this paper makes a solid contribution to our understanding of pacemaker currents in these cells; the results may also have relevance to other neurons that exhibit similar firing properties and may utilize the same underlying conductances.

Reviewer #2 (Public Review):

Ki Bum Um and collaborators show that application of a TRPC3-channel inhibitor (Pyr10) ablates spontaneous firing and decreases membrane potential in midbrain dopaminergic neurons recorded from brain slices and acutely dissociated neurons from young mice, whereas HCN or voltage-activated calcium channels do not contribute under these conditions to pacemaking because firing is not affected by inhibitors of these two channel types. Injection of current in the presence of Pyr10 can restore spontaneous firing, with a firing frequency proportional to the magnitude of the injected current, indicating that Pyr10 did not affect neuronal properties other than the leak current that contributes to subthreshold depolarization. Neurons from TRPC3-deficient mice exhibit spontaneous firing as in wild-type, and their membrane potential becomes insensitive to Pyr10 and another wide-spectrum TRPC channel inhibitor, indicating that another type of channel must compensate for the absence of TRPC3. Application of a NALCN channel inhibitor (L-703,606) in neurons from TRPC3 KO animals, as well from wild type, has a comparable effect to that of Pyr10 on firing and membrane potential, suggesting that NALCN can compensate for the absence of TRPC3, and that both NALCN and TRPC3 contribute to the current that drives periodic firing in these neurons. Consistently, the authors find that neurotensin-sensitive NALCN currents are enhanced in TRPC3 KO mice, as well as mRNA and protein levels for this channel detected by RTPCR of tissue and single cells or immunofluorescence, respectively. Finally, the authors estimate the contribution of each channel to the subthreshold membrane depolarization by measuring membrane potential in the presence of pharmacological inhibitors and estimate that TRPC3 and NALCN each contribute >30%, with a remaining third of the current remaining unaccounted for.

The data are clearly presented and of high quality, and the observations are robust, proving appropriate support for the authors' conclusions, which represent a substantive advancement regarding a long-standing and relevant open question in the field of neuroscience. No direct measurements of the NALCN and TRPC3-mediated currents are performed. No evidence for dose-dependence of Pyr10 or L-703,606 is provided, which would lend further support that the observed effects on membrane potential are caused by a reduction in channel-mediated currents that occurs when they bind the inhibitors. In addition, the specificity of the TRPC3 inhibitor Pyr10 is not discussed; this would be relevant because the cited references do not establish its specificity and in turn show some inhibitory effect on TRPC6. It remains a possibility that TRPC6 and TRPC7 could form heteromers with TRPC3 channels and contribute to pacemaking, but this consideration certainly does no affect the authors' conclusions. Importantly, the number of mice used for recordings is not indicated in all figures.

Reviewer #3 (Public Review):

The authors set out to identify the main ion channels that underlie the regular pacemaking of the dopamine neurons of the substantia nigra compacta. Previous work has pointed to sodium permeable ion channels, but the identity of these channels has been elusive because of the nonspecificity of some pharmacological tools and the difficulties in interpreting gene knockouts.

This work provides convincing evidence for a key role for two particular Na-permeable channels, TRPC3 and NALCN. It shows the puzzling discrepancy that TRPC3 blockers abolish pacemaking but that in TRPC3 knockout animals, pacemaking is normal--and it nicely resolves this discrepancy, first by showing that TRPC3 blockers no longer abolish pacemaking (arguing against a nonspecific effect of the blockers) and then by showing that NALCN channels are upregulated in the knockout animals. A newer drug that targets NALCN channels is used, in combination with TRPC3 blockers, to dissect the relative contributions of these two channels (and of other channels) to the pacemaking.

Reviewer #1 (Public Review):

Enrico and colleagues identify a genetic link between cyclin F and the RB-CDK network using DEPMAP genome-wide datasets with p130 identified as a cyclin F substrate potentially explaining why p130 is degraded in SKP2 KO MEFs. The cyclin F interaction was mapped to an RxL motif on p130, which when mutated prevent proliferation, cell cycle progression, and cell cycle gene expression in NHF-1 cells. However, this RxL motif on p130 was previously implicated in cyclin/CDK complex binding, implicating a deficiency in CDK phosphorylation rather than an increase in protein stability to explain its activity.

Reviewer #2 (Public Review):

The manuscript provides several lines of evidence to support the conclusion that cyclin F interacts with and regulates the stability of p130. The authors first note the connection between Rb/CDK pathway and cyclin F by analysis of the DepMap cell survival data, and then identify p130 as a candidate substrate of cyclin F. The authors use a series of well-designed experiments to demonstrate that overexpression of cyclin F promotes the degradation of retinoblastoma family member p130 in HEK293T cells, and identify a conserved amino acid sequence 680-RRL-682 in a linker domain of p130 that is responsible for cyclin F recognition and ubiquitination. Mutation of this site in p130 results in an increased protein stability and enhanced ability to suppress cell proliferation by repressing the DREAM target genes. The authors show evidence that this regulation is direct using in vitro ubiquitination assay with a reconstituted cyclin F E3 ligase complex. Studies with ectopically expressed p130 mutant refractory to cyclin F degradation demonstrate that loss of this regulation could significantly inhibit cell proliferation and possibly increase apoptosis, which could be especially relevant for ALS pathogenesis. Overall, the data support the role of cyclin F in regulation of the levels of p130, and increases our understanding of the functional significance of both p130 and cyclin F in control of cell proliferation.

Although the data presented in the manuscript is extensive and convincing, this study could be strengthened by additional supportive evidence that cyclin F is indeed a direct physiological regulator of p130 during the cell cycle progression. The authors demonstrate that depletion of cyclin F causes a robust upregulation of p130 in human fibroblasts cells. Given that cyclin F regulates many substrates involved in cell cycle progression, including activator E2Fs and B-Myb transcription factor, these changes in p130 levels could be caused by changes in the cell cycle status, which was not assessed in this study. Furthermore, major conclusions in this study rely on assays using proteins overexpressed in HEK293T or HeLa cells. Since these cells express viral oncoproteins (SV40 Large T antigen and HPV E7 protein, respectively), which are known to bind Rb family members including p130 and target them to proteasomal degradation, it is possible that some of the observed effects in these cell lines are mediated by viral proteins that could be influenced by cyclin F. For example, additional validation of the interaction between the two proteins in non-virus transformed cell line would strengthen the conclusion the cyclin F is indeed a physiological regulator of p130. Notably, the identity of E3 ligase(s) responsible for p130 degradation by E7 or Large T, is not known, and it would be interesting to see if cyclin F is involved. Furthermore, phosphorylation of p130 by cyclin-CDKs has been shown to play a major role in promoting its cell-cycle dependent degradation by proteasome. The role of phosphorylation in regulation of p130 by cyclin F was not investigated in this study, and it remains to be established whether cyclin F controls the levels of p130 in coordination with, or independent of the cyclin-CDKs. The conclusion that stabilized p130 mutant refractory to cyclin F regulation could promote apoptosis also could be strengthened by providing additional evidence.

Reviewer #3 (Public Review):

The authors showed that p130 is a substrate of cyclin F. In addition, the RxL motif in the p130 pocket domain mediates binding, ubiquitination and stability. Expression of mutant p130 which can't be ubiquitinated and degraded impairs cell proliferation and cell cycle progression. These data are straightforward.

Reviewer #1 (Public Review):

The authors claim that nerves regulate the size of the regenerating limb are convincing and the conclusions justified based upon the results presented. A strength of the manuscript is the clear manipulation of limb size through both gain and loss of function experiments. The major weakness of the manuscript is that mechanism by which nerves regulate limb size is not determined, which leaves an open question on how this phenomenon is regulated. How is it that nerves communicate with cells in the regenerating limb to regulate limb growth?

Reviewer #2 (Public Review):

The manuscript by Wells-Enright et al. details an in-depth characterization and experimental analysis of the signals that regulate how relative proportion is interpreted and maintained through an analysis of a regenerating limb model. The work is grounded on classical experimental analysis by Harrison and Twitty in the early 20th century on the regulation of size of limbs during development. Wells-Enright et al. detail distinct phases of regenerative growth 1) blastema stage, incorporating patterning, 2) tiny limb, and late tiny-limb growth in the re-establishment of size comparable to the contralateral limb (not the original size). The growth to the current size of the animal hints that broad-scale systemic signaling (nerve-mediated?) remains a measure of coordination of proportion. The authors further show through elegant experimental approaches that nerves are necessary for late growth, and that relative nerve/tissue ratio is correlated with size and outcomes of experimental analysis. This model would fit with human overgrowth conditions as well. Lastly, the authors use an intriguing new assay in which nerve explants are shown to be sufficient to maintain growth of a regenerate. However, the authors find that these explants, when removed from the normal area of function, do not provide specific information as to size.

Reviewer #1 (Public Review):

1) The findings reported in this manuscript provide support for Liepmann's (1905) seminal observations in apraxia patients in which he proposed lateralization of skilled movement representations in the left hemisphere and identification of the premotor-parietal complex as the principal candidate network involved in the formation of these representations. The prominent role of the left hemisphere in movement control has been documented in several medical imaging studies looking into brain activations associated with dominant and nondominant limb motions as well as bimanual motions. The present study adds new information to this general claim by making use of intracranial recordings (ECoG) in a patient group (n=6, n=665 electrodes meeting inclusion criteria) to explore hemispheric dominance for kinematic encoding in the upper limbs during reaching movements to different targets presented parallel to the frontal plane. Previous single cell recording studies have not provided clear answers to this lateralization question for several reasons, one being the limitations associated with the study of nonhuman primates who exhibit differences in brain lateralization as compared to humans.

2) The authors looked into movement encoding during an instruction (planning) phase and a movement execution phase and provided compelling evidence for more prominent bilateral encoding in the left than right hemisphere, leading to greater overlap between representations of contralateral and ipsilateral movement in the left hemisphere. The data suggest that the more prominent bilateral encoding in the left hemisphere is already present during the movement preparation phase and further strengthens during the execution phase. This is also associated with better across-arm generalization of neural signals in the left hemisphere. The work also points to a prominent role of left premotor-parietal brain areas for this distinct control architecture. Overall, the study appears to have been conducted with great care in a group of 6 participants and the findings are statistically well supported. The different analyses also provide converging support for bilateral encoding processes in the left hemisphere.

3) I am sympathetic towards the employed encoding model that makes use of kinematic features to predict neural activity for each electrode in order to retain the high spatial and temporal resolution of the ECoG signal. This approach provides an interesting way to map kinematics to neural activity in a time-resolved manner.

4) The evidence for more consistent bilateral encoding in the left hemisphere prompts another important question that the authors only address indirectly, i.e., the locus of abstract (effector-independent) representations of movement in the human brain that may also provide the neural architecture for 'motor equivalence', a hallmark of central nervous system flexibility in reaching action goals. To that extent, the current findings may have implications for the neural basis of movement control that may extend beyond the simple reaching movements studied here.

5) The reported data point to an important role of the premotor and parietal regions of the left as compared to the right hemisphere in the control of ipsilateral and contralateral limb movements. These are also the regions where the electrodes were primarily located in both subgroups of patients. I have 2 concerns in this respect. The first concern refers to the specific locus of these electrodes. For premotor cortex, the authors suggest PMd as well as PMv as potential sites for these bilateral representations. The other principal site refers to parietal cortex but this covers a large territory. It would help if more specific subregions for the parietal cortex can be indicated, if possible. Do the focal regions where electrodes were positioned refer to the superior vs inferior parietal cortex (anterior or posterior), or intra-parietal sulcus. Second, the manuscript's focus on the premotor-parietal complex emerges from the constraints imposed by accessible anatomical locations in the participants but does not preclude the existence of other cortical sites as well as subcortical regions and cerebellum for such bilateral representations. It is meaningful to clarify this and/or list this as a limitation of the current approach.

6) The evidence for bilateral encoding during unilateral movement opens perspectives for a better understanding of the control of bimanual movements which are abundant during every day life. In the discussion, the authors refer to some imaging studies on bimanual control in order to infer whether the obtained findings may be a consequence of left hemisphere specialization for bimanual movement control, leading to speculations about the information that is being processed for each of both limb movements. Another perspective to consider is the possibility that making a movement with one limb may require postural stabilization in the trunk and contralateral body side, including a contribution from the opposite limb that is supposedly resting on the start button. Have the authors considered whether this postural mechanism could (partly) account for this bilateral encoding mechanism, in particular, because it appears more prominent during movement execution as compared to preparation. Furthermore, could the prominence of bilateral encoding during movement execution be triggered by inflow of sensory information about both limbs from the visual as well as the somatosensory systems.

Reviewer #2 (Public Review):

Summary:

Merrick et al. studied kinematic encoding of arm reaching movement in the intracranial recording (ECoG) data in 6 human patients. Of these six patients, 3 had electrodes on the left hemisphere surface, and the rest had electrodes on the right hemisphere surface. They performed an unconstrained instructed delayed-reach task with either the left or the right hand.

The main claim of the paper is that, in right-handers, the left hemisphere, especially premotor and parietal regions, encodes ipsilateral arm movement more strongly than the right hemisphere does. The claim is mostly supported by the results of a set of kinematic encoding model analyses applied to the high-frequency cortical activity (HFA) during the reaching task. First, the prediction accuracies for contra- vs. ipsilateral arm movements were more equally high for the left hemisphere electrodes than the right hemisphere electrodes. Second, the cross-arm generalization of encoding model weight was also better for the left hemisphere electrodes than the right hemisphere, indicating shared movement representation in the left hemisphere between contra- and ipsilateral limbs. The electrodes with higher cross-arm generalization included the left premotor and parietal areas.<br> As the authors emphasize, this paper is the first to explicitly compare movement encoding between the two hemispheres in human ECoG recording data and show the left-hemisphere dominance. While the presented results seemingly support the authors' key claims, the details regarding the statistical analyses need to be clarified to judge whether the results generalize to the population.

Strengths:

1) As noted by the authors, the current set of data is valuable for making hemispheric comparisons in human ECoG data; three patients with electrodes implanted on the left hemisphere and the other three patients with electrodes on the right hemisphere.

2) The choice of encoding modeling approach on each electrode has an advantage over the conventional decoding approach in unambiguously interpreting the contribution of each electrode to the movement as recommended by Kriegeskorte & Douglas (2019). Moreover, the combination of the simple encoding model of each limb's kinetics and the cross-limb generalization test would be a powerful approach to study ipsilateral movement representation, allowing one to dissect "unshared" and "shared" ipsilateral movement representations.

3) The time-resolved analysis (and the use of sufficient time delays in the kinematic feature matrix in the encoding model) can give further insight into how ipsi- and contralateral movement representations unfold over the course of movement planning and execution.

Weaknesses:

1) Although the current human ECoG data set is valuable, there is still large variability in electrode coverage across the patients (I fully acknowledge the difficulty). This makes statistical assessment a bit tricky.

The potential factors of interest in the current study would be Electrode (=Region), Subject, Hemisphere, and their interactions. The tricky part is that Electrode is nested within Subject, and Subject is nested within Hemisphere. Permutation-based ANOVA used for the current paper requires proper treatment of these nested factors when making permutations (Anderson and Braak, 2003). With this regard, sufficient details about how the authors treated each factor, for instance, in each pbANOVA, are not provided in the current version of the manuscript. Similarly, the scope of statistical generalizability, whether the inference is within-sample or population-level, for the claims (e.g., statement about the hemispheric or regional difference) needs to be clarified.

Impact and utility of data:

Whereas this paper is the first to report the left-hemisphere dominance in the movement encoding, the finding itself seems not as striking as seen in the authors' tone of the claim. There have been several previous studies with results implying the asymmetry in the movement representation though not explicitly tested (e.g., Fig. 2B and 5A in Diedrichsen et al., Cereb Cortex, 2013; Fig. 3B in Wiestler and Waters-Metenier et al., J Neurosci, 2014; Figs. 2, 4, and 5 in Haar et al., J Neurosci, 2017), as well as the general notion of the left-hemisphere dominance in praxis, as noted by the authors. That said, the importance of this work in the field is still clear in terms of it being the first evidence of left-hemisphere dominance for arm movement representation in human electrophysiological recording data and the value of the data set available for more detailed analyses in the future.

Additional contexts that would help readers interpret or understand the significance of the work:

The greater amount of shared movement representation in the left hemisphere may imply the greater reliance of the left arm on the left hemisphere. This may, in turn, lead to the greater influence of the ongoing right arm motion on the left arm movement control during the bimanual coordination. Indeed, this point is addressed by the authors in the Discussion (page 15, lines 26-41). One critical piece of literature missing in this context is the work done by Yokoi, Hirashima, and Nozaki (2014). In the experiments using the bimanual reaching task, they in fact found that the learning by the left arm is to the greater degree influenced by the concurrent motion of the right arm than vice versa (Yokoi et al., J Neurosci, 2014). Together with Diedrichsen et al. (2013), this study will strengthen the authors' discussion and help readers interpret the present result of left hemisphere dominance in the context of more skillful bimanual action.

Reviewer #3 (Public Review):

In the present work, Merrick et al. analyzed ECoG recordings from patients performing out-and-back reaching movements. The authors trained a linear model to map kinematic features (e.g., hand speed, target position) to high frequency ECoG activity (HFA) of each electrode. The two primary findings were: 1) encoding strength (as assessed by held-out R2 values) of ipsilateral and contralateral movements was more bilateral in the left hemisphere than in the right and 2) across-arm generalization was stronger in the left hemisphere than in the right. As the authors point out in the Introduction, there are known 'asymmetries between the two hemispheres in terms of praxis', so it may not be surprising to find asymmetries in the kinematic encoding of the two hemispheres (i.e., the left hemisphere contributes 'more equally' to movements on either side of the body than the right hemisphere).

There is one point that I feel must be addressed before the present conclusions can be reached and a second clarification that I feel will greatly improve the interpretability of the results.

First, as is often the case when working with patients, the authors have no control over the recording sites. This led to some asymmetries in both the number of electrodes in each hemisphere (as the authors note in the Discussion) and (more importantly) in the location of the recording electrodes. Recording site within a hemisphere must be controlled for before any comparisons between the hemispheres can be made. For example, the authors note that 'the contralateral bias becomes weaker the further the electrodes are from putative motor cortex'. If there happen to be more electrodes placed further from M1 in the left hemisphere (as Supplementary Figure 1 seems to suggest), than we cannot know whether the results of Figures 2 and 3 are due to the left hemisphere having stronger bilateral encoding or simply more electrodes placed further from M1.

Second, it would be useful if the authors provided a bit of clarification about what type of kinematic information the linear model is using to predict HFA. I believe the paragraph titled 'Target modulation and tuning similarity across arms' suggests that there is very little across-target variance in the HFA signal. Does this imply that the model is primarily ignoring the Phi and Theta (as well as their lagged counterparts) and is instead relying on the position and speed terms? How likely is it that the majority of the HFA activity around movement onset reflects a condition-invariant 'trigger signal' (Kaufman, et al., 2016). This trigger signal accounts for the largest portion of neural variance around movement onset (by far), and the weight of individual neurons in trigger signal dimensions tend to be positive, which means that this signal will be strongly reflected in population activity (as measured by ECoG). This interpretation does not detract from the present results in any way, but it may serve to clarify them.

Reviewer #1 (Public Review):

This is a well written manuscript in which the authors describe experiments in which they have used restricted viral tracing to identify premotor neurons which contact multiple motor pools. The finding that single premotor interneurons contact both flexor and extensor motor pools is interesting, and raises the possibility that these neurons may form the neural circuitry involved in regulating muscle stiffness around a joint. Generally speaking, the anatomical data could be improved by providing specific information regarding the rostra-caudal location of the various classes of premotor interneurons. While results of the experiments which investigate the neurotransmitter phenotype, and genetic background, of these divergent premotor interneurons can be used to devise testable hypotheses regarding the characteristics of the neurons, drawing conclusions based on the data presented seems premature. It is likely that this work will drive subsequent studies that attempt to manipulate the activity of these neurons, and definitively determine their function, however caution should be shown interpreting the results here, as the data is solely anatomical and does not shed light into the physiological role of this circuitry.

Reviewer #2 (Public Review):

The study by Ranzano et al. set out to reveal if the spinal cord contain motor circuits that can support co-activation and co-inhibition of diverse flexor and extensor motor neuron pools in the mouse spinal cord. For this they use modified rabies virus in a mouse model and with a set-up that will allow selective mono-synaptically restricted labeling of premotor neurons projecting to functional synergist or antagonist ankle motor neuron pools along the entire spinal cord. They show that a minor percentage of premotor motor neurons projecting to either synergist or antagonist pair of motor neuron pools diverge. Divergent premotor neurons were seen both close and rostral to the target lumbar motor neuron pools, but with an increased proportion with distance from the lumbar cord. In the cervical spinal cord the largest proportion of divergent neurons where commissural excitatory neurons with molecular characteristics of the V0 class. The study provide important, new and convincing data on the spinal anatomical landscape of distributed motor networks that may coordinate synergistic activity as well as mediate co-contraction of antagonist muscles across multiple motor pools in the same limb or across limbs.

Overall the claims are well supported by the data. Some aspects of methods could need clarification and some aspects of the claims are weakened by lack of identification of premotor neuron populations. The discussion of the data could perhaps be made stronger by linking the present data to functional studies.

1) Transsynaptic method. The authors use a different model for trans-synaptic tracing than most previous studies in the spinal cord: namely the RGT mouse line crossed with ChAT-cre mice and combined with a retrograde labelling of motor neurons. The distribution of transsynaptic flexor and extensor related premotor neurons in this model is different from previously reported. Data for this are presented in (Ronzano et al. 2021 BioRxiv). But it will be useful to mention this here as well. The authors discuss why the labeling is not caused by a second jump from V0C neurons or motor neurons labelled by collaterals. Another course of contamination is at the level of the muscle. All injections are done in newborn mice with tiny muscle. It would be useful to know how the authors secured that there is no viral spread peripherally.

2) Identity of neurons. A limitation of the study is that there is no transmitter phenotyping of the divergent premotor neurons in the lumbar and thoracic region. The divergent neurons can be either excitatory or inhibitory and cause coactivation or co-inhibition, respectively of synergist or antagonists. Except for the cervical CNs there is no evidence for transmitter-phenotype in the data. Perhaps the authors could just mention that this would have required in situ hybridization in the double injected animals or a third color RabV togheter with the GlyT2-GFP mice. The identification of V0 neruons interneurons is suboptimal. The use of specific AB (Evx) for the V0 population could have provided a better characterization (see Crone et al. 2008). Maybe also mention that the commissural neurons could be SIM1.

3) The discussion is insightful but should perhaps link the data more directly to functional studies for example by considering how synergies are bound together across limb during locomotion which could both involve co-activation of synergies or co-inhibition of antagonists from commissural neurons (see Bellardita and Kiehn 2015; Butt and Kiehn 2003) or ipsilateral neurons (Levine et al. 2014; among others). It would also be useful to discuss how co-inhibition of synergists leads to functional movements.

Reviewer #3 (Public Review):

The study by Ronzano et al uses monosynaptically-restricted transsynaptic labeling to reveal populations of "divergent" premotor interneurons, neurons that are presynaptic to multiple motor neuron pools. Various pairings of muscle injections are analyzed, including hindlimb synergists and antagonists, and hindlimb-forelimb combinations. They show that divergent neurons innervating both synergist and antagonist motor pools are similarly located and found in similar numbers. These neurons exist throughout the cord, in decreasing number but higher proportions of the total labeled neurons with more rostral locations (lumbar to thoracic to cervical cord). A subset of the population of the long descending divergent neurons is identified as part of the V0 class, with some similarly located (and possibly overlapping) neurons projecting the forelimb muscles in addition to hindlimb muscles. Other studies have shown distributions of premotor interneurons from single motor neuron pools. The novelty here is the focus on interneurons with divergent innervations of multiple motor pools.

One of the major differences (and advantages) of this study from prior muscle injections of transsynaptic virus is that rather than co-injecting the AAV containing the G glycoprotein, necessary for transsynaptic transfer, into the muscle with the modified Rabies virus, they genetically specify it to motor neurons (and other cholinergic neurons) using Chat-Cre. The advantage of this is that the potential confounds of transfer via the sensory neuron and/or selective AAV transfection/G expression by a particular subpopulation of motor neurons is removed. Although the possibility of 'double jumps' (MNs to ChAT interneurons to upstream neurons) is possible, it is less likely to occur in sufficient numbers in the time of the experiment.

The data presented are comprehensive for the motor pools examined. The location analyses are extensive. Divergent neurons demonstrated by the dual virus strategy are further supported by the demonstration of terminals of hindlimb premotor interneurons synapsing on thoracic and cervical motor neurons. The manuscript is well written. Overall, data are clearly presented and limitations are fully discussed. This study can form the basis for future studies regarding specific identity and function of the divergent populations.

Main limitations of the study are related to the tracing technique used. The authors are fully transparent about these limitations in the Discussion. As pointed out, this technique is not optimal for quantifying double labeled neurons. Conclusions regarding the existence and location of neurons projecting to at least two motor pools can be made. However, these are likely to be severely underestimated and it is not possible to determine if these neurons are more broadly presynaptic to other motor pools in the limb (or beyond). The reduced efficiency of infection by two viruses due to viral interference is mentioned and relevant sources demonstrating the limitation are cited. Therefore, the data are solid but the functionally-related interpretations and conclusions are somewhat limited and speculative.

The other related limitation inherent in the technique is the efficiency of transfer of even a single virus. Analysis is presented regarding a comparison with a more efficient virus, suggesting transsynaptic efficiency may be ~25%, but this does not fully get at the issue. The efficiency of the starter or seed cell labeling is not mentioned. Quantification of motor neurons taking up the RabV would be helpful as this will be directly related to the potential number of presynaptic neurons. This is especially crucial with the forelimb injections, in which 0-6 muscles were injected.

The percentage of divergent interneurons is also underestimated in that the denominator is single labeled neurons presynaptic to either motor neuron pool. Information may be gained by determining whether different portions of premotor neurons to one over another are more likely to be divergent. This is particularly the case with antagonist injections but also for comparisons of relative proportions of dual labeled neurons premotor to synergists and antagonists. This would likely need to be combined or controlled by the percent (or number) of motor neurons from each pool that are labeled to indicate potential differences in starter cells as mentioned above. Counterbalancing is mentioned in the Methods but it is not clear that is fully possible with an n=2 or 3.

Reviewer #1 (Public Review):

In this manuscript the authors developed an inducible and reversible system for inhibition of miRNA-mediated gene repression in vivo. The authors achieved their aims by demonstrating the use of this system in inhibition of global miRNA activity in adult animals. Interestingly, they found that global loss of miRNA function has different effects on different tissues and organs and in some tissues the effect is context dependent. Overall, the findings presented are interesting and data presented is high quality. The results are presented support their conclusions. It is expected that this strategy is a significant advance and will impact many fields including but not limited to RNA biology, organ homeostasis and cancer biology.

Reviewer #2 (Public Review):

The authors have engineered a mouse line harboring an inducible transgene encoding a peptide that blocks AGO-TNRC6 interaction. Activation of this transgene allows the rapid and reversible inhibition of miRNA function in vivo. This approach offers several advantages over inducible genetic loss of function of miRNA biogenesis factors, the more standard approach to impair miRNA function in vivo. Most notably, the extreme stability of most miRNAs generally results in long-term residual miRNA expression even after deletion of miRNA processing factors. This new model circumvents this limitation, allowing much more rapid inhibition of miRNA silencing. Using this system, the authors confirm that miRNA-mediated regulation is needed for embryogenesis, but is surprisingly dispensable for homeostasis in most adult tissues, with the notable exception of heart and skeletal muscle.

This work is of extremely high quality, the analyses are thorough and convincing, and the findings are significant.

Reviewer #3 (Public Review):

Here, La Rocca and colleagues establish a versatile mouse model to probe the function of miRNA-guided gene silencing in vivo. Guided by results from protein structures, the Meister group previously designed a peptide that directly inhibits the function of miRNA-guided silencing complexes (miRISC) by competing for the interaction with the effector factor TNRC6/GW182. The Ventura lab now adapted this clever approach to generate an elegant in vivo model. Their transgenic mouse expresses the inhibitory peptide (T6B) in a doxycycline inducible manner, and allows for acute and reversible inhibition of miRNA-silencing.

The authors perform a careful characterization of their model system, and then investigate the consequences of miRISC inhibition in homeostasis and during regeneration after injury in different tissues. Interestingly, the acute inhibition of miRISC activity is well tolerated by most tissues under homeostatic conditions. However, upon injury - at the example of dextran sulfate sodium (DSS)-induced colitis- lack of miRNA-guided regulation results in increased tissue damage and severity of clinical symptoms. Similarly, hematopoiesis fails to regenerate after 5-fluoro-uracil treatment.

Finally, the authors generate a second adjusted mouse model to interrogate miRISC function in heart and skeletal muscle. Inhibition of miRISC induces severe cardiomyopathy and degenerative lesions in skeletal muscles. Taken together, this well-designed and carefully executed study elucidated a differential requirement for miRNA-guided gene regulation in adult tissues under homeostatic and stressed conditions.

Results from this study generate important hypotheses for the pathophysiology of heart and skeletal muscle diseases, and tissue regeneration. The established mouse presents a valuable resource for a broad scientific community and can be used to study any cell type, tissue or disease in vivo.

It is a pleasure to learn from this important, careful and well-presented study.

Reviewer #1 (Public Review):

The key question that the authors were addressing was how ethnicity differentially affects the microbiota of subjects living in a particular area (in this case East Asians and Caucasians living in San Francisco that have been enrolled in an 'Inflammation, Diabetes, Ethnicity and Obesity cohort - although inflammatory disease was apparently excluded in these subjects).<br> The existence of differences between different populations allows potential discrimination of the underlying factors - such as host genetics, diet, lifestyle, physiological parameters, body habitus or other environmental influences. In this case body habitus has been selected as a stratification factor between the two ethnicities. Immigration potentially allows distinction of environmental and host genetical influences.<br> The strength of the study is in the level of robust analysis of the microbiotas by a very experienced group of researchers, distinguishing the microbiota differences, especially in lean subject, with analysis of associations that may be driving the differences. It is interesting that diet is not one of the apparent associations in this study, yet the relationship of microbiota diversity to body habitus is strong in Caucasian subjects. These associations cannot easily be extrapolated to causation or mechanism - a fact well recognized in the paper - but remain important observations that rationalize in vivo modeling with experimental animals or in vitro analyses of microbial interactions between different taxa simulating the context of differences in the intestinal milieu. The paper includes work showing that differences of the microbiota can be recapitulated after transfer to germ-free mice, at least over the short term: this is important to provide tools to model the reasons for differences in consortial composition.<br> A very large amount of work required to assemble the samples and the clinical phenotypic metadata set making the data an important and definitive contribution for the subjects studied. Of course, this is one sample of extremely variable human conditions and lifestyles that will help build the overall picture of how differences in our genetics and environment shape our intestinal microbiota.

Reviewer #2 (Public Review):

The study's primary aims are to test for the differences in the microbiome between self-identified East Asian and White subjects from the San Francisco area in the new IDEO cohort. The study builds on an growing literature which describes variations among ethnic groups. The major conclusion of "emphasize the utility of studying diverse ethnic groups" is not novel to the literature.

Overall, the strength of the results is that they confirm patterns from different cohorts/studies and demonstrate that ethnic-related differences are common. The results are subject to sample size concerns that may underpin some of the conflicting or lack of significant results. For instance, there is no overlap in highlighted species-level taxonomy differences between 16S and metagenomic analyses, which precludes a clear interpretation of the meaning of those differences and whether taxa should be highlighted in the abstract; there are low AUC values for the random forest modelling; and there is a lack of significance in correlations between BMI and East Asian subjects in F4a where there may be a correlation. While a minor point, it serves to highlight the sample sizes as the range of the variation in East Asian subjects is not as substantial as the White subjects because there are fewer East Asian data points above a 30 BMI (~N=5) relative to those of White subjects (~N=11).

The microbiome transfers from humans to mice also demonstrate that certain features of interpersonal or ethnic-related differences can be established in mice. This is useful for future studies, but it is not unexpected in and of itself given the robustness of transferring microbiome differences in other human-to-mouse studies. If the phenotype data were more compelling, then the utility of these transfers could be valuable. However, in the current state, I am concerned with the experimental design since the LFPP experiments used N=1 donor per ethnicity for establishing the mice colonies and are resultantly confounded by mice pseudo-replication with recipient mice derived from one donor of each ethnicity. This concern is relevant to interpreting results back to interpersonal or interethnic variation. Are phenotypic differences due to individual differences or ethnic differences? It's not clear. The HFHS experiment also used N=5 donors that somewhat mitigates these concerns, but mixed sexes were used here and there can be sex-specific human microbiome differences. Finally, experimental results are not always consistent and sometimes show opposite trends that may be related to the sampling sizes. For instance, fat and lean mass increased and decreased respectively in LFPP, but there were no statistically-similar differences in HFHS. Moreover, the metabolic fat mass outcomes in mice do not match the expected human donor data. For instance, in LFPP1, White subjects had lower fat mass in humans but recipient mice on average gained more fat. It is difficult to reconcile these differences to a biological or sampling scheme reason.

Reviewer #3 (Public Review):

The authors aimed to characterise how gut microbiota changes between different ethnic group for bacterial richness and community structure. They also wanted to address how this is associated with ethnic group within a defined geographical location. They have started to their story by comparing the fecal microbiota of relatively small cohort consisting of 46 lean and obese East Asian and White<br> participants living in the San Francisco Bay Area. For that reason they used 16S and shotgun metagenomics. They demonstrated that ethnicity-associated differences in the gut microbiota are stronger in lean individuals and obese did not have a clear difference in the gut microbiota profile between ethnic groups, either suggesting that established obesity or its associated dietary patterns can overwrite long-lasting microbial signatures or alternatively that there is a shared ethnicity-independent microbiome type that predisposes individuals to obesity. The authors did also show the metabolic differences between these ethnic groups and the major differences were in the branched chain amino acid and the short-chain fatty acids. To prove their point, at this stage they have also used different metabolomic methodology. Although some aspects of the work are not very novel, the work does provide additional insights into the effect(s) of ethnicity, current living location and diet on shaping microbiota. Honestly, while reading through the manuscript, I have several questions where I believed that clarification was needed. But somehow, I felt like the authors have been reading my mind every step of the way. At the end of each section whatever I questioned was addressed in the next paragraph There are, however, a few points that I think would like to hear the authors' clarification.

- The authors pursued the story using 16S data. However, they have shotgun Metagenomics data which gives more power and resolution to microbiota profile. Is there any specific reason why the story was not build with shotgun Metagenomic data? However, if this is the case it will be nice to justify in the text or legend which figure was built with what dataset exactly?<br> - Even though the authors mentioned in the discussion that they have not used the same inocula from a donor to different diet, it will be nice if the authors further comments whether they would expect the same results or slightly different results which each different inocula.

Overall, the study is well executed and claims and conclusions seem relatively well justified by the provided evidence. The findings are interesting for a broad audience of biologists. The findings are interesting for a broad audience of biologists.

Reviewer #1 (Public Review):

In this manuscript, Berne et al apply state-of-the-art methodology for quantifying animal behavior to identify distinct behavioral components associated with the repeated application of mechanical stimuli. A central strength of this manuscript is the development of a sophisticated system for precisely applying mechanical stimuli and measuring behavior. This is a significant advance over commonly used approaches and has the potential to broadly impact the field. I have some concerns about the methods used to define discrete behaviors and the interpretations drawn from them (see point 2), the opposing phenotypes of memory mutants, and the circuit modeling. However, the overall results provide strong evidence that a small set of behaviors reflect the intensity of response to stimuli, and these combine to reflect an overall complex behavioral response to mechanical stimuli. Overall the manuscript is well written, and clearly communicates results. The level of analysis has the potential to broadly impact many fields examining innate and learned responses to sensory stimuli.

1) A central strength of this manuscript is the resolution of behavioral analysis. Implicit in this is the potential to use a wealth of genetic analysis and sophisticated genetic tools to dissect the neural basis of these behaviors. These implications would be clearer if the introduction provided more description of this literature.

2) It is unclear how the 4 discrete behaviors were decided upon, and whether there are rarer behaviors, or subcategories within them (for example, sideways crawl).

3) From figure 1A it looks like the mechanical transducer remains in the center independently of where the larvae is. Could it be possible that subtle differences in mechanical force are detected across the arena and this impacts the response? Does the degree of turning matter?

4) I am not clear about the application of statistics. For example, 2D states that as a general trend, increasing vibration also increases reversals. I can see this, clearly but is there reason not to run statistics on these data?

5) The importance of vibration behavior in research is discussed but the ecological relevance of these behaviors is not described.

6) The results of habituation times in mutants are not clear to me. One might predict dnc and rut would have the same phenotype but they have opposing phenotypes with rut being a super-habituate.

7) I appreciate the application of circuit modeling, but it would seem that this would be strengthened by including what is already known about the biological circuit.

Reviewer #2 (Public Review):

Berne et al. establish the responses of Drosophila larvae to mechanical vibrations as a novel paradigm to study habituation. The authors first comprehensively quantify the different types of locomotor responses to vibrations and find that larvae respond to faster and stronger vibrations with more avoidance-type behaviors, like pauses, turns, and reversals. The authors then combine genetic and computational methods to characterize the strong de-sensitization of avoidance responses to vibrations. De-sensitization of reversals follows a simple, exponential decay with a single time constant. By contrast, re-sensitization dynamics are more complex and strongly accelerate after repeated exposure to a vibration stimulus. The authors then test mutants for genes involved in learning and memory (rut, dnc, cam) and find altered de-sensitization and re-sensitization dynamics, suggesting that these genes mediate this behavior. Finally, a simple and intuitive electrical circuit model is used to explain these complex dynamics results. Overall, the results are interesting and they successfully combine behavioral characterization, genetic manipulations, and computational modeling to explain the behavior.

The analyses are all sound and support most of the conclusions but additional control experiments and analyses are required.

1) To convincingly show that the computational models capture the key aspects of the behavior and therefore provide insight into the underlying phenomenon, model predictions and behavioral data need to be compared systematically and quantitatively. This is not sufficiently done for the electrical circuit model, and the analyses shown in Fig. 7C need to be extended. The model should be fitted to the data and the match between model and data should be A) quantified using a suitable measure of goodness-of-fit and B) illustrated by overlaying behavioral data and model predictions. Moreover, the contribution of individual circuit elements should be quantified, for instance by removing key elements from the model like the second capacitor. If a good quantitative fit is for some reason hard to obtain, then more effort should be spent to demonstrate a good qualitative agreement between model and data.

The same goes for the phenomenological model in Fig. 5. Predictions of model variants with a constant re-sensitization time constant and a time constant that changes with pulse number should be shown and their fit to the data should be quantified.

2) The Markov model in Fig. 3 is used to state that habituation is a one-way process from reversals to other behaviors, with only rare transitions back to reversals. However, the low transition rates to reversals (Fig. 3) seem at odds with the fast re-sensitization after repeated stimulation (Fig. 5). This should be explained and both results should be linked.

3) Based on altered de-sensitization and re-sensitization dynamics in mutants, the authors claim that three different genes - rut, dnc, cam - are involved in the molecular pathway that mediates habituation of larval locomotor responses to vibrations. This is interesting and deserves further study. However, it is unclear whether the observed effects are specific to the genes that were altered or whether the effects stem from differences in the genetic background across the mutants. This could be resolved in two ways: Ideally, with rescue experiments; if this is not feasible, then data from different wild-type strains could be used to show that the de-sensitization and re-sensitization dynamics are similar across wild types and somewhat robust to genetic background.

Reviewer #3 (Public Review):

In this work the authors seek to characterize in detail how Drosophila larvae are de-sensitized and re-sensitized to aversive stimuli. They perform technically sophisticated high-throughput measurements of the animal's behavior in response to mechanosensory stimuli, and they set out to provide a simple model that captures the key attributes of de-sensitization and re-sensitization. The authors succeed in their effort. Their characterization is, to my knowledge, the most detailed to-date. They also provide a simple mathematical description, and show that the same math suggests an elegant analog electrical circuit. The circuit provides a useful conceptual model, and, as the authors hint, may even map onto certain biological mechanisms. Moreover, the authors show that their method could be useful for an investigation into biological mechanisms by characterizing mutants that are defective for certain genes thought to be involved in de-sensitization. If there were to be a weakness of the work, it would be that the authors leave to the future the challenging task of chasing down details of the underlying mechanisms of de-sensitization and re-sensitization.

Reviewer #1 (Public Review):

Magliozzi et al. study the interaction of three proteins that have earlier been identified to control cell shape in fission yeast. Two of these are the kinases Pak1 and Orb6. The third is an RNA-binding protein called Sts5. It has been shown earlier that Sts5 uses an intrinsically disordered region (IDR) to concentrate in P bodies, and Orb6-dependent phosphorylation of Sts5-IDR reduces the association of Sts5 with P bodies.

It was unknown if Sts5 is also phosphorylated by Pak1. The authors begin by using biochemistry to show that Pak1 phosphorylates Sts5-IDR at residues S261 and S264. Orb6 has been shown earlier to phosphorylate Sts5 at residue S86. Using non-phosphorylation-competent mutants of Sts5 in vivo, the authors demonstrate that phosphorylation at these residues is important to maintain diffuse distribution of Sts5 in the cytoplasm. In absence of phosphorylation by Orb6 and/or Pak1, increasing fractions of Sts5 protein concentrate in cytoplasmic puncta. This is accompanied by defects in cell morphology, septation and viability. The authors claim that these Sts5+ puncta are P bodies, based on a) colocalization of some of these puncta with the P body marker Sum2, and b) decrease of cellular levels of a protein SSp1 concomitant with concentration of Sts5 in puncta (Sts5 is known to bind ssp1 mRNA).

Next, the authors show that the intracellular localization of Pak1 depends on the amount of glucose in the growth medium (2% vs 0% glucose). In absence of glucose, Pak1 relocates from the cell cortex to join Sts5 in P bodies (tracked by Sum2). This relocalization is dependent on Pka1 (the catalytic subunit of Protein kinase A) in contrast to the Cdc42-associated cell polarity regulatory module. In contrast to Pak1, absence of glucose in the medium did not have any effect on the localization of the other kinase Orb6. In order to address the consequence of relocalization of the kinase Pak1 to P bodies, the authors generated a construct Pak1-CAAX that remained localized to the cortex in absence of glucose. The authors claim that localization of Pak1 to P bodies increase the rate of dissolution of Sts5 puncta.

Taken together, the authors provide new mechanistic insights into the interplay of Pak1, Orb6 and Sts5 when fission yeast is grown in medium containing different amounts of glucose.

1) The claim that absence of Orb6 and Pak1-dependent phosphorylation of Sts5 concentrates Sts5 predominantly to P bodies and causes downregulation of translation of target mRNA would need to be reconsidered. While Figure 3B shows colocalization among some Sts5(2A)+ and Sum2+ puncta, some Sts5(2A)+ Sum2- and Sts5(2A)- Sum2+ puncta are also present. The effect on translation levels should be validated by investigating at least a few other Sts5 mRNA targets (both the levels of mRNA and their protein products should be tested). The model presented in Figure 3F seems to be premature at this stage.

2) The difference in the rates of Sts5 puncta dissolution in presence or absence of punctate Pak1 (Figure 7C) is modest. The authors may consider investigating the physiological consequence of the observed difference in dissolution rates, which remains unclear. Further, not all Sts5+ puncta contain Pak1 (Figure 4D). It would be good to know whether the authors have considered scoring the dissolution rates of Sts5+ Pak1+ vs. Sts5+ Pak1- puncta separately.

Reviewer #2 (Public Review):

Summary:

In this paper, the authors characterize the role of fission yeast Pak1 kinase in the control of mRNA binding protein Sts5 and in P-bodies assembly. Sts5 was previously identified as a substrate of Pak1 kinase in a previous paper by the same group. In this current manuscript the authors characterize the role of Pak1-dependent phosphorylation of two sites (S261 and S264), in the unstructured domain of Sts5, and show that loss of phosphorylation promotes Sts5 assembly. A sts5 mutant mimicking a dephosphorylated state of these two sites presents increased asymmetric cell growth (monopolarity) and increased septation index.

Interestingly, increased Sts5 assembly and morphological anomalies comprise a phenotype that is very reminiscent of the Sts5-S86A mutant, which is modified on a residue that is phosphorylated by Orb6 kinase and was previously characterized by a different lab. Consistent with previous observations that orb6 and pak1 mutants are synthetically lethal, the S261A mutation displays an additive effect with S86A, in promoting Sts5 granule formation, altering cell morphology, and delaying septation.

Using the Sum2 protein as a P-bodies marker, the authors show that increased granule assembly of the Sts5 S86A/S261A protein leads to increased numbers of P-bodies; further, this effect leads to decreased Ssp1 protein levels of Ssp1, a protein encoded by one of the Sts5-associated mRNAs, also consistent with previous findings.

Next, the authors define a crucial difference between Orb6 and Pak1, in that during glucose deprivation, a stress that leads to P bodies assembly, Pak1 kinase localizes to P bodies (and co-localizes with Sts5), while Orb6 kinase does not. Pak1 localization to P-bodies does not include factors that cooperate with Pak1 in the control of cell polarity, such as Scd1, Scd2, or Cdc42 GTPase. The authors note that other kinases, such as Kin1 and Pck2, also have the ability to associate into granules that, in part, include Sum2. Finally, they find that Pak1 localization to P bodies during stress depends on the glucose sensing pathway mediated by Pka1 kinase. Consistent with a potential role for Pak1 kinase in Sts5 granule dissolution, preventing Pak1 localization to the cytoplasm subtly delays Sts5 granule disassembly upon glucose refeeding.

This paper highlights a novel function for Pak1 kinase in the control of RNP dynamics, and nicely align itself with previously published data. It extends our knowledge of the signal integration that controls the coalescence of Sts5 into granules and the assembly of P-bodies. Further, these observations also identify a role for glucose sensing Pka1 kinase in the control of Pak1 localization to the P-bodies, a function that is independent of the polarity factors Pak1 is known to associate with.

Specific comments:

1. It should be noted that observations regarding Orb6 kinase phosphorylation of Serine 86, and the role of this residue in regulating Sts5 granule assembly were previously published (Chen et al., 2019). The role of Pak1 in Sts5 phosphorylation, and the specific residues phosphorylated by Pak1 kinase were also previously identified (Magliozzi et al., 2020). Therefore, the fact that Orb6 and Pak1 phosphorylate distinct residues of Sts5 or that Orb6 regulates Sts5 IDR is not a novel discovery in this paper (as mentioned repeatedly in lines 83-84 , 99-100, 158-161, 177-78, 186-87, etc.). However, the fact that Pak1 function had additive properties to Orb6 kinase in controlling the state of Sts5 granule assembly, in particular under conditions of glucose deprivation, is a novel, interesting expansion of knowledge.

2. Importantly, the authors refer to "stress granules" in several titles (line 185, line 225, line 244). These statements do not correspond to the data presented and should be corrected. Stress granules (SG granules) are separate membraneless organelles that contain specific factors, differentiating them from P-bodies. The marker Sum2, which is used in the paper, does not identify stress granules, but rather it colocalizes with Dcp1, a component of P-bodies. Therefore, data presented here supports the role of Pak1 in the control of P-bodies, not stress granules.

3. The 2% glucose control cells used for experiments shown in Figure 4 (Figure 4, Supplement 1) appear very stressed. P-bodies (as visualized by Sum2) do not condense in healthy, exponentially growing cells. While this effect does not invalidate the results of the experiments shown in Figure 4, it would need to be corrected.

4. Extending the quantification in Figure 7B, to include numbers of Sts5 granules (not only overall fluorescence intensity) would give a more precise assessment of the effects of Pak1 removal on Sts5 granule disassembly.

Reviewer #3 (Public Review):

Fission yeast RNA-binding protein Sts5 is known to localize to P bodies (intracellular granules consisting of RNAs and proteins (ribonucleoproteins, RNPs)), by which this protein plays an important role in cell morphogenesis and polarity. P bodies-localizing Sts5 binds many species of mRNAs encoding regulatory factors required for polarized cell growth, thereby repressing their translation. The Orb6 kinase (a member of the NDR/LATS kinase family) directly phosphorylates Sts5, which restrains localization of Sts5 to P bodies, resulting in proper polarized growth.

In this manuscript, Magliozzi and Moseley have shown that Sts5 is phosphorylated also by the Pak1 kinase (the p21-activated kinase). The phosphorylation site is different from that mediated by Orb6. Interestingly, this phosphorylation also inhibits P bodies-localization of Sts5, which acts independently of and additively with Orb6. The authors have found that upon glucose deprivation, Pak1 together with Sts5 is recruited to RNP granules (stress granules, SGs). Localization of Pak1, but not that of Sts5, is dependent upon protein A kinase (PKA) signalling. They propose that Pak1 localization to RNP granules promotes rapid dissolution of Sts5 from SGs upon glucose re-addition.

Strengths:

A technically sound dataset is presented. Results are clearly and logically shown.

Weaknesses:

The usage of "RNP granule' is confusing: it sometimes means P body, while in other cases (experiments under glucose deprivation), it stands for SG.<br> A mechanism by which Pak1-dependent phosphorylation of Sts5 inhibits Sts5 localization to P-bodies remains unknown.<br> A mechanism by which PKA signalling promotes Pak1 localization to SGs has not been explored.

Reviewer #1 (Public Review):

This manuscript studies the invasion of red blood cells (RBCs) by malaria parasites (merozoites), a key element of their reproduction cycle during the blood stage of the disease. For successful invasion to occur, the merozoite must first align its apex almost perpendicularly to the RBC membrane. In a previous study (reference Hillringhaus et al., 2020), the authors developed a computational model that incorporates stochastic deformations of the RBC membrane and the discrete nature of the adhesive bonds between the merozoite and RBC (arising from filaments on the merozoite surface); together these effects enable partial wrapping of the membrane around the merozoite to aid alignment. This manuscript builds upon this framework to examine the influence of the parasite shape on the alignment dynamics, using five different reference shapes: the egg-like shape typical of Plasmodium merozoites, a sphere, and ellipsoids of varying aspect ratio. By exploring the influence of various parameters such as the bond kinetics and RBC membrane stiffness, they demonstrate that the parasite shape plays a key role in its alignment dynamics. In particular, the egg-like shape is found to be more robust to different adhesion strengths and membrane deformability: it is relatively mobile compared to the ellipsoidal shapes and, unlike a sphere, does not easily become arrested in the high-adhesion limit due to its lack of spherical symmetry.

The manuscript is excellently written and discusses the simulation results clearly and succinctly. The resolution of the simulations is very impressive and yields unprecedented insight into the effect of merozoite shape on alignment dynamics, which has important implications for how effectively the parasite can survive and multiply. The conclusions reached by the authors are certainly justified by the simulation data. In particular, the authors are careful not to draw conclusions beyond the limits of their study, and acknowledge other factors which may influence the merozoite shape, such as internal structural constraints and the energy of invasion following successful alignment.

Regarding weaknesses of the manuscript, some of the explanations of the trends observed in the simulation data could be expanded slightly, to help gain a deeper understanding of the competition between adhesion and RBC deformability underlying the alignment dynamics. These are described in more detail below.

1. Line 114 and lines 120-129: The discussion here of the trends observed in Figure 1 (including why the LE shape has a larger energy compared to the OB shape despite having a smaller adhesion area) is somewhat vague and should be developed further. For example, currently there is only a video showing the egg-like shape and a second video comparing the LE shape to a spherical shape - it would be helpful to have a further video comparing the LE and OB shapes and the different RBC deformations they cause. Moreover, the explanation of the energy/mobility of each shape in terms of curvatures (e.g. the OB shape having "lower curvature at its flat side") could be made more precise. I would expect that the adhesion area depends on how close the principal curvatures of the merozoite surface are to being equal and opposite to the natural curvatures of the RBC, since this determines the bending energy associated with wrapping the merozoite and forming short bonds. This would explain why the spherical shape is most mobile (its principal curvatures are constant so there is no region where at least one is relatively small), and why alignment is most likely to occur in the dimple of the RBC where the membrane is naturally concave-outward. For a given adhesion area, the deformation energy should depend on the difference in principal curvatures in the contact region, with a larger difference causing more bending of the RBC membrane. This difference is larger for the LE shape, since one principal curvature remains large at each point on the surface, compared to the OB shape whose principal curvatures are both small on the 'flat side' where contact is most likely to occur.

2. Lines 175-176: Given that the ratio A_m/A_s (adhesion area to total surface area) plays a key role in the probability of alignment, the authors should be more quantitative at this point. How does the ratio A_m/A_s (as measured directly, or indirectly e.g. by the area under the probability distributions inside the alignment region in figures 3a,b) scale with the system parameters, such as the adhesion strength and the off-rate k_off? Can it be estimated from an energy balance between RBC bending/stretching and the average adhesion energy?

3. Line 197-198 and Figure 4c: Why is the deformation energy associated with the OB shape much lower than all other shapes for values of k_off/k_on^{long} smaller than 2?

4. Alignment requires that the distance between the merozoite apex and RBC membrane is very small, and the alignment criteria necessitate examining small changes in the apex angle \theta from \pi. Can the authors comment on how sensitive are the results to the numerical discretisation used?

Reviewer #2 (Public Review):

This manuscript seeks to determine the role that malarial shape plays in the ability of this parasite to infect red blood cells. The authors use computational modeling to explore the dynamics of different parasite shapes and the affect of adhesion strength in getting the malaria parasite to bind into the correct orientation for invasion into the red blood cell.

A major strength of the results is that it investigates an unstudied problem in malarial pathogenesis. The results pertaining to adhesion strength may be informative for preventing the organism from invading red blood cells. A primary weakness is that there is too little detail provided in the methods for this reviewer to adequate assess the computational method. Secondly, the results are somewhat inconclusive. While the egg-shape performs better than certain other shapes, there is no clear final understanding why this shape is preferred over the spherical or short ellipsoidal shapes. However, this possibly provides some clues as to why a certain malarial species does actively adopt a spherical shape during red blood cell binding and invasion.

Overall, the authors achieved their aims by quantitatively assessing the affect of parasite shape and adhesion strength on cell alignment, which is a proxy for invasion. The discussion at the end of the manuscript provides an accurate evaluation of the results that puts them into the context of invasion.

While to some extent the results presented here are inconclusive, I do think that this paper achieves an important goal for its field. This is an understudied area pertinent to a major disease. This manuscript has the potential to bring questions of the biophysics of malarial invasion out to the broader community, specifically introducing these questions to biophysicists as well as microbiologists. Furthermore, the results naturally lead to new questions. If the spherical and egg shapes do not confer a strong advantage, then these specific shapes must also play a role in other processes. The authors do suggest some possibilities in the Discussion. That their remain interesting questions is a great spur for future work.

Reviewer #1 (Public Review):

This paper presents a protocol and a set of open source computer scripts for performing high throughput imaging-based single cell phenotyping using the ImageStream Mark II system. Their contributions include new open-source computer codes to streamline data analysis, and a deep-learning based system to classify cell types based on previously generated single-cell phenotype profiles. The authors demonstrated the applicability of their tools by performing single cell phenotyping of zebrafish and a freshwater snail P. canaliculata. The core claim is that their tool, termed Image3C, expands the use of image-based analyses of cell population composition to research organisms in which detailed cellular phenotypes are unknown or for which specific reagents are not available (abstract).

Key strength:<br> - The source code is made available via github.

- The demonstration on the zebrafish and freshwater snail data provides a convincing case for their applicability.

Key issues:<br> - The main claim of the versatility of Image3C comes from the idea that it can extract image features even without reagents such as antibodies. The authors seems to have omitted a large body of work in the field of label-free imaging. There are many optical or computational methods to obtain useful cellular features without any chemical labels.

- One core issue is that the entire pipeline is strongly dependent on the use of the ImageStream Mark II system. In particular, the cell image feature extraction step is performed by the IDEAS software that is associated with the imaging system. This limits the general applicability of the Image3C tool.

- Image3C actually contains many separate pieces of computer code. The R code is available as many separate R scripts, and dependent R packages had to be installed manually prior to running the scripts. The clustering step requires a different Java tool called Vortex, which was developed by another group. The clustering results are then analysed by an R script. For data exploration, we also need a different tool called FCS Expression Plus. The set up of the CNN classifier require another set of python scripts. To ensure the Image3C pipeline is indeed as robust as it claims, the authors could consider converting their codes into R and Python packages, with streamlined installation instructions, and example code for users to test.

- In the software pipeline, cell phenotype feature extraction and clustering are performed by other existing tools. The other main new contribution appears to be training of a CNN model to perform cell-type classification, in which the cell type labels were defined by clusters identified in an unsupervised analysis. From a technical point of view, there is little novelty as all the methods are quite standard.

- The authors claim that the CNN classifier was trained in an 'unsupervised' way. I have a strong reservation about this claim. In machine learning, classification by definition is supervised. The fact that cells are labelled by the cluster label does not make it an unsupervised task with respect to the classification task. I think what the authors really means is that the cell types (class labels) do not have to be known prior to analysing the sample.

Reviewer #2 (Public Review):

The authors present an image-based classification scheme of single cells that were imaged on a specific instrument (the ImageStream Mk2 imaging flow cytometer - a high throughput imaging analyzer analyzer by Amnis). Using a layered set of computational tools, sample data is extracted, scaled, normalized and clustered. The discretized representation can then further classified by convolutional neural networks, which can be the basis for efficient and quantitative inter-sample comparisons.

When applied to whole kidney marrow (WKM) extracted from zebrafish and hemolymph from the non-model organism (the apple snail), clusters emerge that can be associated with specific cell types based on prior information.

Importantly, experimental intervention (infection with S. aureus) allows the observation of cluster emergence and shifting of cellular proportions. Professional phagocytes are identified and functional studies demonstrate validity.

The authors convincingly demonstrate that Image3C is capable of reliably and reproducibly clustering cells based on single cell imaging data and that a tissue's cell type composition can be effectively assessed using alignment to previously (or newly) trained classification models.

The authors show that this analysis scheme will aid in quantitation of tissue compositions and is applicable to model-, as well as non-model organisms. The likely impact of this manuscript will be that it will become more tractable for researchers to quantitatively assess cellular composition of complex samples.

Reviewer #1 (Public Review):

Mammalian sperm are required to mature in the female tract in a process named capacitation and to undergo the acrosome reaction, a unique exocytotic process, that allow sperm to fuse and fertilize the egg. Untimely acrosome reaction is detrimental to capacitation and therefore mechanisms that prevent it are important. This work shows that GIV, a guanine nucleotide-exchange modulator (GEM) for trimeric GTPases, is highly expressed in spermatocytes. It is phosphor-regulated during capacitation in serine and tyrosine residues in mouse and human sperm. Phosphorylation of GIV reported to be located in the flagella and the acrosome in the sperm head, activates its ability to stimulate the PI3K→Akt signaling pathway regulating sperm motility and survival, and in the head controlling premature acrosome reaction. Intracellular Ca2+ oscillations had already been shown to modulate premature acrosome reaction. Interestingly, the work also shows that GIV transcripts are downregulated in the testis and semen of infertile men. The authors reach these conclusions combining KO mice and cell-penetrating peptides. Though the findings are interesting and important, several aspects of their quantitation would require significant improvements (for example, it is not indicated how many independent mice or human samples were used and how many cells were examined). The spatiotemporal segregation of signaling programs in the sperm flagella and head relevant for fertilization are indeed notable.

Reviewer #2 (Public Review):

In Reynoso et al. the authors describe how the spatiotemporal phosphorylation of GIV/Girdin in mammalian sperm connects several important signaling cascades that mediate both sperm capacitation and ultimately the acrosome reaction to prepare sperm for fertilization. The central integration of GIV/Girdin as part of the sperm signalome is elegantly demonstrated via detailed fluorescence microscopy with phosphospecific antibodies in both human and mouse sperm, with extensive corroborating evidence from experiments using both knockout mice and biochemical perturbations. I was especially impressed by the use of a TAT transduction system to experimentally measure how wild type and mutant domains of GIV can perturb sperm maturation. I think the authors convincingly support their central tenant that GIV serves dual roles in enhancing PI3K/Akt signaling during capacitation and inhibit cAMP to prevent premature acrosome reaction. The potential transcriptional correlation with human fertility was also interesting, and will likely motivate further research into GIV as a target of infertility and contraception. I believe this report highlights GIV as one of the most highly pleiotropic proteins in sperm, and presents a significant step forward in our understanding of the mammalian fertilization cascade.

Reviewer #1 (Public Review):

This ms targets an interesting question, whether changes of feedforward inhibition at the DG-CA3 synapses regulate the representational capabilities of contextual fear memory at CA1 and the anterior cingulate cortex (ACC). The paper exploits a recent tool developed by the group (viral-mediated shRNA interference of Ablim3 in DG), to enhance PV+ mediated inhibition of CA3 pyramidal cells by increasing both their recruitment by DG cells and their number of contacts over postsynaptic cells. Using micro-endoscopic imaging of mice experiencing contextual fear conditioning, the authors nicely evaluate the effect of feedforward inhibitory control of CA3 outputs in the formation, stabilization and specificity of contextual fear memory representations in the CA1 and ACC. Data is relevant to understand how specific microcircuit motifs can influence representational dynamics in downstream regions.

I have some methodological comments and recommendations for authors to improve their presentation and to exclude potential confounding factors.

1) Since imaging is performed in CA1 and ACC separately, the study design entails 4 groups: shNT vs shRNA which is the main experimental manipulation, plus CA1 vs ACC. While data is in general carefully presented, some analysis may require additional validation to discard whether some regional effects caused by manipulation may actually reflect group differences. This is important because there may be some differences between ACC and CA1 groups in some behavioral readout (e.g. Fig.2c; Fig.S2b) which may actually explains different effect of manipulation. Formal comparisons of behavior in ACC and CA1 shNT groups may be required to discard this effect.

2) Differences of activity level (calcium rate) are examined using bins of 5 seconds for a total of 360 sec of exploratory activity. To discard motility effects an analysis is implemented using 1 sec bins. Thus, the two data samples are not commensurate. Also, an ANOVA on calcium rate is applied over uneven multiple comparisons to account for statistical effects of region x time or context x time. This is relevant for fig.1g vs 1i and Fig.S2j,l and may require correction.

3) Figure 3 nicely show accurate context classification based on calcium activity from A&C contexts neurons using support-vector machine. The authors report very interesting representational effects for shNT vs shRNA manipulations. Is prediction accuracy of the SVM classifier correlated with behavioral discrimination? That would reinforce conclusions.

Regarding conclusions and physiological relevance, the authors may need to discuss why enhanced feedforward inhibition at DG-CA3 synapses is not naturally established given the beneficial effect in context discrimination.

Reviewer #2 (Public Review):

In this manuscript, Twarkowski and colleagues use a previously published lentiviral technique to boost dentate gyrus (DG) mossy fiber recruitment of CA3 parvalbumin+ inhibitory neurons, thus increasing feedforward inhibition in the DG - CA3 circuit. Using this technique, the authors test the impact of increasing feedforward inhibition (FFI) in this circuit on the formation and maintenance of context-associated neuronal ensembles in CA1 and the downstream anterior cingulate cortex (ACC). The authors begin by replicating their previous work showing that increasing FFI in CA3 enhances context-specific freezing behaviors at remote timepoints and assess the correlation structure of CA1 and ACC populations during recent and remote recall. The authors claim that FFI "facilitates formation and maintenance of context-associated neuronal ensembles in CA1" but most of the direct tests between groups are not performed, and it's unclear how increasing correlation structure within a recall session relates to the maintenance of ensembles and memory performance. The premise of interrogating a circuit on this synapse-specific scale without direct interference/stimulation of broader elements of that circuit is an exciting and important direction for systems neuroscience. Overall, this manuscript is written and organized in an intuitive way, however, many of the findings have been previously published by this group and other claims are not yet supported by the data and need to be amended or clarified.

Reviewer #3 (Public Review):

In this study, Twarkowski et al. aim to understand the role of a specific circuit motif, dentate gyrus (DG) to CA3 feed-forward inhibition (FFI), for memory encoding and consolidation. FFI is a ubiquitous circuit motif in the brain. As a result, providing insights on its function is an interesting and a potentially very impactful contribution to neuroscience.

To tackle this issue, the authors describe how increasing DG-CA3 FFI impacts the ensemble activity in hippocampal area CA1 and the anterior cingulate cortex (ACC) in mice undergoing a contextual fear conditioning paradigm. To selectively increase FFI onto CA3 neurons, the study uses a molecular tool (downregulation of Ablim3 using virally mediated expression of shRNA), which has been developed by the same group (Guo et al, 2018, Nature Medicine). The impact of this manipulation is assessed via chronic in vivo one-photon Ca2+ imaging of dorsal CA1 and ACC neurons on the day of fear conditioning, one day after (recent recall), and 16 days after (remote recall) the fear conditioning. During and after fear conditioning, the results show in both experimental groups (shRNA and control) various population activity changes in both CA1 and ACC. Furthermore, the study finds improved context discrimination in the shRNA group only at the remote recall timepoint. The authors' conclusion is that increasing FFI enhances the formation of learning-specific ensembles, first in CA1 and later in ACC, which is associated with an improved memory recall. The experiments presented here were very technically challenging and produced a comprehensive and valuable dataset describing the parallel ensemble activity changes in CA1 and ACC after fear conditioning, with or without increasing DG-CA3 FFI. However, a causal relationship between the manipulation of DG-CA3 FFI, the network activity changes in CA1 and ACC, and the behavioral improvement is, in my opinion, not fully demonstrated. This is for a couple of reasons:

1) The magnitude of the effect of the shRNA manipulation on the immediate downstream area CA3 remains unclear. Therefore, the findings in the downstream areas CA1 or even ACC (which is at least three synapses removed from CA3) are, in my opinion, difficult to interpret. This uncertainty includes (1) the extent of the virus injection in the dentate gyrus and the extent of subsequent changes in CA3, and (2) the effect of the manipulation on CA3 pyramidal cell activity in vivo. The original paper (Guo et al, 2018) uses in vitro voltage-clamp recordings to record EPSCs/IPSCs in CA3, but does not exclude possible compensatory changes in vivo, e.g., in the excitability of CA3 neurons, which could result from increasing FFI chronically over a few weeks. The data in Figures 1f and g seems to suggest that there are baseline activity changes in CA1, which might be caused by changes in the upstream CA3 network activity. Along the same lines, I am unsure how to interpret the comparisons between CA1 and ACC in Figure 1; within brain region comparisons are more relevant and should be shown instead.

2) Several parameters are used in this study to describe the network activity in CA1 and ACC. These include the number of correlated neuron pairs, the number of neurons active in both the training context and a neutral context (so-called A-C neurons), or the event rate observed in these A-C neurons. Most of the activity changes observed do not appear specific to the shRNA group and occur also under control condition, suggesting that they are not caused by an increase in DG-CA3 FFI. It would be helpful to clarify the sequence, how increasing FFI onto CA3 is hypothesized to cause the changes in CA1 or even ACC.

Joint Public Review:

In this manuscript, the authors analyze multiunit data recorded from macaque motor cortex, and compare the data with theoretical results of a network model that is close to a critical point. Their analysis uncovers two main features of the data: (1) Covariances between spike counts of pairs of neurons depend only weakly on distance, while one would expect a much stronger dependence given the scale of local axonal and dendritic arborizations; (2) Patterns of covariances are dynamic, and differ significantly between different epochs of the behavioral task.

To understand these findings, they turn to a spatially extended network model. The analysis of this model is performed using an extension of tools introduced by a subset of the authors in a recent publication, that analyzed a network with no spatial structure. The authors show that the first feature can be obtained in their model provided the network is close to a critical point, and that the second feature is also observed in their network when external inputs to the network are epoch-dependent.

The recordings are from a standard Utah array and reveal correlations across millimeters during either rest or the task. While the heavy-tailed distribution of both positive and negative correlation is striking, it is not unexpected. Long-range anatomical connections cannot be completely ruled out.

The modeling and analytical results reveal how a network with spatially heterogeneous connections can give rise to a heavy-tailed scaling in the correlation. While long-range correlations arising from a disordered model near a critical point are not surprising, the analytical results obtained here are thorough and show how to obtain rigorous approximations even with heterogeneous 2D models.

The results that the long-range covariance structure in the primate cortex changes during different stages of a reach-to-grasp task is the most intriguing finding in the paper. While more needs to be done to reveal the "why" of this change in network structure and its impact on neural computation, this work shows that this kind of careful dissection of network state should be explored further.

The generality of the result beyond motor cortex is argued for and reasonable, though other data would be needed to substantiate this claim.

Reviewer #1 (Public Review):

This work proposes a new mechanism for the biogenesis of the lipopolysaccharide transporter LptD in the bacterial outer membranes. Authors find that BepA, a periplasmic metalloprotease, plays a dual role, that is, facilitating maturation of the OMP as a chaperone and degrading its misassembled forms as a protease. In this mechanism, the edge strand of BepA mediates substrate binding and proteolytic activity. It is a novel finding that the OMP biogenesis can involve the formation of a ternary complex composed of the barrel assembly machinery (BAM), LptD and BepA, implying that BAM serves as a dynamic platform in which OMP substrates and quality control enzymes/chaperones are recruited.

Authors' rigorous experimental design (based on bacterial genetics and structural biology), solid biochemical assays (including photo-crosslinking, cysteine crosslinking, and Western blotting), and carefully drawn interpretation and conclusions are impressive. Finally, authors delineate the mechanisms of BepA activation and LptD biogenesis, which are supported by the current and previous studies by the authors and other research groups.

While this is overall a wonderful piece of work, this manuscript would be further improved by clarifying the following points:

1) Authors examined how mutations (Pro and Cys scanning) on the edge-strand of BepA affected degradation and maturation of LptD.

It was assumed that these mutations impact the structure of BepA only locally. However, a mutational effect can be propagated in an unexpected way affecting the structural integrity of other regions. Although authors tested that A106P retains proteolytic activity as shown by self-cleavage, a similar test (for example, in vitro experiments using a structureless substrate) may need to be extended to other mutations to support the conclusions.

2) In the result (Line 159), authors report chaperone-like activity of BepA.

Here, the term "chaperone-like" is rather obscure regarding whether this activity facilitates LptD maturation without proteolysis (i.e., via holdase activity), or involves proteolysis as a part of quality control mechanisms. In another experiment, authors show that the chaperone-like activity may not necessarily involve proteolysis. It would be good to describe a possible molecular principle of how the edge-stand binding to the substrate can lead to chaperone activity.

Reviewer #2 (Public Review):

The authors found that a conserved β-strand (edge-strand β2 of BepA) directly contacts with the N-terminal half of the β-barrel-forming domain of an immature LptD; the C-terminal region of the β-barrel-forming domain of the BepA-bound LptD intermediate interacts with a "seam" strand of BamA in the BAM complex. By combining crosslinking and mutational studies, they showed that the edge-strand of BepA may have both the proteolytic and the chaperone-like functions. Based on the authors' previous studies of BepA, they proposed a model that the edge-strand and His switch of BepA regulate BepA in LptD assembly and degradation.

Reviewer #3 (Public Review):

This study aims to understand how the regulatory mechanisms governing outer membrane protein biogenesis. Specifically, it focuses on the role of BepA during the biogenesis of the essential beta-barrel membrane protein, LptD.

By performing an impressive systematic cross linking analysis, combined with previous known findings, the authors are able to dissect the general architecture of how BepA interacts with beta-barrel substrates as they are being assembled by the Bam complex. The experiments presented are nicely executed and the data are of high quality. I am convinced that the edge strand of BepA interacts with LptD, likely as it is assembling on the Bam complex. It is also clear that this interaction is functionally important because mutations in this region that disrupt the BepA-LptD interaction interfere with LptD maturation and degradation. This suggests that the substrate binding to the protease domain of BepA is important for both its chaperone and proteolytic activity. The work is well executed and will be of value to others interested in the regulation of membrane protein folding and, more broadly, in the biogenesis of the bacterial cell envelope.

While the authors conclusively establish a link between this region of BepA and its function, the data do not explain the underlying mechanism of how BepA discriminates between substrates targeted for integration into the membrane and those targeted for destruction. The model proposed at the end incorporates the presence of the edge strand of BepA, but its role in the process remains vague. As mentioned in the discussion, the mechanisms that control the switch from chaperone to protease function in BepA is likely governed by the loops that gate access to the catalytic residues proximal to the edge strand. It is possible that the edge strand may just be reporting on substrate binding to the protease domain active site. While this may be important for substrate recognition, it does not mean that the edge strand-substrate interaction plays a deterministic role in subsequent protein triage during LptD assembly.

Reviewer #1 (Public Review):

Strength: Chromatin remodelers regulate chromatin-templated functions through mobilizing and positioning nucleosomes; however, the molecular mechanisms underlying the binding and target-search remain obscure. Kim et al. utilize the powerful live-cell single-molecule tracking technique to investigate the binding and target-search kinetics of a comprehensive set of ATP-dependent chromatin remodelers. They endogenously tag the catalytic subunits of 6 major chromatin remodeling complexes and find that these remodelers reside at chromatin with 4-7 s mean residence times. Their results indicate these chromatin remodelers frequently transition between bound and free states. By using the remodeler mutants that are defective in binding or hydrolyzing ATP, they uncover that the ATPase activity is critical for dissociation of remodelers from chromatin. They reveal that ATP binding rather than ATP hydrolysis facilitates mobility of chromatin-bound fraction of remodelers at chromatin. Finally, they calculate the temporal occupancy of remodelers. Based upon these novel results, they provide a 'tug-of-war' model that explain how remodelers temporally control accessibility of promoters for transcription initiation. These results well support the authors; claims and conclusions and provide novel insights into the dynamic process underlying how remodelers search, locate, and bind target sites.

Weakness: There is no major weaknesses of the manuscript. Re-analysis of some data will strength this manuscript but will not change the claims and conclusions.

Reviewer #2 (Public Review):

Much progress has been made in the understanding of the mechanisms involved in chromatin remodeling, and recent advances in cryogenic electron-microscopy applied to the field have revealed the structural organization of many remodelers, and the way they engage their nucleosomal substrate. Currently, one of the least explored and documented aspect -and one of the most challenging to address- is the dynamic and the kinetics governing the interaction of remodelers with nucleosomes, as well as the concomitant dynamics of different remodelers at a specific nucleosomal location in vivo. These are interesting questions of broad interest as the vast majority of chromatin remodelers are involved in the regulation of DNA accessibility, particularly in the context of allowing or preventing gene expression and DNA repair.

In this work by Kim et al., (Dr. Carl Wu lab, and colleagues), the authors address these questions using an elegant and powerful approach, single-molecule tracking (SMT), that allows them to directly visualize and characterize the kinetics of various remodelers in vivo. Overall, the work is well designed, executed and presented. Here, the use of C-terminal Halo tags integrated into the genome at their endogenous locations controls for expression levels and helps ensure that full/normal complexes are being assessed. The technical merit and analysis modes of the work are strong. Another strength is the comparison of the in vivo dynamics and kinetics of a broad set of six remodelers (RSC, SWI/SNF, CHD1, ISW1, ISW2, INO80) representing all families of remodelers that act concomitantly at gene promoters or gene bodies. This allows the authors to provide strong evidence and notable comparisons and reach several significant conclusions:

• All remodelers engage their chromatin target very transiently and at high frequency (~4-7 second time scale).

• All remodelers use the ATPase activity to increase their dissociation rates (as revealed by characterizing the kinetics of Walker B mutants) while ATP binding itself enhances their browsing of nucleosomes (as revealed by characterizing the kinetics of Walker A mutants).

• Multiple, including functionally antagonistic, remodelers repeatedly engage and compete in a tug of war in order to maintain or change nucleosome positioning at promoters.

All these conclusions are well supported by the data presented.

Conceptually, it is noticeable that the in vivo dynamics characterized in this work are conserved between remodelers from different families, which widely vary in their composition, structure and function. This work further consolidates the ongoing theme of unification of all remodelers which engage nucleosomes in a very similar manner to perform highly regulated DNA translocation, and now, as highlighted by this work, while following similar dynamics and kinetics in vivo and capitalizing similarly on the ATP binding and hydrolysis.

The manuscript is well written, references to the context and related work are well chosen and cited, and the data are compelling.

Reviewer #3 (Public Review):

The paper by Kim et al uses elegant Halo-tag imaging techniques to study the dynamics of a set of 6 nucleosome remodelers in yeast. They model their data to show two populations of slower and faster turnover or movement, and find that remodeler mobility sits appropriately between that of histones and free Halo tag. The authors find that ATP binding (and not necessarily hydrolysis) regulates the rapid movement, as mutations in the Walker A (and to a lesser extent, Walker B motif in CHD1 and ISW2) reduce movement. They suggest that ATP hydrolysis facilitates a rapid movement along the chromatid fiber. Transcriptional elongation is not a source of remodeler movement, on the other hand.

Complementing other data that supports a model of a "tug-of-war" between various types of remodelers at promoters, the authors argue for nucleosomal "pushing and pulling" (split among pushers and puller enzyme complexes that turn over rapidly) as a mode for balancing promoter accessibility.

Reviewer #1 (Public Review):

Alexander Komkov et al. developed a novel software/algorithm (iROAR) to utilise naturally occurring non-functional clonotypes as a control repertoire to correct for amplification bias associated with multiplex PCR based technologies commonly used in TCR/BCR repertoire analysis. No new data was generated in this study and utilises only publicly available datasets. The authors firstly determine the over amplification rate (OAR) as a metric which is found to be close to 1 under no or little amplification bias and this was validated by calculating the OAR for repertoires determined using 5'-RACE, a method known to have little to no amplification bias. This was a great control to have and is essential for validating the OAR measurement. In contrast, multiplex PCR based protocols such as VMPlex and VJMplex had significant deviations in the distribution of OAR.

Strengths: The authors used publicly available datasets that utilise both biased (multiplex PCR based) and low biased (5'-RACE) methods to determine TCR/BCR repertoires. In addition, the authors generated in silico biased 5'-RACE datasets. These comparisons are critical in determining the effect of bias correction.

Weaknesses: Analysis of TCR/BCR repertoires are very generalised to number of clonotypes. The use of this algorithm could be more widespread if the effect of iROAR on another repertoire analysis tools was determined or discussed. For example, does iROAR affect measures of diversity? Identification of rare but unique clonotypes? The ability to detect true clonal expansions? Additionally, documentation for the software is lacking and largely inaccessible to non-specialists.

Reviewer #2 (Public Review):

In this paper, Komkov et al. describe a novel approach for computational correction of PCR amplification bias in adaptive immune receptor repertoire (AIRR) sequencing data (AIRR-seq). Their correction algorithm is based on using out-of-frame rearrangements to approximate gene-specific amplification bias. Gene-specific relative frequencies among out-of-frame rearrangements are not altered by clonal expansion except to the extent that out-of-frame rearrangements are passengers in clones expanding as a consequence of the specificity of the functional rearrangement. Due to independence between the two rearrangements, it can be reasonably assumed that the effects of clonal expansion are uniform in their impact on the observed V- and J-gene frequencies among out-of-frame rearrangements. Komkov et al. further assume that gene-specific relative frequencies among unique, out-of-frame rearrangements approximate recombination frequencies and that the extent to which gene-specific relative frequencies among all out-of-frame rearrangements deviate from those among unique, out-of-from rearrangements provides an estimate of gene-specific PCR amplification bias. The ratio of V- or J-gene relative frequencies among all out-of-frame rearrangements to the corresponding relative frequency among unique out-of-frame rearrangements provides this estimate and can be used as a correction factor during data processing. It also serves as the basis for a repertoire-level metric of the overall extent of amplification bias in a repertoire.

This is a very nice and, to the best of my knowledge, novel idea. The proposed correction factor and metric have potential utility in all studies conducting AIRR-seq that use a PCR amplification step. While the proposed approach may not have superior or even equal performance when compared to biological spike-ins, it still has great potential utility given the time and financial costs and required expertise of using biological spike-ins and because it can be applied to data sets that have already been generated. Incorporation of this approach into AIRR-seq data processing has the potential to increase the accuracy of downstream analyses. It also has the potential to enhance the comparability of results across studies and to reduce the effects of different sequencing protocols for data re-use when data are integrated across studies.

Enthusiasm is dampened by the fact that the proposed method is not directly compared to the gold standard of biological spike-ins.

Reviewer #1 (Public Review):

Zeng and colleagues present an interesting and timely exposition of how memory for items and generalities across related experiences are formed and influence each other. Across two carefully designed longitudinal studies spanning 1-2 months, the authors integrate insights from ensemble perception research to develop a novel landmark learning paradigm in which a set of landmarks are clustered together. Participants were required to learn the specific location of each landmark (item memory) as well as the spatial centre of the locations (reported gist). Leveraging hierarchical clustering models, the authors computed a gist-based bias measurement, enabling them to comment on the extent to which gist memory influences memory for specific items, as well as an index of estimated centre assembled from the retrieval of individual items. This enabled the authors to establish the amount of gist information available in item memories, and to tease apart the direction of the relationship between item and gist memory. I particularly appreciated Study 2's exploration of how the presence of an "outlier" item in spatial location impacts the gist and the relationship between item and gist representations from Study 1. This innovative approach allowed them to determine the extent to which the gist is robust against outlier items over time.

Overall, I enjoyed reading this manuscript. It elegantly addresses an important and as yet unresolved question in cognitive science, namely the extent to which gist representations become independent of individual item memories as they are extracted during encoding and how this relationship potentially changes over time. The inclusion of a longitudinal dimension further enables us to understand something of the temporality of consolidation over short versus longer time periods. The manuscript is very well written, the experimental studies have been meticulously conducted using a novel interdisciplinary approach, limitations are appropriately acknowledged, and the conclusions drawn are highly appropriate and measured. I believe this study will make a very nice contribution to the memory literature and will certainly spur new lines of enquiry in this field.

Reviewer #2 (Public Review):

It is often said that the amount of information humans can commit to long-term memory is essentially unlimited. However, this large storage capacity partly rests on our ability to compress new information before and during memory consolidation. One way to achieve such compression is by storing the common denominator of related experiences, which is often referred to as gist memory. During later recall, the brain is thought to deduce individual memories from the stored gist representation, rather than reading them out one by one. This not only reduces the memory footprint associated with learning, it also supports adaptive generalization of learning to novel situations.

Zeng and colleagues address two important questions regarding gist memory: First, how do gist memory traces develop over time in comparison to memory traces for individual items? And second, how do the two classes of memory traces influence each other?

The authors developed an experimental protocol in which healthy participants learn the association between landmark labels (e.g. university, park, restaurant) and abstract locations on a computer screen. Memory is subsequently measured in terms of the precision with which participants are able to indicate the learned locations, as well as their geometric center (i.e., the gist location).

A major strength of this approach is that it allows for precise, quantitative assessments of item and gist memory, and how they influence each other. On the other hand, the abstract nature of the task makes it difficult to generalize the results to other forms of gist memory. Moreover, gist memory is explicitly assessed directly after initial learning, which does not allow for clear conclusions as to whether similar memory traces would have developed spontaneously. Nevertheless, the protocol represents a valuable tool for studying the interaction of related memory traces over time.

The authors performed two experiments using this task. In the first experiment, they show that gist memory is more stable over one month compared to item memory; and that gist and item memory are positively related across all measured time-points (i.e., after 24 hours, one week, and one month). However, the nature of this positive correlation changes over time, with gist memory biasing item memory only after one month. The results of a second experiment corroborate these conclusions. They additionally indicate that outlier items (i.e., landmarks that are spatially distant from the cluster of all other landmarks) affect explicit, but not implicit gist memory, with only the latter biasing memories for individual items. Taken together, these results corroborate the notion that the importance of gist memories in guiding recall increases over time. Importantly, the data also provide an estimate of the relevant time window, i.e. between one week and one month after learning.

My only major concern is that the experimental task provides a rather restricted view of gist memory. Thus, it remains unclear what the results mean for other kinds of gist memory, both visual and otherwise. The authors discuss this point on p.16 and offer some interesting speculations. However, the main problem I see is that memory for gist locations may reflect a combination of basic perceptual strategies (as acknowledged by the authors in their references to ensemble perception) and the demand characteristics of the task. This issue is most prominently seen in the difference between the effects for global and local centers observed in Experiment 2. Here, the instruction to explicitly recall the center is what may have created (or at least emphasized) the memory for the global center in the first place. More generally, the fact that gist memory was tested first and early on after learning may have changed the associated memory trace, making it difficult to generalize these findings to cases in which gist memories emerge spontaneously. While this may be the price to pay for precise, quantitative error and bias measures of errors, the authors should discuss these limitations in more detail.

Reviewer #3 (Public Review):

A fundamental puzzle in human memory has been whether we retain individual experiences, and extract their gist by pooling over those experiences only when the gist is needed, or whether we also store a separate representation of the gist (regularities). This study uses locations of landmarks on a screen to argue that such a gist representation (the central tendency of the 2D locations of landmarks) is extracted gradually over time during memory consolidation, i.e, over a period between one day and a month after initial encoding of the landmark locations. Furthermore, the study suggests that this gist representation is not influenced by atypical locations, suggesting it is more than the simple average of all item memories, and that atypical stimuli may be encoded and/or retrieved separately.

I think it would really improve the paper if the authors could compare a number of simple computational models, and show the model corresponding to their favoured conclusion (i.e, that a separate gist representation increases its influence on memory over time) is the only one that is consistent with their data. Ideally, this would entail quantitative fitting of parametrised models, but failing that, it might be sufficient to demonstrate qualitatively that certain models can never explain critical results (like the bias measure above, or the effect of an outlier). For example, I presume that an item-only model in which random noise (in both x,y directions) increased with delay could explain their first result of a greater effect of delay on reported item error (Ir) than Gr? But is this model consistent with their regression results, and most importantly, could it never reproduce their bias results?

Another concern is whether the same results would hold if participants' representation of gist (Gr) were more than the simple average of their reported item locations (Ge). For example, would there be any consequences for the authors' conclusions if some 2D locations were represented more accurately than others - e.g, one might expect the location of landmarks close to the edge of the screen to be stored more accurately than ones closer to the centre of the screen (which could be tested by whether edge locations need fewer training trials?). I appreciate that locations are trained to the same criterion, but it is nonetheless possible that some representations are still more precisely encoded than others even after such criterion-training. Then if participants' reported centre were the weighted average of the reported item locations, weighted by each location's remembered precision, could that affect the authors' bias measure? I appreciate that this would not explain why the difference between reported and true centre changes over retention interval, but if one allowed location and precision information to decay at different rates, could this cause such an interaction with retention interval? This could be another model to simulate?

It is unfortunate that the authors did not counterbalance the order of item and gist memory tests. They do consider this limitation in the Discussion, and note that any additive effect of test order would not explain the interaction between memory type and delay, but of course the presence of nonlinear/multiplicative effects (e.g, floor/ceiling effects) means it is not sufficient to conclude that their results could not depend on test order, i.e, it is always better to empirically test generalisation by running the other counterbalancing. This is particularly important here, where there are good a priori, theoretical reasons why test order might matter, e.g., shift people from strategies based on retrieving items or retrieving gist, depending on what type of memory is probed first. So while the authors state that this could be tested in future studies, the paper would be stronger if the authors could run this counterbalancing themselves and show the same results, so that future researchers do not waste time trying to replicate effects that turn out to be conditional on test order.

Reviewer #1 (Public Review): 

In this study, Lentzer and colleagues perform single-cell sequencing of migratory trunk neural crest population in zebrafish trunk during the early stages of migration. The authors provide a characterisation of gene expression in those cells, identify new genetic markers and identify a subset of Rohon-Beard (RB) neurons as a source of Fgf signalling but remain very descriptive in their analysis. However, the dataset is rather limited, and there is no in-depth validation. The study mostly expands the list of markers (albeit previously known in the nervous and hematopoietic system) and reports on differentiated derivative markers' presence in the pre-migratory NC populations (that were previously reported). 

Major concerns are listed below: 

1) The authors argue that RB cells share a developmental origin path with NCC based on scRNA-seq data of 607 cells at a single time point (20-24hpf). Cell number is quite low if one is attempting to resolve transcriptional dynamics underlying cellular behaviour over time. This finding essentially validates the past work from the same lab that demonstrated the presence of HNK1+ (NV migratory marker) RB neurons in a well-characterised pdrm1 mutant (Hernandez-Lagunas et al., 2014). Such analysis performed at a single stage and at a relatively late time point cannot be used to infer common developmental origin or path. Furthermore, the authors seem to "brush" over the finding that they identify both sox10+ and sox10- RB neurons - both of which are lost in pdrm1 mutant - this finding is not addressed appropriately. This indicates the limitations of single-stage analysis with a relatively limited dataset. 

2) There seems to be a disconnect between the title and what forms the bulk of the discussion and figures in this paper. 

3) The authors claim to have identified new markers for these groups of neurons. However, they do not attempt to compare/integrate their dataset with other more extensive existing datasets (Wagner et al.,2018) to investigate whether markers (fgf13a, cxcr4b) are expressed at earlier time points to help support their hypothesis. Similarly, the authors do not seek to utilise single-cell datasets at later time points obtained using the same transgenic line to verify whether these markers are expressed (Aubrey et al., 2021). Further mining and integration with available datasets would help strengthen the authors' point. 

4) The authors also postulate that some premigratory cells already express differentiated genes as a novelty but fail to cite multiple studies that have shown this in the neural crest in zebrafish and other models (Soldatov et al., 2019, Ling et al., 2019). 

5) The "unknown" cluster 7 described by the authors as a potential new NCC lineage cluster is most likely (authors should verify this) a previously reported mesenchymal cluster expressing a wealth of collagen genes a{section sign}nd this should be verified and rectified. 

6) The claim 'Some of 156 cells (Cluster 5) are presumably neural tube tissue' is unclear, as sox10 found in Cluster 5 does not label neural tube. It is unclear what are the Cluster 5 marker genes. Cluster 5 also seems to be split into two subclusters. It is unclear whether different genes mark these regions. It would have been helpful if the authors elaborated on the differential split of sox10-expressing and non-expressing cells within the cluster (feature plots in 1F indicate that sox10 is downregulated in the top left portion of this cluster and the RB cluster). 

7) One of the primary novelty points emphasized by the authors is the subset of RB neurons. If this is to be confirmed, the authors would need to perform KO of some of the known marker genes specific to this cell population to show their relevance to RB cell development and determine what role these subsets of NCC-RB cells play. 

Important technical points: 

The study lacks sufficient information to verify the data quality and level of rigour in the analysis. For instance, information such as the number of embryos used to get 607 cells would need to be provided (this is important for defining genetic heterogeneity). It would also be important to indicate what proportion of embryos were 20hpf and 24hpf, how many cells were loaded into the channel, and what were the mean number of genes and UMI's that were found per cell. It is alosounclear how the analysis was performed, what were the parameters/cut-offs used and how many cells are found within each cluster. Furthermore, the quality of the data and figure annotations could be improved.

Reviewer #2 (Public Review): 

This work presents a small single-cell RNAseq dataset from 20-24 hpf zebrafish trunk neural crest cells (FACS of sox10::GFP from dissected trunk/tails). Within this data, the authors identify trunk neural crest cells (both pre-migratory and migratory), otic/lateral line cells (also labeled by sox10::GFP), posterior arches, and Rohon-beard neurons. A small selection of novel markers of xanthoblasts and Rohon-Beard neurons are identified, validated by in situ hybridization, and also shown to lose expression in prdm1a mutants (which lack trunk neural crest cells). Moreover, neural crest cells were classified as pre-migratory or migratory. Surprisingly, markers of distinct pigment cell fates were expressed prior to the onset of migration, but markers of neural fates were not observed prior to the onset of migration, indicating that some populations of NCCs express differentiation markers prior to migration (but that this is not universal to all neural crest derivatives). Additionally, this work highlights that a small percentage of Rohon-Beard neurons are marked by the sox10 transgene, suggesting that they have expressed sox10 at some point in their developmental history. 

This is a compelling study and a pleasure to read. The data are of good quality (a sensible isolation protocol, quality metrics seem reasonable) and the processing steps used are well established standards in the field. The data annotation is well presented and convincing, and the novel xanthophore and RB markers validation is superb. The finding that some NCCs initiate differentiation prior to migration seems to provide additional evidence toward a longstanding debate in the neural crest field. 

The major weaknesses are that, while a subset of new markers are presented and extensively validated, there is not a broader presentation of some of the novel gene expression results contained in their data that might be more broadly useful to the field. 

Additionally, the study leaves me wondering: if cell labeling experiments in zebrafish have suggested that NCCs are lineage restricted prior to migration, why are NCCs expressing cell-type specific markers only observed prior to migration for pigment cells and not for other neural crest derivative cell types?

Reviewer #1 (Public Review):

Using the noval 3D reconstruction techniques, Hetherington and colleagues investigated the body plan of Asteroxylon mackiei, which is an extinct lycopsid from the Early Devonian Rhynie Lagerstatte. They demonstrate that the body plan of A. mackiei consisted of three distinct axes types and that the rooting axis was developed from root-bearing axes by anisotomous dichotomy.

The main claims of the manuscript are supported by the data in overall, and the 3D reconstruction techniques used here are informative for broader readers. However, the discussion part of this manuscript should be strengthened to a larger extent, in order to clearly demonstrate the significance or implication of this discovery.

Reviewer #2 (Public Review):

Hetherington et al. present a detailed three-dimensional reconstruction of the Rhynie chert vascular plant Asteroxylon mackei, an early lycophyte (club-moss). Rhynie chert fossils preserve exquisite details of anatomy, but our knowledge of the plants preserved in it are based primarily on dissociated thin sections rather than on relatively whole pieces of single plants. The authors present a novel method of three-dimensional reconstruction of Asteroxylon by cutting a length of axis into 31 thick sections, polishing and digitally photographing both sides and digitally stitching them together to produce a nearly 5 cm long reconstructed axis. Using this reconstruction, alongside details of the anatomy of the thick sections, they demonstrate the body plan of this early club-moss was divided into 3 main organs: leafy, largely orthotropic axes, sparsely leaved, plagiotropic root-bearing axes and rooting axes. Importantly, they show that both root-bearing axes and rooting axes in Asteroxylon originated at anisotomous branching points, providing additional support for the centrality of anisotomy in the evolution of complex morphology in vascular plants. In addition, the authors demonstrate that what they term rooting axes lack root caps and root hairs, in contrast to modern lycophytes (and other extant vascular plants). Finally, utilizing images of unrelated peels of another axis, they demonstrate that rooting axes in Asteroxylon underwent dichotomous branching, rather than forming endogenously like all modern lycophytes. These data show that the evolution of true roots proceeded in a stepwise pattern, and that true roots arose twice in the evolution of vascular plants.

This is an excellent paper: it presents a novel technique for reconstruction of fossil plants, and demonstrates the value of this technique in elucidating the morphology of a basal lycophyte from the Rhynie chert, as well as important aspects of the evolution of roots. The illustrations and 3D reconstructions are clear, and fully support the authors' points.

Reviewer #3 (Public Review):

This aim of the work is to investigates the structure and development of one of the oldest known rooting systems. It is based on exceptionally well-preserved fossil plants found in a 407-million-year-old geological site in Scotland.

The authors use modern imaging techniques to assemble sections through stems and roots of petrified fossils to visualize the development of their organs and tissues. This is feasible because cellular details are preserved within the tissue systems. They demonstrate that the roots of the fossil plants are significantly different to those of modern lycopods (i.e., clubmosses), which are their closest living relatives. This extinct form of rooting system therefore appears to represent an early stage in the evolution of roots. The results are significant for understanding the evolution of roots, and they are relevant to interpreting results from molecular development research on the roots of modern plants, especially lycopods.

A strength of the work is that it brings multiple lines of enquiry to bear on the central questions of root structure and development in the fossils. This is considered in relation to various types of organ category, including creeping rhizomes and upright leafy shoots. New preparations were made of fossil materials housed in the University of Munster (Germany), and historic collections were examined in The Natural History Museum, London (UK), and in the University of Wales, Cardiff (UK). These materials were prepared in different ways, providing complementary perspectives. The authors carefully document and explain how they interpret the fossils, and the inferences that they draw are well reasoned. The 3D reconstructions created from serial sections are particularly helpful in visualizing how the rooting system developed.

The work is scholarly, explaining how it builds on previous research, and it references and discusses appropriate work on related living plants. The authors' claims and conclusions are well justified in so far as they apply to the rooting system and basal region of the plant, which is the focus of this work. The form and development of the leafy branching aerial system of the plant are less well evidenced. This aspect of their Figure 1G is a synthesis from other works, and I would say that it is less well supported by the data than their main conclusions about the rooting system.

Reviewer #1 (Public Review): 

How the tolerance of gene overexpression varies across closely related organisms remains poorly understood and this manuscript offers the first systematic functional genomic screen to address this gap. Thus, the approach itself is clearly original and yeast is a great model system for such a study. The data itself would be an important resource for the functional genomics community. The broad picture emerging from this screen is also interesting: a subset of genes is commonly toxic when overexpressed, while many genes are toxic only in specific strains. Importantly, the commonly toxic genes are highly enriched in certain functional classes and often encode for protein complex members. All these make a lot of sense based on what is known about gene dosage sensitivity in baker's yeast. 

The more interesting and also riskier part is to identify and understand strain-specific overexpression phenotypes. The authors made great efforts to offer possible explanations for these, however, I had the impression that some further analyses could strengthen the conclusions and yield more insights. I see four broad issues: 

1) Statistical analysis: I failed to find data on the reproducibility of the screen and how it varies across strains. Variation in reproducibility may hugely influence some of the conclusions as the number of genes with a significant fitness effect depends on measurement noise (and number of replicates). On a related note, I'm somewhat puzzled by the claim that strains with large median fitness effects do not generally show more OE sensitive genes. Visually, it appears that this relationship is borne out for commonly toxic genes (Fig 3B), although not mentioned or interpreted. 

2) The authors show that the number of deleterious OE genes is strongly correlated with the amount of growth defect caused by expressing the empty Moby 2.0 vector (Figure 4D). This is a pretty strong correlation (r=0.7) and might influence the conclusions drawn from the data. In particular, the strong effect of empty Moby 2.0 should be taken into account when defining strain-specific fitness effects. For example, fitness effects that are present in 2-3 strains might be shared between strains that exhibit a similar cost of empty Moby and therefore need to be interpreted with caution. Previous genetic interaction studies suggest that slow growing mutants tend to show many epistatic interactions with any other mutations (Costanzo et al. 2010). I'm left with the feeling that the strain-specific differences in the number of OE sensitive genes might be a manifestation of this more general phenomenon. 

3) Lack of phylogenetic context: The investigated strains come from several distinct populations with different lifestyles and varying phylogenetic distances. I would have expected some further investigations on how strain-specific OE effects depend on lifestyle or phylogenetic relationship. 

4) The tryptophan depletion story is a nice example of strain-specific difference in physiology. Overall, the presented analyses on tryptophan-enriched genes are highly suggestive, however, it lacks a negative control, that is, other genes that have similar functions but are not enriched in tryptophan.

Reviewer #1 (Public Review): 

Using chemical mutagenesis to screen for mutants in mESC that are resistant to tunicamycin, the authors of this manuscript found that mutations in N-acetylglucosamine deacetylase AMDHD2 confer resistance. Furthermore, mESC expresses glutamine fructose-6-phosphate amidotransferase 2 (GFAT2) instead of GFAT1. The authors showed GFAT1 is more efficiently inhibited by UDP-GlcNAc than GFAT2. Thus, the hydrolysis of N-acetylglucosamine by AMDHD2 helps to control the level of HBP in mESC by counteracting the action of GFAT2. This can also explain why mutations in AMDHD2 could confer resistant to tunicamycin as the mutations would increase UDP-GlcNAc levels in mESC. The authors also solved the structure of AMDHD2 and showed the many of the mutations either decreased the catalytic activity, folding or protein stability. This study thus provides important new insight into the hexosamine biosynthesis pathway (HBP). 

Overall, this is a very nice study with many strengths. The chemical mutagenesis and screening for tunicamycin resistant is a nice method that allows the identification of the role of AMDHD2. The structural and biochemical characterization of AMDHD2 is beautifully done. The differential expression patterns and feedback inhibition profiles of GFAT1 and GFAT2 also provided important insights to understand their functional differences and why AMDHD2 mutations more specifically affects mESC that expresses GFAT2. 

I could not point out any major weakness with the study. If I have to be very critical, I would say that it is not clear how GFAT2 and AMDHD2 avoid forming a futile cycle in HBP. However, this is a question better suited for future studies. One very minor weakness is that the down-regulation of GFAT2 protein level during neuronal differentiation is very modest, which in contrast to the very dramatic differences in mSEC and N2a cells.

Reviewer #2 (Public Review): 

The paper by Kroef et al. is aimed at identifying novel regulators of hexosamine biosynthesis - a key pathway that feeds into different types of essential glycosylation processes. The authors use a recently developed haploid stem cell line, and a tunicamycin sensitivity phenotype to perform a genetic screen for novel recessive alleles. Building on their previous work that shows how UDP-GlcNAc provides direct feedback inhibition of GFAT1, here they uncover that AMDHD2 provides another feedback loop for conditions where GFAT2 is the dominant isoform. A crystal structure of this enzyme and a discussion of its mechanism is also included. There were also attempts to identify the role of AMDHD2 using a mouse knockout. This represents a significant advance of knowledge in this area and is of considerable interest to the field and the wider eLife readership. However, the authors do not provide sufficient data to support their proposed mechanism of GFAT2/ AMDHD2 regulation of the HBP, and several important controls are missing. These issues will need to be addressed in a revised manuscript.

Reviewer #1 (Public Review):

This work challenges current models for the regulation of Ca2+ uptake by the MCU channel complex in mitochondria, an important mechanism that matches the rate of ATP generation to cellular metabolic needs. Current models posit that the MICU1 subunit blocks the MCU pore at low cytosolic [Ca2+]i, and that this block is relieved by [Ca2+]i elevation, causing a steep uptake of Ca2+ once a threshold is released. This paper challenges this view, based on a series of cutting-edge patch-clamp measurements of MCU activity in mitoplasts derived from wild type and CRISPR MICU1 knockout mitochondria. This is a powerful approach as it is directly monitors MCU activity and allows precise control of the ionic environment. The mitoplast system is well validated, both in terms of the expression and knockout of MCU, MICU1/2, EMRE subunits. Using this approach, the authors have succeeded in directly addressing the function of MICU1 in regulating the activity of MCU, as well as previous claims that MCU can act as a Ca2+ release channel and that MICU1 selectively inhibits the entry of Mn2+, which can be toxic.

The results show that the characteristic inward rectification of current through MCU as well as Mn2+ permeation are independent of MICU, and that MCU conducts Ca2+ only into mitochondria and does not act as a Ca2+-activated release channel. Most importantly, they find that in the absence of divalent cations, which allows Na+ to permeate the channel, MICU1 does not affect current amplitude, arguing strongly against its role as a blocker at low Ca2+ levels. Rather, it acts to potentiate channel activity at higher levels of Ca2+, which is dependent on Ca2+ binding to the MICU1 EF hands. By measuring single-channel currents, they show that MICU1 potentiates flux by increasing the open probability of the channel.

These studies do much to clarify the mechanism of MICU1 modulation of Ca2+ uptake, yet there remain some open questions. The evidence against MICU1 as a pore blocker was collected in the absence of Ca2+ and Mg2+, which may affect the association of MICU1 with the channel. Ca2+ uptake by intact mitochondria is suppressed by MICU1 at low Ca2+ levels, and it remains to be shown what mechanisms account for this if MICU1 does not plug the channel.

Reviewer #2 (Public Review):

The uptake of Ca2+ into mitochondria through the mitochondrial Ca2+ uniporter complex (MCUcx) is important for ATP production and cellular bioenergetics. The MCUcx consists of five subunits - MCU and EMRE which form the channel pore in the inner mitochondrial membrane, and MICU1-3 subunits which lie in the intermembrane space. While several previous studies have examined the roles of MICU1-3 in regulating Ca2+ uptake via MCUcx, there are ambiguities regarding their precise mechanisms. This study seeks to clarify the impact of MICU1-3 on MCUcx and the mechanisms of action of these proteins. The authors generated cell lines featuring knockout of individual MCUcx subunits; measure mitochondrial Ca2+ uptake in intact cells and isolated mitochondria; and conduct patch-clamp recordings of macroscopic and single channel MCUcx currents using Ca2+ and Na+, respectively, as charge carrier. The authors find that MICU1/MICU2 heterodimer (or MICU1 homodimer in MICU2 knockouts) potentiates MCUcx open probability in a Ca2+-dependent manner. They further conclude from their results that MICU1 does not occlude the MCUcx pore at low [Ca2+]i as has been previously reported.

Overall, the challenging experiments in this work are technically well done and the results produce some new insights into MICU1 regulation of MCUcx. The conclusion that MICU1/MICU2 heterodimer potentiates MCUcx activity in a high [Ca2+]i-dependent manner is well supported by the data (Ca2+-dependence of Ca2+ conductance in mitochondria isolated from WT and MICU1-/- cells; single channel recordings of Ca2+ and Na+ currents in WT and MICU-/- cells).

A fair amount of effort is put in the paper to argue that the results discount a previously proposed model that MICU1 occludes the MCUcx at resting Ca2+ levels in cells. In my view, the data supporting this conclusion is not compelling. The main issue is that the conditions which the authors use to make this argument are essentially 0 [Ca2+]i. This is different from the case in a cell where resting Ca2+ is ~100 nM. It is possible that Ca2+ has a biphasic effect on the action of MICU1/MICU2 heterodimers - inhibition of MCUcx activity at 'low' [Ca2+] ({less than or equal to} 3 microM) and potentiation of activity at high [Ca2+]i ({greater than or equal to} 10 microM). MICU1/MICU2 heterodimers have several EF hands between them and there is ample precedent for Ca2+ binding proteins with multiple EF-hands, such as calmodulin, bifurcating Ca2+ signals and to have functionally opposite effects on a target protein.

Reviewer #3 (Public Review):

In this study, Gard et.al performed rigorous electrophysiology analysis of MCU channel complex in intact isolated mitochondria using whole mitoplast patch clamp method. Their results suggest that the MCU/EMRE channel complex (which form the channel pore) is not blocked by MICUs in the absence or presence of extramitochondrial Ca2+ as has been widely reported. Instead, MICUs potentiate activity of MCU complex by increasing the channel open probability when extramitochondrial Ca2+ is elevated. This study indeed provides very different views on multiple aspects of MCU complex functions. Particularly, the finding of non-inhibitory role of MICU1 is opposite to the current view of MICU1 blocking of MCU at low Ca2+ based on classical mitochondria Ca2+ uptake assays. This finding is also different from the recent structural studies of MCU/EMRE/MICU1/MICU2 holocomplex which showed that MICU1/MICU2 can bind and block the external entrance of the MCU/EMRE channel pore in the absence of Ca2+. Despite these discrepancies, this study provides quantitative analysis of MCU activity by using a highly challenging mitoplast patch clamp technique that very few labs are capable of and gives some novel insights into MCU complex activity and regulation. How to reconcile the different views of MICUs' regulation of MCU will be an interesting and exciting subject in MCU field that warrants further studies and the findings of this study will certainly stimulate this effort.

Reviewer #1 (Public Review): 

By sequencing a large number of SARS-CoV-2 samples in duplicate and to high depth, the authors provide a detailed picture of the mutational processes that shape within-host diversity and go on to generate diversity at the global level. 

1) Please add a description of the sequencing methods and how exactly the samples were replicated (two swaps? two RNA extractions? two RT-PCRs?). Have any limiting dilutions been done to quantify the relationship between RNA template input and CT values? Also, the read mapping/assembly pipeline needs to be described. 

2) I find the way variants are reported rather unintuitive. Within-host variation is best characterized as minor variants relative to consensus (or first sample consensus when there are multiple samples). Reporting "Major Variants" along with minor variants conflates mutations accumulated prior to infection with diversity that arose within the host. The relative contributions of these two categories to the graphs in Fig 1 would for example be very different if this study was repeated now. Furthermore, it is unclear whether variants at 90% are reversions at 10% or within-host mutations at 90%. I'd suggest calling variants relative to the sample or patient consensus rather than relative to the reference sequence (as is the norm in most within-host sequencing studies of RNA viruses). 

3) It is often unclear how numbers reported in the manuscript depend on various thresholds and parameters of the analysis pipeline. On page 2, for example, the median allele frequency will depend critically on the threshold used to call a variant, while the mean will depend on how variation is polarized. Why not report the mean of `p(1-p)` and show a cumulative histogram of iSNV frequencies on a log-log scale including. I think most of these analyses should be done without strict lower cut-offs or at least be done as a function of a cut-off. In contrast to analyses of cancer and bacteria, the mutation rates of the virus are on the same order of magnitude as errors introduced by RT-PCR and sequencing. Whether biological or technical variation dominates can be assessed straightforwardly, for example by plotting diversity at 1st, 2nd, and 3rd codon position as a function of the frequency threshold. See for example here: 

https://academic.oup.com/view-large/figure/134188362/vez007f3.tif [academic.oup.com] 

There are more sophisticated ways of doing this, but simpler is better in my mind. 

It would be good to explore how estimates of the mean number of mutations per genome (0.72) depend on the cut-offs used. A more robust estimate might be 2\sum_i p_i(1-p_i) (where p_i is the iSNV frequency at site i) as a measure of the expected number of differences between two randomly chosen genomes. Ideally, the results of viral RNA produced of a plasmid would be subtracted from this. 

4) This paper provides an important baseline characterization of within-host diversity, while the patterns themselves are not extremely surprising. It is thus important that the data are provided in a form that facilitates reuse. It would be helpful to provide intermediate analysis results in addition to the raw reads in the SRA and the shearwater calls. I would like to see simple csv tables with the number of times A,C,G,U,- was observed at every position in the genomes for every sample. This would greatly facilitate the reuse of the data.

Reviewer #2 (Public Review): 

The paper by Tonkin-Hill and colleagues describes the analysis of intra-host variation across a large number of SARS-CoV-2 samples. The authors invested a lot of effort in replicate sequencing, allowing them to focus on more reliable data. They obtained several important insights regarding patterns of mutation and selection in this virus. Overall, this is an excellent paper that adds much novelty to our understanding of intra-host variation that develops during the time course of infection, its impact on transmission, and what we can or cannot learn on relationships between samples.

Reviewer #3 (Public Review): 

This study by Tonkin-Hill et al. analyzes the intrahost diversity of SARS-CoV-2 in patient samples collected in early 2020. The authors sequenced >1000 samples in duplicate to decrease errors in variant calling. They show that sequencing replicates have good concordance at higher viral loads. They investigate the abundance of within-host variants per specimen, strand biases in within-host variants, and assess within-host purifying selection by dN/dS analysis. They show that within-host variants arise recurrently across disparate genetic backgrounds, which is consistent with either mutation hotspots or positive selection. They also find evidence for a relatively small number of mixed infections. 

Within-host diversity of SARS-CoV-2 is a topic of high interest in the fields of viral evolution and genomic epidemiology. This is a strong and timely analysis with several unique features that set it apart from previous studies. To my knowledge, this is the largest dataset for which there are sequencing duplicates, for which the authors should be commended. The finding of recurrent mutations across genetic backgrounds is highly important for genomic epidemiology. The authors rightly interpret these data as grounds for caution when using intrahost variants for transmission inference. My comments on this paper are largely related to data presentation and organization of the manuscript. There are also a few points where the authors could be clearer about their analytic choices and perhaps consider some caveats to their conclusions.

Reviewer #1 (Public Review): 

Authors reported here the results of two experiments. The first is about the effects of continuous theta burst transcranial magnetic stimulations on single cell responses of lateral parietal cortex of the monkey. This experiment is very challenging, requiring to obtain a stable and long-lasting signal from single cortical neurons and to stimulate constantly the same cell for an hour (they succeded also for longer periods). The paper represents a technical advance in the field and deserves attention and suggests a useful, through difficult, protocol to be replicated by other scientists. 

The second experiment tests the behavioral grasp-related effects of two TMS theta burst protocols. Authors demonstrate a long-lasting increase in the grasping time after TMS. 

A negative aspect is that the two experiments are not carried out on the same animals, and the results of the second experiment seem somehow not completely logically connected to the results of the first. Particularly important for the scientific community is the first experiment, that shows that the neural excitability is significantly reduced within the first hour after rTMS. This experiment demonstrated also a variability of effects (hyperexcitation, hypoexcitation, variable delays of recovery), that can be seen as a potential disadvantage in such TMS protocols, and an index of the different effects that may be obtained in the same experiment across subjects. 

Overall, the paper represents a step forward in the neuromodulation experiments in nonhuman primates.

Reviewer #2 (Public Review): 

Romero and colleagues designed an experiment to describe the neural and behavioral effects of continuous theta burst stimulation with the explicit aim of solving the problem of inter-subject variability of cTBS effects on human behavior. They describe two independent experiments in which cTBS was applied to the inferior parietal lobule of two monkeys per each experiment. In the first experiment the authors measure the activity of single units in response to light-on and to single-pulse TMS (spTMS). In the second experiment the authors describe the effect of cTBS on reaching time in a reach-grasp task. The results indicate a great variability on single neurons that follow different patterns of response to cTBS. In the second experiments the results show a systematic increase in reach-grasp time following cTBS. The authors provide a reasonable description of neuronal activity following their cTBS protocol but do not respond to the main issue of explaining inter-subject variability of human cTBS. The data has the merit of providing neural bases of the "delayed" effects of human cTBS.

Reviewer #1 (Public Review):

This study focuses on how the vmPFC supports delay discounting. The authors tested patients with vmPFC lesions (N=12) and healthy controls (N=41) on a delay discounting (DD) task with two additional conditions: (1) reward magnitude and (2) cues that should evoke episodic future thinking (EFT). 

The authors replicate their previous finding that patients with vmPFC lesions show steeper DD, and report two novel findings: (1) DD in patients is insensitive to reward magnitude, suggesting that vmPFC is critical for reward magnitude to modulate DD; (2) vmPFC patients show normal effects of EFT cues on DD, such that all subjects discounted less in the presence of cues that promote episodic future thinking. These findings have important implications for how vmPFC contributes to delay discounting, as they suggest that vmPFC is not necessary for prospective thinking to affect the evaluation of future rewards. 

1) A potential issue with the EFT finding is that it rests on accepting the null hypothesis of no group differences. However, there are reasons to assume this is not a trivial null result due to a lack of statistical power. Specifically, there is a significant effect of EFT within the vmPFC patient group and there is a significant group difference for the effect of reward magnitude. Assuming comparable power to detect effects of EFT and reward magnitude, it seems unlikely that the non-significant EFT effect is simply a lack of power. In any case, this caveat has to be considered when interpreting the effect. 

2) It is somewhat surprising that the authors had such a strong prediction about the absence of group differences for the EFT effect. Based on previous work (Bertossi et al., 2016a, b), one could expect a smaller EFT effect in the VMPFC group. The authors appear to put much weight on the results by Ghosh et al. 2014, which suggest that vmPFC is critical for schema reinstatement. The rationale for this strong prediction is not very clear from the introduction.

Reviewer #2 (Public Review): 

Ciaramelli et al. address a timely and theoretically important issue with respect to the functional role of the vmPFC in decision-making more generally, and temporal discounting in particular. Strong points of the paper include 1) a theoretically important research question and 2) much-needed lesion data on two important behavioral effects in temporal discounting: the magnitude effect, and a modulation of discounting via episodic future thinking. Weaker points of the paper include 1) lack of clarity for a number of methodological issues (group comparisons & control group for the AI data, inconsistency analysis) and 2) many remaining open questions with respect to how vmPFC patients might have utilized the EFT cues, and whether different processes were at work compared to controls. 

Major points: 

1) The authors note that their interpretation of the preserved EFT effects in the vmPFC patients in terms of e.g. semantic processing remains speculative, but is supported by the finding of intact external details production following vmPFC damage in earlier studies. But was this also the case in the present data set? This remains unclear, because for the AI data, only z-scores relative to some earlier control group (Kwan et al. 2015) are reported (Table 1 and Supplement p. 30). Was this control group matched to the patients? And since the referenced Kwan et al. (2015) paper reports only on six patients (presumably the patients from the Canada site?) - what about the patients from the Italian site, which control group were their AI data compared to? 

2) Directly related to my previous point: The methods section states that external details were in the normal range in the vmPFC group (mean z-score for EFT = -.73) but from Table 1 we can see that 8/10 patients in fact exhibit a negative z-score. This suggests that a direct group comparison of the external details scores would very likely reveal a significant group difference. Generally, it would help to report to actual control data here, not just the z-scores, and report the respective group comparisons. 

3) The description of the inconsistency analysis was somewhat unclear. The authors use the procedure suggested by Johnson & Bickel (2008), which makes sense, given the overall analytical approach that focuses on the analysis of indifference points. However, this procedure is based on a comparison of adjacent indifference points. In contrast, the authors are referring to the number of inconsistent choices - this is either a typo, or a different procedure. I think the former, because the reported absolute numbers (e.g. means around 1) and the single subject plots in the supplement appear to reflect the number of inconsistent ID points rather than choices. If this is the case, I disagree with the statement that the "mean number of inconsistent choices was very low" (p. 10) - as this probably reflects the mean number of inconsistent indifference points and not choices, about 1 out of 6 ID points was inconsistent in the vmPFC group, which is a lot. 

4) The EFT cues are suggested to help vmPFC patients to "circumvent their initiation problems" (p. 12) but I am not sure I follow this logic. First, the AI procedure typically entails external cues as well, and here vmPFC patients showed impairments (Table 1, but see my point 1 above). Second, some of the cited papers (e.g. Verfaellie et al., 2019) also used specific event cues, and still observed reduced internal details production in vmPFC patients. 

5) One shortcoming with the paper is that no data are available that could inform *how* vmPFC patients might have utilized the EFT cues, and whether the processes at work might have differed from those in controls. Many points mentioned in the discussion (self-referential processing, semantic processing, activation of schemata, self-initiation vs. external cueing etc.) thus necessarily remain conjecture.

Reviewer #3 (Public Review): 

In this manuscript, Ciaramelli et al. examined the decision-making behavior of 12 patients with vmPFC damage in a delay discounting task. The authors carried out two manipulations in this task: 1. They presented participants with small and large offers for both the immediate and delayed reward (magnitude manipulation), 2. They prefaced decisions with a cue prompting participants to vividly imagine an event in their future that was expected to occur at the same delay as the proposed larger offer (episodic future thinking (EFT) manipulation). Compared to age and education matched healthy controls, patients with vmPFC damage showed steeper discounting of delayed rewards, particularly when the amounts offered were large (reduced effect of magnitude). However, like controls, vmPFC damaged patients displayed shallower discounting of delayed rewards following the EFT manipulation. 

The manuscript is clear and concise in its presentation of the results, while still providing a detailed description of the behavior of these patients. This paper is also a good example of how pooling participants from multiple institutions can increase statistical power in a study of patients with focal brain damage targeting a fairly specific cognitive question. The positive results of the study mostly replicate previous findings. While the null result for the EFT manipulation is novel, the finding is hard to interpret. The authors state that they predicted that the EFT manipulation would not change discounting behavior in vmPFC damaged patients a priori despite the deficits of these patients in EFT in previous papers, which are also replicated here. However, I do not know why the authors would design their task in such a way to test for a null result. It is also not clear if this null result is observed for the reason proposed by the authors (that the EFT cues externally activate this process), or if this result is null for some other reason that is not accounted for here. As the authors do not provide a direct test for their hypothesized rationale for predicting this null result, the findings are hard to interpret. 

Overall, this manuscript makes a relatively modest contribution to our knowledge about the function of vmPFC during inter-temporal choice. It bolsters previous claims about how vmPFC damage impacts delay discounting and EFT, while not revealing new information about how vmPFC specifically contributes to the processes involved in these behaviors and why damage to this region impacts intertemporal choice in this way.

Reviewer #1 (Public Review):

The article Cortical magnification in human visual cortex parallels task performance around the visual field used the HCP public data and reported the asymmetries of cortical magnification in the human visual cortex. The 7T data source enabled the analysis to be conducted with high resolution, and the results can be compared with behavioral patterns. Given the HCP data coverage, the article also analyzed the radial asymmetries in different participant groups, including monozygotic twins, dizygotic twins and unrelated pairs, and showed the contribution of genetic factors in this functional asymmetry. In general, I think this article is a good example of utilizing public data for further analysis for new research question.

My main suggestion would be to expand discussions on the results of twins. To me that is the most interesting part of the present study, which contributed further from previous findings such as Silva et al., 2018.

Also, as the authors noted too, "behavioral pattern may vary with task". It would be helpful if the relationship between the present cortical magnification finding and behavioral results could be discussed with further details.

Reviewer #2 (Public Review):

In this study Benson and colleagues measure the radial anisotropy of human visual cortex. They relate the observed pattern of cortical magnification to radial variation in perceptual performance and retinal organization. The study is motivated by many years of careful measurement of visual performance that demonstrates overall improved perception along the horizontal as compared to vertical meridian, and relatively better performance in the inferior as compared to superior visual field along the vertical meridian. The authors fit 7T retinotopic mapping data from 181 people and measured the surface area of strips of cortex in V1/V2 corresponding to different polar angle representations. They find a clear over-representation of the horizontal as compared to vertical meridian, and a similar finding for the lower as compared to upper visual field on the vertical meridian. This pattern of results matches previous behavioral measurements. The radial variation in cortical representation is demonstrated to be shared in monozygotic twins. Finally, the authors build on prior modeling work to demonstrate that the radial asymmetries that they observe in their cortical measurements (as a function of eccentricity) are larger than is explained by a first order effect of asymmetries in the number of cones, or midget retinal ganglion cell receptive fields.

There is much to like here. The cortical measurements are lovely. The authors have performed anatomically informed, Bayesian smoothing of the HCP 7T retinotopy dataset and have extremely small error bars on the surface area of the visual regions. Comparisons with retinal anatomy and prior behavioral work strongly support the case that the cortical measures are best related to perceptual performance. My areas of critique and question are organized below, ordered generally from more general to smaller issues.

1) Representation of 45 degrees. The results demonstrate that 45{degree sign} angles are relatively under-represented on the cortical surface, but without a concomitant decline in perceptual performance (Figure 3). While a prior study demonstrated radial asymmetries in cortical magnification (Silva 2018), that prior study did not have the power and resolution to show the clear reduction in surface area around 45{degree sign} that is shown here. This result is one of the more novel findings of the current study, but is not discussed. I looked at the Barbot (2021) paper, and I gather that acuity was tested with a grating that was oriented at 45{degree sign}. Could this property of the stimulus interact with the radial orientation bias that has been shown in perception and cortical response (e.g., Sasaki 2006).

2) While the correlation in the MZ twins is impressive, I am not sure that it is an independent source of information. One would not want to conclude, for example, that there is a genetic influence specifically for radial asymmetry of the visual cortex. Instead, there may be genetic influences upon the general shape, folding, and functional organization of the cortex as a whole, of which the visual cortex is just one part. It would be informative, for example, if the correlation in MZ twins for visual cortex radial asymmetry is GREATER than the correlation that is observed for any other cortical property (Chen 2013). It would also be informative to examine perceptual data from these twin pairs, but I understand why this is not available.

3) I know that these authors have thought carefully about how cortical curvature might influence their measurements. There is the obvious confound that the horizontal meridian is represented in the depth of a sulcus, while the vertical meridian is represented close to the gyral crowns. I would appreciate some consideration in the methods or discussion of why cortical folding can't account for the current results.

4) The Silva 2018 paper included a more "fine scale" analysis of cortical magnification as a function of polar angle (Figure 4B). The error bars in this prior report are an order of magnitude larger than in the current measurements, but I would appreciate an evaluation of the degree to which the current measures agree with this prior work.

5) The cortical surface representation is described as an "amplification" of asymmetries that are present in the retina. Looking at Figure 5, however, it doesn't appear to me that a multiplicative scaling of the cone or midget RF functions would fit the cortical data. The cortical asymmetries are certainly larger, but they are of a different form with eccentricity. This might be worth acknowledging, and perhaps considering that perceptual measures as a function of eccentricity and polar angle could deepen the correspondence with the cortical data.

References:<br> Sasaki, Yuka, et al. "The radial bias: a different slant on visual orientation sensitivity in human and nonhuman primates." Neuron 51.5 (2006): 661-670.<br> Chen, Chi-Hua, et al. "Genetic topography of brain morphology." Proceedings of the National Academy of Sciences 110.42 (2013): 17089-17094.<br> Silva, Maria Fatima, et al. "Radial asymmetries in population receptive field size and cortical magnification factor in early visual cortex." NeuroImage 167 (2018): 41-52.

Reviewer #3 (Public Review):

Noah C. Benson and colleagues investigated the cortical magnification at a fine angular resolution around the visual field using using HCP multimodal imaging data. They report that asymmetries in the primary visual cortex map closely parallel asymmetries in behavior, are larger than asymmetries in retinal cell density and are correlated between twins. These data add in an interesting way to the ongoing discussion on the topic whether and how the visual field asymmetries are shaped by both the genetic and environmental factors, which are reflected in the cortical topography.

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

1) The statistical model on repeated measurements: in the present work, there are lots of repeated measurements recorded (e.g., Figure 1, across angular distance and meridian). It is a need of clear and comprehensive description on the statistical methods to be reported in the method part.

2) Measurement reliability: this is a fundamental concept of individual differences, which the present work is based on to assess the link between brain, behavior and genetics. The reliability levels of these measurements should be reported due to the importance of understanding the correlational outcomes. For example, In Figure 3, a surprisingly high correlation was reported (r = 0.96). How we interpret this correlation in terms of the psychometric theory of individual differences. Again, how this correlation was derived from such a setting on the repeated measurements.

3) ICC: should be non-negative. In Figure 4, the negative ICCs appeared for DZ twins for some polar angle widths. Please clarify the reason.

4) Credit HCP data use: Please visit https://www.humanconnectome.org/study/hcp-young-adult/document/hcp-citations

5) A systems-neuroscience perspective: These is an interesting way of discussing the present findings of the human vision system by looking them at the level of the global brain system (e.g., connectomics), for example, how these vision-related heritable features are related to or implicated for their connectome-level findings (https://pubmed.ncbi.nlm.nih.gov/26891986)?

Reviewer #1 (Public Review): 

This paper is generally well written, and it represents a lot of hard work. 

Key strengths: 

1) The design and methods for extraction of time-series of neuronal activity, corrections for motion artifacts, and correlations with locomotion; <br> 2) The direct evidence for the dramatic impact on population dynamics performed under different experimental conditions (moving versus paralyses); <br> 3) The conclusion that the simplest linear regression model predicts locomotion the best. 

Key weakness: 

The current paper seems to emphasize the following conclusions: two largely distinct and small neuron populations predict the two features of locomotion, and population-based prediction outperforms single neuron prediction. To me, scientifically, neither offers surprises nor reveals truly exciting new insights. It was perhaps too predictable from the perspective of the systems neuroscience, but too vague (lacking the necessary biological details for the groups of neurons) to be more informative for experimental neuroscience. The most informative new takeaway for me was that the simple linear model works the best in behavioral prediction, however I did not see insightful discussions on its potential implication on the property of the C. elegans neural network or the brain's locomotory presentation.

Reviewer #2 (Public Review): 

In the submitted manuscript, Hallinen et al. dissect how neural activity across a large population of C. elegans neurons gives rise to the animal's locomotion. First, they analyze single neurons in their population-level recordings and find that different cells have different "tunings" with respect to the animal's velocity and curvature. They also show that individual identified neurons (AVAL/R) display their well-known activity patterns in these brain-wide datasets. They then use ridge regression to predict velocity and curvature from single neurons, as well as the full set of neurons, and show that the prediction is better when the full population is used. They present exemplary data suggesting that different neurons predict different aspects of the animal's behavior (for example, forward/backward transition vs. high-frequency changes in forward velocity). They also estimate the number of neurons that are necessary to fully predict these behavioral variables by training a range of models with different #s of neurons. Finally, the authors perform recordings where animals are immobilized partway through the recording and show that the correlational structure of neural activity changes after immobilization. 

This paper uses state-of-the-art methods to address an interesting problem -- how the brain gives rise to behavior. As of now, there have been very few large-scale C. elegans brain recordings performed in freely-moving animals in order to address a biological problem (just a handful of papers primarily focused on methodology), so this work represents an important advance for the field. The analysis of single neuron "tunings" could be improved (or at least further unpacked) and concerns about noise levels in the data also impact some of the interpretations of these otherwise very interesting datasets.

Reviewer #3 (Public Review): 

A. Summary of what the authors were trying to achieve 

The authors seek to understand how whole-animal behavior is represented in the nervous system. They approach this problem utilizing high-speed volumetric calcium imaging in freely moving nematodes (C. elegans). In recording from a majority of neurons in the head, this approach is state-of-the art in C. elegans and, arguably, far beyond what is likely to be achieved in most other organisms in the foreseeable future. Imaging data are analyzed by training a linear decoder to predict the instantaneous locomotion velocity and body curvature from instantaneous neuronal activity at single neuron resolution. 

B. Major strengths and weaknesses of the methods and results 

The paper has numerous strengths: 

1) State-of-the art simultaneous imaging of brain-wide neuronal activity and unrestrained behavior. 

2) The overall approach has been published in two papers by this group and one from another group, but this is the first paper that actually takes the next logical step: connecting the recordings back to behavior. This is a major strength. 

3) Comparison of neuronal dynamics during locomotion and immobilization in the same worm. 

4) Rigorous data collection and modeling. 

The paper in its current form has a number of weaknesses: 

1) Several of the main findings of the paper seem rather obvious. (i) "We report that a neural population more accurately decodes locomotion than any single neuron (Abstract)". Similarly, "We conclude that neural population codes are important for understanding neural dynamics of behavior in moving animals." (ii) "Our measurements suggest that neural dynamics from immobilized animals may not entirely reflect the neural dynamics of locomotion." Consider rephrasing, as this sentence is almost a tautology: "...neural dynamics in the absence of locomotion may not entirely reflect the dynamics in the presence of locomotion (line 379)." Can these conclusions be rephrased, or put in a more significant context? 

2) The rationale for the decoding exercises seems underdeveloped. Figs. 3-6 are motivated by the question of whether "activity of the neural population might be more informative of the worm's locomotion than an individual neuron." It just seems obvious this will be the case. There might be a missed opportunity, here. Perhaps a stronger motivation would be to ask whether locomotion related signals can be found in the subset of neurons found in the head. The alternative hypothesis would be that head neurons alone are not sufficient, the implication being that the ventral cord and/or tail ganglia must be included. 

3) The logic of how decoding exercises are interpreted also seems underdeveloped: (i) Why isn't the finding of locomotion-related signals in the head a forgone conclusion? After all, the worm's head is literally "carving the furrow" that the rest of the body follows, leading to body curvatures that ought to be correlated with with neuronal activity in the head. Furthermore, a substantial fraction of head neurons are nose and neck muscle motor neurons. These contribute to overall thrust, which in the worm's fluidic regime is proportional to velocity. Thus, as stronger head motor neuron activation would generate more thrust, there a correlation with velocity is expected. (ii) What does it mean to say, "The distribution of weights assigned by the decoder provides information about how behavior is represented in the brain (p. 8)"? Who or what is reading this representation? Is the representation detected by the decoder necessarily in the same or similar language used by the worm's brain? If not, how are the decoder findings significant for understanding locomotion in the worm? (iii) It seems likely that the decoder picks up signals of neurons that causally regulate locomotion, but also signals that follow from it (e.g., efference copy, proprioception, re-entrant signals, etc.). Assuming this is true, again: how are the decoder findings significant for understanding locomotion in the worm? (iv) In what ways, if at all, is the decoder a model for worm locomotion? If it's not a model, how does it improve our understanding of locomotion, or our future ability to construct and informative model? 

4) The Discussion seems to miss key points: (i) What are the main limitations of the approach (paucity of identified neurons, inability of Ca imaging to report inhibition, etc)? (ii) Why are the limitations non-fatal? (iii) What are the broader impacts of the main conclusions? For example, what is this significance of the finding of locomotion representations in the C. elegans nervous system or, indeed, in any nervous system? How do the results illuminate neural mechanisms of behavior? 

C. Appraisal of whether the authors achieved their aims, and whether the results support their conclusions. 

The authors convincingly demonstrate that locomotion-related signals are present in their recordings. The effects are fairly robust. But if an implied aim was also to elucidate mechanisms of locomotion in C. elegans, this was not achieved. 

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

The authors have not made the case that their main findings are broadly significant. We learn what the linear decoder finds in the neuronal data - sustained and transient locomotion signals and distinct populations of velocity and curvature tuned neurons - but we do not learn what these properties of the decoder have to say about biological mechanisms. This problem is especially acute given: (i) the likelihood of neural correlations with behavior that are not functional representations of behavior and (ii) the absence of evidence that the decodable information is in fact used by the worm. We also learn that, as one would expect, immobilization alters the correlation structure of neural activity, but this finding has not been placed in a mechanistic context.

Reviewer #1 (Public Review): 

This manuscript shows cell to cell variability in the relative levels of Sox2 and Brachyury (Bra) expression by individual cells within the region of the epiblast containing axial progenitors (the progenitor zone, PZ). Accordingly, some cells express high Bra and low Sox2 levels, others high Sox2 and low Bra and a third group expressing equivalent levels of both transcription factors. They then show that by experimentally promoting high Sox2 expression cells enter neural tube (NT) fates, whereas high Bra brings cells in the progenitor zone to enter the presomitic mesoderm (PSM). The authors then complement these experiments with evaluation of cell movements within the PZ, NT and PSM to show that cells in the NT are much less motile than those in the PZ and PSM. These data led the authors to propose a fundamental role for Sox2/Bra heterogeneity to maintain a pool of resident progenitors and that it is the high cell motility promoted by high Bra levels what pushes cells to join the PSM, whereas high Sox2 levels inhibit cell movement forcing cells to take NT fates. To validate their hypothesis, the authors generated a mathematical model to show that those expression and motility characteristics can indeed lead to axial extension generating NT and PSM derivatives in the proper positions, while keeping a PZ at the posterior end. 

Some specific comments on the manuscript are specified below. 

1) Although the description of cells within the PZ containing different Sox2 and Bra expression ratios is more explicit and quantitative in the present manuscript, this has already been previously reported by different methods including immunofluorescence (e.g., Wymeersch et al, 2016). Similarly, that breaking the Sox2/Bra balance towards high Sox2 or Bra is an essential step to bring the progenitors towards NT or PSM fates has also been previously shown in different ways. These observations are, therefore, not totally new. The novel contribution of this paper is the authors' interpretation that "heterogeneity among a population of progenitor cells is fundamental to maintain a pool of resident progenitors". In this work, however, this conclusion is only supported by their mathematical simulation, as the experiments described in this manuscript are not aimed at homogenizing Sox2/Bra expression levels in the progenitor cells (meaning keeping the double positive feature) but, instead, forcing the progenitors to express Sox2 or Bra alone, which permits evaluation of differentiation routes rather than how to maintain the resident progenitor pool. Interestingly, their alternative mathematical model in which the relative Sox2/Bra levels follow an anterior-posterior gradient (which is actually a feature observed in the embryo) was also successful in producing an extending embryo. This model was not favored by the authors (but see my comment below). According to this model, the progenitor zone could be maintained by a cell pool containing equivalent Sox2/Bra levels; when this balance is broken cells eventually enter NT or PSM routes. Therefore, while expression heterogeneity can be observed in the PZ, I am not sure that the work shown in this manuscript is conclusive enough to claim an essential role of such heterogeneity to maintain the progenitor pool. 

2) The other main novelty of this manuscript is the idea that differences in cell motility derived from their Sox2 or Bra contents are a major force driving the generation of NT and PSM from the progenitors in the PZ. While there are clear differences between cell motility in the NT and the other two regions, the differences between what is observed in the PSM and PZ is not that high (actually, from the data presented it is not clear that such differences actually exist). However, independently of motility differences, there is no experimental evidence demonstrating that the essential driver of the cell fate choices is motility itself.  Differences in cell motility could be just one of the results of more fundamental (and causal) changes in cell characteristics triggered by Sox2 or Bra activity. Indeed, NT and PSM cells are different in many different ways, including adhesion properties, which are normally a major determinant of tissue morphogenesis. Cell motility could, therefore, be one of the factors but it is not clear that it plays the essential role proposed by the authors. (see also next comment). 

3) The authors developed a mathematical model to confirm their hypothesis that Sox2/Bra expression diversity combined with different motility of cells with high, low or intermediate relative levels of Sox2 and Bra expression are the key to guarantee proper axial elongation from the PZ. I am, however, not sure that the model, the way it was designed, actually proves their point. In particular, because it introduces an additional variable that might actually be the essential parameter for the success of the mathematical model: physical boundaries between NT and PSM cells, meaning that cells with high Sox2 or high Bra are unable to mix. As I commented above, this variable reflects a key biological property of the two tissues involved, one epithelial and the other mesenchymal in nature, which might be more relevant that the motility of the cells themselves (e.g. by different cell adhesion properties). How would a model that does not include such physical barriers work? Conversely, how would a model work in which only physical barriers are applied, using similar starting conditions: a prefigured central neural tube (Sox2 high), flanked at both sides by PSM (Brachyury high) and with the PZ (variable Sox2/Bra levels) just posterior to the neural tube? 

4) The authors generate two mathematical models, differing in whether they start with a random distribution of Sox2 and Bra expression throughout the PZ or with prefigured opposing Sox2 and Bra expression gradients, somehow resembling the image observed in the embryo. The two models generated structures resembling the elongating embryo, although with small differences in the extension process and the extension rate. After analyzing the behavior of those models, they concluded that the random model fits better with the expectations from the in vivo characteristics in the embryo. I am however not sure that I agree with the authors' interpretation. First, because the gradient model includes a natural characteristic observed in the embryo, which the random model does not. Second, because one of the deciding characteristics, namely the slower extension rate observed in the gradient model, does not necessarily make it worse than the random model, as it is not possible to properly determine which extension rate actually resembles more accurately axial extension in the embryo. Third, because the observation that in the gradient model the PZ undergoes fewer transient deformations and self-corrective behaviour is in my view an argument to favor, instead of to disfavor the gradient model, both because the final result is at least as good as the one obtained with the random model and it is actually not clear that in the embryo the PZ undergoes such clearly visible deformations and self-corrections during axial extension. In addition, the gradient model generates a "pure" PZ (just yellow cells) in the posterior end of the structure, while in the random model the PZ contains some islands of NT cells, which is not what is observed in the embryo. According to the last features, the gradient model seems better than the random model.

Reviewer #2 (Public Review): 

In this manuscript, Romanos et al show firstly that there is extensive cell-to-cell heterogeneity in the relative levels of Sox2 and Bra in the region containing progenitors for neural and paraxial mesoderm, gradually resolving towards high Bra/low Sox2 in the mesoderm or high Sox2/low Bra in emerging neurectoderm. They then show that overexpression of Sox2/morpholino-based inhibition of Bra or vice versa lead cells to favour neurectoderm or mesoderm respectively. Next they show that cells expressing high Bra are more motile than those expressing Sox2, and show using mathematical modelling that these behaviours can explain many aspects of the eventual segregation of Sox2-high neurectoderm and Bra-high mesoderm. 

This interesting and well-presented work leads to the elegant and novel hypothesis that random cell motility induced by Bra and inhibited by Sox2 are sufficient to explain the segregation of NMps towards mesoderm and neurectoderm respectively. The work will be of broad interest to developmental and mathematical biologists interested in the cell biological basis of self-organising cell behaviours. Nevertheless there are some concerns to address in order to solidify the claims in the manuscript. 

1) The section where Sox2 and Bra levels are manipulated (line 152 onwards) is somewhat under-analysed. Results are presented as supporting a model where the two proteins mutually repress each other and lead to segregation of neural (high Sox2) and mesodermal (high Bra) cells. However the data presented does not unequivocally support the claims in the manuscript and would require further clarification. 

2) The mathematical model may be an oversimplification of the role of these two genes in organising a balanced production of neurectoderm and mesoderm.

Reviewer #3 (Public Review): 

The manuscript by Romanos and colleagues examines how Sox2 and Brachyury control the behavior and cell fate of neuro-mesodermal progenitors (NMPs) in avian embryos. Using immunohistochemistry, the authors showed that the cells residing in the progenitor zone (PZ) display high variability in Sox2/Bra expression. Manipulation on the levels of the two transcription factors affected NMPs' choice to stay or exit the PZ and their future tissue contributions. This motivated the authors to employ an agent-based computational model and additional functional experiments to explore the importance of Sox2/Bra for cellular motility. The results led the authors to propose that (i) heterogeneity in Sox2/Bra ratio is important for the spatial organization of the PZ and its derivatives and that (ii) Sox2/Bra determine the fate of progenitor cells by controlling cellular movements. 

This is a technically sound report that combines single-cell analysis, in vivo functional experiments, and mathematical modeling to explore the link between cell motility and cell identity. While the model proposed by the authors is intriguing, I found that the study should provide evidence placing Sox2/Bra as primary regulators of cell motility in the context of the PZ. Given the extensively-studied role of these transcription factors in NMPs, it is challenging to decouple cellular behavior from cellular identity during tissue formation. The study would benefit from further demonstration that cell fate commitment is regulated by - and not a regulator of - cell migration of NMPs. 

Strengths and Weaknesses: 

- The idea that heterogeneity in cellular behaviors within a progenitor field may act as a driver of morphogenesis is interesting and nicely supported by the agent-based model. 

- One of the premises of the model (Fig 4) is that Sox2/Bra ratio determines how much cells move, but this is not clear from the in vivo experiments and seems speculative. A clear demonstration of correlation between Sox2/Bra ratio and cellular motility is necessary for proper support of the model. 

- The authors found that manipulation in the levels of the TFs results in changes in NMP motility, but it is not clear if this the cause or a consequence of commitment to a neural or mesodermal fate. Could Bra-High cell moving more because they have been specified to a mesodermal fate? Conversely, Sox2-High cells might migrate less since they get incorporated into the neural tube. Establishing the timing of cell fate commitment is necessary to resolve this issue 

- The study's impact and novelty depend on the demonstration that the primary function of Sox2/Bra in NMPs is to drive cell movement. This is not sufficiently explored in the study, and there are no proposed mechanisms for how Sox2/Bra modulate cellular behavior.

Reviewer #1 (Public Review):

In this work, Bakkeren et al study the ability of tissue lodged Salmonella to reseed the gut, initiate clonal expansion and serve as recipients for transconjugation of antibiotic resistance plasmids by gut luminal donor bacteria. The authors use i.p injection of Salmonella to induce tissue reservoir recipient cells. They then treat with streptomycin to clear the microbiota and introduce donors orally (either e.coli or Salmonella). The authors show that in this model, tissue resident recipients migrate to the gut, and produce transconjugates that then reseed the tissue. Using tagged strains and modelling, the authors suggest that recipient migration from the tissue is a rate-limiting step. The authors further confirm that these tissue reservoirs can serve for plasmid transfer in future reinfections of the gut. Finally, the authors suggest that vaccines and gut luminal e.coli harboring beta lactam resistance plasmids can protect tissue resident recipients for further transconjugation.

This work describes an important feature of within-host acquisition of antibiotic resistance. This is a follow-up to their recent publication (Bakkeren et al 2019), and complements their finding of persister cells in the tissues, to show that also chronic, tissue residing bacteria can provide plasmid tissue reservoirs. The experiments the authors performed are elegant and timely. However, their conclusions require further experimental evidence. Importantly, the authors should provide experimental support in a chronic model of infection. Furthermore, insights into the mechanism of tissue reseeding and possible effects on the intact microbiota are required.

Reviewer #2 (Public Review):

In the study "Pathogen invasion-dependent tissue reservoirs and plasmid-encoded antibiotic degradation boost plasmid spread in the gut", Bakkeren, Hardt and colleagues investigate the dynamics of antibiotic resistance-encoding plasmid acquisition in Salmonella Typhimurium, using mouse models of persistent Salmonella infection and antibiotic persistent Salmonella infection. Specifically, the authors initially show that tissue-resident Salmonella can re-seed the gut lumen, acquire antibiotic resistance plasmids through trans-conjugation, and subsequently re-invade the mesenteric lymph nodes following trans-conjugation. They go on to show that trans-conjugation can occur if the donor is either Salmonella or E. coli, and that trans-conjugation can occur both in the absence of antibiotic treatment (persistent/chronic infection model) or following antibiotic treatment (antibiotic persistent Salmonella infection model). The authors show that re-seeding of the gut lumen by recipient tissue-resident Salmonella is a rare event that is the rate-limiting step for trans-conjugant formation. They also show that, following trans-conjugation, recipient Salmonella can occasionally re-invade the mesenteric lymph nodes (MLNs). Using a mathematical model based on their experimental data, the authors predict that trans-conjugant formation and re-invasion of MLNs from the gut lumen are dependent on the carrying capacity of the recipient population in the gut, implying that colonisation resistance is likely an important factor in limiting plasmid spread dynamics. The authors go on to show that newly formed transconjugants that have re-invaded MLNs can act as both donors and recipients in further trans-conjugation events following antibiotic treatment and re-seeding of the gut lumen. This highlights that re-seeding of the gut lumen, trans-conjugation, and re-invasion of host tissue can occur cyclically. Finally, the authors show that ESBL resistance plasmids from luminal donors facilitate the survival and expansion of tissue-resident Salmonella recipients that re-seed the gut lumen during treatment with beta-lactam antibiotics, and that, in turn, this enables trans-conjugation of ESBL resistance plasmids to beta-lactam-susceptible recipient Salmonella during beta-lactam treatment.

The study is well done and focusses on a very timely and important subject (the acquisition and spread of antibiotic resistance plasmids) using an appropriate model system. I note however that this work is largely a validation and further expansion of the work this group has recently published (Bakkeren et al., Nature 2019; PMID: 31485077). The primary claims of the paper are supported by the data presented, with the following exception: I am not fully convinced that the authors have clearly shown that the second trans-conjugation event (described in Figure 3B, S7A-B) is necessarily between the initial trans-conjugant and the second recipient. Although unlikely, it does remain formally possible that the initial E coli donor acted as donor in this second trans-conjugation event.

Reviewer #3 (Public Review):

Previously, Bakkeren and colleagues (Nature, 2019) showed that 'persister' cells of Salmonella Typhimurium, that survive antibiotic treatment in host tissues, could act as a source of antibiotic resistance plasmids when they re-seed the gut. Here, the authors expand on this work to show that tissue-resident Salmonella can also act as a plasmid recipient when it re-seeds the gut, and can then essentially capture gut-resident plasmids into long-term storage when it re-invades tissues. These experiments are performed by sequentially infecting mice with different combinations of plasmid-free and plasmid-containing Salmonella and E. coli and looking for the emergence of transconjugants in different tissues over time. Using these data, the authors parameterise a mathematical model of infection, enabling them to infer likely rates of conjugation and bacterial migration between gut and tissues.

The concept is intriguing and extends our understanding of plasmid ecology and infection biology. The ability of plasmids to become acquired from the gut to the tissue reservoir has interesting implications for the maintenance and dissemination of plasmid-borne genes in microbial communities.

This work meaningfully extends the previous study, which showed that plasmids could emerge from persistent reservoirs into the gut, to show that plasmids can be captured from the gut into persistent reservoirs. The experiments are well-designed and generally suitable for testing the specific questions under consideration. The use of mathmatical models enables enhanced insight to be gained from the experimental data. In general, the data are clearly presented and interpreted.

The main scientific issue I have with the manuscript is that the experimental setup presented by the authors is very artificial, involving intraperitoneal inoculation of gut pathogens, antibiotic clearance of resident gut microbiota, and strong selection for plasmid carriage. The authors convincingly justify their choices in the manuscript, and I think that for a proof-of-principle study their decisions are appropriate. Still, I feel that if tissue 'storage' of plasmids indeed plays an important role in epidemiology and evolution, some predictions could be made about diversity of mobile genetic elements in tissues vs. gut lumen under natural conditions (for example), which, if not testable with existing genomic and metagenomic data within the scope of the current work, is an important subject of investigation in future studies.

A further issue with the authors' conceptual model that requires some attention: for plasmids to be 'stored' as 'a record' in tissues, it suggests that plasmids and bacteria do not change significantly in tissues over time. What evidence is there for tissue-lodged bacteria to undergo conjugation, or lose plasmids, or become displaced by newly-incoming populations? If plasmids can conjugate or displace one another in tissues, the role for reseeding into the gut becomes less dominant. There may also be implications for the mathematical model, if tissues can act as a source of transconjugants as well as recipients.

Reviewer #1 (Public Review):

In this manuscript, Huber et al. tested the function of three putative beta-glucosidases in the common cockchafer larva by silencing those genes using RNAi. Genetic modifications of TA-G synthesis in the common dandelion and TA-G deglycosylation in the herbivore allow to directly access the role of the herbivore digestive enzyme both in the plant defense sabotage and the host finding behavior of herbivore. The authors nicely combined a series of various experiments which are molecular biology, analytical chemistry, and bioassay. Based on the convincing approaches, the authors conclude that herbivore digestive enzyme (Mm_bGluc17) enhance herbivore performance and involve in the host finding behavior. I think the conclusions of this study are well supported by solid data and the overall manuscript is well-written. Particularly, the method sections are very informative that the readers can judge and replicate the experiments.

Reviewer #2 (Public Review):

The paper by Huber et al. analyzes the detoxification strategy of an insect herbivore, the cockchafer, against a prominent defensive compound, taraxinic acid glucopyranosyl ester (TA-G), of dandelions, one of the beetle larvae's preferred food plants. As the authors can convincingly show a beta glucosidase, a digesting enzyme in the herbivore's gut, acts as a detoxification enzyme and simultaneously seems to induce the beetle larvae to avoid plants with this defensive compound. The data presented cover the full range from physiological and chemical analytical data exploring the fate of the plant defense compound in the larvae's digestive system to transcriptomic analyses identifying the beetle's relevant beta glucosidase with their tissue and diet specific expression level to cell culture expression of those beta glucosidases and functional verification whether they are able to deglycosylate TAG. The authors thereby home in on one specific enzyme that has the strongest TAG cleaving activity and further demonstrate its effect by silencing it via RNAi. This knock down results in significantly reduced growth of larvae exposed to the toxin, on the other hand, the presence of the enzyme was necessary to elicit the TAG avoidance behavior previously reported for cockchafer larvae.

The manuscript flawlessly follows up on the observed detoxification ability of the beetle larvae from one organismic level to the next and provides an in depth analysis of the phenomenon. All analyses have been carefully performed, correctly analysed and fully support the conclusions drawn. The manuscript is well written and provides a well laid out case study on how digestive enzymes of an insect herbivore can be coopted to provide a specific adaptation to a preferred host plant and not only circumvent its main defense but also modulate the herbivores behavior.

Reviewer #3 (Public Review):

The manuscript describes the metabolic profile of the plant defensive glucoside, TA-G, in the root-feeding beetle larvae and the identification of an insect beta-glucosidase enzyme that hydrolyze TA-G. The gene expression and enzyme activity assays after RNAi further demonstrate that the enzyme is not only responsible for the detoxification, but also for the larval deterrent behavior. This study shows a novel and interesting involvement of an herbivore-derived hydrolyase in the deglucosylation, providing an important insight on how the below-ground herbivorous insect can cope with the root-producing toxin.

Reviewer #1 (Public Review):

The paper by Karim et al aims to explore the overlap (co-localization) of genetic variants associated with COVID-19 outcomes in COVID-19 Host Genetics Initiative (HGI) and protein expression in the blood of uninfected population controls reported by several studies. They use an approach of mendelian randomization (MR), considering both cis- and trans-effects of these genetic variants. In addition to several previously reported results, they report three relatively new results.

The strengths of this paper, compared to a previous report (PMID: 33633408) that uses the same public datasets is a more detailed analysis of both cis- and trans-eQTLs and co-localization-first approach. Having identified an association between rs8176719 within the ABO gene on chromosome 9 and CD209 encoded on chromosome 19, the authors experimentally tested the interaction between recombinant CD209 and SARS-CoV-2 spike protein, suggesting it as a potential viral receptor.

The limitations of this paper include significant overlap with already reported results, limited additional analyses and functional inferences.

Overall, this paper, together with previously reported results, indicates that genetic variants associated with several COVID-19 outcomes may have functional effects on proteins already in uninfected population controls. This knowledge can help identify relevant therapeutic approaches targeting proteins and pathways affected by the associated genetic variants.

Reviewer #2 (Public Review):

Karim et al. take an analytical approach using data from previously published proteomic GWAS along with publicly available COVID-19 GWAS data to identify host proteins that may influence COVID-19 outcomes. The findings suggest that the mechanism underlying the previously identified association of the ABO gene with COVID-19 susceptibility and severity might involve regulation of another gene, CD209. Additional proteins that may regulate COVID-19 outcomes are also reported (e.g. OAS1, THBS3 and FAS).

Although I cannot fully evaluate analytical methods used, the study seems to be well-designed and comprehensive.

Reviewer #3 (Public Review):

These studies test the effect of stimulation of the contralateral somatosensory cortex on recovery, evoked responses, functional interconnectivity and gene expression in a somatosensory cortex stroke. Using transgenic mice with ChR2 in excitatory neurons, these neurons are stimulated in somatosensory cortex from days 1 after stroke to 4 weeks. This stimulation is fairly brief: 3min/day. Mice then received behavioral analysis, electrical forepaw stimulation and optical intrinsic signal mapping, and resting state MRI. The core finding is that this ChR2 stimulation of excitatory neurons in contralateral somatosensory cortex impairs recovery, evoked activity and interconnectivity of contralateral (to the stimulation, ipsilateral to the stroke) cortex in this localized stroke model. This is a surprising result, and resonates with some clinical findings, and a robust clinical discussion, on the role of the contralateral cortex in recovery.

This manuscript addresses several important topics. The issue of brain stimulation and alterations in brain activity that the studies explore are also part of human brain stimulation protocols, and pre-clinical studies. The finding that contralateral stimulation inhibits recovery and functional circuit remapping is an important one. The rsMRI analysis is sophisticated.

Concerns:

1. The studies in the manuscript utilize brain stimulation, and use it as a goal of modeling behavioral limb use. In particular, the optogenetic stimulation protocol of primary somatosensory forelimb cortex is said to mimic overuse of the limb. It is not clear how stimulation of a subset of neurons in somatosensory cortex mimics limb overuse. Further, this stimulation is, in totality, very brief. Limb overuse does not appear to align with this brain stimulation protocol.

2. The parameters of the stimulation are set and not altered in these studies. They were chosen based on Cheng et al (ref 25). This is interesting because in this publication, the optical stimulation parameters, delivered into peri-stroke cortex, produced recovery. Would different stimulation parameters have a different outcome on recovery or functional connectivity? Or is this contralateral stimulation site the main determinant of the negative effect on these two? The stimulation setup is very different in this Cheng et al study, apparently to the present study. However, this is not clear as the actual stimulation is not described. In Cheng et al., the stimulation was specifically to layer V pyramidal neurons with an optrode in Thy1-ChR2 mice. A cranial window is described in the methods section, but this appears to be for imaging. How were the ChR2 neurons stimulated in this present study?

3. Relative to the last issue in #2, if the stimulation was done with an implanted optrode, what was done with control: the -stim condition. Was this an implanted optrode but not activated? Or, if stimulation was done with an LCD on the skull or a window, was this done in -stim?

4. There is substantial and ever-deeper analysis of the rsMRI data (Figs. 4-6). This is all supportive of the overall core finding, that contralateral stimulation of neurons with ChR2 in this protocol impairs recovery. But each successive level of rsMRI analysis does not really add a new amount of evidence-this array of figures is not a new or independent set of data.

5. The gene expression data is to be expected. Stimulation of the brain in almost any context alters the expression of genes.

Minor points:

- Was the behavior and the functional imaging done while the brain was being stimulated?

- It would be useful to understand what is being stimulated. The stimulation method is not described. Is an entire cortical width of tissue stimulated, and this is what is feeding back onto the contralateral cortex? Or is this stimulation mostly affecting excitatory (CaMKII+) cells in upper or lower layers? This will be important to be able to compare to the Chen et al study that gave rise to the stimulation approach here. This gets to the issue of the circuitry that is important in recovery, or in inhibiting recover. One might answer this question by doing the stimulation and staining tissue for immediate early gene activation, to see the circuits with evoked activity. Also, the techniques used in this study could be applied with OIS or rsMRI during stimulation, to determine the circuits that are activated.

- Also, it is possible that contralateral stimulation is impairing recovery, not through an effect on the contralateral cortex (the site of the stroke), but on descending projections, or theoretically even through evoking activity or subclinical movement of the contralateral limb (ipsilateral to the stroke). By more carefully mapping the distribution of the activity of the stimulated brain region, and what exactly is being stimulated, these issues can be explored.

Reviewer #1 (Public Review):

Bice et al. present new work using an optogenetics-based stimulation to test how this affects stroke recovery in mice. Namely, can they determine if contralateral stimulation of S1 would enhance or hinder recovery after a stroke? The study provides interesting evidence that this stimulation may be harmful, and not helpful. They found that contralesional optogenetic-based excitation suppressed perilesional S1FP remapping, and this caused abnormal patterns of evoked activity in the unaffected limb. They applied a network analysis framework and found that stimulation prevented the restoration of resting-state functional connectivity within the S1FP network, and resulted in limb-use asymmetry in the mice. I think it's an important finding. My suggestions for improvement revolve around quantitative analysis of the behavior, but the experiments are otherwise convincing and important.

However, the behavioral readout is not well documented and, to me, is a key readout for the main claims in the paper. For example, in the methods; "The laser power ranged between 0.2mW - 1mW and was set to a level just below that which elicited overt behavioral output (e.g. forepaw or whisker motor movements in sync with stimuli)." for example, how is this measured? By eye? Quantitatively with tracking approaches? Also, the cylinder test I have the same concern - it notes there is only 1 blinded scorer, but how consistent is this person? Why not use a machine vision approach (if the videos are saved, I strongly suggest this is quantified differently). If not, they should add this to the limitations section in discussion.

Other comments - Data and paper presentation:

- Figure 1A is misleading; it appears as if optogenetic stimulation is constant (which indeed would be detrimental to the tissue). Also, the atlas map overlaps color-wise with conditions; at a glance it looks like the posterior cortex might be stimulated; consider making greyscale?

Reviewer #2 (Public Review):

These studies test the effect of stimulation of the contralateral somatosensory cortex on recovery, evoked responses, functional interconnectivity and gene expression in a somatosensory cortex stroke. Using transgenic mice with ChR2 in excitatory neurons, these neurons are stimulated in somatosensory cortex from days 1 after stroke to 4 weeks. This stimulation is fairly brief: 3min/day. Mice then received behavioral analysis, electrical forepaw stimulation and optical intrinsic signal mapping, and resting state MRI. The core finding is that this ChR2 stimulation of excitatory neurons in contralateral somatosensory cortex impairs recovery, evoked activity and interconnectivity of contralateral (to the stimulation, ipsilateral to the stroke) cortex in this localized stroke model. This is a surprising result, and resonates with some clinical findings, and a robust clinical discussion, on the role of the contralateral cortex in recovery.

This manuscript addresses several important topics. The issue of brain stimulation and alterations in brain activity that the studies explore are also part of human brain stimulation protocols, and pre-clinical studies. The finding that contralateral stimulation inhibits recovery and functional circuit remapping is an important one. The rsMRI analysis is sophisticated.

Concerns:

1. The studies in the manuscript utilize brain stimulation, and use it as a goal of modeling behavioral limb use. In particular, the optogenetic stimulation protocol of primary somatosensory forelimb cortex is said to mimic overuse of the limb. It is not clear how stimulation of a subset of neurons in somatosensory cortex mimics limb overuse. Further, this stimulation is, in totality, very brief. Limb overuse does not appear to align with this brain stimulation protocol.

2. The parameters of the stimulation are set and not altered in these studies. They were chosen based on Cheng et al (ref 25). This is interesting because in this publication, the optical stimulation parameters, delivered into peri-stroke cortex, produced recovery. Would different stimulation parameters have a different outcome on recovery or functional connectivity? Or is this contralateral stimulation site the main determinant of the negative effect on these two? The stimulation setup is very different in this Cheng et al study, apparently to the present study. However, this is not clear as the actual stimulation is not described. In Cheng et al., the stimulation was specifically to layer V pyramidal neurons with an optrode in Thy1-ChR2 mice. A cranial window is described in the methods section, but this appears to be for imaging. How were the ChR2 neurons stimulated in this present study?

3. Relative to the last issue in #2, if the stimulation was done with an implanted optrode, what was done with control: the -stim condition. Was this an implanted optrode but not activated? Or, if stimulation was done with an LCD on the skull or a window, was this done in -stim?

4. There is substantial and ever-deeper analysis of the rsMRI data (Figs. 4-6). This is all supportive of the overall core finding, that contralateral stimulation of neurons with ChR2 in this protocol impairs recovery. But each successive level of rsMRI analysis does not really add a new amount of evidence-this array of figures is not a new or independent set of data.

5. The gene expression data is to be expected. Stimulation of the brain in almost any context alters the expression of genes.

Minor points:

- Was the behavior and the functional imaging done while the brain was being stimulated?

- It would be useful to understand what is being stimulated. The stimulation method is not described. Is an entire cortical width of tissue stimulated, and this is what is feeding back onto the contralateral cortex? Or is this stimulation mostly affecting excitatory (CaMKII+) cells in upper or lower layers? This will be important to be able to compare to the Chen et al study that gave rise to the stimulation approach here. This gets to the issue of the circuitry that is important in recovery, or in inhibiting recover. One might answer this question by doing the stimulation and staining tissue for immediate early gene activation, to see the circuits with evoked activity. Also, the techniques used in this study could be applied with OIS or rsMRI during stimulation, to determine the circuits that are activated.

- Also, it is possible that contralateral stimulation is impairing recovery, not through an effect on the contralateral cortex (the site of the stroke), but on descending projections, or theoretically even through evoking activity or subclinical movement of the contralateral limb (ipsilateral to the stroke). By more carefully mapping the distribution of the activity of the stimulated brain region, and what exactly is being stimulated, these issues can be explored.

Reviewer #1 (Public Review):

The authors created a new GPR161 mutant mouse (Gpr161mut/mut) in which GPR161 does not localize to the primary cilium but is still cAMP signaling competent based on an over-expression assay in 293T cells. Through a detailed analysis of the Gpr161mut/mut mouse and its comparison to a previously generated Gpr161 knockout mouse (Gpr161ko/ko), the authors try to discriminate the ciliary and non-ciliary roles of GPR161. The current prevailing model is that GPR161 (localized to the primary cilium in the absence of Hh pathway activation) is constitutively active and elevates cAMP levels within the primary cilium. Elevated ciliary cAMP then activates ciliary (or ciliary adjacent) PKA, driving the processing of bifunctional GLI proteins into transcriptional repressors (GLIR). According to this model, the ciliary pool of GPR161 is critical for suppressing Hh signaling activity, and one would predict that the Gpr161mut/mut embryos would look identical to the Gpr161ko/ko embryos. However, this was not the case. Across multiple developmental tissues, the Gpr161mut/mut phenotype is less severe than the complete knockout, suggesting a role for non-ciliary GPR161 in suppressing Hh signaling activity. The observations made in this paper are interesting, but the data fails to make a clear distinction between the ciliary and non-ciliary roles of GPR161.

Strengths:

1. The loss of ciliary GPR161 has a more robust phenotype in specific tissues (i.e., the limbs and face). As a result, the limb data (in Figure 6) and craniofacial data (in Figure 7) are well presented and clear. In these figures, the authors directly compare and highlight differences between primarily two genotypes (wt and Gpr161mut1/mut1 embryos) and quantify the changes (digit number and distance between nasal pits). Overall, these two figures support the existing GPR161 model, showcasing that a loss of ciliary GPR161 results in a tissue-specific loss of GLI3R (Figure 6D) and consequently the development of additional digits (Figure 6E) and craniofacial defects (Figure 7D and 7E).

Weaknesses:

1. There is no data in the paper showing that Gli3 repressor function is affected preferentially compared to Gli Activator function. In Figure 4C, Gli3 FL/R ratios are not different between wt/wt and mut/mut embryos. The data can be explained by the fact that the mutant Gpr161 is a partial loss of function allele and the resultant weaker phenotypes (compared to the full KO) show some tissue specificity. Linking this allele to a specific biochemical mechanism is not justified by the data.

2. The authors use an endpoint assay based on overexpression in 293T cells to claim that cAMP production is unaffected by the Gpr161mut allele. However, weak effects (very likely given the weak phenotypes) may not be evident this assay. We also do not know if the mutant allele is defective in some other biochemical function or in localization to other places in the cell. One way to address this is to measure ciliary and extraciliary cAMP in their knock-in cells. In Gpr161mut1/mut1 cells, is ciliary cAMP reduced to levels comparable to Gpr161ko/ko cells? Is extraciliary cAMP unchanged compared to WT cells? Or, is cAMP able to diffuse into the cilia from GPR161mut1 localized to vesicles at the ciliary base (Figure 1B)? Many of the conclusions made in the paper equate a loss of ciliary GPR161 to a loss of ciliary cAMP, but this loss of ciliary cAMP is not definitively shown in the paper.

3. Compared to Figures 6 and 7, the data presented in Figures 3 and 5 are very confusing and difficult to interpret. On the one hand, this is understandable, the Gpr161mut/mut phenotypes are complex, and some tissues (like the developing spinal cord) are more resistant to change due to a loss of GliR. On the other hand, the data collected from the numerous genotypes analyzed could be easier to interpret by (i) providing a penetrance of the phenotypes and (ii) quantifying the phenotypes. Below are a few examples of data that could be improved with quantifications:

— In Figure 3, the authors are trying to convey that the Gpr161mut allele is partially functional and produces a milder phenotype than the Gpr161ko allele. However, the Gpr161ko/ko, Gpr161mut/ko, and Gpr161mut/mut phenotypes showcased in the figure all look quite severe, and it is difficult to appreciate the differences in the defects fully. An accompanying table summarizing the phenotypes and their penetrance in the affected genotypes would help to convey this point.

— In Table 1, the authors note that the Gpr161mut1/mut1 mouse is embryonic lethal by e14.5, but the analysis in Table 1 appears to be incomplete. In the table titled "breeding between Gpr161 mut1/+ parents," the authors indicate that they only assessed one litter of e14.5 and e15.5 embryos. Oddly, the authors note that additional litters were collected, but the embryos were not genotyped because the embryos exhibited no phenotypes. The absence of phenotypes could be due to an absence of viable Gpr161mut1/mut1 embryos; however, the embryos need to be genotyped and a chi-square analysis conducted to verify this. Death can be a measure of phenotype severity, but I think it is important to surmise why the embryos are dying. It is unclear whether the embryos are dying due to the heart defects mentioned in the discussion. If the embryos are dying due to the heart defect, then it would be important to know whether the heart defects are more severe in the Gpr161ko/ko embryos.

— In Figure 5, quantifying the progenitor domains would greatly assist in discerning differences between the various genotypes. For example, a quantification would help readers assess differences in NKX6.1 across the various genotypes. On an unrelated note, the PAX7 staining of the Gpr161mut1/ko spinal cord looks very strange because the line adjacent to the image does not accurately represent the dorsal-ventral patterning of PAX7 seen in the image. This image would need to be replaced.

Reviewer #2 (Public Review):

The premise of the entire study is predicated on GPR161mut1 failing to target to cilia and being WT in every other aspect. The Gs coupling of GPR161mut1 is examined. The ciliary localization of GPR161mut1 is carefully assessed by conducting staining not just in WT cells but also in INPP5E cells where GPR161 ciliary levels are known to be elevated. Another prediction is that GPR161mut1 is found in an intermediate biosynthetic compartment. Some insights into the compartment where GPR161mut1 is found would help interpret the phenotype of the GPR161mut1 animals. It would be important to know whether the GPR161mut1 mimics a pre-cilia targeted GPR161 (say at the plasma membrane) or whether it mimics a post-ciliary exit state (say recycling endosomes). In the past few years, work from the von Zastrow lab and others has shown that GPCRs keep activating their downstream partners after endocytosis from the plasma membrane. If GPR161mut1 were to mimic the post-ciliary exit state of GPR161, it may assume some of the signaling functions of ciliary GPR161.

A second point that the authors may wish to address is whether GPR161mut1 may fail to enrich in cilia because it is hyperactive and undergoes constitutive exit from cilia. The hypothesis here is that GPR161mut1 couples to beta arrestin better than WT GPR161. Blocking GPR161mut1 exit via depletion of beta arrestin or BBSome is a simple way to test this hypothesis.

Finally, it would be good to learn about the levels of expression of GPR161mut1 compared to WT GPR161 using immunoblotting. If GPR161mut1 were to be expressed at much higher levels than WT GPR161, it may compensate for its lack of ciliary localization by elevated total cellular activity.

Reviewer #1 (Public Review):

This manuscript is an interesting extension and major follow-up on a previous paper by some of the same authors, currently published as a preprint (Reference as listed by the authors in the current manuscript: DELLA-FLORA NUNES, G., WILSON, E. R., MARZIALI, L. N., HURLEY, E., SILVESTRI, N., HE, B., O'MALLEY, B. W., BEIROWSKI, B., POITELON, Y., WRABETZ, L. & FELTRI, M. L. 2020. Prohibitin 1 is essential to preserve mitochondria and myelin integrity in Schwann cells. Available at Research Square, Preprint (Version 1)).

The current manuscript uses mainly a mouse model lacking Prohibitin 1 specifically in Schwann cells to elucidate the mechanistic connections between mitochondrial dysfunction and demyelination (much of the basic analyses of these mice is described in the preprint mentioned above). Employing an elaborate combination of mouse genetics, pharmacological interventions and cell culture experiments, the authors provide evidence that the mTORC1 and JUN pathways are involved. Furthermore, individual contributions of these pathways have been examined.

The major strengths of the manuscript lay in the thorough genetic approaches in vivo and the extensive data analysis. Furthermore, the presented data are sound and the approaches suitable.

Reviewer #2 (Public Review):

Here the authors genetically perturb mitochondria in Schwann cells (SCs) by conditional knockout of Phb1 in mice. The work aims to address mechanisms by which mitochondrial dysfunction in SCs affects myelin maintenance. Prohibitin 1 (and prohibitin 2) are ubiquitously expressed proteins, found in the cytosol, nucleus and mitochondria, that play roles in oxidative phosphorylation, mitochondrial biogenesis, the unfolded protein response and mitochondrial dynamics, amongst others. The authors find that conditional knockout of Phb1 leads to progressive failure of myelin maintenance (demyelination) and secondary axon degeneration; demonstrated convincingly using electron microscopy of sciatic nerve. To probe the pathological mechanisms, the authors examine candidate signalling pathways using western blotting of whole peripheral nerve lysates. Quantification of their high quality blots show increased steady state levels of JUN and increased levels of total and phosphorylated TORC1 targets, S6 and 4E-BP1. The last could be ameliorated slightly by pharmacologically blocking the intracellular stress response (ISR), in vivo, suggesting the ISR partially contributes to activation of 4E-BP1. In contrast, levels of AKT/p-AKT and mTOR/p-mTOR in whole nerve lysates are unchanged. Importantly, others have shown that very highly elevated levels of JUN lead to a hypomyelination pathology in vivo.

To confirm that primary mitochondrial dysfunction directly activate these pathways, the authors demonstrate, using one of each of three compounds, that pharmacologically interfering with mitochondrial function in primary Schwann cells grown in high glucose medium, can activate mTORC1 and pathways involved in cell stress; 7 days' intervention being more potent than 24 h. Notably, levels of JUN were significantly decreased after 7 days in the presence of two of these factors, which the authors suggest might reflect already elevated levels of JUN in vitro.

Comment: Possibly, replacing glucose with pyruvate would have been appropriate here, to prevent the cells relying on glycolysis for ATP synthesis, and might have resulted in response more compatible with the in vivo observations.

As western blotting does not provide spatial resolution, the authors turned to histological evaluation to show that mitochondrial damage, assessed using a fluorescent reporter, was significantly associated with JUN expression in the SC nucleus in teased fibres from the conditional mutant. However, they found no similar correlation for mTORC1 activation. This, despite that p-S6 was detected by immunostaining in the conditional mutant fibres.

Comment: This histological evaluation raises the possibility that JUN and S6 are each activated in different fibres, or at different time points in the same fibres, despite that western blotting shows both are simultaneously elevated. In this respect, the majority of JUN positive fibres contained myelin ovoids, whereas the association was more tenuous for p-S6 staining.

Next, the authors demonstrate convincingly by western blotting that steady state levels of several targets of JUN are up- or down-regulated in nerve lysates from conditional knockout mice, including myelin genes and factors involved in autophagy. They then genetically knockout or deplete JUN in SCs in conditional Phb1 mice, leading to partially reduced levels of p-S6 and p-4E-BP1, but not total S6 or 4E-BP1. The authors interpret this to suggest the possibility that mTORC1 is activated downstream of JUN activation.

Comment: However, in the histological analyses described above, p-S6 staining did not correlate with mitochondrial disruption, which would be expected to be the case if (i) JUN activation is secondary to mitochondrial disruption, as suggested (Fig 3A-C) and (ii) mTORC1 is activated downstream of JUN.

Appropriately, the authors show that JUN deletion per se does not affect p-S6 and p-4E-BP1 levels, suggesting that JUN depletion in the conditional Phb1 mutant works through lowering the JUN elevation. Notwithstanding that depletion of JUN led to a decrease in the numbers of demyelinated axons, it did not rescue the numbers of myelinated axons or the neuropathy phenotype, possibly because of unrelated primary effects.

Finally and most importantly, the authors find that pharmacological inhibition of mTORC1 in vivo restores myelin maintenance in conditional Phb1 knockout mice, and improves conduction velocity, whilst also reducing levels of JUN (albeit not significantly) and ISR pathway components.

The authors convincingly demonstrate that physical mitochondrial dysfunction correlates with upregulation of JUN in the SC nucleus. However, the evidence that activation of JUN is causally related to demyelination is tenuous and is somewhat difficult to prove with the available models. In contrast, the evidence that the mTORC1 pathway is causally involved in demyelination is strong. The suggestion that mTORC1 is activated downstream of JUN is not well supported by the data, based on the histological observations.

The work has wider relevance for mitochondrial dysfunction in other disorders.

Reviewer #3 (Public review):

This manuscript was built on their recent observation that Schwann cell (SC)-specific loss of the mitochondrial protein Prohibitin-1 results in a rapid, progressive demyelinating peripheral neuropathy in mice associated with mitochondrial dysfunction. Although several mechanisms have been well-studied in SCs, the potential novelty here is establishing those pathways as downstream effectors of mitochondrial dysfunction in SCs. The authors provide a comprehensive evaluation of these pathways following the loss of SC Prophibitin-1 and identify JUN and mTORC1 as potential mediators of myelin disruption. This manuscript includes a substantial amount of data. However, some data are not directly related to the primary mechanistic conclusions. In addition, the manuscript relies heavily on descriptive, rather than mechanistic, data regarding the roles for JUN and mTORC1. Specific issues to be addressed are listed below:

1) Figure 1: The authors suggest that increased JUN expression and mTORC1 activation are associated with the demyelinating in Phb1-SCKO mice with "peaking around P40 - P60" (Line 82). However, it appears the most profound effects on number of myelinated and demyelinated axons were observed at P90. Interestingly, immunoblots for JUN and mTORC1 targets suggest that increases in these signaling pathways are much greater at P20 and P40 when compared to P90. This may suggest that JUN and mTORC1 are important for early demyelination, but other mediators play a more prominent role in chronic changes. It would be nice to have data from a time point between P40 and P90 to further understand the time course of JUN and mTORC1 changes. If not, the authors should discuss these possibilities in further detail.

2) Figure 4: Since these are teased nerve fibers, not adjacent sections, please describe the detailed methods for immunofluorescence detection of DAPI and protein targets (JUN, P0, MBP, p-S6).

3) For Figure 2 and Figure 3, the authors state "mTORC1 and JUN may be activated in different stages of the SC response to mitochondrial damage, with mTORC1 preceding JUN temporally" (Lines 293 - 294). However, the data presented here are somewhat confusing for this conclusion. The immunoblots provided in Figure 1 suggest a similar time course for both JUN and mTORC1 activation after Phb1 loss in SCs at both P20 and P40. However, in Figure 3, teased nerve from Phb1-SCKO at P40 shows reduced JUN but not p-56 expression. The authors may consider repeating the PhAM-DAPI-JUN/p-S6 studies at the P20 and P90 time points to clarify this issue.

4) Figure 7: Significant recovery of the demyelinating phenotype and nerve conduction velocity were noted after blockade of the mTORC1 pathway using rapamycin in Phb1-SCKO mice. However, this did not result in recovery of CMAP or overall functional improvement using the Rotarod assay. Given that demyelination was reversed, does this suggest that an important trophic function of SC mitochondria for associated axons is disrupted in Phb1-SCKO mice? Alternatively, could rapamycin delivery at P20 be too late to rescue degenerating axons, leading to incomplete functional recovery? The authors should discuss these possibilities in further detail.

Reviewer #1 (Public Review):

This study tests the function of the gap junction protein, Gjd2b, in early stages of synaptogenesis in larval Zebrafish cerebellar Purkinje neurons. It uses morpholino-mediated knockdown of Gjd2b and talen-mediated knockout of Gjd2b, both of which resulted in decreased mEPSP frequency and mEPSPs with faster decay kinetics compared to controls. The decreased mEPSP frequency suggests there are fewer synapses. In EM analyses, comparing synapse density and maturation in the cerebellar molecular layer in WT and Gjd2b KO animals, the authors find the KO animals have decreased synaptic density, but that the maturation index is unaffected, suggesting that Gjd2b-/- animals form fewer synapses, but once synaptogenesis is initiated, maturation occurs normally.

In vivo time-lapse imaging of PC dendritic arbors was used to analyze effects of Gjd2b on arbor structure. This beautiful series of experiments demonstrates that Gjd2b-/- decreases dendritic arbor growth by modifying branch extensions, but not branch retractions. Further analysis shows that dendritic branches with Gjd2b puncta extend more than branches without puncta. This level of cellular mechanistic resolution provides considerable insight into the processes by which electrical junctions influence synapse formation and dendritic arbor growth. The authors attempt to identify cells that are coupled to PCs using dye labeling, but relatively few cells are dye labeled. Most dye-labeled cells are not PCs and can't be identified. Nevertheless, PC expression of functional Gjd2b is sufficient to rescue dendritic arbor growth defects seen in gjd2b-/- animals, suggesting that heterotypic gap junctions between PC and non-PCs regulate dendritic arbor growth, and furthermore that Gjd2b has cell autonomous effects on arbor development. Finally, it appears that gjd2b-/- animals have increased levels of camk2 transcripts, and that inhibiting CaMKII activity rescues the effect of gjd2b knockdown on dendritic arbor development.

The study provides convincing evidence that gap junctions are important for glutamatergic synaptogenesis and dendritic arbor development, but how this happens is less clear. As I was reading the paper, I thought the data support the synaptotrophic hypothesis, which states that synapse formation facilitates dendritic arbor development, and that they added an essential early step that gap junctions are required for development of glutamatergic synapses. This would suggest that the sequence of local events is: gap junction formation -> synaptogenesis->branch extension, repeat. The authors suggest an alternate sequence: gap junction formation -> branch extension-> synaptogenesis, repeat. I don't think the current data allow us to choose one or the other of these options and the implied causality, but the study clearly demonstrates a role for gap junctions in the process, which was the goal of the study. Given this ambiguity, the discussion should be modified to accommodate both interpretations, or to explain why one interpretation is favored over the other.

The implied mechanistic link between camk2 transcript expression and pharmacological inhibition of CaMKII enzymatic activity on dendritic arbor growth is not convincing to me. It is clear that the transcript observation is unexpected and suggests that somehow interfering with gjd2b affects camk2 transcript expression. Perhaps other synaptic proteins are affected as well. This point would be worth commenting on. But transcript level does not necessarily correlate with protein level or function, particularly for a calcium activated kinase, which is itself tightly regulated in terms of protein expression and function by multiple mechanisms. The main issue concerns causality. The authors state that the gjd2b regulates glutamatergic synaptogenesis by reducing CaMKII levels. The authors do not provide evidence for this statement of cause and effect.

Reviewer #2 (Public Review):

Summary:

The paper by Seetharaman, Yadav et al. establishes zebrafish as a new model system to investigate the role of gap junctions as mediators of glutamatergic synapse formation and dendrite elaboration in Purkinje neurons. After performing an initial set of experiments with morpholinos to knockdown the gap junction protein Gjd2b, the authors invest a substantial amount of effort to establish a mutant line where the gap junction protein Gjd2b is knocked out. Using electrophysiology and electron microscopy in mutant fish, the authors find reduced numbers of AMPAR synapses though only the electrophysiology data are unequivocal in attributing the reduced synapse number to Purkinje neurons. The authors then investigate dendritic development of Purkinje neurons in the mutant fish and find that mutant Purkinje neurons have significantly shorter dendrites likely because of the reduced elongation rate of dendrites. The authors then find that the dendritic structure of mutant neurons can be rescued by expressing functional Gjd2b in individual neurons in knockout fish. Finally, the authors find that CaMKII levels are elevated in mutant neurons and blocking CaMKII activity restores dendritic arbors.

Strengths:

1) The sheer amount of work that has gone into this paper is impressive. Each technique is tedious, time consuming and labor intensive so it is quite impressive that the authors have a substantial number of Ns for their experiments. All the experiments and analysis have been performed carefully and the data is of high quality.

2) The authors have done a thorough job of generating and characterizing Gjd2b mutant fish which will be useful for the entire zebrafish neuroscience community.

Weaknesses:

1) Overall, while the experiments and data are clearly presented, several experimental results have significant technical limitations, which in turn open them up to alternative explanations which cannot be easily ruled out based on current data.

2) Knocking out gap-junctions will affect spontaneous activity in early development which is propagated via gap junctions. Given that spontaneous activity is likely dampened in Gjd2b knockout fish, a substantial concern is that effects that the authors attribute to the absence of gap junction mediated activity could equally likely be a consequence of homeostatic changes in synaptic input. One possible way to alleviate this issue is to perform transplant experiments from mutant fish to wild type fish which ensures that the rest of the circuit is unaffected.

3) Rescuing Gjd2b is an interesting experiment, but its unclear how functional electrical synapses form by expressing the functional protein in only one neuron.

4) The authors suggest that dendritic growth is reduced because of the absence of gap junctions, but its unclear whether the reduced dendritic growth is simply a consequence of fewer excitatory synapses, and thus a downstream consequence of the absence of gap junctions rather than specific information being transmitted through gap junctions.

Reviewer #3 (Public Review):

The authors tried to address the question of whether the gap-junction protein Gjd2b was involved in the development of cerebellar Purkinje cells. Using a number of complementary approaches, including electrophysiology, EM, and morphological analysis, they show that excitatory synapses are affected when it is reduced in two different ways, and that dendritic structure is affected, thus supporting their hypothesis.

The strength of this manuscript is its thoroughness, and that key findings are shown with more than one method, for example, by the use of both morpholinos and knock-out fish. Combining electrophysiology, EM analysis, and dendritic structure analysis is excellent, and gives weight to the conclusions of the paper.

The results are convincing, and support a role for Gjd2b in Purkinje cell maturation. The mechanism by which this acts is also addressed, although not fully elucidated. I think this makes the paper interesting and important to a number of different groups, including developmental neurobiologists, those interested in gap junction function and signaling, and cerebellar researchers.

Reviewer #1 (Public Review): 

This study shows using ribosome profiling that m1G37 deficiency in E. coli achieved by depletion of TrmD leads to ribosome stalling at codons specific for tRNAs that are methylated by TrmD, including strong pauses at all Pro CCN codons and at the Arg CGG codon, and weak pauses at the Leu CUA codon (in one of two mutants studied). The stalling occurs primarily when the affected codons are in the ribosomal A site, indicating defects in decoding. Biochemical experiments show that m1G37 deficiency reduces rates of tRNA aminoacylation in all isoacceptors examined, and rates of peptide-bond formation in some of the isoacceptors, suggesting that the reduced rates of one or both of these reactions lead to ribosome stalling at the A site. RNA-seq in the mutants shows changes in mRNA expression, some of which can be rationalized by translational pausing in leader peptides of the Leu and Ilv operons at the TrmD-dependent Leu or Pro codons, respectively, leading to loss of attenuation and derepressed transcription of the operons. Many of the other transcriptional changes exhibit the signature of the stringent response, known to be activated by binding of deacylated tRNA to the A site of translating ribosomes, which would be consistent with reduced aminoacylation of the TrmD-dependent tRNAs in vivo. Some of the mRNA changes shared by the TrmD mutant and the stringent response are expected to up-regulate enzymes of the glyoxylate cycle, which could help cells deal with amino acid deficiency-the biological goal of the stringent response. Other changes involving down-regulation of mRNAs encoding enzymes of glycolysis and up-regulation of mRNAs for enzymes of fatty acid metabolism, are predicted to shift metabolism from catabolism of glucose to fatty acids, which would support the glyoxylate cycle. These latter changes in mRNA levels are not shared by the stringent response, however, and the mechanism for the role of TrmD and m1G37 modification in tRNA is unknown. 

The evidence is strong from the ribosome profiling data that loss of m1G37 in tRNAs leads to slow decoding of the triplets in the ribosome A site that are decoded by the known TrmD-modified Pro, Arg and Leu tRNA isoacceptors. Similar results have been published previously for certain mutants of tRNA modifying enzymes in yeast. It is particularly interesting here because most previous work apparently implicated m1G37 in suppressing +1 frameshifting by influencing codon-anticodon interactions in the P site, whereas here they found no evidence for increased +1 frameshifting in their profiling data on the trmD mutants. The biochemical data indicating reduced rates of aminoacylation and peptide bond formation by the unmodified tRNAs appear to be sound, and they have demonstrated reduced aminoacylation of tRNAPro(UGG), which could be mitigated by overexpressing the Pro tRNA synthetase, providing strong evidence that reduced charging of this tRNA is a major determinant of the changes of gene expression on depletion of TrmD. Overexpressing the Pro and Arg synthetases also improved cell viability, particularly for ProRS. They also performed genetic experiments to show that the derepression of the Leu and Ilv operons results from stalling at the relevant codons in the leader peptides, involving replacing the CUA Leu codons with other Leu codons in the case of the Leu operon, and replacing the Pro codons with Ala codons in the leader peptide sequence of the Ilv operon, in operon reporter constructs. Again, overexpressing the ProRS dampened derepression of the Ilv operon reporter, strongly supporting their model. Thus, the authors have provided strong evidence to support all of their major claims.

Reviewer #2 (Public Review): 

This manuscript explored the effect N1-methylation of G37 of tRNAs in bacteria. The authors found that loss of methylation, through the depletion of trmD, results in defects in aminoacylation and peptidyl-transfer, leading to ribosome stalling and activation of the stringent response (as mediated by accumulation of deacylated tRNAs). Briefly, the authors conducted ribosome profiling on trmD conditional-knockout E coli cells and compared it to "wild-type" cells, and documented increased ribosome stalling on codons decoded by tRNAs modified by trmD. Stalling occurs when the ribosome is decoding these codons, i.e. when they occupy the A site. Further biochemical characterization showed that stalling is likely to occur due to defects in aminoacylation and peptide-bond formation for the trmD-substrate tRNAs, primarily for tRNAPro. Finally, analysis of gene expression shows that loss of trmD results in the activation of the stringent response as well as rewiring of central-carbon metabolism. 

Overall, this is a comprehensive study of an essential and universally conserved tRNA methylation. The manuscript expands on the role of m1G37 in translation, beyond its established role in reading-frame maintenance. However, the novelty of the findings was not immediately clear to me, and in particular whether they significantly advance our understanding of tRNA modification. For instance, it is known that defects in tRNA methylation (albeit different than N1-methylation of G37, discussed here) activates Gcn2 in yeast, which arguably is equivalent to the stringent response in bacteria. 

Furthermore, the authors made the claim "In contrast, while m1 G37 deficiency reduces peptide bond formation for some tRNAs at the A site, it consistently reduces the rate of aminoacylation for all tRNAs examined, which has not been shown for other metabolically deficient tRNAs." in the discussion section, which is inaccurate. Previous data, some from the same group, has shown that thiolation of the wobble base in tRNAGln is important for aminoacylation, tRNA selection by the ribosome and reading-frame maintenance. The argument that m1G37's pleiotropic effect on translation is unique is not convincing.

Reviewer #3 (Public Review): 

The study expands upon the previous findings of the Hou lab that the lack of TrmD-catalyzed modification in the anticodons of several bacterial tRNAs leads to +1 frameshifting when the undermodified tRNA is positioned in the ribosomal P site. In the current study, the authors show that a number of other aspects of translation are affected when the m1G modification in the tRNA anticodon is lacking. 

Specifically, the study shows that undermodified tRNAs are less efficiently aminoacylated by the corresponding aminoacyl-tRNA synthetases leading to excessive presence of deacylated tRNAs. One of the consequences is ribosome pausing when the respective codons need to be decoded. The shift in the balance of aminoacyl-tRNA relative to deacyl-tRNA resembles the one caused by amino acid starvation. Indeed, the authors show that changes in the transcriptome triggered by reduced tRNA modification resemble those observed at stringent response. 

While the paper is generally good and interesting in its current version it is not perfectly focused: discussion of the metabolic changes resulting from transcriptome remodeling are relatively fuzzy and do not contribute much to the main story. Another problem is that some of the claims (e.g. that the lack of anticodon modification affects peptide bond formation) are not properly termed and thus, misleading. In fact, the lack of tRNA modification affects dipeptide formation (possibly by interfering with decoding or tRNA accommodation) rather than influencing the rate of peptidyl transfer per se.

Reviewer #1 (Public Review):

Zang and colleagues present an intriguing manuscript on circadian oscillations in retinal gene expression and function in zebrafish and mice. They find that many key regulators of phototransduction-shutoff cascade show marked oscillations that persist in darkness, and that are shared across adult and larval zebrafish. Moreover, the same set of regulators oscillate in antiphase in mice, which unlike zebrafish are nocturnal. Using ERGs, the authors then go on to show that zebrafish photo-recovery is modulated in a circadian manner, and this continues to occur in constant darkness, indicating that it is indeed intrinsic oscillations (rather than light entrainment alone). This also appears to be mirrored in flicker fusion rates and perhaps at the level of some behaviours.

Overall, the manuscript is very interesting and usefully adds to our understanding of retinal/ photoreceptor function in vertebrates in the context of circadian rhythms. Moreover, the manuscript is well presented and written.

Reviewer #2 (Public Review):

Circadian clock function is an intrinsic property of most cells and tissues. The retina is no exception with many lines of evidence pointing to retinal physiology being under the control of a local circadian clock thereby matching the very different physiological demands of the retina at different time of day. In zebrafish, previous studies have documented response threshold, photoreceptor retinomotor movement and cone synaptic ribbon assembly to be under clock control. This manuscript takes some important steps forward in our understanding of the links between the circadian clock and retinal physiology in zebrafish. Rhythms of photoresponse recovery are visualised convincingly by electroretinography and these are shown to be mirrored consistently by rhythms in optokinetic and optomotor responses. The authors also present clear data revealing that this correlates with circadian clock-regulated rhythmic mRNA and protein expression of key regulators of the visual transduction cascade, specifically those involved in so-called shut-off kinetics. What is particularly elegant is that the phase of the equivalent gene expression rhythms in the mouse, a nocturnal species, are 12 hours phase-shifted with respect to the diurnal zebrafish, probably reflecting the different timing of visual function in these two species with respect to the day-night cycle.

The main weak point of this work in my opinion concerns the many questions that are raised, but not convincingly answered, regarding how the clock is coupled to these retinal outputs. Attempts to identify enhancer elements in the promoter region of the rhythmically expressed visual transduction cascade genes which are targeted by the core circadian clock machinery (one assumes, E-boxes) were apparently not conclusive (Lines 23 24, page 21 and lines 1 and 2, page 22: "Although we identified some conserved binding sites of core clock proteins in our analyzed genes, neither of them was conserved in mammalian genomes (....), suggesting that the regulatory pattern of circadian regulation is more complex."). Although this statement is based on bioinformatic inspection, rather than unbiased promoter analysis, this leaves much uncertainty as to precisely how, or indeed if the core clock machinery within the cone photoreceptors cells directly regulates these gene targets. If not direct regulation by core clock transcription factors, which mechanism might operate? One possibly related issue is that in the experiments examining retinal gene expression in larvae raised under constant darkness, rhythmicity was observed where previous studies might have anticipated clock desynchronisation. The authors extrapolate from these findings with the following prediction: (line 7-9, page 22, "We did not observe this phenomenon in our study of visual transduction genes in the retina, suggesting the existence of an inheritable maternal clock in the eye (Delaunay et al., 2000)"). The data presented in this manuscript is insufficient to test this hypothesis and indeed many other studies have been unable to provide any evidence for maternal inheritance of the clock in this species. However, the persistence of rhythmicity in larvae raised in DD conditions is a result that might provide some clues as to how the circadian clock mechanism is operating here.

So, in summary, while making some important contributions to the circadian clock and retinal physiology field, the work tends to raise more questions than it answers.

Reviewer #3 (Public Review):

The authors investigate the putative molecular mechanisms underlying circadian changes in visual behavior using the diurnal zebrafish as model. They focus on arrestins, G-protein receptor kinases, Regulator of G protein signallin 9, and recoverins, which are all regulators of the termination of the visual transduction cascade, hence modulators of visual temporal resolution. Despite two exceptions (i.e. rcv2a which was not fluctuating and rcv1b which was not expressed in the larval retina) the authors find that the expression levels of all tested genes show time dependent fluctuations over the 24 hours in both larvae's and adult eyes. To support a circadian regulation of the tested genes, the observed fluctuations are maintained in constant darkness (where circadian rhythms should be self-sustained) and disrupted in constant light (where circadian rhythms should be lost). The authors also test for fluctuations in the expression levels of the proteins encoded by 2 of the tested genes, namely GRK7a and ARR3.These might also be present in the eyes of adult fish at different amounts over the 24 hours. The fluctuations in the expression levels of the regulators of the visual transduction cascade correlate with changes in visual behavior and physiology. In particular:

• the photoreceptors take longer to recover from a flash of light in the evening compared to the morning, and the difference is lost when the animals are reared in constant light

• the animals are less able to resolve flicker frequencies in the evening

• startle responses following lights-off and lights-on are more intense in the morning or evening, respectively

• optokinetic responses (i.e. velocity of eye movement in response to a contrast stimulus) change over the day being lower in the evening compared to noon.

These results suggest that circadian modulation of visual behavior might depend on the cyclic expression of the genes involved in the visual transduction cascade. These conclusions are well supported by the molecular and behavioral data presented, especially by the fact that time dependent fluctuations are observed in several aspects of visual behavior. A potential weakness is that only one visual behavior is tested under constant darkness or constant light, namely the cone photo response recovery. Testing a second visual behavior (e.g. visual motor response) under these conditions would probably strengthens the correlation between changes in the expression levels of genes of the visual transduction cascade and behavioral rhythms. In addition, while it seems quite clear that transcripts level are changing over time in a circadian manner, this cannot be stated for sure with the statistical analysis performed. Indeed, the One Way ANOVA allows to test for an effect of time, but can not provide proof for circadian oscillation. This is even more important in case of GRK7a and ARR3, where the effect of time on protein expression. The authors investigate the putative molecular mechanisms underlying circadian changes in visual behavior using the diurnal zebrafish as model. They focus on arrestins, G-protein receptor kinases, Regulator of G protein signallin 9, and recoverins, which are all regulators of the termination of the visual transduction cascade, hence modulators of visual temporal resolution. Despite two exceptions (i.e. rcv2a which was not fluctuating and rcv1b which was not expressed in the larval retina) the authors find that the expression levels of all tested genes show time dependent fluctuations over the 24 hours in both larvae's and adult eyes. To support a circadian regulation of the tested genes, the observed fluctuations are maintained in constant darkness (where circadian rhythms should be self-sustained) and disrupted in constant light (where circadian rhythms should be lost). The authors also test for fluctuations in the expression levels of the proteins encoded by 2 of the tested genes, namely GRK7a and ARR3.These might also be present in the eyes of adult fish at different amounts over the 24 hours. The fluctuations in the expression levels of the regulators of the visual transduction cascade correlate with changes in visual behavior and physiology. In particular:

• the photoreceptors take longer to recover from a flash of light in the evening compared to the morning, and the difference is lost when the animals are reared in constant light

• the animals are less able to resolve flicker frequencies in the evening

• startle responses following lights-off and lights-on are more intense in the morning or evening, respectively

• optokinetic responses (i.e. velocity of eye movement in response to a contrast stimulus) change over the day being lower in the evening compared to noon.

These results suggest that circadian modulation of visual behavior might depend on the cyclic expression of the genes involved in the visual transduction cascade. These conclusions are well supported by the molecular and behavioral data presented, especially by the fact that time dependent fluctuations are observed in several aspects of visual behavior. A potential weakness is that only one visual behavior is tested under constant darkness or constant light, namely the cone photo response recovery. Testing a second visual behavior (e.g. visual motor response) under these conditions would probably strengthens the correlation between changes in the expression levels of genes of the visual transduction cascade and behavioral rhythms. In addition, while it seems quite clear that transcripts level are changing over time in a circadian manner, this cannot be stated for sure with the statistical analysis performed. Indeed, the One Way ANOVA allows to test for an effect of time, but can not provide proof for circadian oscillation. This is even more important in case of GRK7a and ARR3, where the effect of time on protein expression seems to be somewhat milder. To better test for circadian oscillation, the authors could perhaps try to use RAIN, which allows to detect rhythms in time series independently of the wave form.

One very interesting finding of this study is that the expression profiles of genes involved in the visual transduction cascade is antiphasic when comparing diurnal fish to nocturnal mice. Nevertheless, zebrafish and mouse are quite distant species.Ti generalize these findings it would be necessary to discuss data also from diurnal rodents or nocturnal fish.

In general, the study, and its conclusions, are straightforward and provide new insights into the mechanisms underlying circadian regulation of visual behavior.<br> n expression seems to be somewhat milder. To better test for circadian oscillation, the authors could perhaps try to use RAIN, which allows to detect rhythms in time series independently of the wave form.

One very interesting finding of this study is that the expression profiles of genes involved in the visual transduction cascade is antiphasic when comparing diurnal fish to nocturnal mice. Nevertheless, zebrafish and mouse are quite distant species.Ti generalize these findings it would be necessary to discuss data also from diurnal rodents or nocturnal fish.

In general, the study, and its conclusions, are straightforward and provide new insights into the mechanisms underlying circadian regulation of visual behavior.

Reviewer #1 (Public Review): 

In this paper McPherson et al investigated fibrin clot properties and fibrinolysis with recombinant fibrinogen variants lacking parts of the fibrinogen alphaC-region. The aim was to understand the contribution of two subregions, the alphaC-connecter and the alphaC-domain, which are known to be involved in the lateral aggregation of fibrin fibers and their cross-linking. The study measured the contribution of subregions to fibrin fiber growth, mechanical strength, how fibrinolysis proceeds in their absence, their impact on clot retraction, and how the variants affect whole blood clot features. 

Strengths and weaknesses: 

The major strengths of this report lie in the broad range of appropriately selected assays used to characterize the fibrinogen variants described, the clarity of the data presented, and a clear discussion of the mechanistic implications of the findings compared to previous work. We can now understand more clearly how the alphaC-subregions contribute to clot structure, initial fibrin fiber growth, clot strength and stiffness, fibrinolysis, clot contraction linked to erythrocyte retention and platelet binding, and a clinically relevant assessment of the clotting and lysis of whole blood. 

The methodology does not have weaknesses in the context presented. One issue that arises from such a study, that is centered on the characterization recombinant molecules assessed in vitro, is to what extent each of the characteristics described would have physiological impact in vivo. The study has the advantage of clearly separating out different roles for the fibrinogen alphaC-region, but the more complex interplay of the variants with a complete vasculature and blood composition, in an organism that produced the variants, would enhance the study claims. This issue is hinted at in the paper, albeit in vitro/ex vivo. The fibrin density and fiber thickness of alpha390 clots was different in a purified setting compared to whole reconstituted blood clots post-thromboelastography made using blood from Fga-/- mice. It therefore seems reasonable that characteristics of the alphaC-region functions described may show more or less importance when assessed in vivo. 

Aims, results and conclusions of the paper: 

The authors achieved their aims, separating functional roles for the two alphaC-subregions, and assessing their relative impact in a broad range of relevant experimental settings. The results clearly support the author's conclusions and are a solid basis on which to design future work with additional variants, and to further assess the role of the alphaC-region in vivo. 

In more detail, the authors produced and purified two novel recombinant fibrinogen variants, alpha390 and alpha220, lacking the alphaC-domain and the alphaC-region (alphaC-connector and alphaC-domain), respectfully. Using turbidity and microscopy, alpha390 gave thinner, denser fibrin clots while alpha220 clots were porous and stunted, compared to control. Alpha220 fibrin polymers formed slowly and were short, implicating directly the alphaC-region in fibrin fiber growth. Surprisingly, both alpha chain variants underwent cross-linking, but alpha220 was extremely sensitive to fibrinolysis and alpha220 clot stiffness could not be measured without FXIIIa. Using blood from Fga-/- mice, which cannot support clotting, clot retraction and size was severely affected with alpha220 supplementation whereas alpha390 behaved like wild-type fibrinogen. This highlights the importance of the alphaC-connector in this mechanism which involves FXIIIa-catalyzed cross-linking. The observation was reinforced by measuring platelet binding, which was also affected but not lost in the alpha220 variant, and similar to wild-type fibrinogen with alpha390. Finally, EXTEM and APTEM thromboelastography assays were made on reconstituted Fga-/- blood as a global assessment of clot properties and the contribution of fibrinolysis. This was important because it demonstrated that both truncations failed to reach wild-type clot firmness, clotting was slowed, and that both variants were susceptible to a decline in clot strength due to concurrent clotting and fibrinolysis. Also, imaging on these ex vivo whole blood clots confirmed the short, sparse nature of alpha220 fibers seen in vitro, and highlighted a certain "normalization" of the thinner, denser alpha390 fibrin when cells were present. 

Impact and utility to the field of study: 

This work has clinical relevance for patients with fibrinogen alpha chain truncation mutations and gives insights to clinicians measuring clot properties in patients. Perhaps more importantly at this stage, the study implicates the alphaC-subregions in fundamental aspects of fibrin formation, fibrinolysis and overall clot mechanics. The methods represent a state-of-the-art panel of tests and therefore a useful guide to the research field on how to approach aspects of fibrinogen function in clotting, particularly using recombinant molecules. The impact of this work therefore lies in both the discovery of roles for the fibrinogen alphaC-subregions, and a cautious extrapolation to the understanding of whole blood clot mechanics measured in patients. 

Additional context: 

Readers can put into context these findings by familiarizing themselves with the clinical presentations and impact on clot properties of known dysfibrinogenemia (or hypodysfibrinogenemia) mutations in the fibrinogen alphaC-region. Also, there are published experimental studies of fibrinogen alphaC variants. The significance of the variants described can be put into a broader context by comparing the thromboelastography and subsequent imaging shown here with whole blood clot analysis used for clinical haemostasis decision-making.

Reviewer #2 (Public Review): 

McPherson H et al designed two fibrinogen variants with truncated alfa- C terminal region: the alfa-390 and alfa-220. The alfa-390 lacks the alfa-C domain, while the alfa-220 lacks both the alfa-C domain and alfa-C connector region. By using different type of optical and electronic microscopy they characterize the fibrin structure at different resolution, and the early fibrin oligomers formation of these homozygous fibrinogen variants, and compared them to the WT fibrinogen. The functional implications of removing these protein stretches in the fibrin mechanical properties and stability were studied by turbidity, FXIIIa fibrin crosslinking, fibrin microrheology, clot contraction (retraction), and rotational thromboelastometry. It was found for the first time the differential roles of these two regions: the role of the alfa-C connector in the longitudinal protofibril/fibre growth, mechanical and fibrinolytic stability, while the alfa-C domain (variant 390) was implicated in the lateral protofibrils association, since its removal gave rise to denser fibrin networks with thinner fibres, already described in the literature. Their finding has clinical implications since support the design of antithrombotic drugs that can limit the thrombus size or growth. Their conclusions are supported by the results. The confocal microscopy fibrin structure was confirmed by the scanning electron microscopy images, and highlight the importance of coupling the fibrinogen under study to the fluorophore in order to do not bias the fibrin structure. 

In order to study the differential implications of these alfa-C subregions on the susceptibility of fibrinogen/fibrin plasmin degradation that will support thromboelastometric and turbidity results, it would be interesting in the future to perform fibrinogen/fibrin plasmin degradation kinetic of these variants monitored by SDS/PAGE.

Reviewer #1 (Public Review):

The results provide a useful compendium of proteins IDed in SNpc dopaminergic cells using a very rigorous and powerful approach. In short, an engineered peroxidase that rapidly biotinylates nearby proteins is coupled to restricted expression in dopaminergic cells, particularly those dopaminergic cells previously transduced in the midbrain with AAV vectors. The spatial aspect to proximity IDs are the main strength, which is a well-known feature harnessed in many studies to understand proteomic compositions of particular circuits.

It is unclear how the different concentrations of the peroxidase factor into the relative abundance calculations of IDed proteins. Further, it would be useful to assess unexpected protein IDs at the synapse with an orthogonal measure instead of full reliance of peroxidase labeling, for example, with in situ analysis or immunohistochemistry. Finally, the associations with GWAS lists from Parkinson's disease with lists of compartment-restricted proteins are somewhat unconvincing and might benefit from a more rigorous analysis.

The strengths of the study include the in vivo catalogue of the cytosolic proteome of an important cell type using a suitable and current methodology. Most of the data is consistent with prior expectations, although there are some apparently postsynaptic proteins that are enriched in the synaptosomes that may represent more novel observations.

Set against these positives, there are three principal weaknesses. First, only dopamine neurons are evaluated. Hence, we do not know which observations are specific to this cell type vs being generic to other projection neurons. While we see the expected accumulation of dopamine synthesis and cytosolic handling, much of the rest of the catalogue would be expected in all neurons, including the accumulation of postsynaptic machinery in the axonal projection compartment. A particularly intriguing observation was the potential enrichment of autophagy machinery in the axonal proteome but we don't know if this is specific to DA neurons or more generally true, which impact both interpretability and level of novel insight.

Second, there is a lack of orthogonal approaches to validate or invalidate some of the major observations. This is particularly crucial in evaluating unexpected accumulation of postsynaptic proteins, for which the authors proffer up to four possible explanations including some that would appear to represent artifacts of the labeling procedure. Thus showing that some of these enrichments can be recapitulated in situ would strongly improve interpretation.

Third, the enrichment of PD genes and GWAS hits suffers from a lack of precision as to what is considered a true PD gene or not and how one considers those genes that are not enriched in the dataset. Here, again, the lack of comparator datasets is an omission as what we don't know is whether there would be a similar level of enrichment in any neuronal context - or what the non-neuronal proteome might be. On a more technical level, the inference here is that this set of genes shows enrichment in axonal vs somatic compartments whereas a better question is whether this gene set (29) is enriched over total detected (>1700) proteins more than chance alone. As presented, the conclusion that this dataset provides insights into PD genetics is not supported.

Reviewer #2 (Public Review):

The manuscript by Hobson et al. uses APEX2, a proximity biotinylation approach, to resolve the subcellular proteomes of midbrain dopaminergic neurons. They used both viral and genetic tools to restrict expression of APEX2 to specific sets of dopaminergic neurons, then exploited the anatomy of the cells to separate the soma and dendrites from axons and axon terminals in the striatum. It is a clever approach and their experimental results are quite impressive. Since APEX2 cannot be used in vivo, they developed a brain-slice method that is very robust. They next purified biotinylated proteins from multiple controls and replicates - the studies were quite rigorous. As an independent approach, they also prepared striatal synaptosomes and performed proximity biotinylation using the synaptosomes. The results were remarkably and impressively similar to the results obtained with the biotinylation in slices. The similarity gives great confidence in the robustness of their approaches and data sets. The final aspect of the paper focused on combining several different resources including single cell RNAseq, GWAS studies of PD patients, and then comparing these to the subcellular dopaminergic neuron proteomes. They identified a set of proteins linked to PD that are preferentially enriched in axons.

I found this paper to be extremely interesting and important not only for the insight into differential protein localization and enrichment, but also because it points to candidate proteins that may play especially important and previously unrecognized roles in dopaminergic axons. This will no doubt serve as a resource for neurobiologists and PD researchers. Finally, the study design and execution is excellent and provides a model for how to carry out these kinds of experiments in the rodent brain using APEX2 for other questions.

Reviewer #1 (Public Review):

The authors developed an effective aorta-on-a-chip system to investigate the potential mechanism of Bicuspid Aortic Valve-Thoracic Aortic Aneurysm, BAV-TAA. They found that the impaired mitochondrial dynamics were involved in the development of BAV-TAA. Also, the drug screening tests involving mitochondrial fusion activators or mitochondrial fission inhibitor, which could rescue the dysfunctional VSMCs, were conducted on this system. These results could provide us an insight that this microphysiological system could serve as the potentially effective research platform for BAV-TAA. The physiologic mimicry of the aortaon-a-chip proved to be excellent and was superior to the 2D aortic cell research-in-a-dish. However, there are still some minor issues that need to be addressed.

1) The authors used aortic smooth muscle cell as a basis for their chips. Although the aortic smooth muscle cells and its related tunica media play a central role in the development of aortopathy, other multiple types of cells, including endothelial cells, fibroblasts and macrophages, are also involved in the aortic wall and contribute to the pathological process. Indeed, this work could inspire future work of integration of other cell types on the chip to study their network effect involved in the aortopathy. However, the present study cannot represent the whole aortic wall on the chip. Therefore, the authors should rephrase the statement of 'aorta-on-a-chip' in the entire manuscript since the current wording leaves the impression that they integrate entire aortic cells. The "aorta tunica media-on-a-chip" could be more precise for the topic.

2) The authors used primary smooth muscle cells form patients with BAV-TAA. However, the population of VSMCs comprising the aortic microstructures is consequently heterogeneous. In the ascending aorta and aortic arch, VSMCs are derived from the neural crest; VSMCs in the descending thoracic aorta are derived from the paraxial mesoderm, while neural crest and secondary heart field-derived VSMCs intermingle in the aortic root. The heterogeneity of VSMCs could lead to section-specific microphysiology in the aortic wall and differences in the vulnerability of VSMCs to pathogenic stimuli. Therefore, the authors should clarify which section of aorta they used and explain why they used that section.

3) Although this is the first report of aortic function unit-on-a-chip to study aortopathy, the authors should clarify in more detail what discriminates this study from the other reported 'artery-on-a-chip' platforms (Artery-on-a-chip platform for automated, multimodal assessment of cerebral blood vessel structure and function, Lab Chip 2015;15(12):2660-9.doi: 10.1039/c5lc00021a) and what the level of novelty is.

4) In Figure 6, the patients-derived cells were used on the chip. The effect of drug testing on aorta-on-a-chip model was desirable, even though the results did not exhibit all the positive responses. The authors should explain the potential reasons for the unevenness.

5) The authors should explain why the commercial primary cells and primary isolated cells should be used together.

Reviewer #2 (Public Review):

In this manuscript, Abudupataer et al. have picked an important scientific/clinical problem and have made some interesting discoveries. They described a microphysiological device for the purpose of building an in vitro model of the aortic aneurysm. After design validation, they used the model to study the role of impaired mitochondrial dynamics and tested drugs to implicate the potential therapeutic targets. Impaired mitochondrial dynamics has already been linked to several cardiovascular conditions including ischemia-reperfusion injury and pulmonary arterial hypertension; and this study provides in vitro data suggesting its role in bicuspid aortopathy-related aortic aneurysm. This manuscript has considerable merit to the field.

Reviewer #3 (Public Review):

Abudupataer et al. investigated the association between mitochondrial dynamics and NOTCH1 deficiency in BAV-TAA using an engineered aorta-on-a-chip model. They found that NOTCH1 insufficiency induced phenotypic switching in human aortic smooth muscle cells (HAoSMCs) and that MFN1/2 agonists and DRP1 inhibitors could be a potential therapeutic approach to reverse the imbalance in mitochondrial dynamics. The authors performed systematic in vitro evaluation of mitochondrial function and dynamics in HAoSMCs. The main conclusions of this paper are supported well by the data provided. However, it can be improved by addressing the following questions.

1) Although microfluidics-based organ-on-a-chips have provided tremendous benefits in the biomedical researches, it is still premature to establish this method as a standard preclinical model. Thus, it is strongly recommended that the authors can conduct in vivo validation using animal model to explore the outcomes of leflunomide, Mdivi-1, and teriflunomide in the treatment of BAV-TAA.

2) It would be more convincing if the authors provide sound justification regarding the strain of the ascending aorta, including its definition, the common value range, and its correlation with vacuum pressure, in the section of "Construction of aorta-on-a-chip model" rather than "Discussion".

3) In Figure 1f, the z score of each identified signaling pathway should be provided.

4) In the horizontal axis of Figure 1h, is it log2 or log10? Please specify.

5) How about the stability or repeatability of the chip for a long time? Please discuss and provide supporting evidence.

6) What the implication of the enhancement of cell contractility? Further discussion is needed.

7) Could the authors give a further explanation why there is a decrease in long rod-shaped of mitochondrial for NOTHC1-KD HAoSMCs?

8) What's the criteria to decide upon the right vacuum pressure? Will the different tensile strain greatly influence the performance of the chip? How about the shape of the HAoSMCs under strain in the chip compared to the real sample?

9) Could the author describe in greater details the major advantages of the aorta-on-a-chip model proposed in this manuscript compared to the conventional cell culture model?

Reviewer #1 (Public Review): 

In this paper, Lynch and colleagues seek to develop a mathematical framework for estimating rates of chromosome missegregation based on known chromosomal properties and observed aneuploidy rates. They derive a model that they validate using live-cell imaging and apply it to several previously-described datasets from tumors and organoids. Overall, this paper is interesting, and its subject matter is of high interest to aneuploidy and genome evolution researchers. Below, I suggest a few areas where this manuscript could be improved: 

1) It seems like this model treats chromosome gains and losses equivalently. Is this appropriate? Chromosome loss events are much more toxic than chromosome gain events - as evidenced by the fact that haploinsufficiency is widespread, and all autosomal monosomies are embryonically-lethal while many trisomies are compatible with birth and development. Can the authors consider a model in which losses exert a more significant fitness penalty that chromosome gains? 

2) Chromosomes do not missegregate at the same rate (PMID: 29898405). This point would need to be discussed, and, if feasible, incorporated into the authors' models. 

3) It would be helpful if the authors could clarify their use of live cell imaging (e.g., in Fig 6G). Certain apparent errors that are visible by live-cell imaging (like a lagging chromosome) can be resolved correctly and result in proper segregation. It is not clear whether it is appropriate to directly infer missegregation rates as is done in this paper. 

4) The authors would need to discuss in greater detail earlier mathematical models of CIN, including PMID: 26212324, 30204765, and 12446840 and explain how their approach improves on this prior work.

Reviewer #2 (Public Review): 

The authors have developed in silico model of CIN that explores how rates of missegration and selection pressure impact population structure and intra-tumor karyotypic variance in human tumors. This extends an already established framework for exploring impact of CIN on karyotypic evolution (PMID: 26212324) in two major ways: (1) accounting for selection pressure on aneuploid karyotypes and (2) using Bayesian inference to fit observed karyotype distributions from single cell DNA sequencing data to those generated in silico through sweeps along parameter space. The latter permits quantitative inference of CIN as a rate, in contrast to a state (i.e. aneuploidy), which is a biologically-important distinction the authors well articulate. Subsequently, they perform sensitivity analyses for the impact of sample size (number of cells that must be karyotyped at single cell level) on inferred rates of missegregation, which is highly relevant to the use of quantitative CIN as a clinical biomarker. The latter is of particular interest to the research and clinical communities, given the pervasive association of CIN with metastasis, drug resistance and poor survival outcomes. 

Strengths of the framework include: 

1) Careful distinct of CIN as a rate (not a state of aneuploidy) as a biomarker for cancer progression and therapeutic resistance. 

2) Acknowledges surprisingly low karyotypic variation observed in published single cell DNA sequencing datasets, despite obvious mitotic errors and karyotypic abnormalities observed by chromosome painting in tumor samples. These conflicting observations are yet to be resolved, and largely limit confidence in accuracy and robustness of single cell DNA sequencing in recovering tumor karyotypes. The author's hypothesize this discrepancy stems from karyotypic selection, which we suggest they expand upon in modeling and validation. 

3) Sensitivity analysis of inferred CIN rate to sample size (number of single cells sequenced) is extremely useful and important for clinical biomarker development. 

Weakness of the framework include: 

1) Most notably, the presented framework is lacking expanded characterization and validation of selection models that are biologically relevant. The current framework simply applies a scalar exponent to already published fitness models for selection. It is unclear what this exponent mirrors biologically, beyond amplifying the selection pressures already explored in existing gene abundance and driver density models. 

2) Towards this, how is the CIN ON-OFF model in which CIN is turned off after so many cell divisions relevant biologically? Typically CIN is a considered a trait that evolves later in cancer progression, that once tolerated, is ongoing and facilitates development of metastasis and drug resistance. A more relevant model to explore would be that of the effect of a whole genome duplication (WGD) event on population evolution, which is thought to facilitate tolerance of ensuing missegregation events (because reduce risk of nullisomy). 

3) The authors utilize two models of karyotype fitness - a gene abundance model and driver density model - to evaluate impact of specific karyotypes on cellular fitness. They also include a hybrid model whose fitness effects are simply the average of these two models, which adds little value as only a weighted average. In silico results shows inferred missegregation rates are extremely disparate across the two primary models. And while a description of these differences is provided, the presented analyses do not make clear the most important question - which of these models is more clinically relevant? Toward this, in Figure 2F, the authors claim the three models approach a triploid state - which is unsupported by the in silico results. Clearly the driver model approaches a triploid state, as previously reported. But the abundance model does not and hybrid only slightly so, given that it is simply a weighted average of these two approaches. Because the authors have developed a Bayesian strategy for inferring which model parameters best fit observed data, it would be very useful to see which model best recapitulates karyotypes observed in cancer cell lines or patient materials. 

4) Topological features of phylogenetic trees, while discriminatory, are largely dependent on accurate phylogenetic tree reconstruction. The latter requires more careful consideration of cell linkages beyond computing pairwise Euclidean distances and performing complete-linkage clustering. For example, a WGD event, would appear very far from its nearest cell ancestor in Euclidean space. 

5) Finally, experimental validation of the added selection exponential factor is imperative. Works have already shown models of karyotypic evolution without additional selection exponential coefficient can accurately recover rates of missegregation observed in human cell lines and cancers by fluorescent microscopy. Incorporation of this additional weight on selection pressure has not been demonstrated or validated experimentally. This would require experimental sampling of karyotypes longitudinally and is a critical piece of this manuscript's novelty.

Reviewer #3 (Public Review): 

This manuscript develops an agent-based computational model describing the evolution of chromosomal alterations in a growing population of cells. The model is then compared to various datasets - a single cell DNA sequencing dataset of paclitaxel treated cells generated by the authors themselves and two public single cell sequencing datasets - using Bayesian inference to infer the model parameter describing the chromosomal alteration rate. 

The novelty and strength of the paper is the mathematical modelling-led approach to quantitatively measure chromosome missegregation rates from single cell sequencing data, and to make the measurements in a way that attempts to be robust to the potentially confounding influence of clonal selection on karyotypic alterations. 

The modelling includes a number of different paradigms of how selection acts upon the karyotype, and the authors show that each paradigm strongly effects the diversity of chromosomal alterations that persist in the evolving population of cells. As expected, the rate of missegregation and overall selection strength also strongly effect diversity. 

Given the importance of the selection paradigm in determining the observed karyotypic heterogeneity, a significant weakness of the work is that there is no attempt to learn the selection paradigm from the observed data. This is important because there is an interrelationship between selection, the chromosomal alteration rate, and the observed data and so the accuracy of the inferred alteration rate is likely to be compromised if an inappropriate model of selection is used. Somewhat relatedly, how the population of cells grows (e.g. exponential growth vs constant population size) also effects the observed karyotype heterogeneity, but the modelling only allows for exponential growth which may be an inappropriate of the public datasets analysed. 

There are some technical concerns about the approximate Bayesian computation analysis (choice of prior distributions, testing for convergence, matching of the growth model to cell growth patterns in the data, and temporal effects) which need to be addressed to ensure this part of the analysis is robust.

Reviewer #1 (Public Review):

This paper addresses an important question in the opioid field, whether the mu opioid receptor (MOR) and the delta opioid receptor (DOR) are likely to occur as independent receptors or whether their signaling is coupled and could be the result of interactions. The authors take advantage of a fluorescent label, NAI-A594, which binds to both receptors, to do live imaging experiments in the cholinergic neurons of striatum and test how the responses to a selective agonist to one receptor affects subsequent responses to an agonist of the other. They use receptor internalization and electrophysiological recordings to gauge the likelihood that the two receptors are independently expressed or act as a unit. The work is carefully done, and the authors conclude that the two receptors act independently in these neurons; the data support the idea that at all of the receptors are not necessarily linked. However, the work cannot exclude that some of the receptors also act together. One issue is that opioid receptors and GPCRs generally can produce distinct effects: recruitment of the beta-arrestin pathway promotes desensitization and receptor internalization, while signaling via Galpha or beta-gamma produces other signaling events. While the agonists used in the present study likely target both pathways in the MOR and DOR, it remains possible that in a heterodimer, signaling might instead be biased. Moreover, the similar downstream signaling pathways make it more difficult to untangle the possible interactions between the two receptor types.

Reviewer #2 (Public Review):

Strengths:

1) The authors use a nice combination of pharmacology, in situ hybridization, and fluorescence analysis to demonstrate that MORs and DORs are both expressed on ChIs.

2) The authors specifically analyze expression patterns based on sex or dorsal vs ventral striatal zones.

3) The authors use extracellular recordings of ChIs to demonstrate that their firing patterns are sensitive to MOR or DOR stimulation.

4) The authors use pharmacology and extracellular recordings to demonstrate that MOR and DOR mediated inhibition is relatively independent of each other.

5) The authors use live cell imaging and pharmacology to examine whether selective agonist administration results in receptor internalization.

Weaknesses:

1) It remains unclear why Met-enk pretreatment results in MOR desensitization.

Reviewer #3 (Public Review):

This is an important study that addresses a standing question regarding whether different types of opioid receptors expressed in the same cell signal independently of one another or operate as functional units (heterodimers). This study specifically explores this question by investigating the co-expression of mu and delta opioids receptors (MORs and DORs respectively) in cholinergic interneurons of the striatum. It has been known for some time that these neurons express both MORs and DORs, but functional interactions between them in these neurons has not been explored. The study uses a variety of methodologies to investigate these interactions and the experiments were generally well designed to test this hypothesis. The results are quite striking, suggesting that this study will be of high impact to help show that while these receptors may co-exist within cells, that they do not necessarily have to act in concert with one another, which is especially relevant in deciphering opioid signaling in neural function and neurotransmission. There are some concerns related to how the data were analyzed, raising some questions about how to interpret the data and whether certain conclusions are warranted, but these do not detract too heavily from a very interesting study.

Strengths:

1) The use of the NAI compounds in combination with receptor-specific antagonists is a nice way to measure receptor expression and internalization, especially across the whole of the striatum.

2) Performing measures in both male and female tissue is a strength.

3) There was a nice comparison made between receptor-specific ligands and the endogenous opioid peptide met-enkephalin.

4) The experiments were generally thorough with proper controls and used a variety of methodologies to address the hypothesis.

5) Even without detailed statistical reporting (see below) many of the findings appear to be robust.

Weaknesses:

1) The manuscript lacks detailed statistical analyses, mostly relying on descriptions of the data as interpreted by the authors. In most places there are no indications of which statistical analyses were utilized and what the outcomes of those analyses were. In the few places where analyses were indicated, it is not clear that the appropriate tests were used (e.g. using an unpaired t-test when an ANOVA, and possibly a repeated measures ANOVA should have been used). The description of the statistical analyses utilized in the paper also conflicts with what is actually used in most places. It is difficult to evaluate the data without this information. Figure 3 uses standard deviation as a measure of variability, but other figures use standard error and there is no explanation for why this is the case. In many cases there are statements regarding data being different than baseline or 100%, but these are not supported by any statistical measures as being truly different.

2) The results section makes claims about the kinetics of desensitization as a result of met-enkephalin treatment (referring to Figure 5E), but there is no indication that a time by treatment factor was significantly different. The authors cannot make claims about the rate of desensitization without an actual assessment of rate. Relatedly, the authors do not fully discuss that while MORs and DORs have different degrees of desensitization at the times they measure, the two receptors may have similar maximal extents of desensitization, just at different time scales. Figure 5D has the implication that MORs are beginning to desensitize, just at a slower rate than DORs. Essentially, the authors are trying to have it both ways: ignoring rates in most cases and implicating rates in one case without actually testing them.

3) The authors conclude that MORs do not internalize, whereas DORs do, but their time course does not align with previous experiments, involving a very long treatment followed by a long washout period. The treatment differences could play a role in their differential outcomes (MOR recycling v. DOR recycling). The authors should address this disparity either experimentally or discuss it as a limitation.

4) The staining of DORs (as inferred by CTAP treatment) in Fig 1Bc does not match the pattern of DOR expression in the literature, appearing like there is no DOR anywhere besides the most dorsolateral region of the striatum. This also conflicts with their data in Figure 3. This is curious and should be addressed/discussed. The species differences between figures could play a role in this or it could be experimental methods.

5) The authors used a variety of pharmacological agents and curiously failed to discuss instances where some of the agents didn't produce expected results. For example, morphine only partially decreased firing, which was surprising, but also wasn't discussed. CTAP and naloxone did not fully reverse the effects of DAMGO (Figure 5C), but this was glossed over.

6) It is curious that the authors found heterologous desensitization with met-enkephalin treatment, but did not explicitly test this with their receptor-specific ligands. This relates to a larger concern, and one that is lightly touched upon in the discussion: the indication that depending on the signaling pathway (G protein v. arrestin) there could be different outcomes for receptor function and regulation (i.e. biased signaling). It would be important for the authors to discuss this given that some of the pharmacological treatments they employ have different biases in their signaling which could affect their measured outcomes.

7) Experiments were performed on tissue from both male and female mice, but the proportion of each sex used in each experiment was not clear, aside from Figure 3 and its accompanying supplemental figure. While overall expression may not differ between sex, sex differences could account for variability in functional data and the sexes used should be indicated in each experiment or at least discussed as a limitation of the study.

8) The use of MOR knockout mice is a good control, but there are no details provided of how cholinergic interneurons were identified in these mice.

9) The description of the methods used to calculate desensitization (lines 236-240) did not seem to match what was actually performed and the methods did not clarify this. It is difficult to evaluate the data when it is not clear how the data were obtained.

10) The descriptions of MOR desensitization was muddled. It was described as having persistent inhibition (i.e. implied lack of desensitization), but the Table and Figures indicated that MORs do desensitize, just not to the extent that DORs do.

11) The authors cite literature that assessed cholinergic interneuron function in dorsal and ventral striatum and their staining data show expression of opioid receptors in both dorsal and ventral striatum, but they chose to focus on cholinergic interneurons in the ventral striatum. The authors should provide a clear rationale in the results section where this decision was made.

Reviewer #1 (Public Review): 

In this study the authors test whether and how serotype, genomic background, and genetic features are associated with host age in pneumococcal disease. 

The strength of the work lies in the high-quality analyses and the large dataset of whole genome sequences. The dataset has >4,000 genomes collected from infants and adults from a vaccinated Dutch dataset and an unvaccinated Thai dataset. The sets do not have much overlap regarding their genomic makeup. 

Authors find that within each group, there is a solid signal for genetic background. Specifically, when the data is plotted by serotypes and sequence types they find association with age (however, these differ between cohorts). Further the association with serotype was also observed from additional analyses investigates genes associated with carriage age. Together these data suggest that serotype and/or their genomic context, are associated with age. 

The authors also investigated whether any genetic variations are associated with age. Their analysis was not dependent on presence/absence alone, but also considered variations in the genome. The signal did not reveal a clear set of genomic regions that likely influence the molecular mechanisms of disease in an age-dependent manner. Nonetheless, the association of an adhesin factor with age deserves further consideration. 

Overall, the study suggests that age may be a consideration for vaccine design. Similar studies in additional datasets are warranted, and a bioinformatic framework for such studies is presented.

Reviewer #2 (Public Review): 

The authors looked for pneumococcal traits specific to host-age, by comparing pneumococcal genomics in carriage isolates from infants to that of their parents in the Netherlands and Maela. The authors report that host age was to some extend explained by pneumococcal genetic variability. Items to evaluate are potential sampling bias and the conclusions inferred from the data. 

Clear strengths of this study are an interdisciplinary team, robust bioinformatics analyses, and the large study populations. 

It is unclear what differences between pneumococcal isolates from infants and their parents were expected. The study design may be better motivated. 

Whether the results support the conclusion, depends on certain methodological aspects that require additional clarification. 1) An overview of the cohorts (in terms of percentage of carriers + degree of parent-child relatedness between pneumococcal isolates) is necessary to interpret the results. 2) Inclusion of repeat samples from infants and/or parents would mimic overrepresentation of genetic variants in a category. 

In the discussion the results should be put into context of previous pneumococcal GWAS studies that reported on relation to host age and to geography. In addition, it would be nice if alternative explanations for the observations and claimed causality would be evaluated. 

Provided high quality, the Dutch pneumococcal carriage genomes would add a rich source of data in the field. Because both serotype as well as lineage predicted host age to some degree, based on this study the necessity of a capsular polysaccharide-based vaccine-target seems not that evident. And even if specific serotypes (capsular polysaccharides) are targeted, these often co-occur with specific proteins. If the authors could demonstrate a stratified vaccination strategy for the populations involved, that would support their conclusion.

Reviewer #3 (Public Review): 

The goal of this study is to determine association between pneumococcal genome sequence variations with host age. The authors performed whole genome sequencing analysis of 4320 samples isolated from infants and adults from the Netherlands and Myanmar. While the manuscript is well written, it falls short of readily understandable data presentation and conclusive findings, which will hamper the translation of the sequencing data into the understanding of pneumococcal carriage dynamics and population-based vaccine design. 

Strengths:

1) Large sizes of pneumococcal carriage isolates from child and adult populations in two countries. The authors performed admirable whole genome sequencing of 1,329 pneumococcal isolates from the adult and Dutch cohort and 3,085 isolates from the Myanmar cohort. This should represent the largest sample size in any of this kind studies on pneumococcal carriage. 

2) Whole genome sequencing analysis of large numbers of bacterial strains. This study undertook genome sequencing analysis of 4320 pneumococcal strains, and presents a comprehensive set of data. 

3) Identification of the Sec-dependent serine-rich glycoprotein adhesin locus as an association candidate. Since the function of this locus has not been well characterized, this information is highly valuable for future investigation of pneumococcal differential carriage in child and adult populations. 

Weaknesses: 

1) The result presentation is too sketchy. While it is understandable that the sequencing data need to be compressed to a presentable format, essential information needs to be logically displayed for the sake of readers' understanding. As examples, the first section of the result section mentioned total numbers of isolates and serotypes from each cohort, but did not say how many of them were from children/adults. Figure 1 does have age information, but it is difficult to evaluate due to data transformation. Sequence clusters were mentioned without elaboration on what they mean. This style of data presentation may be readily comprehensive for sequencing gurus, but is hard to digest to the experimentalists like myself. 

2) There is a lack of experimental confirmation of any sequencing data. This manuscript is a nice example for traditional sequencing analysis of large pneumococcal carriage isolates. It is desirable for the authors to test the key finding to certain extents in the model systems - the Sec-dependent serine-rich glycoprotein adhesin locus as an association candidate.

Reviewer #1 (Public Review): 

Involvement of persistent neuronal activity in behavioral-cognitive functions represents an important research field of neuroscience. Persistent activity may be caused by intrinsic biophysical properties, neural circuit dynamics (i.e., synaptic reverberation in recurrent circuits) or a combination of both. Intrinsically generated persistent firing of excitatory neurons has been frequently observed in acute slices obtained from various brain areas. However, it is still unclear how cell-autonomous persistent firing is involved in cognitive functions (e.g. working memory) and whether intrinsically generated persistent firing underlies other types of behavior. 

The manuscript by Korvasová et al investigated glutamatergic (VGluT2+) neurons of medial septum and diagonal band of Broca (MSDB) and revealed that persistent activity driven by intrinsic excitability of these neurons controls locomotor activity. This group previously described how septo-hippocampal glutamatergic (VGluT2+) neurons control the initiation and velocity of locomotion as well as the entrainment of theta oscillations (Fuhrmann et al., 2015). The manuscript by Korvasová et al represents a continuation and important extension of their previously published research, providing important insights into the underlying cellular mechanisms. 

The experiments and data analysis have been carefully performed. The article is compact and well written. In vivo data and in vitro experiments show good coherence and data are represented in a well-structured, comprehensive manner. The main finding is novel and of significant importance. 

However, while the experiments have been carried out very competently and the paper is well written, I am a bit concerned that the manuscript in the present form is rather descriptive, without going deeper into the investigation of mechanisms underlying activation of intrinsic persistent activity. The authors point out that analysis of the intrinsic mechanisms and conductances underlying persistent firing in the MSDB is beyond the scope of the present paper and thoroughly debate possible mechanisms in the discussion section.

Reviewer #2 (Public Review): 

The medial septum is thought to be a central hub where generation of theta rhythm is coalesced with the regulation of movement. VGluT2-expressing (glutamatergic) MS neurons were identified as interdependently controlling both movement initiation and theta rhythm genesis. Stimulation of these neurons triggers movement and theta outlasting the duration of the stimulus. The Authors explored the mechanism whereby triggered activity persists beyond stimulation and whether movement-induction is independent of the emergence of theta. In behaving head-fixed VGluT2-Cre mice they demonstrate that specific activation of VGluT2 neurons initiated motion and theta paralleled by the persistent activity of MS neurons. Blocking synaptic transmission within the MS attenuated theta induction and reduced persistent neuronal activity without affecting movement initiation by brief VGluT2-activation. The latter manipulation reliably evoked persistent firing in MS slice preparations weakened by synaptic blockers. They conclude that movement is controlled by VGluT2 neurons independent of theta whereas for the latter interaction among the glutamatergic and other major MS neuron populations (cholinergic and especially GABAergic) is pivotal. They also claim that VGluT2 neurons' persistent activity depends on the intrinsic dynamics of these neurons modulated by the MS network. The study is nicely designed, and the Authors used well-established methods. The conclusion is in line with the major findings. However, the analysis falls short in several respects and there are some missed opportunities because of which this study is only an incremental step beyond what we already know about the "third" major neuron class of the MS. 

First, the Authors simultaneously registered the activity of multiple MS units, but they did not exploit the potential of multichannel data for separating and characterizing the response and single stimulus-triggered interaction of the major MS neuron types (seemingly, only multiunit activity was used). The timing (latency and duration) and dynamics (gradually accelerating, fluctuating or dampening alteration of activity) of their stimulus-triggered activity would reveal key details about what happens to the MS network following the injection of a brief excitatory pulse. What types of cells show persistent activity: only the regular, tonically firing (putative glutamatergic) neurons or even the theta bursting ones maintain their elevated activity outlasting the stimulus? A particularly important point would be to correlate the timing of theta and movement with that of the identifiable firing pattern types. Uncovering causal relationship among the activated interacting neurons would also be interesting: would it be possible to explain altered activity of a given type by the stimulus-evoked change of another type? 

Light stimulation would have given the opportunity of identifying the stimulated VGluT2 neurons, for example by applying a train composed of very short (1 ms) tagging light pulses at the end of a recording session for the later identification and isolation of VGluT2 units. Then, the response of these optically tagged VGluT2 neurons could have been compared to the other, unidentified neuron types. 

As stated, the in vitro experiments are especially suitable for exploring the mechanisms of persistent activity. Unfortunately, the question about the mechanism remains unanswered. While we are informed about the network-independence of persistent activity, no further attempts have been made to uncover cell-autonomous processes. We could also learn from the Results that the network dampens persistent activity probably by recurrent inhibition. Demonstrating the facilitated activity of putative inhibitory (fast, rhythmic spiking) neurons locked to the light-activation of VGluT2 neurons would disclose how stimulus-outlasting activity of VGluT2 neurons is controlled by inhibition. 

Sensory stimuli reliably evoke theta and movement comparable to what was detected in response to VGluT2 neurons' activation. Hence, an opsin-lacking reporter control should be added to the results for separating the animal's reaction to light from the effect elicited by selective VGluT2-stimulation.

Reviewer #3 (Public Review): 

In this study, the authors have discovered a novel activity type of activity within the MSDB - persistent activity. They show strong evidence that brief stimulation of glutamatergic neurons within the MSDB generates a sustained increase in neuronal firing that lasts long beyond the stimulus (and continues after animals stop running). They go on to determine the circuit mechanisms of this activity, and find that the persistent activity is maintained in the presence of synaptic blockers (in vivo and in vitro). There are subtle differences between the in vivo and in vitro results which slightly weaken the conclusions here. Overall, it seems true that synaptic connections within MSDB are not necessary for the persistent activity, therefore the following critique should be considered as minor. In vitro - the blocking of synapses reduces the magnitude of the firing rates during the persistent firing, whereas in vivo no reduction of magnitude is observed. It is unclear whether the difference between in vivo and in vitro data are because of slice dynamics or because the blockers are not as effective in vivo - there is no clear cut control that the blockers are working in vivo. Synaptic blockers in vivo do inhibit hippocampal theta (suggesting that the connections from glutamatergic -> PV interneurons are indeed blocked). More analysis of MSDB spiking in the blocked condition or more in depth presentation of the inhibited theta could bolster the claim that the synaptic block is effective in vivo - which would strengthen the conclusion that intra-septal circuitry is not necessary for the persistent activity. In lieu of that, it may be better to soften the conclusion (the in vitro data suggest that persistent activity does not require intra-septal circuitry (as concluded), however the magnitude of the activity is dependent on intra-septal circuitry). 

A more serious weakness of the work concerns whether the persistent firing occurs under normal physiological conditions (i.e. - with no optogenetic push). The title of the manuscript suggest that persistent activity is linked to locomotion and this suggests that the persistent activity is a physiologically relevant mechanism. There are some data presented that show that during voluntary running the MSDB neural activity is increased - however there is not a clear presentation of the data that shows us that increased activity during voluntary running is persistent. In the stimulation experiments, the persistent activity is sustained (with lower magnitude) well after the animal stops running. Is this the case for voluntary running? A clear presentation of persistent firing associated with voluntary running epochs would greatly strengthen the manuscript - in that it would prove that persistent firing occurs under physiological conditions.

Reviewer #1 (Public Review): 

In multicellular eukaryotes,  reproduction usually proceeds through a single-cell stage via propagule cells (germ cells) of some kind, like the zygotes resulting from gamete fusion in animals and flowering plants. In such organisms, inheritance of nuclear genomes from one generation to the next is a relatively straightforward problem when compared to that of inheriting non-nuclear genomes (e.g. mitochondrial or chloroplast genomes), which often exist at very high copy numbers that are not always the same throughout the life cycle of the reproductive cell lineage that gives rise to the gametes. This complex problem is nevertheless important in evolution because allelic changes in these non-nuclear genomes can impact the phenotypes, and therefore potentially the fitness, of the cells, tissues, and organisms that house them. 

In animals, the observations that (a) the gamete precursor cells (primordial germ cells = PGCs) in embryos, or postembryonic gamete precursors (oogonia that have not yet become mature oocytes) typically have far fewer copies of mitochondria than the oocyte that will give rise to the zygote and (b) mitochondrial genome allelic variance is typically higher in embryonic PGCs than in post-embryonic germ cells, have led to the acceptance that some kind of regulated mitochondrial culling occurs at some point between initial PGC specification and the end of gametogenesis. What is less clear is exactly when along this germ cell life cycle trajectory this culling takes place, what the specific evolutionary, cellular or molecular mechanisms are that regulate it, and which mechanism(s) best explain the observed pattern of inherited mitochondrial genomes in populations. 

This manuscript addresses these problems with the approach of developing a computational evolutionary model to see how well different assumptions about when and how mitochondrial culling takes place, are able to predict the observed distribution of mitochondrial mutations in some human populations for which data are available. The authors test the fit of three hypotheses to these data: (1) imposing a bottleneck at the PGC stage by limiting the number (and variance) of mitochondria at PGC stages; (2) selection against oogonia that have "bad" mitochondria; (3) preferential accumulation of "good" mitochondria, pooled from multiple oogonia, into those oogonia that will go on to complete oogenesis. They find that the third model fits the data better than the first two. They then compare these hypotheses in a multi-generational model. They report that the third model fit the data better over a wider range of selective pressures, than the first two, although all three models have some explanatory power within the range of mutation rates explored. 

This problem is an important one and the modeling approach could add important complementary perspective to existing empirical data, or suggest new avenues of experimentation for the future. The authors have tried to extract much biological data from the empirical data to inform their parameter and boundary choices for the model, and explained quite clearly their choices, which is an excellent approach. However, a weakness of the study is that the parameters that inform the model, and many of the assumptions that underlie the logic they use to interpret their results, are drawn from a wide range of different biological systems, but the model aims to test the fit of specific hypotheses to human data only. There are many differences in every aspect of germ line segregation, PGC development, oogenesis, and mitochondrial behaviour across animals, and which aspects of these things have strong evidence for universal conservation remains unclear. Nevertheless, in this MS the authors make broad claims about universality of conclusions in some cases, and in others appear to be restricting their conclusions to explaining human data only. A second area for improvement is that some well-documented observations on mitochondrial and germ line biology that are relevant to interpreting their observations, are not considered or claimed to be absent or irrelevant (e.g. paternal mitochondrial inheritance, germ lineage separation in flowering plants), and the existing empirical literature providing evidence for these things in at least some systems is not discussed at all, not even to explain why the authors deem this evidence unimportant for their model or for the conclusions they draw from it.

Reviewer #2 (Public Review): 

Colnaghi, Pomiankowski and Lane develop models to investigate the effects of population genetic forces on mtDNA variation within germline cells to address unanswered questions about the selective pressures on mitochondrial genomes. The models are based on updated information about germline development in mammals, including humans. Realistic parameters of mutation, selection and sampling drift are applied to the demography of cells from stem cell through mature oocytes. Three selective processes are considered: at the level of the individual (zygote), the cell, and the mitochondria. The results indicate that selection among mitochondria is the most likely process to match empirical, clinical data for mitochondrial mutation loads. This is based on modeling the mixing of mitochondria following cytoplasmic transfer of cellular contents among individual oogonia in germline cysts into the emergent primary oocyte. The proportion of mutant mtDNAs, or the strength of selection on mutant vs. wild type mtDNAs, proved to have the most impact on model outcomes and correspondence to clinical data. 

The paper is clearly written and addresses controversies that have emerged in earlier studies. Notably, the results suggest that the bottleneck effects on the mtDNAs population during germline development has less of an effect that previously thought on the selective landscape that may permit mtDNA to persist despite the consequence of Muller's ratchet decay. A pleasant aspect of the paper is its clear presentation of quantitative approaches used in both the computational and evolutionary models presented. The paper presents an advance of interest to a general readership.

Reviewer #1 (Public Review): 

This is an interesting study focusing on the under-investigated role of mitochondria distribution and function for postnatal lung development in the mice. The study focuses on the impact of deleting two mitochondria related proteins: Tfam, a master regulator of mitochondrial transcription, and Miro1, a protein regulating normal subcellular distribution of mitochondria. Both proteins have been deleted in globally as well as lung epithelial- or mesenchymal-specific in mice and the lungs have been anayzed by histological analysis, immunostainings and PCR. Consistently, a defect in postnatal alveolar formation was found. As a potential mechanisms the author report that the number of myofibroblasts marked by PDGFRA was reduced in the absence of epithelial Tfam and that secretion of PDGF ligand from Tfam- and Miro1-deficient alveolar epithelial cells was compromised, while transcription activity was not altered. While most of the results were largely based on descriptive gene and protein data generated in transgenic mice, myofibrobasts were further isolated from these mouse lungs and subjected to a migration assays. The data suggest a new concept of mechanisms involved in lung injury and potentially human disease, which needs further exploration.

Reviewer #2 (Public Review): 

The authors in this manuscript present data, showing defection of mitochondrial activity and distribution in both alveolar epithelial cells and mesenchymal cells impairs alveolar formation. The initial rationale of the manuscript is gaining the insight that mitochondria display dynamic distribution during alveolar formation. They demonstrate two different ways through which the defection of mitochondria in alveolar epithelial cells and mesenchymal cells impairs secondary septa formation respectively. Further studies identify mTORC1 pathway as a central player in controlling mitochondrial function during alveolar formation. What is more interesting, authors indicate a connection between mitochondrial function and pathogenesis of COPD/emphysema. Considering that mitochondria is the hub of cellular metabolic network, this manuscript may raise broad attention on mitochondria as well as cellular metabolism as drivers of tissue remodeling. 

It is of interest that authors conducted experiments on various mouse models with defective mitochondria in either alveolar epithelial cells or mesenchymal cells. However, confirmation of the inactivated genes (eg. Tfam, Mirol) and subsequently impaired mitochondrial function is missing. Therefore, the conclusion that loss of mitochondrial activity disrupts alveologenesis is questionable with insufficient mitochondrial function analysis. Additionally, since high expression of β-galactosidase is a feature of senescent cells and Tfam-deficient cells show premature senescence, mouse line of Tfamf/f; Pdgfaex4COIN/+; Sox9Cre/+ can not properly indicates Pdgfa expression.

Reviewer #3 (Public Review): 

In the manuscript entitled "Acquisition of cellular properties during alveolar formation requires differential activity and distribution of mitochondria" Zhang et al. argue that mitochondrial function and spatial distribution within specific cells accounts for secondary septation. Use of transgenic mouse models, in vitro experiments and data from human tissue provides support for the importance of the mitochondria in alveolar epithelium and myofibroblasts. The use of multiple transgenic models to relatively selectively deplete mitochondrial number or function in a cell specific manner is a a strength. These models will be of interest to the broader pulmonary biology community. However, the conclusions of the manuscript would be strengthened further still by evidence demonstrating that alveolarization was not affected by loss of mitochondria in some cell-specific manner. As currently configured, loss of mitochondria in any of the cell types compromised alveolarization. This observation prompts the question of whether constraining energy availability in any cell in the alveolus might result in compromise of alveolarization? For example, does loss of mitochondrial function in endothelial cells in the microcirculation have the same effect? It is important to distinguish between a fundamental cellular process that is required for normal function of any given cell type and one that is especially significant in the cell types emphasized in the present study. Given the close relationship between cellular bioenergetics and cell survival and proliferation, cell-specific interrogation of these properties in the context of altered mitochondrial number or distribution would be important.

Reviewer #1 (Public Review): 

The manuscript by Lalanne and Li aims to provide an intuitive and quantitative understanding of the expression of translation factors (TFs) from first principles. The authors first find that the steady-state solutions for translation sub-processes are largely independent at optimality. With a coarse-grained model, the authors derive the optimal expression of translation factors for all important sub-processes. The authors show that intuitive scaling factors can explain the differential expression of translation factors. 

The results are impressive. However, as detailed in the major comments, the choice of some important parameters is not sufficiently justified in the current version. In particular, it is not clear to what extent parameter choice and rescaling was biased toward achieving a good agreement with the experimental data. 

Major comments:

1) The work assumes that reaction times per TF are constant. That may be true at the highest growth rates, but it might not hold for conditions with lower growth rates. The data of Schmidt et al. (Nat. Biotechnol. 34, 104 (2016)) would allow to compare the predictions to proteome partitioning in E. coli across growth rates. It is ok to restrict the present work to maximal growth rates, but then this caveat should be made explicit. This last point also concerns ignoring the offset in the bacterial growth laws, which is only permissible at fast growth; that also should be stated more prominently in the manuscript; see also the legend of Fig. 1, "Our framework of flux optimization under proteome allocation constraint addresses what ribosome and translation factor abundances maximize growth rate". 

2) The diffusion-limited regime considers only the free and idle reactants. For some translation factors, the free state only accounts for a small fraction of its total concentration. In this case, the diffusion-limited regime only explains a small fraction of the TFs. For example, most of EF-Ts may not be in its free state: in simulations with in vitro kinetics, free EF-Ts accounts for 6%-48% of its total concentration (Supplementary Data 3 in [21]). Can the authors use in vitro parameters (or other ways) to provide a rough estimate of the fraction of free TFs? Including this might allow to make quantitative statements about some of the deviations seen in Fig. 4, as most of the TFs are underestimated. 

3) "A factor-independent time τ_ind (e.g., peptidyl transfer), which does not come into play in our optimization framework, was added to account for additional steps making up the full elongation cycle." - what happened to this time? I couldn't find it anywhere else in the paper. What value was chosen, and by what rationale? 

4) Fig. 4: The agreement is very impressive, especially given the simplifying assumptions. However, there are some questions relating the choice of parameters. 

a) Were any parameters fitted? Which, how? What about τ_ind, for example (see above)? 

b) The "predicted" value for ribosomes is calculated from observed data (in a way described on p. S34 that I found incomprehensible, and would likely look very similar regardless of the predicted values for the TFs). According to the section "Equipartition between TF and corresponding ribosomes", the corresponding ribosomes can be quantified in the authors' scheme, too, by the method used for deriving optimal TF concentrations in equation 5. Why didn't the authors directly use the sum of these estimations as the optimal ribosome concentration in Fig. 4? In the current state, it does not seem fair to include the ribosome with the other predictions. 

c) Predictions are for a specific growth rate (doubling time 21min). Was this growth rate also averaged over the three organisms? What were the individual values? <br> These points would need to be discussed in the main text. 

5) In the same vein, in a footnote (!) to Table S4: "#For the ternary complex, the total mass of tRNA+EF-Tu was converted to an equivalent amino acid length." - I can see that this is important to get reasonable results, but it constitutes a major deviation from the strategy proclaimed throughout the main text: that the predicted effects result from a competition for fractions of the limited proteome. That rationale has to be changed (and explained in the main text), or the predictions in Fig. 4 should be based on calculations using only the protein part of TCs (i.e., EF-Tu). 

6) S9: "we anchored our association rates to the estimated in vivo association rate for the ternary complex, 𝑘^𝑇𝐶 = 6.4 μM−1s−1 [13], and rescale the association rate by diffusion of related components" - in comparison, the diffusion limited k^TC is >100. If I understand this correctly, you simply rescale ALL on-rates by 100/6.4 = 15.6. If that is (qualitatively) correct, you would need to discuss this point (and the derivation of the scaling factor) explicitly in the main text.

Reviewer #2 (Public Review): 

This paper presents a theoretical analysis of the abundance of components of the translation machinery (ribosomes, initiation, elongation and release factors, tRNA synthetases) in bacteria. These proteins make up a large fraction of the total proteome and their abundance is closely linked to cell growth. That the abundance of these proteins is adjusted such as to maximize the growth rate has been postulated a long time ago, but was so far only studied in detail for ribosomes and EF-Tu, the most abundant elongation factor. Here, the authors provide a complete analysis based on this idea and derive the optimal stoichiometry for all these factor, which they find to be in good agreement with the observed abundance in different bacteria (abundance ratio are conserved between species). 

The fundamental idea behind the line of though used here is that high abundance of all factors increases the protein synthesis flux via the rate of the corresponding step, but also decreases the abundance of other factors or of ribosomes, because of a proteome constraint (basically a finite budget constraint, if more ribosomes/translation resources are used to make one translation factor, fewer ribosomes are available to make the others). 

The authors also give an interesting new interpretation to the principle that different factors are equally limiting, which they phrase as these factors taking up the same effective proteome fraction. 

A nice feature of their analysis is that the optimal ratios can all be obtained analytically, which provides biophysical interpretations for the quantitative ratios. 

Some limitations of the analysis remain, and these are discussed in the paper. The most important one is that the fully quantitative analysis is only possible assuming that all reactions are diffusion limited, so that only minimal information about in vivo kinetics of the reactions is required. Diffusion limitation is not unreasonable and has been assumed in earlier work as well, but is at this point an assumption.

Reviewer #3 (Public Review): 

The authors provide a reasonable and useful way to coarsegrain complex aspects of translation for finding the optimal TF and ribosome stoichiometry. They provide a relatively simple yet accurate way of finding this optimum. They have also provided good examples of application of their method to find this optimal stoichiometry. 

That said, a convincing first-principles-based approach must simultaneously optimize all three proteomic sectors: the metabolic protein sector; the TF sector; and the ribosome sector. The authors' method optimizes the latter two while making the metabolic protein sector follow the experimentally determined scaling with growth rate: φ_P = λ/v (where v is the experimentally obtained nutrient dependent scaling parameter and λ is the growth rate). This scaling parameter is obtained from systems that are already at the optimal stoichiometry, but the authors' method requires this scaling to hold with the same scaling parameter even when the system is not at optimal stoichiometry. I expect that these three proteomic sectors are interrelated through more than just the scaling with growth rate (the scaling is just the emergent behavior at the optimum), and their effects on each other need to be considered in greater depth when the system is away from the optimum. 

Upon simultaneously optimizing all three sectors, it is reasonable to assume that optimal φ_P ultimately will follow the observed scaling (since that is as observed from cells that are assumed to have optimal stoichiometry). Thus, the authors' method seems to involve fixing one of the three sectors at the observed optimum beforehand and then optimizing the other two, thus arriving at partitioning that matches experimental observations. That is, the optimal partitioning of TF and ribosome sector can indeed be calculated accurately, but this require *prior* knowledge of the behavior at optimum of the metabolic protein sector. Similarly, one could take the observed optimum for ribosome sector as given and then try to optimize metabolic protein and TF sectors etc. All these cases are likely to yield the correct optimum stoichiometry, but do not properly answer the question of how all three sectors are simultaneously optimized. Thus, they do not accurately describe the behavior of the system away from the optimum. 

Despite these shortcomings, what is reported here is a significant step forward: a way to find the optimal stoichiometry of TFs and ribosomes given the optimal stoichiometry of metabolic proteins. Clarity in acknowledging the challenges in describing the behavior of the system away from the optimum would help. Specifically, the limitations of assuming the optimal stoichiometry of metabolic proteins. Alternately, clear justification for why considering the simultaneous optimization of metabolic protein partitioning is not important or relevant.

Reviewer #1 (Public Review): 

In this paper, Strauss et al. examined the molecular identity of the viroplasms that form in rotavirus infected cells. They demonstrate using smFISH and DNA-PAINT that rotavirus RNA transcripts localize to viroplasms, and that these RNA aggregates begin forming around 4 hours post infection. They use a combinatorial method, universal DNA exchange with smFISH, to visualize all eleven unique RNA transcripts in viroplasms. They use RNA-seq and smFISH analyses to determine that RV RNA transcripts comprise 17% of all coding transcripts in an infected cell, but that transcripts are not present in stoichiometric amounts. Finally, they construct a virus carrying a seg9-EGFP fusion gene with UTRs to explore the role of the 3' UTRs in transcript localization to viroplasms. 

The main conclusion that most viroplasms contain all 11 segments of the virus is well supported, although it is somewhat expected. The work would be improved by more detailed quantification of the viroplasms examined. Specifically, is the differential stoichiometry of each segment in viroplasms true in every viroplasm in the same manner (perhaps reflecting a fundamental difference in partition mechanisms), or is there variation between individual assemblies? 

An interesting conclusion of the work is that Nsp2 is required for formation of the viroplasms, however, this knockdown also reduces the levels of various viral RNAs. Thus, it remains unclear, from the work in this manuscript, if Nsp2 plays a structural role in forming viroplasms, or if Nsp2 is required for efficient expression of sufficient vRNAs to drive viroplasm assembly. 

Another intriguing observation is that a viral RNA expressed from the virus efficiently assembles into viroplasms, while a viral RNA expressed from a nuclear encoded gene with a poly(A) tail is less efficiently recruited into viroplasms. This observation is used to argue that the 3' UTR drives the specificity of partitioning into viroplasms. However, this experiment should be interpreted carefully given the absence of quantification of the RNA partitioning into viroplasms, the differences in the coding sequences of the two RNAs, and the fact that one is produced in the nucleus, which could lead to additional differences (e.g. m6A modifications), and one is a cytoplasmically synthesized RNA. Improving the quantification and interpretation of this experiment will improve the manuscript.

Reviewer #2 (Public Review): 

Strauss et al. studied the mechanism of RNA recruitment to ribonucleoprotein condenstates using rotavirus. They used multiplexed DNA-barcorded smFISH and DNA-PAINT for direct visualization of the RNP condensates in cells. They observed the early onset of viral transcript oligomerization before the formation of viroplasms and the process of enrichment in RNP condensates. They imaged all eleven transcript in a RNP condensate and quantified the amount of the transcripts. Based on these findings, they suggested a selective RNA enrichment mechanism of rotavirus. The authors conducted well the experiments with good control measurement. The results look significant enough for understanding the RNA recruitment to RNP condensates and provide a potential usefulness of their approach in future work.

Reviewer #1 (Public Review): 

In this manuscript, the authors used a very innovative approach to address crucial questions in HHV-6 biology. The hypervariable regions within the DR shed light on exciting aspects including virus transmission, phylogeny and the prediction of integration sites in iciHHV-6 individuals. The authors provide a tremendous amount of data and carefully interpreted these in the context of HHV-6 biology. Their analysis was done on a pretty sizeable data set, which included a large number of families and provided important insights, including the observation that a mother with iciHHV-6 can horizontally transmit her inherited virus to her non-iciHHV-6 son. The sequence analyses are complemented with several state-of-the-art STELA assays that demonstrated that a single DR as well as a complete DR-U genome can be excises from the telomere integrated state. In addition, another STELA approach allowed the identification of integrated virus genomes in non-iciHHV-6 genomes that should be also used in follow up studies. Overall, this manuscript is very well written (despite the huge amount of complex data), the conclusions are justified and it addressed critical question in the field. Only minor changes should be made prior to publication as outlined below.

Reviewer #2 (Public Review): 

Wood et al. use amplicon sequencing of a highly variable (DRR-pvT1) regions in the HHV-6A/6B genome to track transmission chains of inherited and acquired HHV-6. The authors sequenced the DRR-pvT1 regions from 102 cases and demonstrate the variability of the HHV-6B DRR-pvT1 between unrelated cases and within related groups. Based on similarity between sequenced iciHHV-6B DRR-pvT1 regions to those of genomes with a previously determined integration site, they predicted the integration sites of iciHHV-6B genomes, and some were confirmed by subtelomere-iciHHV-6B junction sequencing. Variability in DRR-pvT1 is also used to discuss within family inheritance patterns, and demonstrate passing of a reactivated ici-HHV6B genome from mother to son as an acquired infection. 

In addition, using PCR based methods, the authors estimate the rate of telomere integration in-vivo (saliva and kidney samples). Next, they study the phenomenon of partial iciHHV-6B excision and telomere truncation that occurs through strand invasion of the telomer 3' overhang into the DR region forming a t-loop that can is excised out. Using PCR with specific primers, the authors estimate the rate of excision and subsequent telomere lengthening in different samples.

Reviewer #3 (Public Review): 

Overall, this is an impressive, tour-de-force study on HHV6 genetics, telomere integration, reactivation, and transmission. There are some minor concerns with the PCR methods of detection and the limited functional analysis with respect to viral latency, reactivation, and transmission.

Reviewer #1 (Public Review): 

The authors have conducted an investigation in to the impact of evolution on the endometrium. 

A major strength of this work is the use of published single cell RNA seq and ChIPseq data sets to support their findings. The major weakness is the use of an algorithmic approach to deconvolve a specific endometrial signal. 

The authors have broadly acheived their aims and the results support the conclusions. The weakness with the alogrithmic approach to determine specific transcriptomic signal have been carefully addressed and the data presented in figure 1 is persuassive. I would be still slightly concerned that issues with the comparison of a receptive and non-receptive endometrium have not been fully accounted for. It would be nice to see this. It would also be of interest to understand the impact of diapause on this analysis. Can the authors comment? 

The evolutionary impact on the devloping endometrium is of major importance to translational investigation of advserse events during human pregnancy. The methods presented are well described and straight forward fpor a computional group to follow.

Reviewer #2 (Public Review): 

The authors of this manuscript did a wonderful job explaining how gene expression patterns have evolved during recent mammalian evolution, particularly within primates. They used a wide variety of approaches including tissue-based RNA-Seq data, single cell omics, and cell and culture approaches to make the case that a number of changes have occurred during the evolutionary history of eutherian mammals. Most interesting was the finding that the serotonin system, normally associated with neuronal function, appears to play a large role in the pregnant endometrium. Additionally, the roles of genes involved in maternal fetal immune-tolerance and tissue remodeling were confirmed. The methods used for data analysis are rigorous and reproducible and the conclusions of the study are warranted. All data are publicly available furthering the transparency of the author's approach. For those interested in the evolution of pregnancy this is an excellent model of how to study particular species and tissues.

Reviewer #3 (Public Review): 

Strengths:

Mika and colleagues used a comparative transcriptomics approach to identify genes (based on binary {plus minus} expression calls) that were recruited or eliminated in the evolutionary biology of the human endometrium. The recruited genes were then analyzed for potential roles in pregnancy pathophysiology using bioinformatic approaches. The study contributes to ongoing interest in the effect of human evolution on the pathophysiology of human pregnancy, and it is proposed that evolutionary studies of this kind, in combination with traditional methods, can be used to better characterize the genetic architecture of disease. 

Weaknesses: 

The conclusions of the paper are mostly supported by the analyses. However, it is unclear how the evolution of endometrial cell gene expression would contribute to adverse pregnancy outcomes since such conditions would compromise reproduction and therefore be selected against.' 

It is stated that hundreds of genes that gained or lost endometrial expression in the human lineage were identified but these are not listed. Three genes were examined in detail for their roles in pregnancy and human-specific maternal-fetal communication but the rationale for selecting these genes is lacking. 

The uncertain quality of the source transcriptome data is a weakness. The level of transcriptome "noise" in the data sets is unclear. It appears that the transcriptome data from most species was from bulk tissue total RNA and stage of pregnancy and anatomical site (e.g., over the placenta or at the fetal membranes) is not specified. Dissecting and isolating pregnancy endometrium is not trivial and as such this is a likely source of significant variation. Data on placenta-specific gene expression is provided to demonstrated lack of trophoblast cell contamination, however, this does not mean that the RNA was exclusively from endometrial cells since numerous non-endometrial cells are present a the maternal-fetal interface. Consequently, binary gene expression as on/off based on a 2 TPM threshold is problematic since it may be affected by the proportion of endometrial cells in the sample rather than gene expression in endometrial cells. In addition, although the application of binary encoding is understandable, important biology may be missed because gene function extends beyond on/off state. 

Use of the Vento-Tormo scRNAseq data set (Nature 2018, 563:347-353) to establish the first trimester endometrial cell transcriptome is a strength. The study would be improved, however, if those data were compared with the term maternal-fetal interface scRNAseq data set produced by the Gomez-Lopez group (Pique-Regi et al. eLife 2019;8:e52004). 

In Caveats and Limitation, the authors admit that they are unable to identify truly human-specific gene expression changes in pregnancy endometrium, yet sweeping conclusions are made about the changing transcriptome of the human endometrium and how the presumed changes in gene expression contribute to extant pathophysiology. The claim that the comparative transcriptomic approach (based on binary gene expression) provides and insight into human pathophysiology is therefore questionable.

Joint Public Review:

The similar degenerative conditions DM1 and DM2 are respectively caused by tetranucleotide expansions in DMPK and CNBP encoding genes. This paper provides strong evidence that the disease mechanism underlying DM2 muscular dystrophy is mediated not just by the well accepted mechanism of pathogenic RNA transcribed from simple repeat sequences. The paper presents a detailed characterisation of multiple CNBP fly knock-down strains, all displaying similar motor impairments. The authors link the dysfunction to reduce translation of ODC, a key enzyme in polyamine metabolism, and to a reduction in putrescine. They go on to show that feeding putrescine or upregulating ODC can rescue the CNBP mutant defect. This strongly suggests that the primary reasons for the motor defects in CNBP mutants is a polyamine metabolism defect. This is significant because polyamines such as putrescine and spermidine are important for muscle function. The experiments are well done, the data robust and convincing. What remains to be proven how well the Drosophila model mimics human disease and how relevant the CNBP - ODC - polyamine axis will prove to be to the pathology, therapy or prevention of human DM2.

Reviewer #1 (Public Review): 

Previous studies have indicated that neurons in different cortical areas have different intrinsic timescales. However, the functional significance of the difference in intrinsic timescales remains to be established. In this study, Pinto and colleagues addressed this question using optogenetic silencing of cortical areas in an evidence accumulation task in mice. While head-fixed mice performed in an accumulating-towers task in visual virtual reality, the authors silenced specific cortical regions by locally activating inhibitory neurons optogenetically. The weight of sensory evidence from different positions in the maze was estimated using logistic regressions. The authors observed that optogenetic silencing reduced the weight of sensory evidence primarily during silencing, but also preceding time windows in some cases. The authors also performed a wide-field calcium imaging and derived auto-regressive term based on a linear encoding model which include a set of predictors including various task events, coupling predictors from other brain regions in addition to auto-regressive predictors. The results indicated that inactivation of frontal regions reduced the weight of evidence accumulation on longer timescales than posterior cortical areas, and the autoregressive terms also supported the different timescales of integration. 

The question that this study addresses is very important, and the authors used elegant experimental and analytical approaches. While the results are of potential interest, some of the conclusions are not very convincing based on the presented data. Some of these issues need to be addressed before publication of this work. 

Major issues: 

1) There are several issues that reduce the strength of the main conclusion regarding the timescale of integration using cortical silencing. 

a) The main analysis relied on the data pooled across multiple animals although individual animals exhibited a large amount of variability in the weights of integration across different time windows. Also, some mice which did not show a flat integration over time were excluded. This might also affect the interpretation of the analysis based on the pooled (and selected) data. How the individual variability affected the main conclusion needs to be discussed carefully. 

b) The main conclusion that the frontal areas had longer integration windows largely depends on a few data points which relied on a very small number of samples (n = 4 or 3). This is, in part, because of the use of pooled data and because the number of samples comes from the alignment of the data with different timing of inactivation. This analysis also appears to suffer from the fact that the number of sample is biased toward the time of inactivation (y = 0 which had n = 6) compared to the preceding time windows (y = 50 and 100, which had n = 4 and 3, respectively). 

c) The clustering analysis uses only 7 data points corresponding to the cortical areas examined. The conclusions regarding the three clusters appear to be preliminary. 

2) The authors' conclusion that "the inactivation of different areas primarily affected the evidence-accumulation computation per se, rather than other decision-related processes" can be a little misleading. First, as the authors point out in the Results, the effect can be "the processing and/or memory of the evidence". Given that the reduction in the weight of evidence occurs during the inactivation period, the effect can be an impairment of passing the evidence to an integration process, and not accumulation process itself. Second, as discussed above (1b), the evidence supporting a longer timescale process (characterized as "memory" here) is not necessarily convincing. Additionally, the authors' analysis on "other decision-related processes" is limited (e.g. speed of locomotion), and it remains unclear whether the authors can make such a conclusion. Overall, whether the inactivation affected the evidence accumulation process and whether the inactivation did not affect other cortical functions remain unclear from the data. 

3) Different shapes of the autoregressive term may result from different sensory, behavioral or cognitive variables by which neurons in each brain area are modulated. In other words, if a particular brain area tracks specific variables that change on a slow timescale, the present analysis might not distinguish whether a slow autoregressive term is due to the intrinsic properties of neurons or circuits (as the authors conclude), or neuronal activities are modulated by a slowly-varying variable which was not included in the present model.

Reviewer #2 (Public Review): 

Pinto et al use temporally specific optogenetic inactivation across the dorsal cortex during a navigation decision task to examine distinct contributions of cortical regions. Consistent with their previous findings (Pinto et al 2019), inactivation of most cortical regions impairs behavioral performance. A logistic regression is used to interpret the behavioral deficits. Inactivation of frontal cortical regions impairs the weighting of prior sensory evidence over longer timescale compared to posterior cortical regions. Similarly, the autocorrelation of calcium dynamics also increases across the cortical hierarchy. The study concludes that distributed brain regions participate in evidence accumulation and the accumulation process of each region is related to the hierarchy of timescales. 

Identify the neural substrate of evidence accumulation computation is a fundamentally important question. The authors assembled a large dataset probing the causal contributions of many cortical regions. The data is thus of interest. However, I have major concerns regarding the analysis and interpretation. I feel the results as presented currently do not fully support the conclusion that the behavioral deficit is related to evidence accumulation. Alternative interpretations should be ruled out. Another major concern is the variability of the inactivation effect across conditions. The assumptions for pooling inactivation conditions should be better justified. Finally, some framing in the text should more closely mirror the data. Most notably, the data does not casually demonstrate that the hierarchy of timescales across cortical regions is related to evidence accumulation since the experiments do not manipulate the timescales of cortical regions. The two phenomena might be related, but this is a correlation based on the present findings.

Reviewer #3 (Public Review): 

This study examines how the timescale over which sensory evidence is accumulated varies across cortical regions, and whether differences in timescales are causally relevant for sensory decisions. The authors leverage a powerful behavioral paradigm that they have previously described (Pinto et al., 2018; 2019) in which mice make a left vs. right decision in a virtual reality environment based on which side contains the larger number of visual cue "towers" passed by the "running" head-fixed mouse. The probability of tower presentation varies over time/space and between the left and right sides, requiring the mice to integrate tower counts over the course of the trial (several seconds/meters). To examine the contribution of a particular cortical region to sensory evidence accumulation, the authors optogenetically inactivated activity during several sub-epochs of the task, and examined the effect of inhibition on a) behavioral performance (% correct choices) and b) the strength of the contribution of sensory evidence to the decision as a function of time/space from the inhibition onset. Finally, the authors qualitatively compared the timescale of evidence accumulation identified for each region to the autocorrelation of activity in that region, calculated from reanalyzing the author's published calcium imaging data set (Pinto et al., 2019) with a more sophisticated regression model. 

The methodology and analyses are leading edge, ultimately allowing for a comparison of evidence accumulation dynamics across multiple cortical regions in a well-controlled behavioral task, and this is a nice extension of the authors' previous studies along these lines. The study can potentially be built on in two broad directions: a) examining how circuits within any of the regions studied here function to accumulate sensory evidence, and b) addressing how these regions coordinate to guide behavior. Overall, while the study is generally strong, addressing some points would increase confidence in the interpretation of the results. Specifically: 

In describing the contribution of evidence to the decision, and how it is affected by inhibition (primarily Fig. 2), there is a confusing conflation of time and space. These are of course related by the mouse's running speed. But given that inactivation appears to consistently cause faster speeds (Fig. 2-Fig. S1), describing the effect of inhibition on the change of the weight of evidence as a function of *space* does not seem like the optimal way to examine how inactivation changes the *time*scale of evidence accumulation. The authors note in Fig. 2-Fig S1 that inactivation does not decrease speed, but it still would confound the results if inactivation increases speed (as appears to be the case; if not, it would be helpful for the authors to state it). Showing the data (e.g., in Fig. 2) as a function of time, and not distance, from laser on would allow the authors to achieve their aim of examining the timescale of evidence accumulation. 

Performing the analyses mouse by mouse, instead of on data aggregated across mice, would increase confidence in the conclusions and therefore strengthen the study. Mice clearly exhibit individual differences in how they weight evidence (Fig. 1C), as the authors note (line 81). It therefore would make sense to compare the effect of inactivation in a given mouse to its own baseline, rather than the average (flat) baseline. If the analyses must be performed on data aggregated across mice, some justification should be given, and the resulting limitations in how the results should be interpreted should be discussed. For example, perhaps there are an insufficient number of trials for such within-mouse comparisons (which would be understandable given the ambitious number of inactivated regions and epochs)? 

The method of inactivating cortical regions by activating local inhibitory neurons is quite common, and the authors' previous paper (Pinto et al., 2019) performed experiments to verify that light delivery produced the desired effect with minimal rebound or other off-target effects. Since this method is central to interpreting the results of the current study, adding more detail about these previous experiments and results would reassure the reader that the results are not due to off-target effects. Given that the cortical regions under study are interconnected, do the previous experiments (in Pinto et al., 2019) rule out the possibility that inactivating a given target region does not meaningfully affect activity in the other regions? This is particularly important given that activity is inhibited in multiple distinct epochs in this study.

Reviewer #1 (Public Review):

1) The user manual and tutorial are well documented, although the actual code could do with more explicit documentation and comments throughout. The overall organisation of the code is also a bit messy.

2) My understanding is that this toolbox can take maps from BigBrain to MRI space and vice versa, but the maps that go in the direction BigBrain->MRI seem to be confined to those provided in the toolbox (essentially the density profiles). What if someone wants to do some different analysis on the BigBrain data (e.g. looking at cellular morphology) and wants that mapped onto MRI spaces? Does this tool allow for analyses that involve the raw BigBrain data? If so, then at what resolution and with what scripts? I think this tool will have much more impact if that was possible. Currently, it looks as though the 3 tutorial examples are basically the only thing that can be done (although I may be lacking imagination here).

3) An obvious caveat to bigbrain is that it is a single brain and we know there are sometimes substantial individual variations in e.g. areal definition. This is only slightly touched upon in the discussion. Might be worth commenting on this more. As I see it, there are multiple considerations. For example (i) Surface-to-Surface registration in the presence of morphological idiosyncracies: what parts of the brain can we "trust" and what parts are uncertain? (ii) MRI parcellations mapped onto BigBrain will vary in how accurately they may reflect the BigBrain areal boundaries: if histo boundaries do not correspond with MRI-derived ones, is that because BigBrain is slightly different or is it a genuine divergence between modalities? Of course addressing these questions is out of scope of this manuscript, but some discussion could be useful; I also think this toolbox may be useful for addressing this very concerns!

Reviewer #2 (Public Review):

This is a nice paper presenting a review of recent developments and research resulting from BigBrain and a tutorial guiding use of the BigBrainWarp toolbox. This toolbox supports registration to, and from, standard MRI volumetric and surface templates, together with mapping derived features between spaces. Examples include projecting histological gradients estimated from BigBrain onto fsaverage (and the ICMB2009 atlas) and projecting Yeo functional parcels onto the BigBrain atlas.

The key strength of this paper is that it supports and expands on a comprehensive tutorial and docker support available from the website. The tutorials there go into even more detail (with accompanying bash scripts) of how to run the full pipelines detailed in the paper. The docker makes the tool very easy to install but I was also able to install from source. The tutorials are diverse examples of broad possible applications; as such the combined resource has the potential to be highly impactful.

The minor weaknesses of the paper relate to its clarity and depth. Firstly, I found the motivations of the paper initially unclear from the abstract. I would recommend much more clearly stating that this is a review paper of recent research developments resulting from the BigBrain atlas, and a tutorial to accompany the bash scripts which apply the warps between spaces. The registration methodology is explained elsewhere.

I also found parts of the paper difficult to follow - as a methodologist without comprehensive neuroanatomical terminology, I would recommend the review of past work to be written in a more 'lay' way. In many cases, the figure captions also seemed insufficient at first. For example it was not immediately obvious to me what is meant by 'mesiotemporal confluence' and Fig 1G is not referenced specifically in the text. In Fig 3C it is not immediately clear from the text of the caption that the cortical image is representing the correlation from the plots - specifically since functional connectivity is itself estimated through correlation.

My minor concern is over the lack of details in relation to the registration pipelines. I understand these are either covered in previous papers or are probably destined for bespoke publications (in the case of the surface registration approach) but these details are important for readers to understand the constraints and limitations of the software. At this time, the details for the surface registration only relate to an OHBM poster and not a publication, which I was unable to find online until I went through the tutorial on the BigBrain website. In general I think a paper should have enough information on key techniques to stand alone without having to reference other publications, so, in my opinion, a high level review of these pipelines should be added here.

There isn't enough details on the registration. For the surface, what features were used to drive alignment, how was it parameterised (in particular the regularisation - strain, pairwise or areal), how was it pre-processed prior to running MSM - all these details seem to be in the excellent poster. I appreciate that work deserves a stand alone publication but some details are required here for users to understand the challenges, constraints and limitations of the alignment. Similar high level details should be given for the registration work.

I would also recommend more guidance in terms of limitations relating to inter-subject variation. My interpretation of the results of tutorial 3, is that topographic variation of the cortex could easily be driving the greater variation of the frontal parietal networks. Either that, or the Yeo parcel has insufficient granularity; however, in that case any attempt to go to finer MRI driven parcellations - for example to the HCP parcellation, would create its own problems due to subject specific variability.

Reviewer #3 (Public Review):

The authors make a point for the importance of considering high-resolution, cell-scale, histological knowledge for the analysis and interpretation of low-resolution MRI data. The manuscript describes the aims and relevance of the BigBrain project. The BigBrain is the whole brain of a single individual, sliced at 20µ and scanned at 1µ resolution. During the last years, a sustained work by the BigBrain team has led to the creation of a precise cell-scale, 3D reconstruction of this brain, together with manual and automatic segmentations of different structures.<br> The manuscript introduces a new tool - BigBrainWarp - which consolidates several of the tools used to analyse BigBrain into a single, easy to use and well documented tool. This tool should make it easy for any researcher to use the wealth of information available in the BigBrain for the annotation of their own neuroimaging data.<br> The authors provide three examples of utilisation of BigBrainWarp, and show the way in which this can provide additional insight for analysing and understanding neuroimaging data.<br> The BigBrainWarp tool should have an important impact for neuroimaging research, helping bridge the multi-scale resolution gap, and providing a way for neuroimaging researchers to include cell-scale phenomena in their study of brain data.<br> All data and code are available open source, open access.

Main concern:

One of the longstanding debates in the neuroimaging community concerns the relationship between brain geometry (in particular gyro/sulcal anatomy) and the cytoarchitectonic, connective and functional organisation of the brain. There are various examples of correspondance, but also many analyses showing its absence, particularly in associative cortex (for example, Fischl et al (2008) by some of the co-authors of the present manuscript). The manuscript emphasises the accuracy of their transformations to the different atlas spaces, which may give some readers a false impression. True: towards the end of the manuscript the authors briefly indicate the difficulty of having a single brain as source of histological data. I think, however, that the manuscript would benefit from making this point more clearly, providing the future users of BigBrainWarp with some conceptual elements and references that may help them properly apprise their results. In particular, it would be helpful to briefly describe which aspects of brain organisation where used to lead the deformation to the different templates, if they were only based on external anatomy, or if they took into account some other aspects such as myelination, thickness, ...

Minor:

1) In the abstract and later in p9 the authors talk about "state-of-the-art" non-linear deformation matrices. This may be confusing for some readers. To me, in brain imaging a matrix is most often a 4x4 affine matrix describing a linear transformation. However, the authors seem to be describing a more complex, non-linear deformation field. Whereas building a deformation matrix (4x4 affine) is not a big challenge, I agree that more sophisticated tools should provide more sophisticated deformation fields. The authors may consider using "deformation field" instead of "deformation matrix", but I leave that to their judgment.

2) In the results section, p11, the authors highlight the challenge of segmenting thalamic nuclei or different hippocampal regions, and suggest that this should be simplified by the use of the histological BigBrain data. However, the atlases currently provided in the OSF project do not include these more refined parcellation: there's one single "Thalamus" label, and one single "Hippocampus" label (not really single: left and right). This could be explicitly stated to prevent readers from having too high expectations (although I am certain that those finer parcellations should come in the very close future).

Reviewer #1 (Public Review):

Harper DM, et al. focus on understanding how cancer risk perceptions and provider communication behaviors influence the uptake of both cervical and CRC screening among multiethnic women 50-65 years old in southeast Michigan.

They investigate different predictors for CRC and cervical cancer screening together and alone. Their work shows that race, age and physician communication behavior are three independent influencers of completing both CRC and cervical cancer screening.

Through their modeling and analysis of self-reporting, the authors find that involvement by physician in health care decision-making was one communication behavior significantly associated with women having both screens compared to only cervical cancer screening. They find that women who had completed both screenings were 99 percent more likely to agree that her physician involved her in the decisions about her health care as much as she wanted.

The authors provide a new insight with reference to age as a screening predictor and show that younger women participate more in the cervical cancer screen and older women participate more in the CRC screen. Understanding the differences for completion of one screen but not another is important to explore opportunities to present options for other screenings and the need for a more holistic integrated prevention approach.

The study has been also able to show new deficits in screening in women from Middle-East and North Africa (MENA). The work shows that MENA and Black women were significantly less likely than white women to have both the screens and that MENA women are rarely screened for CRC cancer, be it alone or in addition to cervical cancer screening.

The conclusions of the paper are well-supported by data. However, all outcomes were self-reported with an opportunity to over-report actual screening frequencies and also discrepancies from Arabic language translation. Also, only 9 percent of the 394 respondents in the survey were women from MENA race.

Reviewer #2 (Public Review):

Harper and colleagues investigate the potential association between cervical and colorectal screening uptake in a multiethnic sample population of women from 50- to 65-years women from Southeast Michigan with personal risk perceptions, cancer risk perceptions and knowledge along with physician communication behavior. The authors adapted and administered validated behavioral questions sampled from the Health Information National Trends Survey (HINTS) to a multiethnic population sample in Southeast Michigan. The variable outcomes were the self-reported cancer screenings for cervical and colorectal cancer, as defined by the United States Preventive Services Task Force (USPSTF) updated guidelines.

Harper and colleagues have shown that race, age, and physician communication behavior were different and independent predictors for completing both cervical and colorectal cancer screening. Moreover, by using a self-reporting methodology, the investigators have indirectly shown that involvement by physician in health care decision-making was one communication behavior associated with women having both screens compared to only cervical cancer screening. In particular, Harper and colleagues have again recognized cervical and colorectal cancer screening deficits in women from Middle-East/North Africa (MENA) origin or ancestry. They demonstrate that MENA and Black women were less likely than Caucasian women to undergo cancer screenings for both tumor types and that MENA women. They have also demonstrated that virtually all of the MENA and Black women who had completed both of the recommended cancer screenings were more likely to agree that the physician involved her in the decisions about her health care as much as she wanted. Finally, the current study by Harper and colleagues has provided two potentially actionable insights. First, that positive communication with the provider, which includes the woman in her health care as much as she wants associates with completion of both cervical and colorectal cancer screenings not only relatively to no preventive screening at all but also when compared to cervical cancer-only screening. This finding might perhaps have implications for so-called "primary care physicians" that either do not routinely perform or are reluctant to include pelvic exams in their scope of practice in female patients. Second, the internal age range of the screened population is an apparent outcome predictor with the younger women completing more in the cervical cancer screenings and the older women completing more of the colorectal cancer screenings. While the reason(s) for this evident age-dependent and/or tumor-dependent phenomenon remains unclear at this point, this empiric hypothesis-generating finding might generate further research to address this potentially actionable dichotomy.

Reviewer #1 (Public Review):

In a previous eLife paper, the authors showed CTP-dependent ParB spreading from a parS site in vitro on closed DNA substrates. But how ParB binds and spreads from the parS site to flanking DNA remained unclear. In this manuscript the authors provide rigorous structural and biochemical evidence showing how CTP-binding and -hydrolysis regulates (1) the loading of an open ParB dimer onto parS, (2) the sliding of the closed ParB clamp on flanking DNA, and (3) opening of the ParB clamp through CTP-hydrolysis and DNA release. The manuscript currently stands as one of the first papers with strong biochemical and structural support of a mechanism that explains how CTP-binding and CTPase activity can regulate a proteins transition from specific binding to sliding on chromosomal DNA.

The authors provide a strong structural basis for the conformational transition in the N-terminal domain of ParB from the open clamp, where parS is captured by its DNA-binding domain, to the closed clamp upon CTP-binding, where parS is driven from the DNA-binding domain into a compartment of the ParB dimer that allows for sliding on flanking DNA. A series of clever in vitro cross-linking assays provide further biochemical support that ParB entraps DNA in a compartment between the DNA-binding domain and the C-terminal dimer domain after binding CTP. The authors first used CTPgammaS and cross-linking to trap the ParB dimer as a closed clamp. The authors go one step further and strategically mutated the CTP-binding pocket of ParB, allowing them to identify several mutant classes, such as CTP-binding defective and CTP-hydrolysis defective mutants, among others. The authors convincingly show through a suite of biochemical characterization (CTP-binding, CTPase, dimer cross-linking, and parS association assays) that ParB[E102A] is a CTP-trap mutant, capable of binding CTP but unable to hydrolyze CTP. The authors fairly conclude that [E102A] maintains a more stable closed-clamp conformation on closed DNA substrates containing a parS site.

The authors take ParB[E102A] in vivo and performed ChIP-seq in Figure 8A. As the authors note, the mutant profile is significantly lower in height compared to WT. As a result, it is difficult to conclude whether ParB[E102A] has a more pronounced spreading activity from any of the parS sites on the chromosome. However, the authors do note a very interesting and dramatic ParB[E102A] signal amplified upstream of all parS sites. From this upstream "more extended" coverage, the authors propose ParB[E102A] can bind parS and then slide further away. As an interested reader, an explanation as to why this extension only occurs upstream of all parS sites, and particularly over the parAB operon, would have been appreciated.

Overall the biochemical conclusions and mechanism of action proposed in Figure 9 are well justified by the data presented. The findings have major implications in our understanding of the most common DNA segregation system across the bacterial world.

Going forward it is important to place these findings and mechanism in the context of how a ParB dimer oligomerizes with other ParB dimers as shown by many others in the field, as well as how these CTP-dependent activities regulate interactions with the ParA ATPase on the nucleoid that drives the chromosome segregation reaction.

Reviewer #2 (Public Review):

Bacterial ParB partition proteins have the novel property that they employ a CTP nucleotide cofactor for complex assembly at their specific DNA binding site, parS. Here the authors present structural and biochemical data using Caulobacter crescentus ParB, and examine how CTP binding promotes ParB clamp formation around the DNA substrate which in turn results in parS release so the clamp can move away from parS along the DNA ("spreading"). In addition, they examine the role of CTP hydrolysis via isolation of mutant ParBs altered in interactions with CTP. Their data support the proposal that CTP hydrolysis opens the clamps to unload ParB from DNA and limit DNA spreading.

The authors solve the crystal structure of ParB lacking the C-dimer domain in two forms, one with parS DNA and one with CTPgS, representing the prehydrolysis state of ParB. The latter is a new addition to ParB-CTP structures as the original B. subtilis structure was with CDP. The CTPgS structure is "closed" and shows how this closure creates clashes with DNA binding. The CTPgS structure provides a more detailed description of the CTP binding site, and allowed the authors to target a number of residues for mutagenesis to further probe the role of CTP hydrolysis. They use crosslinking of full length ParB to show that closure at the N-term/DBD region and at the C-dimer domain is essential for the clamp. The crosslinking at the DBD further implies that DNA has moved into the region between the DBD and dimer domain, since it can be released when this region is cleaved at an engineered TEV protease site. Overall the data are convincing and they provide important new information about this new class of CTP-dependent DNA binding proteins. The combination of structures, crosslinking and mutagenesis provides a relatively comprehensive analysis and supports the model they propose (Fig 7).

Reviewer #3 (Public Review):

Jalal et al. investigated the mechanism of gating that enables ParB to clamp onto DNA in a reversible manner, using a combination of X-ray structures and in vitro assays. They find that a truncated version (delta CTD) of ParB from Caulobacter crescentus adopt two distinct conformations when bound to parS, revealing an open conformation. By contrast, the structure in the presence of the non-hydrolysable CTPgS nucleotide display a closed conformation of the NTD, similar to other ParB-CTP structures (B. subtilis and M. xanthus) indicating that this closed conformation is a conserved feature. By comparing the ParB-deltaCTD structures formed in the presence of parS or of CTPgS, they unravel the conformational changes upon CTP binding that convert ParB from the open to the closed conformation. They also characterize the clash with DNA in the closed conformation thus providing a molecular explanation for the escape of ParB dimer from parS site upon CTP binding. By performing a well-designed and carefully controlled double cross-linking assay, they fully demonstrate that the DNA is entrapped between the DBD and the CTD, in both a parS- and a CTP-dependent manner. These data clearly demonstrate that the ParB dimer functions as a molecular clamp that entraps parS-containing DNA within the 20 amino acids-long DBD-CTD compartment upon CTP binding. To investigate the role of CTP-hydrolysis in this mechanism, they perform an alanine scanning mutagenesis to uncover and characterize a ParB variant (E102A) defective in CTP hydrolysis but still able to self-dimerize. Comparison of ChIP-sequencing data performed with ParB WT and E102A display enrichment differences, both in intensity and extend. The authors suggest that the clamped state of the ParB variant is more stable explaining the extended profile compare to WT, and thus that CTP hydrolysis might be involved in opening the closed conformation.

The manuscript is clearly written and well presented, and all the experiments are thoroughly controlled. This study thus provides novel structural and molecular insights of the two ParB dimer states - open and closed conformations - that are controlled by ParS and CTP binding. The conclusions of this paper are well supported by data, but some aspects of the ChIP-sequencing data analysis need to be clarified and discussed.

Reviewer #1 (Public Review):

The manuscript attempts to analyze the process of Pavlovian conditioning in fly larvae, where the conditioned stimulus (CS) is the presence of CO2 (usually an repulsive signal), and the unconditioned stimulus (US) is an ontogenetic manipulation of reward neurons. In the course of the manuscript, the authors try an astonishing variety of different conditioning protocols, changing the order and the duration of CS and US presentation and the strength of the CS, introducing extinction phases, testing the duration of persistence of the association, and so on.

Major findings of the manuscript include that:

1) This is, indeed, a classical Pavlovian system, where the order of the CS/US presentation matters (and not just their co-occurrence).

2) It is impossible for larvae to be trained to like CO2, and the strongest learning achieved is to become indifferent to it.

3) The learning and the extinction in these animals is supposedly all-or-nothing - every presentation of CS/US pairing makes a fixed fraction of the animals fully trained, and similarly extinction only changes the fraction of fully trained animals.

4) Memories persist overnight.

I find the manuscript illuminating and thought-provoking. I did not expect (2) above, for example. The studies are quantitative, done with high statistical power. and focus on individual animals, rather than ensemble-averaged. Thus I believe the manuscript will be a gold standard for associative learning work in small animals.

Nonetheless, the manuscript left me wondering about a few serious things.

1) The choice of CO2 as a CS is both a curse and a blessing. The experimentalists must overcome innate avoidance of the signal, instead of the value of the signal being neutral to a naive animal. The authors speculate that the conditioning here is through inhibition of avoidance, and the picture they try to build (and it would be useful to have this as a simple mathematical model rather than just a picture) is that an unconditioned optogenetic stimulus decreases avoidance of the conditioned stimulus. This is not the standard Pavlovian scheme, where, traditionally, positive reinforcement increases preferences (+ / ++) and negative reinforcement increases avoidance (- /+-) or decreases preference (- /-+). Instead it's an unusual structure where positive reinforcement decreases avoidance (+ / --). This is uncommon -- and results in precisely the same behavior limitations that the authors noted: the most one can do is to decrease avoidance to zero, and then subsequent presentation of CS/US pairs does not lead to emergence of the preference. I think the manuscript would become stronger if the authors tried to speculate what aspects of the animal's ecology would make this uncommon functional organization favored.

2) Potentially a bigger issue is that the training in these experiments last for a very short time (from 30 s to 15 min or so), while the readout of the behavioral preference takes an hour, during which many unrewarded presentations of CS happen. In the paper, the authors themselves show that unrewarded CS presentations lead to reduction in the behavioral response (fig 3), to the point that overnight memory consolidation is not observed (fig 4). Thus this long scale of the assay compared to the time scale of dynamics of the learning and extinction themselves makes interpretation of the findings very hard, at least for me. For example, is the 50% maximum choice of CO2 due to the animal not being able to establish the preference to it (and only being able to suppress the avoidance), or is it because the animal establishes a strong preference, which then gets partially washed away during the one hour of testing? There are a few ways that this and similar concerns can be addressed. First, a different assay can be established, where the preference is measured as quickly as it gets established and extinguished. Second, one can explore if the preference of animals does not change during the course of the testing phase. This could be done by analyzing the preference over fifteen minute segments, and checking for a drift (one could even combine animals to do so). Third, one can try to establish a mathematical model of conditioning and extinction, which would account for unrewarded CS presentations, and then see whether all of the data can be explained within this model. Or maybe one can do something totally different -- but I believe that some analysis of the effects of the assay on the conditioning state must be performed.

3) The authors talk about quantized response as compared to gradual learning. This makes it seem that there are only two states that the animals can be in. But this is, in fact, unclear from the data. It's clear that there are two modes: indifferent to CO2 and avoiding it, but the modes are wide. Is there an additional signal there? Where is the width of the modes coming from? Is it simply the counting statistics of making, on average, pN out of N choices? Or are the data hiding something more interesting? This could be addressed by being a bit more careful with statistical analysis, and not treating the data as being fit by two Gaussians with arbitrary widths, but as a mixture of two Bernoulli distributions -- would such model work? If not, then why?

Reviewer #2 (Public Review):

The authors have developed a novel apparatus that promises to accelerate mechanistic studies of how individual Drosophila larvae learn odor preferences. Previous studies of olfactory learning in Drosophila larvae have often used mass assays, testing groups of freely moving larvae trained under different conditions (but see, for example, Gerber and Stocker, 2007). Here, the authors trained individual animals via Pavlovian conditioning in a Y-maze, using an innately aversive olfactory cue, carbon dioxide (CO2), as a conditioned stimulus, and optogenetic stimulation of a pair of reward neurons as the unconditioned stimulus. They then assessed changes in the animal's preference for carbon dioxide versus air in the Y-maze after systematically varying the temporal relationship between carbon dioxide presentation and reward stimulation during training.<br> The results show that, consistent with the associative nature of the learning, the aversion of larvae to CO2 decreases when CO2 presentation is paired with reward stimulation (with the necessary and sufficient condition being only that CO2 predicts reward stimulation), and that the learned preference increases as a function of the number of training cycles, extinguishes when reward stimulation is removed after paired training, and under the right conditions, can be shown to be protein-synthesis dependent (i.e., a long-term memory).<br> Notably, by using their apparatus to repeatedly assess the choices made by individual animals given different amounts of training, they were also able to demonstrate that the learning underlying the change in preference occurs in an all-or-none fashion for each animal, rather than in a graded manner in which the preference gradually changes across training cycles for each animal. This is an observation that could not have been made using the conventional mass assays in which groups of animals trained together.

Strengths: The authors clearly demonstrate the power of their new apparatus for testing learned odor preferences in Drosophila larvae, in providing researchers with improved experimental control over the presentation of olfactory cues and reward stimulation using optogenetic activation. Using their device, they show systematically the training conditions under which Drosophila larvae show changes in preferences to an innately aversive odor, and that this learning undergoes extincion, exhibits protein-synthesis dependent long-term memory, and occurs in an all-or-none mode. As the authors present their remarkably clean findings with equally clear rationales, their conclusions are well supported by data. As the authors claim, this apparatus promises to shed greater light on the neural circuit mechanisms underlying olfactory learning.

Weaknesses: While the results shown in the paper are thorough and demonstrate that many of the major findings shown in adults with respect to the neural basis of behavior can be replicated in larvae, the authors appear uncertain about what the main focus of their article should be. Whereas the title suggests the authors' desire to highlight the evidence on the all-or-none nature of learning, other sections, including the introduction and conclusions, seem to suggest their desire to emphasize the utility of the new technique. If the former is the case, readers might be interested in knowing what neural mechanisms underlie the behavioral evidence showing that learning occurs in a switch-like fashion. For example, is a switch-like change also induced at the level of neurons?

The authors also clearly showed that the "Forward Paired" condition is necessary and sufficient to induce the change in preference to CO2. Why did they not use this condition to carry out all of their experiments, if this shows the crucial learning of interest (not contaminated coincident stimulation)? Have the authors used the "forward paired" condition to examine extinction and overnight memory retention? If so, did they differ from the findings using the "coincidence" condition, or were they the same?

Reviewer #3 (Public Review):

Over the past two decades, the Drosophila larva has proven to be an advantageous system to study the neural basis of memory and its effects on orientation behavior. While larvae clearly learn, this behavior has been mostly characterized through en masse assays. To this date, it has been extremely difficult - if not impossible - to characterize learning at the level of single larvae. Gershow and colleagues present a truly ingenious assay to control the frequency and the exact timing of the presentation of the conditioned and unconditioned signals. With their new assay, they demonstrate the switch-like nature of learning in individual larvae - a really exciting finding, which alone justifies the assay development. But, the authors did not stop there. Their work revisits multiple aspects of the theory of associative learning in the Drosophila larva, including the role of repeated training, the emergence of memory extinction and overnight consolidation of memory.

This manuscript will have a major impact on the field of memory and learning in Drosophila. It provides a groundbreaking tool that will enable a wealth of new experimental work that would have been impossible until now. The study of various parameters influencing memory formation is controlled by the right set of conditions (e.g., the use UAS-Chrimson alone with ATR to preclude leaking expression of the effector; the potential effect of sensory habituation, etc.). The data analysis and statistical models are simple but powerful. I have no major criticism about the methodology and key conclusions of the study.

Reviewer #1 (Public Review):

Through a very substantial set of experiments, involving both cultured primary human melanocytes and cell lines, these authors show that nevus cell growth arrest is reversible, is primarily a G2-arrest, and depends on the actions of microRNAs on levels of Aurkb, which are themselves dependent on TPA-regulable aspects of cell state. The result is a phenomenon of reversible, Braf-dependent arrest that is strongly dependent on cell context. They continue on to make observations using clinical samples that support this view in vivo. The experiments are well done and generally well-explained, and the results should be of wide interest to those interest in melanocyte biology as well as those interested in cancer initiation more generally.

Reviewer #2 (Public Review):

This manuscript puts forth new insight into the complicated nature of phenotypic plasticity often seen in melanoma progression, both in the clinic and on the bench. As many in the melanoma field have demonstrated time and time again, identifying clinically relevant molecular hallmarks that can be used to differentiate the stages and treat melanoma is made all the more difficult by the adaptive and plastic nature of the disease. The classical BRAF oncogene has been widely studied leading to many advancements in the mechanistic understanding of melanoma, and yet the details of its role in tumorigenesis and metastasis still remains largely elusive as the path from one stage to the next is not as straight forward as originally thought.

Utilizing publicly available RNA datasets and clinical samples, McNeil et al. were able to successfully demonstrate that proliferative arrest in melanocytes is induced by MIR211-5p/MIR328-3p-facilitated AURKB inhibition, limiting BRAF V600E's hyperproliferative tendencies. However, BRAF V600E proliferation is also dependent on environmental stimuli and melanocyte differentiation, which could provide new directions in diagnostics and therapeutic options. Overall, the authors' conclusions are well supported by the data, however in addition to the strengths of the manuscript, there remain a few weaknesses that lessen the scope of their findings in a clinical setting.

Strengths:

The ability to compare matched patient samples across multiple stages of disease remains a difficult hurdle that McNeil et al. was able to overcome by utilizing previously published datasets as well as by analyzing tissue obtained from partnering clinics. Throughout the manuscript, there are aspects of each experiment that demonstrate a well thought out analysis in order to properly justify and increase the impact of the authors' conclusions. In particular, the partial rescue of melanocytes from miRNA-induced proliferative arrest using the lentiviral-based AURKB and GPR3 mRNA expression constructs aided in solidifying the relationship, both indirect and direct, of these computationally predicted mRNA targets to the pseudo-senescence-associated miRNAs. Additionally, the in-depth analysis demonstrating how the role of the BRAF V600E oncogene in melanocytic proliferation arrest is highly dependent on environmental stimuli and melanocytic differentiation was clearly summarized in a single figure panel (4H). Finally, the QPI imaging was a unique addition, which visually drove home the claims of mitotic failure making it a hard conclusion to refute. Despite the quality of data, the authors took the time to recognize their own weaknesses within the manuscript by pointing out how the use of an artificial stimulant like TPA does not translate in identifying clinically relevant environmental stimuli that may be involved in miRNA expression regulation in skin. They do, however provide well thought out hypotheses to these unknowns they were not able to identify, which would provide a basis for future work.

Weaknesses:

While the authors present strong conclusions supported by their data, the limited sample sizes (n=3) in earlier experiments in addition to the lack of in vivo work lessens the potential clinical impact this manuscript could have on the melanoma community. They also make limited mention of future directions apart from investigating whether dysregulation of the spindle checkpoint may be a contributor to copy number variations in nevi.

Reviewer #1 (Public Review): 

Sayre et al. report on an impressive data set of over 1,300 neuronal arbours reconstructed, without synapses, from mesoresolution volume electron microscopy, therefore providing a first data set from a densely labelled image volume of a complete central complex of an insect other than a Drosophila. The authors readily acknowledge the limitations of their study, namely the choice of a coarse resolution of 126 nm/voxel which precluded the reconstruction of neuronal arbours with axons thinner than 600 nm, affecting primarily one class of neurons (the tangential cells) which interlink different compartments within the same neuropil region (be it the ellipsoid body (EB), fan-shaped body (FB) or the protocerebral bridge (PB)) and therefore do not preclude the analysis of inter-neuropil regions, which is dominated by columnar neurons of thicker caliber and therefore reconstructed sufficiently here. The low-resolution volume EM was then complemented with both less coarse volume EM (at 100 nm/voxel) and light-microscopy labellings. 

The courage it takes to work with an animal as large as a Bombus terrestris worker using volume electron microscopy is commendable. Obtaining even a projectome, namely, the low-order branches of neurons without the synapses, is already an extraordinary achievement. 

The authors draw direct comparisons with the locust (and briefly the butterfly) and particularly with the fly Drosophila, where the synaptic connectivity circuits and functional data is most abundantly known and published. The comparisons are apt, and the focus on the differences interesting: in resolving the lack of thoroidal shape of the ellipsoid body-equivalent in the bumblebee, and in relating the differences in the relative number of neurons to additional features known to be implemented by the central complex such as path integration. All the above makes for a lengthy discussion, but the specialists will appreciate the detailed one-to-one comparisons between fly, bee, butterfly and locust. 

To remark as well the willingness of the authors to use the Drosophila-centered nomenclature in the naming of compartments and neurons of the bumblebee, which helped perform these revisions and will most certaintly assist a number of fly-trained readers to distill and take home the key findings of this work. 

As dry as anatomy can be, the authors manage to bring in putative functional roles, based on computational models and functional data acquired on the central complex of other species, which make the findings lighter to read and immediately relatable. The authors have carefully acknowledged the limitations of their study data and comparisons appropriately and transparently.

Reviewer #2 (Public Review): 

This study by Sayre et al aims to better characterize neural circuitry of the central complex (CX) in the bee, a neuropil that is important for the control of many navigational behaviors. Much of the current knowledge of the CX circuits and function currently comes from flies, while many complex navigational behaviors have been described in other insects. Thus, this study is significant because (a) it begins to close the gap between the investigations of neural circuits in the fly and behavioral work in other insects, and (b) because it allows for cross-species comparisons of CX circuits and identification of evolutionary adaptations to perform certain behaviors. 

Besides the biological insights, which I will address below, this work demonstrates an approach to electron microscopy that balances imaging throughput with dataset completeness. Rather than image the full bumble bee CX at resolution sufficient for a complete reconstruction of all neurons in the CX, the authors collect lower resolution data and focus on reconstructing main neurites of a selected number of cell types. The authors present a "projectome", a map of neural projections between neuropils of the central complex (and in several cases to sub-regions of said neuropils). However, the method has limitations that prevent the authors from (a) fully tracing neuron processes below a certain size and (b) measuring connections between neurons. Furthermore, the morphology of some neurons cannot be fully reconstructed because the respective neurons leave the imaged volume. This limits the extent and level of detail at which different neurons can be grouped into types. The authors are very transparent about these limitations. They compare three data sets with different resolution to identify which information is lost at lower resolution and focus their analysis on cell types that can be characterized to a high degree with the data at hand: columnar neurons. 

Columnar neurons have characteristic projection patterns that are tightly linked to the function of the CX circuitry. The authors provide an atlas of the different types of columnar neurons and their numbers and compare their findings to the fly and where possible the locust. This reveals a beautiful functional homology of the head direction circuitry across the three species despite different anatomical implementations. The authors also identify several differences that could point at circuit adaptations that allow bees to excel at path integration-based navigation. 

For one region, the noduli (NO), the authors collected EM data with higher resolution, which enabled characterization intra-neuropil organization. Many studies suggest that in the central complex structure is often tightly linked with function, which makes this NO dataset a valuable contribution to further understand the organization of one of the main input structures to the central complex. In the larger neuropils, the ellipsoid body (EB) and fan-shaped body (FB), the resolution of the projectome unfortunately does not allow for detailed characterization of the intra-neuropil structure. Here, instead, the authors provide a rough characterization based on layers defined by immunolabelling of TH and 5TH as well as entry sites of primary neurites. 

This work presents an important first step to better relate findings about the central complex circuitry from fruit flies to the function of the homologous circuits in (bumble)bees, which might help us understand the adaptations that give rise to the astonishing behavioral repertoire of bees such as their ability to navigate accurate on a relatively large spatial scale.

Reviewer #3 (Public Review): 

The insect central complex (CX) is a brain part, which processes multimodal sensory input to guide orientation and directed locomotion. It is built by a large number of different neuron types with intriguing and unique connectivity patterns, which together form several interconnected and midline spanning neuropils. So far, the many neuron types have mainly been identified by sparse marking of cells in a number of insect species, which allowed for precise determination of projection patterns of selected neurons. This approach has revealed an overall conserved CX architecture but it suffers from the possibility that neuron types may be missed and that the relative position of the projections among neurons cannot be determined exactly. Recently, a comprehensive connectomics map was generated for the fruit fly Drosophila melanogaster providing projection and connectivity information in unprecedented detail. While the most thorough understanding of CX function is based on the elaborate toolkit available in the fly, the most complex navigation behaviors are known from ants and bees, calling for more comprehensive work in those species. 

Sayre et al. now provide a comprehensive projectome of the bumblebee CX by using serial block face EM and subsequent 3D reconstruction of 1,300 neurons. The projectome of the entire CX is complemented by analyses of sub-parts (with focus on the noduli) with higher resolution and with some immunohistochemical data. With this work, the authors provide a very extensive and valuable resource allowing for comparisons of neuron types and projections between species. This approach has the power to reveal on one hand the conserved core of CX projections and the specific differences, which might underlie the different navigational abilities of these insects. 

On the technical level, they find that significant additional information can be gained by adding high resolution SBFEM to the less resolved overall reconstruction. Further, the authors confirm that single cell reconstructions based on confocal microscopy can be well mapped onto the projectome, allowing for adding more detail to the reconstruction in the future. 

On the scientific side, they first establish a comprehensive resource for the bumblebee CX - the first such dataset outside Drosophila. They describe the setup of the entire bumblebee CX, defining the neuron types and their projection patterns with a focus on columnar neurons. Second, they mine this resource for conserved and diverged aspects compared to Drosophila with respect to numbers of certain neural cell types, conservation of key projection circuits and - importantly - identifying specific differences between these insects. Based on this careful analysis, several hypotheses are formulated as to what the differences might mean for the functions of the bumblebee CX. For instance, the authors confirm an overall conserved CX architecture and suggest a very conserved head direction circuit. But they also find differences in fan shaped body layering, noduli organization and other aspects like the lack of obvious deltaV and FX cells. Interestingly, they describe changes in projection patterns that could underlie the different morphologies of the ellipsoid body (bar like versus donut shaped) and the potential functional meaning of a number of bee-specific aspects of noduli projections. 

In summary, the strength of the paper lies in the establishment of a quite comprehensive cell atlas of the bumblebee CX displayed in extensive and clear figures. Further, the analysis for conservation and divergence with respect to the fly CX has been done in a very careful and comprehensive way. Intriguing divergences lead to hypotheses on the functional implications. All claims are well founded in the data. 

Almost unavoidably for data gained outside the Drosophila cosmos, the resource does not have single cell resolution when it comes to the fine terminal projections and, hence, does not provide information on specific cell-cell connectivity. Further, cells with small diameter neurites are likely missing outside of the noduli dataset. The authors are aware of these issues calling their resource projectome rather than connectome. 

The paper describes very interesting differences and respective hypotheses are presented but these are not tested. <br> The reconstructions will be available as interactive datasets, which is essential for future use.

Reviewer #1 (Public Review): 

Systematic reviews and meta-analyses are essential tools for synthesizing empirical evidence to advance our knowledge in life science. In this rigorously conducted meta-analytic study, the authors analyzed the data from 119 experiments from 110 published articles (92 on brain functional experiments from 87 articles and 27 on brain structural experiments from 23 papers) and investigated the functional and structural abnormalities associated with developmental dyslexia across languages. Convergent and divergent functional and structural changes as well as language-universal and language-specific brain alternations related to dyslexia are found. In general, the study has generated important results and the findings are interesting. 

I have the following comment: 

Dyslexia in alphabetic languages is generally related to phonological deficits, so there are many neuroimaging experiments using phonology-based tasks. In Chinese, the core deficits of dyslexia are unknown, and neuroimaging tasks devised in the literature are more diverse. Although the authors have done well in Table 1 to specify the experimental tasks in various languages, the meta-analysis did not take into account the task types. I believe that in this article, it is not necessary to conduct task-type based meta-analyses, but one sentence or two in the Discussion section to mention this possibility and the limitation is necessary.

Reviewer #2 (Public Review): 

In the present study, Xiaohui Yan and colleagues attempt to summarize the existing evidence on neurofunctional and neuroanatomical impairments in dyslexia (aka specific reading disorder) in different languages in a meta-analytic manner. The research questions the authors asked are essential but remain largely open. The meta-analysis is a powerful approach to address these problems, and the findings are appealing. Both universal and language-specific neural manifestations in dyslexia are revealed. With this knowledge, the researchers can design experiments to reveal/examine more specific hypotheses, while the educators can refine diagnostic methods and intervention programs. This study has several strengths, including (1) the research questions are explicitly and precisely declared at the beginning; (2) an advanced meta-analytic method (AES-SDM) was used; (3) a series of complementary analyses are done, including a confirmation study with well-matched English and Chinese studies was conducted; (4) a comprehensive discussion is given. At the same time, as too many questions are asked, the analyses/results/interpretations became quite complicated and sometimes hard to follow. In addition, several factors need to be further taken into consideration in data analysis and result explanation. Finally, it should be noted that given meta-analysis is a way to summarize the previous findings, it is necessary to conduct further studies based on it to directly examine the hypotheses. All in all, the main claims are supported by the data, while additional analyses would provide further support. 

I have three main concerns:

1) The imbalanced numbers of studies in alphabetic and morpho-syllabic languages may bias the results of primary meta-analysis pooling all studies together. Specifically, since there are many more alphabetic studies in this field, the results will mainly reflect patterns in these languages. This can be seen, e.g., when comparing figures S1-S2 with figures S3-S6. In this case, the result cannot answer whether there are the same functional and structural impairments in dyslexia in alphabetic and morpho-syllabic languages (i.e., the "both" question). The same issue exists for the multi-modal analysis across languages. 

2) Age range difference may significantly influence the results of the primary meta-analysis across languages. It is shown in p. 15, lines 297-298, "the mean age was 16.55 years for controls and 16.23 years for participants with DD." However, such adolescent age ranges are a result of pooling studies in children and adults together. Given that (1) it has been revealed in previous literatures that participant's age modulates the neural manifestation in dyslexia, (2) fewer adolescent studies exist in alphabetic languages, and (3) research on morpho-syllabic languages is almost with children, the findings of the primary analysis might be influenced by age-related effect. 

3) Some statements are out of the scope of this study and can be misleading. For instance, in the abstract (p.2, lines 17-19), it says, "...it is still not totally understood where and why the structural and functional abnormalities are consistent/inconsistent across languages." However, while the "why" question is an important one, unfortunately, the current meta-analysis does not answer it.

Reviewer #1 (Public Review): 

The manuscript "Two different cell-cycle processes determine the timing of cell division in Escherichia coli" by Colin et al. presents an experimental approach to investigate the role of two governing cell-cycle processes, namely, DNA replication-segregation and cell division cycle, in size regulation. Authors tackle the problem by first decoupling these two cell-cycle process via sub-lethal dosages of A22, and then analyze the role of each process in the timing of cell division. Modern imaging and analysis techniques are used in this work to monitor cell division with single-cell resolution and chromosome replication with sub-cellular resolution. The large pool of data allows the authors to perform correlation analysis of cell-size and the cell cycle parameters, which led to the conclusion that the two processes have a "balanced contributions in non-perturbed cells." 

The question studied in this manuscript is important and timely. The investigation of the two concurrent processes chosen by the authors is perhaps the right direction which may eventually lead to a complete understanding of the E. coli cell-cycle and size regulation. The high-resolution imaging and analysis accomplished in this work is also commendable. There is, however, a major concern about this manuscript, which is the entire conclusion is based on the cell-cycle and size perturbations by A22. The caveat of the A22 perturbations is that an aberrant cell shape could affect both of the cellular processes simultaneously. Even though the C-period and initiation size are largely unchanged, a possible, but unknown, cross-talk between the two processes may be affected by A22. Therefore, additional evidence is necessary to show whether the two processes independently determine cell division.

Reviewer #2 (Public Review): 

This is an interesting paper which makes important contributions to an interesting and highly controversial topic: how does an E.coli cell decide when to divide. 

As the authors describe in clear and careful detail, two main camps have argued (often dogmatically) for "single process" models in which division is either a direct, downstream consequence of replication initiation (which is the regulated step) or of effects that act directly on division (irrespective of replication and, more generally, the chromosome cycle). The authors of this paper have, instead, proposed that both types of effects are important, in different proportions according to the circumstances. They refer to this idea as a "concurrent cycles" hypothesis. In previous work they have presented arguments and data which they interpret as being incompatible with any single process model and consistent with their alternative hypothesis. 

This work now investigates the consequences of treatment with A22, a drug which inhibits MreB, with the result that it increases cell width and, concomitantly, increases the length of time between completion of a given round of DNA replication and the immediately ensuing cell division (an interval known as the "D period"). The idea to analyze this situation was motivated by the authors previous hypothesis: by the concurrent cycles idea, increasing the length of the D-period should prolong the replication-independent inter-division process such that it becomes rate limiting in determining the timing of division (relative to the replication-dependent process). 

The data presented confirm the authors' expectation. <br> They first show that progressively increasing the amount of A22 does not (dramatically) alter either: (i) the basic "adder" behavior in which a fixed amount of cell length is added irrespective of the length of the cell at birth or (ii) the finding that a fixed amount of cell length is added per replication origin during the period from one round of replication initiation to the next, which is consistent with (and generally considered to be supportive of) a role for a replication-dependent process. <br> However, they also discover an interesting additional effect by examining the amount of cell length added (per origin) during the entire period comprising replication plus the immediately ensuing division ("C+D"). In the unperturbed case, cells that are longer at the time of initiation of replication also add more length during the ensuing (C+D) period. In contrast, in the presence of increasing amounts of A22, this effect is progressively reversed such that, finally, at high drug levels, cells which are longer (per origin) at the time of initiation of replication add much less length during the ensuing (C+D) period. Since the length of the C period is essentially constant in all conditions, the relevant effect is the variation in the length of the D period. And since the observed effect becomes more and more prominent with increasing A22 concentration, variation in the D period dominates more and more as the length of that period gets longer and longer. The authors interpret this effect to mean that, with increasing D-period length, division timing is decreasingly dependent on replication initiation. They go on to infer that "with increasing average D period, a process different from DNA replication is likely increasingly responsible for division control". This is a sensible, relatively formal restatement of the finding. This statement allows for diverse specific interpretations. The authors focus on one possible interpretation: they show that their previously proposed concurrent cycles hypothesis can quantitatively explain these data. In essence, given a replication-independent and a replication-dependent process, the observed findings are explained by an increased contribution of the replication-independent process. This scenario also does a better job of explaining the presented data, as well as other findings, than other recent "single process" models, for reasons that are discussed in straightforward detail in the Discussion. The authors also do an excellent job of laying out the assumptions upon which their model (and other existing models) are based, thus laying open the possibility for future studies to consider other possible scenarios. 

This work is important for four reasons. First, provides interesting new data which must be accommodated by any synthetic explanation for cell division control. Second, it makes it abundantly clear that the validity of any proposed single process model remains to be further substantiated. Third, it suggests an interesting alternative model which can accommodate a diversity of data, including that presented in the current work, and which has the potentially attractive feature of combining the two existing single-process models. Fourth, and perhaps most importantly, the authors discussion of the available data in this field clear, thoughtful and thought-provoking and leaves open the possibility of some as-yet unimagined mechanism. Overall, this work provides an important counterpoint to other published work and is a very valuable contribution to thinking and discussion in this field. 

[It can also be noted specifically that this work provides an important counterpoint to the model proposed in a previous eLIFE paper on this topic by Witz et al., 2019 (eLife 2019;8:e48063 doi: 10.7554/eLife.48063).]

Reviewer #3 (Public Review): 

Colin, Micali et al. investigated slow-growing E. coli cells' division and replication over cell cycles at single cell level with the perturbed cellular dimension. They found that the time between replication termination and division increased by perturbing cell width as recently reported, and that chromosome replication became decreasingly limiting for cell division. These results well supported the 'concurrent-processes model' previously proposed by some of the authors. 

1) Cell length can be used to represent the cell size (adder) only if the cell width keeps constant. In the current form of the manuscript, it is unknown whether or not the cell width varies significantly at single-cell level with A22 treatment (e.g., 1µg/ml A22). In this case, cell volume might not be nicely correlated with cell length. The interpretation of Figure 3 therefore would be devalued. 

2) The negative value of 𝜁C+D in Figure 3F (treated group) indicates that the division length is negatively correlated with the cell length at replication initiation. It is not obvious that this can rule out the possible contribution of DNA replication/segregation in offsetting the length difference at initiation and thus contribute to cell division. Since Figure 3F is the key observation to validate the model, more explanations are required to help readers understand how a negative 𝜁C+D can lead to a conclusion that a process different from DNA replication is likely responsible for division control with A22-treatment. 

3) As an important input for the model, the QC+D' is assumed to be equal to QC+D in unperturbed conditions and remains constant regardless of the A22 concentration (Line 548-554). This assumption is reasonable if the minimum time interval for segregation (D') is irrelevant to the change of cell width. But how D' and QC+D' changes with cell width are unknown. Earlier molecular studies revealed that the polymerization of MreB affects the activity of topoisomerase IV, an enzyme mediates the dimerization of sister chromosomes, which implies that changing cell width may affect D'. Given the importance of QC+D' to the model, it is vital for the authors to make this assumption clear in maintext and explain why such assumption is reasonable.

Reviewer #1 (Public Review):

Faulkner et al., has described asymmetric antibody response in SARS-CoV-2 alpha (B.1.1.7) patient sera on parental Wuhan-hu-1 or G614 variant and Beta (B.1.351) variant. This work is highly significant and the information could be very useful for next-generation vaccine development. One of the limitations of the current study is SARS-CoV delta variant (B.1.617.2) is not included, which I think will be highly relevant due to the high transmissibility of the delta variant around the world.

Reviewer #2 (Public Review):

The authors’ study compares neutralisation as well as antibody binding of serum collected from individuals infected with either WT or B.1.1.7 SARS-Cov2 variants. They assess these Ab responses against WT, B.1.1.7 and B.1.351 variants in in vitro assays, ultimately concluding that infection with B.1.1.7 leads to a lower cross-reactive neutralisation than WT because sera from WT maintain neutralisation against B.1.1.7 virus but sera from B.1.1.7 loose neutralisation to WT. The authors provide interesting data on an important question, and a robust analysis of the data. They consider the limitations of the data acquisition process and perform analysis to control for the different sources of the WT and B.1.1.7 sera samples, by matching, disease type (i.e. asymptomatic disease versus symptomatic) and timing of sera collection. However, there is one main point of interpretation that requires a bit more discussion, as it appears possible to interpret the data in the opposite way regarding the authors main conclusion.

In sup. Fig S9 the authors present their most controlled (like-for-like) comparison of sera from WT and B.1.1.7 infected individuals. They conclude that B.1.1.7 infection leads to nAbs with lower cross reactivity. But this is only true for the fold drop. If you look at the absolute level of nAbs in these two groups, it seems to indicate the opposite conclusion is plausible.

That is, if you get an asymptomatic B.1.1.7 infection it seems you may have a higher neutralisation against B.1.1.7 and WT (and even B.1.351) compared with someone who receives an asymptomatic WT infection. This would seem to indicate that a B.1.1.7 infection leads to a higher overall response including a higher cross-reactive response. This interpretation is the opposite of the authors, and the difference is whether you consider absolute level or fold-drop as the better measure of cross-reactivity. It may be that B.1.1.7 infections tend to be asymptomatic with higher viral loads and so lead to higher overall Ab responses with asymptomatic infection.

It is not clear which of these two opposite interpretations of the data is the "correct" interpretation and the authors approach is very reasonable - it's just not clear which is the most meaningful at this stage. Therefore, the authors should include some discussion that another interpretation is possible when looking at the absolute level of nAbs in B.1.1.7 infected individuals and offer a balanced justification for why they favour the interpretation that B.1.1.7 leads to lower cross-reactivity instead of higher cross reactivity.

Reviewer #3 (Public Review):

In this manuscript, Faulkner et al. determine the binding and neutralisation of sera from D614G and B.1.1.7 infected patients against circulating SARS-CoV-2 variants of concern. They show that antibodies arising from B.1.1.7 infection have reduced binding and neutralisation against the parental strain. Additionally, in contrast, the antibodies elicited by patients infected with the D614G strain retained binding to B.1.1.7 spike and a less pronounced decrease in neutralisation. Both cohorts had a significant decrease in neutralisation to the South African B.1.351 variant.

I think this work is well designed, executed, clearly written and well organised. The findings are important and may help inform the design of future SARS-CoV-2 vaccines.

Reviewer #1 (Public Review):

Ence Yang and colleagues report circFL-seq, a method for sequencing full-length circular RNAs with rolling circle reverse transcription and nanopore sequencing. The authors carried out comprehensive computational analyses and experimental validations of circFL-seq data, and reported novel biological findings (e.g. fusion circular RNAs).

Overall, this paper will be a valuable addition to a growing body of literature on long read sequencing of circular RNAs. A major strength of this work is that it combines comprehensive computational analyses with rigorous experimental validations. Specifically, the authors assessed circFL-seq results using short-read RNA-seq data as well as RT-PCR experiments. They also honed into a specific type of novel circular RNAs (i.e. fusion circular RNAs), and demonstrated that circFL-seq discovery of fusion circular RNAs can be validated by RT-PCR. Additionally, the authors compared circFL-seq data to data generated by isoCirc, a recently published method for sequencing full-length circular RNAs, that combines rolling circle amplification followed by nanopore sequencing.

A notable weakness of the present manuscript is with the comparison to existing methods. While the authors compared circFL-seq to isoCirc, they only cited CIRI-long but didn't compare circFL-seq to CIRI-long. Given that both circFL-seq and CIRI-long are based on rolling circle reverse transcription while isoCirc is based on rolling circle amplification, a three-way comparison would have been quite informative. Additionally, given that all three methods were developed independently at almost the same time, the authors would need to provide a more objective discussion about the strengths and weaknesses of individual methods. The current manuscript appears to present circFL-seq as a more superior method for nanopore sequencing of full-length circular RNAs, but the claims and conclusions are not sufficiently supported by their data and the current literature.

Reviewer #2 (Public Review):

In the manuscript entitled "circFL-seq reveals full-length circular RNAs with rolling circular reverse transcription and nanopore sequencing", the authors develop a technology combining RCRT and nanopore long-read sequencing to identify the circRNA isoforms in cell lines and human tissues. The authors claim that the circFL-seq gives the advantages of detection and quantification of circRNA isoforms in comparison to short-read sequencing. The authors also claim that circFL-seq has the advantages of less linear RNA residual contamination and more cost-efficient than RCA long-read sequencing. Finally, the authors can detect fusion circRNAs in MCF7 cells with circFL-seq. Nevertheless, as pointed out by the authors themselves, similar approaches of detecting circRNA isoforms using long-read nanopore sequencing already exists (eg. isoCirc); hence it is the authors' burden to prove that circFL-seq has advantages over isoCirc. However, the authors do not provide enough evidence showing that RCRT indeed gives less linear RNA residual contamination. Moreover, the fact that circFL-seq identifies fewer circRNA isoforms than isoCirc also raises a flag of possible low capture efficiency of RCRT (see major concerns below). In addition, while discovering full length circRNAs using long-read sequencing is interesting, the authors do not provide data suggesting the biological relevance of these identified transcripts. Overall, the authors would would need to provide more data and findings to really strengthen the novelty and the significance of the manuscript.

Reviewer #3 (Public Review):

In the manuscript entitled "circFL-seq reveals full-length circular RNAs with rolling circular reverse transcription and nanopore sequencing", the authors developed a novel strategy to use nanopore sequencing to profile circRNA at the isoform level. Compared with previous short-read RNA sequencing methods, circFL-seq largely increased the sensitivity of circRNA detection. The authors applied circFL-seq to seven human cell lines and two human tissues and discovered novel fusion circRNAs that might play important roles in cancer. The manuscript is well written and the conclusions are well supported by their analyses.

Comment:

In two recently published studies, isoCirc (PMID: 33436621) and CIRI-long (PMID: 33436621) have also used nanopore sequencing to characterize circRNA isoforms and alternative splicing events. It would have been good to see a detailed comparisin between these studies and the current work. For example, both studies have reported a relatively higher percentage of retained introns (isoCirc: Fig. 4b, CIRI-long: Supplementary Fig. 13) compared to the number of 3.5% of intron retention events in line 139. It would be helpful if the authors could clarify the reason behind this difference.

Reviewer #1 (Public Review):

Collective cell motion plays important roles in embryogenesis, wound healing and for cancer progression. Speed and directionality is modulated by numerous parameters including gradients of soluble biochemicals and ECM substrates, substrate rigidity, etc. In the present study, Russo and colleagues investigated how collective migration of a keratinocyte cell line is influenced by integrin ligand nanospacing at different substrate rigidities. The authors perform the studies with gold particles conjugated with alpha5beta1-binding peptidomimetica in PAA hydrogels. They find that the nanospacing of integrin alpha5beta1 promotes the collective movement of keratinocytes (faster focal adhesion dynamics, better keratinocyte co-ordination) independent of substrate stiffness. In addition, the authors showed the migration efficiency by optimal ligand spacing depends on effective propagation of stresses and intercellular stress propagation/co-ordination mediated by E-cadherin.

Reviewer #2 (Public Review):

This manuscript investigates the mechanism of collective keratinocyte migration in vitro. The study focuses on the manipulation of the precise distribution of the main integrin adhesion receptor, alpha5-beta1 integrin, required for migration. It finds that the nano spacing of alpha5-beta1 integrin ligands in the extracellular matrix is key to control collective cell migration. These findings are interesting as they go significantly beyond previous studies that largely focused on the stiffness of the extracellular matrix only and do not take their spatial arrangement into account to regulate collective cell migration. It is likely that similar principles are also relevant in vivo, although this manuscript is not directly testing this in in vivo models.

Strengths:

In collective cell migration, the cell sheet - extracellular traction forces need to be well coordinated to enable coordinated migration of the entire sheet. How this is precisely achieved and which are the underlaying mechanisms is only partially known to date. In particular, the role of the extracellular matrix is unclear. Using state of the art nano-patterning technology Spatz and colleagues have produced nanopatterned hydrogels decorated with artificial highly specific alpha5-beta1 integrin ligands, mimicking a fibronectin matrix with a defined geometry. Hence, not only the stiffness of the matrix but also the spacing of the integrin ligand could be precisely controlled.

Using this technique, the authors find that optimal integrin ligand spacing (50 nm) strongly impacts sheet migration speed, similar as has been shown before in single cell migration assays. Optimal spacing (50 nm) is also needed for optimal coordination of the migrating cell sheet therefore explaining how it results in faster net migration of the entire sheet. This is supported by faster turnover of integrin adhesions, as assayed by tracking paxillin foci turnover.

To quantitate traction forces present in cell sheets, the authors incorporated fluorescent beads into the hydrogels and performed traction force microscopy. The results are intriguing. While the forces are largest at lowest ligand density (70 nm), the stress correlation, which is a measure of force coordination in the sheet, is longest at the intermediate density (50 nm) that also resulted in fastest sheet migration speed.

By varying the stiffness of the hydrogels, the authors make the interesting finding that not only the stiffness but to a larger extend the optimal density (50 nm) of the integrin ligands result in optimal migration speed of the cell sheets. The authors further suggest that the cells need Cadherin to coordinate the forces in the sheets effectively in order to migrate at maximal speed at the optimal ECM ligand density.

Together, these data support that optimal ECM protein distribution can strongly impact how well tissues can regenerate if collective migration is involved as is the case for wound healing. This is summarized in a nice model in Figure 6.

Weakness:

Traction forces were not measured in migrating sheets but only in stationary sheets. Hence, if the force regime on the leading edge of the migrating sheets similarly depends on ligand density as in stationary sheets is not known.

Reviewer #3 (Public Review):

The manuscript by Di Russo et al., "Integrin a5b1 nano-presentation regulates collective keratinocyte migration independent of substrate rigidity" deals with the fundamental question of how the nanoscale organization and mechanical properties of the extracellular matrix control and regulate collective cell migration. In particular, the authors studied how the combined effects of integrin ligands nanoscale spacing and substrate stiffness control the collective migration of keratocytes.

The manuscript of Di Russo et al. builds on previous articles from the same group, in particular a study studying collective cell migration of keratocytes but focusing on the emergence of leader cells (Vishwakarma et al., Nat. Comm. 2018). Importantly, the authors developed their analysis pipeline in this previous article. The current manuscript also uses integrin-specific ligands designed in a previous article (Rechenmacher et al., Angewandte Chemie 2013).

The main message of the manuscript is that the nanoscale spacing of a5b1 integrin ligands is a crucial parameter that controls collective cell migration. Importantly, this parameter prevails over the effects of substrate stiffness. This is particularly interesting since in isolated cells, it was recently demonstrated, that both the nanoscale spacing of integrin ligands and substrate stiffness control the formation on integrin-based adhesions. The authors used as a model system of collective cell migration, keratinocytes monolayers during wound healing.

The authors used nano-patterned hydrogels to control both integrin ligand spacing and substrate stiffness. The nano-patterned hydrogels were functionalized with a ligand highly specific for a5b1 integrin or a cyclic RGD (c(RGDfK)) more selective for avb1 integrin and avb6 integrin of keratocytes. They started with a stiffness of 23 kPa, which corresponds to the stiffness of wounded skin. The authors compared inter-ligand distances of 35 nm, 50 nm, 70 nm and showed that 50 nm is the most effective distance for a5b1 integrin to promote collective migration of keratocytes.

At 50 nm inter-distance, the migration speed and coordination of cell migration within the monolayer were optimal compared to 35 nm and 70 nm. The authors then explored the dynamics of integrin adhesion under the different experimental conditions. Their results suggest that the turnover of integrin-dependent adhesions was faster for cells migrating on 50 nm inter-distance compared to 35 nm and 70 nm. In contrast to a5b1 integrin-specific ligands, keratinocytes on cRGD-functionalized nanopatterns exhibited faster migration on 35-nm inter-ligand spacing (versus 50 nm on a5b1-presenting substrates), and the correlation length did not change with integrin ligand densities. Thus, at 50 nm integrin ligand spacing, integrin a5b1 fosters the maximal migration speed and the most efficient coordination of cell migration within the monolayer.

The authors then tested whether a5b1 integrin ligand density regulates integrin-generated traction and intercellular stress in the keratocyte monolayer. For this, the authors used traction force microscopy on nanopatterned hydrogels. The traction increased with decreasing integrin ligand density, but did not show an optimal value at 50 nm inter-distance. However, the stress correlation length follows the bell-shaped curves observed for migration speed and coordination of cell migration within the monolayer. The authors then calculated the stress vectors within the monolayers using an analysis algorithm developed previously called monolayer stress microscopy (MSM)(Vishwakarma et al., Nat. Comm. 2018). Using this MSM analysis on substrate of different rigidities: 11, 23, 55, and 90 kPa, the authors also found that the spatial correlation length of stress vectors were optimal on 50 nm ligand spacing. Thus, the optimal ligand spacing is independent of the traction forces exerted on the substrate but depends on the correlation length of stress vectors in the monolayer, and this independently of the substrate stiffness.

Since E-cadherin cell-cell interactions are responsible for the mechanical connections crucial for the coordination of collective cell migration, the authors explored whether E-cadherins are participating with integrin ligand spacing in defining the optimal speed of collective cell migration. They inhibited E-cadherins homophilic interactions using a blocking antibody against E-cadherins. This treatment increased collective migration on ligand spacing of 35 nm and 70 nm, while decreasing collective migration on 50 nm inter-distance. These results indicate that cell-cell interactions are also important for optimizing a5b1 integrin-mediated collective cell migration.

The conclusions of the manuscript are, in most cases, convincingly supported by the results. The authors have performed a very comprehensive characterization of the physical parameters (ligand spacing, substrates stiffness) at the base of collective epithelial cell migration. The study represents a huge amount of state-of-the-art biophysical experiments that nicely support their findings. In particular, they have a body of evidence showing that optimal stress propagation within monolayers, which promotes efficient collective cell migration, depends on both integrin ligand spacing and cadherin-mediated cell-cell interactions. However, the results found concerning the dynamics of integrin-based adhesions are more preliminary and need to be further analyzed to extract potentially interesting parameters of cell-ECM interactions important to control collective cell migration. In addition, it would be very interesting to unveil the relative contribution of b1-class integrins versus av-class integrins in collective cell migration. Indeed, it is not clear if the findings of the manuscript, which focus on a5b1 integrin, are relevant during collective cell migration on permissive substrates for b1-class and av-class integrins (e.g. fibronectin).

Reviewer #1 (Public Review):

Johnson et al. aim to unravel the mechanisms, through which the mismatches between human physiology and the Western diet result in chronic diseases. They performed a randomized, preclinical, nonhuman primate trial with two kinds of diet, the Mediterranean and Western diets, and followed the monkeys for 15 months (~4 human years). This study design, with well-defined diets and well-controlled environment, overcomes many challenges/limitations in human studies and enables potential causal inferences for the diet effects. The use of whole diets is able to capture the complicated interactions among individual components. Through standard and solid data analysis, they convincingly showed the effects of the two diets on differential gene expression in monocytes, differential correlations of gene expression, and varying social behaviors. Their results highlight genes related to the subtypes of monocytes: proinflammatory (M1) and regulatory/reparative (M2). Their mediation analysis further suggest that differential gene expression and behavioral changes may mediate the effect of diets on each other. The conclusions are supported by their results. Moreover, the manuscript is well-written and enjoyable to read. Overall, this study provides support for a possible molecular mechanism (monocyte polarization) underlying the negative health impacts of the Western diet, and many candidate genes and pathways for future follow-up studies.

Reviewer #2 (Public Review):

Johnson and colleagues investigate the impact of Western or Mediterranean diet on monocyte gene expression using a macaque model. There are two nested rationales for this work, one more ultimate and the other more proximate. The first is to test the hypothesis that an "evolutionary mismatch" between humans and the so-called "Western" diet is to blame for some inflammation-linked chronic diseases, and the second is to begin to identify a mechanism (currently unknown) that links the Western diet to inflammation, with a focus on the hypothesized role that inflammatory polarization of monocytes may play.

Strengths:

Overall, the work represents a major advance in our understanding of how diet, gene expression, and inflammation are linked, and additionally provides intriguing early results on the connection between diet and behavior which may mediate some aspects of the diet-disease relationship. The conclusions will be of interest to researchers in numerous fields, including those focused on human evolution and public health nutrition.

The current work improves upon previous studies by being a longer-term intervention, and although the whole diet manipulation limits the power to know what particular diet components are mechanistically to blame, the work does have the massive benefit of being able to capture potential emergent properties that might exist when the full Western or Mediterranean diet is consumed. Such "synergistic effects" are likely crucial, given the inability of studies focused on single nutrients in animal models to explain inflammation differences.

The analyses appear very well done and the conclusions justified by their data. I particularly enjoyed reading about the identification of pairs of genes with correlated expression specific to one diet, and the identification of hub genes. It was an elegant analysis, and a model for other work that attempts to identify gene regulatory network perturbations which--as the authors note--may be at the heart of evolutionary mismatches. The link between the one non-coding RNA and KLF11 is a neat result and a testament to the power of this approach.

Weaknesses:

Though I found the work largely beyond critique technically, I would have appreciated additional discussion of the limitations of the use of a captive non-human primate to model human dietary response. I think the caveat that humans and macaques differ is essential enough to address as early as the abstract and certainly in the Discussion. My worry is that macaques are so ill-adapted to the Western human diet that the behavioral and inflammation differences seen are explained by this macaque-Western diet mismatch, which dwarfs the human-Western diet mismatch that likely nonetheless exists. This concern can be partially mitigated by careful discussion of this study limitation.

One critique of dietary interventions that attempt to correct the evolutionary mismatch (which would be useful to address when discussing human-macaque differences) is that human evolution continuing to the present day has been marked by putative selection regime changes associated with multiple major dietary shifts, including meat eating and those arising from cooking and domestication of plants and animals. Such selection may have differentiated humans from macaques in key ways that influence macaque suitability as a dietary model. All that being said, reading the paper made me want to eat less butter, which is an indication the results and conclusions drawn are convincing.

Reviewer #3 (Public Review):

Authors provide compelling evidence for dietary mismatch increasing the risk of chronic diseases, by using a whole diet manipulation experiment in a non-human primate model. They performed a solid suite of behavioural assays and transcriptome analysis of specific immune cells as a proxy for physiological effects of the Mediterranean vs Western diets to mimic the human diet prevalent in a traditional hunter-gatherer society and the modern western world respectively. Their interpretation of dietary effects on gene expression in monocyte populations and immune cell polarization (pro-inflammatory vs regulatory Monocyte cells), correlated gene expression, identification of hub genes was convincing and quite thoughtful. Finally, the use of mediation analysis to propose how both differential immune gene expression and behavioural changes might influence their respective outcomes of dietary changes was appropriate and opens up avenues for future research. Overall, the manuscript is well-written and delivers the message clearly.

However, my major concern is the suitability of these results to explain human relevance and how far they can address the actual evolutionary significance. I think they should tone down a little. For example, is there really any strong reason to assume that macaques will mimic dietary responses in humans? I appreciate the fundamental importance of macaque-specific responses, but I am unclear how captive primates can model human effects─ how do authors factor their (obvious?) fundamental differences between different immune response profiles activated against similar cues and standing microbiome, warranting divergent interactions with the said dietary manipulations. I think these are caveats that need to be carefully discussed as early as possible (e.g. briefly in abstract & results, & certainly in the 1st paragraph of the discussion) to avoid building over expectations among readers.

On a similar note, I am also concerned that macaques must already be poorly adapted to diets used in the experiments.

If so, will this not dilute the proposed role of evolutionary mismatch theory in the observed results, given that they have no evolutionary history with either of the experimental regimes? Also, this is slightly unfortunate because there is no full control treatment where macaques are maintained in their regular diet (i.e., standard monkey chow) and then compared with groups switched to the Mediterranean vs western diet to estimate the relative deviations from their expected physiological processes and behavioural traits. I think this limitation must be highlighted as much as possible.

Could there be more discussion on the relevance of differentially expressed macaque genes in humans?

What are the possible fates of other immune pathways after dietary manipulations? It will be helpful to add some brief speculations.

Reviewer #1 (Public Review):

The authors used a wide range of systems and approaches to demonstrate the importance of the transmembrane potential for the internalization of Cell permeable peptides (CPPs). At a stronger (more negative) transmembrane potential, the three studied peptides (R9, TAT, and Penetratin) internalized easier, while their internalization stopped when the membrane polarization was decreased by the removal of potassium channels. The results are further supported by computer simulations and in vivo experiments providing consistent new insight into uptake of selected CPPs.

Reviewer #2 (Public Review):

While the role of membrane potential in CPP translocation have been consistently described in artificial systems, this multi scale study, combining cell biology, genetic and in silico approaches, further extends this topic to a live cell context and proposes an original mechanism of CPP translocation based on water pore formation.

Contrasting with numerous studies focusing on the role of carbohydrates and lipids in this process, here the authors address the role of proteins, using a CRISPR screen. Depending on the cell context, distinct potassium channels were shown to be required for translocation and based on genetic and pharmacological approaches, the authors propose that their action mostly rely on their ability to modulate membrane potential. Hyperpolarisation enhances CPP translocation while depolarization has opposite effects. MD simulation revealed that hyperpolarization favors the transient formation of water pores in the membrane, induced by a massive and local hyperpolarization (megapolarisation) due to CPP accumulation at the membrane interface. Co-translocation of small soluble molecules with CPP observed in live cells is in agreement with the formation of pore proposed in silico model.

Quantification of CPP uptake that takes into account the intracellular localization of the CPP (cytosolic versus vesicular) is a critical issue in cellular models. In this study, quantification relies either or the toxicity induced by the cytosolic accumulation of the Tat-RasGAP peptide or by the direct visualization of fluorescently-labelled CPPs. In the latter case, the choice made by the authors to define the 3 categories used for quantification has to be justified. More precisely, merging the low and strong cytosolic signal categories into a single one is questionable as these two categories might correspond to distinct functional fates due to the highly variable extent of the cytosolic staining (much more than between vesicular and low cytosolic). Indeed, the water pore mechanism invoked by the authors might better fit with an all or none mechanism for cytosolic delivery that would correspond to low and high cytosolic content respectively. How each of the two categories (low and high cytosolic categories) are affected by membrane potential might be highly relevant. In order to link functional (toxicity) and live imaging analysis assays, one possibility would be to analyze the remaining live cells following long incubation (16-24h) with FITC-TAT RasGAP to estimate which staining categories are actually killed (ideally with or without hyperpolarization).

A second striking observation is the huge heterogeneity of the translocation efficiency within a same cell culture, which unless I missed some points, is not explained. Measuring the membrane potential of each cell (DiBAC4(3) labelling or one of the numerous genetically encoded membrane potential sensor) together with CPP translocation (TMR labelled if using DiBAC), would allow evaluating if the heterogeneity of CPP translocation within a same culture correlates with membrane potential variations.

Although distinct types of potassium channels have been characterized in the screen, the authors only consider their action on the membrane potential, supported by the effects of drugs that specifically act on membrane potential. However, translocation efficiency does not strictly correlate with membrane potential (e. g. KCNJ2 expression in WT and KCNN4 KO Hela figure 3B). It would be interesting to evaluate if KCNQ5 expression would rescue or even increase internalization (additive effect) in SW6.4 and HeLa cells (WT and KCNN4 KO) and vice versa (KCNN4 expression in Raji). This would also avoid any potential interference of the CRISPR system on ectopic expression. Indeed, the kinetic of CPP uptake significantly differs between cell lines (Figure 1B, almost 100% negative cells for Raji at 20 minutes and only 20% for the 2 others), suggesting partially distinct mechanisms.

This study brings new data not only in the CPP field but also in our vision of membrane permeability. The authors proposed an original mechanism of CPP translocation, based on water pore formation. To be fully conclusive, it would require the actual detection of megapolarisation events through electrophysiological recordings to corroborate the in silico model. Whether this mechanism only accounts for very high CPP concentrations or could also apply to lower concentrations often used in functional CPP-delivery strategies remains an open question.

Joint Public Review:

The Mismatch Repair (MMR) pathway removes mismatched bases from newly synthesized DNA strands. Strand discrimination is driven by single strand breaks in the daughter strands. MMR can also recognize some adducts formed by methylating chemotherapeutics, such as temozolomide (TMZ), the standard treatment for glioblastoma. TMZ, and the mimic N-methyl-N-nitrosourea (MNU), methylate guanine at N7 (7mG) and adenine at N3 (3mA). These account for 80-90% of total adducts and are repaired by the Base Excision Repair (BER) pathway. However, they also form 8-9% O6-methylguanine (O6-mG), which is cytotoxic and mutagenic and not repaired by BER. O6-mG can pair with T during replication giving rise to O6-mG:T lesions. This mismatch is recognized by MMR but provokes a "futile cycle" of repair in which the T, since it is in the daughter strand, is removed, after which repair synthesis restores the O6-mG:T. It has been proposed that, in the subsequent S phase, replication across gaps generated during the futile cycles results in toxic double strand breaks (DSBs). The key feature of this model is the requirement for two cycles of replication, the first to generate the provocative O6-mG: T mismatch, the second to produce the breaks. Versions of this scenario have been the primary concern of the field for many years.

The submission from Fuchs and colleagues presents an additional and non-conventional model for MNU/TMZ toxicity. Their experimental approach departs from the requirement for replication and emphasizes the initial O6-mG:C lesion rather than O6-mG:T. They follow repair synthesis in plasmids treated with either MNU or methyl methane sulfonate (MMS) which produces high levels of 7mG and 3mA, but low levels of O6-mG:C. The plasmids were incubated in Xenopus egg extracts that support repair but not replication. They found that MMR proteins bound the MNU treated plasmid but not the MMS treated plasmid and that there was greater repair synthesis in the plasmid treated with MNU than with MMS. They also observed that the BER pathway was important for repair synthesis of the MNU treated plasmid. Experiments with a plasmid carrying a single defined O6-mG:C with or without MMS treatment supported this conclusion. Based on these and other observations they argue that BER of the 7mG and 3mA adducts introduced nicks that were exploited by MMR to drive gap formation and repair synthesis at sites of O6-mG:C. DSBs were formed in the plasmids undergoing both BER (against the N methyl adducts) and MMR against O6-mG:C. Their results support a model in which BER nicking at sites of N methyl adducts provides an enhanced opportunity for MMR of the O6-mG:C lesions. Extended exonuclease digestion by MMR reaches sites undergoing BER on the other strand thus generating DSBs.

Although there is an extensive literature on replication-dependent production and processing of the MNU/TMZ O6-mG:T lesion, this report is novel in the attention to replication-independent repair of the primary mismatch product. Chemotherapy has typically been premised on targeting replicating cells. However, the majority of cells in a glioblastoma tumor are not proliferating, and insight into attacking non dividing cells might be very useful in treating this almost always fatal tumor. The author's data support their model, although some of the implications of their conclusions could be more fully developed. Additional data on two aspects would strengthen the paper.

The first reflects the considerable interest in manipulations of DNA repair pathways that would enhance the toxicity towards tumors of DNA reactive chemotherapy drugs. The authors propose that the introduction of nicks during the early steps of BER are responsible for the enhanced efficacy of MMR in generating the DSBs. However, the later steps of BER act to reverse the nicks. The extract system would appear to lend itself to the identification of the later steps in the BER pathway which, if inhibited, would increase DSB formation by MMR mediated gap formation on one strand past nicks on the other.

The second would extend the approach beyond the extracts. The authors have effectively exploited this system to identify key proteins responding to model substrates and address certain mechanistic questions with those substrates. However, the extracts cannot recapitulate all the features of repair/toxicity of MNU/TMZ adducts in the chromatin environment of the human genome. Although the authors allude to future cell-based assays, the paper would benefit by an initial test of the new model in a live cell system.

The authors should also consider an apparent discrepancy with earlier work. Figure 1 describes the recovery of MMR proteins bound to the plasmid treated with MNU. This treatment would yield O6-mG:C in addition to the guanine and adenine N-alkylation products. Several years ago the Hsieh lab found that purified MutS alpha failed to bind O6-mG:C but recognized O6-mG:T (Mol Cell 22, 501, 2006). However, in this submission the authors report binding of MutS alpha to the plasmid with O6-mG:C. Current models suggest that mismatch binding by MutS alpha initiates the repair process (see Ortega, Cell Res. 31, 542, 2021). In the light of the report from the Hsieh lab the authors' results would seem to imply that something in the extract in addition to MutS alpha is required for that binding. The recognition of O6-mG:C is central to their model, and it would be useful for them to discuss how they reconcile their results with those of the Hsieh lab. In addition, there is a discrepancy with an earlier publication (Olivera Harris et al. 2015 DNA Repair about the effectiveness of the MGMT inhibitor Patrin-2 in Xenopus extracts that should be reconciled.

Reviewer #2 (Public Review):

Valentini et al. explore the contribution of inexperienced homing pigeons in a pair, while finding the most efficient route back home. My comments below mostly concern the need of broadening the scope of the introduction and discussion by discussing and citing literature beyond homing pigeons as at the moment the manuscript could be characterized as too specific for the readership.

The authors use and present transfer entropy methods which regard the transmission of information from one individual to the other and effect of this information on behaviour. I haven't used such methods myself, but I think the methodology is nicely explained and easy to follow as it's written here. However, I would still encourage the authors to avoid jargon and un-introduced terms while first presenting their methods and results in the introduction and results sections. I also think that the paragraph in the introduction (L92-104) that refers to transfer entropy (TE) has to be extended and also direct readers to reviews such as [1] that attempt to make TE accessible to a broad audience of non-physicists. Behavioural ecologists and primatologists that study leadership and influence in animals, using less data hungry methods than TE, will probably be interested in reading this manuscript. Because eLife is a journal that attracts a very broad audience I would suggest investing more on better introducing TE to biologist and anthropologists.

A thought I had while reviewing this work regards the theory of the wisdom of the crowd [2]. This indicates that when a group or a collective averages the different estimates of its members, they reach a more accurate collective estimate. Studies have also shown that animals can average their movement directions to resolve conflicts of interest [3,4]. The current manuscript also shows that pooling infomration leads to better movement decisions. Would it thus make sense for this manuscript to discuss how its findings may support the wisdom of the crowd theory?

As briefly mentioned earlier, I think that the cited literature in this manuscript (especially in L58-138 and throughout the discussion) includes mostly studies on homing pigeons whereas relevant studies to the current manuscript have been performed on other species and by discussing and citing relevant studies on various species the manuscript would become more attractive to a broader audience and wouldn't read as homing-pigeon specific.

References

1. Strandburg-Peshkin A, Papageorgiou D, Crofoot MC, Farine DR. Inferring influence and leadership in moving animal groups. Philos Trans R Soc B Biol Sci [Internet]. 2018;373:20170006. Available from: https://royalsocietypublishing.org/doi/10.1098/rstb.2017.0006

2. Galton F. Vox populi (The wisdom of crowds). Nature. 1907;75:450-451.

3. Strandburg-Peshkin A, Farine DR, Couzin ID, Crofoot MC. Shared decision-making drives collective movement in wild baboons. Science (80- ) [Internet]. 2015;348:1358-61. Available from: https://www.sciencemag.org/lookup/doi/10.1126/science.aaa5099

4. Biro D, Sumpter DJT, Meade J, Guilford T. From Compromise to Leadership in Pigeon Homing. Curr Biol. 2006;16:2123-8.

Reviewer #1 (Public Review):

This paper tests the hypothesis that the transfer of information within a pair of birds flying to their home loft, where one is experienced and the other naive, occurs unidirectionally. The authors use information theoretic methods to analyze trajectory data from a previous study and find interesting results. First, that the passage of information is largely democratic, meaning information passes both ways, second, unexpectedly, that exploration of the route is initiated both by the naive and the experienced bird, and third, mostly in agreement with previous work, that the leading bird is mostly in front of the pair.

The results presented here contributes to large body of information-theoretic methods being applied toward better understanding of natural processes as well as improves our understanding of social learning.

Reviewer #1 (Public Review):

This study reports the function of calsyntenin-3 in the cerebellum. The authors find that cerebellar Purkinje cells selectively express calsyntenin-3. Acute Crispr/Cas-mediated knockout (KO) of calsyntenin-3 in cerebellar Purkinje cells increases the density of excitatory parallel fiber synapses but not excitatory climbing fiber synapses. At the same time, inhibitory synaptic density is decreased in Purkinje cells. This conclusion is supported by multiple lines of morphological and functional evidence. This is the first study showing that a particular synaptic adhesion molecule regulates excitatory and inhibitory synapse toward opposite directions. Although it remains unclear how acute calsyntenin-3 deletion initiated at a juvenile stage leads to opposite changes in excitatory and inhibitory synapses, the current findings are quite comprehensive and set a new stage for follow-up studies.

Reviewer #2 (Public Review):

Calsyntenins (Clstns) are evolutionarily conserved synaptogenic adhesion proteins of the cadherin superfamily, but how it functions remains not completely understood, partly due to the functional redundancy among Clstn members. In this paper, the authors examined roles of Clstn3 in cerebellar Purkinje cells, which predominantly expressed Clstn3, by CRISPR/Cas9-mediated deletion in vivo. They showed deletion of Clstn3 caused a large decrease in inhibitory synapses, but a robust increase in excitatory parallel-fiber synapses, indicating a unique postsynaptic function of Clstn3.

The major strength of this paper is that the authors have clarified the phenotype of Clstn3 deletion using a variety of techniques, including electrophysiology, immunohistochemistry, cell morphology and mouse behaviors. In addition, the phenotype that excitatory and inhibitory synapses showed reciprocal changes is interesting.

A major concern is that the phenotype could not be solely attributable to the postsynaptic function of Clstn3 in Purkinje cells because cell type-specific promoters were not used to express gRNAs. Molecular-layer interneurons, which make inhibitory synapses onto Purkinje cells, likely express Clstn3. Thus, presynaptic functions of Clstn3 cannot be ruled out. Another concern is that although synaptic functions were carefully studied by electrophysiological analyses, it is not completely clear whether the number of inhibitory and excitatory synapses was affected by the deletion of Clstn3. Moreover, it is unclear whether Clstn3 is localized at parallel-fiber, but not climbing-fiber, synapses in wild-type Purkinje cells to explain the parallel fiber-specific phenotype in Clstn3 KO Purkinje cells. Because of these major weak points, the authors' claims and conclusions are not fully justified by their data at this point.

Reviewer #3 (Public Review):

This study investigates the role of calsyntenin-3, an atypical cadherin, in controlling synaptic inputs to cerebellar Purkinje cells. The authors need to do a thorough job of dealing with all of the potential complications given that Purkinje cells were not selectively targeted. The Allen brain atlas shows that Clstn2 and Clstn3 are both expressed by Purkinje cells and that Clstn3 is also expressed by stellate cells and basket cells. The authors used a viral approach that did not select for Purkinje cells. As a result, the effects they see on synaptic transmission could arise from the elimination of all cells in a region rather than the selective elimination of calsyntenin-3 in Purkinje cells. This changes the interpretation of many of the experimental results.

It is difficult to understand such large effects on the total cerebellar mRNA levels and protein levels. The images show large regions of the cerebellum that do not have TdTomato fluorescence, as would be expected given the limitations of injections. This would be expected to be a bigger problem far from the midline. It is also difficult to understand how the protein levels could be reduced to a much larger extent than mRNA levels.

The behavioral data is not very compelling and far from complete.

The manuscript has a tendency to make statements that go beyond the data. For example, the distinction between CF and PF mEPSCs is not easy, and the rise time of 1 ms is unlikely to provide a clear-cut distinction between the two. It is suggestive but far from definitive. Similarly, the kinetics of large and small EPSCs are unlikely to provide a means of discriminating between large and small EPSCs. If the authors really want to make a distinction between basket cells and stellate cells, extracellular recording and rise-times and decay-times are not sufficient, but would require paired recordings and filling of presynaptic cells to better make this distinction.

Reviewer #1 (Public Review):

A strength of the manuscript is that it includes data obtained with a variety of complementary and integrated approaches, such as genetic fate-mapping, neuroanatomy, slice physiology, connectivity tracing and cFos staining. The main message that birthdate determines subsequent CA1 PN heterogeneity is persuasively supported by the experimental data. However, this claim should be a bit mitigated throughout the manuscript because other factors could contribute to adult CA1 PNs diversity.

The data submitted mostly remain at a descriptive/correlative level and should be integrated by attempts of describing the underlying mechanisms and by testing or at least discussing behavioral roles, e.g. anxiety, more specific for the ventral hippocampus.

Reviewer #2 (Public Review):

Currently, glutamatergic neurons within the pyramidal cell layer of the CA1 hippocampus (CA1PNs) are largely classified into superficial or deep populations, based on their location relative to stratum radiatum and accompanying differences in many properties. In the current study, Cavalieri et al. set out to determine whether the embryonic birthdate of CA1PNs was correlated with these properties and whether any further unique CA1PN populations could be identified. While embryonic day (E) 14.5 birthdate neurons largely correspond to previously identified deep CA1PNs and E16.5 resemble superficial CA1PNs, the authors also describe a new E12.5 population of pioneer CA1PNs. The E12.5 CA1PNs show distinct morphological, intrinsic excitability, synaptic connectivity and behaviorally-associated properties that do not fit into a simple linear gradient based on birthdate or laminar position with the other CA1PN populations.

This is an important, novel and interesting study that presents a useful approach of labeling neurons with distinct birthdates so they can be differentiated in experiments performed later in development. The authors then utilize a diverse array of approaches, including immunohistochemistry, slice electrophysiology, retrograde labelling, morphological reconstructions and behavioral assays to evaluate the heterogeneity of CA1PN populations.

While the main point of identifying a new pioneer population of CA1PNs is strongly supported, there remain areas of weakness that should be addressed, mostly related to statistics, rigor of analysis, methodological details, and sample sizes.

Reviewer #3 (Public Review):

The study by Cavalieri et al. is a follow up of Marissal T. et al. 2012 and Save L. et al. 2018 published by the same group. In these articles the authors reported birth date-depended structural and functional features in excitatory cells of the hippocampal formation. They showed that certain cellular characteristics in the dentate gyrus granule cells and the CA3 pyramidal cells track with their date of birth. To label the cells born at E12.5, E14.5 and E16.5 cells the authors used the exact same fate mapping strategy (Ngn2CreER-Ai14 cross and tamoxifen administration) as in this manuscript.

Published work from other groups has utilized various anatomical and functional techniques to study the diversity within the CA1 pyramidal cell layer, which was for a long time considered as one big group of neurons performing the same role. This body of work is reference and described by the authors to suggest that the anatomical and in vitro and in vivo functional diversity previously observed can be better explained, and the CA1 pyramidal cells better segregated, by their embryonic birth day rather than position within the stratum pyramidale. The researchers provide evidence for this claim, by first of all showing that pyramidal cells born at E12.5 and E16.5 seem to have more similar E/I ratio of spontaneous synaptic inputs compared to E14.5. The latter receive more inhibitory synaptic inputs from PV-positive terminals on their cell soma and hence have a skewed distribution towards a reduced E/I ratio. The authors also report the intrinsic electrophysiological properties and apical dendrite extension and ramification of the three groups of cells and identify some differences in both domains.

Following the basic characterization of the properties of the cells, the authors also report a bias towards the projection specificity of the different groups, especially to the Nucleus Accumbens, where they report a projection enrichment of E14.5 fate mapped neurons. In a last set of experiments the authors put an effort to try to uncover the potential involvement of the three fate-mapped groups in a hippocampal-dependent exploration task. By placing the mice in a familiar versus novel environment they show that the E12.5 born cells are proportionally more active (based on cfos labeling) in the former.

Overall the study presents a set of differences between the three fate-mapped cohorts of neurons at different level of analysis, with some intriguing findings on their output connectivity and behavior-dependent activation. The results on the bias in output regions between the cohorts are interesting and provide a possible handle by which to target and manipulate specific populations of CA1 pyramidal cells. Equally interesting is the finding that the environment seems to differentially activate the different cohorts. It nevertheless remains to be determined how the electrophysiological and anatomical properties described in the manuscript assist in the particular function of the cells during exploration. The individual conclusions are largely supported by the data, but extra experiments and analysis would strengthen the claims made.

The message of the study that developmental events and cell trajectories can help us uncover the function of cell types and circuits in the adult brain is an important one for the field.

Reviewer #1 (Public Review):

Summary of the paper

The authors' model is an extension of standard disease models (Kermack & McKendrick, 1927; Yang & Brauer, 2008) that track the spread of an infectious disease within a host population. The authors consider the possibility that individuals' level of activity (and thus their probability of contacting others and potentially transmitting or contracting the infectious disease) may vary in time. Importantly, individual activity levels vary according to a stochastic processes that is not in any way affected by the current disease dynamics in the population or by the individuals' own disease states.

The authors' key result is that if individual social activity levels can spike or crash but then tends to return to their mean value, then synchronous spikes and/or crashes among many individuals' activity levels can lead to corresponding transient changes in the epidemiological dynamics. Waves take off when many individuals are active, but may peak well before herd immunity is reached, because individual activity levels regress to the mean.

Nowhere in the authors' model does individual behavior depend upon individual disease state or population-level disease dynamics. There are many epidemiological models featuring adaptive host behavior; in these, individuals respond behaviorally to the disease. Those adaptive behavior models show disease dynamics that would not be seen in the standard (i.e. constant contact rate) Susceptible-Infectious-Recovered (SIR) model (see for instance Epstein et al., 2008; Fenichel et al., 2011; see Bauch et al., 2013 for an extensive review).

This, then, is the authors' key result: behavioral change that is not responsive to the disease itself can still produce transient plateaus, sub-herd immunity peaks, etc. The authors thus offer a valuable null model that should be considered when responsive behavioral change models are proposed to explain observed epidemiological dynamics.

I believe that this is an important result, especially in light of the explosion of adaptive behavior epidemiology that has accompanied the COVID-19 pandemic thanks to an unprecedented wealth of both epidemiological (e.g. case / hospitalization / death) and behavioral (e.g. Google Mobility) data (Nouvellet et al., 2021). Claims that responsive behavior explains observed epidemiology will need to improve upon this null model in some way in order to be persuasive. My principal reservation about the paper is that the model is presented less as such a null model and more as a mechanistic explanation of observed COVID-19 dynamics. I did not find the case for this interpretation sufficiently convincing, for reasons I will explain below.

The authors find a number of other interesting results, including that stochastically time-varying behavior can reduce the likely "overshoot" of the disease attack rate beyond the herd immunity threshold, and can produce states of "Transient Collective Immunity". These results are a property of a previously-presented model developed by the same authors, in which individual activity levels may vary in time but not necessarily according to a defined stochastic process (Tkachenko et al., 2021). In general, given that this paper builds on that work, I would encourage the authors to be clearer about distinguishing their current results from their prior findings.

The authors characterize the potential endemic state for a pathogen under their model (in the case that previously-exposed individuals can become once again susceptible on some timescale), and show that time-varying heterogeneous contact behavior again alters the dynamics of the approach to endemicity. Notably, they find that behavioral variation can reduce the amplitude of peaks and troughs on the way to endemicity, potentially avoiding stochastic extinction of the disease during troughs.

The authors compare their analytical results to stochastic simulations based on the underlying stochastic process, and find good agreement. Finally, the authors fit their model to COVID-19 death data from United States geographical regions and compare predicted model trajectories to observed deaths.

Key contribution of the paper

In my view, the greatest strength of the paper is in providing a plausible null model for how adaptive behavior can modulate epidemiology even when it does not respond directly to disease, and in developing analytical results that give further insight into the origin and magnitude of these effects given the underlying model parameters.

Concerns regarding the paper

My principal concern about the paper is the implicit claim that the model explains the epidemiological patterns of COVID-19 in the United States during summer and fall 2020.

The authors fit their model to US death data by estimating parameters related to the degree of mitigation as a function of time M(t), as well as some seasonality parameters affecting R0 as a function of time. It is not clear whether baseline R0 was also estimated, since it is not listed as a fixed.

As the authors point out,monotonically increasing R0M(t) in a standard well-mixed SIR far from herd immunity would result in a single peak that overshoots the (ever-increasing) HIT. In the authors' fitted model, deaths in fact initially decline in the northeast and midwest before rising again, and the epidemic in the south displays two peaks separated by a trough.

But I am not sure this is a particularly convincing demonstration of the correctness of a model as an explanation for the observed dynamics. Official distancing policies may have monotonically become more lax over the period June 1 through to, e.g., the fall. But restrictions were tightened in winter in response to surges, and there was clear signal of behavioral response to incresasing transmission that seems unlikely to have been mere regression to the mean.

In the model, the mitigation function is fitted; no actual data on deliberate versus randomly-varying behavior change is used. Given clear empirical signals of synchronous and delibate response to epidemiology, modulated by social factors (Weill et al., 2020), a persuasive demonstration that consideration of random behavioral variation is necessary and/or sufficient to explain observed US COVID-19 dynamics would need to start from mobility data itself, and then find some principled way of partitioning changes in mobility into those attributable to random variation versus deliberate (whether top-down or bottom-up) action.

My other main concern is that the central result of transient epidemiological dynamics due to transient concordance of abnormally high versus low social activity-stems from the choice to model social behavior as stochastic but also mean-seeking. While I find this idealization plausible, I think it would be good to motivate it more.

In other words, the central, compelling message of the paper is that if collective activity levels sometimes spike and crash, but ultimately regress to the mean, so will transmission. The more that behavioral model can be motivated, the more compelling the paper will be.

References

Bauch, C., d'Onofrio, A., & Manfredi, P. (2013). Behavioral epidemiology of infectious diseases: An overview. Modeling the interplay between human behavior and the spread of infec- tious diseases, 1-19.

Epstein, J. M., Parker, J., Cummings, D., & Hammond, R. A. (2008). Coupled contagion dy- namics of fear and disease: Mathematical and computational explorations. PLoS One, 3(12), e3955.

Fenichel, E. P., Castillo-Chavez, C., Ceddia, M. G., Chowell, G., Parra, P. A. G., Hickling, G. J., Holloway, G., Horan, R., Morin, B., Perrings, C., et al. (2011). Adaptive human behav- ior in epidemiological models. Proceedings of the National Academy of Sciences, 108(15), 6306-6311.

Kermack, W. O., & McKendrick, A. G. (1927). A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society of London, Series A, 115(772), 700-721.

Nouvellet, P., Bhatia, S., Cori, A., Ainslie, K. E., Baguelin, M., Bhatt, S., Boonyasiri, A., Brazeau, N. F., Cattarino, L., Cooper, L. V., et al. (2021). Reduction in mobility and covid-19 transmission. Nature communications, 12(1), 1-9.

Tkachenko, A. V., Maslov, S., Elbanna, A., Wong, G. N., Weiner, Z. J., & Goldenfeld, N. (2021). Time-dependent heterogeneity leads to transient suppression of the covid-19 epidemic, not herd immunity. Proceedings of the National Academy of Sciences, 118(17).

Weill, J. A., Stigler, M., Deschenes, O., & Springborn, M. R. (2020). Social distancing responses to covid-19 emergency declarations strongly differentiated by income. Proceedings of the National Academy of Sciences, 117(33), 19658-19660.

Yang, C. K., & Brauer, F. (2008). Calculation of R0 for age-of-infection models. Mathematical Biosciences & Engineering, 5(3), 585.

Reviewer #2 (Public Review):

Current mechanistic modeling approaches lack a unified framework that agrees with data for connecting local well-mixed outbreaks with long-term steady state dynamics of epidemics in highly heterogeneous (e.g., national or global) populations. The model presented here combines overdispersion in how many contacts people have on average (e.g., node degree distribution in a static network) with temporally correlated variability in each individual's degree of sociality. They show the framework can recapitulate agent-based simulations of these processes and provide a qualitative comparison with data from SARS-CoV-2. They suggest that their model describes the time series of COVID-19 deaths observed in different regions of the U.S.

Key features of their model include:

1) progression toward the 'herd immunity threshold' without an overshoot

2) lack of pathogen extinction following an initial outbreak even for relatively small population sizes

3) a long plateau in infection rates after the first (not so huge) wave, which can easily be excited to subsequent waves in the face of changes in transmission rates (e.g., seasonal forcing, lockdown, NPIs, etc)

4) Interactions between multiple time-scales (i.e., infectious period, 'burstiness' of individual social behavior through time, waning of immunity / antigenic variation)

5) An emergent time scale of epidemic relaxation toward the steady state

This model formulation is a step forward for outbreak biologists and infectious disease epidemiologists that can help integrate multiple time scales and achieve a more nuanced understanding of their interplay by quantitatively incorporating them into models. The model formulation may also be useful in design and assessment of control measures, though I would like to see more rigorous comparisons with varied data sets and sensitivity analysis of some of the model parameters before I would use it for prediction. In particular, the fits to SARS-CoV-2 death data are not entirely compelling, and the final sentence of the paper is overstated, claiming quantitative agreement with data on secondary waves of COVID-19 in the US.

While the match with particular dynamic data is not very compelling, their general argument is strong: epidemics rarely burn through populations as deterministic, non-agent-based models predict, even models that include persistent heterogeneity in transmission rates. This modeling framework provides a functional approximation of assumptions regarding individual heterogeneities, and show that it can recapitulate many broad features of transitions from emergence to endemicity.

Reviewer #1 (Public Review):

The physical principles underlying oligomerization of GPCRs are not well understood. Here, authors focused on oligomerization of A2AR. They found that oligomerization of A2AR is mediated by the intrinsically disordered, extramembraneous C-terminal tail. Using experiment and MD simulation, they mapped the regions that are responsible for oligomerization and dissected the driving forces in oligomerization.

This is a nice piece of work that applies fundamental physical principles to the understanding of an important biological problem. It is a significant finding that oligomerization of A2AR is mediated by multiple weak interactions that are "tunable" by environmental factors. It is also interesting that solute-induced, solvent-mediated "depletion interactions" can be a key driving force in membrane protein-protein interactions.

Although this work is potentially a significant contribution to the fields of GPCRs and molecular biophysics of membrane proteins in general, there are several concerns that would need to be implemented to strengthen the conclusions.

1) How reasonably would the results obtained in the micellar environment be translated into the phenomenon in the cell membranes?

1a) Here authors measured oligomerization of A2AR in detergent micelles, not in the bilayer or cellular context. Although the cell membranes would provide another layer of complexity, the hydrophobic properties and electrostatics of the negatively charged membrane surface may cooperate or compete with the interactions mediated by the C-terminal tail, especially if the oligomerization is mediated by multiple weak interactions.

1b) Related to the point above (1a), I wonder if MD simulation could provide an insight into the role of the lipid bilayer in the inter- or intra-molecular interactions involving the tail. Although the neutral POPC bilayer was employed in the simulation, the tail-membrane interaction may affect oligomerization since the tail is intrinsically disordered and possess a significant portion of nonpolar residues (Fig. S4).

2) Ensuring that the oligomer distributions are thermodynamic products.

Since authors interpret the SEC results on the basis of thermodynamic concepts (driving forces, depletion interactions, etc.), it would be important to verify that the distribution of different oligomeric states is the outcome of the thermodynamic control. There is a possibility that the distribution is the outcome of the "kinetic trapping" during detergent solubilization.

3) The claim that the C-terminal tail is engaged in "cooperative" interactions is too qualitative (p. 11 line 274, p.12 line 279 and p.18 line 426).

This claim seems derived from Fig. 3b and Figs. 4b-c. However, the gradual decrease in the dimer level and the number of interactions may indicate that different parts in the C-terminal tail contribute to dimerization additively rather than cooperatively. The large decrease in the number of interactions may stem from the large decrease in the length (395 to 354). Probably, a more quantitative measure would be the number of interactions (H-bonds/salt bridges) normalized to the tail length upon successive truncation. Even in that case, the polar/charged residues would not be uniformly distributed along the primary sequence, making the quantitative argument of cooperativity challenging.

4) On the compactness and conformation of the C-terminal tail:

Although the C-terminal tail is known as "intrinsically disordered", the results seem to indicate that its conformation is rather compact (or collapsed) with a number of intra- and intermolecular polar interactions (Fig. 4) and buried nonpolar residues (Fig. 6), which are subject to depletion interactions (Fig. 5). This raises a question if the tail indeed "intrinsically disordered" as is known. Recent folding studies on IDPs (Riback et al. Science 2017, 358, 238-; Best, Curr Opin Struct Biol 2020, 60, 27-) suggest that IDPs are partially expanded or expanded rather than collapsed.

Reviewer #2 (Public Review):

The authors expressed A2A receptor as wild type and modified with truncations/mutations at the C-terminus. The receptor was solubilized in detergent solution, purified via a C-terminal deca-His tag and the fraction of ligand binding-competent receptor separated by an affinity column. Receptor oligomerization was studied by size exclusion chromatography on the purified receptor solubilized in a DDM/CHAPS/CHS detergent solution. It was observed that truncation greatly reduces the tendency of A2A to form dimers and oligomers. Mechanistic insights into interactions that facilitate oligomerization were obtained by molecular simulations and the study of aggregation behavior of peptide sequences representing the C-terminus of A2A. It is concluded that a multitude of interactions including disulfide linkages, hydrogen bonds electrostatic- and depletion interactions contribute to aggregation of the receptor.

The general conclusions appear to be correct and the paper is well written. This is a study of protein association in detergent solution. It is conceivable that observations are relevant for A2A receptors in cell membranes as well. However, extrapolation of mechanisms observed on receptor in detergent micelles to receptor in membranes should proceed with caution. In particular, the spatial arrangement of oligomerized receptor molecules in micelles may differ from arrangement in lipid bilayers. The lipid matrix may have a profound influence on oligomerization.

The ultimate question to answer is how oligomerization alters receptor function. This will have to be addressed in a future study.

Reviewer #3 (Public Review):

The work of Nguyen et al. demonstrates the relevant role of the C-terminus of A2AR for its homo-oligomerization. A previous work (Schonenbach et al. 2016) found that a point mutation of C394 in the C-terminus (C394S) reduces homo-oligomerization. Following this direction, more mutants were generated, the C-terminus was also truncated at different levels, and, using size-exclusion chromatography (SEC), the oligomerization levels of A2AR variants were assessed. Overall, these experiments support the role of the C-terminus in the oligomerization process. MD studies were performed and the non-covalent interactions were monitored. To 'identify the types of non-covalent interaction(s)', A2AR variants were also analysed modulating the ionic strength from 0.15 to 0.95 M. The C-terminus peptides were investigated to assess their interaction in absence of the TM domain.

The SEC results on the A2AR variants strongly support the main conclusion of the paper, but some passages and methodologies are less convincing. The different results obtained for dimerization and oligomerization are low discussed. The MD simulations are performed on models that are not accurately described - structural information currently available may compromise the quality of the model and the validity of the results (i.e., applying MD simulations to low-resolution models may not be appropriate for the goal of this analysis, moreover the formation of disulfide bonds cannot be simulated but this can affect the conformation and consequently the interactions to be monitored). Although the C-terminus is suggested as 'a driving factor for the oligomerization', the TM domain is indeed involved in the process and if and how it will be affected by modulating the solvent ionic strength should be discussed.

Reviewer #1 (Public Review):

Garcia, AR et al. seek to test out the hypothesis that APOE4 is environmentally mediated and may be protective in a high-pathogen environment. The authors test the presence of at least a single APOE4 allele copy with baseline innate immune function in a Tsimane population in Bolivia by measuring various biomarkers. They showed that being an APOE4 allele carrier is associated with higher circulating levels of lipids combined with lower levels of CRP and eosinophils. This finding among the APOE4+ individuals of the Tsimane population demonstrates further support for the hypothesis that higher loads of lipids are protective in higher loads of infection. This work highlights not only connections to immune response but how we can interpret heart disease/Alzheimer's in an evolutionary context dependent on the environment. Furthermore, a strength is that this work was carried out ethically where work with human subjects was not only approved in US-based institutions, but also by the governing body of the Tsimane. Overall, this is a clear study using fieldwork methods to demonstrate connections difficult to replicate in a controlled laboratory setting.

1) One of the underlying assumptions for the persistence of APOE4 alleles across human populations is because it is or was previously under selection and in the right environment, the APOE4 allele is advantageous. Presumably, in the Tsimane, where the APOE4 allele may be advantageous due to a higher pathogen load and high activity, then wouldn't we expect the allele frequencies to be higher? This section discussing evolution should be a little more fleshed out. Is there any evidence for genetic selection (positive/ balancing) at that locus or is it based on allele frequencies? Given that you do calculate allele frequencies, how do the allele frequencies in Tsimane populations compare to other populations that live in the same geographic region or environment? Would we expect these allele frequencies to be higher than in a post-industrial environment? Do they support selection?

2) Throughout the paper I was wondering if other models were also considered and tested (APOE3/APOE3, APOE3/APOE4, APOE4/APOE4), but I didn't see the reasoning for why the alleles were binned until the methods section. This information should come earlier in the paper, given the way it is structured. If the 3 genotypes were tested, it should be stated in the paper, even if there was no association or there was insufficient sample size and should be discussed in the discussion.

Reviewer #2 (Public Review):

This work investigates the impact of the APOE4 gene variant on inflammation and lipid profiles among the Tsimane subsistence population of Bolivia, a group facing energy constraints and heavy infectious disease burden. APOE4 is associated with greater inflammation, lipids, and downstream cardiovascular disease and Alzheimer's disease in energy-abundant post-industrial populations. Increasingly, human and other model research suggests that the impact of APOE4 on inflammation and lipids may vary under differing conditions of energy availability and infection. It is important to understand this variation to understand how APOE4 impacts disease risk across populations but also to understand why, from an evolutionary perspective, APOE4 frequency is up to 40% in some populations.

Strengths:

*The evolutionary medicine approach used in this study allows for a powerful analysis to probe both proximate ("how") and ultimate ("why") questions relating to variation in APOE4 frequency and associated disease risk.

*The sample size is relatively large and is, it appears, the first to combine this set of measures in a subsistence population experiencing a wide range of energy availability. This allows for the testing of variable interactions and moderating effects using mixed models that can accommodate data clustering and missing data.

*The paper is organized nicely. The findings, as currently described, have important implications for understanding evolved mechanisms of pathogen defense and the rapidly increasing burden of cardiovascular disease in many low-and middle-income countries.

Weaknesses:

*The observational design and correlative nature of the analysis limit causal inference. This is exacerbated by near-single measures of some key variables and the use of proxies of energy availability (e.g., BMI) and pathogen exposure (e.g., community) that lack specificity.

*There may be reporting errors in the key marker of inflammation (CRP) and, potentially, the sample sizes. This adds concern for the analysis.

*While the argument of the paper is based on "baseline" measures of inflammation and lipids, it is unclear given the nature of the data and analysis if representative measures are actually being used. If not, the interpretation of the data could change considerably.

*The paper does not have the sample size to address the impact of having 1 vs. 2 copies of APOE4 and could better discuss population-level variation in APOE4 frequencies and why Tsimane frequency (12%) is, in fact, much lower than in many other populations (e.g., in Central Africa).